WO2012158772A1

WO2012158772A1 - Genetic identification of domestic cat breeds and populations

Info

Publication number: WO2012158772A1
Application number: PCT/US2012/038101
Authority: WO
Inventors: Leslie A. Lyons; Jennifer D. KURUSHIMA; Lutz FROENICKE; Monika J. LIPINSKI; Barbara GANDOLFI
Original assignee: The Regents Of The University Of California
Priority date: 2011-05-19
Filing date: 2012-05-16
Publication date: 2012-11-22

Abstract

This invention provides genetic markers for use in identifying and assigning the population of origin of a feline.

Description

GENETIC IDENTIFICATION OF DOMESTIC CAT BREEDS AND

POPULATIONS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit under 35 U.S. C. § 119(e) of

U.S. Provisional Application No. 61/487,987, filed on May 19, 2011, which is hereby incorporated herein in its entirety for all purposes.

STATEMENT OF GOVERNMENTAL SUPPORT

[0002] This invention was made with government support under Grant No.

R24 RROO 16094 awarded by the National Institutes of Health, National Center for Research Resources (NCRR). The government has certain rights in the invention.

FIELD OF THE INVENTION

[0003] The invention relates to determining the contribution of one or more feline populations to the genome of a feline using a predetermined set of genetic markers, including single nucleotide polymorphisms (SNPs), short tandem repeats (STRs) and DNA- based phenotypic markers.

BACKGROUND OF THE INVENTION

[0004] The domestication of the cat has been a slow and prolonged process, especially when compared to most species associated with human agricultural development. Indeed, the cat is often considered to be only semi-domesticated. Archaeological remains of cats in close proximity to and even buried alongside humans suggest that cats were first domesticated in Cyprus during the Neolithic age 5,000-10,000 BP (Vigne et al, (2004) Science 304, 259-259.) but popular culture suggests cats were domesticated in Egypt (Malek, (1993) The cat in ancient Egypt British Museum Pr. for the Trustees of the British Museum, London; Nowell, (1996) Status Survey and Conservation Action Plan: Wild Cats IUCN, Gland, Switzerland.). Genetic studies using STR and mtDNA analysis of feral and wildcats from throughout Africa and Eurasia identified the Near Eastern Arabian/Northern African wildcat subspecies (Felis silvestris libyca) as the species most closely related to the domestic cat (Driscoll et al., (2007) Science 317, 519-523). Other early human civilizations developed near the Yellow River region of China and the Indus Valley of present day Pakistan. However, sufficient sampling or documentation of wildcats in these regions is inadequate. Only the Fertile Crescent lies within the range of F. s. lybica, which has better documentation and sampling. In addition to the wildcat studies, an independent STR study of both feral and pedigreed cats found the highest genetic diversity of the sampled cats in the region of the eastern Mediterranean Sea, supporting a Fertile Crescent origin of cat domestication (Lipinski et al, (2008) Genomics 91, 12-21.). However, neither study sampled the cats of the Fertile Crescent and Egypt sufficiently to closely examine cat populations in this historically important region, which is necessary for pinpointing the site of cat domestication.

[0005] Genetic markers that arise through a variety of mutational mechanisms help to resolve population stratifications and trace historical migrations (Zeder et al., (2006) Trends in Genetics 22:139-155). STRs and have long been the preferred tool for genetic analyses of recently diverged populations, such as cat breeds, due to their high mutation rate and relative cost effectiveness in comparison to sequencing techniques (Brown et al, (1979) Proc. Natl. Acad. Sci. USA 76: 1967-1971). Analysis of different areas of the mtDNA, particularly gene sequences provide evidence of the matrilineal history of the domesticated cat and of the closest common ancestor, the African wildcat from the Near East (Driscoll et al., 2007, supra). In addition, the mtDNA control region (CR), with its fast rate of mutation, provides evidence of recent admixture of most of the worldwide cat populations (Grahn et al, Forensic Sci Int Genet. (2011) 1 :33-42). The advent of high-throughput SNP typing platforms allows the genotyping of many markers with slower mutation rates, rates which can help define a population's more ancient origins and provide finer-scale evidence for the first domesticated cat populations. Thus, genetic analysis of the same cat populations, using an assortment of DNA markers with a variety of mutation patterns, will better define cat population stratification but not obfuscate the more ancient lineages, further clarifying the domestication progression from ancient to modern cats.

[0006] Data presented herein shows that the domestic cat origins lie within the

Northern region of the Fertile Crescent, where the earliest agriculture and civilizations began. Random bred domestic cat populations from around the world, specifically the region of the Fertile Crescent and Egypt, were genetically investigated to improve the resolution of cat population structures within this important site of cat domestication. Two types of genetic markers, STRs and SNPs, were genotyped in the same cat populations, including several larger populations from the Fertile Crescent region. [0007] The genetic markers further find application in determining the breed pedigree or population origins of a subject feline. Over the past 125 years, mankind has imposed artificial selection to further the previously unchecked process of cat domestication resulting in pedigreed cats. Since the first USA cat show in 1895, which presented five breeds, the development of pedigreed cats has increased in popularity (Gebhardt (1991) The Complete Cat Book. Howell Book House, New York.). Forty-one breeds are currently recognized for competition by the Cat Fanciers' Association (CFA, on the internet at cfa.org/) and 57 are accepted by The International Cat Association (TICA, on the internet at tica.org/). A majority of the breeds recognized by these two large registries is also recognized around the world. A common, sometimes obsessive hobby of cat breeders is feline genealogy, or tracing the true genetic ancestry of the breed and even of one's own random bred pet cat. Many commercial service laboratories are marketing genetic tests for dogs, promising the elucidation of "the breed ancestry of your best friend". Random bred house cats, however, have a different story to their genetic origins. Whereas the average feline mutt found in the streets of most developed countries is more likely a cross-bred individual from multiple purebred breeds, the average random bred cat is not a descendant of their pedigreed counterparts. For cats, the opposite scenario is more likely - pedigreed feline stocks are the descendants of common street cats from distinct parts of the world that have been selected for a distinctive trait (Table 8) (CFA (1993) The Cat Fanciers'

Association Cat Encyclopedia, Simon & Schuster, New York). Random bred cats are the original populations from which the breeds developed, not a population of pedigreed cats gone feral. In addition, also converse to most dog registries, to improve population health and reduce the effects of inbreeding depression, cat breeding associations often seek to diversify their breed populations with random bred cats from their ancestral origin. For this reason, most cat registries use the term "pedigreed" and not "purebred".

[0008] Two studies have evaluated the genetic distinction of cat breeds. Lipinski et al. ((2008) Genomics, 91 : 12-21) defined the connections between the random bred cat populations and their descendant pedigreed lines using a DNA marker panel containing two tetranucleotide and 36 dinucleotide STR markers. Five hundred fifty-five individuals were demarcated into 20 breeds. Four breeds could not be resolved at the breed level.

Furthermore, the breeds sampled by Lipinski et al. were shown to be similar to the populations of street cats found in Europe, the Eastern Mediterranean and Southeast Asia. Menotti-Raymond et al. ((2008) Genomics, 91 :1-11) used a panel of eleven tetranucleotide STR markers and ten regions of SNPs in a subset of their sample set in order to characterize the delineation of cat breeds. Further attempts at population division caused lineages within breeds to resolve before that of the recognized sister breeds. Using only the STR markers, 1040 individuals were demarcated into 8 individual breeds and 9 additional breed groups. Twenty breeds could not be resolved at the breed level. These studies indicated that distinct populations and breeds of cats can be defined genetically, that breeds do have different worldwide regions of origin, tetranucleotide STRs do not perform as well with defining cat breeds as the dinucleotide markers, and that some breeds are so closely related that they cannot be distinguished with even the rapidly evolving dinucleotide STRs.

[0009] The 38 highly polymorphic markers of Lipinski et al. (2008), supra, and a recently developed panel of 148 intergenic autosomal SNPs were recently applied to an extensive sample of random bred street cats collected throughout the world (described herein). Nine hundred forty-four samples were collected from 37 locations spread throughout North and South America, Europe, Africa, and Asia. This study found that while both were efficient at distinguishing five long established lineages, a few

geographically close populations were better delineated with either SNPs or STRs, most likely due to varying mutation rates between the markers.

[0010] Many methods of assignment testing have been developed in the past decade using common population genetic markers and a variety of statistical methods (Rannala & Mountain (1997) Proc Natl Acad Sci USA. 94(17):9197-9201; Pritchard et al. (2000) Genetics, 155:945-959; Baudouin & Lebrun (2001) In: Proc. Int. Symp. on Molecular Markers, pp. 81-94; Paetkau et al. (2004) Molecular Ecology, 13:55-65). These methods have been applied to various breeding populations including pigs, cattle, and dogs

(Schelling et al. (2005) Journal of Animal Breeding and Genetics, 122:71-77; Negrini et al. (2009) Animal Genetics, 40: 18-26; Boitard et al. (2010) Anim Genet. 2010 41(6):608-18. In cattle, Negrini et al. (2009), supra, used 90 SNPs to both allocate and then assign 24 breeds under both the Baysean methods of Pritchard et al. (2000), supra, and Rannala & Mountain (1997), supra, and Baudouin & Lebrun (2001), supra, and the likelihood method of Paetkau et al. (2004), supra. Negrini et al. (2009) concluded that the methods implemented through Rannala & Mountain (Bayesian) (1997) and Petkau et al.

(frequentist) (2004) worked best when attempting to assign unknown individuals to a known database of representative samples from each breed. Previous population studies used Bayesian clustering and neighbor-joining phylogenetic analyses to elucidate the cat breeds and the origins of random bred populations. The present invention demonstrates the utility of a panel of 148 evenly dispersed genome-wide SNPs for population assignment of cats. Different assignment techniques are examined and demonstrated in a species exhibiting many recent and extreme population bottlenecks, comparing the power and efficiency of this 148 SNP panel to 4-fold fewer microsatellites. The power of phenotypic DNA variants is demonstrated for sensitivity and specificity to support individual assignment, specifically for closely related cat breeds that are demarcated by single gene traits.

SUMMARY OF THE INVENTION

[0011] The present invention provides genetic markers useful for the determination of the the population of origin (e.g., ancestral lineage and/or contributing breed(s)) of a test feline. Accordingly, in one aspect, the invention provides computer implemented methods for determining the contributions of feline populations to a feline genome. In some embodiments, the methods comprise:

(a) genotyping a sample comprising genomic DNA obtained from a test feline to determine the identity of one or both alleles of each marker of a set of markers, wherein the set of markers comprises a plurality of single nucleotide polymorphisms (SNPs) listed in Table 1 ;

(b) comparing the identity of one or both alleles for each of the markers in the set of markers determined to be present in the test feline genome to a database comprising one or more feline population profiles, wherein each feline population profile comprises genotype information for the set of markers in the feline population; and

(c) determining the contribution of the one or more feline populations to the test feline genome.

[0012] In a further aspect, the invention provides methods for defining one or more feline populations. In some embodiments, the methods comprise:

(a) determining the identity of one or both alleles for each marker of a set of markers in a test feline genome, wherein the set of markers comprises a plurality of single nucleotide polymorphisms (SNPs) listed in Table 1 ; and

(b) applying a computer-implemented statistical model to define one or more distinct feline populations, wherein one or more distinct feline populations are characterized by a set of allele frequencies for each marker of the set of markers comprising a plurality of

SNPs listed in Table 1. [0013] In a related aspect, the invention provides methods for determining the contributions of feline populations to a feline genome. In some embodiments, the methods comprise performing a genotyping assay on a sample comprising genomic DNA obtained from a test feline to determine the identity of one or both alleles present in the test feline genome for each marker of a set of markers, wherein the set of markers is indicative of the contribution of feline populations to the genome of the test feline, wherein the set of markers comprises a plurality of single nucleotide polymorphisms (SNPs) listed in Table 1.

[0014] In another aspect, the invention provides methods of assigning a feline individual to a population of origin (e.g., an ancestral lineage and/or one or more contributing breeds), which comprises:

(a) genotyping the feline individual to identify one or both alleles of each marker of a set of markers to thereby identify the individual's genotype, wherein the set of markers comprises a plurality of single nucleotide polymorphisms (SNPs) listed in Table 1 ;

(b) applying a computer-implemented statistical model to assign the feline individual to one or more feline populations in a database, wherein the one or more feline populations are characterized by a set of allele frequencies for each marker of the set of markers; and

(c) assigning the feline individual to the one or more most likely populations identified in step (b). In some embodiments, the individual is assigned to the one or more most likely feline populations if the population genotype probability for the most likely feline populations exceeds the value of assignment to any other feline populations of the database.

[0015] With respect to the embodiments, in some embodiments, the plurality of

SNPs comprises at least about 5 SNPs listed in Table 1, for example, at least about 10, 15, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100, 110, 120, 125, 130, 140 or 148 SNPs listed in Table 1. The SNPs listed in Table 1 are as depicted at position 61 of a polynucleotide selected from the group consisting of SEQ ID NO: l to SEQ ID NO: 148 listed in Table 1. In some embodiments, the plurality of SNPs comprises all 148 SNPs listed in Table 1, e.g., as depicted at position 61 of polynucleotides SEQ ID NO: l to SEQ ID NO: 148 listed in Table 1.

[0016] For example, the plurality of SNPs listed in Table 1 are as depicted at position 61 of a polynucleotide selected from the group consisting of SEQ ID NO: l to SEQ ID NO: 148. In some embodiments, the set of markers comprises a plurality of SNPs, wherein the SNPs are selected from the group consisting of position 61 of SEQ ID NO: l, position 61 of SEQ ID NO:2, position 61 of SEQ ID NO:3, position 61 of SEQ ID NO:4, position 61 of SEQ ID NO:5, position 61 of SEQ ID NO:6, position 61 of SEQ ID NO:7, position 61 of SEQ ID NO:8, position 61 of SEQ ID NO:9, position 61 of SEQ ID NO:10, position 61 of SEQ ID NO: 11, position 61 of SEQ ID NO: 12, position 61 of SEQ ID

NO: 13, position 61 of SEQ ID NO: 14, position 61 of SEQ ID NO: 15, position 61 of SEQ ID NO: 16, position 61 of SEQ ID NO: 17, position 61 of SEQ ID NO: 18, position 61 of SEQ ID NO: 19, position 61 of SEQ ID NO:20, position 61 of SEQ ID NO:21, position 61 of SEQ ID NO:22, position 61 of SEQ ID NO:23, position 61 of SEQ ID NO:24, position 61 of SEQ ID NO :25, position 61 of SEQ ID NO :26, position 61 of SEQ ID NO :27, position 61 of SEQ ID NO:28, position 61 of SEQ ID NO:29, position 61 of SEQ ID NO:30, position 61 of SEQ ID NO:31, position 61 of SEQ ID NO:32, position 61 of SEQ ID NO:33, position 61 of SEQ ID NO:34, position 61 of SEQ ID NO:35, position 61 of SEQ ID NO:36, position 61 of SEQ ID NO:37, position 61 of SEQ ID NO:38, position 61 of SEQ ID NO:39, position 61 of SEQ ID NO:40, position 61 of SEQ ID NO:41, position 61 of SEQ ID NO:42, position 61 of SEQ ID NO:43, position 61 of SEQ ID NO:44, position 61 of SEQ ID NO:45, position 61 of SEQ ID NO:46, position 61 of SEQ ID NO:47, position 61 of SEQ ID NO:48, position 61 of SEQ ID NO:49, position 61 of SEQ ID NO:50, position 61 of SEQ ID NO:51, position 61 of SEQ ID NO:52, position 61 of SEQ ID NO:53, position 61 of SEQ ID NO:54, position 61 of SEQ ID NO:55, position 61 of SEQ ID NO:56, position 61 of SEQ ID NO:57, position 61 of SEQ ID NO:58, position 61 of SEQ ID NO:59, position 61 of SEQ ID NO:60, position 61 of SEQ ID NO:61, position 61 of SEQ ID NO:62, position 61 of SEQ ID NO:63, position 61 of SEQ ID NO:64, position 61 of SEQ ID NO:65, position 61 of SEQ ID NO:66, position 61 of SEQ ID NO:67, position 61 of SEQ ID NO:68, position 61 of SEQ ID NO:69, position 61 of SEQ ID NO:70, position 61 of SEQ ID NO:71, position 61 of SEQ ID NO:72, position 61 of SEQ ID NO:73, position 61 of SEQ ID NO:74, position 61 of SEQ ID NO:75, position 61 of SEQ ID NO:76, position 61 of SEQ ID NO:77, position 61 of SEQ ID NO:78, position 61 of SEQ ID NO:79, position 61 of SEQ ID NO:80, position 61 of SEQ ID NO:81, position 61 of SEQ ID NO:82, position 61 of SEQ ID NO:83, position 61 of SEQ ID NO: 84, position 61 of SEQ ID NO: 85, position 61 of SEQ ID NO: 86, position 61 of SEQ ID NO:87, position 61 of SEQ ID NO:88, position 61 of SEQ ID NO:89, position 61 of SEQ ID NO:90, position 61 of SEQ ID NO:91, position 61 of SEQ ID NO:92, position 61 of SEQ ID NO:93, position 61 of SEQ ID NO:94, position 61 of SEQ ID NO:95, position 61 of SEQ ID NO:96, position 61 of SEQ ID NO:97, position 61 of SEQ ID NO:98, position 61 of SEQ ID NO:99, position 61 of SEQ ID NO: 100, position 61 of SEQ ID NO: 101, position 61 of SEQ ID NO: 102, position 61 of SEQ ID NO: 103, position 61 of SEQ ID NO: 104, position 61 of SEQ ID NO: 105, position 61 of SEQ ID NO: 106, position 61 of SEQ ID NO: 107, position 61 of SEQ ID NO: 108, position 61 of SEQ ID NO: 109, position 61 of SEQ ID NO: 110, position 61 of SEQ ID NO: 111, position 61 of SEQ ID NO: 112, position 61 of SEQ ID NO: 113, position 61 of SEQ ID NO: 114, position 61 of SEQ ID NO: 115, position 61 of SEQ ID NO: 116, position 61 of SEQ ID NO: 117, position 61 of SEQ ID NO : 118, position 61 of SEQ ID NO : 119, position 61 of SEQ ID NO : 120, position 61 of SEQ ID NO: 121, position 61 of SEQ ID NO: 122, position 61 of SEQ ID NO: 123, position 61 of SEQ ID NO: 124, position 61 of SEQ ID NO: 125, position 61 of SEQ ID NO: 126, position 61 of SEQ ID NO: 127, position 61 of SEQ ID NO: 128, position 61 of SEQ ID NO : 129, position 61 of SEQ ID NO : 130, position 61 of SEQ ID NO : 131 , position 61 of SEQ ID NO:132, position 61 of SEQ ID NO: 133, position 61 of SEQ ID NO: 134, position 61 of SEQ ID NO: 135, position 61 of SEQ ID NO: 136, position 61 of SEQ ID NO: 137, position 61 of SEQ ID NO: 138, position 61 of SEQ ID NO: 139, position 61 of SEQ ID NO: 140, position 61 of SEQ ID NO: 141, position 61 of SEQ ID NO: 142, position 61 of SEQ ID NO: 143, position 61 of SEQ ID NO: 144, position 61 of SEQ ID NO: 145, position 61 of SEQ ID NO: 146, position 61 of SEQ ID NO: 147, and position 61 of SEQ ID NO: 148.

[0017] In some embodiments, the set of markers comprises a plurality of SNPs, wherein the SNPs are selected from the group consisting of chrAl_10141047,

chrAl l 33621071, chrAl l 51648701, chrAl_175780586, chrAl_208054462,

chrAl_223501140, chrAl_223506906, chrAl_225057933, chrAl_235579538,

chrAl_27523501, chrAl_68485376, chrAl_69424718, chrAl_7429296, chrAl_8742286, chrA2_152258936, chrA2_201526186, chrA2_202225770, chrA2_44241149,

chrA2_554046, chrA3_101420069, chrA3_l 1480952, chrA3_12082294,

chrA3_l 30195244, chrA3_159537633, chrA3_l 62208567, chrA3_38781591,

chrA3_75156179, chrA3_91058022, chrA3_99507784, chrBl l 0420438,

chrBl_12214271, chrBl_195678303, chrB 1 199564532, chrBl_202966562,

chrBl_54775572, chrBl_80161671, chrBl_88148379, chrB2_138312489,

chrB2_146660650, chrB2_41509834, chrB2_45093345, chrB2_6949528,

chrB3_l 04483970, chrB3_l 11000326, chrB3_l 3666494, chrB3_39203469, chrB3_51317931, chrB3_57141954, chrB3_77094074, chrB4_l 05706694, chrB4_142658074, chrB4_143006494, chrB4_144693308, chrB4_146486983,

chrB4_147206961, chrB4_149532846, chrB4_1687419, chrB4_20001848,

chrB4_21098349, chrB4_255106, chrB4_3093827, chrB4_40319102, chrB4_47638578, chrCl_l 16355295, chrCl_123164748, chrCl_l 81852965, chrCl_190502133,

chrCl_215441574, chrCl_216852686, chrCl_24148281, chrCl_28702055,

chrCl_34981315, chrCl_396397, chrCl_44520932, chrCl_52456776, chrC2_106991233, chrC2_147124460, chrC2_l 50774106, chrC2_156491175, chrC2_187325, chrC2_262401, chrC2_5215469, chrDl_101321498, chrD 1 104941557, chrD 1 105498119,

chrDl_10789012, chrDl l 1484008, chrD 1 117527468, chrDl_125811329,

chrDl_126256993, chrDl_126847301, chrD 1_15984279, chrD 1_16242433,

chrDl_18390852, chrDl_18570323, chrD 1 66177762, chrD2_l 020904,

chrD2_l 05772916, chrD2_l 752007, chrD2_56777338, chrD2_717969, chrD2_74293444, chrD2_91989307, chrD3_103840114, chrD3_122502120, chrD3_1810839,

chrD3_24565823, chrD3_24823793, chrD3_28838660, chrD4_41078218,

chrD4_42000379, chrD4_63622083, chrEl_130875919, chrEl_131587399,

chrEl_3912105, chrEl_4114158, chrEl_48228153, chrEl_48700963, chrEl_5453028, chrE2_22632289, chrE2_34027888, chrE2_35914023, chrE2_36986631, chrE2_38860686, chrE2_39211557, chrE2_65436639, chrE2_7950477, chrE2_8422942, chrE3_36044809, chrE3_55434272, chrE3_67006512, chrFl_20309325, chrF 1 21799641, chrFl_26100599, chrFl_27124984, chrFl_38051725, chrFl_565223, chrFl_82068276, chrFl_82716202, chrFl_91517402, chrF2_26886470, chrF2_38395360, chrF2_46855978, chrF2_68572596, chrF2_74863327, chrF2_78303221, chrF2_79632602 and chrF2_8427817.

[0018] In some embodiments, the set of markers further comprises one or more microsatellite markers. For example, in some embodiments, the set of markers further comprises one or more STRs selected from the group consisting of FCA005, FCA008, FCA023, FCA026, FCA035, FCA043, FCA045, FCA058, FCA069, FCA075, FCA077, FCA080B, FCA088, FCA090, FCA094, FCA096, FCA097, FCA105, FCA123, FCA126, FCA132, FCA149, FCA211, FCA220, FCA223, FCA224, FCA229, FCA262, FCA293, FCA305, FCA310, FCA391, FCA441, FCA453, FCA628, FCA649, FCA678 and FCA698.

[0019] In various embodiments, prior to or in addition to genotyping, a most likely population of origin is based on one or more morphological features of the test feline. In some embodiments, prior to or in addition to genotyping, one or more morphological features of the test feline allow the exclusion of one or more of the candidate populations of origin. For example, the feline may be evaluated for coat color (e.g. , chocolate, cinnamon, dilute, orange, white), coat patterning (e.g., agouti, tabby, spotted, ticked, calico, point coloring), coat texture (e.g., straight or rex), coat length (e.g., hairless, short or long), ear morphology (e.g. , normal, curled or folded), paw morphology (e.g. , normal or polydactyl), and tail morphology (e.g., manx, bobtail, long).

[0020] In some embodiments, the set of markers further comprises one or more phenotypic markers. For example, in some embodiments, the set of markers further comprises one or more of the phenotypic markers selected from the group consisting of Phen CM AH G 139 A, Phen ASIP del, Phen_MLPH_T83del, Phen_MClR_G250A, Phen_TYRPl_C298T, Phen_TYRPl_5IVS6, Phen_TYR_del975C, Phen_TYR_G715T, Phen_TYR_G940A, Phen_KIT_G1035C_BI, Phen_FGF5_475, Phen_FGF5_474, phen_FGF5_406, Phen_FGF5_356, Phen_GBLl_G1457C_SIA_KOR,

Phen HEXB Dellntr BUR, Phen_HEXB_del39C_KOR, Phen GBE 1 Ins NFC ,

Phen_KRT71_G/Aintro4_SPX, Phen_MYBPC_G93C_MCC,

Phen_MYBPC_C2460T_RAG, phen MPO ALC, Phen PLAU AG ALC,

Phen FC AT ALC , Phen_PKLR_13delE6_Aby, Phen_PKDl_C10063A_PER,

Phen_SHH_A479G_Hw, Phen_CEP290_PRA_Aby, Phen_CRX_546_Aby,

Phen CMAH del, Phen_HEXB_C667T_DSH, Phen_GM2A_Del_DSH,

Phen GRHPR DSH, Phen LPL G 1234A DSH, Phen LAMAN del PER,

Phen lDUA del DSH, Phen_ARSB_G1558A_SIA, Phen_ARSB_T1427C_Sia,

Phen GUSB Al 052G DSH, Phen_MYBPC_A74T_Poly, Phen NPC 1 G2864C PER, Phen_SHH_G257C_UKl , Phen_SHH_A481T_UK2, Phen_HMBS_del842_SIA, Phen- HMBS l 89TT SIA, Phen_CYP21Bl , Phen TAS 1 R2 CAT,

Phen_TASlR2_G8224A_CAT, Phen_CYP27Bl_Rob, Phen ZFX, KRT71 -Del Drex,

P2RY5_CRex, WNK4_Burm_HKL, and CARTl del Burm. In some embodiments, the set of markers further comprises one or more of the phenotypic markers selected from the group consisting of SEQ ID NO: 149 to SEQ ID NO:202, shown in Table 3.

[0021] In some embodiments, the marker locus genotypes for each candidate population are in Hardy- Weinberg Equilibrium and/or Gametic Phase Equilibrium.

[0022] In various embodiments, the genotype information in each feline population profile comprises identities of one or both alleles of each marker of the set of markers. In some embodiments, the genotype information in each feline population profile comprises allele frequencies for one or both alleles of each marker of the set of markers. In various embodiments, the genotype information in each feline population profile comprises both the identities and the allele frequencies of one or both alleles of each marker of the set of markers.

[0023] In some embodiments, the database of feline population profiles comprises one or more feline population profiles. In various embodiments, the database of feline population profiles comprises a plurality of feline population profiles, for example, between about 5 and about 500 feline population profiles, for example, about 10-400, 15-300, or 20-200 feline population profiles, for example, about 5, 10, 15, 20, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, or more, feline population profiles.

[0024] In some embodiments, the database of feline populations profiles comprise one or more profiles of feline ancestral lineages, i.e., randombred populations of origin. For example, the feline populations profiles may comprise the profiles of one or more ancestral lineages of random bred worldwide populations of cats, including, e.g., Europe,

Mediterranean, Egypt, Iraq/Iran, Arabian Sea, India, Southeast Asia, and East Asia. In some embodiments, the feline populations profiles may comprise the profiles of 1, 2, 3, 4, 5, 6, 7, 8, or more, ancestral lineages of random bred worldwide populations of cats.

[0025] In some embodiments, the database of feline populations profiles comprise profiles of one or more feline breeds. Breeds of interest are recognized by at least one cat breed registry. For example, the breed may be recognized by one or more cat registries selected from the group consisting of The International Cat Association (TICA); the Cat Fanciers' Association (CFA); The Australian Cat Federation (ACF); Co-Ordinating Cat Council of Australia (CCC of A); Federation Internationale Feline (FIFe); Governing Council of the Cat Fancy (GCCF); The New Zealand Cat Fancy (NZCF); The Southern African Cat Council (SACC); The World Cat Federation (WCF); American Cat Fanciers Association (ACFA); The Traditional Cat Association, Inc. (TCA); International

Progressive Cat Breeders' Alliance (IPCBA); Canadian Cat Association (CCA); Cat Fanciers' Federation (CFF); American Association of Cat Enthusiasts (AACE); Australian National Cats (WNCA); Capital Cats Incorporated (CCI); Catz Incorporated; Council of Federated Cat Clubs of Qld (CFCCQ); The Feline Association of NSW (TFA of NSW); Feline Control Council (FCC); Gold Coast Cat Club; The Governing Council of the Cat Fancy of South Australia (GCCFSA); NSW Cat Fanciers^* Association (NSW CFA;);

Queensland Feline Association (QFA); Queensland Independent Cat Council (QICC;); Hong Kong Cat Lovers' Society; Korea Cat Club (KOCC); The Cat Federation of Southern Africa (CFSA); The Asian Cat Association (ACA); Bavarian Cat Fanciers' Association; and Feline Federation Europe.

[0026] In various embodiments, the database comprises profiles of a plurality of feline breeds, for example, profiles of at least about 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more, feline breeds recognized by one or more cat registries. For example, in certain embodiments, the profiles of feline breeds are selected from the group consisting of Persian, Exotic Shorthair (SH), British SH, Scottish Fold, Chartreux, American SH, Sphynx, Japanese Bobtail, Cornish Rex, Ragdoll, Maine Coon, Abyssinian, Siberian, Norwegian FC, Manx, Egyptian Mau, Turkish Angora, Turkish Van, Bengal, Sokoke, Ocicat, Russian Blue, Australian Mist, Burmese, Birman, Havana Brown, Korat, Siamese and Singapura. In some embodiments, the profiles of feline breeds are selected from the group consisting of Abyssinian, American Bobtail, American Bobtail Shorthair (SH), American Curl, American Curl Longhair (LH), American Shorthair, American Wirehair, Balinese, Bengal, Birman, Bombay, British Shorthair, British Longhair, Burmese, Chartreux, Colorpoint Shorthair, Cornish Rex, Cymric, Devon Rex, Don-Skoy, Egyptian Mau, European Burmese, Exotic Shorthair, Havana Brown, Himalayan, Japanese Bobtail, Japanese Bobtail Longhair, Korat, LaPerm, Maine Coon, Manx, Munchkin, Munchkin Longhair, Nebelung, Norwegian Forest Cat, Ocicat, Oriental Longhair, Oriental Shorthair, Persian, Peterbald, Pixiebob, Pixiebob Longhair, RagaMuffm, Ragdoll, Russian Blue, Scottish Fold, Scottish Fold Longhair, Selkirk Rex, Selkirk Rex Longhair, Siamese, Siberian, Singapura, Snowshoe, Somali, Sphynx, Thai, Tonkinese, Toyger, Turkish Angora, and Turkish Van. The profiles of feline breeds may also include one or more of Chausie, Savannah, Bambino, Donskey,

Highlander, Highlander Shorthair, Kurilian Bobtail, Kurilian Bobtail Longhair, Minskin, Ojos Azules, Ojos Azules Longhair, Serengeti and Sokoke.

[0027] In various embodiments, the test feline is suspected of having genetic contributions of 4 or fewer breeds. For example, a test feline may be suspected of being a purebred, having a genetic composition primarily contributed from a single breed, having a genetic composition primarily contributed by two distinct breeds, having a genetic composition primarily contributed by three distinct breeds, or having a genetic composition primarily contributed by four distinct breeds.

[0028] In some embodiments, the set of markers comprises a subset of the 148 SNP markers listed in Table 1 and the method determines the contributions of one or more feline populations to the test feline genome. In various embodiments, the set of markers comprises fewer than about 150 SNP markers and the method determines the contributions of 1, 2, 3 or 4 feline populations to the test feline genome.

[0029] The identity of one or both alleles of a marker can be determined using any method in the art. In some embodiments, the identity of one or both alleles of a marker is determined by amplifying genomic DNA of the test feline using primers specific for each of the set of markers and determining the size of the amplification product. In some embodiments, the identity of one or both alleles of a marker is determined by amplifying genomic DNA of the test feline using primers specific for each of the set of markers and sequencing the amplification product.

[0030] In some embodiments, the algorithm used to compare the identity of one or both alleles for each of the markers in the set of markers to a database comprising the one or more, or a plurality, of feline population profiles comprises a genotype clustering program. In some embodiments, the algorithm used to compare the identity of one or both alleles for each of the markers in the set of markers to a database comprising the one or more, or a plurality, of feline population profiles comprises an assignment program. In some embodiments, the algorithm used to compare the identity of one or both alleles for each of the markers in the set of markers to a database comprising the one or more, or a plurality, of feline population profiles comprises both a genotype clustering program and an assignment program. In some embodiments, the clustering program is a Bayesian clustering program. In some embodiments, the assignment program is a likelihood or frequentist program. In some embodiments, the test feline is assigned to the most likely population of origin if the population genotype probability for the most likely population of origin exceeds the value of assignment to any other population of the database.

[0031] In some embodiments, the contributions of two or more genetically related feline populations to the test feline genome are discriminated by comparing the alleles in the test feline genome to a database comprising profiles of the two or more genetically related feline populations. For example, in various embodiments, the two or more genetically related feline populations being discriminated are selected from the group consisting of (i) Persian and Exotic Shorthair (SH); (ii) British SH and Scottish Fold; (iii) Australian Mist and Burmese; (iv) Singapura and Burmese; (v) Birman and Korat, and (vi) Siamese and Havana Brown. As appropriate, one or more phenotypic markers can be determined, in addition to determining the identity of a plurality of the SNPs listed in Table 1 , to help distinguish between the contributions of two or more genetically related feline populations to the test feline genome.

[0032] For example, the genotype of the FGF5 SNP, which causes long hair, can be determined to affirmatively assign a test feline to one or more breeds selected from the group consisting of Persian, Maine Coon, Turkish Angora, Turkish Van and Birman.

Similarly, a FGF5 genotype indicative of the presence of long hair can be used to exclude assignment to one or more breeds selected from the group consisting of Abyssinian, Egyptian Mau, Sokoke, Ocicat, and short-haired varieties of other recognized feline breeds. In some embodiments, the genotypes of one or both alleles of one or more of the FGF5 SNPs depicted by SEQ ID NOs: 159-162 are determined. In some embodiments, the genotypes of one or both alleles of all four of the FGF5 SNPs depicted by SEQ ID

NOs: 159-162 are determined.

[0033] In various embodiments, the methods further comprise reporting the results of the analysis. In some embodiments, the methods further comprise the step of providing a document displaying the contributions of one or more feline populations to the genome of the test feline genome. In various embodiments, the document provides additional information regarding the one or more feline populations that contributed to the genome of the test feline. In some embodiments, the document provides health-related information. In some embodiments, the document provides a certification of the contributions of one or more feline populations to the genome of the test feline. In some embodiments, the document provides a representation of the one or more feline populations that contributed to the genome of the test feline.

[0034] In another aspect, the invention provides one or more primer sets for determining the identity of one or both alleles a plurality of single nucleotide

polymorphisms (SNPs) listed in Table 1. In various embodiments, primer sets for determining the identity of one or both alleles of at least about 5 SNPs, for example, at least about 10, 15, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100, 110, 120, 125, 130, 140, 148 SNPs listed in Table 1 are provided. The primer sets may be provided in a kit.

[0035] In a related aspect, the invention provides one or more computer-readable media. In some embodiments, the computer-readable media comprise:

(a) a data structure stored thereon for use in distinguishing feline populations, the data structure comprising: (i) marker data, wherein the marker data identifies one or both alleles of each marker of a set of markers in one or more feline population profiles, wherein the set of markers comprises a plurality of single nucleotide polymorphisms (SNPs) listed in Table 1 ; and

(ii) genotype information data, wherein the genotype information data provides genotype information for each marker of a set of markers in a feline population, wherein a record comprises an instantiation of the marker data and an instantiation of the genotype information data and a set of records represents a feline population profile; and

(b) computer-executable instructions for controlling one or more computing devices to:

(i) identify one or both alleles in a test feline genome for each marker of the set of markers; and

(ii) determine the contributions of one or more feline populations to the test feline genome by comparing the identified alleles in the test feline genome to the database comprising one or more feline population profiles, wherein each feline population profile comprises genotype information for the set of markers in the feline population.

[0036] In a further aspect, the invention provides one or more computer-readable media comprising a data structure stored thereon for use in distinguishing feline

populations. In some embodiments, the data structure comprises:

(a) marker data, wherein the marker data identifies one or both alleles of each marker of a set of markers in one or more feline population profiles, wherein the set of markers comprises a plurality of single nucleotide polymorphisms (SNPs) listed in Table 1 ; and

(b) genotype information data, wherein the genotype information data provides genotype information for each marker of a set of markers in a feline population, wherein a record comprises an instantiation of the marker data and an instantiation of the genotype information data and a set of records represents a feline population profile.

[0037] Further embodiments in the computer readable media are as described above and herein. DEFINITIONS

[0038] Unless defined otherwise, all technical and scientific terms used herein generally have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Generally, the nomenclature used herein and the laboratory procedures in cell culture, molecular genetics, organic chemistry and nucleic acid chemistry and hybridization described below are those well known and commonly employed in the art. Standard techniques are used for nucleic acid and peptide synthesis. Generally, enzymatic reactions and purification steps are performed according to the manufacturer's specifications. The techniques and procedures are generally performed according to conventional methods in the art and various general references (see generally, Sambrook et al. MOLECULAR CLONING: A LABORATORY MANUAL, 3rd ed.

(2001) Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. and Ausubel, et al., Current Protocols in Molecular Biology, 1987-2011, John Wiley and Sons), which are provided throughout this document. The nomenclature used herein and the laboratory procedures in analytical chemistry, and organic synthetic described below are those well known and commonly employed in the art. Standard techniques, or modifications thereof, are used for chemical syntheses and chemical analyses.

[0039] The terms "isolated," "purified," or "biologically pure" refer to material that is substantially or essentially free from components that normally accompany it as found in its native state. Purity and homogeneity are typically determined using analytical chemistry techniques such as polyacrylamide gel electrophoresis or high performance liquid chromatography. Genomic DNA or a polynucleotide that is the predominant species present in a preparation is substantially purified. The term "purified" denotes that a nucleic acid gives rise to essentially one band in an electrophoretic gel. Particularly, it means that the nucleic acid or genomic DNA is at least 85% pure, more preferably at least 95% pure, and most preferably at least 99% pure.

[0040] The terms "nucleic acid" and "polynucleotide" are used interchangeably herein to refer to deoxyribonucleotides or ribonucleotides and polymers thereof in either single- or double-stranded form. The term encompasses nucleic acids containing known nucleotide analogs or modified backbone residues or linkages, which are synthetic, naturally occurring, and non-naturally occurring, which have similar binding properties as the reference nucleic acid, and which are metabolized in a manner similar to the reference nucleotides. Examples of such analogs include, without limitation, phosphorothioates, phosphoramidates, methyl phosphonates, chiral-methyl phosphonates, 2-O-methyl ribonucleotides, peptide-nucleic acids (PNAs). [0041] Unless otherwise indicated, a particular nucleic acid sequence also encompasses conservatively modified variants thereof (e.g., degenerate codon substitutions) and complementary sequences, as well as the sequence explicitly indicated. Specifically, degenerate codon substitutions may be achieved by generating sequences in which the third position of one or more selected (or all) codons is substituted with mixed-base and/or deoxyinosine residues (Batzer et al, Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al, J. Biol. Chem. 260:2605 2608 (1985); Rossolini et al., Mol. Cell. Probes 8:91 98 (1994)). The term nucleic acid is used interchangeably with gene, cDNA, mRNA, oligonucleotide, and polynucleotide.

[0042] The terms "identical" or percent "identity," in the context of two or more nucleic acid sequences, refer to two or more sequences or subsequences that are the same or have a specified percentage of nucleotides that are the same (e.g., 80% identity, preferably 85%, 90%, 95%, 96%, 97%, 98%, 99% identity over a specified region such as the nucleic acid sequences of SEQ ID NOs: 1-148 and SEQ ID NOs: 149-202), when compared and aligned for maximum correspondence over a comparison window, or designated region as measured using a known sequence comparison algorithm (e.g. , BLAST, ALIGN) set to default settings or by manual alignment and visual inspection. Such sequences are then said to be "substantially identical." This definition also refers to the complement of a test sequence. Preferably, the identity exists over a region that is at least about 25 nucleotides in length, or more preferably over a region that is 50-100 nucleotides in length, or over the full length of the contextual sequence flanking the genetic marker.

[0043] A "label" or "detectable label" is a composition detectable by spectroscopic, photochemical, biochemical, immunochemical, or chemical means. For example, useful

3 35 32 51 125

labels include radioisotopes (e.g., H, S, P, Cr, or I) fluorescent dyes, electron-dense reagents, enzymes (e.g., alkaline phosphatase, horseradish peroxidase, or others commonly used in an ELISA), biotin, digoxigenin, or haptens and proteins for which antisera or monoclonal antibodies are available (e.g., the polypeptide comprising a sequence encoded by SEQ ID NO: l can be made detectable, e.g., by incorporating a radiolabel into the peptide, and used to detect antibodies specifically reactive with the peptide).

[0044] An "amplification reaction" refers to any chemical reaction, including an enzymatic reaction, which results in increased copies of a template nucleic acid sequence. Amplification reactions include polymerase chain reaction (PCR) and ligase chain reaction (LCR) (see U.S. Pat. Nos. 4,683,195 and 4,683,202; PCR Protocols: A Guide to Methods and Applications (Innis et al, eds, 1990)), strand displacement amplification (SDA) (Walker, et al. Nucleic Acids Res. 20(7): 1691 (1992); Walker PCR Methods Appl 3(1): 1 (1993)), transcription-mediated amplification (Phyffer, et al., J. Clin. Microbiol. 34:834 (1996); Vuorinen, et al, J. Clin. Microbiol. 33 : 1856 (1995)), nucleic acid sequence-based amplification (NASBA) (Compton, Nature 350(6313):91 (1991), rolling circle amplification (RCA) (Lisby, Mol. Biotechnol. 12(1):75 (1999)); Hatch et al., Genet. Anal. 15(2):35 (1999)) and branched DNA signal amplification (bDNA) (see, e.g., Iqbal et al, Mol. Cell Probes 13(4):315 (1999)).

[0045] "Amplifying" refers to submitting a solution to conditions sufficient to allow for amplification of a polynucleotide if all of the components of the reaction are intact.

Components of an amplification reaction include, e.g., primers, a polynucleotide template, polymerase, nucleotides, and the like. Thus, an amplifying step can occur without producing a product if, for example, primers are degraded.

[0046] "Amplification reagents" refer to reagents used in an amplification reaction. These reagents can include, e.g., oligonucleotide primers; borate, phosphate, carbonate, barbital, Tris, etc. based buffers (see, U.S. Pat. No. 5,508, 178); salts such as potassium or sodium chloride; magnesium; deoxynucleotide triphosphates (dNTPs); a nucleic acid polymerase such as Taq DNA polymerase; as well as DMSO; and stabilizing agents such as gelatin, bovine serum albumin, and non-ionic detergents (e.g. Tween-20). [0047] A "plurality" refers to two or more, for example, 2, 3, 4, 5, 10, 15, 20, 25, 30,

40, 50, 60, 70, 75, 80, 90, 100, 1 10, 120, 130, 140, 145, 148, 150, or more (e.g., genetic markers, including SNPs, short tandem repeats (STRs), microsatellites, phenotypic markers; feline population profiles). In some embodiments, a plurality refers to concurrent or sequential determination of about 2-150, 5-148, 50-148, 100-148 markers, for example, about 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 1 10, 120, 130, 140, 145, 148, 150, or more, markers. In some embodiments, "plurality" refers to all markers listed in one or more tables, e.g., all markers listed in Table 1 , and optionally also including all markers listed in Table 3.

[0048] A "single nucleotide polymorphism" or "SNP" refers to polynucleotide that differs from another polynucleotide by a single nucleotide exchange. For example, without limitation, exchanging one A for one C, G or T in the entire sequence of polynucleotide constitutes a SNP. Of course, it is possible to have more than one SNP in a particular polynucleotide. For example, at one locus in a polynucleotide, a C may be exchanged for a T, at another locus a G may be exchanged for an A and so on. When referring to SNPs, the polynucleotide is most often DNA and the SNP is one that usually results in a change in the genotype that is associated with a corresponding change in phenotype of the organism in which the SNP occurs. [0049] A "variant" is a difference in the nucleotide sequence among related polynucleotides. The difference may be the deletion of one or more nucleotides from the sequence of one polynucleotide compared to the sequence of a related polynucleotide, the addition of one or more nucleotides or the substitution of one nucleotide for another. The terms "mutation," "polymorphism" and "variant" are used interchangeably herein to describe such variants. As used herein, the term "variant" in the singular is to be construed to include multiple variances; i. e., two or more nucleotide additions, deletions and/or substitutions in the same polynucleotide. A "point mutation" refers to a single substitution of one nucleotide for another.

[0050] A nucleic acid "that distinguishes" as used herein refers to a

polynucleotide(s) that distinguishes a first polymorphism (e.g., a major allele of a SNP) from a second polymorphism (e.g. , a minor allele of the same SNP) at the same position in the genomic sequence. The nucleic acid that distinguishes can allow for polynucleotide extension and amplification after annealing to a polynucleotide comprising the first polymorphism, but will not allow for polynucleotide extension or amplification after annealing to a polynucleotide comprising the second polymorphism. In other embodiments, a nucleic acid that distinguishes a first polymorphism from a second polymorphism at the same position in the sequence will hybridize to a polynucleotide comprising the first polymorphism but will not hybridize to a polynucleotide comprising the second

polymorphism. The invention provides polynucleotides that distinguish the SNPs and genetic markers listed in Table 1.

[0051] The term "primer" refers to a nucleic acid sequence that primes the synthesis of a polynucleotide in an amplification reaction. Typically a primer comprises fewer than about 100 nucleotides and preferably comprises fewer than about 30 nucleotides.

Exemplary primers range from about 5 to about 25 nucleotides. The "integrity" of a primer refers to the ability of the primer to primer an amplification reaction. For example, the integrity of a primer is typically no longer intact after degradation of the primer sequences such as by endonuclease cleavage. [0052] The term "subsequence" refers to a sequence of nucleotides that are contiguous within a second sequence but does not include all of the nucleotides of the second sequence.

[0053] A "target" or "target sequence" refers to a single or double stranded polynucleotide sequence sought to be amplified in an amplification reaction. Two target sequences are different if they comprise non-identical polynucleotide sequences.

[0054] As used herein a "nucleic acid probe or oligonucleotide" is defined as a nucleic acid capable of binding to a target nucleic acid of complementary sequence through one or more types of chemical bonds, usually through complementary base pairing, usually through hydrogen bond formation. As used herein, a probe may include natural (i.e., A, G, C, or T) or modified bases (7-deazaguanosine, inosine, etc.). In addition, the bases in a probe may be joined by a linkage other than a phosphodiester bond, so long as it does not interfere with hybridization. Thus, for example, probes may be peptide nucleic acids in which the constituent bases are joined by peptide bonds rather than phosphodiester linkages. It will be understood by one of skill in the art that probes may bind target sequences lacking complete complementarity with the probe sequence depending upon the stringency of the hybridization conditions. The probes are preferably directly labeled as with isotopes, chromophores, lumiphores, chromogens, or indirectly labeled such as with biotin to which a streptavidin complex may later bind. By assaying for the presence or absence of the probe, one can detect the presence or absence of the select sequence or subsequence.

[0055] A "labeled nucleic acid probe or oligonucleotide" is one that is bound, either covalently, through a linker or a chemical bond, or noncovalently, through ionic, van der Waals, electrostatic, or hydrogen bonds to a label such that the presence of the probe may be detected by detecting the presence of the label bound to the probe.

[0056] "Biological sample" as used herein is a sample of biological tissue or fluid that contains genomic DNA. These samples can be tested by the methods described herein and include body fluids such as whole blood, serum, plasma, cerebrospinal fluid, urine, lymph fluids, and various external secretions of the respiratory, intestinal and genitourinary tracts, tears, saliva, milk, white blood cells, myelomas, and the like; and biological fluids such as cell extracts, cell culture supernatants; fixed tissue specimens; and fixed cell specimens. Biological samples may also include sections of tissues such as biopsy and autopsy samples or frozen sections taken for histologic purposes. A biological sample can also be skin cells, a cheek swab or a hair bulb sample. These samples are well known in the art. A biological sample is obtained from any mammal including, e.g., a cat. A biological sample may be suspended or dissolved in liquid materials such as buffers, extractants, solvents and the like.

[0057] The term "feline" refers to an animal that is a member of the family Felidae; including without limitation the subfamilies, Felinae, Pantherinae, and Acinonychinae; the genera Caracal, Catopuma, Felis, Herpailurus, Leopardus, Leptailurus, Lynx, Oncifelis, Oreailurus, Otocolobus, Prionailurus, Profelis, Puma, Neofelis, Panthera, Pardofelis, and Uncia; the species felis, lybica, jubatus, caracal, badia, bieti, chaus, margarita, nigripes, silvestris, gordonii, yaguarondi, pardalis, tigrinus, wiedi, serval, canadensis, lynx, pardinus, rufus, colocolo, geoffroyi, guigna, jacobita, manul, bengalensis, planiceps, rubiginosus, viverrinus, aurata, concolor, nebulosa, leo, onca, pardus, tigris, marmorata, and uncial.

[0058] The term "determining the contributions of feline populations" refers to estimating or inferring using statistical methods the contributions of feline populations to draw conclusions regarding whether one or more feline populations contributed to the genome of a test feline.

[0059] The term "feline population" refers to a group of felines related by descent, such as a domestic cat breed.

[0060] The term "breed" refers to an intraspecies group of animals with relatively uniform phenotypic traits that have been selected for under controlled conditions by man. For example, The International Cat Association (TICA) recognizes 57 Championship

Breeds, 2 Advanced New Breeds and 10 Preliminary New Breeds (identified at tica.org). The Cat Fanciers' Association (CFA) lists 40 breeds. The methods of the invention may be used to estimate the genetic contributions of any cat breed, including, but not limited to Abyssinian, American Bobtail, American Bobtail Shorthair (SH), American Curl, American Curl Longhair (LH), American Shorthair, American Wirehair, Balinese, Bengal, Birman, Bombay, British Shorthair, British Longhair, Burmese, Chartreux, Colorpoint Shorthair, Cornish Rex, Cymric, Devon Rex, Egyptian Mau, European Burmese, Exotic Shorthair, Havana Brown, Himalayan, Japanese Bobtail, Japanese Bobtail Longhair, Korat, LaPerm, Maine Coon, Manx, Munchkin, Munchkin Longhair, Nebelung, Norwegian Forest Cat, Ocicat, Oriental Longhair, Oriental Shorthair, Persian, Peterbald, Pixiebob, Pixiebob Longhair, RagaMuffm, Ragdoll, Russian Blue, Scottish Fold, Scottish Fold Longhair, Selkirk Rex, Selkirk Rex Longhair, Siamese, Siberian, Singapura, Snowshoe, Somali, Sphynx, Thai, Tonkinese, Toyger, Turkish Angora, Turkish Van, Chausie, Savannah, Bambino, Donskey, Highlander, Highlander Shorthair, Kurilian Bobtail, Kurilian Bobtail Longhair, Minskin, Ojos Azules, Ojos Azules Longhair, Serengeti and Sokoke, and mixtures thereof.

[0061] The term "marker" refers to any polymorphic genomic locus that is sufficiently informative across the feline populations used in the methods of the invention to be useful for estimating the genetic contribution of these feline populations to the genome of a test feline. A genomic locus is polymorphic if it has at least two alleles.

[0062] The term "allele" refers to a particular form of a genomic locus that may be distinguished from other forms of the genomic locus by its nucleic acid sequence. Thus, different alleles of a genomic locus represent alternative nucleic acid sequences at that locus. In any individual feline genome, there are two alleles for each marker. If both alleles are the same, the genome is homozygous for that marker. Conversely, if the two alleles differ, the genome is heterozygous for that marker.

[0063] Population-specific alleles are alleles that are present at some frequency in one feline population but have not been observed in the sampled feline from comparison feline populations (although they may be present at a significantly lower frequency).

Population-specific alleles may be used to assign an individual to a particular population. Accordingly, the difference in allele frequencies between populations can be used for determining genetic contributions.

[0064] A "set of markers" refers to a minimum number of markers that are sufficient for determining the genetic contribution of the feline populations used in the methods of the invention to the genome of a test feline. The minimum number of markers required depends on the informativeness of the markers for the particular feline populations that are being used, as further described below. The set of markers may comprise at least about 5, 10, 25, 50, 75, 100, 125, 150 markers, or more, as appropriate.

[0065] A "feline population profile" as used herein refers to the collection of genotype information for the set of markers in a feline population. Thus, a feline population profile may comprise genotype information for most or all alleles of most or all markers in the set of markers in the feline population.

[0066] An "allele frequency" refers to the rate of occurrence of an allele in a population. Allele frequencies are typically estimated by direct counting. Generally, allele frequencies in a feline population are estimated by obtaining the identity of one or both alleles for each of the set of markers in at least about five members of that feline population.

[0067] A "database of feline population profiles" refers to the collection of feline population profiles for all of the feline populations used in an exemplary method of the invention. In some embodiments, the database of feline population profiles comprises between about five and about 500 feline population profiles, such as about 20 feline population profiles, about 50 feline population profiles, or about 100 feline population profiles.

[0068] A "computer-readable medium" refers to any available medium that can be accessed by computer and includes both volatile and nonvolatile media, removable and nonremovable media.

[0069] The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[0070] A "data structure" refers to a conceptual arrangement of data and is typically characterized by rows and columns, with data occupying or potentially occupying each cell formed by a row-column intersection.

BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 illustrates a map of random bred cat sampling locations. The pie charts represent the percentage of the eight worldwide lineages found at each location. The shading indicates the strength of the predominating lineage for each region of the world.

[0072] Figures 2A-F illustrate Delta K plots of random bred cat population structuring. Graphs of both the mean Ln(K) and ΔΚ calculations based on the results of Bayesian clustering. Top) SNPs only, Middle) STRs only, Bottom) SNPs and STRs combined. Points where a peaks in a ΔΚ plot occur indicate population stratification with higher likelihood than those where valleys occur.

[0073] Figures 3A-F illustrate Bayesian clustering of random bred worldwide cat populations. Clustering of cat populations using STRUCTURE A) SNPs K=5, B) SNPs K=8, C) STRs K=5, D) STRs K=7, E) SNPS and STRs K=5, F) SNPs and STRs K=8.

[0074] Figures 4A-C illustrate alternate Bayesian clustering of worldwide cat populations. Alternate clustering of cat populations using STRUCTURE based on ΔΚ calculations. A) SNPs at K=l 1 and 23, B) STRs at K=9 and 13, and C) SNPs and STRs combined at K=10, 16, 18, and 22.

[0075] Figures 5A-B illustrate principal coordinate analysis of world cat populations. A) SNPs and B) STRs by sampling location calculated via Nei's Unbiased Distance. Colors indicate the eight random bred populations. Circles indicate the five lineages.

[0076] Figures 6A-B illustrate neighbor-joining trees of world cat populations.

Bootstrap values over 50% indicated on nodes. Branch colors indicate the population as assigned by STRUCTURE. A) SNP-based phylogeny produced with Reynolds, Weir and Cockerham's genetic distance. B) STR-based tree produced with Nei's unbiased genetic distance.

[0077] Figures 7A-D illustrate log likelikhood and Delta K plots from the Bayesian clustering of cat breeds. Graphs of both the mean Ln(K) and ΔΚ calculations based on the results of Bayesian clustering. Points where a peaks in a ΔΚ plot occur indicate population stratification with higher likelihood than those where valleys occur.

[0078] Figure 8 illustrates Bayesian clustering of cat breeds. Clustering of breeds at

K=17 and K=21 as calculated with SNPs and STRs respectively.

[0079] Figures 9A-B. Figure 9A illustrates alternate plots of Bayesian clustering analysis for SNPs. Figure 9B illustrates alternate plots of Bayesian clustering analysis for STRs.

[0080] Figures 10A-B illustrate principal coordinate analysis of cat breeds and worldwide random bred cat populations. Color shades indicate the population membership of the respective random bred populations.

[0081] Figures 11 A-B. Figure 11 A illustrates crossed assignment rate between breeds as a function of the Reynolds distance between populations using SNPs. Figure 1 IB illustrates crossed assignment rate between breeds as a function of the Reynolds distance between populations using STRs.

DETAILED DESCRIPTION

1. Introduction

[0082] The present invention is based, in part, on the discovery of a panel of biomarkers useful for the assignment of domestic cats to specific breeds or world populations based on the frequency of genetic markers in their genome. Assignment testing utilizes microsatellite and/or single nucleotide polymorphism (SNP) biomarkers, as well as genetic biomarkers that are known to confer a physical characteristic or disease state in the cat. The combined panel of over 200 different genetic tests can be used to determine if a cat is from a specific breed or random bred population of origin within a database of approximately 2000 cats. To conduct the test, the genotypes of the panel of biomarkers are determined in a biological sample of the cat (e.g., blood, tissue, hair bulb, buccal swab) comprising genomic DNA. The genotypic "signature" over the panel of biomarkers of the test cat is compared against a database of the same panel of biomarkers with identified frequency associations with known cat breeds and random bred populations of origin. The frequency of the DNA variants of the test cat are compared to the database to match the test cat to the population with the most similar frequencies, allowing assignment to one or more breeds and/or ancestral lineages of origin. Using the biomarker panels described herein, it is possible to determine the geographical region of the genetic origins of the test cat, whether the test cat is highly related to a known breed, or whether the test cat has a parent or grandparent that is of a known breed. The present genetic assignment tests also find use breeding strategies, e.g. , to facilitate the selection of a mating partner that is genetically dissimilar, or as a new foundation for a breed stock.

2. Felines Subject to Testing

[0083] The methods find use in determining the contributing feline populations of origin of any feline, e.g., any member of the family Felidae. Oftentimes, the feline will be a domesticated feline. In various embodiments, the feline is a member of the genus Felis. For example, the feline may be a member of Felis silvestris or Felis catus. The feline further can have one or more identifiable phenotypic or morphological features associated with one or more recognized cat breeds, by a cat registry. For example, the feline may have genetic contributions from a cat breed recognized by one or more cat registries selected from the group consisting of The International Cat Association (TICA; tica.org); the Cat Fanciers' Association (CFA; cfa.org); The Australian Cat Federation (ACF; acf.asn.au); Co-Ordinating Cat Council of Australia (CCC of A; cccofa.asn.au); Federation

Internationale Feline (FIFe; fifeweb.org); Governing Council of the Cat Fancy (GCCF; gccfcats.org); The New Zealand Cat Fancy (NZCF; nzcatfancy.gen.nz); The Southern African Cat Council (SACC; tsacc.org. za); The World Cat Federation (WCF;wcf- online.de); American Cat Fanciers Association (ACF A; acfacat.com); The Traditional Cat Association, Inc. (TCA; traditionalcats.com); International Progressive Cat Breeders' Alliance (IPCBA; ipcba.8k.com); Canadian Cat Association (CCA; cca-afc.com); Cat Fanciers' Federation (CFF; cffinc.org); American Association of Cat Enthusiasts (AACE; aaceinc.org); Australian National Cats (WNCA; ancats.com.au); Capital Cats Incorporated (CCI; cci.asn.au); Catz Incorporated (catzinc.org); Council of Federated Cat Clubs of Qld (CFCCQ; cfccq.org/index.html); The Feline Association of NSW (TFA of NSW;

tfansw.webs.com); Feline Control Council (FCC; hotkey.net.au/%7Efccvic); Gold Coast Cat Club Inc. (goldcoastcatclub.com); The Governing Council of the Cat Fancy of South Australia (GCCFSA; users.chariot.net.au/~gccfsa/index.html); NSW Cat Fanciers'

Association (NSW CFA; nswcfa.asn.au); Queensland Feline Association (QFA;

qfeline.com); Queensland Independent Cat Council (QICC; qicc.org.au); Hong Kong Cat Lovers' Society (hkcls.com); Korea Cat Club (KOCC; kocc.or.kr/link/link.htm or ticakorea.org); The Cat Federation of Southern Africa (CFSA; .cfsa.co.za); The Asian Cat Association (ACA; asiancats.co.uk); Bavarian Cat Fanciers' Association (bavarian- cfa.de/bcfa.htm); and Feline Federation Europe (FFE; ffe-europe.de).

[0084] Illustrative breeds include without limitation Abyssinian, American Bobtail,

American Bobtail Shorthair (SH), American Curl, American Curl Longhair (LH), American Shorthair, American Wirehair, Balinese, Bengal, Birman, Bombay, British Shorthair, British Longhair, Burmese, Chartreux, Colorpoint Shorthair, Cornish Rex, Cymric, Devon Rex, Egyptian Mau, European Burmese, Exotic Shorthair, Havana Brown, Himalayan, Japanese Bobtail, Japanese Bobtail Longhair, Korat, LaPerm, Maine Coon, Manx,

Munchkin, Munchkin Longhair, Nebelung, Norwegian Forest Cat, Ocicat, Oriental

Longhair, Oriental Shorthair, Persian, Peterbald, Pixiebob, Pixiebob Longhair, RagaMuffin, Ragdoll, Russian Blue, Scottish Fold, Scottish Fold Longhair, Selkirk Rex, Selkirk Rex Longhair, Siamese, Siberian, Singapura, Snowshoe, Somali, Sphynx, Thai, Tonkinese,

Toyger, Turkish Angora, Turkish Van, Chausie, Savannah, Bambino, Donskey, Highlander, Highlander Shorthair, Kurilian Bobtail, Kurilian Bobtail Longhair, Minskin, Ojos Azules, Ojos Azules Longhair, Serengeti and Sokoke, and mixtures thereof.

[0085] The feline breed assignment tests were developed based on the

understanding that 44 breeds are genetically definable of the world's 54 major breeds, not including longhaired and shorthaired varieties with the same breed name. The assignment test described herein attempted to include all breeds recognized in three or more of the following registries: CFA (USA), TICA (USA), GCCF (UK), and FIFe (Europe). However, the assignment of a cat to a breed was mainly based on USA populations. Cats representing breeds from other world regions will therefore be assigned to a cat breed in reference to the genetic structuring of USA cats.

[0086] Additional populations can also be added. For example, breeds that are considered preliminary or under development, as well as breeds specific to a particular geographic location (e.g., breeds or populations specific to an island location), can be added to the databases described herein, first as a preliminary breed and then as an established breed. For example, the Selkirk Rex and American Curl breeds are under development. Also, additional analyses could be used to further refine population and breed definitions when compared on a less global and more regional scale.

[0087] It is recognized that the definitions of cat breeds vary between registries around the world, and that different breed registries accept and refuse different color varieties and variants, sometimes even defining a breed. For example, some registries define a Himalayan as a pointed Persian. For the sake of reference, the CFA definitions of breeds were used in the tests described herein to make assignments. Thus, a pointed Manx, which may be a defined breed in another cat registry, may seem to have an inappropriate assignment. Straight-eared Scottish Folds and tailed Manx may be difficult to define if not only by their breed heritage. These nuances of breed definitions need to be considered in the analysis and interpretation of results. It is further recognized that many breeds may have longhaired and shorthaired varieties, some using a different name, such as Manx and Cymric.

[0088] In various embodiments, the feline is a hybrid, e.g. , having genomic contributions from one or more wild felids. For example, the Bengal is a cross of various cat breeds and random bred cats with various sub-species of the Asian Leopard cat (Felis bengalensis, a.k.a. Prionailurus bengalensis). The Chaussie breed is a cross of various cat breeds and random bred cats with various sub-species of the Jungle cat (Felis chaus). The Savannah breed is a cross of various cat breeds and random bred cats with various subspecies of the Serval (Felis Serval). Some cat breeds are mixtures of these various hybrid breeds, e.g., the Desert Lynx.

3. Biological Sample

[0089] The methods may comprise the step of obtaining a biological sample comprising genomic DNA from the feline to be tested. The biological sample may be obtained in the laboratory conducting the analysis or by another party (e.g., a veterinarian, a guardian of the feline). The biological sample can be from solid tissue or a biological fluid that contains a nucleic acid comprising a single nucleotide polymorphism (SNP) described herein, e.g., a genomic DNA sample comprising a plurality of the genetic markers listed in Table 1 , particularly the SNPs depicted in SEQ ID NOs: 1-148. The biological sample can be tested by the methods described herein and include body fluids including whole blood, serum, plasma, cerebrospinal fluid, urine, lymph fluids, semen, and various external secretions of the respiratory, intestinal and genitourinary tracts, tears, saliva, milk, white blood cells, myelomas, and the like; and biological fluids such as cell extracts, cell culture supematants; fixed tissue specimens; and fixed cell specimens. Biological samples can also be from solid tissue, including hair bulb, skin, cheek swab, biopsy or autopsy samples or frozen sections taken for histologic purposes. These samples are well known in the art. A biological sample is obtained from any feline to be tested for the genotype of the genetic markers as described herein. A biological sample can be suspended or dissolved in liquid materials such as buffers, extractants, solvents and the like.

4. Biomarkers Useful to Determine Breed and/or Population of Origin

[0090] Genetic markers useful for the determination of the contribution of one of more feline populations or breeds of origin are listed in Table 1. The methods of the invention analyze in a test feline the genotype of a plurality of genetic markers depicted as SEQ ID NOs: l-148 in Table 1 , also identified by their chromosomal location.

Table 1 - SNP sequences useful for breed identification test

SEQ ID NO: SNP ID Sequence

1 A1J0141047 TCAATAG CAG G AG AMCAAGATCAMCCATG CCCGG GTTTCAATG CCTTG

GGGAAATAAC[A/G]GAGAGAAGGAAACTTTATTAAGGCGATCCCGTCAACT CTACCCATTCCTCGGAGGCGTTT

2 A1J33621071 GTAAAACACGACAACATAGAATGACACTCACTGTGGCAGTCGAAAAGAGG

TACTTGGCAA[A/G]TACCATGGGAATGTCATACGGGATGCATGCTACTGGA GGGATGTCTATAGCCTTTCCACT

3 A1J51648701 TAGCACCAGATCAAAAAATGAGTGGATTTCCCTGTCTAGCTCCTTCACCA

CCACAAGTTC[T/C]GCATGTTTGGTCTCATCAGGCCCCACGATGACATCCA GGGCAAAGTGCTCGCTGGGGGAC

4 A1_175780586 TTGTGGAATGACACCGTCAGAAAGGAGATTTCTTGGGCTACTGTGGTAGC

TAGATTCCCG[T/C]GGAAGGGCGTGCCTTTCGGTTACAACGTATTGGTGC TAGGCTGCCTGGACCACTGGCTTT

5 A1_208054462 GAAACGGAGTCACAGGAAGTAAGGGTTGGTATTATATTTTTAGAAGTATTT

ATTGGGGGA[C/G]GGGGGATAAATAGGTGGGCTCAGAGAATAATATTTCC AAG GTCACAG GG CTAATG AG CCT Table 1 - SNP sequences useful for breed identification test

SEQ ID NO: SNP ID Sequence

6 A1_223501140 AGTGATCCAAGGAGGTGACGAGGGACCATAAGCCTTGATTTATGACCTGA

GGTTTCCATC[T/C]CAGAAGCCACATCATCAGTCCCTCTGGGAAAGAGTTT TAACTGGATGAACTGCCCTTCTA

7 A1_223506906 GTTCATTGAGTAAGATGTTCATCACCCTCTTCTTAGAAATAAATTCCCTTT

GCTTCATCA[A/G]GGAATCATGAACCCTTAGAGCTAGAAACGACTTTGGAG GTTATGTGTTTAAGTGTTTTTG

8 A1_225057933 AATTACCCAATTCCTCCCTAGTTATCGTATTCAGTGACACAGATAACAAAA

GTTAGAAGT[T/G]CTTCGATTCACATTCACAAAGATGCACCATGAAATCATA GTAACTTGGAGTAAGTGGCAG

9 A1_235579538 CTTTTGATTCTATTTTGGGTCACACGTGAAACCCACAGAACAATCGACAAA

AGCCATTTC[A/G]TCTTCTCACTCTCTTCAGTTTACCCTTTTGCTTAGTTTAT TTCATTTCGCCAACATTTTT

10 A1_27523501 ATGCAGTCCTGCCTAAATGTAGGAGAGTCCTGAAGATTTTCTGGATCTAA

TCTCTACCAT[T/G]TTGTGCCAAGTTTGAGGACTCATTATACTTTAGGCTTT ATAAAATATTTCTCCTCTGGGT

11 A1_68485376 ATTATTTGCAGGATCTACGTTCATTACTTGAGACAGGACGATTCATTAAAT

GTTAGAAAT[T/C]AATTCGTGGAGCAAGTAAAAAGGTGGAAGAAGTGTTAG GAAAATCACTTGAGAAAACGTA

12 A1_69424718 AACTCAATCAATCCAGGCATCCTTGTCTGACCAGGAGGAAAAAATAAACA

CAGCAACGTG[A/G]AGGCGGAAGCTCGTGCTCTGGAAACAGTCAGACCTG ACTCAATTCCAAGCTCCCGGATGT

13 A1_7429296 GAGTCAAGCTGTCGCTGTTTCTGGTGCAAAACCAGGCACAAGGTACACA

GTGATATTAAA[A/G]GCTCGTGGGCAAAACACCTTCCTCAGCCCGGGAGC GACACCTGTGGCAATATAATTTGAT

14 A1_8742286 CCTTCCCTTACTGAGAGACAGTCAATAAACCTTCAGAGGAGGGCTAAGCA

TGACCCGCAG[T/G]GATCCAAGAACACACCAGAAGAAAGGGGATCATCAC AGCCAATGCCAACGTAGGGAGTTG

15 A2_152258936 CTAAAACTTCATTTGGTTAAAACAGAAGAAGAGTCAAGCACTTCTCTTCCT

TGTGAGCTA[T/C]CATGTAGCCAACACTCTGAACATAACATGCGCAACGGG AATATACTCAGCTTCCCAACTC

16 A2_201526186 TCGAGAAATAGGGGACACAGCAATTCAATCTCCTGGTTAAACCAAAGCTT

AGATGAAGAC[A/G]TCTGGTTCTTTAAGCCTTTCTGCTGAAAATAATCATCC GAGGTACTAAGGTCCCTTTTGA

17 A2_202225770 GCAGAATTTGTCGTAAAGAGAATTCTCACACGTGAGGACTTTCCCTCTCTT

GTGTTGCAT[T/C]GTCAAACTAGACCTGCATTTAGGCCCCTGGTTGTATAA ACTCCAGCTTAGTTATCCAACG

18 A2_44241149 GTTTTTCCTGAACCTTCCCACCTTTAATGCATCCTGGAGCAGTCCTTCAGC

CTGCTTCCC[T/G]CCAGTCTTCTTACTCTTTCATTTTAAATAATGTAATAAC GTTGACATTTTCATTTAGAGT

19 A2_554046 GAACCCACTTTGCAGATGGGAGAACCATGGGGTTACACTTCGGCATCTC

CCTGAAATCTG[A/G]TGAGACACGGAATGGAGGCCTTCTCAGCAGATACT GGGTGAGAGTCACATTGATGTGCTG

20 A3_101420069 CAG ATTTCAGG G AG CAAAGG G ATCAAATTAACTTTTCTCATG GTTCTATTT

TTGTGACTC[A/C]ATTCTTTTGGAGGAGAGAGTCAGGATGACTGGTGGGAT TTCCAGAAAAGCCAGAAACAGT

21 A3J2082294 CAGACACATTGGGATCATGAAAATCAGCCTCAGTTTCAAAAATAAATCTGT

TACCTCCAT[T/C]AATCATGAAAAACAATTGGTCAATGGCCTGCAGGGGTG GCAGCTGTGTCAAAGCAGGGGC

22 A3J30195244 ATATGCTCAATAAATAACGATCACTCGTTTGCTTATTACTCGTTCGGGTGG

GGATACAGA[T/C]GTATATACCTAAAATTACAAAACAGCGTAAGATCTGTC CTGGTTACATGTACCAGGTGAA Table 1 - SNP sequences useful for breed identification test

SEQ ID NO: SNP ID Sequence

23 A3 159537633 CCGCAAGTTGGGCGGAGGTTAGGTGGGCAGACTCACCTTCTTGTCCATG

AGGCAACCAAA[C/G]AGTAAGATGAAGACACCCTTAACGGGGGGATACGG GGATAAGTTAGTCACGGAGGAAGGA

24 A3 162208567 ACGAGTCGTGTTAAATAAATCGAGGATGGCATTGTTAGTCCTCTTTCCTAA

GTAACCTTC[C/G]AGGCTGTCAGATAAGCTCTCCCTGTGGTTTCTGTTCTC TTTTAGAATGAGCCCTTCCTGA

25 A3_38781591 AGCATTTATCAAAGGAATGATAAGGAGTAAAGTAGAACATTAGTCCATAG

GTGAAAGCCC[A/G]TGAGAAAAAAATTAAATGCCCAAGTCATTAAGTGCAT

GGCTTGACAAATTGTTTAAAAGg

26 A3 75156179 CTATACTAGTTTTATATCATCCTTGCCTGTTATGGATCGAACGAGTGTTTTT

CCTGCTGT[A/G]CAAAAACGTTCCTTGACAATATATTTTCTACCAATAATCA

TTATTTTAATAAAACCAGTG

27 A3 91058022 GTTAAGCCCATCTAACTCCTGAATTTTTTCGTGTGATTGTTTCATAGAAAA

GGTAAGCTT[T/G]CTGGGAAAGCAGGTATGGAGAATTTTTATTTTAAATCCT

TGAAACTGTGACAGATGTTTA

28 A3_99507784 CAGCACAAACATCTCCTATTCCTTCCTTTCTCATTCCATAATCCTTTGAATA

TGCATCCA[A/T]TAAAACACTTAACACATTGTTCTGTGTCAGGAGTGTCTGT CTCTGATTAGACCAGCAGTT

29 B1 10420438 GGGTAGGGTTGGGAATGAGGGTGAAGGTAGAAGGAGGGATAGAAGGAC

AAGAAGAAGCAA[A/G]GAGCATCCTGGACAATCTGTGTCATTAGCTTCTGT TTG CACATG G CCAAG G CACTTGCTT

30 B1 12214271 CCTTCCTCTTCACCCTGCCTCTCGGGCATGAGTCACCATTTCCTCTTAAAA

TATGGAGAA[A/C]TACCAAACGTGGCTTTCATGTGGGTTGCACACGTGGTA ATGACTGAGTTGGGAAGACCAC

31 B1J95678303 TGGAGGGCATCAGAGATGTTAGACACCATGGGCAGGCTTACCTTGAATTT

TAG GTG CTCC[A/G] AG G CTCTGAG GTTCTCCATCATAAAGTCAAG GATTTG ACAAGACTTGAAGTTGTTATTGA

32 B1_199564532 TACTTGGAAATGTTCATTTCTGAGCTTCCTGTCTGTCGTGGAATTGCTGGA

GAATGGAAA[A/G]TGGGTTTCGTTTTCTCTGAGTAGTGAGGACTTTAAGCC TCTGCACACATTTGTTGCCTTT

33 B1 202966562 AGTCATCTATAACTCAGAAAAAATAAGCAACAGTATAATCCTTAACCTTGT

TAACAGGGC[A/G]GGGGTGTAGGGGGCAAGCACAAACTAAAATGACACAG

GGTATTTCACTAGTTTTTTTTTT

34 B1_54775572 TTTTAATGTATGCTCTTTTATAAAATCTGCATGGCCATTCCGTGTATATGC

GTTTTTAGC[A/G]TGTGCATAAATGATATGTTCTGCGTCTCATTTGGTTTCT TATG GTTCAGTCAG CACTGTT

35 B1_80161671 tatGTAAACACTTATTGAGTATCTATTGCCCTAAAGGGATTCAACAACAGCT

CGATCATA[A/G]TTATAAGGCACAAAAGAGAGATGAGTTTAAGTCTCTACTT

TGATTTTAAAAACTTATATT

36 B1 88148379 CAAGGACCCAACTTGGGTCCCATCCCCATGCCCCACCTCACCCCACAGC

AAGACGTCTTT[T/C]CCATATTCACTGCTCTTCCCTTGACTTCTGAGAGCTT TTG CAATCTACATTTTG ACATTT

37 B2_138312489 TAGGAGGTTGATGAAAGGCATCCGGATCAGGGCCAAGGGTTTGATCAAA

AGCCCAGTAAT[A/G]AGAGTGAGCAAAGTGTAATGTTAAAGAATCAGAGTG CATCCTACTCAGATGTGGCAAAGG

38 B2_146660650 ACAGATAAGTGTCCGTGTTCAGTGGGCTCAAGCCTCCTGGCTCAAGAGA

CTATGGTTTGA[T/C]CAGTCTTCTAGGTGAATCAAAGATAGCTGACTCTGA GGCTTTGACCCTGGATTTCAAGAG

39 B2 41509834 ACAGAGGGCAGTCACCATGGTCACTAGTGGGGACAACGAGGGAAGACTC

TCAGGAGACAC[A/G]AAGGTCAGAGTTTACTCTAGTGCCAATAGTAATAAC ATTTACGAGGTCCCCACTGTGTGC Table 1 - SNP sequences useful for breed identification test

SEQ ID NO: SNP ID Sequence

40 B2 45093345 AGCTCTACAGTTCTGGAAGCTTCCTTAGGCTCCTCCTCTACAGTCCTAGG

CTCAGGCACA[T/C]TCTTCTGGGTGAGGAAGCATCTGTTCCAGAGGATGA GTGTTTCAAGGTTGACTTCCTGAA

41 B2 6949528 CAAATGGGCATAGGTGTTCAAAATTAAAATTAAGTCGTCTGGCATCCAGAT

GAGAGAAAT[A/G]GAAGTTAGAAGTCAGGGAGACGTTGTGCACTGGGCCC GTGATTTGCTGCAGGGCCTCCAT

42 B3_104483970 GGGTTACACTGTTTATGCGGTTGTACAGATCTACAGTTCTTAACAGTTGG

GTTCTGCAGT[A/G]TATTACTCTGGTGTTAAATGGAGGATCATCTGTTTTAG

ATCACAGATTTATTATTCTATT

43 B3 111000326 AAGCAACCAAAAGGTGAACTGTTGATGAGCAGTGTCCTGCTTGATGATAT

TTATCATGTG[T/C]AGCCCCTCCTGAACGTTGACTCCGCGTCATGGCACAC AATTTTCTGATGACAATATACTT

44 B3 13666494 AACAAAG CAG CCCAG CTTCTCAGG GAAGTCTTCTACCTG G GTG ATTACTT

AACCTTTCTA[C/G]AGTTCTGCGGTATTTTTCTCTAGGGCAAAGTCGTAAAA

CCGCCAGGTTTGGGTACAGCCC

45 B3J9203469 CCAGACAGCTGCCAGATCCGGGGGGTGGGGATGAGGGTAAAGGGGATG

GGAGTTATCTCC[T/C]TGCCTTTATGGCTGGCACCTGGAGCCAGGCTGGG TGATTCAAAAGCACTTGGCCAGAGAC

46 B3 51317931 GGCAGTGTGGGAATAAATATTTATAGGCTGGGCTCTGAAACCAACATATT

CTCATTTTTT[A/G]TAGAGCCTTGGGCCAGGCCTATGCCAAGTGAAAATTA ATTTACCCAAGAATTTCTTTTCA

47 B3 57141954 TCCTGCCATCACAGTGGGCACCTGTCAGGTCACAACTACTGACCAAAGA

GAAACCCAGCT[C/G]CTCACTTCTGCCCCTCCCACAGATAAGCAGAACCC CCAGGACCACCATCACTGTGAATGA

48 B3_77094074 GCCTGATGTTTTCTGGTTGGTGGGTGTATTTATCCTTGTTCCTTCTGTCCT

GACCAAGTC[A/C]CTTGCTTCTACAGAGTGAATGGAGCCTAGACTAGCTAA AAATCAAGATTCTACCACTTAC

49 B4_105706694 ATTAGAAACATACCAATAAATGTTATTATTTGAAAAAAGATTTTAGACTCAC

TGAAGCCC[A/G]CAAATATTTAGGCTTTGCCCAAATTTATTTCTACACTACA GGAATTTGCTCAACTACTTT

50 B4 142658074 AG G CCAGAACAATAATATG CCCTTCCG GAAAG GTCTATCACATTCTCAG G

AGGCAAAGGT[A/G]GCTTGAAAAAGCATACTGTAATGTACACATCTAGGAA GGTGGAAGGAGCCTTCACCTGAT

51 B4 143006494 TTTAATTATTAGTTGTACATGATGCATAACCACTGAACTTTCTCTGCATTAA

CAGGATGA[A/G]TATCAGGTAATTAGTGCTCTGACAGTGCTCTGATAATTA GTG CTCTGAGTCCTAG CATTT

52 B4_144693308 CTTCTCTTGGCCTGGAGAGAGCATTGAAGCCACTCCCTCTGTGGGTGCC

TCGCTCCATAC[A/G]TAAACACATGTCTACCATATATACACAGGCACACAC ATTTGTCATTCCTTCCCAGAATGA

53 B4_146486983 AGTTTGTGCTCACTTGTGTTTTTTCCTAATTGTTTTATGTGGAAATGTTTTA

TCTTCATG [A/G] CAG CAG AACACATTCCTTG AG G AAAAAACAATATGTCTT CACTTTATTTTGTCCCCTAAT

54 B4 149532846 TGAGGCCTGGCCAGATCTCCCTGGCCATCTGGGTCCCCTGGCACAGCTT

CCTTTGGTGAC[T/C]CGAGTAATCGTAACAGTTGCCATATAATTGAGGAGC GGCCATGGTGTGCCATACGTCAGC

55 B4_20001848 GGTGTAAGGAGAGAAAACGGAAAAGCTATTTTAACATGAGTTCTCTCAGA

ATGGCGTCAT[T/C]GTGAGACCCTTGTACTTTATTCTCTTTTTGCTTATCTG CATTGTTAAGTGATCTGTAGTA

56 B4_21098349 TTTGTTTGTTACAGGAGATTAAGGGCCTGGCTTCTCTTTGACTCTTTCAAG

TTTACCCAA[A/G]TTCAGAAGTAGGATCACAACACAGCATTTTGTGTGGAG AACCATGTCTCAAATATACAAC Table 1 - SNP sequences useful for breed identification test

SEQ ID NO: SNP ID Sequence

57 B4 255106 TCGCCCATATGGAGTGCAGCAGCCCCTCTGTTAAGGCAATTATCTCATTA

TCGTG G GACT[T/C]TTG AG G CTGTCTTTTATG CTCACCATGACTTTCTTAAT GGGTTTCATCATGTTCGCCTTT

58 B4 3093827 GCCCCGTCATCCAAACTGTTCAACAGGGGAAGGGTGTGAGGCTGACCTA

TGTTGTCCACA[T/C]GACCCCAGAAGGTCTAAGAGAAGCAGGTGTCGTTG GTGCTCAGCAAGTGCCCAGAGCCCC

59 B4_40319102 TTCATAATGAAGCCTGGAATGCCTTCCTCCCAGTTCCATTTGTCCTGCTCC

TCACCCCAT[T/C]ACTCTTTATTTCCTCTCCTGCTTAAGTTTTCTTCAGAGC

TCCCACCCTGTGGGACATGCC

60 B4 47638578 ACCCCTGCCTCCTTTCCCTCATACCTAATAGGAGTCCTCTGTATGTTCACC

AGTGCAGCT[A/G]AATCAAGCTGCTGAGAGTTAACCAAATAAAAGAAATAG GTCTAGCTCTCCTGTAACTGGC

61 C1 116355295 ATCTATCTGGCAGCCCTTCCACCAGCAGTGTATTTCCAAATATCCCACGG

GGCTGTCCTC[T/C]GTCTCTACTGTGCCATCCTTTCTGTCTGGACCATCTA AACAGAGTTTTGGAAGCACCACA

62 C1_123164748 GTTAAGTGTAAGCTGCTGTATGGGGATTATCATTATTTCTTTTGTCAGTGA

ACCATCACT[A/G]CTCTTTGCTCCCTTGCCTCCCCACCTCTGTCTGCCCAC CCCCTTCTTGCCAACCTTTCTT

63 C1 190502133 GCCCCTCTCATCTTGGGCAACACTTCACTGGGTGAGTGATTACAGCATTT

TTCCTCTCAC[T/C]GCCAAGCTGGTCCTCCTGTCCCCTACTTTGCTCTCAG TCTATAAAAACTACTTTTTAAGG

64 C1 215441574 TGTTGACTAATGTGAATAATGATTCTTTTTTTCTATTAAGAAAAGAGAGGAA

CGCATCCA[A/C]GTATTGAGTACACTACCAGCCAGGAATGGTGTATTTTAC AG GTG G GCATCTAATCTAAG A

65 C1_24148281 TGACTATCTTGCCCCCTTCCTTTTGTAGCAACCCTGTTCTCCAGGGTCTA

GTAAGACAAG[A/G]CATGGGAAAACACTTTGCCGTTGCAAAAGCATTGTAT

AAAGTGATGCACAGAAAATGGGA

66 C1_28702055 TGTG AG G ATATG G AGACATCACATAAACTAACAG ACAACTAG ACTTAATCA

ATG GTG G CT[A/C]ATTAG G AG AAG ACTCAACAGTG G AAG CTTCTTAGTTG G GCACCGAAGGTTTCCTGGGAGC

67 C1 34981315 TGTTCAGATATTTGGGTGTTCTGGTTCAAAACTGGTTTCTCTCAAGATTCT

GATAGACCT[T/C]GCCAGCTGCAGATCCCAGCCACCTTCAAACCCATCCTT

AGGTGGCCTCCTCTCGAGACTC

C1J96397 AGACAGCGCCATGCCCGCATCCCGCCACCCTCCCCTCACGCGTCTTCCC

TGGTAATTATG[A/G]TTTTCAAAACTCACCTGATATAAAATTTACCACTTAAC CTTTTTTATTACTTACTTATTA

69 C1_44520932 TCAGGTACGAAGGCTCAGGTATGGAAGGCGGGTATTTTTTAGCCTTGAGA

AAG GTTTAAG[A/G]TCAAG AAGG ACAATG G GTTAAG CCATCTGAG CAAAG GATCATCTGGTCAGAGACAGGAAA

70 C1 52456776 GGGGAGAGCACATGGCGGGGAGTGGGGGGGCAGGTATACGAGATGTTG

CTCCAGTGGAAC[A/G]GATAGAAAATGAGAGGAAGAGGTTAGACAGGAAG GGGTTCGGGTTGATGCTTAGTTCTAG

71 C2_106991233 AAAAGTTGTAGAAAGAAAAAGAAGCATACAAAAGTGCTCAGTGAAAAATG

AGAGGATATC[A/G]CCCAAAGGCAAAGGAGCTACAAGCATGTATTGACAT CATAGTACGGAGTACAGACTCAAA

72 C2_147124460 GTTCTCTTCACACACG CCAG GATATAAACACAAACTTTGCAAAG G CACTT

GTG CCTCACC[T/C] GCTCATTTTCATG CATAAATCAACG G CTATTTCTTACA CAAATG G AAACAAATTTAAATT

73 C2 150774106 TTCAGCAATGGTATAATATCTCTGCAGTCTGGATTACCCAACAGGCAGTT

CAAATACACA[A/G]TTAGGTGTCCCAAACTTTTATTGCTATACTTTTTCATTT

TTAATATACCTATGGAGAGTT Table 1 - SNP sequences useful for breed identification test

SEQ ID NO: SNP ID Sequence

74 C2J56491175 TTGCTACTGCTACCAGGCACAGAGACTATTCTGCCAGGGCAGTTAATTTT

CAATCAGGAT[A/G]CACATTTCAGGGTCTGGGGTGCAGGCATATGCAGTT AGGGCAAGTTCCCCAAGAGATTTT

75 C2_262401 TTGTTCCAAGCGGGAGGGTAGACCTGAGTTCTCTTGTCTGAATTATCCTA

TTTCCTCATT[T/C]TTTACCGTGGAGCCGTCTCCTCTGGCCCAGAAGCTTC CTGGAGCCGAGTCTATGGGTTGT

76 C2_5215469 TAAATAAAG GAGCAAATG AATATAATGG G CTTATTCAACTCTCAAG AATAA

AGATGGGTC[A/G]CTGCTTTTAGGAAATGGGATGTGTAAACACCCACCCA CACCGTCCTTCCCTTTCACCATG

77 D1_101321498 AGGAAGAAGGTTATGTTATCCAGTGTACCTGGTGTTCTAAGGGGAGGCTC

TTGGAATTCA[A/G]ATACAGGCCTGTCTAATGCTGAAGGTCTTGACTTCTT GGCCAGGCCATCTTGTTTTCAGT

78 D1J04941557 GATATTCTGTACAGCTGTTGTGCAGCTTGTTGCCACAGCAATGGCTAAAT

TTCAAAG GTG [A/G] GCCCAG GATATGTG AAGTAG GG CATAG AAGATTTCC AACACACCCAGGAACCTTTCCGGG

79 D1J05498119 GCAGATGTCTTGTCAAAACAGAATAAGAAACTGTGAAGAAATAGTCTGTG

AAACCCAGCC[A/G]TGTCCTGCTTCCTCTCATTTCTGGGGTAAAATAGCAG CGGGGGGAAAAATGGCTTTTAGT

80 D1_10789012 GTGAAGAAAGAGGAGTGATGGGGAAATCTGGGCTCACTAGGTGTAGCCA

CTCTCTCCTTT[A/G]GAATGTACTGGCTAAGCACTGACATGTTCTTACTATT CAGTTGCCTTCCTTATACAATTT

81 D1_11484008 TGGGTCCAACCTTTTAACCCATTTCTTCACCCATTTGTCTACAGTGGAGAC

ACCAGAAGT[T/C]GTCTAGCATCTTAAGAACAATTGCTCTCACTCTGAATG GTGGGATAGTCTGGTTGCCTGG

82 D1_117527468 CACCGAAATCTCTTCCATCGATGTCCAGGTGGTGCCAATTGCAAAATATT

GCTTCCCTTC[A/G]ATAGTATAAGGAAGTACACGGTCTTTTGCTCCGTACT CGTGAATAACGAATGGGAAGCTT

83 D1_125811329 CTGGCCTTTGGCTCAGGAATCTGTCGGCGAAACCAGTTCTGATTTCTTTT

GCCCCAAATC[A/G]TTTGCTCAAAGGTAATGAATGCGGTGGCAATTCTCAG TTTGTGTCCTGCTGTAAATATCT

84 D1J26256993 CAGTGAAAGGGCACCTTACCAGCAAATAGGCTTAAGCGACGACCTTACAT

AAAGACAGGC[A/G]TTTGGAACCTTCTGCCCAGAGCAGCTCCACCCTGGA GATCCTGCCCAACTTCCTGATCGC

85 D1J26847301 ATGGGCATGGCTTCCCAAGACATATTTATGAATTGAGGATCTGATAGAGA

AGGTAAGATG[A/G]GGTCAAAGTGGGGAAGTAGTCAGCTCTAAAACAAAA ATGAGAAATCCCGGGGATTTTAAG

86 D1J5984279 GTGGCGTGAACCAAGATAGTAGAAAAAATGGTACCAGTTTCGGGGAGTC

ATCAAAGGGTG[A/G]TGTGTTATCTATCACTCATCTTCGGTACGGAATTCC TGCCTACCCTGAAGATAAGCCTGG

87 D1J8390852 TTCTTGTGAAAGCCTACCCTTCCCACACAGCTGCAGCTGCTTTGAATGAA

TG G GAGTTCA[T/C] CTG GG G GG AAATG GTCTATTG CTCTGG G GAATTG CT GTCCTCCCCAGAGGCTGGAGGGCA

88 D1J8570323 CCCCCCCCCAAGCTTGGGTGGGTTCTCTTACTAACAATTTGTAAGCACCT

GG GAG ATG CA[A/G]AG GTG CTTTTTATTAATTTCTTAACTTAAG GAAACATA GCTGAAAGGAAATTCTGGATTA

89 D1_66177762 AGACCAGAATGGGCACAAAAGGGGTGTTAAGAGAGTGCCACCTAATGGA

AAATAGGAACG[T/C]CTCCCTGGGTTGCAGAGACAGGTTCCAGAGCCCCA GAGACCCAGAGCACCAGCCTTAAAA

90 D2_1020904 TGAGCTAAATTTGCTTTTTGTGAGAAATTGATGTCAGTGTTTTCACTGCAT

TTATGAACA[T/C]GTTATCATCTTTGGGAGAGAGACCAGAAGAGTAAAATT GAAGAAGAGTACAAAGGGATTG Table 1 - SNP sequences useful for breed identification test

SEQ ID NO: SNP ID Sequence

91 D2J05772916 ATGGGAAGAACGGGGACCGCTCCCGAAAGTTCCGAGACTTTCTATCCTG

GAAGAATGAAG[T/C]AATTCCCACTCTTGTCTCCAGCCGGGGAAGGGGGG GGTGGAGAGTCATGTGAGCCGCCCC

92 D2_1752007 AATGGAGGAATTTTGCCTTGTGAGCCTTCACTTACCAACAATGATGTGGG

TACCTAACTT[T/C]TGGCGCATTGATCTCTTGACTTTGCTGTACAATTTTAA CCTCTG CAG CTTTAATTATCTT

93 D2_56777338 TACCAGGTTGGAATCTTCCTTTCTGTGTTTTCTCTAGGGTTTCTAGGACAC

ATCTGCTTC[A/G]TTTGCCCTAAATCTGAGTCCCACGGGGATGTGTAAACA GTTATTACATTTCCCGCAGGAC

94 D2_74293444 GGGGCCCAGACTGTGAGTGTGTGCAGAACCAGAAGAAATAGGAGTGGCC

CATGTAGGCTG[T/C]TCCTTCTAGAAGTGCAAAGAAGAAGCTGGAGGACA GCTCAAGAAGGGTGTGGGGTCAGGG

95 D2_91989307 AG GAAGG ATGACACTG ACTG AATTCAG G CCCTG G G AACTTTAAATTTCAA

TCCTATCTCC[T/C]CTGCTTGACCACAGTGCTCACAGATAGGACCGTGTTT TTGTTGTGAACTTAATTACACAC

96 D3J03840114 GCAATGCTTCATTGTGGTTATTCCAGAGAAGACAGAGCAATCCAAGCCAC

CCCACTGATG[T/C]TCAGGAACAGGTCAACCAATTCATTTGGGGGCACAG CGTATCCTTATCCGTGCCACACCG

97 D3J22502120 AATAAGAGACTGCATTTCATTGAATCCTCTTTGAACCCGTTTTGCTATTCC

AATAAAGAG[A/C]CCTGCAAGTCCTGATTTCTCTGGAAAATGAATAATAATC TTCTAATTTGTTGTTTCCTCA

98 D3_24565823 TTTATGGAATGGAAGATGAACCTTGATGGATGATAAAGCATGTCATGAAA

GTGGATGCAC[A/G]GAAGGCCAGAATTCTTCAAGTTCACAAGAACCTGAC CAGCGCAGTGACTTCTTTATGTAC

99 D3_24823793 TGGTCTCATGGCTGAGTCAGCACCTGTGCAGAGGCGCAGAGGCACTAGA

CTTCTCCCACT[C/G]GTTGCATTTAACTGAGGGGACACAGCCTGTGTGCTT CCTGTAGGGAACAGAACAAGTAAA

100 D3_28838660 CACACAGGCATAGCCAGAAGAAGGCAGCCAAGCCAGATGCTGGACCAGA

CTCCACAGTCT[T/C]CTGGAAAATCATGAGTTGAAAAGGAAACCCTTAGCC ACCATATAATCTTGTGGCTTAGTA

101 D4_41078218 TAGCTGACTTTAATTCTCCCACAAAGAGAGGAAATTAGCCACATTATTTTA

AAAAGAGAT[T/G]CTTCATTTTACAAAAAGATTGCTGAACATTTCCTAAATG ACAATAG GACATTCAAAAAAT

102 D4_42000379 TTCCAGAGTGACGAGGACGCATAGTCGCTGCCCAGAGAAGATGAAGCAC

TCTGGAGAGAT[C/G]AAAGGCCATTGAGGCCTGAGTGGCTGTACAATTTA GCACTGGGGGTTCTATTCAGCAGTC

103 D4_63622083 CCTCAATGTATGGTGGTGGCTGAAAAGCTGTTGCAGCTTCATGCCCAGAA

ATGCACGTGT[A/G]TACATGAATACATGGGTTTGTGGATGGATTTTGCATG AGTGCATATGTGGGTCTGCATGT

104 E1_130875919 CTGCGTGACTGTAATTTTCCCCTTGAGCCCTGGATACAAATAAAGAGCCA

GCTAATACTT[T/G]CTTTGCCCTTTTTCTACTCTCCCTTTGCTTAACCCACA GGTATTTACAAGGTTTTGGTTG

105 E1_3912105 GTGAGTAACATGGTCCACATACCCATGGAATGGGTGTCCTGAACAAGGTC

TGGGCAGTAG[T/G]TGACAATGAACTGAAAATAAGTGGAGTCATTTCCATG ACCGTAATGATAACTCTATGATG

106 E1_4114158 TTTTATTTTGTTGATTTAGCGAACATTTATCATGTGCCTTCCATGTGTGTGG

TCCCAAAG[T/G]TGGAGTAGTATTGAATAAGGAGGGCAATTCTGACCCTCA CCCAAG AG G CG GTATG AAG AA

107 E1_48228153 AACAGGAGAGTAGGGCTCATATTGATTAGATATCTGTCACGGGTTGGGAA

GGTTTACCGT[A/C]ACTGTGCTATCCATTTTGCATATCAGTCTTCTGACATG TCCTCTTACATCCATTTTATAG Table 1 - SNP sequences useful for breed identification test

SEQ ID NO: SNP ID Sequence

108 E1_48700963 GACGCTTAACCAACCGAGCCACCCGGGTGCCCCTCCTAGGAGGCGATCT

TGAAAAGACAG[A/T]CATAACAGTGCTCCATTATGTGATTTTCAACTTTCTC AGTAAGCATTACAGACTGCCCAG

109 E1_5453028 TCATAAATTCCACCTCCTTTTGCAAAGGACTCCTATGCTTCAGCGTTTCAT

TTTAGATCG[A/G]TAGAATTTAAATTTTCTTGGAATTTTAAAATTCACAGAGA ATCTGGTCTTTTCTTTTAAA

110 E2_22632289 GCAAGATTTGCTTTCCTCTGTATCACCGAGGCAGTTTGCTTCAAAGAACA

ACTATTTG AC[T/C]G GTCAGATG CTACTTGCAGG CAG AG G GG AGTCAAG C CTCAG CACCTTTG G GTG GCACATG

111 E2J4027888 TTTACTTGTAGTATAATAAATGCCATTAGCATGACTAATCACACACCTGATT

AGACAAAC[T/C]GCCCCTCCTCCCCTAGTGTATTTCCTGGATCCTACACCC AATAAATCCAGCATTGATGCA

112 E2_35914023 GGAGGGCAGGGCCTGGTCTTACCCAGTATATCAGAAGCAGCCAGGGTCG

GTCGCCTTCCC[T/C]CCATCAAGTGCTCCCTATGTCCTCCTTGGCTGCATT CTGTGAAAGCACTAGAAGGGAGCT

113 E2_36986631 TTAGGGGAGCAGGAGTTCAGAGACAGGCTTTTCTAAGGACAGGCTTTCAA

AAAAAATGTG[T/G]TTTTTTTGGTTTATTTTACCCTTTTGAAGACCTCACAAC GTATAGTAAGTCACGTCAGGG

114 E2_38860686 AAGCCATGTTGGACGATCTCCAGTCTCTGCGAAAACATGTGACACCATTG

TACATTCCCC[T/C]GGTTAAAACACTTCATTCCCAGTTTTATTGTGTTGCCT GTGGCCCCTTACTTTTGAGGCT

115 E2_65436639 TCTGGGGGACGATGGACTCAGGAGGGGACCTGAGAAGTATGTAGGAGAA

GGCAGGCTAAC[A/G]AAAGGAGGGAGAGTCAGCCGAGTACCAGAGGTGG GG GCAGAG AG G CTCAGTG AGG GAG CC

116 E2_7950477 TACATTCTAGTTCAGGATGGAATGAATGAGGGAAAGAAAAAGACGTTTTA

ATTCCTCAAG[T/G]CTTTCTGGTGTGCAAGTCCCTTTCTGGGAAAGCACAG GTGCTGGTCGAATTCGTTCCCTG

117 E2_8422942 GCTAATTTCTCTGAGAAATGGCTATGCCATGGGGACCTCCTGCTAAATGC

ATGCAACAGA[A/G]TATTCTAGAAGCATGCTAAAATAGATTCAGGGTCCCA TGCCAGCCCACCTGGGCTTGCTA

118 E3J6044809 CAGTTAGTAAGCACTCCTTTGGTTAGTACAGAAAAAGTGAAATGTTGGAG

GTGTGAGAAA[T/C]GCGGTTGGGGGCATGTTGAAGGACAGGGACACGCG CTTTGTGACTGCCAGGTTTTGAGAG

119 E3_55434272 TGCCGCTTCAGATTGGGGAGACAGGTTCAAGGTGACTGCCTCAACATAC

CCAAGTTCAGA[A/G]GGAGGAGCTCCGATCATACACTGTGTCTTCCCCGT GACACCACATGCCCTGCCCCTGAGG

120 F1_20309325 TCTGTGGAGGTGATGTGACAGCCAGGGCACTGTTGTCACCCAGAGAATA

CAGGCATTTGG[A/G]ACTGCTATTAAATCTACTGAAAGCCAGTCACTGCAG AAGAAGGCAAGCTATAGGCCTGCT

121 F1_21799641 TGACCCAGGGTCAAATTTGGTGGCCTCTTCCTTAGCGGTCAGCTTAGCAG

TGAAGGTCTG[A/G]CACATGCTTCCACAGCACATGCTTCGATAAGTGGCTA ACGAAGAAATGAATAAAAGAGCT

122 F1_26100599 CCAAAAGAAACAAAACTGCCAAGTAAAATTGCACTCCAAGAACTGGGCAT

ATGCTTTATT[A/T]ACCCAAACCTCATGTTTATAAGACTCAGTACCGACTCT AATTCAGGCTAGTGGGTCACAA

123 F 1 _27124984 CACATTATTTGTCTGTTCCCACATGATCTCAATGAATGTAAATTCCTTTCAT

CCTGAAGT[A/G]GCCAGTAAGAACACACTCTTCCAGTGAGGCTCCCTTCCT TCAGACCTTTCTGATTTGCAC

124 F1J8051725 CTTTACAACGAAGAGGTACACATTGCTAATGGGAGTCACAGTACGGTGTG

GGCAAAGGTT[A/G]ATTTTTTCTTAATTCTTGTAGAGGCGCCAAAAAGTACA CACAACTCCTACTCAAGTTCAC Table 1 - SNP sequences useful for breed identification test

SEQ ID NO: SNP ID Sequence

125 F1 565223 TGAGAGTTTGGACTTGAGGTATCCTCGTGTTAAAAACGTAGACATTGGTG

TTTCGAAATG[A/G]TGGGAGAATCAGACATTGAATATAGTCAACGCGTTGT AAATTAACATTTCCTTCTAGTTG

126 F1 82716202 CTCCTTTTCCAATCTCATCTCTTATCAACTCTTTCTATGCACCTTATATTCA

GGCTATAC[T/G]GAATTACTGGTGGTAGCCTGAAATGCTGACCTCTTTACT GGAATACCACCTTTTCCTTAT

127 F1_91517402 TGCTTATCCACCTAATAATATAATTCAGTAATAGCAATACAAAAGTATGATC

TTTGTTTG[A/G]TCCCGATTCACACAACCCAACTGCAAAAAGACCGTTGAG GATATTTGGGTGCTTACTGGA

128 F2 26886470 TAACCTAAAAACGTACAGTGGGAATGCACGAGTACCCAGCAGTGCTTCAG

GCAATGCGTG[A/G]CTTGTTAGTTTGCTATGTTTGGGGACAGATTCTTAAT

GTCCTCAAATAAAATTCCAGATA

129 F2 38395360 AAGACAGCTACATTCCAGAGGATCCAATTTTGCCTTGTAGAGTATAGACAT

CCATGGCAC[A/G]GGCCTTGAAATAGCCCAGCAGGGGGAATGGTTGAAAC TCCAGGAGCTGTGCCTTTATAGA

130 F2_46855978 TTCCTTTTATGGCCTGGAGAAGGTTTTCTAACTTTCCGCTACATGCCATCC

GGTCTGGTA[T/C]AACTGACGAAAACAATTGTTGAAAGTACTGTCCTCTGG TTAAATAAATTATGGTACGTCT

131 F2 74863327 TGACTGCACTTCCTTCTGAGCTGGAGGAGGGGCATACAATGCACCCACT

AACTAGGTATG[T/C]GATTCCCGGTCCATCAGAACCTGCATTCCCACCAGC

ACCTTAGCCCCTCTCCACGTTCTC

132 F2 78303221 AAAGTGTCATCGATGCCCAAACCACGACTGATGAAGGATGGGAATCAAAT

CGTCTGTCTT[T/C]GTAGAGGCCACCAATGGTACTAACCACGCTGGAAAC

GAACGCCTGTTGGTTAAATGTACA

133 F2_79632602 CCAGTGTACCTTTTGGTAACCATTTGCATGGGTGTTTTGCTGCATTAGAAC

ATGTGAGTC[C/G]CTCTAGCACTTGTGTGCGGGAATCCCACCACCAGGAT

AATCTGGAGGTCATGTTACGGAG

134 F2_8427817 ATTTTGTCACGTAAGCAACACTGGAATTCTGAATGTGTGTTCGCAGGCGC

TCACATAATT[A/G]CCAACTGTGATTTTAGACGAGCCTGTGCCTCGGATCC CAATATTATTTATCATGCACGTT

135 C2 187325 AATCCTAACATTCATTAAAAGGAAACTGTAAGCCGCGACCGTGTGAGATC

ACGTGCTTCT[A/C]GATTTACAAAAGAATTCTAGTCCTTCAGCAGCTGTTTA AAAGTACTTTTAACTAACTAAA

136 D2_717969 aCAGATATTCTTTTTTATAGCCTTTTACTTATTAGAAGAACGATAGGTACTC

GTGATACT[A/G]CGTTTTGAGCTCTGAGAAGATACGTAGAATCATTAAGTC ACCGGGGGTAACTGTCGTCAG

137 B4J687419 CTCTTGCTGCCCTAAAGGTGGACTGAAAATGGAGTTGGGTGATCATCCCC

AGTGGATGCT[T/C]GTAGCACTTAGCTCATGATATGTGCTCTATAAATATCA GCCTTTATAAATTATTAGTGCT

138 D3 1810839 ACTTAACCCAGGGAATTCTTCCAGGAAGCACTTCAGAAAATGGAAAGCAC

CACATGGCAG[T/G]TTTCTTCTGGGTTTAAAAACTGCTATTTGCGGGTACC TTTGTTTGTATTTTCAAGCAGGA

139 A3_11480952 AGAACATTGACCTTGAATGCATGCAGTTTAGGAAGTGAAGGCCAGACCAC

CAAGGAGGCC[T/C]ATGTTGGCTGTGTTTTCACATCTTGCTTCCCCTGGGG AAACCAGGGCTGGGGCAGGAAGG

140 D1J6242433 GGTTTCTGGAAGTGCAATTCCTCAATATCCTGCCTCCCAACCAGGTAGGC

AG G AG AGG AA[A/G]ACCTAAAATCTG CTG AATTACTTTCTTGAATTTG G CC TGTTTTCCAGGCTGTCTACTATG

141 E2 39211557 ACTAACGTCAAGACCCGTCTGCATCCCGAGGACAGGAAATAAGCAACTCT

GTGCATTTGC[A/G]CAAGACCTTGCGGAAGACCTCATTACAGACGAGTGG

ATGTTTGCCCATCAAGCTCTTTGT Table 1 - SNP sequences useful for breed identification test

SEQ ID NO: SNP ID Sequence

142 E3 67006512 CCCGGACGGCCCACTGTGAAGTTCTGCCCCGCATGTCCCTGAGCGTCAT

CCACCACCTG A[T/C] CAG CTCATAATAG AG CTG G ACTCCCCAG CACTCCG GCATCCCTCCCCCTCCCACCTGGGA

143 F2 68572596 AGCTATTGTTCCAATAAAATTACTTCCTAAAACTGCACTCAAATTTCAATTT

ATCCCATG[T/C]GAATCTATTCAGCAATTTCCCCAACTTCTCTTAGCTGTCT GAAACCTCCTTTTCATATTT

144 F1_82068276 CTGTAGTAACATTCCATAATGAGAATATGAGATTATTCCGGATAGTCATAG

ACACAAGAT[A/C]CACTACCCTTCAGTCCCTGATAAATGCTCAGTGTCTGT GGTTTCCCTTCCATTATTATTT

145 E1 131587399 TCG CTG CTGG ATG G CTG ACG GTTTTTCAAAACCTGCAG AAAGAAACAAAG

TTAGTTCTAA[T/C]CACACACTGGAAGCTCCTCGGTATCCCTGTTTAAGCC CTCACCCCCACCCCTCCTAACCC

146 B4 147206961 CCCCGATGTGGAAATGCTAGCTTGGGGCCCAAGTCTCTGTCTTAAGGGT

AACAGGGAATG[A/G]TGTCTAGAAAGGACCTTGTGCCAAATGGCTGTGGC ACATGCCATAAGGCATCCAGGTTGA

147 C1_181852965 CCTTCCTGAATTCTCTGCCCTCCCGTTCCTGCACCATTGAAATCCAGAGG

GCATAAGTTC[A/G]TGAAACTAATAGCAAGTAGAGCGGCATAAACAGAAAT AGTTCTTACTATAAATG GAG CCT

148 C1 216852686 GTTAGATTCAGGGAAATTTGCATGACCTGCCCGAGCTCAGTCTTCTGAGT

GAAATGGAGA[T/C]CGTCACGAGGATGGAGTTGGCTCATGTGTGTGCATG TGCTTGTCAGCCTGTATACACCCA

Table 2a - Intergenic SNP minor allele frequencies by breed and population.

Major Minor

Allele Allele

chrAU 0141047 A G 0.146 0.088 0.175 0.286 0.000 0.036 0.063 0.000 0.107 0.000 0.042 0.000 0.458 0.028 0.000 0.094 0.045 0.063 0.125 0.08 chrA1_133621071 C T 0.270 0.315 0.245 0.167 0.769 0.367 0.528 0.526 0.667 0.079 0.500 0.467 0.643 0.316 0.045 0.250 0.065 0.316 0.406 0.41 chrA1_151648701 G A 0.065 0.086 0.055 0.267 0.231 0.100 0.000 0.000 0.028 0.000 0.000 0.000 0.036 0.079 0.154 0.000 0.000 0.079 0.176 0.32 chrA1_175780586 G A 0.394 0.572 0.306 0.500 0.400 0.933 0.563 0.421 0.750 0.750 0.500 0.667 0.773 0.583 0.545 0.528 0.618 0.656 0.533 0.50 chrA1_208054462 C G 0.129 0.185 0.102 0.393 0.150 0.000 0.156 0.083 0.235 0.028 0.346 0.167 0.214 0.194 0.000 0.118 0.045 0.263 0.206 0.12 chrA1_22350114C A G 0.133 0.193 0.099 0.533 0.083 0.100 0.344 0.475 0.083 0.083 0.063 0.000 0.321 0.176 0.818 0.000 0.000 0.237 0.235 0.05 chrA1_223506906 A G 0.260 0.123 0.327 0.533 0.000 0.042 0.100 0.000 0.000 0.000 0.417 0.423 0.000 0.079 0.000 0.167 0.025 0.194 0.029 0.13 chrA1_22505793 C A 0.233 0.128 0.283 0.400 0.038 0.067 0.139 0.375 0.028 0.132 0.250 0.233 0.115 0.026 0.038 0.417 0.060 0.053 0.265 0.02 chrA1_23557953£ G A 0.101 0.027 0.138 0.033 0.000 0.000 0.000 0.000 0.000 0.028 0.000 0.067 0.000 0.026 0.000 0.000 0.025 0.031 0.063 0.06 chrA1_27523501 C A 0.564 0.473 0.605 0.233 0.308 0.714 0.500 0.850 0.406 0.342 0.231 0.000 0.536 0.333 0.458 0.235 0.682 0.412 0.441 0.47 chrA1_68485376 A G 0.059 0.089 0.045 0.000 0.385 0.000 0.000 0.000 0.000 0.000 0.200 0.071 0.000 0.194 0.227 0.167 0.000 0.206 0.188 0.08 chrA1_69424718 C T 0.378 0.311 0.410 0.100 0.154 0.393 0.265 0.289 0.194 0.395 0.409 0.467 0.400 0.500 0.192 0.235 0.550 0.105 0.412 0.35 chrA1_7429296 C T 0.181 0.152 0.196 0.200 0.154 0.133 0.382 0.000 0.194 0.056 0.167 0.633 0.077 0.028 0.100 0.111 0.000 0.313 0.265 0.03 chrA1_8742286 G T 0.373 0.278 0.416 0.067 0.346 0.167 0.000 0.650 0.278 0.447 0.292 0.667 0.000 0.026 0.583 0.368 0.208 0.278 0.313 0.41 chrA2_152258936 T C 0.386 0.401 0.382 0.233 0.417 0.154 0.056 0.194 0.389 0.353 0.231 0.433 0.036 0.079 0.708 0.235 0.370 0.316 0.412 0.33 chrA2_201526186 C T 0.191 0.211 0.182 0.393 0.154 0.286 0.056 0.025 0.083 0.105 0.042 0.067 0.654 0.316 0.042 0.553 0.160 0.361 0.294 0.27 chrA2_20222577C C T 0.327 0.363 0.311 0.000 0.167 0.077 0.222 0.278 0.306 0.342 0.500 0.767 0.333 0.667 0.591 0.147 0.553 0.250 0.382 0.32 chrA2_44241149 G T 0.016 0.008 0.019 0.000 0.000 0.000 0.000 0.200 0.000 0.000 0.000 0.000 0.038 0.000 0.000 0.000 0.000 0.000 0.000 0.00 chrA2_554046 T C 0.149 0.122 0.165 0.700 0.000 0.033 0.531 0.000 0.056 0.026 0.115 0.000 0.538 0.000 0.125 0.158 0.040 0.316 0.000 0.00 chrA3_10142006£ T G 0.052 0.055 0.049 0.033 0.000 0.033 0.029 0.200 0.000 0.342 0.000 0.000 0.417 0.000 0.000 0.000 0.000 0.000 0.059 0.25 chrA3_11480952 C T 0.025 0.024 0.026 0.000 0.083 0.292 0.000 0.000 0.028 0.000 0.000 0.000 0.000 0.000 0.000 0.026 0.000 0.053 0.100 0.00 chrA3_12082294 G A 0.212 0.288 0.178 0.533 0.731 0.714 0.139 0.000 0.278 0.079 0.375 0.200 0.179 0.500 0.000 0.278 0.000 0.158 0.643 0.25 chrA3_130195244 T C 0.188 0.184 0.188 0.643 0.136 0.536 0.393 0.306 0.000 0.237 0.083 0.000 0.389 0.079 0.150 0.417 0.000 0.219 0.125 0.16 chrA3_15953763 G C 0.471 0.316 0.552 0.200 0.423 0.033 0.559 0.225 0.361 0.132 0.250 0.214 0.429 0.263 0.038 0.658 0.060 0.579 0.500 0.12 chrA3_162208567 C G 0.265 0.374 0.206 0.333 0.500 0.107 0.500 0.889 0.594 0.031 0.318 0.577 0.393 0.658 0.083 0.176 0.000 0.412 0.469 0.53 chrA3 38781591 G A 0.013 0.020 0.010 0.167 0.000 0.133 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.02

Table 2a - Intergenic SNP minor allele frequencies by breed and population.

2. . S ro

Major Minor

Allele Allele

chrA3_75156179 G A 0.240 0.228 0.249 0.533 0.462 0.433 0.029 0.000 0.029 0.000 0.000 0.000 0.115 0.026 0.192 0.222 0.042 0.583 0.250 0.44 chrA3_91058022 A C 0.286 0.391 0.237 0.667 0.538 0.038 0.265 0.025 0.361 0.194 0.000 0.000 0.643 0.211 0.077 0.444 0.304 0.711 0.735 0.75 chrA3_99507784 A T 0.324 0.251 0.356 0.071 0.192 0.367 0.088 0.625 0.147 0.342 0.042 0.033 0.625 0.158 0.077 0.500 0.435 0.206 0.188 0.20 chrB1_10420438 C T 0.074 0.117 0.054 0.000 0.038 0.615 0.156 0.025 0.083 0.000 0.083 0.333 0.038 0.333 0.045 0.000 0.000 0.000 0.059 0.05 chrB1_12214271 T G 0.432 0.413 0.438 0.500 0.250 0.100 0.265 0.842 0.389 0.289 0.222 0.600 0.231 0.471 0.115 0.526 0.182 0.421 0.441 0.22 chrB1_195678303 T C 0.181 0.139 0.201 0.067 0.038 0.714 0.250 0.132 0.000 0.263 0.000 0.067 0.115 0.053 0.364 0.079 0.080 0.053 0.088 0.11 chrB1_199564532 G A 0.091 0.119 0.078 0.633 0.115 0.000 0.324 0.000 0.028 0.000 0.000 0.000 0.538 0.139 0.038 0.139 0.042 0.000 0.088 0.14 chrB1_202966562 C T 0.274 0.353 0.236 0.200 0.250 0.467 0.176 0.395 0.441 0.500 0.591 0.700 0.538 0.368 0.000 0.333 0.326 0.406 0.265 0.20 chrB1_54775572 G A 0.119 0.125 0.118 0.933 0.000 0.167 0.028 0.000 0.278 0.000 0.292 0.000 0.036 0.211 0.000 0.222 0.000 0.184 0.029 0.08 chrB1_80161671 G A 0.151 0.210 0.126 0.567 0.192 0.000 0.188 0.000 0.625 0.079 0.300 0.036 0.192 0.469 0.000 0.000 0.000 0.406 0.344 0.42 chrB1_88148379 T C 0.395 0.353 0.420 0.467 0.385 0.036 0.556 0.075 0.588 0.000 0.292 0.033 0.786 0.222 0.292 0.118 0.409 0.441 0.353 0.32 chrB2_13831248£ G A 0.452 0.447 0.449 0.100 0.038 0.900 0.000 1.000 0.306 0.816 0.636 0.133 0.125 0.421 0.083 0.167 0.840 0.389 0.281 0.67 chrB2_14666065C C T 0.019 0.035 0.012 0.100 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.026 0.000 0.000 0.271 0.000 0.000 0.00 chrB2_41509834 G A 0.380 0.378 0.378 0.067 0.308 0.923 0.118 0.447 0.179 0.861 0.318 0.286 0.000 0.139 0.077 0.605 0.979 0.139 0.063 0.13 chrB2_45093345 A G 0.364 0.406 0.344 0.786 0.583 0.154 0.200 0.263 0.559 0.028 0.409 0.654 0.077 0.658 0.125 0.088 0.261 0.667 0.618 0.53 chrB2_6949528 G A 0.126 0.151 0.115 0.767 0.038 0.067 0.056 0.083 0.167 0.000 0.038 0.333 0.083 0.421 0.000 0.083 0.000 0.211 0.176 0.21 chrB3_10448397C G A 0.324 0.324 0.318 0.233 0.000 0.533 0.222 0.900 0.083 0.842 0.250 0.200 0.269 0.026 0.923 0.441 0.783 0.167 0.147 0.05 chrB3_111000326 A G 0.191 0.123 0.225 0.300 0.000 0.267 0.000 0.075 0.118 0.263 0.042 0.067 0.038 0.053 0.458 0.000 0.180 0.026 0.000 0.00 chrB3_13666494 C G 0.491 0.560 0.454 0.567 0.583 0.833 0.472 0.625 0.500 0.868 0.269 0.900 0.077 0.722 0.846 0.676 0.813 0.611 0.382 0.35 chrB3_39203469 G A 0.215 0.128 0.259 0.000 0.308 0.200 0.028 0.000 0.088 0.028 0.083 0.167 0.000 0.263 0.000 0.118 0.023 0.139 0.063 0.20 chrB3_51317931 C T 0.304 0.322 0.293 0.571 0.038 0.433 0.000 0.471 0.000 0.737 0.385 0.167 0.000 0.053 0.846 0.083 0.978 0.088 0.147 0.08 chrB3_57141954 G C 0.285 0.394 0.234 0.000 0.667 0.167 0.000 0.100 0.444 0.026 0.409 0.000 0.692 0.611 0.125 0.412 0.000 0.694 0.588 0.59 chrB3_77094074 A C 0.082 0.135 0.060 0.033 0.231 0.364 0.206 0.031 0.250 0.088 0.150 0.192 0.091 0.200 0.050 0.067 0.000 0.361 0.094 0.10 chrB4_105706694 T C 0.456 0.427 0.464 0.067 0.462 0.077 0.639 1.000 0.471 0.471 0.458 0.500 0.818 0.528 0.550 0.324 0.457 0.324 0.324 0.50 chrB4_142658074 A G 0.137 0.175 0.115 0.600 0.045 0.143 0.233 0.553 0.265 0.026 0.273 0.071 0.167 0.088 0.000 0.412 0.000 0.053 0.206 0.33 chrB4_143006494 A G 0.254 0.220 0.273 0.000 0.808 0.100 0.500 0.000 0.059 0.059 0.045 0.067 0.154 0.147 0.222 0.618 0.000 0.206 0.469 0.53 chrB4_14469330£ G A 0.390 0.401 0.380 0.385 0.773 0.667 0.367 0.947 0.194 0.889 0.292 0.321 0.107 0.158 0.636 0.206 1.000 0.324 0.529 0.21 chrB4_146486982 T C 0.278 0.273 0.282 0.750 0.000 0.786 0.029 0.025 0.133 0.737 0.125 0.033 0.154 0.053 0.700 0.278 0.896 0.111 0.088 0.00

Table 2a - Intergenic SNP minor allele frequencies by breed and population.

Major Minor

Allele Allele

chrB4_147206961 T C 0.382 0.446 0.354 0.679 0.050 0.750 0.194 0.235 0.115 0.778 0.450 0.600 0.273 0.143 0.808 0.321 0.896 0.208 0.235 0.20 chrB4_149532846 C T 0.074 0.125 0.050 0.767 0.000 0.071 0.406 0.000 0.333 0.000 0.042 0.036 0.107 0.053 0.000 0.281 0.042 0.194 0.118 0.08 chrB4_1687419 C T 0.057 0.024 0.077 0.000 0.000 0.000 0.031 0.000 0.028 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.056 0.031 0.29 chrB4_20001848 T C 0.030 0.058 0.018 0.433 0.038 0.107 0.382 0.000 0.000 0.028 0.000 0.000 0.045 0.000 0.000 0.028 0.000 0.412 0.000 0.05 chrB4_21098349 C T 0.406 0.479 0.376 0.750 0.708 0.300 0.375 0.125 0.844 0.389 1.000 0.607 0.182 0.567 0.077 0.447 0.000 0.618 0.781 0.67 chrB4_255106 G A 0.063 0.095 0.047 0.400 0.250 0.000 0.321 0.000 0.029 0.000 0.364 0.250 0.038 0.206 0.000 0.000 0.000 0.088 0.059 0.02 chrB4_3093827 C T 0.132 0.227 0.086 0.333 0.000 0.714 0.444 0.083 0.063 0.526 0.083 0.107 0.000 0.000 0.923 0.000 0.636 0.079 0.059 0.00 chrB4_40319102 C T 0.546 0.478 0.578 0.393 0.167 0.786 0.647 0.421 0.438 0.778 0.000 0.633 0.625 0.559 0.955 0.781 0.222 0.353 0.206 0.29 chrB4_47638578 A G 0.055 0.101 0.032 0.033 0.038 0.000 0.083 0.000 0.278 0.000 0.038 0.000 0.000 0.412 0.000 0.000 0.000 0.250 0.235 0.11 chrC1_11635529E C T 0.272 0.295 0.258 0.167 0.154 0.267 0.118 0.525 0.294 0.158 0.346 0.000 0.042 0.158 0.000 0.184 0.413 0.618 0.647 0.50 chrC1_12316474£ C T 0.217 0.233 0.212 0.923 0.192 0.033 0.600 0.000 0.083 0.158 0.577 0.300 0.538 0.263 0.038 0.000 0.400 0.083 0.063 0.20 chrC1_18185296E G A 0.216 0.301 0.156 0.278 0.417 0.400 0.438 0.115 0.556 0.167 0.214 0.500 0.200 0.542 0.444 0.400 0.029 0.423 0.063 0.16 chrCU 9050213 - G A 0.241 0.146 0.291 0.133 0.231 0.000 0.056 0.025 0.333 0.028 0.900 0.167 0.538 0.194 0.000 0.026 0.000 0.211 0.176 0.05 chrC1_21544157^ C A 0.374 0.430 0.348 0.267 0.154 0.615 0.647 0.118 0.176 0.763 0.417 0.857 0.808 0.605 0.091 0.344 0.455 0.118 0.559 0.25 chrC1_216852686 G A 0.326 0.353 0.314 0.857 0.364 0.214 0.265 0.050 0.324 0.029 0.208 0.000 0.864 0.605 0.000 0.318 0.020 0.531 0.500 0.35 chrC1_24148281 T C 0.202 0.198 0.206 0.033 0.231 0.143 0.147 0.075 0.406 0.053 0.308 0.321 0.036 0.132 0.792 0.000 0.000 0.735 0.382 0.22 chrC1_.28702055 G T 0.070 0.091 0.060 0.000 0.077 0.000 0.000 0.000 0.324 0.000 0.038 0.000 0.000 0.026 0.000 0.079 0.080 0.472 0.088 0.28 chrC1_34981315 A G 0.043 0.044 0.043 0.000 0.038 0.067 0.028 0.000 0.000 0.026 0.000 0.000 0.000 0.000 0.000 0.000 0.042 0.000 0.118 0.03 chrC1_396397 G A 0.254 0.269 0.244 0.000 0.292 0.367 0.471 0.579 0.500 0.079 0.273 0.615 0.179 0.447 0.000 0.222 0.000 0.267 0.344 0.32 chrC1_.44520932 C T 0.074 0.087 0.068 0.133 0.208 0.000 0.375 0.000 0.118 0.079 0.200 0.000 0.179 0.026 0.000 0.059 0.000 0.000 0.094 0.05 chrC1_.52456776 G A 0.477 0.510 0.459 0.000 0.808 0.385 0.438 0.938 0.556 0.467 0.167 0.767 0.393 0.711 0.458 0.692 0.696 0.500 0.469 0.39 chrC2_10699123 - C T 0.278 0.248 0.294 0.100 0.154 0.600 0.250 0.111 0.029 0.816 0.000 0.067 0.071 0.147 0.458 0.053 0.370 0.263 0.235 0.23 chrC2_14712446C T C 0.377 0.327 0.404 0.733 0.115 0.429 0.500 0.079 0.333 0.105 0.417 0.467 0.769 0.105 0.545 0.579 0.021 0.316 0.559 0.36 chrC2_150774106 A G 0.062 0.090 0.049 0.033 0.000 0.000 0.188 0.000 0.028 0.000 0.192 0.000 0.464 0.079 0.000 0.000 0.000 0.167 0.094 0.17 chrC2_15649117E T C 0.479 0.463 0.488 0.250 0.346 0.800 0.500 0.158 0.444 0.722 0.500 0.067 0.071 0.219 0.909 0.618 0.760 0.294 0.294 0.26 chrC2_187325 C A 0.193 0.195 0.192 0.433 0.136 0.464 0.353 0.265 0.029 0.579 0.000 0.357 0.042 0.028 0.727 0.033 0.150 0.059 0.000 0.06 chrC2_262401 G A 0.264 0.299 0.245 0.467 0.083 0.714 0.441 0.350 0.111 0.947 0.000 0.429 0.036 0.026 0.808 0.176 0.913 0.265 0.029 0.03 chrC2_5215469 T C 0.405 0.327 0.436 0.533 0.000 0.633 0.083 0.925 0.059 0.895 0.000 0.107 0.091 0.079 1.000 0.375 0.958 0.088 0.000 0.05

Table 2a - Intergenic SNP minor allele frequencies by breed and population.

Major Minor

Allele Allele

chrD1_10132149£ A G 0.315 0.399 0.276 0.700 0.708 0.321 0.036 0.206 0.611 0.053 0.708 0.300 0.385 0.579 0.000 0.222 0.048 0.658 0.500 0.76 chrD1_10494155/ C T 0.119 0.091 0.134 0.267 0.125 0.033 0.250 0.000 0.088 0.026 0.083 0.000 0.077 0.132 0.083 0.139 0.000 0.111 0.059 0.08 chrDU 0549811. T C 0.121 0.144 0.109 0.179 0.042 0.033 0.382 0.083 0.281 0.000 0.462 0.200 0.000 0.342 0.038 0.000 0.020 0.105 0.176 0.20 chrD1_10789012 G A 0.330 0.319 0.339 0.300 0.077 0.433 0.139 0.000 0.333 0.235 0.038 0.100 0.115 0.289 0.308 0.306 0.523 0.588 0.294 0.38 chrD1_11484008 C T 0.138 0.076 0.170 0.133 0.000 0.000 0.038 0.000 0.000 0.026 0.000 0.071 0.107 0.026 0.250 0.235 0.159 0.000 0.000 0.05 chrD1_11752746Σ G A 0.021 0.039 0.013 0.000 0.308 0.000 0.000 0.000 0.059 0.000 0.000 0.000 0.000 0.176 0.000 0.000 0.000 0.237 0.000 0.05 chrD1_12581132£ G A 0.243 0.313 0.205 0.133 0.500 0.233 0.441 0.500 0.222 0.395 0.143 0.333 0.250 0.219 0.500 0.206 0.955 0.214 0.313 0.26 chrD1_12625699 - T C 0.299 0.381 0.259 0.467 0.115 0.321 0.133 0.289 0.265 0.789 0.350 0.200 0.115 0.289 0.923 0.176 0.960 0.088 0.176 0.30 chrD1_126847301 T C 0.086 0.086 0.086 0.033 0.269 0.167 0.029 0.265 0.056 0.026 0.000 0.542 0.000 0.000 0.000 0.375 0.000 0.029 0.038 0.11 chrD1_15984279 A G 0.193 0.213 0.185 0.250 0.538 0.192 0.100 0.059 0.028 0.105 0.577 0.038 0.077 0.053 0.250 0.132 0.214 0.306 0.294 0.33 chrDU 6242433 G A 0.284 0.337 0.263 0.227 0.150 0.650 0.294 0.105 0.083 0.731 0.200 0.455 0.083 0.393 0.417 0.333 0.375 0.344 0.250 0.11 chrDU 8390852 A G 0.148 0.230 0.110 0.733 0.308 0.033 0.031 0.025 0.594 0.053 0.455 0.033 0.107 0.528 0.000 0.000 0.000 0.083 0.471 0.20 chrD1_18570323 A G 0.401 0.370 0.412 0.167 0.385 0.067 0.441 0.842 0.278 0.211 0.455 0.500 0.538 0.316 0.917 0.382 0.600 0.441 0.367 0.33 chrD1_66177762 G A 0.101 0.121 0.091 0.167 0.308 0.000 0.056 0.000 0.222 0.026 0.269 0.033 0.429 0.132 0.000 0.079 0.000 0.447 0.294 0.19 chrD2_1020904 A G 0.466 0.336 0.531 0.100 0.346 0.500 0.471 0.150 0.194 0.711 0.038 0.214 0.321 0.605 0.231 0.313 0.140 0.389 0.382 0.29 chrD2_10577291£ A G 0.306 0.356 0.275 0.033 0.115 0.538 0.294 0.825 0.167 0.737 0.227 0.067 0.269 0.053 0.917 0.118 0.680 0.056 0.156 0.13 chrD2_1752007 G A 0.131 0.182 0.108 0.333 0.038 0.375 0.111 0.000 0.000 0.342 0.000 0.133 0.000 0.079 0.875 0.235 0.200 0.250 0.029 0.05 chrD2_56777338 C T 0.004 0.013 0.000 0.233 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.040 0.000 0.000 0.00 chrD2_717969 C T 0.404 0.518 0.340 0.633 0.458 0.143 0.382 0.972 0.324 0.737 0.042 0.464 0.269 0.194 0.958 0.583 0.913 0.500 0.235 0.40 chrD2_74293444 C T 0.195 0.191 0.197 0.000 0.167 0.071 0.029 0.158 0.056 0.158 0.000 0.615 0.731 0.194 0.227 0.056 0.208 0.139 0.313 0.32 chrD2_91989307 G A 0.066 0.100 0.050 0.467 0.000 0.367 0.028 0.000 0.000 0.000 0.154 0.100 0.000 0.105 0.833 0.147 0.000 0.056 0.094 0.05 chrD3_1038401 C T 0.042 0.054 0.036 0.100 0.038 0.033 0.000 0.000 0.056 0.000 0.038 0.000 0.269 0.000 0.000 0.000 0.000 0.000 0.000 0.00 chrD3_12250212C G T 0.230 0.184 0.248 0.467 0.038 0.346 0.028 0.694 0.029 0.278 0.182 0.357 0.000 0.211 0.292 0.125 0.786 0.056 0.156 0.18 chrD3_1810839 T G 0.349 0.405 0.319 0.923 0.545 0.250 0.179 0.167 0.471 0.278 0.350 0.714 0.458 0.500 0.000 0.300 0.000 0.767 0.600 0.69 chrD3_24565823 G A 0.081 0.156 0.044 0.367 0.423 0.100 0.028 0.000 0.559 0.000 0.231 0.233 0.000 0.184 0.000 0.000 0.000 0.289 0.324 0.35 chrD3_24823793 C G 0.193 0.132 0.224 0.000 0.038 0.133 0.000 0.225 0.028 0.342 0.269 0.500 0.000 0.079 0.818 0.079 0.326 0.026 0.059 0.05 chrD3_28838660 T C 0.082 0.139 0.053 0.000 0.000 0.000 0.000 0.447 0.000 0.389 0.042 0.267 0.000 0.000 0.077 0.083 0.800 0.000 0.000 0.00 chrD4_41078218 A C 0.448 0.430 0.456 0.071 0.500 0.083 0.406 0.579 0.125 0.206 0.364 0.821 0.417 0.605 0.364 0.567 0.412 0.658 0.536 0.32

Table 2a - Intergenic SNP minor allele frequencies by breed and population.

2. . S ro

Major Minor

Allele Allele

chrD4_42000379 G C 0.081 0.106 0.069 0.000 0.000 0.536 0.441 0.000 0.033 0.395 0.000 0.000 0.042 0.000 0.000 0.000 0.000 0.000 0.125 0.15 chrD4_63622083 G A 0.138 0.198 0.111 0.308 0.750 0.100 0.000 0.083 0.176 0.056 0.500 0.633 0.179 0.353 0.000 0.028 0.022 0.028 0.375 0.36 chrE1_13087591£ C A 0.125 0.134 0.123 0.038 0.269 0.033 0.000 0.000 0.111 0.000 0.038 0.600 0.375 0.079 0.000 0.324 0.000 0.406 0.206 0.40 chrE1_13158739£ G A 0.089 0.143 0.056 0.071 0.385 0.536 0.100 0.500 0.528 0.000 0.450 0.100 0.036 0.211 0.154 0.028 0.000 0.184 0.000 0.19 chrE1_3912105 G T 0.170 0.249 0.132 0.467 0.375 0.143 0.111 0.147 0.417 0.079 0.778 0.200 0.143 0.237 0.000 0.029 0.109 0.306 0.176 0.26 chrE1_4114158 g t 0.477 0.499 0.467 0.400 0.417 0.433 0.083 0.000 0.056 0.316 0.000 0.300 0.500 0.000 0.731 0.105 0.521 0.063 0.094 0.02 chrE1_48228153 C A 0.218 0.254 0.202 0.400 0.417 0.433 0.083 0.000 0.056 0.316 0.000 0.300 0.500 0.000 0.731 0.105 0.521 0.063 0.094 0.02 chrE1_48700963 A T 0.062 0.093 0.048 0.733 0.000 0.143 0.088 0.000 0.111 0.000 0.000 0.100 0.077 0.056 0.000 0.000 0.000 0.278 0.294 0.20 chrE1_5453028 G A 0.312 0.352 0.296 0.214 0.731 0.267 0.559 0.000 0.353 0.000 0.231 0.200 0.679 0.474 0.192 0.444 0.020 0.278 0.600 0.66 chrE2_22632289 G A 0.439 0.411 0.449 0.750 0.731 0.385 0.688 0.861 0.382 0.079 0.308 0.115 0.346 0.441 0.708 0.389 0.545 0.184 0.206 0.25 chrE2_34027888 G A 0.082 0.155 0.044 0.000 0.125 0.038 0.031 0.447 0.528 0.237 0.462 0.300 0.083 0.694 0.000 0.026 0.000 0.028 0.250 0.08 chrE2_35914023 C T 0.215 0.195 0.226 0.700 0.417 0.033 0.028 0.000 0.118 0.026 0.038 0.200 0.143 0.000 0.038 0.563 0.043 0.056 0.688 0.44 chrE2_36986631 G T 0.435 0.438 0.433 0.300 0.346 0.286 0.353 0.579 0.618 0.361 0.636 0.633 0.500 0.763 0.269 0.400 0.440 0.053 0.500 0.33 chrE2_38860686 C T 0.132 0.188 0.106 0.667 0.423 0.214 0.118 0.000 0.088 0.026 0.227 0.500 0.125 0.222 0.000 0.139 0.022 0.211 0.429 0.25 chrE2_39211557 C T 0.424 0.434 0.418 0.389 0.722 0.000 0.375 0.667 0.542 0.056 0.750 0.583 0.750 0.429 0.083 0.455 0.269 0.533 0.545 0.34 chrE2_65436639 C T 0.277 0.296 0.267 0.567 0.308 0.533 0.222 0.194 0.056 0.556 0.167 0.393 0.321 0.211 0.885 0.105 0.313 0.250 0.235 0.25 chrE2_7950477 C A 0.516 0.518 0.513 0.167 0.750 0.654 0.500 0.639 0.250 0.722 0.346 0.667 0.269 0.676 0.308 0.643 0.550 0.733 0.594 0.41 chrE2_8422942 A G 0.082 0.050 0.098 0.133 0.000 0.000 0.000 0.000 0.000 0.000 0.273 0.214 0.000 0.000 0.000 0.000 0.020 0.028 0.029 0.20 chrE3_36044809 G A 0.374 0.263 0.428 0.100 0.269 0.100 0.088 0.361 0.472 0.139 0.077 0.600 0.179 0.556 0.182 0.382 0.271 0.211 0.118 0.41 chrE3_55434272 C T 0.347 0.361 0.341 0.800 0.417 0.107 0.094 0.200 0.306 0.026 0.462 0.000 0.077 0.441 0.625 0.667 0.217 0.500 0.441 0.30 chrE3_67006512 C T 0.115 0.160 0.093 0.071 0.208 0.033 0.036 0.000 0.536 0.031 0.423 0.536 0.042 0.188 0.000 0.167 0.000 0.235 0.281 0.18 chrF1_20309325 G A 0.085 0.127 0.065 0.367 0.000 0.000 0.235 0.200 0.000 0.278 0.000 0.067 0.375 0.026 0.615 0.000 0.083 0.026 0.029 0.00 chrF1_21799641 C T 0.163 0.230 0.130 0.333 0.077 0.750 0.188 0.150 0.056 0.500 0.250 0.033 0.250 0.158 0.208 0.139 0.000 0.306 0.344 0.37 chrF1_26100599 A T 0.220 0.308 0.180 0.133 0.292 0.167 0.139 0.079 0.433 0.143 0.154 0.308 0.038 0.289 0.292 0.571 0.152 0.237 0.406 0.53 chrF1_27124984 C T 0.316 0.271 0.336 0.367 0.000 0.654 0.361 0.675 0.000 0.158 0.000 0.133 0.429 0.053 0.542 0.294 0.980 0.132 0.118 0.14 chrF1_38051725 A G 0.470 0.448 0.480 0.167 0.308 0.733 0.188 0.417 0.639 0.395 0.538 0.567 0.536 0.417 0.615 0.206 0.500 0.139 0.607 0.47 chrF1_565223 G A 0.496 0.529 0.483 0.107 0.154 0.500 0.676 0.368 0.694 0.684 0.417 0.538 0.821 0.184 0.400 0.471 0.500 0.342 0.471 0.65 chrF1_82068276 G T 0.216 0.361 0.143 0.269 0.318 0.133 0.375 0.139 0.375 0.529 0.682 0.133 0.682 0.357 1.000 0.111 0.900 0.100 0.094 0.17

Table 2a - Intergenic SNP minor allele frequencies by breed and population.

Major Minor

o Allele Allele

chrF1 82716202 C A 0.196 0.181 0.204 0.067 0.038 0.067 0.389 0.026 0.222 0.000 0.333 0.067 0.269 0.139 0.000 0.029 0.000 0.395 0.471 0.36 chrF1 91517402 C T 0.188 0.173 0.192 0.467 0.308 0.100 0.133 0.447 0.333 0.079 0.250 0.067 0.077 0.250 0.154 0.105 0.000 0.147 0.235 0.11 chrF2 26886470 G A 0.298 0.322 0.282 0.100 0.115 0.607 0.059 0.650 0.361 0.889 0.417 0.000 0.000 0.361 0.875 0.194 0.739 0.000 0.088 0.05 chrF2 38395360 C T 0.295 0.319 0.285 0.167 0.269 0.357 0.313 0.053 0.778 0.000 0.545 0.607 0.091 0.868 0.045 0.194 0.000 0.278 0.500 0.38 chrF2 46855978 T C 0.329 0.359 0.314 0.333 0.385 0.367 0.156 0.342 0.313 0.441 0.182 0.233 0.357 0.474 0.500 0.316 0.457 0.263 0.375 0.32 chrF2 68572596 G A 0.291 0.241 0.309 0.033 0.115 0.233 0.194 0.912 0.029 0.656 0.077 0.000 0.423 0.158 0.909 0.026 0.471 0.118 0.088 0.13 chrF2 74863327 G A 0.253 0.264 0.249 0.267 0.083 0.567 0.294 0.132 0.206 0.316 0.167 0.200 0.321 0.105 0.682 0.118 0.340 0.053 0.147 0.13 chrF2 78303221 T C 0.475 0.401 0.517 0.600 0.538 0.214 0.412 0.025 0.722 0.028 0.500 0.000 0.750 0.765 0.077 0.579 0.125 0.278 0.647 0.38 chrF2_ 79632602 G C 0.026 0.039 0.019 0.100 0.000 0.000 0.000 0.000 0.111 0.000 0.000 0.200 0.000 0.211 0.000 0.000 0.000 0.028 0.029 0.00 chrF2 8427817 A G 0.402 0.475 0.374 0.692 0.600 0.167 0.154 0.200 0.567 0.500 0.571 0.577 0.222 0.462 0.045 0.750 0.423 0.346 0.500 0.57

Table 2b - Intergenic SNP minor allele frequencies by breed and population.

Minor Allele Frequency

o ^■o 73 73 cs> Ό m m

o 03 o o o^" U> in o O)^' σ o

in in ><

in 3 o co^'

in > ro < in o. co^'

Major Minor

Allele Allele

chrA1_ 10141047 A G 0.000 0.033 0.000 0.100 0.029 0.000 0.286 0.000 0.000 0.000 0.237 0.294 0.11 0.28 0.31 0.26 0.10 0.04 0.10 chrA1_ 133621071 C T 0.250 0.333 0.600 0.000 0.529 0.000 0.133 0.029 0.000 0.559 0.143 0.184 0.304 0.222 0.309 0.283 0.189 0.316 0.129 chrA1_ ,151648701 G A 0.050 0.067 0.200 0.028 0.000 0.000 0.033 0.500 0.000 0.147 0.026 0.000 0.120 0.063 0.056 0.013 0.027 0.023 0.000 chrA1_ 175780586 G A 0.833 0.400 0.357 0.382 0.794 0.767 0.462 0.563 0.250 0.433 0.333 0.618 0.423 0.410 0.299 0.085 0.088 0.162 0.581 chrA1_ 208054462 C G 0.611 0.200 0.367 0.200 0.294 0.000 0.071 0.000 0.000 0.559 0.190 0.139 0.142 0.138 0.182 0.013 0.014 0.055 0.125

Table 2b - Intergenic SNP minor allele frequencies by breed and population.

Major Minor

Allele Allele

chrA1_223501140 G 0.222 0.100 0.067 0.333 0.118 0.267 0.071 0.588 0.000 0.219 0.147 0.075 0.162 0.082 0.074 0.039 0.111 0.167 0.076 0. chrA1_223506906 G 0.000 0.000 0.033 0.000 0.000 0.000 0.091 0.625 0.000 0.179 0.194 0.147 0.154 0.195 0.329 0.367 0.708 0.425 0.183 0. chrA1_225057933 A 0.111 0.067 0.267 0.056 0.029 0.133 0.133 0.000 0.000 0.000 0.190 0.132 0.221 0.218 0.284 0.401 0.000 0.259 0.139 0. chrA1_235579538 A 0.000 0.036 0.036 0.000 0.000 0.033 0.036 0.000 0.071 0.059 0.000 0.132 0.054 0.144 0.090 0.299 0.324 0.125 0.056 0. chrA1_27523501 A 0.150 0.267 0.733 0.735 0.382 0.800 0.667 0.531 0.000 0.406 0.667 0.700 0.426 0.628 0.636 0.745 0.568 0.649 0.786 0. chrA1_68485376 G 0.000 0.107 0.067 0.000 0.118 0.000 0.067 0.000 0.000 0.235 0.053 0.184 0.141 0.076 0.009 0.000 0.000 0.006 0.015 0. chrA1_69424718 T 0.000 0.500 0.400 0.433 0.118 0.367 0.500 0.000 0.143 0.088 0.316 0.556 0.292 0.343 0.332 0.755 0.542 0.335 0.412 0. chrA1_7429296 T 0.050 0.167 0.100 0.000 0.382 0.000 0.000 0.000 0.375 0.533 0.105 0.028 0.183 0.150 0.161 0.244 0.581 0.296 0.028 0. chrA1_8742286 T 0.650 0.033 0.167 0.611 0.000 0.400 0.267 0.031 0.500 0.353 0.250 0.250 0.273 0.406 0.426 0.683 0.257 0.476 0.514 0. chrA2_152258936 c 0.833 0.333 0.600 0.853 0.324 0.333 0.357 1.000 0.714 0.567 0.711 0.361 0.409 0.399 0.319 0.565 0.446 0.314 0.471 0. chrA2 201526186 T 0.050 0.233 0.200 0.000 0.206 0.000 0.133 0.176 0.000 0.412 0.214 0.361 0.207 0.156 0.269 0.161 0.176 0.253 0.103 0. chrA2_202225770 T 0.650 0.633 0.333 0.094 0.324 0.733 0.308 0.107 0.000 0.563 0.300 0.375 0.317 0.319 0.232 0.203 0.108 0.440 0.672 0. chrA2_44241149 T 0.000 0.000 0.000 0.000 0.000 0.000 0.067 0.000 0.000 0.000 0.000 0.000 0.062 0.016 0.009 0.006 0.000 0.017 0.014 0. chrA2_554046 c 0.050 0.000 0.200 0.111 0.059 0.000 0.133 0.000 0.000 0.147 0.150 0.053 0.133 0.169 0.217 0.186 0.027 0.144 0.028 0. chrA3_101420069 G 0.000 0.000 0.000 0.000 0.000 0.000 0.200 0.000 0.000 0.118 0.000 0.000 0.056 0.057 0.065 0.000 0.054 0.087 0.000 0. chrA3 11480952 T 0.000 0.000 0.000 0.000 0.029 0.000 0.067 0.000 0.000 0.059 0.024 0.000 0.053 0.021 0.027 0.000 0.014 0.000 0.048 0. chrA3_12082294 A 0.313 0.467 0.300 0.400 0.406 0.000 0.286 0.833 0.000 0.118 0.071 0.225 0.336 0.171 0.252 0.126 0.095 0.063 0.103 0. chrA3_130195244 C 0.222 0.038 0.100 0.077 0.059 0.167 0.115 0.192 0.700 0.156 0.179 0.059 0.199 0.284 0.368 0.075 0.111 0.124 0.138 0. chrA3_159537633 C 0.050 0.333 0.433 0.222 0.176 0.233 0.367 0.088 0.000 0.344 0.475 0.825 0.472 0.595 0.650 0.699 0.405 0.506 0.243 0. chrA3_162208567 G 0.167 0.786 0.200 0.441 0.794 0.000 0.536 0.100 0.000 0.594 0.382 0.250 0.377 0.194 0.196 0.260 0.068 0.064 0.016 0. chrA3_38781591 A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.158 0.053 0.022 0.016 0.004 0.006 0.000 0.011 0.000 0. chrA3_75156179 A 0.350 0.000 0.467 0.281 0.118 0.000 0.467 0.233 0.000 0.267 0.333 0.605 0.266 0.375 0.348 0.243 0.068 0.127 0.069 0. chrA3_91058022 C 0.313 0.433 0.633 0.233 0.147 0.133 0.633 0.706 0.786 0.294 0.625 0.342 0.484 0.256 0.241 0.104 0.230 0.106 0.121 0. chrA3_99507784 T 0.100 0.233 0.467 0.333 0.235 0.433 0.033 0.219 0.000 0.176 0.250 0.333 0.231 0.302 0.296 0.578 0.162 0.355 0.456 0. chrB1_10420438 T 0.444 0.267 0.333 0.000 0.125 0.000 0.167 0.000 0.500 0.118 0.071 0.025 0.104 0.082 0.034 0.023 0.027 0.034 0.014 0. chrB1_12214271 G 0.722 0.333 0.533 0.382 0.688 0.267 0.607 0.941 0.714 0.441 0.525 0.105 0.401 0.392 0.591 0.350 0.514 0.554 0.333 0. chrB1_195678303 C 0.056 0.067 0.133 0.500 0.029 0.033 0.143 0.000 0.667 0.088 0.175 0.132 0.102 0.258 0.230 0.358 0.351 0.116 0.071 0. chrB1_199564532 A 0.200 0.067 0.100 0.206 0.147 0.000 0.250 0.000 0.000 0.147 0.095 0.075 0.156 0.101 0.154 0.003 0.027 0.052 0.000 0. chrB1_202966562 T 0.100 0.400 0.267 0.833 0.382 0.167 0.462 0.125 0.000 0.500 0.250 0.316 0.381 0.263 0.198 0.183 0.149 0.184 0.235 0.

Table 2b - Intergenic SNP minor allele frequencies by breed and population.

Major Minor

Allele Allele

chrB1_54775572 A 0.000 0.100 0.033 0.059 0.176 0.000 0.000 0.324 0.000 0.059 0.190 0.075 0.188 0.204 0.135 0.059 0.216 0.000 0.097 0. chrB1_80161671 A 0.000 0.231 0.000 0.036 0.594 0.033 0.222 0.583 0.000 0.147 0.056 0.028 0.169 0.096 0.058 0.241 0.068 0.122 0.097 0. chrB1_88148379 C 0.150 0.267 0.567 0.265 0.382 0.500 0.643 0.235 0.333 0.156 0.525 0.605 0.360 0.477 0.483 0.443 0.622 0.369 0.182 0. chrB2_138312489 A 0.400 0.433 0.800 0.433 0.353 0.567 0.400 0.125 0.750 0.735 0.275 0.750 0.422 0.462 0.422 0.282 0.284 0.698 0.700 0. chrB2_146660650 T 0.050 0.000 0.000 0.000 0.000 0.000 0.000 0.467 0.000 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.014 0.034 0.139 0. chrB2_41509834 A 0.333 0.233 0.600 0.000 0.000 0.833 0.269 0.857 0.714 0.412 0.605 0.400 0.278 0.257 0.155 0.624 0.351 0.464 0.614 0. chrB2_45093345 G 0.333 0.700 0.500 0.063 0.625 0.067 0.808 0.393 0.100 0.469 0.389 0.474 0.542 0.433 0.404 0.093 0.257 0.227 0.258 0. chrB2_6949528 A 0.000 0.467 0.000 0.083 0.324 0.000 0.214 0.000 0.000 0.029 0.262 0.100 0.201 0.195 0.111 0.029 0.149 0.029 0.014 0. chrB3_104483970 A 0.333 0.133 0.367 0.719 0.000 0.821 0.233 0.471 0.143 0.029 0.050 0.132 0.202 0.328 0.321 0.172 0.122 0.506 0.855 0. chrB3_111000326 G 0.222 0.133 0.033 0.333 0.029 0.567 0.067 0.206 0.000 0.000 0.025 0.100 0.111 0.200 0.261 0.171 0.149 0.333 0.676 0. chrB3 13666494 G 0.667 0.667 0.767 0.563 0.706 0.667 0.286 0.563 0.083 0.353 0.361 0.225 0.475 0.439 0.504 0.255 0.243 0.560 0.765 0. chrB3_39203469 A 0.000 0.200 0.067 0.393 0.147 0.000 0.036 0.000 0.083 0.219 0.333 0.294 0.184 0.378 0.625 0.211 0.108 0.071 0.100 0. chrB3_51317931 T 0.786 0.250 0.233 0.056 0.029 0.893 0.167 1.000 0.125 0.219 0.353 0.325 0.138 0.294 0.276 0.407 0.095 0.390 0.750 0. chrB3_57141954 c 0.350 0.571 0.667 0.882 0.471 0.067 0.533 0.767 0.000 0.531 0.324 0.588 0.377 0.173 0.171 0.242 0.230 0.137 0.069 0. chrB3_77094074 c 0.417 0.036 0.033 0.115 0.467 0.000 0.000 0.000 0.300 0.143 0.105 0.000 0.168 0.006 0.022 0.007 0.139 0.056 0.067 0. chrB4 J 05706694 c 0.056 0.533 0.433 0.344 0.265 0.733 0.286 0.176 0.786 0.500 0.417 0.250 0.374 0.410 0.397 0.480 0.459 0.585 0.371 o. chrB4_142658074 G 0.000 0.200 0.300 0.059 0.353 0.000 0.227 0.000 0.000 0.125 0.167 0.194 0.108 0.190 0.187 0.066 0.014 0.030 0.065 0. chrB4_143006494 G 0.050 0.167 0.733 0.029 0.088 0.000 0.462 0.000 0.000 0.469 0.105 0.139 0.338 0.344 0.345 0.243 0.097 0.110 0.074 0. chrB4 144693308 A 0.625 0.167 0.133 0.156 0.029 0.833 0.367 0.333 0.750 0.406 0.190 0.289 0.311 0.426 0.460 0.450 0.473 0.335 0.469 0. chrB4_146486983 T C 0.050 0.033 0.067 0.528 0.156 0.767 0.269 0.433 0.000 0.029 0.200 0.075 0.193 0.194 0.124 0.288 0.541 0.134 0.641 0. chrB4_147206961 C 0.556 0.179 0.292 0.719 0.000 0.900 0.500 0.938 0.917 0.222 0.294 0.100 0.350 0.276 0.434 0.293 0.176 0.544 0.750 0. chrB4_149532846 T 0.200 0.000 0.233 0.000 0.324 0.000 0.115 0.063 0.000 0.067 0.000 0.025 0.126 0.056 0.030 0.016 0.014 0.023 0.057 0. chrB4_1687419 T 0.000 0.000 0.000 0.000 0.000 0.000 0.115 0.000 0.000 0.063 0.000 0.029 0.108 0.141 0.145 0.013 0.000 0.009 0.000 0. chrB4_20001848 c 0.000 0.000 0.000 0.000 0.063 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.046 0.008 0.000 0.003 0.000 0.029 0.029 0. chrB4_21098349 T 0.350 0.500 0.808 0.250 0.923 0.000 0.462 0.281 0.000 0.647 0.737 0.475 0.581 0.408 0.375 0.359 0.357 0.318 0.118 0. chrB4_255106 A 0.050 0.200 0.067 0.000 0.147 0.036 0.000 0.133 0.071 0.000 0.071 0.105 0.120 0.025 0.065 0.036 0.000 0.006 0.015 0. chrB4_3093827 T 0.400 0.033 0.000 0.900 0.088 0.800 0.000 0.219 0.000 0.063 0.000 0.000 0.109 0.046 0.055 0.000 0.041 0.071 0.452 0. chrB4_40319102 T 0.688 0.643 0.767 0.000 0.412 0.867 0.467 0.813 0.500 0.118 0.316 0.368 0.468 0.470 0.600 0.500 0.730 0.560 0.818 0. chrB4_47638578 G 0.100 0.367 0.067 0.000 0.559 0.000 0.115 0.000 0.000 0.029 0.048 0.026 0.086 0.038 0.004 0.006 0.000 0.017 0.014 0.

Table 2b - Intergenic SNP minor allele frequencies by breed and population.

Major Minor

Allele Allele

chrCU 16355295 C T 0.500 0.233 0.200 0.000 0.500 0.500 0.300 0.265 0.000 0.206 0.350 0.667 0.332 0.262 0.272 0.203 0.284 0.320 0.235 0. chrC1_123164748 C T 0.150 0.167 0.067 0.000 0.088 0.000 0.467 0.588 0.000 0.059 0.389 0.275 0.261 0.237 0.196 0.267 0.000 0.229 0.242 0. chrC1_181852965 G A 0.500 0.400 0.500 0.125 0.417 0.125 0.167 0.591 0.000 0.308 0.136 0.000 0.406 0.108 0.167 0.022 0.000 0.113 0.056 0. chrC1_190502133 G A 0.300 0.133 0.000 0.139 0.235 0.133 0.067 0.000 0.000 0.000 0.200 0.275 0.176 0.381 0.293 0.576 0.306 0.215 0.028 0. chrC1_215441574 C A 0.056 0.700 0.367 0.118 0.265 0.200 0.462 1.000 0.125 0.588 0.206 0.583 0.394 0.346 0.272 0.235 0.459 0.323 0.574 0. chrC1_216852686 G A 0.250 0.607 0.500 0.375 0.265 0.000 0.375 0.607 0.750 0.133 0.605 0.529 0.356 0.339 0.507 0.312 0.200 0.231 0.340 0. chrC1_24148281 T C 0.063 0.133 0.333 0.029 0.324 0.000 0.385 0.000 0.000 0.281 0.059 0.147 0.180 0.222 0.209 0.247 0.292 0.324 0.059 0. chrC1_28702055 G T 0.000 0.000 0.000 0.000 0.294 0.000 0.133 0.000 0.000 0.382 0.050 0.100 0.122 0.041 0.000 0.129 0.027 0.029 0.043 0. chrC1_34981315 A G 0.000 0.000 0.000 0.706 0.000 0.067 0.000 0.000 0.000 0.000 0.025 0.000 0.046 0.050 0.099 0.000 0.000 0.012 0.103 0.

£ chrC1_396397 G A 0.050 0.467 0.433 0.000 0.281 0.000 0.357 0.000 0.333 0.235 0.528 0.325 0.392 0.313 0.425 0.139 0.230 0.109 0.030 0. chrC1_44520932 C T 0.250 0.033 0.000 0.000 0.063 0.033 0.033 0.000 0.000 0.000 0.194 0.441 0.134 0.087 0.137 0.003 0.014 0.040 0.015 0. chrC1_52456776 G A 0.750 0.714 0.400 0.281 0.719 0.400 0.192 0.643 0.125 0.643 0.467 0.214 0.342 0.352 0.454 0.747 0.569 0.506 0.529 0. chrC2_106991233 C T 0.438 0.067 0.000 0.147 0.000 0.786 0.100 0.700 0.000 0.206 0.525 0.125 0.261 0.250 0.274 0.279 0.068 0.335 0.578 0. chrC2_147124460 T C 0.444 0.200 0.067 0.647 0.324 0.000 0.321 0.000 0.750 0.147 0.238 0.472 0.516 0.492 0.556 0.416 0.568 0.362 0.186 0. chrC2_150774106 A G 0.000 0.033 0.000 0.607 0.000 0.000 0.367 0.000 0.000 0.029 0.132 0.050 0.072 0.057 0.059 0.032 0.014 0.012 0.014 0. chrC2 156491175 T C 0.786 0.433 0.167 0.765 0.412 0.200 0.464 1.000 0.600 0.545 0.405 0.447 0.381 0.408 0.307 0.672 0.662 0.667 0.313 0. chrC2_187325 C A 0.450 0.038 0.143 0.029 0.000 0.567 0.000 0.429 0.300 0.071 0.105 0.000 0.105 0.195 0.289 0.168 0.216 0.211 0.383 0. chrC2_262401 G A 0.444 0.067 0.133 0.059 0.094 0.333 0.107 0.531 0.571 0.059 0.125 0.306 0.188 0.283 0.277 0.122 0.153 0.280 0.591 0. chrC2_5215469 T C 0.050 0.000 0.400 0.344 0.088 0.800 0.179 0.286 0.417 0.235 0.286 0.395 0.241 0.342 0.250 0.728 0.500 0.313 0.838 0. chrD1_101321498 A G 0.250 0.607 0.000 0.618 0.765 0.100 0.462 0.000 0.643 0.500 0.375 0.368 0.441 0.279 0.281 0.258 0.432 0.134 0.061 0. chrD1_104941557 C T 0.200 0.100 0.000 0.028 0.088 0.000 0.107 0.000 0.000 0.438 0.024 0.105 0.144 0.197 0.147 0.136 0.243 0.052 0.014 0. chrD1_105498119 T C 0.000 0.231 0.033 0.472 0.156 0.000 0.100 0.000 0.000 0.324 0.095 0.100 0.182 0.094 0.180 0.040 0.081 0.177 0.042 0. chrD1_10789012 G A 0.389 0.133 0.333 0.111 0.176 0.267 0.467 1.000 0.750 0.412 0.306 0.400 0.322 0.362 0.230 0.401 0.149 0.149 0.368 0. chrDU 1484008 C T 0.167 0.067 0.000 0.176 0.000 0.133 0.000 0.250 0.071 0.088 0.156 0.025 0.068 0.073 0.044 0.178 0.189 0.247 0.081 0. chrD1_117527468 G A 0.100 0.067 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.125 0.000 0.040 0.01 1 0.004 0.003 0.000 0.000 0.000 0. chrD1_125811329 G A 0.167 0.133 0.233 0.094 0.059 0.633 0.192 0.733 0.500 0.206 0.150 0.200 0.308 0.151 0.206 0.096 0.324 0.169 0.518 0. chrD1_126256993 T C 0.833 0.267 0.333 0.559 0.059 0.667 0.500 0.676 0.250 0.375 0.325 0.056 0.233 0.228 0.188 0.144 0.378 0.221 0.819 0. chrD1_126847301 T C 0.000 0.000 0.000 0.000 0.059 0.179 0.1 15 0.000 0.000 0.147 0.000 0.147 0.100 0.137 0.218 0.010 0.054 0.052 0.043 0. chrD1_15984279 A G 0.222 0.100 0.000 0.794 0.059 0.033 0.417 0.000 0.167 0.235 0.289 0.316 0.245 0.278 0.265 0.050 0.014 0.100 0.368 0.

Table 2b - Intergenic SNP minor allele frequencies by breed and population.

Major Minor

Allele Allele

chrD1_16242433 A 0.167 0.333 0.583 0.125 0.118 0.792 0.409 1.000 0.300 0.300 0.235 0.346 0.336 0.256 0.273 0.139 0.143 0.198 0.517 0. chrD1_18390852 G 0.167 0.300 0.233 0.056 0.765 0.067 0.292 0.563 0.000 0.200 0.175 0.105 0.223 0.171 0.111 0.006 0.108 0.080 0.000 0. chrD1_18570323 G 0.300 0.286 0.167 0.361 0.147 0.533 0.500 0.029 0.500 0.324 0.214 0.475 0.401 0.409 0.561 0.243 0.527 0.430 0.379 0. chrD1_66177762 A 0.000 0.300 0.000 0.028 0.294 0.000 0.033 0.000 0.000 0.029 0.048 0.075 0.161 0.112 0.104 0.010 0.097 0.052 0.014 0. chrD2_1020904 G 0.000 0.400 0.167 0.188 0.000 0.033 0.321 0.813 0.571 0.382 0.619 0.605 0.420 0.437 0.689 0.631 0.703 0.667 0.329 0. chrD2_105772916 G 0.600 0.143 0.167 0.969 0.176 0.933 0.267 0.500 0.500 0.250 0.357 0.395 0.192 0.272 0.372 0.284 0.338 0.412 0.426 0. chrD2_1752007 A 0.200 0.133 0.300 0.625 0.088 0.533 0.000 0.000 0.000 0.088 0.167 0.053 0.135 0.056 0.043 0.133 0.041 0.147 0.250 0. chrD2_56777338 T 0.150 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0. chrD2_717969 T 0.643 0.167 0.667 0.906 0.471 0.933 0.200 0.563 0.250 0.781 0.368 0.658 0.402 0.252 0.542 0.256 0.136 0.432 0.719 0. chrD2_74293444 T 0.200 0.200 0.100 0.059 0.059 0.633 0.286 0.000 0.071 0.235 0.250 0.150 0.208 0.151 0.155 0.122 0.189 0.134 0.118 0. chrD2_91989307 A 0.100 0.067 0.133 0.029 0.118 0.200 0.000 0.000 0.000 0.031 0.050 0.000 0.133 0.041 0.065 0.013 0.041 0.029 0.014 0. chrD3_103840114 T 0.150 0.000 0.000 0.000 0.000 0.000 0.000 0.750 0.000 0.000 0.100 0.100 0.022 0.040 0.043 0.039 0.135 0.046 0.000 0. chrD3_122502120 T 0.100 0.143 0.000 0.000 0.000 0.133 0.067 0.118 0.071 0.125 0.275 0.075 0.176 0.236 0.243 0.313 0.392 0.310 0.313 0. chrD3_1810839 G 0.071 0.423 0.500 0.000 0.800 0.000 0.643 0.292 0.167 0.667 0.292 0.750 0.603 0.528 0.427 0.065 0.129 0.211 0.143 0. chrD3_24565823 A 0.250 0.267 0.333 0.000 0.235 0.100 0.033 0.059 0.000 0.176 0.024 0.026 0.151 0.024 0.030 0.003 0.000 0.023 0.028 o. chrD3_24823793 G 0.050 0.000 0.000 0.056 0.000 0.367 0.033 0.133 0.583 0.125 0.025 0.025 0.073 0.184 0.186 0.197 0.541 0.339 0.250 0. chrD3_28838660 C 0.100 0.033 0.067 0.147 0.000 0.200 0.036 0.679 0.500 0.094 0.000 0.000 0.022 0.011 0.009 0.000 0.041 0.100 0.343 0. chrD4_41078218 C 0.700 0.536 0.733 0.231 0.438 0.400 0.654 0.000 0.900 0.038 0.429 0.550 0.458 0.491 0.482 0.493 0.919 0.402 0.212 0. chrD4_42000379 C 0.000 0.033 0.200 0.233 0.000 0.000 0.000 0.000 0.000 0.324 0.167 0.139 0.077 0.134 0.106 0.038 0.014 0.078 0.015 O. chrD4_63622083 A 0.000 0.429 0.500 0.292 0.294 0.000 0.179 0.029 0.333 0.000 0.075 0.139 0.241 0.082 0.065 0.069 0.083 0.135 0.061 O. chrE1_130875919 A 0.000 0.233 0.000 0.000 0.088 0.000 0.067 0.000 0.000 0.250 0.225 0.083 0.271 0.103 0.303 0.013 0.000 0.061 0.061 O. chrE1_131587399 A 0.056 0.269 0.100 0.000 0.382 0.000 0.000 0.000 0.000 0.176 0.083 0.000 0.141 0.047 0.022 0.013 0.000 0.023 0.056 O. chrE1_3912105 T 0.500 0.200 0.233 0.000 0.618 0.367 0.200 0.467 0.214 0.000 0.324 0.450 0.199 0.076 0.097 0.123 0.405 0.092 0.074 0. chrE1_4114158 t 0.200 0.067 0.033 0.382 0.147 0.500 0.367 0.676 0.333 0.147 0.071 0.474 0.438 0.461 0.415 0.507 0.365 0.458 0.439 0. chrE1_48228153 A 0.200 0.067 0.033 0.382 0.147 0.500 0.367 0.676 0.333 0.147 0.071 0.474 0.205 0.196 0.147 0.145 0.176 0.198 0.530 0. chrE1_48700963 T 0.222 0.100 0.067 0.111 0.029 0.000 0.036 0.000 0.000 0.000 0.025 0.050 0.096 0.060 0.048 0.000 0.027 0.034 0.000 O. chrE1_5453028 A 0.000 0.286 0.400 0.278 0.471 0.067 0.462 0.000 1.000 0.647 0.633 0.350 0.522 0.376 0.435 0.211 0.284 0.157 0.029 O. chrE2_22632289 A 0.850 0.500 0.300 0.094 0.353 0.833 0.500 0.031 0.917 0.529 0.200 0.389 0.373 0.389 0.421 0.374 0.608 0.371 0.724 0. chrE2_34027888 A 0.000 0.433 0.167 0.156 0.265 0.000 0.033 0.000 0.000 0.000 0.026 0.025 0.059 0.022 0.089 0.000 0.000 0.076 0.014 O.

Table 2b - Intergenic SNP minor allele frequencies by breed and population.

Major Minor

Allele Allele

chrE2_35914023 C T 0.167 0.000 0.000 0.278 0.059 0.033 0.600 0.1 18 0.083 0.107 0.306 0.333 0.275 0.241 0.152 0.050 0.292 0.194 0.181 0. chrE2_36986631 G T 0.200 0.786 0.533 0.333 0.625 0.200 0.467 0.313 0.143 0.324 0.750 0.526 0.410 0.603 0.618 0.392 0.257 0.471 0.281 0. chrE2_38860686 C T 0.150 0.133 0.133 0.056 0.735 0.000 0.179 0.000 0.500 0.059 0.139 0.000 0.297 0.049 0.039 0.090 0.081 0.059 0.015 0. chrE2_39211557 C T 0.389 0.464 0.542 0.300 0.708 0.464 0.500 0.000 0.833 0.462 0.227 0.333 0.440 0.422 0.495 0.411 0.324 0.402 0.407 0. chrE2_65436639 c T 0.500 0.067 0.233 0.000 0.088 0.400 0.133 0.938 0.071 0.235 0.176 0.325 0.227 0.298 0.283 0.330 0.189 0.310 0.147 0. chrE2_7950477 c A 0.722 0.500 0.467 0.806 0.471 0.667 0.433 0.063 0.400 0.781 0.528 0.368 0.457 0.497 0.417 0.566 0.597 0.579 0.677 O. chrE2_8422942 A G 0.050 0.000 0.133 0.028 0.000 0.033 0.000 0.029 0.000 0.000 0.139 0.184 0.078 0.064 0.066 0.320 0.054 0.017 0.028 0. chrE3_36044809 G A 0.000 0.400 0.536 0.028 0.382 0.067 0.133 0.344 0.286 0.500 0.175 0.200 0.237 0.41 1 0.548 0.530 0.243 0.282 0.191 o. chrE3_55434272 C T 0.250 0.500 0.600 0.471 0.412 0.333 0.583 0.000 0.100 0.633 0.553 0.21 1 0.385 0.383 0.421 0.039 0.324 0.317 0.303 O. chrE3_67006512 C T 0.056 0.067 0.357 0.000 0.235 0.000 0.091 0.000 0.000 0.100 0.265 0.333 0.129 0.123 0.109 0.083 0.045 0.032 0.030 o. chrF1 20309325 G A 0.056 0.067 0.233 0.028 0.000 0.667 0.000 0.029 0.000 0.029 0.071 0.306 0.075 0.081 0.022 0.076 0.097 0.034 0.069 o. chrF1_21799641 C T 0.400 0.300 0.400 0.469 0.147 0.067 0.433 0.281 0.000 0.156 0.071 0.050 0.331 0.079 0.113 0.048 0.108 0.098 0.042 0. chrF1_26100599 A T 0.750 0.400 0.567 0.176 0.353 0.167 0.714 0.286 0.000 0.176 0.417 0.605 0.356 0.194 0.068 0.072 0.189 0.167 0.186 0. chrF1_27124984 C T 0.150 0.033 0.200 0.059 0.029 0.767 0.071 0.577 0.929 0.125 0.094 0.026 0.206 0.253 0.297 0.373 0.292 0.429 0.667 0. chrF1_38051725 A G 0.444 0.286 0.400 0.882 0.382 0.333 0.633 0.250 0.000 0.500 0.675 0.500 0.541 0.644 0.671 0.317 0.122 0.305 0.530 0. chrF1 565223 G A 0.550 0.179 0.733 0.361 0.706 0.750 0.692 0.824 0.417 0.563 0.632 0.600 0.463 0.438 0.425 0.463 0.608 0.536 0.471 o. chrF1_82068276 G T 0.938 0.444 0.143 0.000 0.417 0.700 0.1 15 0.882 0.333 0.269 0.056 0.079 0.187 0.065 0.059 0.007 0.015 0.250 0.379 0. chrF1_82716202 C A 0.000 0.250 0.167 0.853 0.206 0.067 0.107 0.000 0.000 0.324 0.132 0.026 0.288 0.191 0.125 0.339 0.243 0.100 0.100 0. chrF1_91517402 C T 0.350 0.133 0.233 0.029 0.382 0.033 0.107 0.000 0.000 0.235 0.200 0.225 0.214 0.223 0.184 0.176 0.351 0.327 0.044 o. chrF2_26886470 G A 0.444 0.133 0.433 0.344 0.118 0.833 0.143 0.781 0.750 0.235 0.024 0.100 0.104 0.153 0.104 0.459 0.405 0.494 0.591 0. chrF2_38395360 C T 0.643 0.833 0.367 0.235 0.765 0.033 0.269 0.000 0.214 0.156 0.333 0.025 0.349 0.270 0.279 0.334 0.338 0.360 0.029 0. chrF2_46855978 T C 0.125 0.433 0.500 0.433 0.382 0.433 0.433 0.500 0.200 0.219 0.389 0.316 0.332 0.280 0.309 0.229 0.392 0.407 0.444 0. chrF2_68572596 G A 0.688 0.107 0.133 0.156 0.118 0.964 0.107 0.667 0.125 0.176 0.105 0.000 0.211 0.270 0.318 0.302 0.500 0.323 0.717 0. chrF2_74863327 G A 0.650 0.133 0.400 0.194 0.088 0.433 0.200 0.618 0.071 0.206 0.119 0.553 0.181 0.278 0.282 0.171 0.122 0.306 0.338 0. chrF2_78303221 T C 0.000 0.633 0.500 0.194 0.500 0.000 0.769 0.265 0.857 0.469 0.595 0.275 0.478 0.531 0.470 0.743 0.716 0.429 0.273 0. chrF2_79632602 G C 0.000 0.167 0.000 0.063 0.125 0.000 0.000 0.000 0.000 0.059 0.000 0.000 0.025 0.01 1 0.004 0.062 0.000 0.006 0.000 O. chrF2_8427817 A G 0.222 0.464 0.267 0.679 0.412 0.433 0.417 1.000 0.100 0.781 0.556 0.533 0.390 0.408 0.387 0.227 0.257 0.378 0.350 0.

[0091] As appropriate, the genotypes of at least about 3, 5, 10, 15, 20, 25, 30, 40,

50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 145, 148 or more, SNPs from Table 1 are determined. In some embodiments, the expression levels of all listed SNPs of SEQ ID NOs: 1-148 listed in Table 1 are determined.

[0092] In addition to determination of the plurality of SNPs listed in Table 1 , one or more morphological features and/or the genotype of one or more phenotypic markers can be determined. The morphologic and phenotypic markers can relate to hair length, coat color, coat texture, ear, paw and tail morphology, or a known disease marker. For example, the feline may be evaluated for coat color (e.g., chocolate, cinnamon, dilute, orange, white), coat patterning (e.g., agouti, tabby, spotted, ticked, calico, point coloring), coat texture (e.g., straight or rex), coat length (e.g., hairless, short or long), ear morphology (e.g., normal, curled or folded, paw morphology (e.g., normal or polydactyl), and tail morphology (e.g., manx, bobtail, long). Such phenotypic and morphologic features can be evaluated by visual inspection and/or genetic analysis of the feline.

[0093] Some phenotypic markers can be evaluated by genetic analysis, without visual inspection of the feline. In various embodiments, the methods may further comprise determining the genotype of one or more phenotypic markers identified in Table 3, i.e., as SEQ ID NOs: 149-202.

[0094] As appropriate, the genotypes of at least about 3, 5, 10, 15, 20, 25, 30, 40, 50, or more, phenotypic markers, e.g., listed in Table 3, are determined. In some

embodiments, the expression levels of all listed phenotypic marker of SEQ ID NOs: 149-202 are determined.

[0095] Genetic markers for phenotypic traits, especially traits that are breed specific, find use to distinguish genetically related feline populations. For example, Birman or Siamese are homozygous for the G940A TYR mutation. This same mutation would exclude a Havanna Brown from very closely related breeds, e.g., Siamese, Birman and Himalayan. Colorpoints in the chocolate variety, such as Havana Brown, cannot be homozygous for the mutation. A Korat, Russian Blue or Charteux are by definition solid blue; therefore no other colors or patterns are present. Thus, the presence of mutations that would exhibit other patterns and colors would exclude a cat as one of these breeds.

[0096] A genetic marker for long hair, e.g., the A475C FGF5 mutation, can be used as a means for identifying members of the Persian, Maine Coon, Turkish Angora, Turkish Van and Birman breeds, and likewise a means for discrimination as an exclusion maker for breeds such as the Abyssinian, Egyptian Mau, Sokoke and Ocicat. The long hair mutations can also be used to distinguish different breeds that are long haired varietes within the breed family. For example, a Balinese is a longhaired Siamese and a Cymric is a longhaired Manx. Many cat breeds are designated as a breed, such as Oriental Longhair or Oriental Shorthair, just based on the FGF5 mutations. The frequency of the long hair mutations could also be used to support a breed selection. For example, the Norwegian Forest Cat and the Ragdoll have FGF5 mutations that are less common in other breeds.

[0097] Other single gene traits may be used to identify members of certain cat breeds as well. For example, the G715T TYR mutation, which defines Burmese points, is found in the genomes of felines of Burmese and Singapura breeds. The cinnamon mutation, C298T TYRP1, is common to the red Abyssinian. Certain dominant traits can be homozygous or heterozygous, such as the ear curl of American Curls or the bobtail of the Japanese Bobtail. Tonkinese felines are genetically compound heterozygous for the G940A and the G715T TYR mutations and can produce both pointed and sepia cats, and genetically resemble a Siamese or Burmese, respectively. However, breeding restrictions would not allow these Tonkinese variants to be registered as Siamese or Burmese. Additional phenotypic SNPs that find use include the Norwegian Forest Cat color variant amber (Peterschmitt et al. (2009) Animal Genetics, 40:547-552), three additional long-haired mutations (Kehler et al. (2007) Journal of Heredity, 98:555-566), and the mutations responsible for hairless of Sphynx and rexing of the Devon Rex (Gandolfi et al., Mamm Genome. (2010) (9-10):509-15). A mutation in KIT (c.1035_1036delinsCA) for Birman glove white spotting should be restricted to the Birman breed. Phenotypic genetic markers, as well as disease mutations, find use to further delineate cat breeds.

[0098] Accordingly, morphological and/or phenotypic markers find use to distinguish between genetically related feline breeds, e.g., (i) Persian and Exotic Shorthair (SH); (ii) British SH and Scottish Fold; (iii) Australian Mist and Burmese; (iv) Singapura and Burmese; (v) Birman and Korat, and (vi) Siamese and Havana Brown. For example, determination of whether the feline has the phenotype for long hair can be used to distinguish between Persian and Exotic Shorthair; determination of whether the feline has curled ear morphology can be used to distinguish between British SH and Scottish Fold; determination of fur color and/or pattern can be used to distinguish between Australian Mist and Burmese; between Singapura and Burmese; between Birman and Korat; or between Siamese and Havana Brown. Whereas a Burmese will lack barring and/or spotting, a Singapura possesses the dominant ticked tabby gene, Ta; and an Australian Mist will have spotting and barring. Whereas a Birman and a Siamese will have the mutation for Siamese points (i.e, homozygous for the G940A TYR mutation), a Korat and a Havana Brown will not.

[0099] The use of phenotypic markers to further refine the assignment of a test feline to a breed or ancestral population is demonstrated in Tables 4 and 5.

Table 3 - Phenotypic Markers useful for breed identification test

SEQ ID NO: SNP ID Sequence

149 Phen CMAH G139A AATTTCTTG AG AAACAAG AAGACC RG CAAAGATTTCATTCTGTACAAG AG C

AAG AATYGY[G/A]TG AG GG CGTG CAAG AACGTGTG CAAG CATCAAG GAG K CCTGTTCRTAAAAGACATCGAAG

150 Phen_ASIP_del CCACCCTGCTGGTCTGCCTGTGCCTCCTCACTGCCTACAGTCACCTGGC

ACCTGAG GAAAAACCCAG AG ATG ACAG GAACCTG AG GAG CAACTCCT[CA /-]TGAACATGTTGGATCTCTCTTCTGTCTCTATTGTAGCGCTGAACAAGAA ATCCAAAAAGATCAG CAG AAAAG AG G CG GAAAAG AAGAG ATCTTCCAAGA AAAAGGCTTCG

151 Phen_MLPH_T83del GGCAGAGATGGGGAAAAAACTGGATCTTTCCAAGCTCACGGACGACGAG

GCCAAGCACATCTGGGAAGTGGTTCAGCGGGACTTTGATC[T/-]AGAAGGA

AAGAAGAGGAAAGGCTGGGGTGAGTGTATGAGGCCGAGCCCGTCCCTC

CGGTGTCCCCTGGAAGGGGGGGCCTCCCCGGGACGCTGGGGGTGAGC

A

152 Phen_MC1 R_G250A CCTGGGGCTGGTGAGCGTGGTGGAGAACGTGCTGGTGGTGGCCGCCAT

TGCCAAGAACCGCAACCTGCACTCGCCCATGTATTACTTCATCTGTTGCC TGGCCGTGTCC[G/A]ACCTGCTGGTGAGCGTGAGCAGTGTGCTGGAGAC GGCCGTCATGCTGCTGCTGGAGGCAGGCGCCCTGGCCGGCCGGGCCG CCGTGGTGCAGCGGCTGGA

153 Phen TYRP1 C298T CCGCATGATGGCAGAGATGATCGGGAGGCTTGGCCCACGAGGTTCTTCA

ACAG GACATG C[T/C]G ATGCAATG G CAATTTCTCAG G ACACAACTGTGG G ACTTGCCGTCCTGGATGGAAAGGAG

154 Phen TYRP1 5IVS6 CCTTCACAATTTGGCTCATCTATTCCTGAATGGAACAGGGGGACAAACCC

ATTTATCTCCAAACGATCCTATTTTTGTCCTCCTGCACACTTTCACTGACG CAGTCTTTGATGAATGGCTGAGGAGATATAATGCTGGTGA[G/A]ACATTCT CCTATG CTTTTTTG CAG GCTCAG CAAG

155 Phen_TYR_del975C TTAGCCGATTGGAGGAGTACAATAGCCGTCAGGCTTTATGTGATGGAACT

CCAGAGGGACCATTACTGCGCAATCCCGGAAACCATGACAAAGCCAGGA CCCCAAGGCT[C/-]CCTCCTCTGCTGATGTGGAATTTTGCCTAAGTCTGAC ACAATATGAATCGGGTTCCATGGATAAAGCTGCA

156 Phen_TYR_G715T TTCCTGCCTTGGCACAGACTCTTCTTGTTGCTGTGGGAACAAGAAATCCA

GAAGCTGACC[T/G]GGGATGAGAACTTCACTATTCCATATTGGGATTGGC

GAGATGCTAAAAGCTGTGA

157 Phen TYR G940A TACAATAGCCGTCAGGCTTTATGTGATGGAACTCCAGAGGGACCATTACT

GCGCAATCCC[A/G]GAAACCATGACAAAGCCAGGACCCCAAGGCTCCCCT CCTCTGCT Table 3 - Phenotypic Markers useful for breed identification test

SEQ ID NO: SNP ID Sequence

158 Phen_KIT_G1035C_BI TTTCATTAACATCTTCCCTATGATGAATACCACAATATTCGTGAATGATGG

CGAGAATGTGGATCTGATTGTCGAATATGAGGCATATCCCAAGCCTGA[G/

C]MACCAACGGTGGGTCTATATGAACAGAACCCTCACTGATAAATGGGAA

GATTATCCCAAGTCTGACAACGAAAGTAATATCAGGTAAGAAAAAAGCCTT

ACGCTGAGGATCAGATGTTTTCC

159 Phen_FGF5_475 TGCTGAAAATATTATTACTATTTACAAGTTTTTCTTCTTTCCCCCCACCAGG

CCAAATTT[A/C]CCGATGACTGCAAGTTCAGGGAGCGATTCCAAGAAAACA GCTATAATACCTATGCCTCAG

160 Phen_FGF5_474 TGCTGAAAATATTATTACTATTTACAAGTTTTTCTTCTTTCCCCCCACCAGG

CCAAATT[T/-]ACCGATGACTGCAAGTTCAGGGAGCGATTCCAAGAAAACA GCTATAATACCTATGCCTCAG

161 phen_FGF5_406 ATTTCTGTCATCCTAGGTATTTTGGAAATATTTGCTGTGTCTCAGGGGATT

GTAGGAATA[C/T]GAGGAGTTTTCAGCAACAAATTTTTAGCGATGTCAAAA AAAGGAAAACTCCATGCAAGTGTAAGTAGAACCACTTTATGTT

162 Phen_FGF5_356 GTGGAGCCCCTCGGGGCGCCGGACCGGCAGCCTCTACTGCAGAGTGGG

CATCGGTTTCCATCTGCAGATCTACCCGGATTGGCAAAGTCAATGGCTCC

CATGAAGCCAATA[T/-]GTTAAGTAAGTTGCTCGCCCTCCTGGCAAAGCGC

GTCCTAAGCGGGCGATGGGGGGRTTCGGGAGGGACAGAGGCTATTCCC

TGGCCCACAGGCGCACCTTCCGGAGCCCTGGCTCCTGGGACTCAGCTGT

CCCTCGGAACC

163 Phen_GBL1_G1457C CCCAGGGAGTCCTGGAGCGAAGTTACGTGATCACTCTGAACATAACAGG SIA_KOR GCAAGCCGGAGCCACTCTGGACCTTCTGGTGGAGAACATGGGGC[G/C]T

GTG AACTATG G CAG ATACATCAATG ATTTCAAG GTAGG ACCAGCCTCG CT GTCGAGGTCGATAGGACTGTGTCTGTGCCACCCGAGGA

164 Phen_HEXB_Dellntr_B ACCATGAACTGACCAAGGGACCAGTAATTGCCTCTGTCAGACTACTACTG UR CATTTTGCCTATTGCCTCTGCAACTACTTCATTTACAGCCATTCAATGATTT

[TAATGTAGGTTCTCA/-]AGAACAGAAAAAACTTGTCATTGGTGGAGAAGCT

TGTCTGTGGGGAGAATTTGTGGATGCAACTAACCTTACTCCAAGATTATG

GTATGGAAT

Phen_HEXB_del39C GGCTGGGGCTGGCGGCGCTGCTGGCGCTGCTGGCGGCCGTGGCCCCG KOR CGCTCCTCCGCCGCCGCGGAGCCGCCCTGTGGCCTATGCCGCTCTCGG

TGAAGACGTCTCCGCGCCTGCTGCA[C/-]CTCTCCCGCGACAACTTCTCCA

TCGGCTACGGCCCCTCGTCCACCGCCGGCCCCACCTGCTCCCTCCTGCA

GGAAGCTTTTCGGCGATATCACGAATACATTTTTGGTTTCGACAAGAGG

166 Phen GBE1 Ins NFC TTAAGAATATTCATTCTAGGGGCGCCTGGGTGGCGCAGTCGGTTAAGCG

TCCGACTTCAGCCAGGTCACGATCTCGCGGTCTGTGAGTTCGAGCCCCG

CGTCGGGCTCTGGGCTGATG[C/-]CCAGAGCCCGACGACATGGGTCAAAC

TCACCGACCGCGAGATCGTGTACCTGAGCTGAAGTCGGACGCTTCACCG

TACTGAGCCACCCAGGC

Phen_KRT71_G/Aintro GCCAATAAGGAGGAGCTCCAGGCCAAGGTGGACTCCATGGATCAGGAGA 4_SPX TCAAGTTTTTCAAGTGCCTCTATGAAGCC[G/A]TAAGTCTGTCTCTCCACC

CACCCCTCTGAGGGCAGCCAGCGGGTAAAACTCTGTTCTGG

Phen_MYBPC_G93C GCCTTCAGCAAGAAGCCAAGGTCAGTGGAAGTGGCAGCCAGCAGCTCTG MCC CTGTGTTCGAG[C/G]CCGAGACAGAGCGGTCAGGAGTAAAGGTGCGCTG

GCAGCGGGGGGGCAGTGACATCAGCG

Phen_MYBPC_C2460 GTCCCCCCTTCATCAGGCTACATCCTGGAGCGCAAGAAGAAGAAGAGCT T_RAG TCCGGTGGATG[T/C]GGCTGAACTTTGACCTGCTGCAGGAGCTGAGCCAC

GAGGCACGGCGCATGATTGAGGGCG Table 3 - Phenotypic Markers useful for breed identification test

SEQ ID NO: SNP ID Sequence

170 phen_MPO_ALC GTGCAGTTGGGGGATCGCAGAAGAGGGCTGGGCACATGCAGCTCCTGG

TGCCAAGGTCAC[T/C]CCAGTGGGCAGGTCTTCACCAGGGAATCAAGGTC TCAAGGTATAATGCCATTCAGACTTG

171 Phen_PLAU_AG_ALC TGTATCTAAGGAAGAATGTGAGGATGAGAGATATTAGAAAGAGGAGGAAA

TTCAGACAGG[T/C]GTTTTAGAGATCCTGTCAGGCCTTGCATGATTTCAGA CCTGG

172 Phen_FCAT_ALC AAAAGCTGATGTACTTGTGCCCAGGGAAGATGTCAATATTTACCATTAACT

GTTGTGAAA[A/G]ATTCAACCAATGTACTCAGTTGAAGTTCTTTTCATTTTA TTG G ATTAAAAAAATCACTAT

173 Phen_PKLR_13delE6_ CGCCCACCGGTGCCTGTTCCGTGCACGGCCCAGGCCCCAAGGTGGACA Aby GGCAATAGGACACGGGTTCCTGATTTCCTGGGGGCCCACGCCCCGTGCC

CCCGCTCCAC[G/A]ACTCTGCCCCCGGCTYGCCCCTGACCTGCGCTGGC TCTTCCCATGCCTGCAGGCCCGGAGGGGCTGGAGACCCACGTGGAGAA CGGCGGCGTCCTGGGC

174 Phen_PKD1_C10063A CTCCCTCTGGGACCGGCCTCCTCGGAGCCGCTTCACCCGCGTCCAGCG _PER GGCCACCTGTTG[A/C]GTCCTCCTCGTCTGCCTCTTCCTGGGCGCCAATG

CTGTGTGGTACGGGGTCGTGGGAGAC

175 Phen_SHH_A479G_H CCAGTGGCTAATTTGTCTCAGGCCTCCGTCTTAAAGAGACAC[A/G]GAAAT w GAGTAGGAAGTCCAGCGTGGTCTCAGAGAGCT

176 Phen_CEP290_PRA_ AACAGAGAGGGAGCAAAAAGCTAAGAAATACACTGAAGACCTTGAGCAAC Aby AAGCAAGTAA[T/G]TTTTCTTTATGAGAAAATAATGCATTTCATCTCAAGCC

ACTCCTTGGCATTGTTTTAA

177 Phen_CRX_546_Aby TCCCCCACCTCGGCCGTGGCCACGGTGTCCATCTGGAGTCCCGCCTCG

GAGTCCCCTCTGCCCGAGGCCCAGCGGGCGGGGCTGGTGGCGGC[C/-]G GGCCCCCTCTGACCTCCGCGCCCTACGCCATGACCTACACCCCCGCCTC TGCTTTCTGCTCTTCCCCCTCGGCCTACGGGTCTCCGAGCTCCTATTTCA GTGGCC

178 Phen_CMAH_del AGTAGTGAACCGCGTGCATATGCATCCTCCGTCTCATACTTTGTGGGAGC

A[AACGAGCAACCGAAGCTG/-]AACGAGCAACCACCGTCCTTTCAGAATTC CCAGGGAGAGGCAGCTGCGGACCATGGGCAGGCAAGTGACAGGGGCAT TGGGTCTGGAGGAACCCGAGACCAACACTGAGCA

179 Phen_HEXB_C667T_ TCTCTGTCTCATGTTTATACACCAAACGATGTCCATACGGTGATTGAATAT DSH GCCAGATTA[T/C]GAGGGATTCGAGTCATACCAGAATTTGATAGCCCTGGA

CATACACAGTCTTGGGGAAAAG

180 Phen_GM2A_Del_DS TCACTGCCGGAGAGTGATTTCACCCTGCCCCAGCTGGAGGTGCCCGGCT H GGCTTAGCTCTGGGCACTACCGCATCAAGAGGT[C/T]CCTCAGCAGCGGT

GGGGAGCGTCTGGGCTGTGTCAAGATCTCTGCCTCTCTGAAGGGCAAAT AATGTGGCACCAGCCACA

181 Phen_GRHPR_DSH ACCTGGAGAGGATGATCTTCTCCCCCTGGGGGTTCTGGGTCTGTGAGTT

TGCAGTGAGCCCTCATCTTTGCCCAAGGTGGGGTCCTCTTACCCACCCCT TCTCTCCTCACA[G/A]CGGTGGCTGGACCTCATGGAAGCCCCTGTGGATG TGTGGCTATGGACTCACGCAGAGCACTGTCGGCATCATCGGGCTGGGGC GC

182 Phen_LPL_G1234A_D GAG CG ATTCATACTTCAG CTGGTCAG ACTG GTG GAG CAG CCCTG G GTTT SH ACTATTGAGAAGATCAGAGTAAAAGCA[G/A]GAGAGACTCAAAAAAAGGTA

ATCTTCTGTTCCAG GG AG AAAGTATCTCATCTG CAG AAAG GAAAG G CATC TGTGGTATTTGTGAAATGCCATG Table 3 - Phenotypic Markers useful for breed identification test

SEQ ID NO: SNP ID Sequence

183 Phen_LAMAN_del_PE ACGGTCAGGAACTGCTTTTCCCAGCCTCGGTGCCTGCCCTGGGCTTCAG R CATCTACTCAGTAAGCCAGGTGCCTG[GCCA/-]KCGCCCCCACGCCCACA

AACCCCAGCCCAGATCCCAGCGGCCCTGGTCCCGTGTCTTGGCCATCCA AAATGAGCACATCCGGGC

184 Phen_IDUA_del_DSH GGACATCCCGACCCCCTGGACCCCCGCAGGTCATTGCGCAGCACCAGAA

CCTGCTGGTGGCCAACACCAGCTCCCCCGTGCGCTACGCGCTCCTGAGC

AAC[GAC/-]ACGCCTTCCTGAGCTACCACCCGCACCCCTTCACGCAGCGC

ACGCTCACGGCGCGCTTCCAGGTCAACAACACCCGCCCGCCGCACGTG

CAGCTGCTGC

Phen_ARSB_G1558A CTCCAGTTCTACCACAAACATTCAGTGCCTGTGCATTTCCCGGCACAG[A/ SIA G]ACCCCCGCTGTGACCCCAAGGGCACTGGGGCCTGGGGCCCTTGGGTA

TAG G AC

Phen_ARSB_T1427C CGTCTCCATACAACGATTCTGCGATACCCTCATCAGACCCACCGACCAAG Sia ACCCTCTGGC[T/C]CTTTGATATTGATCAGGACCCAGAAGAAAGACATGAC

CTGTCAAGAGACTATCCCCATAT

Phen_GUSB_A1052G GATTCGCACGGTGGCTGTCACAGAGCACCAGTTCCTCATCAATGGGACC DSH TTTCTATTTCCACGGGGTCAACAAGCAC[G/A]AGGATGCAGATATCCGAG

GGAAGGGCTTTGACTGGCCACTGCTGGTGAAGGACTTCAATTTGCTTCG CTGGCTCGGGGCCAACGCCTCCGCACCAGTCACTACCCCTA

Phen_MYBPC_A74T CTTCAGCAAGAAGCCAAGGTCAGTGGAAGTGGCAGCCAGCAGCTCTGCT Poly GTGTTCGAGSCCGAGACAGAGCGGTCAGGAGTAAAGGTGCGCTGGCAG

CGGGGGGGCAGTGACATCAGCGCCAGTGACAAGTATGGCCTAGCARCC

GAGGGCACGAGGCACACTCTGACAGTGCGGGACGTGGGCCCC[G/A]CCG

ACCAGGGACCCTACGCAGTCATCGCT

Phen_NPC1_G2864C GCTTTGCTCCCTCTTCCTGGATCGACGATTACTTTGATTGGGTCAAGCCT PER CAGTCTTCTT[C/G]CTGTAGAGTCTACAACAGCACCGATCGGTTCTGCAAT

GCTTCAGGTACTTTCATCTCCTT

Phen_SHH_G257C_U TAATTAGACTGACCAGGTGGCAGCAAAGAGCCGGGTGCC[C/G]GTGCTG K1 GGAAGGCCTATAAAGCTGAGCGCTGTGACAGCACA

Phen_SHH_A481T_U CCAGTGGCTAATTTGTCTCAGGCCTCCGTCTTAAAGAGAC[A/T]CAGAAAT K2 GAGTAGGAAGTCCAGCGTGGTCTCAGAGAGCT

Phen_HMBS_del842_ GTCCCCGTGCACGTCAAGTTGTCCACAAGAGCCCAGGTTTCTAACCAGTT SIA CTCTCAGAATATGCTGAGATAACCATTTTCTTTCCCAGCTGTACCTGACAG GAG [G AG/-]TCTG G AGTCTAG ACG G CTCAGATAGCATG CAAG AGACCATG CAGGCCACCATTTGTGTCACTGCCCAGGTGCCAAAGCTGGAGGGTGAGG GAGAGGGTGAG

193 Phen- TTAGTACAGTGCTGGGCTCAGTAGGGGCTCGTTAAACGCCAATAGATGA

HMBS_189TT_SIA GCACGTGACTGATTCTCTCCTCAGTTGCTATGTCCACCACAGGGGACAAG

ATT[-/T]CTTGATACTGCGCTCTCTAAGGTAACGTGTTCCCCCCAATCCCTT

TCCCTCCTTTCTCTTTCCTTCCCCCAAAAGATTCACTCTGAGGCTTTTTCT

TGACC

194 Phen CYP21 B1 GGAGGCACCCGCCTGGGTTTCTCAGTGCCCTGACAGCGCCCCCTCGCG

CCCAGGGATAGCCGCCGTGCTCCTGGGTTCACGCCTGGGCTGCCTGGA GGCCGAAGTGCCTCCAGACACAGAGACCTTCATCCGCGCGGTGGGATC GGTATTTGTGTCCACGCTGCTGACCATGGCGATGCCTAGCTGGCTTCAC

C[G/-]CCTCGTGCCCGGACCCTGGGGCCGCCTCTGCCGAGACTGGG Table 3 - Phenotypic Markers useful for breed identification test

SEQ ID NO: SNP ID Sequence

195 Phen_TAS1 R2_CAT CCCTCCTGGGCCACCAGATCTTTTTTGACCAGCGAGGGGACCTACTCAT

GCGCCTGGAGATCATCCAGGGACGGTGGGACCTGAGCCAGAAC[T/-]TTT CTGGAGCGTCGCCTCCTACTGCCCGGTGCTACGACGGCTGAGGGCCAT CCGTGACGTCTCCTGGCACACGGCCAACAACAC

196 Phen_TAS1 R2_G8224 TTGCAGACGAGTTTGGCTGCCGGCCCTGCCCGAGTTGCGGGTGGTCCC A_CAT GGAGGAACGACGCTTCGTGCTTCAAGCGGCGGCTGGCCTCCCTTGAATG

[G/A]CGCGAGGCACCCGCCGTCGCTGTGGCCGTGCTGTCCATCCTGGGC

TCCCTCTGCACCCTGGCCATCCTGGTGATCTTCTGGAGGCACCGCCACG

CGCC

197 Phen_CYP27B1_Rob GGAGGCACCCGCCTGGGTTTCTCAGTGCCCTGACAGCGCCCCCTCGCG

CCCAGGGATAGCCGCCGTGCTCCTGGGTTCACGCCTGGGCTGCCTGGA

GGCC[G/T]AAGTGCCTCCAGACACAGAGACCTTCATCCGCGCGGTGGGAT

CGGTATTTGTGTCCACGCTGCTGACCATGGCGATGCCTAGCTGGCTTCA

CC

198 Phen ZFX GGTTTTCGTCACCCGTCAGAGCTCAAGAAGCACATGCGAATCCATACTGG

GGAGAAGCCGTACCAGTGCCAGTACTGCGA[G/A]TATAGGTCTGCAGACT CTTCTAACTTGAAAACGCATGTAAAAACTAAGCATAGTAAAGAGATGCCAT TCAAGTGTGACATCTGTCTTCTGACT

199 KRT71-Del Drex GGTGGGCATCCCTGCCNGGGAATCAGCAAGCNCTAGTGNGATTTGGATT

TGGATGACTCGAATTACCCCTTCCAGTTTTTGAACTTCTCCAACTCCCTGT

[TTAGGCTTCCAACCTGGAGACGGCCATCGCCGATGCCGAGCAGCGGGG

CGACAGTGCCCTGAAGGATGCCCGGGCCAAGCT/-][-/AGTTGGAG]GGAC

GAGCTGGA[-/T]GTCCGCCCTGCACCAGGCCAAGGAGGAGCTGGCCCGG

ATGCTGCGGGAGTACCAGGAGCTCATGAGCCTGAAGCTGGCCCTGGACA

TGGAGATCGCCACCTACC

200 P2RY5 CRex ACACTTTGTATGGCCGCATGTTTAGTATGGTATTTGTGCTTGGGTTAATAT

CTAACTGTGTTGCCATATACATTTTCATCTGCACCCTCAAAGTGCGAAATG

AAACTACAACATACATGATTAACTTGGCAATGTCAGACTTGCTTTTCGTTT

TTACTTTACCCTTCAGGATTTTTTACTTTGCAACCCAGAATTGGCC[GTTT/-

]GGAGATCAACTCTGTAAAATTTCAGTGATGCTATTCTATACCAACATGTAT

GGAAGCATTCTGTTCTTAACCTGTATTAGTGTCGATCGGTTTCTGGCAAT

201 WNK4 Burm HKL TGCCCCTCTGCTTCCCCCCTTCCGTCCACCACAGCAGCCCCTCTCCTCTC

TCTGGCTAGTGCCTTCTCACTGGCTGTGATGACTGTGGCCCAGTCCCTG

CTGTCTCCCTCACCTGGGCTCCTGTCCCAGTCTCCTCCAGCCCCTCCTG

CTCCCCTCCCTAGCTTGCCCCTGCCCCCTCCCCCTGCTCCTTGTGGC[C/T

]AGGATAGGCCTTCACCCCCAACAGCTGAGACCGAGAGTGAGGTGAGTA

GGAAACCAAGAGGGATGGTTAGGGGAGCTCCACTCTGGATCATTTCCCT

TCTCATGGACCCACACTTTGCAGGTCCCGCCAAATCCTGCTCGGCCACTC

202 CART1 del Burm CTCCCGTGAAGGGGATGCCAGAAAAGGGAGAACTAGATGAACTTGGGGA

TAAATGTG ACAG CAACGTATCCAG CAGCAAGAAG CG GAG ACACAG AACC ACCTTCACCAG CCTG CAG CTCG AG GAGCTG GAG AAG GTCTTTCAG AAAA CCCATTACCCGGATGTATACGTCAGAGAACAGCTTG[CTCTCAGGACTG/-] AGCTCACGGAGGCCAGAGTCCAGGTAGGAGCCAAATGAAGGACGTGGG TGTGCGTGTTGGGGGCCGGTGTGTGGAGATACTGTTAGAATAATTCAGT GGTTGCATTTTGCCAAAAGGAAGAAACTGATCCTCTCACTAAAGACTAGA ACC Table 4

No. Defining Variant Phenotypic SNPs / genotypes (Inclusive (I) / Exclusive (E))

Registry Breed Category Family for family Agouti Brown Color Dilute Extension Glove Hairless Inhibitor Orange Spotting White Long Rex Rex 2

4 Abyssinian 1 Abyssinian aa - E bb = E CC = I E E E E E E E L- = l E E

2 American Shorthair 1 cbcb = E E E E L- = l E E

4 Birman 1 aa = I cscs = I E I E E II - I E E

4 British Shorthair 1 Persian C- = l E E E E E

4 Burmese 1 Burmese aa = I cbcb=l E E E E E E E

3 Chart re ux 1 aa = I BB, Bb = I C- = l dd = I E E E E E E E

4 Cornish Rex* 1 Rex cbcb = E E E E I E

4 Devon Rex 1 cbcb = E E E E E rere = I

4 Egyptian Mau 1 C- = l E E E E E E E

3 Japanese Bobtail 1 cbcb = E E E E E E

4 Korat 1 aa = I BB, Bb = I C- = l dd = I E E E E E E L- = E E E

•J\

4 Maine Coon 1 C- = l E E E II - I E E

4 Manx? 1 C- = l E E E E E

4 Norwegian Forest 1 C- = l I E E E E

4 Persian 1 Persian cbcb = E E E E E E

4 Russian Blue 1 aa = I BB, Bb = I cbcb = E dd = I E E E E E E E

4 Siamese 1 Siamese aa = I blbl = E cscs = I E E E E E E

3 Turkish Angora 1 cbcb = E E E E E E

4 Turkish Van 1 cbcb = E E E E E E

X Asian 2 Burmese E E E E E

4 Balinese 2 Siamese Long/Brown/Dilute aa = I bibi = E cscs = I E E E E E E

3 Bombay 2 Burmese Color aa = I B- = l C- = l dd - E E E E E E E E

2 Colorpoint 2 Siamese Agouti cscs = I E E E E E

2 Cymric^ 2 Manx Long cbcb = E E E E E E

4 Exotic Shorthair 2 Persian Long cbcb = E E E E E E

3 Havana Brown 2 Siamese Color aa = I bb = I C- = l dd = E E E E E E E E

X Himalayan 2 Persian Color cscs = I E E E E E

4 Oriental 2 Siamese Color cscs = E E E E E E

Table 4

•J\

Disease info: F disease SNP is frequent in the breed and may be breed specific

V the SNP variant has different frequencies in different breeds,

Categories: founder breed (N = 19, genetically defined by SNPs)

2 = Breed variants (N = 16), distinguish with other phenotypic SNPs or DNA variants

3 = Breeds may be like random breds (N = 9), but if found mutation, could distinguish.

4 = Hybrid breed (N = 2), need wildcat diagnostics, Y STRs

5 = Ftendom bred (N = 4), regional, 3 completed, Euro SH underway, but define as breeds in breed only comparison

6 = concoction breeds (N = 3), will show mixture, defining variants will be of assistance

7 = Foreign Burmese may have distinct gene pool from USA Burmese, separate breed

§Variant found, unpublished

•Could share fur type variant

Table 5

Structural Trait Disease Traits

Aby Aby

Defining Variant Blood Korat Korat Burmese NFC MCC Ragdoll PRA PRA Persian Aby

Ear Poly- Type

Breed Family for family Dwarf Curl Fold Bobtail Tailless dactyla B GM1 GM2 GM2 GSD HCM HCM CEP290 CRX PKD PKLR

Abyssinian Abyssinian E E E E E E V F F F American Shorthair E E E E E E V

Birman E E E E E E V

British Shorthair Persian E E E E E E V

Burmese Burmese E E E E E E V

Chartreux E E E E E E V

Cornish Rex* Rex E E E E E E V

Devon Rex E E E E E E V

Egyptian Mau E E E E E E V

Japanese Bobtail E E E 1 E E V

Korat E E E E E E V

Maine Coon E E E E E F V

Manx§ E E E E 1 E V

Norwegian Forest E E E E E E V

Persian Persian E E E E E E V

Russian Blue E E E E E E V

Siamese Siamese E E E E E E V

Turkish Angora E E E E E E V

Turkish Van E E E E E E V

Asian Burmese E E E E E E V

Balinese Siamese Long/Brown/Dilute E E E E E E V

Bombay Burmese Color E E E E E E V

Colorpoint Siamese Agouti E E E E E E V

Cymric§ Manx Long E E E E 1 E V

Exotic Shorthair Persian Long E E E E E E V

Table 5

Structural Trait Disease Traits

Aby Aby

Defining Variant Blood Korat Korat Burmese NFC MCC Ragdoll PRA PRA Persian Aby

Ear Type

Breed Cat. Family for family Dwarf Curl Fold Bobtail Tailless B GM1 GM2 GM2 GSD HCM HCM CEP290 CRX PKD PKLR

Havana Brown 2 Siamese Color E E E E E E V

Himalayan 2 Persian Color E E E E E E V

Oriental 2 Siamese Color E E E E E E V

Ragamuffin 2 Ragdoll Not pointed E E E E E E V

Singapura 2 Burmese Agouti E E E E E E V

Somali 2 Abysinnian Long E E E E E E V

Sphynx 2 Devon Rex Hairless/Rex E E E E E E V

Tiffanie 2 Burmilla Long/Silver E E E E E E V

Scottish Fold 3 Persian Fold E E I E E E V

European Shorthair 5 E E E E E E V

Ragdoll 5 Ragdoll E E E E E E V

Siberian 5 E E E E E E V

Sokoke 5 E E E E E E V

Australian Mist 6 Burmese E E E E E E V

Burmese Non-USA 7 E E E E E E V

Tonkinese 2/6 Burmese/Siamese Color E E E E E E V

Bengal 1/4/6 Aby/Mau Ticked E E E E E E V

Snowshoe ? Birman? bicolor, sh E E E E E E V

Ocicat 1 (6) Aby/Siamese Color/Ticked/Tabby E E E E E E V

[0100] In addition to determination of the plurality of SNPs listed in Table 1 , the genotype of one or more microsatellite markers and/or short tandem repeats (STRs) can be determined. For example, the genotype of one or more feline STRs selected from the group consisting of selected from the group consisting of FCA005, FCA008, FCA023, FCA026, FCA035, FCA043, FCA045, FCA058, FCA069, FCA075, FCA077, FCA080B, FCA088, FCA090, FCA094, FCA096, FCA097, FCA105, FCA123, FCA126, FCA132, FCA149, FCA211, FCA220, FCA223, FCA224, FCA229, FCA262, FCA293, FCA305, FCA310, FCA391, FCA441, FCA453, FCA628, FCA649, FCA678 and FCA698. The identity and location of feline microsatellites and STRs have been characterized and mapped, as described, e.g., in Menotti-Raymond, et al., Genomics (1999) 57(l):9-23; Menotti-

Raymond, et al., Journal of Heredity (2003) 94(1):95— 106; and Menotti-Raymond, et al., Genomics (2009) 93(4): 305-313.

[0101] As appropriate, the genotypes of at least about 3, 5, 10, 15, 20, 25, 30, 35,

38, or more, feline STRs are determined. In some embodiments, the expression levels of all STRs selected from FCA005, FCA008, FCA023, FCA026, FCA035, FCA043, FCA045, FCA058, FCA069, FCA075, FCA077, FCA080B, FCA088, FCA090, FCA094, FCA096, FCA097, FCA105, FCA123, FCA126, FCA132, FCA149, FCA211, FCA220, FCA223, FCA224, FCA229, FCA262, FCA293, FCA305, FCA310, FCA391, FCA441, FCA453, FCA628, FCA649, FCA678 and FCA698 are determined.

5. Methods of Detecting Biomarkers

[0102] In some embodiments, methods comprise obtaining the identity of one or both alleles in a test feline genome for each marker of a set of markers. The genetic markers described herein, including the SNPs, STRs and phenotypic markers, can be detected using any methods known in art, including without limitation amplification, sequencing and hybridization techniques. Detection techniques for evaluating nucleic acids for the presence of a single base change involve procedures well known in the field of molecular genetics. Methods for amplifying nucleic acids find use in carrying out the present methods. Ample guidance for performing the methods is provided in the art.

Exemplary references include manuals such as PCR Technology: PRINCIPLES AND APPLICATIONS FOR DNA AMPLIFICATION (ed. H. A. Erlich, Freeman Press, NY, N.Y., 1992); PCR PROTOCOLS: A GUIDE TO METHODS AND APPLICATIONS (eds. Innis, et al, Academic Press, San Diego, Calif, 1990); CURRENT PROTOCOLS IN MOLECULAR BIOLOGY, Ausubel, 1990-2008, including supplemental updates;

Sambrook & Russell, Molecular Cloning, A Laboratory Manual (3rd Ed, 2001).

[0103] According to one aspect of the present invention, there is provided a method for assigning a feline to one or more breeds and/or populations of origin based on the genotypes of a set of gene polymorphisms. The method comprises the steps of first isolating a genomic DNA sample from a feline, and then detecting, e.g., amplifying a region genomic DNA including the one or more of the genetic markers using an oligonucleotide pair to form nucleic acid amplification products of the one or more gene polymorphism sequences. Amplification can be by any of a number of methods known to those skilled in the art including PCR, and the invention is intended to encompass any suitable methods of DNA amplification. A number of DNA amplification techniques are suitable for use with the present invention. Conveniently, such amplification techniques include methods such as polymerase chain reaction (PCR), strand displacement amplification (SDA), nucleic acid sequence based amplification (NASBA), rolling circle amplification, T7 polymerase mediated amplification, T3 polymerase mediated amplification, SP6 polymerase mediated amplification, and GoldenGate amplification assays. The precise method of DNA amplification is not intended to be limiting, and other methods not listed here will be apparent to those skilled in the art and their use is within the scope of the invention.

[0104] In some embodiments, the polymerase chain reaction (PCR) process is used (see, e.g., U.S. Pat. Nos. 4,683,195 and 4,683,202. PCR involves the use of a

thermostable DNA polymerase, known sequences as primers, and heating cycles, which separate the replicating deoxyribonucleic acid (DNA), strands and exponentially amplify a gene of interest. Any type of PCR, including quantitative PCR, RT-PCR, hot start PCR, LA-PCR, multiplex PCR, touchdown PCR, finds use. In some embodiments, real-time PCR is used.

[0105] The amplification products are then analyzed in order to detect the presence or absence of at least one polymorphism in the feline genome that is associated with the desired genotypes and/or phenotypes, as discussed herein. By practicing the methods of the present invention and analyzing the amplification products it is possible to determine the genotype of individual animals with respect to the polymorphism.

[0106] In some embodiments, the genetic markers may be detected by restriction fragment length polymorphism (RFLP) analysis of a PCR amplicon produced by amplification of genomic DNA with the oligonucleotide pair. In order to simplify detection of the amplification products and the restriction fragments, those of skill will appreciate that the amplified DNA will further comprise labeled moieties to permit detection of relatively small amounts of product. A variety of moieties are well known to those skilled in the art and include such labeling tags as fluorescent, bioluminescent, chemiluminescent, and radioactive or colorigenic moieties.

[0107] A variety of methods of detecting the presence and restriction digestion properties of amplification products are also suitable for use with the present invention. These can include methods such as gel electrophoresis, mass spectroscopy or the like. The present invention is also adapted to the use of single stranded DNA detection techniques such as fluorescence resonance energy transfer (FRET). For FRET analysis, hybridization anchor and detection probes may be used to hybridize to the amplification products. The probes sequences are selected such that in the presence of the SNP, for example, the resulting hybridization complex is more stable than if there is a G or C residue at a particular nucleotide position. By adjusting the hybridization conditions, it is therefore possible to distinguish between animals with the SNP and those without. A variety of parameters well known to those skilled in the art can be used to affect the ability of a hybridization complex to form. These include changes in temperature, ionic concentration, or the inclusion of chemical constituents like formamide that decrease complex stability. It is further possible to distinguish animals heterozygous for the SNP versus those that are homozygous for the same. The method of FRET analysis is well known to the art, and the conditions under which the presence or absence of the SNP would be detected by FRET are readily determinable.

[0108] Suitable sequence methods of detection also include e.g., dideoxy

sequencing-based methods and Maxam and Gilbert sequence (see, e.g., Sambrook and Russell, supra). Suitable HPLC-based analyses include, e.g., denaturing HPLC (dHPLC) as described in e.g., Premstaller and Oefner, LC-GC Europe 1-9 (July 2002); Bennet et al, BMC Genetics 2: 17 (2001); Schrimi et al, Biotechniques 28(4):740 (2000); and Nairz et al, PNAS USA 99(16): 10575-10580 (2002); and ion-pair reversed phase HPLC-electrospray ionization mass spectrometry (ICEMS) as described in e.g., Oberacher et al.; Hum. Mutat. 21(1):86 (2003). Other methods for characterizing single base changes in alleles of genetic markers include, e.g., single base extensions (see, e.g., Kobayashi et al, Mol. Cell. Probes, 9: 175-182, 1995); single-strand conformation polymorphism analysis, as described, e.g, in Orita et al, Proc. Nat. Acad. Sci. 86, 2766-2770 (1989), allele specific oligonucleotide hybridization (ASO) (e.g., Stoneking et al, Am. J. Hum. Genet. 48:70-382, 1991 ; Saiki et al, Nature 324, 163-166, 1986; EP 235,726; and WO 89/1 1548); and sequence-specific amplification or primer extension methods as described in, for example, WO 93/22456; U.S. Pat. Nos. 5, 137,806; 5,595,890; 5,639,61 1 ; and U.S. Pat. No. 4,851 ,331 ; 5*- nuclease assays, as described in U.S. Pat. Nos. 5,210,015; 5,487,972; and 5,804,375; and Holland et al, 1988, Proc. Natl. Acad. Sci. USA 88:7276-7280.

[0109] Methods for detecting single base changes well known in the art often entail one of several general protocols: hybridization using sequence-specific oligonucleotides, primer extension, sequence-specific ligation, sequencing, or electrophoretic separation techniques, e.g., singled-stranded conformational polymorphism (SSCP) and heteroduplex analysis. Exemplary assays include 5' nuclease assays, template-directed dye-terminator incorporation, molecular beacon allele-specific oligonucleotide assays, single-base extension assays, and SNP scoring by real-time pyrophosphate sequences. Analysis of amplified sequences can be performed using various technologies such as microchips, fluorescence polarization assays, and matrix-assisted laser desorption ionization (MALDI) mass spectrometry. In addition to these frequently used methodologies for analysis of nucleic acid samples to detect single base changes, any method known in the art can be used to detect the presence of the genetic markers described herein.

[0110] For example FRET analysis can be used as a method of detection.

Conveniently, hybridization probes comprising an anchor and detection probe, the design of which art is well known to those skilled in the art of FRET analysis, are labeled with a detectable moiety, and then under suitable conditions are hybridized an amplification product containing the genetic marker of interest in order to form a hybridization complex. A variety of parameters well known to those skilled in the art can be used to affect the ability of a hybridization complex to form. These include changes in temperature, ionic concentration, or the inclusion of chemical constituents like formamide that decrease complex stability. The presence or absence of the genetic marker is then determined by the stability of the hybridization complex. The parameters affecting hybridization and FRET analysis are well known to those skilled in the art. The amplification products and hybridization probes described herein are suitable for use with FRET analysis.

6. Methods of Analyzing Biomarkers

[0111] The methods comoprise determining the contributions of one or more feline populations (e.g., ancestral lineage and/or breed contributions) to the test feline genome by comparing the alleles at the predetermined genetic markers in the test feline genome (e.g. , a plurality of the SNPs listed in Table 1; optionally one or more phenotypic markers and/or microsatellite markers) to a database comprising feline population profiles, wherein each feline population profile comprises genotype information for alleles of the markers in the set of markers in the feline population. For example, a feline population profile may comprise genotype information for each allele of each marker in the set of markers in the feline population. The genotype information in a feline population profile may comprise information such as the identity of one or both alleles of most or all of the markers in the set of markers in one or more felines that are members of that feline population, and/or estimated allele frequencies for at least one allele of most or all of the markers in the set of markers in that feline population. The collection of feline population profiles can be collected in a database for use in practicing the invention. In some embodiments, the database of feline population profiles comprises one or more feline population profiles. In various embodiments, the database of feline population profiles comprises a plurality of feline population profiles, e.g., between about five and about 500 feline population profiles, such as about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, or more, feline population profiles.

[0112] Determining the contributions of feline populations to the test feline genome can encompass both assigning a feline genome to one or more particular feline populations and/or determining the fraction of the feline genome that was derived from one or more feline populations. In one embodiment, the test feline is suspected of having at least about 25% of the feline genome, e.g., at least about 30%, 40%, 50%, 60%, 70%, 80%, 90% or 100%), derived from a single defined feline population (e.g., ancestral lineage and/or breed).

[0113] Many methods of assignment testing have been developed in the past decade using common population genetic markers and a variety of statistical methods (Rannala & Mountain (1997) Proc Natl Acad Sci USA. 94(17):9197-9201 ; Pritchard et al. (2000) Genetics, 155:945-959; Baudouin & Lebrun (2001) In: Proc. Int. Symp. on Molecular Markers, pp. 81-94; Paetkau et al. (2004) Molecular Ecology , 13:55-65). These methods have been applied to various breeding populations including pigs, cattle, and dogs

(Schelling et al. (2005) Journal of Animal Breeding and Genetics, 122:71-77; Negrini et al. (2009) Animal Genetics, 40: 18-26; Boitard et al. (2010) Anim Genet. 2010 41(6):608-18. See also, U.S. Patent Nos. 7,729,863 and 6,770,437. In some embodiments of the method, a Bayesian model-based clustering approach is used, e.g. , as described by Rannala &

Mountain, supra; Pritchard, supra; and/or Baudouin & Lebrun, supra.

[0114] There are two broad classes of clustering methods that are used to assign individuals to populations (Pritchard, supra). Distance-based methods calculate a pairwise distance matrix to provide the distance between every pair of individuals. Model-based methods proceed by assuming that observations from each cluster are random draws from some parametric model; inference for the parameters corresponding to each cluster is then done jointly with inference for the cluster membership of each individual, using standard statistical methods. In some embodiments of the method, a likelihood or frequentist model- based approach is used, e.g., as described by Paetkau, supra; and/or Negrini, supra. Any standard statistical method may be used in the methods of the invention, including maximum likelihood, bootstrapping methodologies, Bayesian methods and any other statistical methodology that can be used to analyze genotype data. These statistical methods are well-known in the art. [0115] Many software programs for population genetics studies have been developed and may be used in the methods of the invention, including, but not limited to TFPGA, Arlequin, GDA, GENEPOP, GeneStrut, POPGENE (Labate (2000) Crop. Sci. 40: 1521-1528), Geneclass2 (Piry et al, (2004) Journal of Heredity 95, 536-539) and STRUCTURE (Pritchard et al. (2000) Genetics 155:945-59).

[0116] An exemplary Bayesian model-based clustering approach is provided by the genotype clustering program STRUCTURE (Pritchard et al. (2000) Genetics 155:945-59), which has proven useful for defining populations within a species (Rosenburg et al. (2001) Genetics 159:699-713; Rosenburg et al. (2002) Science 298:2381-5; Falush et al. (2003) Genetics 164(4): 1567-87). The clustering method used by STRUCTURE requires no prior information about either phenotype or genetic origin to accurately place an individual or set of related individuals in a population.

[0117] Any algorithms useful for multi-locus genotype analysis may be used in the methods of the invention, for example, classic assignment algorithms. Suitable algorithms include those described in Rannala & Mountain (1997) Proc. Natl. Acad. Sci. U.S.A. 94:9197-9201, Paetkau et al. (2004) Molecular Ecology, 13:55-65 and Cornuet et al.

(1999) Genetics 153: 1989-2000 and variations thereof. Exemplary programs available for multi-locus genotype analysis include Doh (available on the internet at

biology .ualberta.ca/jbrzusto/Doh.php) and GeneClass (available at montpellier.inra.fr/URLB/geneclass/genecass.htm). Cluster iterations can be combined, e.g., through the program CLUMP (Jakobsson & Rosenberg, Bioinformatics 23(14): 1801-6) and DISTRUCT (Rosenberg, (2004) Molecular Ecology Notes 4, 137-138) to create a consensus clustering. Migrants within populations can be detected, e.g., using the program Geneclass2 (Piry et al, (2004) Journal of Heredity 95, 536-539.

[0118] In some embodiments, the methods of the invention comprise determining the probability that a specific feline population contributed to the genome of the test feline by determining the conditional probability that the alleles in the test feline genome would occur in the specific feline population divided by the sum of conditional probabilities that the alleles in the test feline genome would occur in each feline population in the database.

[0119] Some embodiments of the methods of the invention comprise discriminating between the contributions of two or more genetically related feline populations to the test feline genome by comparing the alleles in the test feline genome to a database comprising profiles of the two or more genetically related feline populations. In various embodiments, the two or more genetically related feline populations may comprise (i) Persian and Exotic Shorthair (SH); (ii) British SH and Scottish Fold; (iii) Australian Mist and Burmese;

(iv) Singapura and Burmese; (v) Birman and Korat, and (vi) Siamese and Havana Brown.

[0120] Using an assignment algorithm on genotype information for all 148 SNPs listed in Table 1, 38 microsatellite markers and 5 phenotypic markers listed in Table 3 from 477 felines representing 29 feline breeds, the methods of the invention have been used to assign individual test felines to its breed with at least about 50% sensitivity and specificity, for example, at least about 60%, 70%, 75%, 80%, 85%, 90%, 95% sensitivity and specificity, or greater. As used herein, sensitivity specifically indicates the percentage of individuals sampled from a breed that could be assigned back to that breed. Specificity takes into account individuals sampled from other breeds that were misassigned to that breed. Sensitivity and specificity are properly used to describe the power of the testing in assignment testing. See, Example 2.

[0121] The methods of the invention are also useful for determining the

contributions of feline populations to felines having genetic contributions from more than one breed or defined ancestral lineage. Preferably, the test feline has at least 25% of the markers, e.g., at least about 30%, 40%, 50%, 60%, 70%, 80%, 90% or 100% of the markers, associated with a defined ancestral lineage or breed. Models that detect an individual's admixed state can be considered to group into two classes: models that require a combinatoric set of unique alleles for each of the possible mixtures of ancestral populations (Nason & Ellstrand (1993) J. Hered. 84: 1-12; Epifanio & Philipp (1997) J. Hered. 88:62- 5), and Bayesian methods where ancestral populations are not required to contain a combination describing unique alleles, but instead assign individuals to admixed states probabilistically based on differences in allele frequencies between populations (Corander et al. (2003) Genetics 163(1): 367-74; Anderson & Thompson (2002) Genetics 160: 1217- 29, Pritchard et al. (2000) Genetics 155:945-59, Rannala & Mountain (1997) Proc. Natl. Acad. Sci. U.S.A. 94:9197-9201. The latter set of models are more informative for most populations and data sets as they allow for a Bayesian posterior probabilistic assignment vector for each population/generation combination, thereby allowing for uncertainty analysis to be incorporated into the assignment vector; but existing models for the exact, recent admixture assignments of individuals from multiple ancestral populations are limited in their scope as they have been developed thus far only for two generation prediction and allow for only a few ancestral populations. For example, the methods of Anderson & Thompson (2002) are developed for a two generation, two population model with unlinked microsatellite data.

7. Reporting Results of Analysis

[0122] In various embodiments, the methods may further comprise the step of reporting the results of the assignment analysis, e.g., to the purchaser, to the owner or guardian of the feline, to a breed registry, to a veterinarian or another interested individual. The methods may further comprise the step of providing a document displaying the contributions of one or more feline populations to the genome of the test feline genome. The document may be a chart, certificate, card, or any other kind of documentation. The document may be electronic or paper copy. The document may display the contributions of one or more feline populations to the test feline genome in a numeric format or in a graphic format. For example, the document may include photographs or other depictions, drawings, or representations of the one or more feline populations. The document may also provide confidence values for the determined contributions (such as 80%, 85%, 90%> 95%, or 99% confidence). In some embodiments, the document provides a certification of the

contributions of one or more feline populations to the genome of the test feline genome.

[0123] In some embodiments, the document additionally provides information regarding the one or more feline populations that contributed to the genome of the test feline or the test feline. The information regarding feline populations that contributed to the genome of the test feline may include information related to the characteristics and origin of the feline population (e.g., ancestral origin and/or contributing breed(s)) or any other kind of information that would be useful knowledge concerning the test feline. In some

embodiment, the information includes health-related information. Many feline populations have predispositions to particular diseases or conditions. For example, heart disease in the Maine Coon and Ragdoll (Meurs et al. (2005) Human Molecular Genetics, 14:3587-3593; Meurs et al. (2007) Genomics, 90:261-264), polycystic kidney disease in the Persian (Lyons et al. 2004, supra), progressive retinal atrophy in the Abyssinian (Menotti- Raymond et al. (2007) Journal of Heredity, 98:211-220) and a craniofacial defect and hypokalemia in Burmese. Therefore, information regarding the contributions of one or more feline populations to the genome of the test feline genome is particularly valuable to mixed-breed feline owners or caretakers (both professional and non-professional) for the purpose of proactively considering health risks for individual tested animals. For example, a mixed breed cat that is found to be a mixture of a breed with known association or predisposition for certain disease conditions could be proactively monitored for such disease conditions that occur with rare frequency in the general population of cats, but occur with significant frequency in these specific breeds.

[0124] Health-related information may also include potential treatments, special diets or products, diagnostic information, and insurance information.

8. Computer-Readable Media

[0125] In a further aspect, the invention provides one or more computer-readable media comprising a data structure stored thereon for use in distinguishing feline

populations. In some embodiments, the data structure comprises: a marker field, which is capable of storing the name of a marker (for example, an SNP marker) or the name of an allele of a marker; and a genotype information field, which is capable of storing genotype information for the marker (for example, the identity of one or both alleles of the marker in a feline genome or an estimate of the frequency of an allele of the marker in a feline population), wherein a record comprises an instantiation of the marker field and an instantiation of the genotype information field and a set of records represents a feline population profile.

[0126] A "computer-readable medium" refers to any available medium that can be accessed by computer and includes both volatile and nonvolatile media, removable and nonremovable media. Computer readable media that are non-transitory find use. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other computer storage media. Communication media typically embody computer-readable instructions, data structures, program modules or other data in a modulated data signal, such as a carrier wave or other transport mechanism that includes any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired media, such as a wired network or direct- wired connection, and wireless media, such as acoustic, RF infrared, and other wireless media. A combination of any of the above should also be included within the scope of computer-readable media.

[0127] A "data structure" refers to a conceptual arrangement of data and is typically characterized by rows and columns, with data occupying or potentially occupying each cell formed by a row-column intersection. In some embodiments, a data structure in the computer-readable medium can comprise a marker field and a genotype information field, as described above. The instantiation of the marker field and the genotype information field provides a record, and a set of record provides a feline population profile. Thus, the data structure may be used to create a database of feline population profiles.

[0128] In some embodiments, the computer readable medium comprises a substrate having stored thereon: (a) a data structure for use in distinguishing feline populations, the data structure comprising: (i) a marker field, which is capable of storing the name of a marker or of an allele of a marker; and (ii) a genotype information field, which is capable of storing genotype information for the marker, wherein a record comprises an instantiation of the marker field and an instantiation of the frequency field and a set of records represents a feline population profile; and (b) computer-executable instructions for implementing a method for determining the contributions of feline populations to a feline genome, comprising: (i) obtaining the identity of one or both alleles in a test feline genome for each marker of a set of markers; and (ii) determining the contributions of feline populations to the test feline genome by comparing the alleles in the test feline genome to a database comprising feline population profiles, wherein each feline population profile comprises genotype information for the set of markers in the feline population.

9. Kits

[0129] In another aspect, the invention provides nucleic acid sequences for determining the identity of one or both alleles in a feline genome for each marker of a set of markers, e.g., as listed in Table 1. The nucleic acid sequences can be primer sets. In some embodiments, the primer sets are provided in a kit.

[0130] The invention further provides kits useful for determining the population of origin {e.g., ancestral lineage and/or breed contributions) of a feline. In general, the kits comprise one or more oligonucleotide primer pairs as described herein suitable to amplify the portions of a feline genome comprising a plurality of the SNPs listed in Table 1. In some embodiments, the kit comprises oligonucleotide primer pairs for determining all 148 SNPs listed in Table 1. In various embodiments, the kits may further comprise one or more oligonucleotide primer pairs for determining one or more biomarkers listed in Table 3, e.g., for determining the SNPs in one or more of SEQ ID NOs: 149-202. The kits comprise forward and reverse primers suitable for amplification of a genomic DNA sample taken from a feline. As described above, the biological sample can be from any tissue or fluid in which genomic DNA is present. Conveniently, the sample may be taken from blood, skin {e.g., cheek swab) or a hair bulb.

EXAMPLES

[0131] The following examples are offered to illustrate, but not to limit the claimed invention. Example 1

Genetic Structure of Worldwide Random Bred Cat Populations

[0132] This example describes the specific pinpointing of the geographic source of domestic cats. For this study, 944 cats from 37 random bred worldwide populations particularly from within the Middle East, Egypt and other Old World areas were genotyped at 148 SNPs and 38 cat-specific STRs. Thirty-eight wildcats were examined as outgroups. Principal Coordinate Analysis (PCA) and Bayesian clustering methods indicated eight modern worldwide cat populations belonging to at least five distinct ancestral groups;

populations were geographically distributed, consistent with isolation by distance. Genetic indices were a gradient across the world, with the highest genetic diversity and lowest inbreeding in the region of historical Mesopotamia and the Levant, current day Iraq, Lebanon and Israel of the 37 sampled locations, more so than cats from Egypt, suggesting cats shared their cradle of domestication with the earliest human civilization and only later branching out towards Europe and Asia.

Materials and Methods:

[0133] Cat Sample Collection. This study included 944 domestic cats from 37 locations worldwide, including 20 locations and 481 cats novel to this study (Figure 1). Samples (n = 463) from a previous study included random bred cats from 17 locations (Lipinski et al, (2008) Genomics 91, 12-21). Samples were collected via buccal (cheek) swabs and extracted with a QIAamp DNA blood mini kit following the manufacturer's protocol (Qiagen, Valencia, CA, USA), or as whole blood spotted onto an FTA Card (Whatman International Ltd.) followed by a modified whole genome amplification

(REPLI-g Mini Kit, Qiagen) as follows. A 0.12 cm punch was taken from the bloodstained card and washed with 5 minutes of gentle rocking a total of 5 times: 3 times with 0.5 ml of FTA purification mix made out of one part FTA-Reagents (Whatman International Ltd.), two parts PBS and 0.5% TWEEEN, and 2 washes with 0.5 mL of lx TE Buffer (10 mL Tris HC1 1M, 2 mL EDTA 0.5M). This was dried at 60°C for 30 min. Two clean, dry punches were combined with 2.5 μΐ PBS and processed following the REPLI-g whole genome amplification kit protocol for amplification of genomic DNA from blood or cells (Qiagen, Valencia, CA, USA). Following whole genome amplification, consumed punches were discarded. Wildcat samples (N=38) were collected as part of other studies and provided as extracted DNA that had been whole genome amplified.

[0134] SNP and STRs. Thirty-eight autosomal STRs were genotyped following the

PCR and analysis procedures of a previous study (Lipinski et al., 2008, supra). Unlinked non-coding autosomal SNPs (n=169), which were defined by one Abyssinian cat, were selected to represent all autosomes from the 1.9x coverage cat genomic sequence (Pontius, et al., 2007, (2007) Genome Research 17, 1675-1689). Primers were designed with the

VeraCode Assay Designer software (Illumina Inc., San Diego, CA, USA). Only SNPs that received a design score of 0.75 or higher (with a mean design score of 0.95) (n = 162) were included in the analysis (Table 6). [0135] Golden Gate Assay amplification and BeadXpress reads were performed per the manufacturer's protocol (Illumina Inc.) on 50-500ng of DNA or whole genome amplified product. BeadStudio software v. 3.1.3.0 with the Genotyping module v. 3.2.23 (Illumina Inc.) was used to analyze the data. Any samples with a call rate less than 0.80 (n = 21) were removed from further clustering analysis. Additionally, only SNPs with a Gentrain Score of > 0.55 (n = 148) were included in the study (Table 6).

Table 6

SNPs for the analysis of worldwide random bred cat populations.

Minor Allele

Locus Name Call Frequency Frequency Gentrain Score Desiqi chrA1_10141047 0.753 0.099 0.8314 0.967

chrA1_133621071 0.822 0.316 0.6292 0.851

chrA1_151648701 0.82 0.161 0.9071 0.872

chrA1_175780586 0.745 0.257 0.797 0.908

chrA1_208054462 0.788 0.094 0.8803 1

chrA1_223501140 0.777 0.201 0.9354 0.979

chrA1_223506906 0.775 0.168 0.7732 0.911

chrA1_225057933 0.824 0.179 0.9035 0.934

chrA1_235579538 0.782 0.066 0.9023 0.953

chrA1_264488084 - - < 0.55 0.952

chrA1_27523501 0.816 0.408 0.7117 0.98

chrA1_68485376 0.806 0.116 0.6293 0.953

chrA1_69424718 0.796 0.369 0.8457 0.95

chrA1_7429296 0.755 0.121 0.933 0.976

chrA1_8742286 0.826 0.264 0.9066 0.9

chrA2_152258936 0.807 0.266 0.7745 0.941

chrA2_18603206 - - < 0.55 0.909

chrA2_201526186 0.822 0.253 0.9045 0.967

chrA2_202225770 0.794 0.222 0.9004 0.955

chrA2_44241149 0.834 0.014 0.8447 0.919

chrA2_554046 0.806 0.105 0.8098 0.754

chrA2_63928585 - - <0.55 0.977

chrA3_101420069 0.825 0.036 0.9005 0.949

chrA3_11480952 0.756 0.017 0.8013 0.847

chrA3_12082294 0.813 0.148 0.8723 0.941

chrA3_130195244 0.765 0.126 0.9275 0.915

chrA3_159537633 0.812 0.382 0.9266 0.986

chrA3_162208567 0.768 0.278 0.8971 0.959

chrA3_38781591 0.818 0.06 0.6781 0.913

chrA3_75156179 0.808 0.28 0.8108 0.991

chrA3_91058022 0.808 0.208 0.8533 0.969

chrA3_99507784 0.817 0.223 0.7373 0.94

chrB1_10420438 0.812 0.168 0.8177 0.973

chrB1_12214271 0.813 0.302 0.9113 0.977

chrB1_173872554 - - < 0.55 0.984

chrB1_195678303 0.82 0.122 0.8838 0.958

chrB1_199564532 0.816 0.068 0.8779 0.967

chrB1_202966562 0.807 0.304 0.9003 0.921

chrB1_54775572 0.826 0.086 0.7206 0.932 Table 6

SNPs for the analysis of worldwide random bred cat populations.

Minor Allele

Locus Name Call Frequency Frequency Gentrain Score Desiqn Score chrB1_80161671 0.755 0.158 0.8431 0.971 chrB1_88148379 0.811 0.272 0.881 0.954 chrB2_138312489 0.816 0.315 0.9085 0.972 chrB2_146660650 0.834 0.013 0.9346 0.932 chrB2_165334691 - - < 0.55 0.978 chrB2_41509834 0.8 0.252 0.8068 0.988 chrB2_44611679 - - < 0.55 0.902 chrB2_45093345 0.779 0.252 0.8423 0.842 chrB2_6949528 0.824 0.096 0.9261 0.955 chrB3_104483970 0.818 0.223 0.8287 0.924 chrB3_111000326 0.827 0.129 0.8451 0.97 chrB3_13666494 0.813 0.337 0.8875 0.949 chrB3_144896949 - - < 0.55 0.93 chrB3_39203469 0.807 0.151 0.8987 0.91 chrB3_51317931 0.806 0.204 0.7156 0.96 chrB3_57141954 0.79 0.31 0.8199 0.814 chrB3_77094074 0.762 0.135 0.9005 0.945 chrB4_105706694 0.795 0.371 0.8855 0.936 chrB4_142658074 0.796 0.094 0.666 0.953 chrB4_143006494 0.79 0.18 0.8963 0.915 chrB4_144693308 0.806 0.267 0.925 0.906 chrB4_146486983 0.817 0.187 0.9402 0.978 chrB4_147206961 0.738 0.232 0.87 0.965 chrB4_149532846 0.756 0.108 0.856 0.992 chrB4_1687419 0.696 0.036 0.8797 0.992 chrB4_20001848 0.813 0.05 0.8896 0.983 chrB4_21098349 0.779 0.278 0.8934 0.929 chrB4_255106 0.8 0.154 0.8312 0.964 chrB4_3093827 0.812 0.094 0.9242 0.935 chrB4_40319102 0.79 0.363 0.8632 0.981 chrB4_47638578 0.813 0.084 0.9279 0.923 chrC1_104161375 - - < 0.55 0.909 chrC1_116355295 0.821 0.196 0.6692 0.924 chrC1_123164748 0.813 0.148 0.8197 0.94 chrC1_181852965 0.389 0.11 0.9008 0.954 chrC1_190502133 0.825 0.254 0.7788 0.968 chrC1_214768780 - - < 0.55 0.94 chrC1_215441574 0.794 0.255 0.863 0.956 chrC1_216852686 0.694 0.198 0.9473 0.958 chrC1_23778400 - - < 0.55 0.957 chrC1_24148281 0.811 0.143 0.8695 0.987 chrC1_28702055 0.819 0.168 0.9367 0.901 chrC1_34981315 0.814 0.031 0.8551 0.937 chrC1_396397 0.785 0.178 0.7129 0.976 chrC1_44520932 0.796 0.053 0.9208 0.982 chrC1_52456776 0.747 0.339 0.9 0.987 chrC2_106991233 0.802 0.302 0.8821 0.983 chrC2_147124460 0.824 0.394 0.8973 0.99 chrC2_150774106 0.821 0.046 0.7475 0.976 chrC2_156491175 0.799 0.376 0.8444 0.944 Table 6

SNPs for the analysis of worldwide random bred cat populations.

Minor Allele

Locus Name Call Frequency Frequency Gentrain Score Desigi chrC2_187325 0.689 0.116 0.7213 0.936 chrC2_262401 0.771 0.176 0.8529 0.981 chrC2_5215469 0.806 0.268 0.8223 0.958 chrC2_955732 - - < 0.55 0.947 chrD1_101321498 0.802 0.226 0.9247 0.904 chrD1_104941557 0.816 0.148 0.8874 0.956 chrD1_105498119 0.798 0.084 0.8834 0.942 chrD1_10789012 0.818 0.231 0.8845 0.904 chrD1_11484008 0.797 0.095 0.8929 0.922 chrD1_117527468 0.823 0.093 0.8557 0.931 chrD1_125811329 0.761 0.166 0.7921 0.906 chrD1_126256993 0.815 0.202 0.8932 0.977 chrD1_126847301 0.804 0.06 0.8391 0.96 chrD1_15984279 0.806 0.256 0.8615 0.964 chrD1_16242433 0.728 0.178 0.8532 0.988 chrD1_17005687 - - < 0.55 0.955 chrD1_18390852 0.816 0.213 0.933 0.902 chrD1_18570323 0.811 0.279 0.9196 0.943 chrD1_66177762 0.813 0.073 0.7002 0.974 chrD2_1020904 0.82 0.383 0.7066 0.922 chrD2_103984864 - - < 0.55 0.933 chrD2_105772916 0.808 0.211 0.8808 0.996 chrD2_1752007 0.81 0.09 0.8115 0.991 chrD2_56777338 0.832 0.004 0.7186 0.949 chrD2_717969 0.771 0.263 0.8805 0.932 chrD2_74293444 0.784 0.134 0.8692 0.985 chrD2_91989307 0.817 0.165 0.8417 0.945 chrD3_103840114 0.829 0.029 0.8386 0.867 chrD3_122502120 0.807 0.173 0.7726 0.982 chrD3_1810839 0.515 0.191 0.8856 0.951 chrD3_24565823 0.827 0.057 0.7063 0.93 chrD3_24823793 0.792 0.128 0.8819 0.901 chrD3_28838660 0.824 0.057 0.8921 0.968 chrD4_41078218 0.77 0.301 0.8384 0.84 chrD4_42000379 0.785 0.055 0.8177 0.941 chrD4_63622083 0.786 0.196 0.886 0.937 chrE1_130875919 0.803 0.09 0.6753 0.947 chrE1_131587399 0.762 0.146 0.8134 0.966 chrE1_3912105 0.81 0.119 0.9033 0.997 chrE1_4114158 0.81 0.366 0.8524 0.997 chrE1_48228153 0.814 0.155 0.8877 0.963 chrE1_48700963 0.828 0.043 0.8647 0.981 chrE1_5453028 0.822 0.347 0.9203 0.947 chrE2_22632289 0.804 0.298 0.8704 0.989 chrE2_34027888 0.807 0.168 0.6515 0.994 chrE2_35914023 0.805 0.152 0.906 0.834 chrE2_36986631 0.815 0.361 0.927 0.968 chrE2_38860686 0.811 0.212 0.7932 0.989 chrE2_39211557 0.651 0.24 0.6053 0.964 chrE2_65436639 0.818 0.253 0.9099 0.937 Table 6

SNPs for the analysis of worldwide random bred cat populations.

Minor Allele

Locus Name Call Frequency Frequency Gentrain Score Design Score chrE2_7950477 0.8 0.347 0.6547 0.989

chrE2_8422942 0.771 0.115 0.8173 0.96

chrE3_36044809 0.81 0.256 0.8796 0.833

chrE3_42164232 - - < 0.55 0.967

chrE3_55434272 0.782 0.241 0.7735 0.936

chrE3_67006512 0.681 0.071 0.8178 0.96

chrF1_20309325 0.824 0.059 0.7635 0.983

chrF1_21799641 0.824 0.122 0.8749 0.937

chrF1_26100599 0.804 0.271 0.8394 0.951

chrF1_27124984 0.814 0.213 0.9122 0.929

chrF1_38051725 0.819 0.331 0.9303 0.993

chrF1_565223 0.802 0.348 0.8739 0.958

chrF1_82068276 0.682 0.157 0.7686 0.91

chrF1_82716202 0.81 0.253 0.8563 0.943

chrF1_91517402 0.809 0.166 0.7746 0.933

chrF2_26886470 0.799 0.198 0.8643 0.957

chrF2_38395360 0.801 0.317 0.877 0.993

chrF2_46855978 0.808 0.277 0.7193 0.963

chrF2_68572596 0.796 0.191 0.8183 0.956

chrF2_74863327 0.82 0.18 0.9059 0.976

chrF2_78303221 0.812 0.4 0.911 0.911

chrF2_79632602 0.827 0.019 0.9445 0.959

chrF2_8427817 0.695 0.302 0.8707 0.955

Locus name includes the chromosome and location of each SNP.

[0136] Data analysis. Data sets were analyzed with the Bayesian clustering program STRUCTURE (Pritchard et al, (2000) Genetics 155, 945-959) under the admixture model with correlated allele frequencies and a burn-in of 100,000 with 100,000 additional iterations. Values of K were calculated from K = 1 to K = 25, each run was replicated 10 times. Posterior log likelihoods were used in the calculation of ΔΚ in order to best estimate the number of ancestral populations through the program Harvester (Evanno et al, (2005) Molecular Ecology 14, 2611-2620) (Figure 2). To assess the effects of varying marker types on the final results, STRUCTURE analysis was conducted on the data in three different permutations: only SNPs, only STRs, and SNPs and STRs together.

Images were created with CLUMPP v. 1.1.2 (Jakobsson and Rosenberg, (2007)

Bioinformatics 23(14): 1801-6) to combine replicates and DISTRUCT v. 1.1 (Rosenberg, (2004) Molecular Ecology Notes 4, 137-138) to create final images. A map of cat domestication was created using an inverse distance function. Each point on the map has an interpolated likelihood for each cluster; the color is the most likely cluster. Color saturation is based on the value of the likelihood (e.g. low saturation means low likelihood)

(Figure 1).

[0137] First generation migrants within populations identified by STRUCTURE were detected with the program Geneclass2 (Piry et al, (2004) Journal of Heredity 95, 536- 539) under the Rannala and Mountain (Proc Natl Acad Sci USA. (1997) 94(17):9197-201) Bayesian model, with 1000 Monte-Carlo re-sampling simulations (Paetkau et al., (2004) Molecular Ecology 13, 55-65), and the p-value threshold set at 0.01. From both STRs and SNPs, samples that were below the threshold of 0.01 for originating from the same lineage as that assigned for the majority of the cats from the same sampling location were considered recent immigrants and removed from subsequent analyses. These individuals were compared to the first generation migration test applied to the output from

STRUCTURE as in previous studies (Randall et al, (2010) Conservation Genetics 11, 89- 101), where individuals that clustered with a lineage other than that of the majority of their sampling location with a posterior probability of >0.5 were considered to be first generation immigrants to that sampling location (n = 56). All migrants were removed from further analyses (n = 63). FIS was calculated with Fstat v. 2.9.3.2 (Goudet, (1995) J Hered (1995) 86(6): 485-486) and observed and expected heterozygosites with GenAlEx v.6.3 (Peakall, Smouse, (2006) Molecular Ecology Notes 6, 288-295). F-statistics were calculated both by sampling location and based on the populations as assigned by STRUCTURE.

[0138] Principal coordinates analyses were conducted on a matrix of Nei's unbiased genetic distance and plotted via the standardized co-variance using the software GenAlEx (Peakall, Smouse, 2006, supra).

[0139] Phylogenetic trees were created with the software package PHYLIP version

3.67 (Felsenstein, (1989) Cladistics 5, 164-166). Allele frequencies for each data set were analyzed sequentially with SeqBoot, Genedist, Neighbor, and Consense to create a consensus tree. Trees were replicated with 1,000 bootstraps; genetic distance was calculated following Nei's unbiased method (Nei, Roychoudhury, (1974) Genetics 1 '6:379- 390) with the STRs to account for not only genetic drift, but also the fast mutation rate of the markers. The method of Reynolds et al. (Reynolds et al, (1983) Genetics 105, 767- 779) was applied to the SNP data to account for genetic drift only. Final unrooted trees were produced with the neighbor-joining method and visualized with FigTree vl .3.1 (on the internet at tree.bio.ed.ac.uk/software/figtree). Results:

[0140] The final analyses consisted of cat DNA samples (n = 944) from worldwide populations, including 463 domestic cats used previously (Lipinski et al, 2008, supra), and 481 domestic cat samples collected from 20 additional locations in the Middle East, Kenya, India, and Japan (Figure 1). Thirty-eight wildcats of known genetic origin (F. silvestris silvestris (Western Europe), F. s. libyca (assorted African locations), and F.s. tristrami (Israel)) (Lipinski et al, 2008, supra) were used as outgroups for the analyses. The 38 STRs had an average call rate of 92.3% and the 148 SNPs had an average call rate of 95.5%.

[0141] Bayesian clustering suggested a value of K = 5 (Figure 2) for both SNPs and

STRs resulting in five groupings corresponding to Europe, Mediterranean (including Western portions of the Middle East), Iraq/Iran, South Asia, and East Asia (Figure 3A, C) (alternate plots are available as Figure 4). The sampling locations along the Indian Ocean, including India and Sri Lanka, appeared to be admixtures of all five ancestral lineages, which are more strongly depicted by the STR data (Figure 3C). A secondary peak in ΔΚ values was observed at K=7 in the STRs and K=8 in SNPs, however, the two marker types resolved with some discrepancies (Figure 3B, D). SNPs and STRs both separated the Egyptian cats from the other Mediterranean cats, STRs indicating a stronger distinction (Figure 3D) than the SNPs (Figure 3B). STRs additionally indicated the Arabian Sea (Dubai, Pate, and Lamu), to be distinct from the cats of India, Sri Lanka and Southern Asia (Thailand and Vietnam). SNPs were able to discern the Arabian Sea cats and the Southern Asian cats as distinct populations but maintained the cats of India as highly admixed.

[0142] When SNPs and STRs were combined for analysis, a consensus of both the individual SNP and STR analyses is observed, suggesting five distinct ancestral lineages (Figure 3E) and eight modern populations (Figure 3). The division between the Arabian Sea and the Indian populations is supported by the STR data, but not the SNP data set, while the division between Indian and South Asian populations is supported by the SNP but not the STR data. Besides the Indian cats, the cats from Taiwan and Sapporo, Japan have the most amount of admixture.

[0143] When analyzed for first generation migrants, 21 individuals had a p-value of less than 0.01 indicating that these cats were likely not native to the sampling location. Additionally, detection of first generation migrants using the posterior probabilities of assignment with STRUCTURE detected 56 individuals (42 additional migrants), for a combined total of 63 individuals when the cats from the two methods were combined (Table 7). Europe is the source for a majority of the migrant cats that are found in other parts of the world (33%). The remainder of migrant cats appears to have traveled a short distance to adjoining populations. Individuals that were categorized as first generation migrants were removed from further analyses.

Table 7

First generation migration test of eight worldwide cat populations^*.

Total First Generation Migrants from Each Source

Sampling East South East

Lineage location Europe Mediterranean Egypt Iraq/Iran India Asia Asia

Europe Germany 1

Italy-Milan 4

Kenya-Nairobi 1 1

East

Mediterranean Turkey 2 1 4

Cyprus 4

Lebanon 1 6 1

Israel 2

Egypt Cairo 6 2 1

Luxor 1

Arabian Sea Dubai 1 1

India Sri Lanka 1 1

South Asia Vietnam 1

East Asia Taiwan 7 1

Japan-Oita 2

Japan-

Kanazawa 2

Japan-Ohmiya 1

Japan-Sapporo 4 2

South Korea 1

Total N = 63 21 11 12 1 4 13 1

*Number of individuals that were determined migrants with a p-value < 0.01 calculated using Geneclass2 (Peakall, Smouse, 2006, supra). Populations with no detected migrants are not presented.

[0144] Population statistics are presented both for each sampling site (Table 8) and population (Table 9) including the average effective number of alleles, inbreeding coefficients (FIS) and observed heterozygosity (HO) based on SNPs and STRs. No population had a diagnostic / unique SNP. The range of the SNP minor allele frequencies (MAF) throughout the world suggests insignificant ascertainment bias (Table 10). The populations with the highest number of STR alleles were the Mediterranean lineage and the modern Egyptian population, the lowest were found in East Asia. Private alleles were most common in the lineages from the Arabian Sea/ Asia, Mediterranean and Iraq/Iran, and the modern lineages of the Arabian Sea and Iraq/Iran. Table 8

Cat population statistics based on sampling locations^*.

Sampling Location n Private FlS (SNP) Fis Ho (SNP) Ho (STR)

(Migrants Alleles (STR)

1

Europe USA-NY 9 2 0.032 0.128 0.312 0.639

(sites=9) USA-MS 10 0 0.026 0.063 0.309 0.68

USA-HI 10 0 0.084 0.142 0.304 0.615

Brazil 29 1 0.014 0.072 0.3 0.652

Finland 11 0 0.174 0.082 0.259 0.628

Germany 28 3 0.209 0.101 0.255 0.626

Italy-Milan 10 1 0.051 0.039 0.316 0.711

Italy-Rome 15 2 0.018 0.098 0.315 0.651

Kenya-Nairobi 34 6 -0.015 0.056 0.336 0.725

Eastern Turkey 44 4 0.118 0.064 0.267 0.689

Mediterr. Basin Cyprus 26 2 0.002 0.078 0.309 0.704

(sites=4) Lebanon 50 4 0.025 0.058 0.298 0.717

Israel-Tel Aviv 44 2 0.006 0.076 0.301 0.751

Egypt Cairo 75 6 0.053 0.107 0.292 0.688

(sites=4) Assuit 10 1 -0.064 0.031 0.298 0.704

Luxor 27 5 -0.008 0.043 0.303 0.745

Abu Simbel 5 1 0.027 0.099 0.286 0.673

Iraq/Iran Iraq-Western 12 1 -0.06 0.102 0.295 0.684

(sites= 3) Baghdad 33 7 -0.025 0.043 0.284 0.721

Iran 110 4 0.045 0.121 0.239 0.646

Arabian Sea Dubai 8 2 0.03 0.024 0.244 0.699

(sites=3) Kenya-Pate 9 0 -0.015 0.056 0.241 0.597

Kenya-Lamu 20 2 0.04 0.118 0.249 0.59

India Udaipur/Agra 15 2 -0.08 0.073 0.268 0.68

(sites=5) Hyderbad 20 2 -0.008 0.103 0.266 0.688

Andhra 23 3 0.02 0.076 0.259 0.665

Kolkata 7 1 0.07 0.144 0.261 0.639

Sri Lanka 24 2 0.015 0.054 0.289 0.719

South Asia Thailand 14 1 0.171 0.153 0.183 0.563

(sites=2) Vietnam 19 0 0.038 0.065 0.261 0.639

East Asia Taiwan 21 0 0.008 0.025 0.287 0.681

(sites=7) Japan-Oita 15 1 0.11 0.101 0.248 0.613

Japan-Kanazawa 13 0 0.1 0.111 0.237 0.604

Japan-Ohmiya 15 0 0.071 0.163 0.259 0.594

Japan-Sapporo 9 0 0.031 0.075 0.26 0.641

China-Henan 20 3 0.012 0.043 0.219 0.627

South Korea 37 0 0.044 0.04 0.246 0.619

Total sites = 37 881 (63) 71 0.275 0.66

*First generation migrants were not included. N = sample size. F_IS = average inbreeding coefficient of an individual relative to its source population, H₀ = observed heterozygosity. Table 9

Population statistics of worldwide cat populations

Total Private Aver.

Population n Alleles Alleles N_e F,s(SNP) F,s(STR) Ho(SNP) Ho(STR)

Ancient Basal Lineage

Europe 156 452 5 4.908 0.087 0.107 0.302 0.678

Mediterranean 281 516 7 5.466 0.051 0.089 0.293 0.71

Iraq/Iran 155 408 7 4.866 0.038 0.118 0.252 0.682

Arabian

Sea/Asia 159 455 9 5.197 0.102 0.153 0.254 0.657

East Asia 130 402 3 4.299 0.087 0.097 0.249 0.652

Modern Lineage

Europe 156 452 5 4.941 0.087 0.107 0.302 0.678

East

Mediterranean

Basin 164 481 3 5.284 0.053 0.079 0.291 0.715

Egypt 117 447 3 5.353 0.04 0.096 0.295 0.704

Iraq/Iran 155 408 7 4.866 0.038 0.118 0.252 0.682

Arabian Sea 37 288 8 3.802 0.066 0.121 0.245 0.632

India 89 416 4 5.193 0.042 0.122 0.27 0.683

South Asia 33 285 4 3.554 0.112 0.11 0.225 0.617

East Asia 130 402 4 4.442 0.087 0.097 0.249 0.652

*First generation migrants were not included, n = sample size. Aver. N_e = average effective number of alleles F_IS = average inbreeding coefficient of an individual relative to its source population, H₀ = observed heterozygosity.

Table 10

Minor Allele Frequency based on population

<

Locus

chrA1_10141047 0.108 0.277 0.308 0.260 0.100 .045 .097 .027 chrA1_133621071 0.304 0.222 0.309 0.283 0.189 .316 .129 .108 chrA1_151648701 0.120 0.063 0.056 0.013 0.027 .023 .000 .058 chrA1_175780586 0.423 0.410 0.299 0.085 0.088 .162 .581 .363 chrA1_208054462 0.142 0.138 0.182 0.013 0.014 .055 .125 .090 chrA1_223501140 0.162 0.082 0.074 0.039 0.111 .167 .076 .100 chrA1_223506906 0.154 0.195 0.329 0.367 0.708 .425 .183 .529 chrA1_225057933 0.221 0.218 0.284 0.401 0.000 .259 .139 .437 chrA1_235579538 0.054 0.144 0.090 0.299 0.324 .125 .056 .071 chrA1_27523501 0.574 0.372 0.364 0.255 0.432 .351 .214 .437 chrA1_68485376 0.141 0.076 0.009 0.000 0.000 .006 .015 .014 chrA1_69424718 0.292 0.343 0.332 0.755 0.542 .335 .412 .322 chrA1_7429296 0.183 0.150 0.161 0.244 0.581 .296 .028 .125 chrA1_8742286 0.273 0.406 0.426 0.683 0.257 .476 .514 .303 chrA2_152258936 0.409 0.399 0.319 0.565 0.446 .314 .471 .182 chrA2_201526186 0.207 0.156 0.269 0.161 0.176 .253 .103 .104 chrA2_202225770 0.317 0.319 0.232 0.203 0.108 .440 .672 .338 chrA2_44241149 0.062 0.016 0.009 0.006 0.000 .017 .014 .003 chrA2_554046 0.133 0.169 0.217 0.186 0.027 .144 .028 .236 chrA3_101420069 0.056 0.057 0.065 0.000 0.054 .087 .000 .061 chrA3_11480952 0.053 0.021 0.027 0.000 0.014 .000 .048 .054 chrA3_12082294 0.336 0.171 0.252 0.126 0.095 .063 .103 .119 chrA3_130195244 0.199 0.284 0.368 0.075 0.111 .124 .138 .096 chrA3_159537633 0.528 0.405 0.350 0.301 0.595 .494 .757 .519 chrA3_162208567 0.377 0.194 0.196 0.260 0.068 .064 .016 .139 chrA3 38781591 0.022 0.016 0.004 0.006 0.000 Table 10

Minor Allele Frequency based on population

Locus

.01 1 .000 .000 chrA3_75156179 0.266 0.375 0.348 0.243 0.068 .127 .069 .167 chrA3_91058022 0.484 0.256 0.241 0.104 0.230 .106 .121 .165 chrA3_99507784 0.231 0.302 0.296 0.578 0.162 .355 .456 .41 1 chrB1_10420438 0.104 0.082 0.034 0.023 0.027 .034 .014 .033 chrB1_12214271 0.401 0.392 0.591 0.350 0.514 .554 .333 .437 chrB1_195678303 0.102 0.258 0.230 0.358 0.351 .1 16 .071 .107 chrB1_199564532 0.156 0.101 0.154 0.003 0.027 .052 .000 .028 chrB1_202966562 0.381 0.263 0.198 0.183 0.149 .184 .235 .175 chrB1_54775572 0.188 0.204 0.135 0.059 0.216 .000 .097 .028 chrB1_80161671 0.169 0.096 0.058 0.241 0.068 .122 .097 .069 chrB1_88148379 0.360 0.477 0.483 0.443 0.622 .369 .182 .371 chrB2_138312489 0.422 0.462 0.422 0.282 0.284 .698 .700 .482 chrB2_146660650 0.000 0.000 0.004 0.000 0.014 .034 .139 .010 chrB2_41509834 0.278 0.257 0.155 0.624 0.351 .464 .614 .489 chrB2_45093345 0.542 0.433 0.404 0.093 0.257 .227 .258 .347 chrB2_6949528 0.201 0.195 0.1 1 1 0.029 0.149 .029 .014 .070 chrB3_104483970 0.202 0.328 0.321 0.172 0.122 .506 .855 .420 chrB3_111000326 0.1 1 1 0.200 0.261 0.171 0.149 .333 .676 .260 chrB3_13666494 0.475 0.439 0.504 0.255 0.243 .560 .765 .528 chrB3_39203469 0.184 0.378 0.625 0.21 1 0.108 .071 .100 .138 chrB3_51317931 0.138 0.294 0.276 0.407 0.095 .390 .750 .254 chrB3_57141954 0.377 0.173 0.171 0.242 0.230 .137 .069 .281 chrB3_77094074 0.168 0.006 0.022 0.007 0.139 .056 .067 .059 chrB4_105706694 0.374 0.410 0.397 0.480 0.459 .585 .371 .628 chrB4_142658074 0.108 0.190 0.187 0.066 0.014 .030 .065 .121 chrB4 143006494 0.338 0.344 0.345 0.243 0.097 Table 10

Minor Allele Frequency based on population

Locus

.1 10 .074 .261 chrB4_144693308 0.31 1 0.426 0.460 0.450 0.473 .335 .469 .215 chrB4_146486983 0.193 0.194 0.124 0.288 0.541 .134 .641 .568 chrB4_147206961 0.350 0.276 0.434 0.293 0.176 .544 .750 .291 chrB4_149532846 0.126 0.056 0.030 0.016 0.014 .023 .057 .026 chrB4_1687419 0.108 0.141 0.145 0.013 0.000 .009 .000 .006 chrB4_20001848 0.046 0.008 0.000 0.003 0.000 .029 .029 .014 chrB4_21098349 0.581 0.408 0.375 0.359 0.357 .318 .1 18 .239 chrB4_255106 0.120 0.025 0.065 0.036 0.000 .006 .015 .031 chrB4_3093827 0.109 0.046 0.055 0.000 0.041 .071 .452 .161 chrB4_40319102 0.532 0.530 0.400 0.500 0.270 .440 .182 .188 chrB4_47638578 0.086 0.038 0.004 0.006 0.000 .017 .014 .031 chrC1_116355295 0.332 0.262 0.272 0.203 0.284 .320 .235 .155 chrC1_123164748 0.261 0.237 0.196 0.267 0.000 .229 .242 .120 chrC1_181852965 0.406 0.108 0.167 0.022 0.000 .1 13 .056 .239 chrC1_190502133 0.176 0.381 0.293 0.576 0.306 .215 .028 .127 chrC1_215441574 0.394 0.346 0.272 0.235 0.459 .323 .574 .389 chrC1_216852686 0.356 0.339 0.507 0.312 0.200 .231 .340 .21 1 chrC1_24148281 0.180 0.222 0.209 0.247 0.292 .324 .059 .1 16 chrC1_28702055 0.122 0.041 0.000 0.129 0.027 .029 .043 .024 chrC1_34981315 0.046 0.050 0.099 0.000 0.000 .012 .103 .040 chrC1_396397 0.392 0.313 0.425 0.139 0.230 .109 .030 .096 chrC1_44520932 0.134 0.087 0.137 0.003 0.014 .040 .015 .024 chrC1_52456776 0.342 0.352 0.454 0.747 0.569 .506 .529 .331 chrC2_106991233 0.261 0.250 0.274 0.279 0.068 .335 .578 .402 chrC2_147124460 0.516 0.492 0.556 0.416 0.568 .362 .186 .086 chrC2 150774106 0.072 0.057 0.059 0.032 0.014 Table 10

Minor Allele Frequency based on population

Locus

.012 .014 .060 chrC2_156491175 0.381 0.408 0.307 0.672 0.662 .667 .313 .565 chrC2_187325 0.105 0.195 0.289 0.168 0.216 .21 1 .383 .185 chrC2_262401 0.188 0.283 0.277 0.122 0.153 .280 .591 .283 chrC2_5215469 0.241 0.342 0.250 0.728 0.500 .313 .838 .628 chrD1_101321498 0.441 0.279 0.281 0.258 0.432 .134 .061 .186 chrD1_104941557 0.144 0.197 0.147 0.136 0.243 .052 .014 .084 chrD1_105498119 0.182 0.094 0.180 0.040 0.081 .177 .042 .046 chrD1_10789012 0.322 0.362 0.230 0.401 0.149 .149 .368 .524 chrD1_11484008 0.068 0.073 0.044 0.178 0.189 .247 .081 .471 chrD1_117527468 0.040 0.01 1 0.004 0.003 0.000 .000 .000 .010 chrD1_125811329 0.308 0.151 0.206 0.096 0.324 .169 .518 .172 chrD1_126256993 0.233 0.228 0.188 0.144 0.378 .221 .819 .361 chrD1_126847301 0.100 0.137 0.218 0.010 0.054 .052 .043 .01 1 chrD1_15984279 0.245 0.278 0.265 0.050 0.014 .100 .368 .124 chrD1_16242433 0.336 0.256 0.273 0.139 0.143 .198 .517 .340 chrD1_18390852 0.223 0.171 0.1 1 1 0.006 0.108 .080 .000 .066 chrD1_18570323 0.401 0.409 0.561 0.243 0.527 .430 .379 .450 chrD1_66177762 0.161 0.1 12 0.104 0.010 0.097 .052 .014 .095 chrD2_1020904 0.580 0.563 0.31 1 0.369 0.297 .333 .671 .525 chrD2_105772916 0.192 0.272 0.372 0.284 0.338 .412 .426 .154 chrD2_1752007 0.135 0.056 0.043 0.133 0.041 .147 .250 .135 chrD2_56777338 0.000 0.000 0.000 0.000 0.000 .000 .000 .000 chrD2_717969 0.402 0.252 0.542 0.256 0.136 .432 .719 .195 chrD2_74293444 0.208 0.151 0.155 0.122 0.189 .134 .1 18 .430 chrD2_91989307 0.133 0.041 0.065 0.013 0.041 .029 .014 .025 chrD3 103840114 0.022 0.040 0.043 0.039 0.135 Table 10

Minor Allele Frequency based on population

Locus

.046 .000 .01 1 chrD3_122502120 0.176 0.236 0.243 0.313 0.392 .310 .313 .194 chrD3_1810839 0.603 0.528 0.427 0.065 0.129 .21 1 .143 .104 chrD3_24565823 0.151 0.024 0.030 0.003 0.000 .023 .028 .035 chrD3_24823793 0.073 0.184 0.186 0.197 0.541 .339 .250 .368 chrD3_28838660 0.022 0.01 1 0.009 0.000 0.041 .100 .343 .127 chrD4_41078218 0.458 0.491 0.482 0.493 0.919 .402 .212 .313 chrD4_42000379 0.077 0.134 0.106 0.038 0.014 .078 .015 .01 1 chrD4_63622083 0.241 0.082 0.065 0.069 0.083 .135 .061 .088 chrE1_130875919 0.271 0.103 0.303 0.013 0.000 .061 .061 .032 chrE1_131587399 0.141 0.047 0.022 0.013 0.000 .023 .056 .083 chrE1_3912105 0.199 0.076 0.097 0.123 0.405 .092 .074 .120 chrE1_4114158 0.438 0.461 0.415 0.507 0.365 .458 .439 .550 chrE1_48228153 0.205 0.196 0.147 0.145 0.176 .198 .530 .223 chrE1_48700963 0.096 0.060 0.048 0.000 0.027 .034 .000 .053 chrE1_5453028 0.522 0.376 0.435 0.21 1 0.284 .157 .029 .071 chrE2_22632289 0.373 0.389 0.421 0.374 0.608 .371 .724 .656 chrE2_34027888 0.059 0.022 0.089 0.000 0.000 .076 .014 .063 chrE2_35914023 0.275 0.241 0.152 0.050 0.292 .194 .181 .431 chrE2_36986631 0.410 0.603 0.618 0.392 0.257 .471 .281 .191 chrE2_38860686 0.297 0.049 0.039 0.090 0.081 .059 .015 .094 chrE2_39211557 0.440 0.422 0.495 0.41 1 0.324 .402 .407 .357 chrE2_65436639 0.227 0.298 0.283 0.330 0.189 .310 .147 .216 chrE2_7950477 0.543 0.503 0.583 0.434 0.403 .421 .323 .496 chrE2_8422942 0.078 0.064 0.066 0.320 0.054 .017 .028 .053 chrE3_36044809 0.237 0.41 1 0.548 0.530 0.243 .282 .191 .686 chrE3 55434272 0.385 0.383 0.421 0.039 0.324 Table 10

Minor Allele Frequency based on population

Locus

.317 .303 .538 chrE3_ _.67006512 0. ^E1u^ro^pe29 0.123 0.109 0.083 0.045 .032 .030 .077 chrF1_ _.20309325 0.075 0.081 0.022 0.076 0.097 .034 .069 .050 chrF1_ _.21799641 0.331 0.07 M^ditee^rranean9 0.113 0.048 0.108 .098 .042 .108 chrF1_ _.26100599 0.356 0.194 0.068 0.072 0.189 .167 .186 .170

^Etgyp

chrF1_ _.27124984 0.206 0.253 0.297 0.373 0.292 .429 .667 .461 chrF1_ _.38051725 0.541 0.644 0.671 0.317 0.122 .305 .530 .434

^I/Ira^qran

chrF1_ _.565223 0.537 0.562 0.575 0.537 0.392 .464 .529 .399 chrF1_ _.82068276 0.187 0.065 0.059 0.007 0.015 .250 .379 .289

A^bi S^raanea

chrF1_ _.82716202 0.288 0.191 0.125 0.339 0.243 .100 .100 .124 chrF1_ _.91517402 0.214 0.223 0.184 0.176 0.351 .327 .044 .064

^Idina

chrF2_ _.26886470 0.104 0.153 0.104 0.459 0.405 .494 .591 .390 chrF2_ _.38395360 0.349 0.270 0.279 0.334 0.338 .360 .029 .184

S^h A^itousa

chrF2_ _.46855978 0.332 0.280 0.309 0.229 0.392 .407 .444 .310 chrF2_ _.68572596 0.211 0.270 0.318 0.302 0.500 .323 .717 .3 ^E A^itassa13 chrF2_ _.74863327 0.181 0.278 0.282 0.171 0.122 .306 .338 .336 chrF2_ _.78303221 0.522 0.469 0.530 0.257 0.284 .571 .727 .595 chrF2_ _.79632602 0.025 0.011 0.004 0.062 0.000 .006 .000 .007 chrF2_ _.8427817 0.390 0.408 0.387 0.227 0.257 .378 .350 .489

[0145] Principal coordinates analyses (PCA) (Figure 5) broadly correlated with the

STRUCTURE analyses. PCA consistently grouped individuals from the same geographic cluster together as delineated by Bayesian clustering (Figure 3). Differences of SNP (Figure 5A) versus STR (Figure 5B) based analyses are most notably in the distance of the Arabian Sea populations from those of the Iraqi/Iranian populations (more distant with SNPs and less so with STRs). Also, SNPs position the South Asian populations close to that of the Indian populations as opposed to closer to the East Asian populations when STRs are used. Despite these slight changes in comparative distance, both PCA analyses show genetic distances to be correlated with that of geographical or physical distances of the populations and sampling sites.

[0146] The SNP (Figure 6A) and STR-based (Figure 6B) Neighbor-joining phylogenetic trees were also broadly concordant with the STRUCTURE and PCA defined lineages through forming groups along geographical lines. Very high bootstrap values are noted between cats that show strong associations in the other analyses, including, cats from Vietnam and Thailand, cats from the Kenyan Islands, Lamu and Pate, and Dubai, and those from various cities of Japan. However, at a fine-scale, minor discrepancies are observed. For example, mainland Kenyan cats, when analyzed with STRs, are in the middle of the European branch with a bootstrap value of 100%, but in the SNP -based tree, mainland Kenyan individuals are in between the European/Mediterranean and the Iraq/Iran/ Asian clades with a lower bootstrap of 43%. Additionally, the northwestern populations in India weakly grouped with either the Iraq/Iran clade or the Indian depending upon the marker.

[0147] The wildcat outgroups also showed significant differences between markers.

With SNPs, Felis s. libyca is more closely associated with the Mediterranean cats, while F. s. silvestris associated with the European cats. With STRs, the two wildcat groups form their own clade, in between the Mediterranean/European/ Iraqi/Irani populations and more eastern populations.

Discussion

[0148] Cultural histories, archeological evidence, and more recently, genetic investigations all support the theory of at least one cat domestication event in the 450,000 km2 region of the Fertile Crescent. The Fertile Crescent spreads from the land between the Tigris and Euphrates Rivers (currently Iraq), extending into modern Syria and Turkey, along the Mediterranean coast of Israel (the Levant region) and into the fertile regions of the Nile River Valley in Egypt. As agriculture and civilization spread, the provision of vermin population control to surrounding refuse piles and grain stores was an important benefit of the symbiotic relationship between the regional wildcats and humans. Bold wildcats may have repeatedly approached humans as the sedentary agricultural lifestyle proliferated throughout the Fertile Crescent. Over time, some cats may have moved with mobile tribes over short distances. Both natural and human-induced barriers have posed limits to feline migrations over the past 10,000 years, as well as varying human migrations due to stochastic political barriers. But as humans became increasingly mobile, so too have the cats, by hitching rides around the world on ships and colonizing new regions with limited founders. DNA sampling and genetic evaluations of modern cats provide only a present- day snapshot of cat population stratifications around the world. However, large DNA sample sets of cats from diverse geographical regions combined with an assortment of genetic markers with a variety of resolution powers can help define these cat populations, tracing recent migrations, and clarifying those more ancient relationships of the cats around the world.

[0149] This study focused on cat populations within the Fertile Crescent, Egypt, and eastern regions, examining the effect of different genetic markers on refining the site of cat domestication and clarifying the paths of migration. STRUCTURE analysis of SNPs and STRs genetically refined the cat population stratifications of the world, especially with the addition of cats from the Middle East and Asia. The previous STR study by Lipinski et al. (2008) suggests that worldwide cats divide into 4 basal lineages; Western European, Mediterranean basin, East African (Lamu and Pate), and Asian. By doubling the sample set and including 148 random SNP markers, the previous population demarcations were reinforced and an additional lineage in Iraq/Iran was detected. The previously defined African lineage, cats from the Kenyan islands of Lamu and Pate, appears to be rather a lineage that defines cats of Indian Ocean/ Arabian Sea including cats from Dubai, India and Sri Lanka, Thailand, and Vietnam. The SNPs more clearly defined this lineage than the STRs.

[0150] Additional stratifications can be resolved, dividing cats into seven modern populations for each marker type, eight overall populations considering a combined analysis. These population divisions align mostly along geographic regions, specifically Europe (including the Americas), East Mediterranean Basin (Turkey, Cyprus, Lebanon, and Israel), Egypt, Iraq/Iran, Arabian Sea, India (including Sri Lanka), Southern Asia (Thailand and Vietnam), and Eastern Asia (Taiwan, Japan, China, and South Korea). Both marker types support Egyptian cats as distinctive, perhaps a result of ancient breeding practices causing drift or strong isolation due to cultural and geographical barriers in the region. SNPs and STRs were discordant with the distinction of the cats from India and Sri Lanka. With SNPs, the Indian cats group with cats around the Arabian Sea, but with STRs, these same cats cluster with a distinctive Southern Asian group. The slower mutation rate of SNPs should strengthen their resolving power in the older cat populations; STRs should illuminate the more recent migrations that may not be visible with SNPs. Perhaps the differences in the SNP versus STR demarcations suggest that historically the India cats arrived from the West, whereas more recent migrations have come from the East.

[0151] In support of recent cat movements, approximately 66% of migrants appear to have originated from geographically close neighbors. However, the cat migrants in Taiwan and Japan are most likely due to human-mediated movement subsequent to human colonization or migration by European people. For example, 9.5% of the Japanese cats and 24.1% of Taiwanese cats appear to have a significant portion of European ancestry. The Nairobi Kenyan cats appear to be highly correlated with that of Europe, consistent with the European colonization of Kenya, which is readily depicted by the STR neighbor-joining phylogenetic analysis. A similar result was indicated in the previous study with cats from Tunisia, which appeared to also have European origins (Lipinski et al., 2008). An individual example is a cat sample that was collected in Luxor, Egypt, a city deep within the Nile Valley. This longhaired cat belonged to a woman who had recently moved from the coastal Mediterranean city of Alexandria. Her cat clearly owes most of its ancestry to that of European cats. While signs of historical colonization by western countries can be detected, Iraq's short history of British rule resulted in very little influence of the European cats in the feral populations of Iraq and Iran. Overall, the stratification of cats from these analyses is sufficient to define regional origins of modern cats, and recent migrations can be tracked by the cat's genetic constitution.

[0152] PCA analyses depicted a great divide between the regions of Europe, East

Mediterranean Basin, and Egypt versus cats from Southern Asian, and Eastern Asian populations, suggesting these larger regional groupings as the most genetically distinct. The phylogenetic relationships presented in the neighbor-joining trees also relay a significant difference between Eastern and Western domestic cats. Both approaches place the

Irani/Iraqi and Arabian Sea groups somewhat in between the Eastern - Western divisions. The wildcat species group with the Western European clusters of cats, leaving speculation as to whether the significant divide between western and eastern cats is due to more than one domestication event, or the ancient diaspora of early cat domesticates, followed by a decrease of migration and isolationism.

[0153] Population statistics assist the identification of the older cat populations and lineages, indicating that Egypt and the Mediterranean lineage have the lowest inbreeding and highest heterozygosity based on STRs, as well as the highest averages for effective number of alleles of the populations included in this study. FIS calculations based on SNPs show the Asuit, Egypt and Iraqi/Irani lineage to have the least amount in inbreeding but likely not statistically significant from other Egyptian and Mediterranean areas.

Additionally the Assuit collection was partially made up of cats that had been presented to the Veterinary School of Assuit University, and may likely represent cats from a variety of origins. Thus, the oldest cat lineages are represented in the Near East, primarily the Eastern Mediterranean countries and Egypt, supporting sites of cat domestication. The isolation of the cat populations in Iraq and Iran has undoubtedly prevented admixture, despite this, the population has not suffered from inbreeding. Cats from India, including Sri Lanka, show significant admixture with representation from all groups. Bootstrap values are low for the Indian cats, while STRUCTURE analysis indicated gene flow from surrounding areas, suggesting the Indian peninsula as a potential mixing pot for domestic cats. Overall, the regions of highest diversity focus around the location of the first human agricultural settlements, including Egypt, Israel, Lebanon, and Iraq.

[0154] Together, the various SNP and STR data of this study indicate that cats were first domesticated in the Fertile Crescent and Levant regions, potentially near Lebanon and Iraq. The Iraq/Iran clade is clearly delineated early in the Baysean clustering analysis as one of the first lineages, show little sign of admixture with the other lineages, and maintains a high diversity even when compared to the modern populations. Iraq has also had the benefit of relatively few influences from Europe; the modern populations found there are most likely to represent some of the first domesticated cats. The high diversity of other populations is a result of large numbers of migrations and introgressions from other regions of the world (such as northwestern India and northern Egypt). From the northern Fertile Crescent, domesticated cats have spread throughout the world, first out towards Iraq, the Eastern Mediterranean and then down through Egypt before extending to Asia and Europe. Since then, as Europeans traveled throughout the world, they brought with them their fellow felines, which have subsequently left their genetic pawprints in areas such as Kenya, Taiwan, Japan, and the Americas.

Example 2

Assessment of Genetic Assignment of Domestic Cats to Breeds and Worldwide

Random Bred Populations

[0155] In this example, 477 cats representing 29 fancy breeds were analyzed with

38 microsatellites (STRs), 148 intergenic SNPs, and 5 causative and linked phenotypic SNPs. Results of this study suggest that contrary to previous studies, the frequentist methods of Paekau (accuracy SNPs = 0.78, STRs = 0.88) surpass the Bayesian methods of Rannala and Mountain (SNPs = 0.56, STRs = 0.83). Additionally, a post-assignment verification step with the phenotypic SNPs will accurately identify between 0.31 and 0.58 of the mis-assigned individuals.

Materials and Methods:

[0156] Sample Collection. Twenty-nine breeds were represented by 477 cats. This study included 354 cats from the work of Lipinski et al. ((2008) Genomics 91 : 12-21) that analyzed 22 breeds. The 123 additionally collected samples represented seven additional breeds (Scottish Fold, Cornish Rex, Ragdoll, Manx, Bengal, Ocicat, and Australian Mist). All cats were representatives of their breed as found within the USA, except for a few Turkish Angora and Turkish Vans from an international collaboration. All cats were pedigreed and verified to be unrelated to the grandparent level. Worldwide random bred data (N=944) was included from results discussed in Example 1 in order to assess the origins of each of the breed populations. New samples were collected via a buccal (cheek) swab and extracted following the manufacturer's protocol (Qiagen).

Table 11

Origins of cat Breeds^*

Indicative Phenotypic Traits

Origins Based on Historical

Breed Inclusion Exclusion Evidence¹

Persian LH SH British RB, Siamese, Maine Coon

Persian, American SH, Abyssinian,

Exotic SH SH LH Burmese

British SH SH LH British RB, Persian

Scottish Scottish RB, British SH, American SH,

Fold Ear Fold Persian, Exotic SH

Chartreux Blue French RB, Persian, British SH

American British RB, American RB, Persian,

SH SH LH Burmese

Sphynx Hairless American RB

Japanese

Bobtail Bobtail Japanese RB

Cornish

Rex Cornish curl British RB, Siamese

Ragdoll LH American RB, Birman

Maine Coon LH American RB

Abyssinian SH, Ticked, Agouti LH British RB, Egyptian RB, African RB

Siberian LH Russian RB²

Norwegian

FC LH Norwegian RB

Manx Tailless, SH LH British RB Table 11

Origins of cat Breeds^*

Indicative Phenotypic Traits

Origins Based on Historical

Breed Inclusion Exclusion Evidence¹

Egyptian

Mau Egyptian RB

Turkish

Angora Turkish RB

Turkish

Van Turkish RB

Bengal Asian leopard cat, American RB

Sokoke Kenyan randombred³

Ocicat Spotted, SH LH Abyssinian, Siamese

Russian

Blue Blue, SH British RB, Siamese

Australian

Mist Burmese, Abyssinian, Australian RB⁴

Non-Agouti, Burmese RB, Siamese, European RB,

Burmese Agouti, Sepia Silver British SH

Non-Agouti,

Singapura Agouti Silver Singaporean RB, Abyssinian

Birman LH, Points Burmese RB, various breed outcrosses

Korat Blue, Non-agouti Thai RB

Havana Chocolate, Non-

Brown Agouti Points Siamese, British SH, Russian Blue

SH, Siamese Points,

Siamese Non-Agouti Thai RB

RB = randombred, SH = shorthair, LH = Long hair. Unless noted origins are according to: 'Gebhardt, 1991(Gebhardt (1991). The Complete Cat Book. Howell Book House, New York, ²The Royal Canin Cat Encyclopedia, 2000, Groupe Royan Canin, Paris, France, ³The International Cat Association (on the internet at tica.org), or ⁴Australian Mist Breed Council (on the internet at australianmist. info/Home .html).

[0157] Thirty eight micro satellites were genotyped in the 123 newly acquired cats following the PCR and analysis procedures of Lipinski et al. (2008), supra. The 148 intergenic SNPs were assayed in all 477 breed cats for this study as described in Example 1. Five additional phenotypic SNPs were also evaluated in all cats. The phenotypic SNPs consisted of a causative mutation for the most common form of long hair in cats (FGF5 A475C) (Kehler et al. (2007) J Hered 98, 555-66.), Burmese and Siamese color points (TYR G715T and G940A, respectively) (Lyons et al. (2005) Animal Genetics, 36, 1 19- 126), and the mutations for the color variants chocolate and cinnamon

(Phen_TYRPl_5IVS6 and C298T) (Lyons et al. (2005) Mammalian Genome, 16, 356- 366). Golden Gate Assay amplification and BeadXpress reads were performed per the manufacturer's protocol (Illumina Inc., San Diego) on 50-500ng of DNA, using the same oligo primer pool as used in Example 1. Each run of the SNP assay contained both an internal positive and negative control in order to validate repeatability and contamination. [0158] Population Statistics. Hardy- Weinberg Equilibrium (HWE) with associated chi squared tests was performed by population, as well as observed and expected

heterozygosites with GenAlEx v.6.3 (Peakall & Smouse 2006, supra). FIS and FST were calculated with Fstat v. 2.9.3.2 (Goudet 1995, supra). F-statistics were calculated both by sampling location, and based on the populations as assigned by STRUCTURE, regardless of sampling location. Reynold's genetic distance was calculated between all pairs of breeds due to the predicted recent separation of these populations (Reynolds et al. 1983, supra).

Population Structuring

[0159] Bayesian Clustering. Data sets were analyzed with the Bayesian clustering program STRUCTURE (Pritchard et al. 2000) under the admixture model with correlated allele frequencies and a burn-in of 100,000 with 100,000 additional iterations. Values of K were calculated from K = l to K = 33, each run was replicated 10 times. Posterior log likelihoods were used in the calculation of ΔΚ to best estimate the number of ancestral populations through the program Harvester (Evanno et al. 2005, supra). All ten iterations were then combined through the program CLUMP (Jakobsson & Rosenberg 2007, supra) to create a consensus clustering. To assess the effects of varying marker types on the final results, STRUCTURE analysis was conducted on the data with the two different

permutations: SNPs and STRs.

[0160] Principal Coordinate Analysis . Principal components analyses were conducted through the calculation of Nei's genetic distance using the software GenAlEx v.6.3 (Peakall & Smouse 2006, supra). For the PCA plots, both the data in the present example and data from the worldwide random bred populations discussed in Example 1 were considered to show the relationship of the cat breeds and their random bred population origins.

[0161] Breed Lineage Assignment. Cat breed populations were assigned to the eight ancestral lineages of random bred worldwide populations of cats (Europe, Mediterranean, Egypt, Iraq/Iran, Arabian Sea, India, Southeast Asia, and East Asia) identified in Example 1 by calculating -log(Likelihood) values using the Bayesian population assignment methods available in the software Geneclass2 (Piry et al. 2004, supra).

[0162] Assignment Testing. Ten sets of 50 individuals were randomly selected from the sample set and assigned to a population of origin using the remaining samples as the reference populations. The Bayesian method of Rannala and Mountain (1997), supra, and the Frequentist method suggested by Paetkau et al. (2004), supra, were compared as they performed best in the previous assignment study of Negrini et al. (2009) Animal Genetics, 40: 18-26) when compared to the Pritchard et al. (2000) Genetics, 155:945-959) and the Baudoulin & Lebrun methods (2001) Proc. Int. Symp. on Molecular Markers, pp. 81-94. Additionally, the assignment tests were performed in three iterations: intergenic SNPs, intergenic and phenotypic SNPs combined, and STRs. Tallies of type I error (an individual not reassigning to its population of origin) and type II error (an individual not from that population assigning to it) were used to calculate the sensitivity and specificity of the assignment method (Negrini et al. 2009, supra).

[0163] Phenotypic SNPs were also used post-assignment test to compare both STRs with and without phenotypic SNP input, as well as comparing the use of phenotypic SNPs combined with intergenic SNPs for the assignment test as opposed to only post assignment testing. Cats were considered mis-assigned if they had genotypes exclusionary for the breed. For example, an individual assigned to the Exotic short haired group was identified as mis-assigned if it was homozygous for long-hair.

Results:

[0164] Summary Statistics. Pedigreed cats (N = 477), representing 29 recognized breeds were included in this study (Table 12). The number of cats per breed ranged from 7 to 25 with an average of 16.4 individuals per breed. STRs had a call rate of 88.2% and SNPs had a 94.0% average call rate. While the chi-squared goodness-of-fit test indicated that 126 of the 148 SNPs and 36 of the 38 STRs were not in HW equilibrium in at least one breed group, only one SNP marker (chrF2_46855978) was found to be not in HW equilibrium in more than 50% of the breeds (Table 13). Twenty-seven breeds have 10-25 SNPs not in HWE; however the Russian Blue and Turkish Van breeds have 31 and 33 of the total 186 genetic markers out of HWE. This is suggestive of potential population substructuring or recent inbreeding. The frequency of the genotypes and alleles for the phenotypic SNPs were calculated by counting (Table 14). The FGF5 A475C mutation causing long coated cats in the homozygous state was by far the most prevalent of the phenotypic SNPs as it was found in all but eight of the breeds. In contrast, cinnamon, caused by TYRPl C298T, was only seen in five breeds two of which had a frequency lower than 0.1. Table 12

Population statistics of cat breeds.

Total Total PA PA Na Na Ho Ho Fis Fis(

Alleles Allele

Breed N S

Persian 15 276 181 1 0 1.865 4.763 0.285 0.502 -0.023 0.145

Exotic SH 19 279 178 1 1 1.885 4.684 0.253 0.526 0.073 0.068

British SH 18 276 192 2 0 1.865 5.053 0.242 0.546 0.101 0.064

Scottish Fold 17 269 180 2 1 1.818 4.737 0.261 0.574 0.001 0.047

Chartreux 13 264 151 0 0 1.784 3.974 0.237 0.557 0.1 0.042

American SH 13 269 168 0 0 1.818 4.421 0.281 0.552 -0.023 0.035

Sphynx 17 277 178 2 0 1.872 4.684 0.271 0.549 0.047 0.05

Japanese Bob. 19 267 191 4 0 1.804 5.026 0.223 0.578 0.146 0.076

Cornish Rex 15 262 163 2 0 1.77 4.289 0.238 0.555 0.049 0.026

Ragdoll 15 265 178 4 0 1.791 4.684 0.29 0.624 -0.057 0.001

Maine Coon 19 282 210 2 1 1.905 5.526 0.256 0.602 0.114 0.043

Abyssinian 15 277 130 1 1 1.872 3.421 0.287 0.415 0.022 0.11

Siberian 17 275 227 4 2 1.858 5.974 0.26 0.705 0.093 -0.06

Norwegian FC 16 284 248 8 0 1.919 6.447 0.28 0.667 0.063 0.023

Manx 17 282 233 6 2 1.905 6.132 0.304 0.696 -0.002 -0.018

Egyptian Mau 14 268 160 1 0 1.811 4.211 0.246 0.495 0.029 0.114

Turkish Angora 21 284 275 10 1 1.919 7.237 0.251 0.674 0.109 0.055

Turkish Van 20 277 259 6 0 1.872 6.816 0.236 0.595 0.12 0.123

Bengal 18 274 192 10 2 1.851 5.053 0.244 0.579 0.065 0.038

Sokoke 7 222 92 0 0 1.5 2.421 0.166 0.368 0.003 0.004

Ocicat 10 264 142 3 2 1.784 3.737 0.236 0.495 0.035 0.048

Russian Blue 17 259 146 2 1 1.75 3.842 0.185 0.453 0.16 0.063

Australian Mist 15 273 156 4 0 1.845 4.105 0.268 0.569 -0.01 -0.045

Burmese 19 262 158 2 1 1.77 4.158 0.196 0.423 0.08 0.158

Birman 20 247 133 3 0 1.669 3.5 0.169 0.437 0.134 0.027

Havana Brown 14 245 113 1 0 1.655 2.974 0.168 0.423 0.115 -0.015

Korat 25 246 150 2 0 1.662 3.947 0.168 0.516 0.077 0.032

Siamese 15 242 133 2 1 1.635 3.5 0.2 0.466 0.002 0.024

Singapura 17 232 94 1 0 1.568 2.474 0.182 0.342 0.055 0.019

Total 477 296 490 1.794 4.544 0.237 0.534 0.058 0.044

N implies number of samples, PA_B implies private alleles between breeds, PA_W implies private alleles between breeds and worldwide randombred populations, Na implies average effective number of alleles, Ho implies observed heterozygosity, F_IS implies inbreeding coefficient

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cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn 3ritish SH

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Scottish Fold cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Chartreux

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn American SH

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Sphynx

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Japanese BobtailiJ cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Cornish Rex cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn agdoll

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cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Siberian

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Norwegian FC cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Vlanx

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Egyptian Mau

cn cn cn cn cn cn cn cn cn cn cn cn cn Turkish Angora cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Turkish Van cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn 3engal

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Sokoke

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Dcicat

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Russian Blue cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Australian Mist cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn 3urmese

cn cn cn cn cn 3irman

cn cn cn cn cn cn cn cn cn cn cn cn cn Havana Brown cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Korat

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Siamese

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Bingapura ho o -t_^ o cn o co co cn

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Scottish Fold Chartreux cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn American SH

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cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Dcicat

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Russian Blue

Australian Mist 3urmese

3irman

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Korat

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CO C3 0 0 CO C C CO C3 C3 0 CO C C CO co co co co co co co co ³ersian

CO C3 C3 0 0 CO CO C C CO CO ϊ CO CO co co co co co co co co Exotic SH

co co co co co co co co co co co co co co co co 3ritish SH

co co co co co co co co co co co co co co co co co co co co co co co Scottish Fold co co co co co co co co co co co co co co co co co co co co co co co co Chartreux

co co co co co co co co co co co co co American SH

co co co co co co co co co co co co co co co co co co co co co Sphynx

co co co co co co co co co co co co co co co co co co co co co Japanese Bobtail|

co co co co co co co co co co co co co co co Cornish Rex co co co co co co co co co co co co co co co co co agdoll

co co co co co co co co co co co Vlaine Coon co co co co co co co co co co co co co co co co co co co co co co co Abyssinian

co co co co co co co co co co co co co co co co co Siberian

co co co co co co co co co co co co co co co co co co co Norwegian FC co co co co co co co co co co co co co co co co co co co Vlanx

co co co co co co co co co co co Egyptian Mau

Turkish Angora co co co co co co co co co co co co co Turkish Van

co co co co co co co co co co co co co 3engal

co co co co co co co co co co co co co co co co Sokoke

co co co co co co co co co co co co co Dcicat

co co co co co co co co co co co co co co co Russian Blue

co co co co co co co co co co co co co co co Australian Mist

3urmese

3irman

Havana Brown co co co co co co co co co co co co co co co co co Korat

co co co co co co co co co co co co co co co co co Siamese

Bingapura

Total

T0T8£0/ZT0ZSil/I3d ZZ,Z,8£I/ZI0Z OAV o

3- σ

σ>

—^

-j

σ>

r

_•

_• h

T0l8C0/Zl0ZSil/X3d ZLLHSIIZIOZ OAV

H

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn ³ersian

Exotic SH

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn 3ritish SH

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Scottish Fold cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Chartreux

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn American SH

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Sphynx

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Japanese Bobtail) cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Cornish Rex cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn agdoll

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Vlaine Coon cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Abyssinian

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Siberian

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Norwegian FC cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Vlanx

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Egyptian Mau r o cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Turkish Angora Si r cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Turkish Van cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn 3engal a cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Sokoke

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Dcicat

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Russian Blue cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Australian Mist cn cn cn cn cn cn cn cn cn cn cn cn 3urmese

cn cn cn 3irman cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Havana Brown

cn cn cn cn cn cn cn Korat

cn cn cn cn cn cn cn cn cn cn cn cn cn Siamese

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Singapura

ho O ho ho ho o cn cn ->■ o cn cn ->■ ho

Total

T0l8£0/Zl0ZSil/I3d £./.8£l/£^'l0£^' OAV o o o o O o o o o o o o o o o o o o O o o o o o

3- 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3" 3"

ro ro ro ro

σ> -^. co ro to co co cn co ro ro ro ro cn cn co co cn co co co co co

co σ> co cn —^ ro ro cn co cn —^ CD -J cn cn -^. to cn to co cn cn -^.

n co co co cn CD cn CD —^ —^ co CD -^. CD ro cn ro co to to CD

-j cn to co —^ —^ cn ro cn ro CD to CD CD co ro CD co ^ cn co ^ ro

ro cn cn cn -J cn co ro —^ -^. CD to to cn -^. to cn ^ CD cn -j

cn to co -^. ro ro co to cn cn co cn ro CO ■ε- cn cn cn cn CD co

to ~-l σ> -j CD CD -j ro co to -^. ro -j CD ro co cn co co ro co

cn co CD CD ro ro cn cn -^. to —^ cn ro ro to to -J cn co co

H

co co co co co co co co co co co ³ersian

co co co co co co co co co co co co co co co co co Exotic SH

cn cn cn cn cn cn cn cn cn cn cn cn 3ritish SH n co co co co co co co co co co co co co co co co co co co co co co Scottish Fold

»Q S

co co co co co co co co co cn cn cn cn cn cn cn cn cn cn cn cn Chartreux

3 ^E

cn cn cn cn cn cn co co co co c³ ^Eo co co co co cn cn cn cn American SH cn cn cn cn cn cn co co co co co co co co ³c ^Eo co co Sphynx

^E2

co co co co co co co co cn cn cn cn cn cn cn cn cn Japanese Bobtail

^E2

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn c ^E2n cn cn cn cn cn Cornish Rex

^E2

cn cn cn cn cn cn cn cn cn cn co co co co co co co co agdoll

^E2

cn cn cn cn cn cn cn cn cn cn cn cn co co co co co co co c ^E2o co co Vlaine Coon co co co co co co co co co co co co cn cn cn cn cn cn c ^E2n Abyssinian

^E2

cn cn cn cn cn cn cn cn cn co co co co co co co co co co Siberian

co co co co co co co co co co co co co co co co co co co co co Norwegian FC cn cn cn cn co co co co co co co co co co co co co co co co co Vlanx

cn cn cn co co co co co co co co co co co co cn cn cn Egyptian Mau r o co co co co co co co co co co co co co co co co co co co co co Turkish Angora Si r cn cn cn cn co co co co co co co co co co co co cn cn cn cn Turkish Van co co co co co co co co co co co co co co co co co co co co co co 3engal a co co co co co co co co co co co co co co co co co co co cn cn cn cn Sokoke

co co co co co co co co co co co co co co co cn cn cn cn cn cn cn Dcicat

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Russian Blue cn cn cn cn co co co co co co co co co co co co co co co co co Australian Mist co co co co co co co co co co co cn cn cn cn cn cn 3urmese

co co co co co co co co co co co cn cn cn 3irman

co co co co co co co co co co co co cn cn cn cn Havana Brown co co co co co co co co co co co co co cn cn cn cn cn cn Korat

cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn cn Siamese

Bingapura

Total

T0T8£0/ZT0ZSil/I3d £ ./.8£T/ZT0Z OAV o o o o

o O 3" 3" 3" 3"

> O > O > > O > O o O > O o o O O O O O O O O > o

CD CD C >D C >D CD C >D C >D C >D C >D C >D C >D C >D C >D C >D C >D CD C >D -n -n -n -n

r ro CD to to oo co -J -J cn cn co ro ro CD CD ro ro ro ro

σ> co cn -J cn CD co CD -J cn to co cn co cn cn co co cn co -j -j -j

ro to co

ro cn co co

-j co CD cn

co ro co co

cn ro co

CD ro ro

ro

³ersian

Exotic SH

3ritish SH

Scottish Fold Chartreux

American SH

Sphynx

Japanese Bobtail Cornish Rex agdoll

Vlaine Coon Abyssinian

Siberian

Norwegian FC Vlanx

Egyptian Mau Turkish Angora Turkish Van 3engal

Sokoke

Dcicat

Russian Blue Australian Mist 3urmese

3irman

Havana Brown

Korat

Siamese

Bingapura ho ho cn co ho ho ho co ho

Total

T0T8£0/ZT0ZSil/I3d ζζ,ζ,8£τ/ζτοζ OAV Table 13 - Chi-squared test for Hardy- Weinberg Equilibrium by cat breed

FCA132 ns ns ns ns • ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns • ns • ns ns • ns ns 4

FCA149 ns ns ns • • ns ns ns ns ns ns ns ns ns ns • ns ns ns ns ns ns ns ns • ns ns ns ns 4

FCA211 ns ns • ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns • ns ns 2

FCA220 ns ns ns ns ns ns ns • ns ns ns ns ns ns ns • ns ns ns • ns ns ns ns ns ns ns ns ns 3

FCA223 ns ns • ns ns ns ns ns ns ns ns • • ns ns • ns • ns • ns ns ns ns ns ns ns • ns 7

FCA224 ns ns ns ns ns ns ns ns ns ns • ns ns ns ns • ns • ns ns ns ns ns ns • ns ns ns ns 4

FCA229 ns • ns ns • ns ns ns ns • ns ns ns ns ns ns ns • ns ns ns ns • ns ns ns ns ns ns 5

FCA262 • ns ns ns ns ns ns ns ns • ns ns ns ns ns • ns • ns ns ns ns ns ns • ns • ns ns 6

FCA293 ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns • ns ns ns ns ns ns ns 1

FCA305 ns ns ns ns ns ns • ns ns ns ns • ns • ns ns ns ns • ns ns ns ns ns ns ns ns ns ns 4

FCA310 ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns 0

FCA391 ns • • ns ns • • ns • ns • • • ns • ns • ns ns ns ns • ns ns ns ns ns ns ns 11

FCA441 ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns • ns ns ns ns ns ns ns ns ns 1

FCA453 ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns • ns ns ns ns ns ns ns ns ns ns 1

FCA628 ns ns ns ns ns ns ns ns ns ns • ns ns ns ns ns ns ns ns ns ns ns ns • ns ns • • ns 4

FCA649 ns ns ns • ns • ns ns ns ns • ns ns ns ns ns ns ns ns • ns ns ns ns ns ns • ns ns 5

FCA678 ns ns ns ns ns ns ns ns ns ns • ns ns ns ns ns ns • ns ns ns ns ns ns ns ns ns ns • 3

FCA698 • ns ns ns ns ns ns ns ns ns ns ns ns • ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns 2

Total 14 21 17 11 18 21 20 25 13 11 23 17 20 16 15 25 25 31 14 14 10 33 12 17 23 16 18 13 16 ns = not significantly out of Hardy- Weinberg Equilibrium, · = significantly out of HWE with p-value < 0.05

14 - Phenotypic SNP Frequencies

Long Hair Burmese Points Siamese Points Chocolate Cinnamon

FGF5 A475C TYR G715T TYR G940A Phen_TYRP1_5IVS6 TYRP1 C298T

Freq. Freq.

Breed N AA AC CC C N GG GT TT T N GG GA AA A N CC CG GG Freq. N CC CT TT

Persian 15 0 1 14 0.97 15 15 0 0 0 15 5 4 6 0.53 15 12 2 1 0.13 15 15 0 0

Exotic SH 17 5 10 2 0.41 19 19 0 0 0 17 14 2 1 0.12 19 15 3 1 0.13 19 19 0 0

British SH 18 16 2 0 0.06 18 18 0 0 0 17 13 0 4 0.24 18 11 2 5 0.33 18 15 3 0

Scottish Fold 16 13 3 0 0.09 17 17 0 0 0 15 14 1 0 0.03 17 13 4 0 0.12 17 17 0 0

Chartreux 10 5 5 0 0.25 13 13 0 0 0 11 11 0 0 0 13 13 0 0 0 13 13 0 0

American SH 13 10 2 1 0.15 13 11 2 0 0.08 13 13 0 0 0 13 12 0 1 0.08 13 13 0 0

Sphynx 16 9 1 6 0.41 16 6 6 4 0.44 12 9 1 2 0.21 17 8 5 4 0.38 17 17 0 0

Japanese Bobl 14 8 2 4 0.36 18 18 0 0 0 15 13 2 0 0.07 19 19 0 0 0 19 19 0 0

Cornish Rex 15 14 1 0 0.03 15 15 0 0 0 14 3 4 1 0.21 14 13 1 0 0.04 15 15 0 0

Ragdoll 15 4 3 8 0.63 15 15 0 0 0 15 0 0 15 1 15 13 2 0 0.07 15 15 0 0

Maine Coon 14 0 0 14 1 18 18 0 0 0 17 17 0 0 0 19 16 2 1 0.11 19 19 0 0

Abyssinian 15 15 0 0 0 15 15 0 0 0 15 15 0 0 0 15 12 3 0 0.1 15 4 6 5

Siberian 14 1 3 10 0.82 16 16 0 0 0 15 8 6 1 0.27 17 16 1 0 0.03 17 17 0 0

Norwegian FC 13 8 3 2 0.27 16 16 0 0 0 15 15 0 0 0 16 15 0 1 0.06 16 16 0 0

Manx 15 8 6 1 0.27 17 17 0 0 0 16 16 0 0 0 17 16 1 0 0.03 17 17 0 0

Egyptian Mau 12 12 0 0 0 14 14 0 0 0 12 12 0 0 0 14 14 0 0 0 14 14 0 0

Turkish Angors 20 0 0 20 1 21 21 0 0 0 20 17 1 2 0.13 20 15 5 0 0.13 21 21 0 0

Turkish Van 18 0 0 18 1 19 19 0 0 0 20 19 1 0 0.03 19 14 2 3 0.21 20 20 0 0

Bengal 16 15 1 0 0.03 18 16 2 0 0.06 14 9 4 1 0.21 18 16 2 0 0.06 17 17 0 0

Sokoke 6 6 0 0 0 7 7 0 0 0 4 3 0 1 0.25 6 5 1 0 0.08 7 7 0 0

Ocicat 8 8 0 0 0 10 10 0 0 0 9 9 0 0 0 10 4 1 5 0.55 10 6 3 1

Russian Blue 15 14 0 1 0.07 17 16 1 0 0.03 15 11 3 1 0.17 17 17 0 0 0 17 17 0 0

Australian Mist 13 11 2 0 0.08 15 2 0 13 0.87 12 10 1 1 0.13 15 6 6 3 0.4 15 7 7 1

Burmese 19 19 0 0 0 19 0 1 18 0.97 16 16 0 0 0 19 9 4 6 0.42 19 18 0 1

Birman 19 0 0 19 1 20 20 0 0 0 16 0 0 16 1 20 12 5 3 0.28 20 20 0 0

Havana Brown 11 11 0 0 0 14 14 0 0 0 12 10 1 1 0.13 14 0 1 13 0.96 14 14 0 0

Korat 23 22 1 0 0.02 25 25 0 0 0 21 20 1 0 0.02 25 1 2 22 0.92 25 25 0 0

Siamese 15 15 0 0 0 15 15 0 0 0 13 0 0 13 1 15 2 6 7 0.67 15 15 0 0

Singapura 16 16 0 0 0 15 0 0 15 1 14 14 0 0 0 17 17 0 0 0 17 17 0 0

[0165] Genetic Diversity. The population genetic data is presented in Table 12. The average effective number of SNP alleles observed averaged 1.794 across breeds, ranging from 1.500 in Sokoke to 1.919 in Norwegian Forest and Turkish Angora. The average effective number of STR alleles observed averaged 4.54 across breeds, ranging from 2.42 in Sokoke to 7.23 in Turkish Angora. Private STR alleles within breeds ranged from 0 - 10, however, when the breeds were compared to worldwide random bred populations, private alleles within breeds dropped to between 0 - 2 per breed. No SNPs had private alleles in a breed.

[0166] The average SNP -based observed heterozygosity was 0.287, ranging from 0.166 in Sokoke to 0.304 in Manx. The average STR-based observed heterozygosity was 0.534, ranging from 0.342 in Singapura to 0.705 in Siberian. Inbreeding coefficients (FIS) were lowest in the Ragdoll (-0.057) and Siberian (-0.060) with SNPs and STRs,

respectively, and highest within the Australian Mist Cats (0.160) and Burmese (0.158). Between population variation FST values were 0.237 ± 0.007 with SNPs and 0.269 ± 0.019 with STRs.

[0167] Breed Clustering. The most likely value of K, the number of structured groupings, could not be decisively determined. A significant difference between the log likelihoods was not evident for either marker type between K = 17 - 33 (Figure 7), however, a plateau was suggested near K = 21 for STRs and K = 17 for SNPs (Figure 8). As a result, a combination of the ΔΚ plots and common sense directed selection of the most likely number of populations. For STRs, at K > 24 (Figure 9B), different lineages within specific breeds, such as Norwegian Forest Cat and Turkish Angora, became apparent before five other breed groups would split: Persian / Exotic SH, British SH / Scottish Fold, Australian Mist / Burmese, Birman / Korat, and Siamese / Havana Brown. Similar results were found for the SNPs-based analyses; however the associations of the Asian based breeds varied. SNPs appear to resolve the Birman and Singapura breeds from the other Asian breeds more readily. Considering both SNPs and STRs, Persians appear to have influenced several younger and older breeds: Exotic shorthair, Scottish Fold, British Shorthair and Chartreux. For breeds with Asian heritage, Siamese have a strong influence on the Havana Brown, Korats, Birmans, and Singapura (Figure 8).

[0168] The principal coordinate analyses indicate the relationship of the sub-groups of breeds and their likely closest random bred origins (Figure 10). The breeds that originated solely from European and American random bred cats cluster with the random bred populations of Europe and America. Likewise, breeds with Asian decent grouped with the South Asian populations of random bred cats, and the Sokoke shows a strong influence from its roots in Kenya. The breeds that do not share similar coordinates with a random bred population such as, Russian Blue, Ocicat, Singapura, Australian Mist and Birman, show a strong influence from both Europe and Asia.

[0169] Using Bayesian clustering, the breeds were then assigned back to the eight random bred lineages discussed in Example 1 (Table 15). Four regional areas have contributed to the development of cat breeds. Asian breeds, such as Birman, Burmese, Siamese and others grouped with Southern Asian cats, Western breeds, such as Persian, Norwegian Forest Cat, Maine Coon and others grouped with the Western European random bred cats. Turkish Angora and Turkish Van assigned to the Eastern Mediterranean cats and the Sokoke to the Arabian Sea region. Three breeds showed regional variation depending on the marker type used for assignment. When analyzed with data from SNPs and STRs, the Turkish Angora assigned to the Eastern Mediterranean or Europe, Bengal assigned to the Arabian Sea or Europe, and the Ocicat assigned to Europe or South Asia, respectively.

Table 15a Assignment of cat breeds to random bred cat populations using SNPs.

Breed SNPs (N=148)

Lineage "-log(L)"

South East

Europe East Med South Medraqlran Arabian India Asia Asia

Birman South Asia 562 .439 514, .619 489. 325 571 , .811 466 .884 398 .272 357 .638 494 .121

Burmese South Asia 537 .957 494, .594 510. 756 554 .652 452 .18 353 .1 232 .359 408 .667

H. Brown South Asia 498 .346 470, .353 477. ,771 538 .807 421 , .824 376 .835 266 .337 404 .7

Korat South Asia 783 .086 710, .194 728. 915 728 .005 569 .748 497 .518 241 , .988 544 .729

Siamese South Asia 521 .766 481 , .206 493 .075 520 .538 417, .91 356 .165 196 .049 363 .29

Singapura South Asia 610, .855 635 .495 634. ,003 697 .695 541 , .126 518 .324 402, .416 539 .635

Abyssinian Europe 425 .881 506, .972 520. , 173 739 .043 523 .387 571 , .166 539 .713 582 .183

Russian Blue South Asia 426 .363 443, .64 443, .657 549 .342 451 , .198 439 .924 406, .281 478 .247

American SH Europe 214, .445 294 .034 300. 891 436 .278 342 .32 356 .071 430, .155 363 .917

British SH Europe 266 .189 351 , .574 383. ,015 509 .425 386 .181 402 .229 499 .062 426 .489

Chartreux Europe 243 .214 287. 905 310. 595 392 .542 328 .31 306, .122 390 .676 340 .836

Japanese Bob. Europe 241 , .661 256 .018 267. 56 369 .44 306, .997 289 .681 354 .146 274 .931

Maine Coon Europe 233 .644 336 .954 359. ,517 507, .38 383 .73 400 .377 481 , .154 409 .93

Norwegian FC Europe 186 .918 268 .319 297. ,021 459 .99 364 .374 361 .486 460, .332 367 .953

Persian Europe 210, .224 280 .822 298. 776 425 .561 327 .435 320 .381 410, .002 328 .219

Exotic SH Europe 275 .475 367 .195 380 .41 532 .774 391 .854 408 .132 500, .594 423 .104

Sphynx Europe 229 .867 268 .333 294. 366 421 , .316 343 .479 304, .696 385 .86 333 .086

Siberian Europe 177, .505 205 .628 244. , 135 368 .507 282 .501 286 .135 350, .747 277 .186

Egyptian Mau Europe 278 .503 303, .627 303. 291 421 , .446 345 .331 349 .259 422 .453 366 .832

Sokoke W Indian 215 .491 213 .151 215 .151 222 .711 175, .088 194 .761 214, .181 212 .471 Table 15a Assignment of cat breeds to random bred cat populations using SNPs.

Breed SNPs (N=148)

Lineage "-log(L)"

South East

Europe East Med South Medraqlran Arabian India Asia Asia

Turkish Angora * Europe 206.309 215.369 239.522 346.148 308.088 297.611 395.989 324.211

Turksih Van East Med 251.489 233.29 268.56 383.923 339.409 320.684 396.447 367.681

Cornish Rex Europe 317.191 368.372 358.46 488.121 383.104 360.937 413.799 403.094

Manx Europe 183.172 266.261 293.483 465.86 343.149 354.55 469.19 382.31

Ragdoll Europe 238.285 277.549 295.736 393.95 342.598 302.199 364.068 309.321

Scottish Fold Europe 285.089 410.698 439.66 579.88 419.796 453.876 548.531 480.68

Australian Mist South Asia 366.442 372.814 392.118 473.959 391.023 357.53 299.831 371.146

Bengal* Europe 277.481 312.259 314.506 444.415 343.165 327.977 410.917 369.303

Ocicat* South Asia 250.81 272.05 285.371 362.082 289.028 245.677 244.482 272.293

*Indicates breeds that assigned to varying origins based on the genetic marker type.

Table 15b Assignment of cat breeds to random bred cat populations using STRs.

Breed STRs (N=38)

Lineage "-log(L)"

South East Europe East Me South Me Iraqlran Arabian India Asia Asia

Birman South Asia 619.802 624.199 691.233 784.48 627.696 611.122 404, .846 619.715

Burmese South Asia 538.86 574.707 602.871 650.149 543.725 469.352 328 .296 480.689

H. Brown South Asia 405.814 452.844 463.474 490.494 410.516 391.736 290 .886 348.305

Korat South Asia 724.631 720.932 769.557 773.995 605.694 574.06 298 .912 623.147

Siamese South Asia 440.302 476.476 511.011 515.028 452.232 377.384 268 .941 373.961

Singapura South Asia 529.943 593.678 581.331 723.339 530.853 516.217 377, .292 562.624

Abyssinian Europe 391.775 431.049 448.175 569.783 452.082 452.751 420, .303 462.802

Russian Blue South Asia 459.628 550.271 565.88 694.372 553.199 522.684 440, .046 531.117

American SH Europe 284.004 352.415 373.617 519.61 407.953 404.881 411. .122 413.971

British SH Europe 365.836 462.364 494.534 697.257 542.932 520.762 538 .64 553.998

Chartreux Europe 288.091 333.218 364.422 471.369 420.547 380.17 383 .442 377.357

Japanese Bob. Europe 442.684 492.594 504.049 586.241 498.245 472.916 486 .841 466.728

Maine Coon Europe 325.993 413.915 454.08 635.061 519.562 481.193 513, .901 510.841

Norwegian FC Europe 265.706 325.855 373.736 530.702 476.455 437.795 466 .9 454.453

Persian Europe 304.615 380.994 409.462 529.928 455.066 438.168 449, .052 414.575

Exotic SH Europe 431.298 514.268 536.769 706.318 563.227 579.773 541 , .912 574.013

Sphynx Europe 381.955 446.644 456.938 607.529 461.321 471.637 426 .769 491.009

Siberian Europe 272.587 305.993 346.585 514.722 453.233 405.521 428 .93 411.038

Egyptian Mau Europe 322.916 350.668 362.843 446.658 386.768 374.387 399 .565 412.49

Sokoke W Indian 275.548 271.011 274.846 286.448 218.947 248.466 244, .002 300.134

Turkish Angora * East Med 398.178 381.553 406.446 556.699 524.253 483.876 549 .764 539.647

Turksih Van East Med 452.401 387.724 427.429 577.663 525.23 477.131 505, .265 549.053

Cornish Rex Europe 369.25 437.788 446.038 555.609 468.858 428.47 394 .753 477.392

Manx Europe 280.006 354.58 393.541 589.955 496.761 463.125 499 .578 482.289

Ragdoll Europe 341.134 393.51 404.545 592.622 471.899 425.202 397 .716 405.643

Scottish Fold Europe 353.106 438.254 455.217 674.159 506.12 514.422 530, .18 525.99

Australian Mist South Asia 372.752 415.4 428.335 503.159 433.821 388.385 318 .639 384.275 Table 15b Assignment of cat breeds to random bred cat populations using STRs.

Breed STRs (N=38)

Lineage "-log(L)"

South East

Europe East Me South Me Iraqlran Arabian India Asia Asia

Bengal* W Indian 496.347 512.793 499.54 583.542 490.251 513.431 503.627 594.543 Ocicat* Europe 283.111 305.428 301.617 383.669 328.11 300.313 287.939 319.107

[0170] Assignment Testing. The accuracy of assignment testing varied depending upon not only the assignment method, but also the marker type used to differentiate the cat breeds. For example, when comparing the Bayesian method of Rannala & Mountain (1997) versus the Frequentist method of Paetkau et al. (2004), supra, the average sensitivity of assignment for the 148 non-phenotypic SNPs was 0.56 and 0.78, respectively (Table 16). When the five phenotypic SNPs were included with the random SNPs, the average assignment sensitivity was 0.54 and 0.83, respectively. Overall, the STRs had higher average sensitivities of 0.83 and 0.88, respectively. In six breeds, adding phenotypic SNPs into the Frequentist assignment of individuals reduced the sensitivity of the test, and in five breeds it reduced specificity. As this may be due to the strength of selection imposed on these markers we looked to use the phenotypic SNPs as a method of post assignment verification of breed origin for these cats. For many breeds these single-gene traits are the sole selection criteria for breed allocation, and can be seen in the frequency of the causative mutation in the breed (Table 14). Using just the five phenotypic SNPs included in this study, it was possible to correct for miss-assignment of individuals post-assignment testing in 57.5% of the individuals miss-assigned by the Bayesian method and in 50% of the individuals incorrectly assigned by the Frequentist method (Table 17). This would increase the sensitivity and specificity of the Bayesian method to 0.746 and 0.772 respectively, and the Frequentist to 0.89 and 0.888, resulting in better resolution than the use of STRs alone. The influence of recent breed development on the mis-allocation of individuals may be further visualized by plotting the crossed assignment rate as a function of the Reynold's distance between breeds (Figure 14). In all cases, the crossed assignment rate increased as the distance between breeds decreased. Table 16a - Assignment accuracy with the Bayesian method of Rannala & Mountain (Rannala & Mountain 1997).

Intergenic SNPs Intergenic and phenotypic SNPs STRs

Ave. Ave.

Breed n Ei EN Sens. Spec. Ave. Prot Ei Ell Sens. Spec. Prob. Ei Ell Sens. Spec. Prob.

* * *

Persian 12 12 0 0 10 0 0.17 1 2 13 0.83 0.43 0.57

Exotic SH 22 16 8 0.27 0.43 1 17 6 0.23 0.45 1 6 1 0.73 0.94 0.69

British SH 17 17 10 0 0 1 13 5 0.24 0.44 0.99 7 1 0.59 0.91 0.24

Scottish Fold 19 18 0 0.05 1 1 16 0 0.16 1 1 6 0 0.68 1 0.67

Chartreux 1 1 1 13 0.91 0.43 1 1 7 0.91 0.59 1 1 1 0.91 0.91 0.61

American SH 17 8 0 0.53 1 0.99 11 0 0.35 1 1 4 0 0.76 1 0.54

Sphynx 25 16 0 0.36 1 0.99 14 0 0.44 1 0.99 3 0 0.88 1 0.34

Japanese Bob. 18 2 33 0.89 0.33 1 7 34 0.61 0.24 1 1 0 0.94 1 0.55

Cornish Rex 23 12 0 0.48 1 0.97 14 0 0.39 1 0.98 5 0 0.78 1 0.58

* *

Ragdoll 16 16 0 0 16 0 0 5 0 0.69 1 0.6

Maine Coon 27 3 21 0.89 0.53 1 10 32 0.63 0.35 1 6 1 0.78 0.95 0.61

Abyssinian 1 1 4 0 0.64 1 0.98 4 0 0.64 1 0.98 2 0 0.82 1 0.54

*

Siberian 9 9 0 0 6 23 0.33 0.12 1 1 9 0.89 0.47 0.27

Manx 22 20 1 0.09 0.67 1 21 1 0.05 0.5 1 4 16 0.82 0.53 0.48

Egyptian Mau 14 3 0 0.79 1 1 4 0 0.71 1 1 1 0 0.93 1 0.59

Turkish Angora 18 5 125 0.72 0.09 1 11 134 0.39 0.05 1 11 2 0.39 0.78 0.46

Turkish Van 14 10 3 0.29 0.57 0.98 13 3 0.07 0.25 0.99 3 3 0.79 0.79 0.69

Bengal 23 7 0 0.7 1 1 2 0 0.91 1 0.99 0 0 1 1 0.79

Sokoke 5 0 0 1 1 1 0 0 1 1 1 0 0 1 1 0.81

Ocicat 7 4 0 0.43 1 0.99 3 1 0.57 0.8 0.99 1 0 0.86 1 0.63

Russian Blue 19 0 0 1 1 1 4 0 0.79 1 1 3 0 0.84 1 0.93

Australian Mist 20 0 1 1 0.95 1 0 2 1 0.91 1 2 15 0.9 0.55 0.92

Burmese 16 4 1 0.75 0.92 1 2 0 0.88 1 1 4 1 0.75 0.92 0.86

Birman 22 4 0 0.82 1 0.96 6 0 0.73 1 0.99 1 0 0.95 1 0.72

Havana Brown 15 2 2 0.87 0.87 1 2 1 0.87 0.93 1 0 0 1 1 0.93

Korat 24 0 15 1 0.62 1 0 17 1 0.59 1 2 0 0.92 1 0.55

Norwegian FC 16 8 4 0.5 0.67 1 5 25 0.69 0.31 1 2 25 0.88 0.36 0.41

*

Siamese 19 19 0 0 19 0 0 1 0 0.95 1 0.63

Singapura 19 1 0 0.95 1 1 0 0 1 1 1 2 0 0.89 1 0.86

All Breeds 500 221 237 0.56 0.54 0.99 231 291 0.54 0.48 1 86 88 0.83 0.82 0.63

* Essentially zero due to lack of sensitivity, n = Number of samples from this breed tested over ten iterations, Έι = Members of a breed that were incorrectly assigned to another breed, E_n Members of a different breed that were incorrectly assigned to the breed in question, Sens. = Sensitivity, Spec. = Specificity, Ave. Prob. = Average Probability of assignment.

Table 16b - Assignment accuracy with the Frequentist method of Paetkau et al. (Paetkau et al. 2004).

Intergenic SNPs Intergenic and phenotypic SNPs STRs

Ave.

Breed n Ei EN Sens. Spec. Ave. Prob Ei EN Sens. Spec. Ave. Prob. Ei En Sens. Spec. Prob.

Persian 12 9 19 0.25 0.14 0.39 6 10 0.5 0.38 0.45 1 6 0.92 0.65 0.26

Exotic SH 22 19 7 0.14 0.3 0.43 10 5 0.55 0.71 0.37 4 1 0.82 0.95 0.39

British SH 17 10 6 0.41 0.54 0.45 5 4 0.71 0.75 0.33 5 3 0.71 0.8 0.16

Scottish Fold 19 10 0 0.47 1 0.84 10 0 0.47 1 0.85 2 0 0.89 1 0.45

Chartreux 11 2 0 0.82 1 0.31 2 0 0.82 1 0.31 0 0 1 1 0.15

American SH 17 1 0 0.94 1 0.53 4 0 0.76 1 0.6 2 0 0.88 1 0.27

Sphynx 25 3 2 0.88 0.92 0.32 3 2 0.88 0.92 0.31 1 0 0.96 1 0.25

Japanese Bobtail 18 4 0 0.78 1 0.29 3 0 0.83 1 0.26 1 0 0.94 1 0.29

Cornish Rex 23 5 0 0.78 1 0.29 4 1 0.83 0.95 0.3 2 0 0.91 1 0.25

Ragdoll 16 3 0 0.81 1 0.26 2 0 0.88 1 0.26 4 0 0.75 1 0.32

Maine Coon 27 5 8 0.81 0.73 0.44 1 13 0.96 0.67 0.44 6 5 0.78 0.81 0.35

Abyssinian 11 0 0 1 1 0.32 0 0 1 1 0.32 2 0 0.82 1 0.33

Siberian 9 5 3 0.44 0.57 0.19 4 3 0.56 0.63 0.22 0 18 1 0.33 0.11

Manx 22 8 11 0.64 0.56 0.33 5 9 0.77 0.65 0.4 4 12 0.82 0.6 0.14

Egyptian Mau 14 1 0 0.93 1 0.29 2 0 0.86 1 0.32 3 0 0.79 1 0.18

Turkish Angora 18 10 1 0.44 0.89 0.23 6 8 0.67 0.6 0.37 9 7 0.5 0.56 0.21

Turkish Van 14 5 3 0.64 0.75 0.27 4 2 0.71 0.83 0.37 2 3 0.86 0.8 0.18

Bengal 23 2 0 0.91 1 0.43 2 0 0.91 1 0.43 0 0 1 1 0.21

Sokoke 5 0 0 1 1 0.41 0 0 1 1 0.42 0 0 1 1 0.46

Ocicat 7 0 1 1 0.88 0.27 0 2 1 0.78 0.3 1 1 0.86 0.86 0.1

Russian Blue 19 0 0 1 1 0.31 1 0 0.95 1 0.32 3 0 0.84 1 0.39

Australian Mist 20 0 2 1 0.91 0.57 0 3 1 0.87 0.58 2 1 0.9 0.95 0.27

Burmese 16 2 2 0.88 0.88 0.51 3 0 0.81 1 0.51 0 2 1 0.89 0.26

Birman 22 1 0 0.95 1 0.39 1 0 0.95 1 0.38 1 0 0.95 1 0.34

Havana Brown 15 1 0 0.93 1 0.48 2 1 0.87 0.93 0.49 0 0 1 1 0.37

Korat 24 1 0 0.96 1 0.41 0 0 1 1 0.42 0 0 1 1 0.45

Norwegian FC 16 1 46 0.94 0.25 0.33 3 20 0.81 0.39 0.37 1 3 0.94 0.83 0.06

Siamese 19 1 0 0.95 1 0.33 0 0 1 1 0.32 0 0 1 1 0.17

Singapura 19 1 0 0.95 1 0.45 0 0 1 1 0.44 3 0 0.84 1 0.32

All Breeds 500 110 111 0.78 0.78 0.39 83 83 0.83 0.83 0.39 59 62 0.88 0.88 0.27

Table 17

Total mis-assigned individuals identified post-assignment by phenotypic SNPs.

Assigned by SNPs Assigned by STRs

Bayesian Frequentist Bayesian Frequentist

Total Freq. Total Freq. Total Freq. Total Freq.

Long Hair 105 0.49 37 0.34 11 0.128 11 0.18

Burmese Points 15 0.07 3 0.03 1 0.016 2 0.03

Siamese Points 15 0.07 16 0.15 6 0.07 3 0.05

Chocolate 8 0.04 0 0 2 0.023 0 0

Cinnamon 14 0.07 5 0.05 4 0.047 4 0.07

Total* 127 0.58 55 0.5 22 0.256 19 0.31

Frequency (SNPs: Bayesian = 221, Frequentist = 110 STRs: Bayesian = 86, Frequentist = 62) *A few individuals were identified as mis-assigned with multiple phenotypic SNPs.

Discussion:

[0171] The artificial selection and population dynamics of domestic cats and its associated fancy breeds are unique amongst domesticated species. Cats are one of the more recent mammalian domesticates, arguably they exist in a unique quasi-domesticated state. Unlike other agricultural species and the domestic dog, for thousands of years, cats have had minimal artificial selection pressures on their form and function as they have naturally performed their required task of vermin control. Cats are transported between countries via accidental human-mediated travel or by direct importations, reducing barriers to gene flow; however, rabies control legislation has reduced the migration of cats between some countries. Overlapping niches between the wildcat progenitors, random bred feral cats, random bred house cats, and fancy breeds likely produces continual, however limited, gene flow throughout domestic cat world.

[0172] The overall selection on the cat genome may be predicted as less intense than in other domesticated species and their breeds. The cat fancy is less than 200 years old, and a majority of cat breeds were developed in the past 50 - 75 years. Human selection has focused on aesthetic qualities, such as coat colors and fur types, as opposed to complex behaviors, such as hunting skills, meat or milk production. Many of the cat's phenotypic traits, even ones that affect body and appendage morphologies, are simple traits with basic Mendelian inheritance patterns. One simple genetic change, such as long hair of the Persian versus short hair of the Exotic Shorthair, can be the defining characteristic between two breeds. Cat registries have recognized that some breeds are "natural", such as the Korat and Turkish Van, being specific population isolates, therefore random bred cats of similar origins can be used to augment the gene pools of these selected breeds. Other breeds are recognized as "hybrids", developed from purposeful cross-breeding. Breeds that are interspecies hybrids also exist in the cat fancy, such as the Bengal, which is a hybrid between an Asian leopard cat and various domestic breeds, such as Abyssinian and

Egyptian Mau. Thus, some cat breeds may be a concoction of various genetic backgrounds.

[0173] Breed assignment studies of cats can be of great value to humans and the cat itself in a variety of applications. As models for human disease, cat population structuring is important to the proper selection of cases and controls in the study design of genome- wide association studies. Cross-bred cats may unknowing transport undesired mutations into na^'ive breed populations, and affect the breed's health and veterinary care. Polycystic kidney disease (PKD) is found in roughly 40% of Persians worldwide and has been documented in breeds with Persian influence, such as Scottish Folds and British Shorthairs (Lyons et al. (2004) Journal of the American Society of Nephrology, 15:2548-2555). The identification of migrants or hybridized individuals may affect the registration policies of a breed association. Out of curiosity, many of the lay public may like to know the origins of their cat and if their cat has pedigreed roots. Thus, this study has focused on the feasibility and power of genetic markers to delineate 29 of the world's cat breeds. SNPs and STRs were evaluated to assess the effects of genetic markers with different mutation rates on domestic cat regional clustering, breed clustering, and individual breed assignment. The selected 29 breeds were expected to represent the major breeds of the cat fancy. Several breeds were purposely selected that had been clearly derived from another breed, such as Persians and Exotics, while others were selected because they were recently developed hybrid breeds, such as the Ocicat and the Australian Mist. More slowly evolving SNPs and relatively quickly evolving STRs were examined as to their power to resolve these cat breeds that have different patterns and ages of ancestry.

[0174] Genetic Diversity. Significant genetic variation is present in many cat breeds. The Turkish Angora, a breed from Turkey, an area near the seat of cat

domestication, had the highest effective number of alleles for both SNPs and STRs. A continuum of increasing heterozygosity and decreasing inbreeding, whether SNP- or STR- based, is found between the least variable and most variable domesticated cat breeds. Two of the more popular breeds of the USA and the world are Persians and Bengals (on the internet at tica.org/). Persians were one of the first breeds to be recognized and Bengals, although only introduced in the past 40 years have risen to worldwide fame. Both breeds had moderate levels of heterozygosity and inbreeding. Several less popular breeds, such as the Cornish Rex, had fairly high levels of variation and low inbreeding. Two relatively new breeds, the Siberian and Ragdoll, have high variation, perhaps a reflection of their recent development from random bred populations. Thus, levels of variation and inbreeding cannot entirely be predicted based on breed popularity and breed age, implying management by the cat breeders may be the most significant dynamic for breed genetic population health. Interestingly, the Burmese had one of the highest levels of inbreeding and lowest levels of genetic variation. Burmese were established in the post- World War II breed boom, and has been a moderately popular breed. However, concerns for two diseases, a craniofacial defect and hypokalemia, has limited migration of cats between countries and within the USA. Fractionation of the breeding pool by color preferences within the USA has also caused poor breeding dynamics. Thus, a reduction in observed heterozygosity due to the Wahlund- effect may be likely, resulting in an under-estimation of the already severely high inbreeding coefficients, possibly sending the Burmese into extinction. A breed management plan that balances diversity, health and breed type may need to be implemented to help the Burmese breed survive.

[0175] Breed Clustering. The most likely value of K, the number of structured groupings, could not be decisively determined. Several factors that violate many of the assumptions in the models implemented by Structure may have caused this difficulty. Cat breeds have variation in age of development, significantly different genetic population origins, and the variation in breeding practices can create distinct lines within one breed that may be as unique as one of the more recently established breeds. Many breeds were created through the crossing of two often highly divergent populations of cats resulting in a hybrid of sorts while other breeds still allow the introduction of cats from random bred populations. However, the Bayesian cluster analysis supported the breed demarcations from previous studies, especially the STR analyses of Lipinski et al. (2007) Animal Genetics, 38, 371-377. Previously, 22 breeds, which included 15 of 16 "foundation" cat breeds designated by the Cat Fanciers Association, delineated as 17-18 separate populations. This study added seven additional breeds, including the missing 16th "foundation" breed, the Manx. As in previous studies, the novel breeds that were not deemed significantly distinct from another breed can be very clearly explained by the breed history. The two large breed families of Siamese and Persians were re-identified and the Persian family expanded with Scottish Folds. In addition to the previously recognized grouping of the Siamese / Havana Brown, the Australian Mist, which was created by cross-breeding with Burmese, grouped with the Burmese. However, more recent breeds, such as Ragdoll and Bengal, are resolved as separate breed populations, suggesting STRs alone can differentiate about 24 of 29 breeds, as well as Turkish-origin versus USA-origin Turkish Angoras. At K = 17, SNPs could not differentiate the Singapura, however, the Birman separation from other Asiatic breeds could be defined. Thus, both sets of markers provide valuable insight to the relationship of the breeds.

[0176] Regional Clustering. Regardless of the marker assayed and using both

Bayesian assignment testing and principal coordinate analyses, the majority of the breeds assigned back to the random bred population most influential to the creation of that breed, as suggested by popular breed histories. Sixteen breeds originate from European populations, six breeds form South Asian populations, two breeds from the Eastern

Mediterranean, the Turkish Van and the Turkish Angora, and the Sokoke from the Arabian Sea region. However, some exceptions were noted depending on the marker of analysis. For SNPs versus STRs, the Turkish Angora assigned to the Eastern Mediterranean or

Europe, respectively, Bengal assigned to the Arabian Sea or Europe, respectively, and the Ocicat assigned to Europe or South Asia, respectively. The Turkish Angora breed was reconstituted from the Persian (European) pedigree post World Wars and recently, has been increasing genetic diversity via the outcrossing of pedigreed Turkish Angora cats to the random bred cats of Turkey. The identified lineages within the breed may be identifying the recent influx of random bred cats. The confusion in the Bengal and the Ocicat assignments could be a result of the contribution of the Abyssinian and Egyptian Mau and the Abyssinian and the Siamese, respectively, which are breeds with different regional assignments.

[0177] Assignment Testing. Overall, the Frequentist method of Paetkau et al.

(2004), supra, outperformed the Bayesian method of Rannala and Mountain (1997), supra. In addition, while the 38 highly polymorphic STRs consistently outperformed the SNPs, the addition of phenotypic SNPs as a post-assignment verification significantly improved the assignment rates using the frequentist method. For the 29 breeds, when intergenic SNPs were used with the frequentist method for assignment, the sensitivity of assignment was equal to or better than that of the STRs in 12 breeds and the specificity in 19 breeds. With the addition of only five phenotypic determining SNPs, specificity improved to 15 breeds, equaling or surpassing in the sensitivity of assignment of the STRs and 19 breeds equaling or outperforming in specificity of assignment.

[0178] In six breeds, adding phenotypic SNPs into the frequentist assignment of individuals reduced the sensitivity of the test, and in five breeds it reduced specificity. As this may be due to the strength of selection imposed on these markers we looked to use the phenotypic SNPs as a method of post assignment verification of breed origin for these cats. For many breeds these single-gene traits are the sole selection criteria for breed allocation, and can be seen in the frequency of the causative mutation in the breed (Table 14). In general, breeds that were more inbred and both not used in outcrosses, nor developed through the crossing of pre-existing breeds had a higher accuracy in reassignment. Breeds such as the Russian Blue, Sokoke and Abyssinian are examples of such. In contrast, breeds where outcrossing is common either with other breeds or random bred populations tended to confuse the assignment algorithm and had a high probability of both Type I and II error. These would be breeds such as the Persians, Turkish Angoras, Siamese and Ragdoll. The most common error in assignment by far was cross assignment between Exotic shorthairs and Persians -a problem easily remedied through exploiting the FGF5 SNP shown to cause long hair in Persians.

[0179] The influence of recent breed development on the mis-allocation of individuals may be further visualized by plotting the crossed assignment rate as a function of the Reynold's distance between breeds (Figure 11). In all cases, the crossed assignment rate increased as the distance between breeds decreased. This correlates exactly with what would be expected. Breeds that are considered separate solely on a color variant or hair length would be very close genetically and as a result, more prone to cross assignment.

[0180] The advantage was tipped in favor of SNPs when the five phenotypic SNPs were included as a post-assignment check. Initially, cats could be localized to a regional population and a breed family by STRs and / or SNPs. Secondary differentiation within the breed family could be determined by genotyping mutations for phenotypic traits, especially traits that are breed specific to or fixed within a breed. Some traits are required for breed membership; a Birman or Siamese must be pointed, implying homozygous for the G940A TYR mutation. Some traits are grounds for exclusion, all Korats are solid blue, no other colors or patterns are acceptable. Therefore, a trait such as the long haired A475C FGF5 mutation could be used as a means for identifying members of the Persian, Maine Coon, Turkish Angora, Turkish Van and Birman breeds, and likewise a means for discrimination as an exclusion maker for breeds such as the Abyssinian, Egyptian Mau, Sokoke and Ocicat. Other single gene traits may be used to identify members of a small family of cat breeds as well, such as the Burmese points, G715T TYR, are a prerequisite for membership to the Burmese and Singapura breeds, and Siamese points, G940A TYR, as their name suggests, are a requirement for Birmans, Himalayans and, of course, Siamese cats. The cinnamon mutation, C298T TYRP1, is very rare and is common to the red Abyssinian. Certain dominant traits can be homozygous or heterozygous, such as the ear curl of American Curls or the bobtail of the Japanese Bobtail. Some dominant traits are homozygous lethal in utero, such as tailless of the Manx (Todd (1961) Journal of Heredity, 52:228-232), or cause health problems, such as osteochondroplasia caused by the ear fold mutation in Scottish

Folds (Malik et al. (1999) Australian Veterinary Journal, 77:85-92). As a result, the breed may have cats that conform to the breed except do not express the breed-specific trait, such as straight-eared Scottish Folds, or tailed Manx. These varieties would currently be difficult to distinguish within the breed family or region. [0181] Cat fancy registries may not agree with assignments due to breeding restrictions. The Tonkinese, which is genetically compound heterozygous for the G940A and the G715T TYR mutations, can produce both pointed and sepia cats, thus they would genetically resemble a Siamese or Burmese, respectively. However, breeding restrictions would not allow these Tonkinese variants to be registered as Siamese or Burmese. Since the development of this SNP panel, additional phenotypic SNPs have been discovered in cats including the Norwegian Forest Cat color variant amber (Peterschmitt et al. (2009) Animal Genetics, 40:547-552), three additional long-haired mutations (Kehler et al. (2007) Journal of Heredity, 98:555-566), and the mutations responsible for hairless of Sphynx and rexing of the Devon Rex (Gandolfi et al, Mamm Genome. (2010) (9-10):509-15). These additional mutations, as well as disease mutations, could further delineate cat breeds.

[0182] Conclusions. Aside from the public interest in knowing their prized family pet is descendent from a celebrated pedigree, breed assignment is a vital tool in tracing the spread of genetically inherited diseases throughout the cat world. Much like humans and dogs, certain populations of cats are known to be at higher risk for particular diseases, such as heart disease in the Maine Coon and Ragdoll (Meurs et al. (2005) Human Molecular Genetics, 14:3587-3593; Meurs et al. (2007) Genomics, 90:261-264), polycystic kidney disease in the Persian (Lyons et al. 2004, supra), and progressive retinal atrophy in the Abyssinian (Menotti-Raymond et al. (2007) Journal of Heredity, 98:211-220). Knowing if a particular feline descended from one of these at risk populations may influence treatments in a clinical setting and help us to better care for our animal companions. With additional phenotypic and perhaps disease-causing SNPs, the power of this STR / SNP panel to accurately assign individuals to specific cat breeds would be greatly increased, in particular those breeds that are defined expressively by single-gene traits.

Example 3

Tables of Population Clustering at Different K Values

Table 18 - Population clustering of each pedigreed individual in the database by STRs at K = 21

Feline ID Missing Groups

Population No. Data 8 10 11 12 13 14 15 16 17 18 19 20 2

Persian 1250 7 0.7866 0.0682 0, 0114 0.0031 0.0048 0.0039 ,0064 0.0035 0031 0.039 0.0066 0.0059 0.0096 0.0033 0.0022 0.0014 0.0065 0.0085 0.0035 0.0099 0. Persian 1939 13 0.5479 0.0033 0. 134 0.0038 0.022 0.002 ,004 0.0053 001 0.003 0.2157 0.002 0.0043 0.0023 0.0033 0.0033 0.0021 0.0033 0.001 0.0354 0. Persian 2071 18 0.7477 0.0207 0. 0379 0.0036 0.0045 0.0095 ,0277 0.0095 00330.0028 0.006 0.0073 0.0095 0.0048 0.0032 0.0043 0.003 0.0199 0.0082 0.0066 O. Persian 2088 21 0.9342 0.0063 0, 0037 0.0028 0.0063 0.0038 ,0028 0.006 0021 0.002 0.0034 0.0092 0.0019 0.0026 0.0023 0.0017 0.0018 0.0019 0.002 0.0021 O. Persian 2140 10 0.8648 0.0117 0, 0038 0.0056 0.0028 0.0059 ,0063 0.0127 003 0.0079 0.0117 0.0031 0.0047 0.0023 0.0019 0.0024 0.0199 0.0065 0.0036 0.0135 O. Persian 2174 10 0.9585 0.002 0, 0016 0.001 0.002 0.001 ,001 0.002 001 0.001 0.0019 0.002 0.0018 0.0021 0.0116 0.0022 0.0013 0.001 0.001 0.003 O. Persian 2209 10 0.6727 0.0023 0, 0058 0.0048 0.0176 0.002 ,0021 0.0022 002 0.1571 0.0055 0.0065 0.002 0.0022 0.0017 0.0019 0.0032 0.0043 0.0818 0.0081 O. Persian 2215 18 0.6827 0.0013 0, 0057 0.0227 0.001 0.0086 0 ,0058 0.0137 01080.0209 0.066 0.0157 0.0094 0.0021 0.0012 0.0026 0.0023 0.0027 0.0026 0.1183 O. Persian 2890 10 0.9745 0.0012 0. 002 0.001 0.0011 0.001 0 ,0019 0.0018 00120.001 0.0011 0.001 0.001 0.001 0.001 0.001 0.0018 0.001 0.001 0.0017 O. Persian 2977 15 0.3524 0.0062 0, 1847 0.003 0.0022 0.001 0 ,0047 0.0155 00190.0392 0.2965 0.002 0.0065 0.0022 0.0115 0.0027 0.001 0.001 0.002 0.0628 O. Persian 4143 7 0.5399 0.0015 0, 1206 0.0053 0.0036 0.0022 0 ,0029 0.025 00770.0071 0.2098 0.002 0.0026 0.0058 0.0025 0.0027 0.0072 0.0019 0.0046 0.0437 O. Persian 4168 7 0.88 0.0049 0, 0032 0.0038 0.0527 0.002 0 ,02 0.002 001 0.003 0.0036 0.0022 0.0025 0.0023 0.0023 0.0019 0.0031 0.0014 0.0017 0.0052 O. Persian 4169 7 0.9097 0.0074 0, 0108 0.0041 0.002 0.0202 0 ,0075 0.0028 0021 0.0051 0.0068 0.0036 0.0023 0.0018 0.0027 0.0017 0.0016 0.002 0.0018 0.0028 O. Persian 4950 7 0.8748 0.0039 0, 0049 0.0049 0.0019 0.0125 0 ,0026 0.0055 001 0.0039 0.0077 0.0049 0.0132 0.0051 0.003 0.007 0.0036 0.0037 0.0302 0.003 O. Persian 4953 15 0.9194 0.0027 0. 0058 0.0024 0.0021 0.0012 0 0022 0.0098 002 0.0025 0.0045 0.0217 0.0057 0.0044 0.0035 0.0014 0.002 0.001 0.001 0.0031 O. Exotic SH 258 15 0.8256 0.0051 0. 003 0.0113 0.0015 0.005 0 ,0082 0.002 002 0.0024 0.006 0.0037 0.0833 0.0028 0.0021 0.0014 0.002 0.0238 0.0044 0.0031 O. Exotic SH 259 7 0.9005 0.0029 0, 0057 0.0075 0.004 0.0066 0 ,0102 0.0187 001 0.0034 0.0026 0.0025 0.0044 0.0011 0.0014 0.0013 0.001 0.001 0.001 0.0215 O. Exotic SH 260 5 0.9608 0.0037 0, 0023 0.0028 0.0035 0.0024 0 ,0021 0.0034 001 0.002 0.002 0.001 0.002 0.0018 0.0015 0.0019 0.001 0.001 0.0012 0.0016 O. Exotic SH 261 7 0.8958 0.0065 0, 0091 0.0085 0.0025 0.0053 0 ,001 0.0033 002 0.0073 0.0114 0.0092 0.0071 0.0072 0.0112 0.0024 0.0012 0.001 0.003 0.0036 O. Exotic SH 262 7 0.9025 0.018 0, 0032 0.0158 0.0016 0.0069 0 ,0028 0.0068 0041 0.003 0.0037 0.002 0.0034 0.0046 0.0036 0.0025 0.0019 0.0043 0.0033 0.0048 O. Exotic SH 263 10 0.9234 0.0056 0, 0041 0.0091 0.0024 0.0019 0 ,0022 0.01 00130.0072 0.0044 0.005 0.0021 0.0034 0.0019 0.0024 0.0026 0.0022 0.001 0.0066 O. Exotic SH 264 7 0.6501 0.0031 0. 0266 0.1467 0.0838 0.0011 0 ,0107 0.0014 002 0.0138 0.0077 0.0023 0.0104 0.0012 0.0024 0.0062 0.0082 0.0057 0.0027 0.0115 O. Exotic SH 265 18 0.8689 0.0033 0, 005 0.0032 0.0029 0.006 0 ,008 0.003 00340.01 0.0069 0.002 0.0026 0.0033 0.0025 0.0021 0.0058 0.006 0.0061 0.004 O. Exotic SH 266 7 0.9553 0.002 0, 0021 0.0041 0.004 0.001 0 ,0019 0.002 001 0.0017 0.0022 0.001 0.001 0.0016 0.001 0.001 0.0116 0.001 0.002 0.0015 O. Exotic SH 267 7 0.9372 0.0019 0, 0015 0.016 0.002 0.002 0 ,0029 0.002 00170.0019 0.0019 0.001 0.0015 0.0041 0.0045 0.0089 0.0015 0.0019 0.0018 0.0025 O. Exotic SH 268 18 0.8783 0.009 0, 0056 0.0105 0.0021 0.0018 0 ,0052 0.0024 00230.0039 0.0053 0.0054 0.0024 0.0023 0.007 0.0016 0.0233 0.0034 0.0219 0.0028 O. Exotic SH 269 5 0.9549 0.0019 0, 0031 0.0046 0.0028 0.0019 0 ,0018 0.0033 0011 0.0018 0.0023 0.0026 0.0019 0.0015 0.0023 0.0017 0.0018 0.0018 0.0035 0.0016 O. Exotic SH 270 5 0.9377 0.0061 0. 0022 0.0131 0.002 0.0042 0 ,0025 0.0027 001 0.0022 0.0024 0.0012 0.0028 0.0011 0.001 0.0011 0.0022 0.0011 0.001 0.0112 O. Exotic SH 271 7 0.9563 0.0043 0. 0029 0.0018 0.0021 0.0023 0 ,001 0.0032 002 0.002 0.0024 0.0019 0.0019 0.0019 0.0016 0.0019 0.0018 0.0025 0.0022 0.003 O. Exotic SH 272 7 0.7489 0.0021 0, 003 0.1869 0.0021 0.0023 0 ,0023 0.005 00330.0044 0.0033 0.002 0.0037 0.0021 0.0085 0.0024 0.0018 0.0032 0.0036 0.0074 O. Exotic SH 273 5 0.9224 0.0056 0, 0036 0.0036 0.0019 0.0028 0 ,002 0.0032 00450.0057 0.006 0.0073 0.0028 0.0035 0.0025 0.0016 0.0114 0.0019 0.0018 0.0033 O. Exotic SH 274 15 0.9178 0.0023 0, 0066 0.0013 0.0054 0.004 0 ,0073 0.0041 00260.0034 0.0056 0.0018 0.0062 0.002 0.0035 0.003 0.0137 0.0022 0.001 0.004 O. Exotic SH 275 10 0.8779 0.002 0, 0029 0.0111 0.0021 0.0028 0 ,0241 0.0014 01040.002 0.0022 0.0018 0.0018 0.0016 0.0011 0.0017 0.0054 0.0022 0.0377 0.0031 O. Exotic SH 276 7 0.9058 0.0039 0, 0076 0.008 0.0011 0.0021 0 ,0044 0.0058 00320.0043 0.0064 0.0028 0.0022 0.0029 0.0017 0.0012 0.002 0.0036 0.0014 0.004 O. British SH 156 5 0.6905 0.0221 0. 0172 0.0044 0.002 0.0108 0 ,0018 0.0048 01120.005 0.0087 0.0042 0.006 0.004 0.0027 0.0013 0.002 0.0028 0.001 0.1936 O. British SH 157 5 0.7973 0.0148 0, 0172 0.0053 0.009 0.0068 0 ,0057 0.0077 00820.004 0.006 0.0034 0.0078 0.0032 0.002 0.0017 0.001 0.0028 0.0041 0.0852 O. British SH 158 5 0.5086 0.0079 0, 1649 0.002 0.0095 0.003 0.004 0.0117 003 0.0101 0.1597 0.0021 0.0036 0.0024 0.0023 0.0024 0.0011 0.0017 0.001 0.0928 O.

Feline ID Missing Groups

Population No. Data 6 8 10 11 12 13 14 15 16 17 18 19 20 21

British SH 159 5 0.6213 0.063 0 0035 0.0065 0.0274 0.0029 0, 0031 0.0062 0.00250.0458 0.0077 0.003 0.002 0.0061 0.0027 0.0023 0.0042 0.0081 0.0037 0.1741 0.

British SH 160 7 0.7633 0.0026 0 01 0.002 0.0018 0.0025 0, 001 0.0037 0.0021 0.0018 0.0016 0.001 0.0019 0.0021 0.001 0.001 0.001 0.001 0.001 0.1958 0.

British SH 161 5 0.6705 0.017 0 0622 0.002 0.0021 0.0085 0, 0056 0.0081 0.002 0.003 0.0025 0.0025 0.0032 0.0025 0.0017 0.0022 0.0141 0.0013 0.001 0.1828 0.

British SH 162 5 0.5013 0.0683 0 188 0.002 0.002 0.0024 0.0019 0.0047 0.00360.0104 0.0055 0.0038 0.0044 0.0061 0.0026 0.0018 0.0359 0.0021 0.0011 0.151 0.

British SH 163 5 0.4522 0.1998 0 0046 0.0033 0.0609 0.004 0.0053 0.0056 0.0021 0.0215 0.0036 0.0137 0.0077 0.003 0.006 0.0013 0.0352 0.0025 0.0421 0.1228 O.

British SH 164 5 0.5118 0.0989 0 1951 0.003 0.0029 0.0044 0, 0013 0.0049 0.002 0.0234 0.0037 0.002 0.0028 0.0019 0.0077 0.0013 0.0034 0.001 0.0027 0.124 O.

British SH 165 10 0.6535 0.0691 0 0123 0.003 0.0018 0.0059 0, 0061 0.0059 0.00390.0145 0.0055 0.0208 0.0065 0.002 0.0023 0.0059 0.0028 0.0021 0.0014 0.1721 O.

British SH 166 7 0.8077 0.0026 0 007 0.019 0.0107 0.0063 0, 0122 0.0104 0.01060.0029 0.0032 0.0041 0.0026 0.0034 0.0011 0.0026 0.0067 0.0023 0.011 0.0688 O.

British SH 167 7 0.4173 0.038 0 314 0.0094 0.0059 0.002 0, 0196 0.0156 0.002 0.023 0.0386 0.002 0.0138 0.005 0.0015 0.0022 0.0145 0.0044 0.0044 0.0638 O.

British SH 168 5 0.721 0.0019 0 0027 0.003 0.0019 0.0017 0, 0259 0.003 0.00560.003 0.0101 0.0145 0.002 0.0019 0.0034 0.0038 0.0012 0.0404 0.0052 0.1342 O.

British SH 169 5 0.8963 0.004 0 0084 0.004 0.0042 0.002 0.0066 0.0042 0.00820.0037 0.0057 0.0047 0.014 0.0036 0.0024 0.003 0.0011 0.001 0.0084 0.0125 O.

British SH 170 7 0.7456 0.0052 0 0136 0.002 0.001 0.001 0, 001 0.0281 0.00180.0027 0.0034 0.002 0.0055 0.0011 0.009 0.0024 0.001 0.001 0.0011 0.1704 O.

British SH 171 5 0.7699 0.0017 0 0014 0.0027 0.001 0.0032 0, 0038 0.0025 0.00420.0021 0.0027 0.0032 0.0021 0.0032 0.001 0.0017 0.0012 0.0013 0.001 0.1891 O.

British SH 172 5 0.7388 0.0026 0 0021 0.0022 0.002 0.0033 0, 0011 0.0034 0.00190.0012 0.0012 0.001 0.001 0.0011 0.001 0.0016 0.0025 0.0085 0.0028 0.2084 O.

British SH 173 7 0.6843 0.0123 0 0075 0.002 0.0019 0.0631 0, 0031 0.0052 0.00350.003 0.0032 0.0018 0.003 0.0061 0.007 0.0016 0.0053 0.0014 0.001 0.1806 O.

Scottish Fold 5655 7 0.6005 0.0301 0 0652 0.0178 0.0021 0.0022 0, 0048 0.0196 0.01090.0086 0.019 0.0021 0.0105 0.0077 0.001 0.0013 0.0578 0.0038 0.0021 0.1304 O.

Scottish Fold 5669 2 0.7467 0.0172 0 0347 0.0032 0.0048 0.0032 0.024 0.0113 0.01780.0021 0.0278 0.002 0.0057 0.0016 0.0016 0.0025 0.0097 0.0017 0.001 0.0803 O. Scottish Fold 7205 5 0.5549 0.1066 0 1134 0.0039 0.0027 0.0024 0.0331 0.0033 0.00290.0128 0.009 0.0014 0.0031 0.002 0.003 0.0176 0.0037 0.0023 0.0032 0.1174 O. Scottish Fold 7260 7 0.4461 0.0055 0 0897 0.0059 0.0046 0.0054 0, 0028 0.0026 0.07850.0443 0.2661 0.0025 0.002 0.0011 0.0013 0.0032 0.0035 0.001 0.001 0.0319 O. Scottish Fold 8552 7 0.7094 0.0031 0 0065 0.0077 0.004 0.0023 0, 0042 0.0148 0.00290.0067 0.0063 0.0019 0.0078 0.0023 0.002 0.0109 0.0085 0.0013 0.0013 0.1806 O. Scottish Fold 9823 5 0.7631 0.0033 0 0035 0.0022 0.0017 0.0018 0, 0012 0.0028 0.00270.0029 0.0042 0.0029 0.0018 0.0011 0.0011 0.0022 0.0018 0.0017 0.001 0.1957 O. Scottish Fold 9824 5 0.7671 0.0015 0 0045 0.0018 0.002 0.0017 0, 002 0.0115 0.00180.002 0.0041 0.001 0.0013 0.0012 0.0013 0.0014 0.001 0.0015 0.001 0.1885 O. Scottish Fold 9825 7 0.7611 0.0037 0 0031 0.0024 0.003 0.0021 0, 0018 0.0028 0.00280.0035 0.0032 0.0018 0.002 0.0014 0.0015 0.0019 0.001 0.0019 0.0014 0.1948 O. Scottish Fold 9826 5 0.7696 0.0032 0 0035 0.0018 0.0018 0.0028 0.001 0.0025 0.00170.0018 0.0023 0.0017 0.001 0.001 0.0011 0.0017 0.0018 0.001 0.001 0.1967 O. Scottish Fold 9827 7 0.7742 0.0012 0 0029 0.0019 0.003 0.001 0, 001 0.0028 0.0011 0.002 0.0018 0.0015 0.0014 0.001 0.001 0.0022 0.002 0.001 0.001 0.195 O. Scottish Fold 9831 5 0.7659 0.0028 0 0026 0.002 0.0021 0.002 0, 001 0.0033 0.0041 0.002 0.0019 0.002 0.0019 0.0017 0.0011 0.0029 0.0018 0.0018 0.001 0.1943 O. Scottish Fold 9832 7 0.7597 0.0036 0 0037 0.002 0.004 0.0019 0, 0018 0.0037 0.00240.0029 0.0058 0.0018 0.0018 0.0011 0.0015 0.0031 0.001 0.001 0.0016 0.1946 O. Scottish Fold 9929 7 0.7189 0.0385 0 0118 0.0024 0.003 0.001 0, 0121 0.004 0.00190.0503 0.0078 0.0015 0.0296 0.0065 0.0028 0.0028 0.002 0.0017 0.001 0.0995 O. Scottish Fold 9930 7 0.5752 0.2529 0 0055 0.002 0.0033 0.0032 0, 0094 0.002 0.00290.0158 0.0039 0.001 0.002 0.0033 0.0016 0.0021 0.0011 0.001 0.001 0.1097 O. Scottish Fold 9931 7 0.5005 0.3098 0 0049 0.0023 0.0079 0.0015 0.0074 0.0027 0.00230.0052 0.0049 0.0031 0.0043 0.0026 0.004 0.0038 0.001 0.0027 0.001 0.1271 O. Scottish Fold 9937 5 0.3795 0.3982 0 0214 0.0035 0.001 0.0049 0. 0064 0.0155 0.00290.0072 0.0167 0.001 0.0056 0.0021 0.0019 0.0027 0.0017 0.0034 0.0027 0.1204 O. Scottish Fold 9965 5 0.4471 0.1745 0 2081 0.0056 0.002 0.002 0, 0017 0.002 0.00230.0057 0.0063 0.001 0.0037 0.0012 0.0095 0.0029 0.0019 0.001 0.0018 0.1188 O. Chartreux 1772 7 0.0063 0.8977 0 0381 0.0023 0.0046 0.0047 0, 0033 0.0023 0.00160.0021 0.0022 0.0018 0.0052 0.0018 0.0014 0.0017 0.0086 0.002 0.0043 0.0051 O. Chartreux 2226 15 0.0037 0.8556 0 0077 0.0397 0.0043 0.0042 0, 0065 0.0067 0.004 0.0123 0.0052 0.0033 0.0197 0.0022 0.0032 0.0026 0.0095 0.0014 0.0019 0.005 O. Chartreux 2229 26 0.028 0.7448 0 0033 0.0025 0.0026 0.0044 0, 0019 0.1111 0.009 0.0064 0.004 0.0019 0.0032 0.0041 0.0041 0.0025 0.0011 0.0011 0.0028 0.06 O. Chartreux 2524 5 0.0025 0.7292 0 1568 0.0113 0.0046 0.0062 0, 0095 0.0022 0.008 0.0094 0.0156 0.004 0.0038 0.0036 0.0042 0.0051 0.0025 0.0057 0.0024 0.0074 O. Chartreux 2787 13 0.0032 0.86 0 0504 0.0167 0.0028 0.002 0.0032 0.0039 0.00280.0032 0.0076 0.003 0.0026 0.0057 0.005 0.0044 0.0047 0.0044 0.0047 0.006 O. Chartreux 2805 15 0.0671 0.7312 0 0491 0.0042 0.0054 0.001 0.001 0.0027 0.00260.0039 0.0091 0.0192 0.0018 0.0017 0.0025 0.0012 0.0026 0.0025 0.0012 0.0866 O. Chartreux 2813 26 0.1312 0.5713 0 1405 0.0048 0.0038 0.006 0.0033 0.0076 0.0021 0.0173 0.0049 0.0074 0.002 0.0021 0.0094 0.0039 0.0041 0.0043 0.0023 0.0684 O.

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2

Chartreux 2979 21 0.2175 0.663 0, 0037 0.0045 0.0032 0.0046 0 ,0067 0.0022 0021 0.0025 0.0054 0.002 0.0048 0.0088 0.0059 0.008 0.003 0.002 0.0056 0.0433 0.

Chartreux 4059 13 0.006 0.956 0, 002 0.0026 0.0026 0.0013 0 ,001 0.0013 001 0.0022 0.0027 0.0048 0.0013 0.0014 0.0017 0.0012 0.0019 0.0023 0.0021 0.0026 0.

Chartreux 4063 28 0.0128 0.9451 0, 0033 0.0035 0.0014 0.0014 0 ,0022 0.0012 002 0.0023 0.0028 0.0016 0.0021 0.002 0.0021 0.0023 0.004 0.0018 0.0021 0.0027 0.

Chartreux 5606 13 0.0031 0.9692 0. 0023 0.0013 0.0016 0.0017 0 ,0013 0.0017 0011 0.0013 0.0023 0.001 0.001 0.0011 0.0015 0.0011 0.0023 0.001 0.0013 0.0018 0.

Chartreux 5609 10 0.0013 0.9309 0. 0016 0.0028 0.0087 0.0021 0 ,0033 0.0025 00740.0024 0.0029 0.002 0.0036 0.0055 0.0013 0.0016 0.0041 0.0041 0.0024 0.0054 O.

Chartreux 5611 18 0.0236 0.9192 0, 0023 0.0114 0.0021 0.0024 0 ,0017 0.0091 00140.0023 0.0032 0.0031 0.002 0.0022 0.0018 0.0011 0.0034 0.0015 0.0013 0.0037 O.

American SH 143 5 0.0105 0.2447 0, 5894 0.008 0.0023 0.01 0 ,002 0.0088 00170.003 0.0082 0.079 0.005 0.0024 0.0027 0.0047 0.009 0.0013 0.001 0.0043 O.

American SH 144 5 0.0025 0.2937 0, 6791 0.0031 0.001 0.001 0 ,001 0.001 00230.0023 0.0017 0.001 0.001 0.001 0.0012 0.0011 0.001 0.001 0.001 0.0011 O.

American SH 145 5 0.0092 0.2629 0, 6655 0.0041 0.006 0.002 0 ,0013 0.0055 00230.0055 0.0044 0.0023 0.004 0.0024 0.0046 0.0014 0.0023 0.0023 0.001 0.0087 O.

American SH 146 5 0.0259 0.1959 0, 5457 0.0053 0.0016 0.0105 0 ,0231 0.0304 00320.0455 0.0179 0.001 0.0021 0.0027 0.0011 0.0021 0.0401 0.002 0.0018 0.0383 O.

American SH 3652 15 0.0039 0.2904 0. 6406 0.0052 0.0019 0.0097 0 ,0024 0.0057 00280.004 0.0094 0.001 0.0039 0.003 0.001 0.0012 0.0048 0.0018 0.0012 0.0036 O.

American SH 3653 5 0.0095 0.2145 0, 5634 0.0112 0.0516 0.003 0 ,0049 0.0235 01070.0135 0.0269 0.0051 0.022 0.0036 0.0033 0.0037 0.0035 0.0143 0.001 0.0056 O.

American SH 4363 10 0.0132 0.277 0, 673 0.0011 0.0013 0.0026 0 ,0025 0.0018 00450.0031 0.0023 0.002 0.0013 0.0017 0.001 0.0017 0.002 0.001 0.001 0.0034 O.

American SH 4370 7 0.0111 0.2027 0, 5732 0.0052 0.0131 0.0154 0 ,0298 0.0066 00250.0449 0.0106 0.001 0.0163 0.016 0.0043 0.0141 0.0023 0.0035 0.0124 0.0071 O.

American SH 4373 18 0.006 0.2785 0, 6691 0.002 0.0021 0.001 0 ,0015 0.0014 00130.0036 0.002 0.0141 0.0024 0.0021 0.0022 0.0019 0.0023 0.001 0.001 0.0019 O.

American SH 6404 13 0.0045 0.1919 0, 6085 0.0108 0.048 0.0023 0 ,002 0.0033 00250.0046 0.0041 0.003 0.0554 0.0014 0.0041 0.007 0.0376 0.0016 0.0022 0.0036 O.

American SH 6406 7 0.1731 0.1501 0. 5087 0.0093 0.0039 0.0624 0 ,0041 0.0121 002 0.0062 0.0153 0.0031 0.0074 0.0021 0.0061 0.0043 0.002 0.0017 0.0064 0.0184 O.

American SH 6410 7 0.2401 0.2059 0. 4241 0.0035 0.0037 0.0022 0 ,0019 0.0039 00140.0057 0.0038 0.0027 0.0033 0.0014 0.0026 0.0023 0.0036 0.002 0.0041 0.0803 O.

American SH 6421 13 0.0173 0.2621 0, 4855 0.0056 0.008 0.0156 0 ,004 0.045 00570.0076 0.0104 0.0434 0.0146 0.0079 0.0043 0.0161 0.002 0.0054 0.0042 0.0258 O.

American SH 2259 5 0.0059 0.2719 0, 6448 0.0012 0.0072 0.002 0 ,0037 0.0037 01780.0034 0.004 0.002 0.0032 0.0021 0.0066 0.0021 0.0035 0.0021 0.0054 0.0028 O.

Sphynx 277 10 0.0077 0.003 0, 0031 0.8871 0.0068 0.0256 0 ,0058 0.0026 00440.002 0.003 0.002 0.0021 0.0035 0.0018 0.0034 0.0021 0.0064 0.0081 0.0045 O.

Sphynx 278 7 0.0054 0.0143 0, 0078 0.9134 0.002 0.001 0 ,003 0.0027 002 0.0054 0.0103 0.0028 0.002 0.0029 0.0063 0.004 0.0064 0.002 0.0027 0.0023 O.

Sphynx 279 7 0.0035 0.0038 0, 0026 0.8939 0.003 0.012 0 ,0057 0.0314 003 0.0022 0.0045 0.0018 0.002 0.0023 0.001 0.0026 0.002 0.0061 0.0018 0.0126 O.

Sphynx 280 7 0.0046 0.0045 0. 0092 0.9188 0.0024 0.002 0 ,0074 0.003 0021 0.0029 0.0078 0.0035 0.0037 0.0017 0.002 0.003 0.01 0.0013 0.002 0.0026 O.

Sphynx 281 7 0.0053 0.003 0, 0075 0.8739 0.0047 0.0327 0 ,0077 0.0021 00320.0066 0.0031 0.002 0.0091 0.0034 0.0016 0.0041 0.002 0.0112 0.0023 0.0058 O.

Sphynx 282 7 0.0035 0.002 0, 0022 0.9577 0.001 0.002 0 ,002 0.002 00220.002 0.0021 0.0066 0.002 0.001 0.001 0.001 0.001 0.0021 0.002 0.0023 O.

Sphynx 283 10 0.0587 0.0023 0, 0041 0.8182 0.002 0.0043 0 ,003 0.0705 0021 0.0047 0.0028 0.0038 0.0029 0.0012 0.0013 0.001 0.003 0.001 0.0054 0.0065 O.

Sphynx 284 7 0.0037 0.0155 0, 0104 0.8288 0.0012 0.0083 0 ,002 0.0068 04460.0094 0.0372 0.0028 0.0026 0.0025 0.0021 0.0052 0.005 0.0027 0.002 0.0046 O.

Sphynx 285 7 0.0025 0.0021 0, 0032 0.9417 0.0033 0.002 0 ,0033 0.002 003 0.0026 0.0026 0.0029 0.003 0.0024 0.0022 0.0052 0.0031 0.0026 0.0037 0.0039 O.

Sphynx 286 7 0.003 0.0029 0. 002 0.9484 0.0012 0.002 0 ,0019 0.0041 003 0.002 0.0029 0.0056 0.0015 0.0016 0.001 0.001 0.0034 0.0039 0.0014 0.005 O.

Sphynx 287 10 0.0259 0.0163 0. 105 0.7 0.0097 0.0042 0 ,0056 0.0048 00320.008 0.0115 0.0318 0.0102 0.0038 0.0026 0.0029 0.0042 0.0082 0.017 0.0108 O.

Sphynx 288 7 0.0022 0.0091 0, 0029 0.9489 0.001 0.0134 0 ,002 0.0012 00290.002 0.002 0.001 0.003 0.0011 0.001 0.0011 0.0011 0.001 0.001 0.0011 O.

Sphynx 289 7 0.0037 0.016 0, 0053 0.8831 0.001 0.0029 0 ,0018 0.0058 03160.0034 0.0063 0.0099 0.0059 0.0044 0.0034 0.0027 0.0027 0.0013 0.001 0.0062 O.

Sphynx 290 10 0.0031 0.0028 0, 0035 0.9513 0.0028 0.0029 0 ,001 0.003 00140.002 0.0034 0.0013 0.004 0.0019 0.0027 0.0054 0.001 0.0025 0.0013 0.0017 O.

Sphynx 291 5 0.0053 0.0147 0, 0173 0.874 0.0046 0.0072 0 ,0021 0.0106 00230.008 0.0075 0.0039 0.0118 0.0045 0.0041 0.0027 0.005 0.0033 0.0015 0.0077 O.

Sphynx 292 5 0.0054 0.0035 0, 0023 0.9266 0.0011 0.0055 0 ,0039 0.0044 00380.002 0.0028 0.002 0.004 0.0027 0.0013 0.0021 0.001 0.0091 0.002 0.0043 O.

Sphynx 293 7 0.0018 0.0061 0. 0076 0.7433 0.0061 0.0041 0 ,031 0.004 022 0.0235 0.032 0.0083 0.0036 0.0166 0.0232 0.0185 0.0062 0.0089 0.0057 0.0084 O.

Japanese BT 1949 7 0.0029 0.0019 0, 0026 0.003 0.9335 0.0031 0 ,0013 0.0058 00130.0087 0.0043 0.0017 0.0105 0.0044 0.003 0.0025 0.001 0.001 0.0022 0.0038 O.

Japanese BT 1966 10 0.0131 0.0107 0, 0875 0.0202 0.7777 0.0023 0 ,002 0.0071 00590.0106 0.0152 0.0183 0.0034 0.0023 0.0021 0.002 0.0037 0.0015 0.0013 0.0055 O.

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2

Japanese BT 2661 10 0.001 0.001 0, 001 0.001 0.9798 0.001 ,0011 0.001 001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0. Japanese BT 2663 5 0.001 0.0012 0, 001 0.001 0.9745 0.001 ,002 0.001 00190.001 0.001 0.002 0.0017 0.0013 0.001 0.0011 0.002 0.001 0.001 0.001 0. Japanese BT 2666 7 0.0029 0.0063 0, 0089 0.0055 0.8864 0.011 ,0132 0.002 0, 0021 0.0092 0.003 0.0023 0.0029 0.0016 0.0029 0.0028 0.0075 0.0047 0.0147 0.0056 0. Japanese BT 2668 10 0.0016 0.0042 0. 0156 0.0024 0.8897 0.003 ,0035 0.0029 0, 00460.0047 0.0211 0.003 0.0027 0.0031 0.002 0.0076 0.002 0.0066 0.0032 0.0034 0. Japanese BT 2973 13 0.0019 0.0023 0. 0044 0.002 0.9499 0.0024 0 ,0018 0.0013 0, 00330.002 0.004 0.002 0.005 0.003 0.0018 0.0025 0.002 0.001 0.0017 0.0024 O. Japanese BT 3324 10 0.0031 0.002 0, 0017 0.001 0.9731 0.001 0 ,0019 0.001 001 0.001 0.001 0.001 0.001 0.001 0.0019 0.001 0.001 0.001 0.001 0.0016 O. Japanese BT 3355 5 0.002 0.0031 0, 0017 0.001 0.9594 0.0011 0 ,003 0.0021 00370.002 0.002 0.001 0.0011 0.0021 0.001 0.0024 0.001 0.0023 0.0018 0.0042 O. Japanese BT 3356 15 0.0047 0.0174 0, 003 0.006 0.8809 0.0049 0 ,009 0.011 003 0.0057 0.0062 0.002 0.0283 0.0019 0.002 0.0055 0.002 0.0012 0.0013 0.003 0. Japanese BT 3523 7 0.0016 0.0048 0, 0049 0.002 0.9443 0.002 0 ,003 0.002 00550.002 0.0038 0.004 0.0029 0.0027 0.0026 0.0028 0.002 0.002 0.001 0.0021 0. Japanese BT 3621 7 0.0044 0.002 0, 0023 0.001 0.9505 0.002 0 ,0039 0.0012 0, 003 0.001 0.002 0.001 0.002 0.0025 0.0038 0.0094 0.003 0.001 0.0011 0.0019 O. Japanese BT 3622 10 0.0076 0.0035 0. 0043 0.004 0.802 0.002 0 ,005 0.0432 0, 01250.0094 0.008 0.002 0.0045 0.0052 0.002 0.0022 0.0258 0.0186 0.0026 0.0319 O. Japanese BT 3673 5 0.0019 0.003 0, 0037 0.0017 0.6722 0.0041 0 ,0031 0.0102 0, 2051 0.0117 0.0084 0.002 0.0148 0.0058 0.0023 0.029 0.0054 0.0068 0.0021 0.0025 O. Japanese BT 3691 5 0.0039 0.0031 0, 003 0.002 0.9432 0.001 0 ,0049 0.001 00480.002 0.0015 0.001 0.001 0.0011 0.0018 0.0022 0.0039 0.0026 0.007 0.0031 O. Japanese BT 3693 5 0.0018 0.0013 0, 0024 0.001 0.9627 0.0015 0 ,0027 0.001 003 0.003 0.002 0.002 0.001 0.0019 0.0028 0.0012 0.0015 0.0013 0.002 0.0026 O. Japanese BT 4027 13 0.0051 0.0289 0, 0052 0.0014 0.8401 0.0024 0 ,0142 0.004 00430.0082 0.0089 0.001 0.0113 0.0017 0.0015 0.0018 0.0481 0.0013 0.004 0.0035 O. Japanese BT 4030 13 0.0031 0.0038 0, 0025 0.0157 0.876 0.0089 0 ,0058 0.002 00360.003 0.005 0.0063 0.0088 0.0039 0.0038 0.0265 0.003 0.006 0.006 0.0036 O. Japanese BT 4043 10 0.0066 0.004 0. 0074 0.0104 0.7678 0.0309 0 ,086 0.0065 0, 00740.0043 0.0052 0.0025 0.004 0.002 0.0021 0.0037 0.01 0.0051 0.0026 0.0042 O. Cornish Rex 2497 7 0.0019 0.0042 0. 0039 0.002 0.001 0.933 0 ,002 0.002 0, 0011 0.002 0.0064 0.0023 0.002 0.0019 0.0019 0.002 0.001 0.0051 0.0122 0.002 O. Cornish Rex 2863 0 0.002 0.001 0, 0017 0.003 0.0036 0.954 0 ,0021 0.002 00720.001 0.001 0.001 0.001 0.0019 0.001 0.0012 0.001 0.0045 0.001 0.0042 O. Cornish Rex 2872 13 0.001 0.0018 0, 002 0.0013 0.001 0.9531 0 ,002 0.001 00250.002 0.002 0.001 0.002 0.0014 0.0019 0.002 0.001 0.0057 0.0056 0.0019 O. Cornish Rex 3283 2 0.0026 0.0026 0, 005 0.004 0.0123 0.8988 0 ,002 0.003 00430.002 0.0022 0.002 0.0021 0.0029 0.0024 0.0034 0.0035 0.0307 0.0047 0.0058 O. Cornish Rex 3306 15 0.0018 0.0034 0, 0023 0.0024 0.002 0.8649 0 ,0177 0.002 00420.011 0.0053 0.002 0.0057 0.0037 0.0084 0.0069 0.0047 0.0077 0.0116 0.0093 O. Cornish Rex 3931 10 0.001 0.0014 0, 0016 0.002 0.0032 0.9654 0 ,0013 0.002 ,0011 0.0031 0.0026 0.001 0.002 0.0012 0.002 0.0012 0.002 0.0018 0.001 0.0014 O. Cornish Rex 3955 7 0.0151 0.0186 0. 0076 0.0022 0.001 0.889 0 ,0191 0.0021 ,0019 0.0052 0.0099 0.0023 0.0059 0.0033 0.0021 0.0022 0.0016 0.0026 0.0025 0.0041 O. Cornish Rex 3992 23 0.0181 0.0022 0, 0042 0.0013 0.002 0.9222 0 ,002 0.0035 0, ,0067 0.0034 0.0027 0.001 0.0023 0.002 0.001 0.0032 0.002 0.004 0.0033 0.0107 O. Cornish Rex 5790 2 0.018 0.0106 0, 0146 0.0067 0.1444 0.7143 0 ,0035 0.0063 0, ,0108 0.0043 0.0234 0.0069 0.0054 0.0041 0.0033 0.002 0.0019 0.0024 0.0047 0.0084 O. Cornish Rex 7031 0 0.0023 0.0142 0, 0032 0.0111 0.0061 0.8151 0 ,003 0.0027 0, ,0023 0.0052 0.004 0.0167 0.0046 0.0131 0.0199 0.0166 0.0019 0.0341 0.0036 0.0173 O. Cornish Rex 7036 0 0.0036 0.0023 0, 0029 0.002 0.002 0.8666 0 ,0164 0.001 0, ,0031 0.0033 0.003 0.0011 0.002 0.002 0.0027 0.0034 0.0241 0.0043 0.0048 0.0034 O. Cornish Rex 8528 18 0.001 0.001 0, 002 0.002 0.0022 0.9638 0 ,002 0.0026 0, ,0013 0.002 0.0022 0.002 0.001 0.001 0.001 0.0016 0.0023 0.0026 0.0021 0.0028 O. Cornish Rex 8529 2 0.0018 0.002 0. 0018 0.0019 0.001 0.9633 0 ,002 0.002 0, ,001 0.002 0.0026 0.0019 0.002 0.001 0.001 0.0011 0.001 0.0017 0.0013 0.0014 O. Cornish Rex 8530 0 0.0012 0.0026 0. 0013 0.0011 0.0017 0.9591 0 ,0021 0.002 0.0064 0.001 0.003 0.001 0.0028 0.0016 0.001 0.0012 0.001 0.0031 0.002 0.0021 O. Cornish Rex 8543 5 0.001 0.0018 0, 0071 0.002 0.001 0.9612 0 ,001 0.002 0.0022 0.002 0.0026 0.002 0.0022 0.0021 0.0015 0.001 0.002 0.001 0.001 0.0016 O. Ragdoll 7429 5 0.002 0.0036 0, 0045 0.002 0.0022 0.0042 0 ,9378 0.001 0.001 0.0063 0.0026 0.002 0.0021 0.0042 0.002 0.0052 0.0082 0.0023 0.002 0.0033 O. Ragdoll 8362 2 0.0118 0.0024 0, 0051 0.0012 0.003 0.003 0 9272 0.002 0.0081 0.003 0.0028 0.002 0.0025 0.0017 0.0019 0.0028 0.0106 0.001 0.003 0.0029 O. Ragdoll 8366 0 0.0018 0.002 0, 0026 0.0015 0.004 0.002 0 ,9558 0.002 0.0022 0.0047 0.0025 0.001 0.0022 0.0014 0.002 0.0015 0.001 0.0023 0.002 0.0021 O. Ragdoll 8367 5 0.0208 0.0035 0, 004 0.0029 0.0018 0.0017 0 ,921 0.0069 0, 0048 0.0031 0.0027 0.0049 0.0045 0.0011 0.0021 0.0025 0.002 0.002 0.0025 0.0028 O. Ragdoll 8368 7 0.0044 0.001 0. 0027 0.002 0.0027 0.0013 0 ,9534 0.002 0, 002 0.002 0.0021 0.002 0.0019 0.0012 0.0014 0.002 0.003 0.004 0.0024 0.0025 O. Ragdoll 8372 13 0.425 0.0027 0, 0023 0.0037 0.0052 0.0051 0 ,5111 0.0031 0.0031 0.002 0.0024 0.003 0.003 0.0021 0.0016 0.0013 0.003 0.003 0.0087 0.0049 O. Ragdoll 8377 0 0.001 0.001 0, 001 0.0012 0.002 0.002 0 ,9636 0.001 0.0011 0.0011 0.0013 0.002 0.001 0.002 0.002 0.0029 0.002 0.004 0.0042 0.0018 O.

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2

Ragdoll 9292 0 0.0151 0.0038 0, 0055 0.0021 0.001 0.0125 0 ,8937 0.0086 0, 0041 0.0035 0.0029 0.002 0.0086 0.0023 0.001 0.0024 0.0052 0.0033 0.0027 0.0057 0. Ragdoll 9295 5 0.004 0.002 0, 0024 0.0066 0.007 0.004 0 ,8912 0.001 0, 0121 0.002 0.0106 0.002 0.0029 0.0032 0.0032 0.0077 0.0052 0.0191 0.0044 0.0052 0. Ragdoll 9299 0 0.0091 0.0038 0, 0301 0.0093 0.0069 0.0026 0 ,666 0.0273 0, 0051 0.0123 0.0978 0.0147 0.0223 0.0045 0.0028 0.0134 0.0117 0.0056 0.0232 0.0151 0. Ragdoll 9300 10 0.0019 0.0022 0.0059 0.003 0.002 0.0047 0 ,9335 0.002 00540.0035 0.0051 0.001 0.0027 0.0097 0.0014 0.0011 0.002 0.0027 0.003 0.0049 0. Ragdoll 9301 2 0.0056 0.0033 0.0153 0.0024 0.0039 0.0034 0 ,9086 0.003 00590.0061 0.0064 0.001 0.0031 0.0034 0.0015 0.0033 0.0055 0.004 0.0073 0.0031 O. Ragdoll 9302 2 0.0049 0.0027 0, 0041 0.003 0.0021 0.0052 0 ,9162 0.0081 00720.0053 0.0083 0.0082 0.0051 0.0021 0.0034 0.0022 0.002 0.002 0.0032 0.0029 O. Ragdoll 9304 10 0.0037 0.0046 0, 0049 0.0078 0.003 0.0161 0 ,9128 0.0031 0011 0.0021 0.0018 0.0042 0.003 0.0019 0.0019 0.0034 0.0097 0.0051 0.002 0.0058 O. Ragdoll 9305 0 0.003 0.003 0, 0023 0.0065 0.001 0.001 0 ,9288 0.003 002 0.002 0.0028 0.001 0.001 0.0015 0.0056 0.0016 0.001 0.0029 0.0249 0.0025 O. Maine Coon 2950 5 0.002 0.0026 0, 0073 0.0039 0.002 0.0028 0 ,002 0.942 00490.0047 0.0045 0.003 0.005 0.0021 0.0026 0.0013 0.0013 0.001 0.0015 0.0019 O. Maine Coon 2959 13 0.0039 0.0083 0, 0084 0.0024 0.0019 0.0079 0 ,0134 0.8793 0, 00850.0044 0.0056 0.0087 0.0075 0.0062 0.0033 0.0028 0.0059 0.0045 0.0044 0.0052 O. Maine Coon 3304 18 0.0123 0.0104 0.0435 0.0023 0.0012 0.0072 0 ,0818 0.7136 0, 002 0.003 0.0795 0.003 0.0031 0.0028 0.0028 0.003 0.0077 0.0013 0.001 0.0175 O. Maine Coon 3311 5 0.0016 0.002 0, 0033 0.002 0.001 0.0029 0 ,002 0.9501 0, 002 0.002 0.0055 0.0022 0.0022 0.0034 0.001 0.0014 0.001 0.0034 0.0013 0.0065 O. Maine Coon 3495 13 0.0047 0.0044 0, 0098 0.003 0.0113 0.0025 0 ,0021 0.8838 0, 0011 0.0174 0.0195 0.0028 0.0133 0.0019 0.0067 0.002 0.0043 0.0027 0.001 0.0029 O. Maine Coon 3925 13 0.0072 0.0089 0, 0029 0.002 0.0042 0.0031 0 ,001 0.9303 0, 00350.002 0.0043 0.0031 0.0026 0.0099 0.0019 0.0028 0.001 0.0017 0.0019 0.004 O. Maine Coon 3941 5 0.0045 0.0025 0, 0062 0.0028 0.0056 0.0081 0 ,0054 0.9368 0, 00180.0039 0.0037 0.002 0.003 0.0018 0.0022 0.0029 0.0012 0.001 0.0013 0.0024 O. Maine Coon 9198 10 0.0043 0.0159 0, 0367 0.0081 0.0045 0.01 0 ,0249 0.3824 0, 0031 0.0077 0.0072 0.0114 0.1519 0.0039 0.0108 0.0067 0.0185 0.092 0.003 0.1198 O. Maine Coon 9775 7 0.0048 0.0014 0.0023 0.002 0.002 0.0036 0 ,0011 0.9566 0, 00150.002 0.0026 0.0045 0.0029 0.0014 0.0012 0.0021 0.002 0.0017 0.0014 0.0019 O. Maine Coon 10662 13 0.002 0.0027 0.0026 0.001 0.0019 0.0031 0 ,0019 0.9558 0, 0031 0.002 0.0025 0.002 0.002 0.0026 0.001 0.001 0.0045 0.0022 0.0023 0.0025 O. Maine Coon 11544 13 0.0058 0.0036 0, 0023 0.0066 0.0053 0.0094 0 ,002 0.916 0, 00580.0106 0.0081 0.0052 0.003 0.0028 0.0015 0.0021 0.003 0.0012 0.002 0.0027 O. Maine Coon 217 7 0.0066 0.0018 0, 0021 0.002 0.002 0.002 0 ,002 0.9577 0, 00560.002 0.0023 0.001 0.0019 0.001 0.0011 0.0012 0.002 0.001 0.001 0.002 O. Maine Coon 218 5 0.1421 0.0922 0, 0719 0.0067 0.0047 0.0057 0 ,217 0.0225 0, 02170.0049 0.098 0.0076 0.0157 0.0047 0.0046 0.0046 0.1483 0.0197 0.0119 0.0844 O. Maine Coon 219 7 0.006 0.001 0, 0012 0.002 0.001 0.001 0 ,002 0.9616 0, 00240.002 0.0021 0.002 0.002 0.0017 0.0029 0.0011 0.002 0.001 0.0013 0.0027 O. Maine Coon 220 7 0.0054 0.0141 0, 0185 0.0091 0.5015 0.2302 0 ,002 0.1042 0, 00580.0231 0.0309 0.004 0.004 0.0032 0.002 0.0036 0.0138 0.0065 0.0032 0.0089 O. Maine Coon 221 5 0.0073 0.0104 0.0101 0.0065 0.002 0.0168 0 ,0038 0.879 0, 00180.0092 0.012 0.0069 0.0088 0.002 0.0058 0.0033 0.0048 0.0014 0.0013 0.0058 O. Maine Coon 222 5 0.0057 0.3721 0, 0464 0.0217 0.0032 0.0192 0 ,0066 0.3519 0, 00430.0456 0.0454 0.0093 0.0131 0.0077 0.0046 0.0091 0.001 0.0043 0.002 0.0242 O. Maine Coon 223 5 0.0066 0.0196 0, 0285 0.0141 0.003 0.0256 0 ,0156 0.7372 0, 05480.0153 0.0317 0.007 0.0055 0.005 0.0023 0.0084 0.0053 0.0011 0.0012 0.0097 O. Maine Coon 224 5 0.0058 0.0033 0, 0146 0.0027 0.0109 0.0052 0 ,146 0.6608 0, 01170.0106 0.018 0.0119 0.0199 0.0029 0.0012 0.0043 0.0028 0.019 0.0091 0.0206 O. Abyssinian 110 5 0.0022 0.0066 0, 0073 0.0065 0.0053 0.0065 0 ,0021 0.0057 0, 77170.0041 0.0038 0.002 0.0031 0.0022 0.0021 0.0848 0.0683 0.0017 0.0011 0.0027 O. Abyssinian 111 7 0.0035 0.0067 0, 0073 0.0099 0.007 0.0362 0 ,002 0.0115 0, 749 0.008 0.0267 0.0108 0.0087 0.0037 0.0012 0.0881 0.001 0.0052 0.0034 0.0059 O. Abyssinian 112 7 0.0015 0.0011 0.0015 0.0011 0.0011 0.0015 0 ,0011 0.0013 0, 87290.0021 0.0046 0.002 0.0011 0.0015 0.0011 0.0952 0.004 0.0012 0.0011 0.0015 O. Abyssinian 113 7 0.003 0.0095 0.0062 0.007 0.002 0.0034 0 ,0014 0.0037 0, 7841 0.0086 0.0208 0.0026 0.0655 0.0037 0.0015 0.0681 0.001 0.0014 0.0011 0.0042 O. Abyssinian 114 5 0.0015 0.0021 0, 0015 0.0024 0.001 0.0023 0 ,0021 0.0017 0, 87630.002 0.0019 0.0011 0.0011 0.002 0.0015 0.092 0.0011 0.0014 0.0011 0.0026 O. Abyssinian 115 5 0.0018 0.0018 0, 0039 0.001 0.0018 0.0041 0 ,0013 0.0023 0, 8641 0.0021 0.0021 0.001 0.0034 0.0016 0.0023 0.0957 0.0013 0.001 0.001 0.0025 O. Abyssinian 116 5 0.0011 0.0011 0, 0012 0.0012 0.001 0.001 0 ,0036 0.001 87860.001 0.0013 0.0011 0.0018 0.0014 0.001 0.0953 0.001 0.0011 0.0014 0.001 O. Abyssinian 117 5 0.001 0.001 0, 0013 0.0154 0.001 0.0011 0 ,0019 0.0011 87190.001 0.0019 0.0032 0.001 0.001 0.001 0.0878 0.0011 0.0018 0.0017 0.0013 O. Abyssinian 118 7 0.0011 0.0011 0, 0011 0.0022 0.0011 0.001 0 ,0021 0.0016 0, 88170.0014 0.0013 0.001 0.0012 0.0014 0.001 0.0893 0.001 0.0025 0.0023 0.0018 O. Abyssinian 119 7 0.0021 0.0051 0.0017 0.0069 0.0016 0.0019 0 ,013 0.0033 0, 8471 0.0021 0.0033 0.0011 0.0053 0.0018 0.001 0.0912 0.001 0.0047 0.0016 0.0024 O. Abyssinian 120 5 0.0017 0.0025 0, 0024 0.001 0.0013 0.002 0 ,0017 0.0011 0, ,87230.001 0.0012 0.002 0.002 0.002 0.001 0.0978 0.0011 0.0011 0.0015 0.0015 O. Abyssinian 121 5 0.002 0.0022 0, 0032 0.0022 0.0021 0.0011 0.0011 0.0022 0.84660.002 0.004 0.0021 0.0031 0.0021 0.0032 0.0961 0.0022 0.0021 0.0066 0.0033 O.

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2

Abyssinian 122 7 0.0021 0.001 001 0.001 0.001 0.001 0 ,002 0.001 86360.001 0.002 0.0027 0.001 0.001 0.0014 0.0944 0.0017 0.0039 0.001 0.0014 0. Abyssinian 123 5 0.0011 0.001 0013 0.0013 0.0016 0.001 0 ,0024 0.0022 86920.0032 0.0017 0.0021 0.003 0.0013 0.0067 0.0929 0.0012 0.0023 0.0017 0.0016 0. Abyssinian 6934 28 0.0051 0.0051 0039 0.0011 0.0013 0.0024 0 ,0048 0.0019 749 0.0023 0.002 0.003 0.0021 0.0023 0.0034 0.0831 0.0949 0.0046 0.0011 0.0033 0.

Siberi lan 3273 21 0.0042 0.0092 0 012 0.0037 0.0059 0.002 0 ,002 0.0059 00940.8101 0.0 3 0.0041 0.0246 0.0635 0.0044 0.0047 0.002 0.002 0.0045 0.0132 0. Siberi ian 4593 21 0.003 0.0049 0049 0.0028 0.0062 0.0025 0 ,0022 0.004 00260.9239 0.0109 0.0025 0.002 0.002 0.0109 0.0025 0.004 0.002 0.0025 0.002 O. Siberi ian 4829 7 0.002 0.003 O 0037 0.0053 0.003 0.002 ,003 0.0154 00330.9021 0.0146 0.002 0.0049 0.0023 0.0055 0.0047 0.0111 0.0025 0.0028 0.0044 O. Siberi ian 4928 5 0.3394 0.007 0064 0.0022 0.001 0.001 ,0018 0.0062 001 0.5831 0.0044 0.0016 0.0032 0.0015 0.0019 0.0018 0.0022 0.0022 0.002 0.028 O. Siberi ian 4930 7 0.0032 0.0153 0025 0.0018 0.0017 0.001 ,0031 0.002 0011 0.8832 0.0028 0.0029 0.0022 0.0145 0.0107 0.002 0.0414 0.002 0.0022 0.0034 O. Siberi ian 5101 13 0.0051 0.0033 0024 0.0011 0.001 0.001 ,0029 0.0034 002 0.9575 0.0021 0.0018 0.0019 0.0019 0.0018 0.0025 0.0026 0.001 0.001 0.0027 O. Siberi ian 5105 15 0.0036 0.0023 0054 0.002 0.0055 0.0065 0 ,0133 0.0023 00240.8245 0.003 0.0289 0.0061 0.0679 0.0054 0.0029 0.0015 0.0017 0.0022 0.011 O. Siberi ian 5107 5 0.0091 0.0067 0. 0403 0.0062 0.0122 0.0739 0 .003 0.0039 00370.7549 0.0113 0.0081 0.0072 0.0144 0.0019 0.0028 0.002 0.003 0.0176 0.0102 O. Siberi ian 5110 10 0.0022 0.0027 O 0054 0.0117 0.0359 0.002 0 ,002 0.3551 002 0.5514 0.0041 0.002 0.0064 0.0021 0.002 0.0029 0.0012 0.003 0.0017 0.0027 O. Siberi ian 5118 7 0.0025 0.003 0082 0.0064 0.002 0.002 0 ,0073 0.0021 0051 0.8052 0.0533 0.0069 0.0053 0.0108 0.0089 0.0018 0.0283 0.0289 0.001 0.0061 O. Siberi ian 5120 5 0.002 0.0029 0047 0.003 0.0039 0.0098 0 ,0037 0.0054 0021 0.8584 0.0526 0.0091 0.0038 0.0031 0.0034 0.0046 0.0093 0.0086 0.002 0.0038 O. Siberi ian 5632 5 0.0368 0.0162 0087 0.004 0.0059 0.0046 0 ,0058 0.0779 00630.6382 0.0097 0.0329 0.0047 0.0024 0.002 0.0014 0.0044 0.013 0.0262 0.0077 O. Siberi ian 6474 5 0.0032 0.0037 0026 0.0028 0.0894 0.0059 0 ,0162 0.0028 002 0.8405 0.0036 0.001 0.0049 0.0036 0.0052 0.0014 0.0018 0.0013 0.0026 0.0032 O. Siberi ian 11562 15 0.0049 0.0091 0 0381 0.0061 0.002 0.0022 0 ,0133 0.0083 01750.7766 0.0398 0.003 0.0139 0.0059 0.0045 0.0045 0.0042 0.0053 0.0241 0.0111 O. Siberi ian 11582 10 0.046 0.0051 0074 0.0069 0.0093 0.0043 0 ,0118 0.0204 0271 0.6859 0.0968 0.0251 0.0024 0.007 0.0081 0.0135 0.002 0.0037 0.001 0.0143 O. Siberi ian 11559 13 0.0042 0.0046 O 0048 0.002 0.0037 0.0099 0 ,0052 0.004 01030.9102 0.0105 0.002 0.003 0.0024 0.0011 0.0022 0.0072 0.0053 0.0017 0.0034 O. Siberi ian 11560 13 0.0116 0.0096 0087 0.0067 0.0032 0.0029 0 ,0214 0.02 00170.7299 0.0303 0.0089 0.0802 0.0048 0.0318 0.0051 0.002 0.0012 0.0044 0.0119 O.

Norwegian FC 2942 15 0.0076 0.0048 0046 0.0052 0.0067 0.002 0.002 0.0062 00180.0224 0.8878 0.0251 0.0036 0.0019 0.0011 0.0029 0.002 0.0044 0.0027 0.003 O. Norwegian FC 3610 47 0.0083 0.1843 0307 0.0031 0.0046 0.0042 0 ,0038 0.0375 0041 0.0255 0.4997 0.0725 0.039 0.0189 0.0113 0.0038 0.0215 0.002 0.0047 0.0153 O. Norwegian FC 3611 13 0.2644 0.01 0142 0.0029 0.0031 0.0204 0 ,0076 0.3742 01460.0658 0.1256 0.0018 0.0073 0.0107 0.002 0.0014 0.0062 0.0022 0.002 0.0615 O. Norwegian FC 3612 7 0.003 0.004 0. 0019 0.0025 0.0033 0.0052 0 ,0025 0.004 00190.021 0.9191 0.0078 0.0027 0.0029 0.0032 0.0025 0.0032 0.002 0.0017 0.0027 O. Norwegian FC 3617 26 0.0042 0.0107 O 0124 0.0084 0.0027 0.003 0 ,0032 0.019 0021 0.8151 0.0663 0.0106 0.009 0.0035 0.006 0.0063 0.0031 0.004 0.002 0.0059 O. Norwegian FC 3661 31 0.0053 0.0473 O 015 0.002 0.0033 0.0035 0 ,0052 0.0054 01320.0095 0.8189 0.0045 0.0037 0.0078 0.0095 0.0036 0.0061 0.0157 0.002 0.0146 O. Norwegian FC 4815 7 0.0033 0.013 0766 0.0174 0.0043 0.0119 0 ,0151 0.026 00920.027 0.6794 0.0062 0.007 0.0066 0.0021 0.004 0.004 0.0047 0.0034 0.0552 O. Norwegian FC 4816 2 0.0021 0.0027 0257 0.0011 0.002 0.0021 0 ,0059 0.0182 00830.0068 0.8951 0.0039 0.0023 0.0016 0.0031 0.0023 0.001 0.0016 0.001 0.0119 O. Norwegian FC 6004 18 0.0049 0.0053 0183 0.0089 0.0046 0.0152 0 ,0065 0.0154 0021 0.0161 0.7258 0.0282 0.0522 0.0683 0.0038 0.0023 0.001 0.0027 0.0017 0.0148 O. Norwegian FC 6932 44 0.007 0.0029 0 0041 0.0092 0.0036 0.0061 0 ,0079 0.0048 00270.0141 0.6163 0.0163 0.0076 0.0032 0.0154 0.0039 0.2592 0.0062 0.003 0.0039 O. Norwegian FC 9321 2 0.0029 0.0426 0147 0.0145 0.0087 0.0042 0 ,0191 0.622 00330.0099 0.1254 0.0072 0.0961 0.0046 0.0021 0.0033 0.0105 0.0013 0.001 0.0052 O. Norwegian FC 10367 34 0.0079 0.0132 O 0173 0.0076 0.0031 0.0138 0 ,0068 0.0053 0111 0.1797 0.6539 0.0315 0.0076 0.0032 0.0026 0.0047 0.0128 0.006 0.0022 0.0073 O. Norwegian FC 10682 13 0.0094 0.0036 0049 0.005 0.0609 0.0034 0 ,0029 0.0096 00350.0196 0.8214 0.0143 0.0082 0.0058 0.0051 0.0049 0.0069 0.0024 0.0023 0.0048 O. Norwegian FC 11548 18 0.0086 0.0763 1058 0.0041 0.002 0.0076 0 ,0044 0.0129 00390.0455 0.5909 0.0508 0.0351 0.002 0.0139 0.0136 0.0038 0.0051 0.0057 0.0053 O. Norwegian FC 242 7 0.0031 0.2604 0126 0.0044 0.001 0.0032 0 ,0084 0.0433 05050.0706 0.476 0.0048 0.0065 0.0211 0.0038 0.0046 0.002 0.0064 0.0022 0.0095 O. Manx 2928 15 0.0049 0.0053 1084 0.0037 0.002 0.0123 0 ,002 0.1142 00650.0166 0.6578 0.0049 0.0038 0.0021 0.0019 0.0021 0.0034 0.0017 0.001 0.0436 O. Manx 2980 31 0.0048 0.0083 0. 2651 0.0023 0.0078 0.002 0 ,0176 0.0103 01890.0232 0.5133 0.0093 0.0045 0.0063 0.0033 0.003 0.002 0.0013 0.0015 0.0934 O. Manx 3926 10 0.0027 0.0036 O 2563 0.0021 0.003 0.0061 0 ,0035 0.0053 00580.0035 0.56 0.0398 0.005 0.0036 0.0022 0.0019 0.001 0.002 0.0023 0.089 O. Manx 4378 0 0.0121 0.1224 1639 0.0065 0.0039 0.0208 0 ,0076 0.0326 00590.004 0.5237 0.0074 0.0033 0.0033 0.0021 0.0032 0.0037 0.0013 0.0013 0.0694 O.

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2

Manx 5757 2 0.0059 0.005 0, 1857 0.0032 0.0043 0.0118 0 ,002 0.2705 00230.0032 0.4171 0.0031 0.0031 0.0026 0.0031 0.0029 0.002 0.001 0.0045 0.0657 0. Manx 6294 5 0.3341 0.0022 0, 167 0.0036 0.002 0.0015 0 ,0122 0.0063 00350.008 0.326 0.0024 0.0111 0.0166 0.0022 0.0013 0.0014 0.0037 0.0013 0.0915 0. Manx 6296 10 0.0018 0.0074 0, 3603 0.0042 0.001 0.0205 0 ,0024 0.0061 00130.0101 0.4823 0.0022 0.0048 0.0033 0.001 0.0019 0.0035 0.0014 0.0017 0.0805 0. Manx 6299 5 0.0035 0.0163 0. 2518 0.0025 0.0054 0.022 0.003 0.0221 02870.0155 0.4937 0.0022 0.0133 0.0119 0.002 0.0021 0.001 0.002 0.001 0.0976 0. Manx 7079 5 0.0189 0.024 0. 2636 0.012 0.0049 0.0045 0 ,0104 0.0067 00440.0152 0.4918 0.0046 0.016 0.0023 0.0121 0.0055 0.0138 0.0019 0.0021 0.083 O. Manx 7082 5 0.0314 0.0239 0, 0066 0.0078 0.0035 0.0129 0 ,0131 0.0171 00630.0412 0.7496 0.0087 0.0085 0.0127 0.0099 0.0044 0.002 0.0041 0.0167 0.0117 O. Manx 7083 0 0.0021 0.017 0, 0032 0.0023 0.0163 0.003 0.009 0.0033 00550.0042 0.8929 0.0069 0.0026 0.0033 0.0026 0.0019 0.001 0.0021 0.0141 0.0032 O. Manx 7105 2 0.0048 0.0022 0, 2381 0.0086 0.0058 0.0161 0 ,0789 0.0026 00460.005 0.4752 0.0509 0.0089 0.0057 0.0019 0.0036 0.0019 0.0023 0.002 0.0786 O. Manx 7108 0 0.0227 0.0037 0, 2805 0.0108 0.002 0.0059 0 ,0071 0.0028 00190.0046 0.5445 0.0014 0.003 0.0011 0.0017 0.001 0.002 0.002 0.0031 0.0959 O. Manx 7112 2 0.0288 0.0056 0, 2782 0.0175 0.003 0.0084 0 ,0095 0.0027 00170.0041 0.5021 0.005 0.0176 0.0045 0.0045 0.0021 0.0019 0.0029 0.0034 0.0944 O. Manx 7708 5 0.2924 0.0192 0. 0126 0.0042 0.004 0.0047 0 ,0132 0.0132 00190.3028 0.0799 0.1553 0.0212 0.0114 0.0012 0.0095 0.0047 0.0013 0.002 0.044 O. Manx 9084 18 0.0062 0.0058 0, 281 0.0054 0.0037 0.0036 0 ,0042 0.0047 003 0.0035 0.5503 0.006 0.0057 0.0047 0.0031 0.0058 0.003 0.0023 0.002 0.0948 O. Manx 9091 7 0.0329 0.0101 0, 1494 0.005 0.008 0.0087 0 ,0031 0.0236 02350.0579 0.4568 0.0469 0.0832 0.0035 0.0028 0.0074 0.0112 0.0075 0.002 0.054 O.

Egypt an Mau 1812 5 0.0044 0.0029 0, 0036 0.0046 0.001 0.001 0 ,0033 0.0051 00520.002 0.0061 0.9301 0.0101 0.0034 0.0019 0.0029 0.001 0.0036 0.001 0.0051 O. Egypt an Mau 2431 18 0.0021 0.0029 0, 0025 0.0029 0.0062 0.0099 0 ,0016 0.0126 001 0.002 0.002 0.9326 0.003 0.002 0.0042 0.0034 0.001 0.0025 0.0017 0.0019 O. Egypt an Mau 2433 7 0.0011 0.0036 0, 0019 0.002 0.001 0.001 0 ,0073 0.001 001 0.0042 0.0024 0.8827 0.0033 0.003 0.0059 0.0628 0.0034 0.0035 0.002 0.0053 O. Egypt an Mau 3331 26 0.0029 0.0027 0. 0023 0.0067 0.0019 0.0162 0 ,012 0.0079 01140.003 0.003 0.8547 0.0038 0.0028 0.0026 0.0108 0.002 0.0347 0.0094 0.0044 O. Egypt an Mau 3332 15 0.001 0.0012 0. 0023 0.0016 0.002 0.002 0 ,0043 0.0041 001 0.0018 0.002 0.9613 0.0027 0.0024 0.002 0.0019 0.001 0.001 0.0015 0.0016 O. Egypt an Mau 5545 15 0.0049 0.0032 0, 0098 0.0026 0.0062 0.003 0 ,0011 0.004 002 0.0075 0.0047 0.9225 0.0034 0.0029 0.002 0.0055 0.004 0.003 0.0018 0.0033 O. Egypt an Mau 5553 15 0.0035 0.0052 0, 0065 0.0041 0.0244 0.0154 0 ,0023 0.0047 00290.002 0.0102 0.7988 0.011 0.0078 0.006 0.0782 0.001 0.0017 0.0104 0.0026 O. Egypt an Mau 5567 28 0.0077 0.0254 0, 0033 0.0044 0.0075 0.0033 0 ,0051 0.004 00330.005 0.0158 0.8821 0.0031 0.0032 0.0011 0.0052 0.0094 0.0027 0.0024 0.0043 O. Egypt an Mau 5568 28 0.002 0.0015 0, 0032 0.0041 0.0013 0.001 0 ,001 0.0019 00220.0019 0.0027 0.9561 0.0021 0.0032 0.0017 0.0023 0.003 0.0023 0.0019 0.003 O. Egypt an Mau 7475 13 0.0068 0.0013 0, 002 0.0021 0.001 0.002 0 ,001 0.0038 001 0.0011 0.002 0.9649 0.001 0.0011 0.0015 0.001 0.0012 0.001 0.001 0.0021 O. Egypt an Mau 7476 28 0.0094 0.0157 0. 0774 0.002 0.0636 0.002 0 ,002 0.0042 00350.0061 0.0122 0.6551 0.0265 0.0139 0.0072 0.0038 0.0019 0.0084 0.0683 0.0038 O. Egypt an Mau 7479 34 0.002 0.0011 0, 0013 0.0023 0.0034 0.0023 0 ,0056 0.0021 00170.002 0.002 0.7461 0.0014 0.0069 0.0015 0.0038 0.0114 0.0336 0.0062 0.0791 O. Egypt an Mau 7480 28 0.001 0.001 0, 0013 0.001 0.0025 0.002 0 ,001 0.001 001 0.001 0.0024 0.9743 0.002 0.0011 0.0011 0.001 0.001 0.001 0.001 0.0012 O. Egypt ian Mau 11410 13 0.017 0.0195 0, 0022 0.0197 0.0017 0.0352 0 ,0019 0.0184 00220.002 0.012 0.6149 0.0165 0.0033 0.0074 0.2079 0.0035 0.0033 0.0016 0.0088 O. Turk. Angora 607 15 0.0696 0.0333 0, 0401 0.0083 0.0253 0.0088 0 ,002 0.0285 03060.0681 0.0823 0.0252 0.4761 0.0349 0.0358 0.0037 0.0066 0.0036 0.0012 0.014 O. Turk. Angora 1832 13 0.0028 0.0025 0, 0024 0.0042 0.002 0.002 0 ,0026 0.0084 00420.0038 0.0072 0.0031 0.9312 0.003 0.0017 0.0059 0.002 0.0034 0.001 0.0036 O. Turk. Angora 1845 13 0.001 0.0013 0. 0017 0.001 0.0021 0.001 0 ,0021 0.0011 00190.0017 0.0024 0.0022 0.9687 0.0018 0.0011 0.0012 0.001 0.0018 0.002 0.0012 O. Turk. Angora 2848 18 0.0114 0.0719 0. 079 0.0184 0.0083 0.0574 0 ,0086 0.0169 0051 0.0042 0.0893 0.0319 0.497 0.0227 0.0027 0.0081 0.0031 0.0042 0.0055 0.0316 O. Turk. Angora 2862 21 0.0043 0.0023 0, 0039 0.002 0.0047 0.0023 0 ,0125 0.004 003 0.002 0.0026 0.0021 0.9333 0.0041 0.0035 0.0034 0.002 0.0017 0.0011 0.0024 O. Turk. Angora 5552 7 0.0031 0.003 0, 0073 0.0058 0.0059 0.0038 0 ,0057 0.0037 00470.0043 0.0025 0.0021 0.9191 0.0016 0.0033 0.0021 0.001 0.0064 0.0062 0.005 O. Turk. Angora 5563 5 0.0093 0.0014 0, 0046 0.005 0.0032 0.0073 0 ,0021 0.0071 00140.0021 0.0078 0.0054 0.9209 0.002 0.0048 0.005 0.0019 0.0013 0.0021 0.004 O. Turk. Angora 5564 5 0.0017 0.0089 0, 0119 0.0021 0.002 0.0271 0 ,0037 0.0037 02680.0534 0.0636 0.0066 0.7368 0.0048 0.0312 0.0047 0.001 0.001 0.001 0.0062 O. Turk. Angora 6350 10 0.0024 0.0046 0, 0036 0.0021 0.0121 0.0057 0 ,0022 0.0027 00270.0081 0.0045 0.0065 0.9169 0.0044 0.0059 0.0031 0.0015 0.0026 0.0033 0.003 O. Turk. Angora 9541 0 0.002 0.0055 0. 0082 0.0072 0.0041 0.0047 0 ,002 0.002 0021 0.421 0.0049 0.0106 0.3367 0.0118 0.1236 0.0329 0.0026 0.0049 0.0054 0.0048 O. Turk. Angora 9542 23 0.0017 0.0013 0, 0013 0.001 0.001 0.0017 0 ,0011 0.001 001 0.0011 0.0011 0.0016 0.0012 0.0023 0.879 0.0963 0.0011 0.0014 0.0012 0.0015 O. Turk. Angora 9584 10 0.0028 0.0478 0, 0107 0.0092 0.0732 0.002 0.0044 0.0064 02870.3881 0.0246 0.0044 0.2958 0.0504 0.0187 0.0063 0.0037 0.0028 0.0037 0.0135 O.

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2

Turk. Angora 9586 2 0.0455 0.0084 0, 0233 0.0068 0.0122 0.014 0 ,051 0.0122 00540.0351 0.3795 0.0211 0.3065 0.0257 0.0029 0.0021 0.002 0.0027 0.0013 0.0336 0. Turk. Angora 9608 10 0.0033 0.005 0, 0146 0.003 0.0031 0.0021 0 ,0053 0.046 0101 0.0193 0.1131 0.003 0.1273 0.538 0.0019 0.0025 0.002 0.0068 0.0022 0.0839 0. Turk. Angora 9609 7 0.0045 0.0029 0, 0043 0.006 0.0045 0.0029 0 ,0091 0.0021 00290.0107 0.0044 0.0044 0.1047 0.6694 0.0581 0.0145 0.0011 0.0017 0.0028 0.0865 0. Turk. Angora 9610 13 0.0076 0.0104 0. 0027 0.0025 0.0057 0.004 0 ,0125 0.0035 01340.003 0.0037 0.0033 0.0947 0.7216 0.0017 0.0013 0.0046 0.003 0.0038 0.094 0. Turk. Angora 9611 5 0.0011 0.001 0. 0011 0.001 0.001 0.0011 0 ,0011 0.0011 0011 0.0011 0.001 0.0011 0.0964 0.7792 0.0073 0.0012 0.0011 0.0011 0.0011 0.0986 O. Turk. Angora 9612 5 0.002 0.0027 0, 0124 0.004 0.0035 0.003 0 ,002 0.2123 0041 0.0187 0.0084 0.0215 0.0823 0.5342 0.0026 0.0013 0.0049 0.0022 0.0019 0.0719 O. Turk. Angora 9613 2 0.005 0.0032 0, 0057 0.0076 0.0136 0.0065 0 ,0033 0.0332 01980.0056 0.0069 0.0551 0.7127 0.0029 0.006 0.0899 0.0015 0.0141 0.0017 0.0034 O. Turk. Angora 9614 2 0.0011 0.0011 0, 0011 0.001 0.0021 0.0032 0 ,0013 0.0017 001 0.0032 0.0023 0.0021 0.0961 0.7776 0.0017 0.0011 0.001 0.001 0.001 0.0983 O. Turk. Angora 9615 7 0.0021 0.0019 0, 0014 0.001 0.001 0.002 0 ,0023 0.0017 00190.0023 0.0022 0.0011 0.0948 0.7738 0.0027 0.0018 0.0019 0.0022 0.0014 0.0986 O. Turkish Van 1789 13 0.001 0.0012 0, 0015 0.001 0.0014 0.001 0 ,001 0.001 001 0.0011 0.0011 0.0015 0.0011 0.001 0.8809 0.0981 0.0011 0.001 0.001 0.001 O. Turkish Van 3013 18 0.0016 0.0045 0. 0087 0.0016 0.0024 0.0015 0 ,0014 0.0015 00130.0018 0.0052 0.0055 0.0045 0.004 0.8524 0.0948 0.0011 0.001 0.0011 0.0018 O. Turkish Van 3056 5 0.0025 0.0372 0, 0025 0.0105 0.0025 0.0042 0 ,001 0.0087 00630.0111 0.0133 0.002 0.0069 0.0031 0.771 0.0915 0.0038 0.0023 0.0135 0.0039 O. Turkish Van 4090 15 0.0022 0.0026 0, 003 0.0015 0.0023 0.0014 0 ,0024 0.0015 00260.0025 0.0025 0.0023 0.0016 0.0016 0.8667 0.0966 0.0015 0.0012 0.001 0.0014 O. Turkish Van 7662 21 0.0022 0.0035 0, 0033 0.0037 0.0021 0.0048 0 ,0029 0.002 00190.0045 0.0047 0.0036 0.0168 0.0301 0.8133 0.0742 0.0024 0.0129 0.0042 0.0049 O. Turkish Van 7663 34 0.0092 0.0231 0, 0065 0.0049 0.0072 0.0041 0 ,0054 0.0055 002 0.0076 0.0059 0.0047 0.0057 0.0249 0.6873 0.1447 0.0048 0.014 0.0094 0.0181 O. Turkish Van 7666 39 0.0076 0.0032 0, 0051 0.0033 0.0223 0.0011 0 ,0125 0.0028 00440.0045 0.0056 0.006 0.0047 0.003 0.7513 0.1171 0.002 0.0046 0.0287 0.0029 O. Turkish Van 7667 36 0.0034 0.0043 0. 0041 0.002 0.042 0.0078 0 ,0024 0.0091 00970.0734 0.006 0.0033 0.0059 0.0093 0.711 0.0879 0.0024 0.0052 0.0019 0.0065 O. Turkish Van 9322 5 0.0109 0.1919 0. 0066 0.0023 0.0019 0.0063 0 ,0054 0.0033 00170.0302 0.0053 0.0026 0.0106 0.0023 0.637 0.0605 0.0018 0.002 0.0112 0.0034 O. Turkish Van 9535 2 0.0042 0.0034 0, 0045 0.002 0.002 0.0015 0 ,001 0.0027 00180.0184 0.009 0.001 0.0036 0.0353 0.82 0.0761 0.0043 0.001 0.0013 0.0055 O. Turkish Van 9538 0 0.0036 0.0031 0, 0044 0.0038 0.0019 0.0025 0 ,0023 0.0024 00240.0034 0.0028 0.0072 0.0053 0.0039 0.8435 0.0946 0.0015 0.0036 0.0016 0.0023 O. Turkish Van 9539 0 0.0104 0.1031 0, 0312 0.0032 0.0022 0.0021 0 ,0024 0.0041 084 0.048 0.0133 0.0117 0.0116 0.0107 0.5446 0.0595 0.001 0.0027 0.0386 0.0129 O. Turkish Van 9543 0 0.0022 0.0077 0, 0183 0.0033 0.0059 0.0049 0 ,0387 0.0042 01 0.0855 0.0432 0.0042 0.6395 0.0083 0.0156 0.0709 0.0026 0.003 0.0132 0.0124 O. Turkish Van 9563 2 0.0225 0.0161 0, 0443 0.0118 0.0041 0.0062 0 ,0038 0.0162 00690.0116 0.0293 0.0145 0.0123 0.0084 0.5507 0.2153 0.0086 0.0033 0.0029 0.0096 O. Turkish Van 9564 2 0.0014 0.0052 0. 0095 0.0019 0.0042 0.0039 0 ,0068 0.0016 00170.0071 0.0026 0.0019 0.0057 0.0077 0.8296 0.1002 0.0017 0.001 0.001 0.003 O. Turkish Van 9573 0 0.002 0.0013 0, 0021 0.0014 0.0024 0.001 0 ,0017 0.0031 00340.0023 0.002 0.0024 0.0063 0.0022 0.8454 0.1118 0.001 0.0019 0.0028 0.0022 O. Turkish Van 9574 0 0.0021 0.003 0, 0047 0.009 0.002 0.0014 0 ,0039 0.002 00330.015 0.0052 0.0096 0.0072 0.1311 0.6738 0.0764 0.016 0.0076 0.003 0.0215 O. Turkish Van 9575 0 0.003 0.0013 0, 0211 0.005 0.0038 0.0081 0 ,0072 0.003 031 0.6143 0.0709 0.0073 0.0119 0.0115 0.1241 0.0357 0.0029 0.0085 0.0031 0.0143 O. Turkish Van 9576 2 0.002 0.0042 0, 0059 0.0148 0.1006 0.003 0 ,0149 0.002 05020.4899 0.0374 0.002 0.0777 0.0106 0.0205 0.0064 0.003 0.0757 0.0331 0.0073 O. Turkish Van 9581 5 0.0039 0.0038 0, 0051 0.0053 0.0049 0.0039 0 ,0112 0.0039 00640.691 0.0077 0.0048 0.0368 0.1393 0.0099 0.0068 0.0022 0.0165 0.0098 0.0252 O. Bengal 2518 52 0.0177 0.0044 0. 0067 0.004 0.0024 0.0037 0 ,012 0.0031 08250.0075 0.0068 0.0123 0.0438 0.0073 0.0124 0.7459 0.0019 0.0051 0.0044 0.008 O. Bengal 3455 44 0.002 0.0022 0. 0028 0.0023 0.0015 0.002 0 ,002 0.0021 00170.0026 0.002 0.002 0.0021 0.002 0.0288 0.9157 0.0049 0.0057 0.0086 0.0026 O. Bengal 3478 39 0.0017 0.0023 0, 0065 0.0046 0.001 0.0025 0 ,0023 0.0044 00350.003 0.0145 0.0785 0.011 0.0033 0.0126 0.8365 0.001 0.0027 0.0019 0.0042 O. Bengal 3522 18 0.0039 0.0072 0, 0059 0.0032 0.0034 0.0065 0 ,0112 0.006 033 0.0044 0.0201 0.0022 0.0069 0.0568 0.0019 0.8101 0.002 0.0016 0.0022 0.0078 O. Bengal 3541 18 0.0084 0.0028 0, 0054 0.0025 0.0038 0.0018 0 ,0171 0.0044 00190.0202 0.0034 0.0085 0.0025 0.0025 0.0015 0.9003 0.0048 0.0012 0.0011 0.0043 O. Bengal 3550 31 0.0011 0.0021 0, 003 0.0029 0.0022 0.0059 0 ,0048 0.0019 0051 0.0028 0.0029 0.0031 0.0102 0.0036 0.0023 0.9346 0.0011 0.0023 0.0025 0.0022 O. Bengal 6678 0 0.0011 0.0025 0, 0031 0.0042 0.0025 0.0019 0 ,0025 0.001 00690.0021 0.002 0.0069 0.0035 0.0033 0.0028 0.9321 0.0011 0.0099 0.0032 0.0057 O. Bengal 6899 0 0.0148 0.0024 0. 008 0.0091 0.0229 0.0988 0 ,009 0.012 01 0.004 0.0236 0.0024 0.0061 0.0049 0.0047 0.7319 0.0028 0.0046 0.0125 0.0062 O. Bengal 6902 0 0.0015 0.0018 0, 0023 0.0013 0.0017 0.0021 0 ,001 0.0016 00140.0011 0.0015 0.006 0.0022 0.0017 0.0013 0.9347 0.0305 0.001 0.0015 0.0016 O. Bengal 6907 2 0.0024 0.0012 0, 0049 0.0052 0.0053 0.0022 0 ,0042 0.0049 05940.0151 0.0043 0.0025 0.007 0.0138 0.0017 0.8549 0.0029 0.001 0.0017 0.0037 O.

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Bengal 8399 2 0.0059 0.0156 0 0011 0.0052 0.0144 0.0093 0, 0025 0.0027 00780.0021 0.0023 0.1794 0.0029 0.0057 0.0018 0.6801 0.0023 0.0115 0.0093 0.007 0.

Bengal 8400 13 0.0015 0.002 0 0024 0.0025 0.0028 0.0085 0, 0043 0.0041 003 0.0023 0.0034 0.0035 0.0028 0.0012 0.0022 0.9358 0.0016 0.0049 0.0022 0.002 0.

Bengal 8766 13 0.001 0.002 0 0023 0.0016 0.0011 0.0026 0, 0015 0.0015 00290.0023 0.0022 0.0101 0.0112 0.0118 0.0113 0.9187 0.0011 0.0041 0.0012 0.0056 0.

Bengal 9053 10 0.0071 0.0029 0 0037 0.0022 0.009 0.015 0, 0244 0.0036 00250.005 0.009 0.0225 0.0132 0.0024 0.0028 0.8564 0.0042 0.0053 0.0021 0.0039 0.

Bengal 9800 2 0.0015 0.0021 0 0022 0.0023 0.0019 0.0023 0, 0027 0.0011 003 0.0013 0.0022 0.0012 0.0012 0.0016 0.0014 0.9639 0.0019 0.0021 0.0012 0.0018 O.

Bengal 10344 23 0.0013 0.0013 0 0027 0.0025 0.0022 0.0023 0, 0034 0.0015 0021 0.0022 0.0011 0.0032 0.0027 0.006 0.0016 0.9561 0.0021 0.001 0.0015 0.0021 O.

Bengal 10946 0 0.004 0.0092 0 0351 0.005 0.0045 0.002 0.002 0.0053 054 0.0041 0.0056 0.0163 0.0019 0.0054 0.0039 0.7789 0.0073 0.0027 0.003 0.0087 O.

Bengal 11194 18 0.003 0.007 1181 0.0076 0.0136 0.0113 0, 0063 0.0023 015 0.003 0.0062 0.0025 0.0051 0.0044 0.0078 0.7445 0.0128 0.0022 0.021 0.0047 O.

Sokoke 1890 7 0.001 0.001 001 0.0012 0.001 0.0014 0, 001 0.001 00120.001 0.0013 0.001 0.001 0.099 0.4893 0.2931 0.001 0.0019 0.0018 0.0988 O.

Sokoke 1898 31 0.001 0.001 001 0.0013 0.0033 0.001 0, 0027 0.001 00170.0017 0.001 0.001 0.0012 0.0985 0.4716 0.2823 0.001 0.0086 0.0062 0.1059 O.

Sokoke 2054 7 0.001 0.001 001 0.0012 0.0013 0.001 0, 0013 0.001 001 0.0013 0.0013 0.0013 0.001 0.0992 0.4901 0.2909 0.001 0.0013 0.0027 0.0989 O.

Sokoke 2061 34 0.0031 0.0044 0 ,0046 0.0023 0.0017 0.0018 0, 0023 0.003 00160.0026 0.0026 0.0016 0.0017 0.0974 0.4793 0.2818 0.0012 0.003 0.0024 0.0979 O.

Sokoke 2063 15 0.001 0.002 0 ,0015 0.0014 0.0021 0.0037 0, 0052 0.0027 003 0.0038 0.0027 0.0017 0.0011 0.0974 0.4689 0.2882 0.001 0.0037 0.0077 0.0975 O.

Sokoke 2067 42 0.0049 0.0057 0 ,0027 0.0706 0.0016 0.0271 0, 0021 0.0022 00250.0059 0.0024 0.0046 0.0021 0.0796 0.3918 0.2925 0.0023 0.009 0.0052 0.082 O.

Sokoke 6615 23 0.001 0.0078 0 ,0045 0.0029 0.0016 0.0109 0, 0023 0.0013 01050.0039 0.0051 0.0017 0.0075 0.0905 0.4162 0.2424 0.0014 0.0078 0.0017 0.0923 O.

Ocicat 2933 44 0.0033 0.0026 0 ,0085 0.0063 0.0094 0.0072 0, 0828 0.4183 019 0.0068 0.007 0.002 0.0035 0.0118 0.0021 0.0022 0.003 0.0688 0.0055 0.06 o.

Ocicat 2951 21 0.001 0.007 0 0028 0.0011 0.0022 0.0029 0, 0038 0.0029 678 0.0048 0.0067 0.0023 0.0086 0.0907 0.0027 0.0074 0.0028 0.0393 0.0205 0.0972 O.

Ocicat 2954 26 0.0019 0.0021 0 ,019 0.089 0.0018 0.0028 0, 002 0.0055 62320.0217 0.0041 0.002 0.0044 0.0907 0.0039 0.0233 0.001 0.0054 0.0018 0.0889 O.

Ocicat 3514 47 0.0056 0.0086 0 ,0032 0.0061 0.0023 0.0022 0, 0054 0.0072 6771 0.0061 0.0257 0.0021 0.0094 0.0951 0.0033 0.0036 0.001 0.029 0.0041 0.0979 O.

Ocicat 5744 5 0.0075 0.0048 0 ,0075 0.0106 0.0075 0.0101 0, 0037 0.011 52480.0095 0.0071 0.0072 0.0098 0.0995 0.0032 0.003 0.0153 0.1045 0.0032 0.1311 O.

Ocicat 9966 13 0.0055 0.0017 0 0022 0.0025 0.003 0.0451 0, 0659 0.0076 39380.0044 0.0126 0.0013 0.0245 0.0975 0.0064 0.0052 0.003 0.1382 0.0086 0.1019 O.

Ocicat 9967 2 0.0603 0.0031 0 ,0054 0.0037 0.0026 0.0023 0, 0077 0.0051 49460.0045 0.0026 0.002 0.0064 0.084 0.005 0.003 0.006 0.0861 0.0095 0.1648 O.

Ocicat 10400 2 0.0025 0.0042 0 ,0108 0.0035 0.0017 0.0168 0, 0046 0.0032 55730.0124 0.0071 0.0311 0.006 0.0823 0.0022 0.02 0.0051 0.0894 0.0051 0.0898 O.

Ocicat 10652 2 0.0102 0.0067 0 0076 0.0042 0.0143 0.0019 0, 0017 0.0073 60080.0077 0.0075 0.0027 0.0188 0.1089 0.0011 0.0036 0.0057 0.0537 0.0015 0.1257 O.

Ocicat 10654 0 0.0292 0.0017 0 ,0065 0.0087 0.001 0.0152 0, 0254 0.0129 55380.0017 0.0029 0.0014 0.0035 0.0967 0.0026 0.0018 0.0017 0.0669 0.0083 0.1257 O.

Russian Blue 1834 7 0.001 0.0015 0 ,0017 0.0021 0.001 0.002 001 0.001 001 0.002 0.0021 0.0015 0.0019 0.0016 0.002 0.0026 0.9642 0.0026 0.002 0.0036 O.

Russian Blue 1835 5 0.001 0.001 0 ,001 0.001 0.001 0.001 001 0.001 001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.98 0.001 0.001 0.001 O.

Russian Blue 2505 13 0.001 0.0022 0 ,0011 0.001 0.001 0.001 001 0.001 001 0.0027 0.001 0.0011 0.001 0.001 0.0019 0.0088 0.9667 0.001 0.0022 0.0011 O.

Russian Blue 4068 13 0.001 0.001 0 ,0013 0.0011 0.001 0.003 002 0.002 001 0.0019 0.001 0.001 0.001 0.0014 0.0014 0.0011 0.9697 0.0016 0.0033 0.0019 O.

Russian Blue 4072 23 0.0032 0.0024 0 ,002 0.002 0.002 0.0011 0, 001 0.002 00190.002 0.0028 0.0022 0.002 0.0019 0.0092 0.0014 0.9493 0.0074 0.0011 0.0015 O.

Russian Blue 4074 10 0.0035 0.0048 0 ,0034 0.002 0.0012 0.0108 0, 001 0.2168 002 0.0038 0.0028 0.0032 0.0019 0.001 0.001 0.0011 0.7309 0.0029 0.002 0.0026 O.

Russian Blue 4076 10 0.002 0.0029 0 ,002 0.002 0.001 0.001 0, 001 0.002 00190.002 0.0013 0.0024 0.001 0.001 0.0012 0.0012 0.9687 0.0014 0.001 0.0019 O.

Russian Blue 4077 7 0.001 0.001 0 ,001 0.001 0.001 0.001 0, 001 0.001 001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.9792 0.001 0.001 0.001 O.

Russian Blue 4078 10 0.0029 0.0047 0 0066 0.0067 0.003 0.0069 0, 003 0.0034 0031 0.004 0.0037 0.003 0.0058 0.0046 0.0033 0.0112 0.915 0.002 0.0019 0.0032 O.

Russian Blue 4302 10 0.0011 0.001 0 0017 0.001 0.0027 0.001 0, 0012 0.0018 00120.0013 0.001 0.003 0.0019 0.0012 0.001 0.001 0.9453 0.0022 0.0012 0.0025 O.

Russian Blue 4867 23 0.0018 0.0034 0 0037 0.0022 0.002 0.001 0, 0053 0.0047 02130.0082 0.003 0.001 0.0025 0.0015 0.0015 0.0051 0.9252 0.001 0.0018 0.002 O.

Russian Blue 4869 21 0.002 0.0038 0 002 0.002 0.0024 0.0019 0, 0028 0.0012 001 0.002 0.002 0.002 0.001 0.0019 0.0018 0.0013 0.9566 0.0025 0.0049 0.0035 O.

Russian Blue 5216 7 0.028 0.0027 0 0043 0.002 0.0022 0.0244 0, 1649 0.0581 05770.0026 0.0138 0.0025 0.0037 0.0059 0.002 0.0036 0.5907 0.0047 0.0111 0.009 O.

Russian Blue 5630 34 0.0051 0.0036 0 005 0.0059 0.001 0.004 0.002 0.003 00190.0037 0.0032 0.0037 0.0028 0.0019 0.0029 0.0036 0.9353 0.002 0.0052 0.0026 O.

Feline ID Missing Groups

Population No. Data 6 8 10 11 12 13 14 15 16 17 18 19 20 2

Russian Blue 5631 15 0.0044 0.0172 0, 0088 0.0043 0.002 0.002 0 ,0025 0.001 00350.0039 0.0023 0.0048 0.0012 0.0022 0.002 0.0021 0.9265 0.002 0.002 0.0026 0.

Russian Blue 5704 7 0.0014 0.001 0, 0017 0.001 0.001 0.001 0 ,002 0.0018 001 0.0018 0.0014 0.001 0.001 0.001 0.001 0.001 0.9758 0.001 0.001 0.0011 0.

Russian Blue 5709 15 0.0022 0.0029 0, 0017 0.0036 0.002 0.003 0 ,001 0.002 001 0.0013 0.001 0.002 0.001 0.0011 0.0012 0.001 0.9667 0.0019 0.001 0.0014 0.

Aust. Mist 4716 86 0.0221 0.0471 0. 1068 0.0059 0.034 0.0167 0 .0471 0.0124 0071 0.0291 0.024 0.0186 0.0092 0.0107 0.1018 0.023 0.1247 0.3026 0.0111 0.0283 0.

Aust. Mist 4718 2 0.0317 0.0024 0. 0108 0.0037 0.0023 0.0081 0 .001 0.0317 008 0.0528 0.0629 0.0488 0.0129 0.0059 0.0019 0.0048 0.001 0.6997 0.0018 0.0059 O.

Aust. Mist 4720 15 0.0024 0.002 0, 0044 0.0047 0.0048 0.002 0 ,0072 0.0042 014 0.0083 0.0069 0.002 0.0091 0.0055 0.0016 0.0027 0.0019 0.8976 0.0024 0.0133 O.

Aust. Mist 4722 13 0.0117 0.0035 0, 0079 0.0103 0.0051 0.0037 0 ,0264 0.0275 00570.0127 0.0258 0.0036 0.0069 0.0051 0.0011 0.0024 0.0021 0.8215 0.0013 0.0138 O.

Aust. Mist 4723 10 0.0038 0.0023 0, 0051 0.0081 0.0065 0.0093 0 ,0296 0.0107 004 0.0124 0.0374 0.0025 0.023 0.0059 0.0075 0.0037 0.0016 0.7855 0.0068 0.0075 O.

Aust. Mist 4724 84 0.013 0.0498 0, 0201 0.0089 0.0067 0.0114 0 ,011 0.0134 02170.0258 0.0154 0.0092 0.6192 0.0728 0.0299 0.0174 0.003 0.0061 0.0054 0.0357 O.

Aust. Mist 4725 21 0.004 0.0142 0, 0084 0.0155 0.0224 0.0023 0 ,0029 0.0171 01470.0142 0.0278 0.0103 0.0061 0.0058 0.0041 0.0475 0.0111 0.749 0.0077 0.0075 O.

Aust. Mist 4727 0 0.002 0.0024 0. 0061 0.007 0.0033 0.0588 0 ,002 0.0028 00640.0219 0.1028 0.0066 0.0022 0.003 0.0016 0.0037 0.0024 0.7511 0.0047 0.0034 O.

Aust. Mist 4731 26 0.0034 0.001 0, 002 0.2342 0.002 0.0079 0 ,0137 0.003 01080.0026 0.0028 0.002 0.0041 0.0018 0.0015 0.0015 0.0012 0.6965 0.002 0.0037 O.

Aust. Mist 4736 44 0.0019 0.0014 0, 0026 0.0022 0.0017 0.0021 0 ,0021 0.0033 006 0.0053 0.0063 0.001 0.0074 0.0128 0.0047 0.0021 0.001 0.9273 0.002 0.0048 O.

Aust. Mist 4739 28 0.0026 0.0076 0, 0048 0.0174 0.0022 0.0115 0 ,0029 0.0094 00840.0034 0.0362 0.015 0.0036 0.0022 0.0018 0.0026 0.002 0.8503 0.0067 0.0066 O.

Aust. Mist 6184 2 0.0067 0.0079 0, 0101 0.0051 0.002 0.0376 0 ,0044 0.0024 00330.0035 0.0225 0.0023 0.0022 0.0024 0.0027 0.0022 0.002 0.8309 0.0039 0.0072 O.

Aust. Mist 6187 2 0.0184 0.0078 0, 0261 0.0051 0.0036 0.0121 0 ,0088 0.0101 01170.0103 0.0233 0.0034 0.0053 0.0066 0.0029 0.0042 0.0084 0.7978 0.0113 0.0163 O. j Aust. Mist 6188 7 0.0053 0.0143 0. 032 0.0228 0.0071 0.0145 0 ,0049 0.0338 0811 0.0126 0.0737 0.002 0.0106 0.0063 0.002 0.0173 0.0036 0.5904 0.0042 0.0133 O.

Aust. Mist 6189 7 0.0164 0.0073 0. 0061 0.0167 0.0065 0.0042 0 ,002 0.0052 00220.0085 0.0064 0.002 0.0014 0.002 0.0011 0.0017 0.0049 0.8746 0.0174 0.0106 O.

Burmese 21 5 0.0049 0.0027 0, 002 0.0026 0.0049 0.001 0 ,0022 0.001 00430.0068 0.0025 0.001 0.001 0.0012 0.0012 0.0011 0.003 0.9204 0.0034 0.0172 O.

Burmese 22 5 0.0013 0.0013 0, 0013 0.0036 0.001 0.0027 0 ,0016 0.0014 00130.0017 0.0018 0.0025 0.0015 0.0014 0.0027 0.0079 0.0026 0.9442 0.009 0.0033 O.

Burmese 23 5 0.0018 0.0023 0, 0017 0.001 0.0011 0.0021 0 ,0029 0.0018 00160.0021 0.0024 0.0025 0.0021 0.0012 0.0024 0.0017 0.0012 0.888 0.002 0.0026 O.

Burmese 24 7 0.0019 0.002 0, 0019 0.002 0.0021 0.0021 0 ,0189 0.0224 002 0.0039 0.0051 0.0333 0.0605 0.0033 0.0018 0.0011 0.0027 0.8254 0.0024 0.0026 O.

Burmese 25 5 0.001 0.0011 0, 001 0.002 0.0021 0.001 0 ,0026 0.0015 00340.0011 0.0017 0.002 0.002 0.0017 0.0023 0.0019 0.001 0.9163 0.0404 0.004 O.

Burmese 26 7 0.0025 0.009 0. 0026 0.01 0.0029 0.0033 0 ,002 0.001 00220.0085 0.0034 0.001 0.0037 0.0025 0.0011 0.0017 0.0016 0.9098 0.0091 0.0067 O.

Burmese 27 7 0.0024 0.0018 0, 0021 0.002 0.001 0.0055 0 ,0312 0.0019 00430.0054 0.0033 0.0099 0.0015 0.0027 0.0017 0.005 0.0047 0.8194 0.0064 0.0725 O.

Burmese 28 10 0.0127 0.0012 0, 0025 0.001 0.002 0.001 0 ,002 0.0026 00490.0013 0.0018 0.0016 0.001 0.0012 0.0012 0.0011 0.0019 0.9462 0.001 0.0063 O.

Burmese 29 7 0.001 0.0013 0, 0013 0.0013 0.001 0.001 0 ,0011 0.001 0021 0.002 0.0023 0.0132 0.0014 0.0041 0.0077 0.0013 0.001 0.9405 0.0062 0.0024 O.

Burmese 4401 13 0.0018 0.0011 0, 001 0.0014 0.0031 0.0014 0 ,0023 0.0044 00270.0022 0.0022 0.0015 0.0013 0.0029 0.0071 0.0076 0.002 0.8144 0.13 0.0033 O.

Burmese 4691 18 0.0054 0.007 0, 0085 0.0042 0.0211 0.0636 0 ,0022 0.0086 00170.019 0.0074 0.0021 0.0034 0.0173 0.0365 0.009 0.0075 0.7569 0.0019 0.0098 O.

Burmese 4781 18 0.001 0.0026 0. 001 0.0013 0.001 0.0025 0 ,0013 0.0013 00130.001 0.0012 0.001 0.001 0.0017 0.0025 0.0017 0.0013 0.9301 0.0079 0.0021 O.

Burmese 4782 18 0.0031 0.0011 0, 0021 0.002 0.001 0.002 0 ,003 0.001 0041 0.0012 0.0031 0.0013 0.002 0.0018 0.0015 0.0022 0.001 0.8325 0.0021 0.0032 0.

Burmese 5425 7 0.0015 0.002 0, 0027 0.004 0.001 0.002 0 ,0013 0.0021 00450.0042 0.0053 0.0047 0.0086 0.0023 0.0139 0.0025 0.0017 0.8858 0.0029 0.0035 O.

Burmese 5800 15 0.0035 0.0045 0, 0018 0.0022 0.001 0.0026 0 ,0065 0.0042 00180.002 0.0028 0.002 0.0013 0.0027 0.0028 0.0027 0.002 0.8922 0.0055 0.007 O.

Burmese 6182 13 0.0038 0.0015 0, 0037 0.0013 0.001 0.001 0 ,0028 0.0044 00130.0088 0.0038 0.0027 0.0013 0.0012 0.0032 0.001 0.0303 0.9028 0.0028 0.0046 O.

Burmese 6471 18 0.0011 0.001 0, 001 0.001 0.001 0.001 0.001 0.001 00660.001 0.001 0.0029 0.001 0.0018 0.0022 0.0022 0.001 0.8969 0.0229 0.0066 O.

Burmese 6962 15 0.0013 0.0013 0, 0022 0.001 0.0011 0.0013 0 ,0026 0.001 001 0.0014 0.0013 0.001 0.001 0.0019 0.0022 0.001 0.0024 0.7499 0.2 0.0028 O.

Burmese 6964 23 0.0017 0.0028 0. 0018 0.0018 0.001 0.0048 0 ,0023 0.0014 00980.0024 0.0034 0.0013 0.0027 0.0026 0.002 0.0026 0.0013 0.9276 0.0186 0.0026 O.

Birman 1760 7 0.0036 0.0092 0, 0078 0.01 0.001 0.001 0 ,0036 0.0051 0011 0.0039 0.0054 0.001 0.0104 0.0031 0.001 0.001 0.0122 0.001 0.914 0.0025 O.

Birman 2917 28 0.001 0.0011 0, 0011 0.001 0.002 0.0018 0 ,0037 0.002 00120.001 0.0017 0.001 0.001 0.0015 0.001 0.001 0.0018 0.001 0.9712 0.0016 O.

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2

Birman 3910 10 0.0034 0.0142 0, 002 0.0018 0.0102 0.0017 0 ,0026 0.0011 0011 0.002 0.0032 0.0015 0.0035 0.0018 0.0026 0.0019 0.003 0.0013 0.9382 0.0018 0.

Birman 5029 7 0.0024 0.0098 0, 0042 0.0064 0.001 0.0051 0 ,0034 0.0019 01580.0042 0.0088 0.0065 0.0024 0.0018 0.0013 0.0018 0.0029 0.0017 0.9133 0.0036 0.

Birman 5033 7 0.001 0.001 0, 0011 0.0011 0.001 0.0033 0 ,0017 0.001 00170.001 0.001 0.001 0.001 0.0012 0.0012 0.0016 0.001 0.0023 0.9717 0.0011 0.

Birman 5034 5 0.0145 0.0221 0. 003 0.0038 0.0178 0.002 0 ,0032 0.003 00290.0061 0.0065 0.002 0.0039 0.0033 0.0018 0.0011 0.0115 0.005 0.8733 0.0089 0.

Birman 5038 10 0.0168 0.0049 0. 0044 0.0032 0.0034 0.0024 0 ,0025 0.0035 00160.0026 0.0033 0.0049 0.0026 0.0023 0.0021 0.0017 0.0237 0.0032 0.9049 0.0038 O.

Birman 5151 7 0.0034 0.0038 0, 0031 0.0015 0.0017 0.0027 0 ,003 0.0021 00240.0019 0.0061 0.001 0.0048 0.0017 0.0086 0.0015 0.0018 0.0027 0.9408 0.0024 O.

Birman 5576 23 0.0038 0.0011 0, 0018 0.0013 0.0036 0.001 0 ,002 0.0019 00380.001 0.001 0.001 0.001 0.001 0.0011 0.0011 0.0017 0.0016 0.9656 0.0016 O.

Birman 5578 15 0.0169 0.0116 0, 0041 0.056 0.0221 0.0129 0 ,012 0.0078 00930.002 0.005 0.0044 0.0038 0.0082 0.0021 0.0039 0.0072 0.021 0.6749 0.0617 O.

Birman 6448 10 0.0046 0.0016 0, 0018 0.0029 0.0016 0.0017 0 ,0024 0.0017 001 0.0027 0.0017 0.016 0.0017 0.0011 0.001 0.002 0.0031 0.002 0.9452 0.0019 O.

Birman 6450 15 0.0019 0.001 001 0.002 0.0016 0.001 0 ,0027 0.0036 00180.001 0.001 0.001 0.001 0.0016 0.001 0.001 0.0011 0.0026 0.9655 0.0025 O.

Birman 6526 21 0.001 0.001 001 0.001 0.001 0.001 0 ,0011 0.001 001 0.001 0.001 0.001 0.001 0.0011 0.0011 0.001 0.001 0.001 0.9793 0.001 O.

Birman 6527 13 0.001 0.001 0017 0.001 0.0038 0.0017 0 ,001 0.0025 00190.0017 0.0017 0.0028 0.0016 0.0013 0.001 0.001 0.0017 0.0013 0.9661 0.0022 O.

Birman 6528 15 0.0013 0.001 0017 0.001 0.0018 0.0011 0 ,001 0.0014 00180.0017 0.0018 0.001 0.001 0.0012 0.0017 0.0011 0.001 0.002 0.9711 0.0016 O.

Birman 6529 15 0.0122 0.0034 0, 0018 0.0019 0.0036 0.0023 0 ,0152 0.0017 00170.0017 0.0018 0.0017 0.0027 0.002 0.0075 0.0021 0.0016 0.001 0.9315 0.0016 O.

Birman 6604 5 0.0019 0.0025 0, 0035 0.003 0.0029 0.0046 0 ,0153 0.0042 00280.0043 0.003 0.001 0.002 0.002 0.0013 0.0022 0.001 0.0016 0.9354 0.0033 O.

Birman 6607 10 0.0022 0.0017 0, 0017 0.001 0.0011 0.0017 0 ,0017 0.001 00170.0011 0.0014 0.001 0.0021 0.001 0.0064 0.0013 0.002 0.001 0.9668 0.0011 O.

Birman 6608 5 0.0014 0.0017 0. 0029 0.002 0.0025 0.0091 0 ,0222 0.0027 00160.0019 0.0045 0.0022 0.0027 0.0019 0.0011 0.002 0.001 0.0016 0.9313 0.0017 O.

Birman 6609 10 0.0451 0.0027 0. 0033 0.0017 0.0034 0.0014 0 ,0176 0.0017 00180.0039 0.0062 0.002 0.0011 0.0072 0.001 0.0011 0.0043 0.0044 0.8781 0.0085 O.

Havana Brn. 787 7 0.0035 0.0021 0, 0028 0.0035 0.0022 0.001 ,0039 0.0019 00150.0014 0.0035 0.001 0.0013 0.0014 0.0012 0.0019 0.004 0.3056 0.0015 0.384 o.

Havana Brn. 2415 47 0.0036 0.0019 0, 0017 0.0026 0.0146 0.002 ,0023 0.0026 00580.0016 0.0025 0.0013 0.0026 0.0019 0.0013 0.0029 0.0054 0.2886 0.003 0.3842 0.

Havana Brn. 2500 10 0.0057 0.0042 0, 0021 0.003 0.0017 0.001 ,002 0.0028 00350.002 0.0028 0.0032 0.002 0.002 0.0017 0.0012 0.0033 0.2846 0.002 0.3804 o.

Havana Brn. 2501 36 0.0014 0.0012 0, 0014 0.0013 0.002 0.001 ,0036 0.0022 0021 0.0011 0.0018 0.002 0.0017 0.0014 0.001 0.001 0.0016 0.292 0.003 0.3808 o.

Havana Brn. 2502 28 0.002 0.0024 0, 0016 0.0016 0.001 0.0014 0 ,0023 0.0013 0021 0.0013 0.0018 0.0013 0.0013 0.0012 0.001 0.001 0.0013 0.2923 0.0018 0.3911 o.

Havana Brn. 3312 34 0.0016 0.0019 0. 0014 0.0016 0.0016 0.001 0 ,0026 0.002 00260.002 0.0019 0.0016 0.0013 0.0012 0.0013 0.0011 0.0022 0.2879 0.0052 0.3881 o.

Havana Brn. 3404 10 0.001 0.003 0, 0033 0.0017 0.0043 0.0017 0 ,0017 0.0101 00320.0031 0.0022 0.0017 0.002 0.0029 0.0017 0.0021 0.0033 0.2916 0.0017 0.3814 o.

Havana Brn. 3513 13 0.0019 0.0037 0, 003 0.0017 0.0032 0.0027 0 ,0919 0.0035 0011 0.0056 0.0154 0.0011 0.0023 0.0015 0.0015 0.0016 0.0023 0.2551 0.0032 0.3547 0.

Havana Brn. 5707 7 0.0019 0.0025 0, 002 0.0019 0.0032 0.0019 0 ,0017 0.0117 00160.0014 0.0017 0.0031 0.0014 0.0018 0.001 0.0013 0.0048 0.2854 0.001 0.3918 o.

Havana Brn. 5708 13 0.0029 0.0035 0, 0031 0.0199 0.0014 0.0017 0 ,0018 0.0177 0041 0.0141 0.0031 0.0134 0.0047 0.0017 0.007 0.003 0.0022 0.2806 0.0012 0.3648 0.

Havana Brn. 6972 13 0.0034 0.0161 0, 0026 0.0097 0.0107 0.0021 0 ,0023 0.0082 00130.0033 0.0023 0.0017 0.0046 0.002 0.0011 0.001 0.0101 0.2627 0.0014 0.3858 0.

Havana Brn. 6973 21 0.0015 0.0046 0. 004 0.0079 0.0086 0.002 0 ,0026 0.002 00170.003 0.0021 0.0017 0.0035 0.0022 0.0013 0.002 0.0057 0.267 0.0021 0.387 o.

Havana Brn. 10350 13 0.0031 0.0014 0, 0019 0.0016 0.0016 0.0034 0 ,0017 0.0062 00320.0019 0.0029 0.0069 0.0019 0.002 0.001 0.0015 0.0016 0.2849 0.0023 0.3909 o.

Havana Brn. 5554 28 0.0014 0.0021 0, 0024 0.0019 0.0059 0.001 0 ,0026 0.0023 00140.0017 0.0019 0.0033 0.0024 0.0012 0.001 0.0021 0.0078 0.2725 0.0013 0.3811 o.

Korat 4708 5 0.0017 0.0034 0, 0021 0.0031 0.002 0.0048 0 ,006 0.0013 00640.0022 0.0025 0.0033 0.0029 0.0038 0.002 0.0021 0.0018 0.0018 0.6506 0.0031 o.

Korat 4711 5 0.001 0.0018 0, 001 0.002 0.001 0.002 0 ,0018 0.001 0011 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.0017 0.6819 0.0014 o.

Korat 5059 7 0.001 0.0018 0, 001 0.0016 0.0019 0.001 0 ,001 0.001 001 0.001 0.001 0.001 0.001 0.0011 0.0014 0.0014 0.001 0.0112 0.6496 0.0283 o.

Korat 5069 5 0.0043 0.0195 0, 0047 0.0173 0.002 0.006 0 ,0011 0.0022 00380.0034 0.0032 0.0067 0.0023 0.0111 0.005 0.0049 0.0012 0.0154 0.6021 0.0085 o.

Korat 5098 7 0.001 0.0022 0. 0012 0.0024 0.0049 0.0026 0 ,002 0.001 0011 0.0013 0.0013 0.0011 0.0013 0.0015 0.0014 0.002 0.0013 0.003 0.6807 0.0018 o.

Korat 5175 5 0.001 0.0013 0, 001 0.001 0.001 0.002 0 ,0029 0.001 00170.0011 0.0016 0.0011 0.0019 0.0012 0.0011 0.0012 0.0013 0.0103 0.649 0.0241 o.

Korat 5176 5 0.001 0.0014 0, 0022 0.0027 0.002 0.001 0 ,0014 0.0019 00290.0031 0.0017 0.001 0.0011 0.0016 0.0019 0.002 0.001 0.0296 0.5911 0.058 o.

Feline ID Missing Groups

Population No. Data 6 10 11 12 13 14 15 16 17 18 19 20 2

Korat 5177 5 0.001 0.001 0, 001 0.002 0.001 0.0014 0 ,0012 0.001 00150.001 0.0014 0.001 0.001 0.0012 0.0146 0.0016 0.0014 0.171 0.5363 0.006 o.

Korat 5178 7 0.001 0.0023 0, 0011 0.002 0.0013 0.0013 0 ,002 0.001 002 0.0013 0.0015 0.0022 0.0017 0.0021 0.0022 0.0012 0.002 0.002 0.679 0.0016 o.

Korat 5224 5 0.0016 0.0017 0, 002 0.0011 0.0017 0.0034 0 ,002 0.0017 0011 0.0018 0.0026 0.0033 0.002 0.0014 0.0018 0.0029 0.0035 0.0034 0.6672 0.0055 o.

Korat 5237 7 0.0126 0.0096 0. 007 0.0088 0.0038 0.002 0 ,002 0.0026 00430.0018 0.0029 0.0234 0.0029 0.0066 0.0118 0.0138 0.001 0.0083 0.6129 0.0075 o.

Korat 5240 5 0.001 0.001 0. 0014 0.002 0.0011 0.0017 0 ,001 0.001 00140.001 0.001 0.001 0.0012 0.0015 0.002 0.0014 0.0012 0.0052 0.6789 0.0026 o.

Korat 5242 5 0.001 0.001 0, 001 0.001 0.001 0.001 0 ,001 0.001 0011 0.001 0.001 0.001 0.001 0.0011 0.0011 0.001 0.0018 0.0024 0.6841 0.0026 o.

Korat 5244 5 0.001 0.0011 0, 001 0.002 0.0015 0.0015 0 ,001 0.001 002 0.001 0.001 0.001 0.001 0.0011 0.0011 0.0013 0.0013 0.0022 0.6983 0.002 o.

Korat 5284 7 0.001 0.001 0, 001 0.001 0.0021 0.002 0 ,002 0.001 00330.001 0.0014 0.0013 0.001 0.0017 0.0017 0.0013 0.001 0.1027 0.5843 0.0074 o.

Korat 5344 5 0.001 0.001 0, 0011 0.001 0.001 0.0013 0 ,0065 0.002 005 0.0017 0.0014 0.001 0.0014 0.0021 0.002 0.0024 0.0034 0.0041 0.6631 0.0053 o.

Korat 5481 5 0.001 0.0017 0, 0016 0.0045 0.001 0.0064 0 ,0014 0.0017 00280.0014 0.0019 0.0013 0.0017 0.0019 0.0024 0.0033 0.0019 0.0115 0.6558 0.0034 o.

Korat 5512 5 0.001 0.0013 0. 0011 0.0037 0.0013 0.0028 0 ,001 0.001 002 0.0019 0.0013 0.0017 0.002 0.0011 0.0012 0.0021 0.0013 0.0057 0.6975 0.0028 o.

Korat 5514 5 0.0017 0.0021 0, 0022 0.0017 0.001 0.002 0 ,001 0.0017 0011 0.001 0.0015 0.001 0.001 0.0011 0.0011 0.001 0.001 0.0025 0.6633 0.0023 o.

Korat 5595 5 0.001 0.001 0, 0016 0.0023 0.002 0.0026 0 ,001 0.001 0011 0.002 0.0017 0.0026 0.0015 0.0035 0.0052 0.0029 0.002 0.0954 0.576 0.0105 o.

Korat 5597 7 0.001 0.002 0, 0032 0.0032 0.0016 0.0033 0 ,0017 0.002 00730.002 0.0037 0.0034 0.0027 0.0043 0.003 0.0054 0.001 0.0474 0.6235 0.0046 o.

Korat 6375 5 0.001 0.0012 0, 0013 0.001 0.0034 0.0021 0 ,003 0.002 00420.002 0.0022 0.001 0.0158 0.0975 0.0045 0.0051 0.0017 0.0791 0.4248 0.088 o.

Korat 6376 7 0.001 0.0017 0, 0022 0.0019 0.0017 0.0051 0 ,001 0.001 00280.0022 0.0116 0.0017 0.0018 0.0028 0.001 0.0022 0.0017 0.0668 0.4568 0.0718 o.

Korat 6377 5 0.001 0.0011 0. 0014 0.0018 0.0012 0.0514 0 ,0023 0.0023 00450.0025 0.0021 0.003 0.0012 0.0073 0.0016 0.0018 0.0021 0.1067 0.429 0.0903 o.

Korat 6378 5 0.0023 0.0021 0. 0019 0.002 0.0021 0.0191 0 ,0044 0.0022 00870.0036 0.0023 0.0014 0.0032 0.0067 0.0104 0.0018 0.0026 0.1016 0.4425 0.1102 o.

Siamese 2868 7 0.001 0.0021 0, 0014 0.0021 0.0032 0.0053 0 ,001 0.002 006 0.0014 0.0017 0.001 0.001 0.0026 0.005 0.0027 0.0011 0.3468 0.0293 0.3171 o.

Siamese 6686 28 0.0068 0.0021 0, 0022 0.0028 0.0014 0.0012 0 ,0037 0.0025 002 0.1366 0.0042 0.0063 0.0135 0.0029 0.0059 0.0024 0.0023 0.2729 0.002 0.3335 0.

Siamese 6688 10 0.0019 0.0022 0, 0018 0.0059 0.005 0.0036 0 ,0113 0.002 00740.0037 0.0033 0.0027 0.0038 0.0026 0.0027 0.0027 0.0035 0.3084 0.006 0.3178 o.

Siamese 6690 18 0.0014 0.0049 0, 0029 0.0092 0.0019 0.0023 0 ,0013 0.0013 00250.0031 0.0021 0.0015 0.0015 0.0034 0.0025 0.0053 0.0038 0.2853 0.002 0.3935 0.

Siamese 6696 15 0.001 0.0012 0, 0023 0.0024 0.0013 0.0028 0 ,0055 0.0018 00320.0018 0.0013 0.001 0.0021 0.002 0.0035 0.0018 0.0068 0.2498 0.1412 0.3094 o.

Siamese 7839 10 0.001 0.001 0. 0011 0.001 0.0016 0.0014 0 ,003 0.001 0021 0.001 0.001 0.001 0.0011 0.003 0.0012 0.001 0.001 0.2979 0.0073 0.3802 o.

Siamese 8181 10 0.001 0.0017 0, 0019 0.0016 0.0017 0.0014 0 ,0026 0.0016 00140.0016 0.0025 0.0014 0.001 0.0011 0.001 0.0013 0.0016 0.2929 0.0022 0.3904 o.

Siamese 8182 7 0.001 0.001 0, 001 0.0013 0.001 0.0013 0 ,0013 0.0016 00130.0012 0.001 0.0017 0.001 0.0011 0.001 0.0033 0.001 0.3154 0.0023 0.3818 o.

Siamese 8184 13 0.0016 0.0023 0, 0018 0.003 0.0017 0.0169 0 ,0509 0.003 00350.0109 0.003 0.003 0.002 0.0028 0.0026 0.0093 0.0023 0.3273 0.0057 0.3575 0.

Siamese 8185 10 0.0037 0.0033 0, 0025 0.0028 0.0019 0.0076 0 ,0197 0.0033 00450.0138 0.0031 0.002 0.0022 0.004 0.019 0.0051 0.0212 0.259 0.008 0.3659 0.

Siamese 8187 5 0.0016 0.0014 0, 0013 0.0027 0.0022 0.002 0 ,0051 0.0013 00160.0015 0.0013 0.001 0.0013 0.0016 0.0014 0.0019 0.0014 0.2767 0.128 0.3118 o.

Siamese 8251 13 0.0024 0.0075 0. 0029 0.0014 0.001 0.0266 0 ,01 1 0.001 00250.0019 0.0024 0.0025 0.0022 0.0055 0.001 0.0011 0.0026 0.2803 0.0854 0.2768 0.

Siamese 8253 5 0.001 0.002 0, 0037 0.0023 0.0029 0.0026 0 ,0121 0.001 00380.0015 0.0012 0.0013 0.0016 0.0025 0.001 0.0029 0.0042 0.3692 0.0088 0.2998 0.

Siamese 8258 10 0.0011 0.0034 0, 0011 0.002 0.0011 0.0015 0 ,0015 0.0013 00150.002 0.001 0.0013 0.001 0.0016 0.0019 0.0013 0.0027 0.2845 0.0262 0.3747 0.

Siamese 8259 5 0.0013 0.0014 0, 0027 0.0042 0.0043 0.0159 0 ,0036 0.002 00220.0026 0.0034 0.0017 0.002 0.0024 0.0027 0.0027 0.0025 0.2744 0.0365 0.3478 0.

Singapura 3428 39 0.001 0.0024 0, 0019 0.007 0.0035 0.002 0 ,0042 0.0026 00390.0027 0.0028 0.0053 0.002 0.003 0.0026 0.0019 0.0046 0.0455 0.0172 0.0123 O.

Singapura 3919 26 0.001 0.0012 0, 0012 0.001 0.001 0.0013 0 ,0013 0.0013 00250.001 0.0012 0.001 0.0013 0.0014 0.0013 0.001 0.001 0.0016 0.001 0.0021 0.

Singapura 4464 7 0.001 0.0012 0, 0013 0.001 0.001 0.0027 0 ,0021 0.0012 00170.001 0.0011 0.0011 0.0013 0.0016 0.001 0.001 0.0017 0.0028 0.0011 0.0011 0.

Singapura 4467 7 0.001 0.001 0. 0011 0.001 0.001 0.0012 0 ,0013 0.001 00180.001 0.001 0.001 0.001 0.001 0.001 0.001 0.0021 0.0025 0.0013 0.0014 0.

Singapura 4468 31 0.001 0.0013 0, 0013 0.0013 0.001 0.0013 0 ,0013 0.0013 00180.0013 0.0013 0.0016 0.0016 0.0013 0.001 0.0011 0.0013 0.0063 0.001 0.0012 0.

Singapura 4469 7 0.001 0.0012 0, 0016 0.001 0.001 0.0013 0 ,0013 0.0013 00390.0013 0.0013 0.0013 0.0013 0.0016 0.001 0.001 0.001 0.0027 0.0013 0.0014 0.

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 t 1 12 13 14 15 16 17 18 19 20

Singapura 4470 5 0, .001 0, .0012 0, .0016 0, .001 0, .001 0, .0013 0, .0013 0, .001 0, .0013 0.0013 0, .0012 0.0013 0, .0013 0, .0016 0, .001 0.0039 0.0011 0, .0026 0, .001 0, .001

Singapura 4471 5 0, .001 0, .0012 0, .0016 0, .001 0, .001 0, .001 0, .0013 0, .001 0, .0086 0.0013 0, .0014 0.0013 0, .0013 0, .0012 0, .001 0.001 0.0044 0, .0016 0, .0037 0, .001

Singapura 4472 5 0, .001 0, .0012 0, .0029 0, .0021 0, .0013 0, .0011 0, .0013 0, .001 0, .0768 0.0013 0, .0029 0.0028 0, .0016 0, .0013 0, .0013 0.0011 0.001 0, .0022 0, .0013 0, .001

Singapura 4473 18 0, .002 0, .0012 0, .0015 0, .0013 0, .005 0, .0013 0, .0046 0, .002 0, .0019 0.0023 0, .0016 0.004 0, .0039 0, .001 0, .001 0.001 0.001 0, .0027 0, .0022 0, .002

Singapura 4474 28 0, .0037 0, .002 0, .0044 0, .0044 0, .0027 0, .0023 0, .0051 0, .0039 0, .0373 0.0026 0, .0049 0.0037 0, .002 0, .0043 0, .0026 0.0016 0.0026 0, .0383 0, .0019 0, .005

Singapura 4485 31 0, .0016 0, .0019 0, .002 0, .002 0, .001 0, .002 0, .0023 0, .001 0, .0056 0.002 0, .002 0.0022 0, .0023 0, .0044 0, .0016 0.0017 0.0017 0, .0028 0, .0045 0, .003

Singapura 4486 18 0, .0012 0, .0019 0, .0027 0, .001 0, .001 0, .0023 0, .0019 0, .0019 0, .0067 0.0016 0, .0014 0.0016 0, .0015 0, .0043 0, .0013 0.0013 0.0013 0, .0044 0, .0013 0, .001

Singapura 4487 18 0, .001 0, .0058 0, .0045 0, .0016 0, .0015 0, .001 0, .0031 0, .0012 0, .0055 0.0028 0, .0028 0.002 0, .0014 0, .0016 0, .002 0.001 0.0029 0, .0068 0, .0381 0, .003

Singapura 4488 21 0, .0012 0, .0027 0, .0041 0, .002 0, .001 0, .0064 0, .0027 0, .0016 0, .0196 0.0036 0, .0024 0.0011 0, .0013 0, .0017 0, .0013 0.0014 0.0015 0, .0038 0, .0022 0, .003

Singapura 6597 5 0, .001 0, .001 0, .001 0, .001 0, .001 0, .001 0, .001 0, .001 0, .0018 0.001 0, .001 0.001 0, .001 0, .001 0, .001 0.001 0.001 0, .0061 0, .0013 0, .001

Singapura 6975 21 0, .0016 0, .0015 0, .002 0, .0019 0, .001 0, .0023 0, .003 0, .0017 0, .0152 0.0019 0, .002 0.001 0, .002 0, .0018 0, .0014 0.0011 0.0017 0, .0072 0, .001 0, .002

o

Table 19 - Population clustering of each pedigreed individual in the database by SNPs at K = 17

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Persian 1250 9 0.0065 0.0352 0.0036 0, .001 0, ,0034 0, .0042 0.0203 0, .0035 0.0022 0.0068 0.885 0.0044 0.0073 0, .0052 0.0029 0, .0046 0.0039

Persian 1939 4 0.0011 0.0013 0.001 0, .001 0, ,0019 0, .0027 0.001 0, .001 0.001 0.001 0.9786 0.001 0.002 0, .0011 0.002 0, .001 0.0013

Persian 2071 8 0.0024 0.0046 0.0022 0. .0029 0. ,0605 0, .002 0.002 0, .0023 0.0025 0.003 0.8831 0.0022 0.0027 0, .002 0.011 0, .0057 0.0089

Persian 2088 0 0.0125 0.0039 0.0021 0, .0192 0, ,0684 0, .0023 0.0026 0, .0022 0.0011 0.0079 0.8201 0.0024 0.003 0, .003 0.0198 0, .002 0.0275

Persian 2140 7 0.001 0.0019 0.0014 0, .0041 0, ,002 0, .0044 0.0023 0, .0016 0.001 0.002 0.966 0.0024 0.0018 0, .0019 0.0025 0, .002 0.0017

Persian 2174 2 0.0018 0.0012 0.0015 0, .0035 0, ,0071 0, .002 0.002 0, .0018 0.002 0.0018 0.9602 0.0011 0.0011 0, .0016 0.0055 0, .003 0.0028

Persian 2209 4 0.0031 0.0063 0.003 0, .002 0, ,0068 0, .0051 0.0107 0, .0099 0.0018 0.003 0.8757 0.0045 0.0073 0, .0173 0.0339 0, .0048 0.0048

Persian 2215 21 0.0024 0.0048 0.0052 0, .0118 0, ,0062 0, .0033 0.0039 0, .0033 0.0018 0.0078 0.9129 0.0035 0.0033 0, .0083 0.0125 0, .0054 0.0036

Persian 2890 8 0.0029 0.003 0.0255 0, .0033 0, ,0126 0, .0033 0.0102 0, .0077 0.002 0.0061 0.8256 0.0146 0.0057 0, .0188 0.0133 0, .003 0.0423

Persian 2977 3 0.0406 0.0043 0.0029 0, .0037 0, ,0896 0, .006 0.0202 0, .0123 0.0022 0.0099 0.7185 0.0189 0.0121 0, .0089 0.0172 0, .0105 0.0223

Persian 4143 8 0.0031 0.0033 0.0032 0. .0163 0. ,004 0, .0074 0.003 0, .0081 0.0032 0.0065 0.9183 0.0026 0.0066 0, .0038 0.0039 0, .0028 0.004

Persian 4168 4 0.0048 0.0061 0.0031 0, .0112 0, ,0092 0, .001 0.0067 0, .0041 0.0028 0.0055 0.9075 0.0039 0.0037 0, .0093 0.0074 0, .0083 0.0054

Persian 4169 2 0.002 0.0056 0.001 0, .001 0, ,002 0, .0026 0.002 0, .002 0.001 0.0028 0.9684 0.0011 0.002 0, .0017 0.001 0, .002 0.0018

Persian 4950 0 0.0019 0.002 0.0013 0, .0045 0, ,001 0, .0032 0.0025 0, .0031 0.001 0.003 0.9638 0.0011 0.003 0, .0026 0.0018 0, .0022 0.002

Persian 4953 1 0.001 0.001 0.001 0, .001 0, ,002 0, .001 0.001 0, .001 0.0009 0.001 0.9831 0.001 0.001 0, .001 0.0011 0, .001 0.001

Exotic SH 258 0 0.001 0.001 0.001 0. .0029 0. ,0044 0, .003 0.001 0, .0011 0.001 0.001 0.9748 0.001 0.001 0, .001 0.002 0, .0012 0.0016

Exotic SH 259 3 0.0038 0.0114 0.002 0, .005 0, ,0171 0, .0027 0.0022 0, .0044 0.0032 0.0029 0.9282 0.0021 0.003 0, .0022 0.0024 0, .002 0.0054

Exotic SH 260 4 0.0061 0.0047 0.0048 0, .0044 0, ,0107 0, .002 0.0304 0, .0067 0.001 0.0068 0.8873 0.0063 0.0063 0, .0102 0.0028 0, .004 0.0054

Exotic SH 261 4 0.001 0.002 0.001 0, .001 0, ,0021 0, .0034 0.001 0, .001 0.0007 0.001 0.9791 0.0011 0.001 0, .001 0.0016 0, .001 0.001

Exotic SH 262 4 0.0236 0.0035 0.0076 0, .0029 0, ,0228 0, .0118 0.022 0, .0024 0.0022 0.0079 0.818 0.0113 0.0316 0, .0032 0.0045 0, .002 0.0226

Exotic SH 263 7 0.001 0.002 0.001 0. .0031 0. ,5009 0, .001 0.002 0, .0012 0.001 0.0012 0.0102 0.001 0.0013 0, .0038 0.4582 0, .0032 0.0078

Exotic SH 264 2 0.0011 0.002 0.001 0, .0012 0, ,6721 0, .0019 0.001 0, .0013 0.001 0.0014 0.0029 0.001 0.001 0, .001 0.3058 0, .002 0.0023

Exotic SH 265 3 0.0022 0.0096 0.0024 0, .0015 0, ,3718 0, .002 0.0031 0, .0022 0.001 0.0031 0.0071 0.0019 0.0024 0, .0067 0.5751 0, .0027 0.0053

Exotic SH 266 5 0.0011 0.0031 0.001 0, .001 0, ,4978 0, .0045 0.0021 0, .002 0.0016 0.0015 0.003 0.001 0.001 0, .0014 0.4721 0, .0021 0.0037

Exotic SH 267 4 0.0206 0.0043 0.053 0, .0031 0, ,475 0, .0068 0.0024 0, .0213 0.0133 0.0076 0.0038 0.0145 0.0051 0, .0194 0.2797 0, .0391 0.0308

Exotic SH 268 5 0.0016 0.001 0.001 0. .0036 0. ,5358 0, .003 0.002 0, .0028 0.0018 0.0022 0.0028 0.0018 0.002 0, .0021 0.4301 0, .001 0.0054

Exotic SH 269 4 0.0026 0.0031 0.0018 0, .0034 0, ,6168 0, .0054 0.0024 0, .0031 0.001 0.0026 0.0057 0.002 0.002 0, .0027 0.3267 0, .0032 0.0156

Exotic SH 270 9 0.0473 0.0046 0.007 0, .002 0, ,2903 0, .0024 0.0103 0, .0031 0.0103 0.0388 0.1262 0.0065 0.0387 0, .0092 0.3427 0, .003 0.0577

Exotic SH 271 0 0.002 0.005 0.0013 0, .0102 0, ,3558 0, .0029 0.0029 0, .003 0.001 0.0022 0.0061 0.0033 0.0026 0, .0025 0.5466 0, .0013 0.0514

Exotic SH 272 1 0.0019 0.0019 0.0011 0, .0015 0, ,5542 0, .0013 0.0025 0, .002 0.0011 0.0018 0.0021 0.0012 0.001 0, .0019 0.4172 0, .0028 0.0045

Exotic SH 273 1 0.018 0.0317 0.1202 0. .0049 0. , 1133 0, .0028 0.0104 0, .0078 0.0064 0.0669 0.0078 0.0127 0.0047 0, .0297 0.5325 0, .0127 0.0176

Exotic SH 274 1 0.0045 0.0104 0.002 0, .0015 0, ,3267 0, .0064 0.0043 0, .0055 0.0018 0.0031 0.0243 0.002 0.0023 0, .0094 0.5775 0, .0069 0.0114

Exotic SH 275 0 0.0023 0.0028 0.0011 0, .0064 0, ,544 0, .0033 0.0019 0, .0028 0.001 0.002 0.006 0.0013 0.0018 0, .0025 0.4129 0, .0043 0.0036

Exotic SH 276 2 0.004 0.0079 0.0026 0. .0026 0. ,5834 0, .0038 0.0087 0, .0073 0.003 0.0111 0.0132 0.0054 0.0101 0, .0037 0.321 0, .0029 0.0093

British SH 156 9 0.0043 0.0055 0.0051 0, .0025 0, ,4419 0, .0029 0.0048 0, .004 0.001 0.0027 0.003 0.0024 0.0022 0, .0031 0.4866 0, .003 0.0249

Table 19 - Population clustering of each pedigreed individual in the database by SNPs at K

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

British SH 157 1 0.2103 0 0049 0.0068 0 0238 0.1071 0 0029 0 0176 0 0041 0.002 0.013 0.1852 0.0434 0.0066 0 0059 0.3426 0 0077 0.0161

British SH 158 5 0.0011 0 0011 0.001 0 0022 0.6183 0 002 0 001 0 001 0.001 0.001 0.0028 0.001 0.001 0 001 0.3604 0 0012 0.0029

British SH 159 3 0.0011 0 0011 0.001 0 0027 0.6988 0 0013 0 0011 0 001 0.001 0.001 0.0038 0.001 0.001 0 001 0.2635 0 001 0.0186

British SH 160 5 0.0023 0 0066 0.001 0 0021 0.4154 0 0026 0 002 0 005 0.0012 0.0019 0.0151 0.002 0.002 0 0049 0.5274 0 0036 0.0049

British SH 161 2 0.0038 0 0025 0.0055 0 0062 0.6298 0 0044 0 0081 0 0034 0.0017 0.012 0.0099 0.0127 0.0044 0 0036 0.0022 0 0054 0.2845

British SH 162 17 0.0022 0 0029 0.0209 0 0052 0.5835 0 001 0 0139 0 0027 0.0029 0.0191 0.0049 0.036 0.002 0 0068 0.0019 0 0024 0.2917

British SH 163 15 0.0135 0 0028 0.0078 0 0055 0.5922 0 0034 0 0139 0 0037 0.0111 0.005 0.0015 0.0074 0.0029 0 0028 0.0057 0 0292 0.2916

British SH 164 8 0.0046 0 0016 0.006 0 0284 0.5898 0 0022 0 0133 0 0024 0.0054 0.0066 0.0156 0.0062 0.0038 0 0031 0.0087 0 0035 0.2988

British SH 165 15 0.1608 0 002 0.0035 0 0038 0.4208 0 0108 0 0313 0 0287 0.0118 0.1931 0.002 0.0171 0.0098 0 0042 0.0057 0 0362 0.0584

British SH 166 5 0.0057 0 008 0.0032 0 0058 0.6486 0 0062 0 0017 0 002 0.0031 0.0051 0.0058 0.0034 0.0035 0 0021 0.0043 0 0036 0.2879

British SH 167 18 0.0014 0 0012 0.0011 0 0032 0.6795 0 001 0 0018 0 0011 0.001 0.0015 0.005 0.0013 0.0017 0 0015 0.0058 0 0016 0.2903

British SH 168 15 0.0023 0 0018 0.002 0 0081 0.663 0 0049 0 0025 0 0022 0.0017 0.0029 0.0011 0.0023 0.0015 0 0014 0.0044 0 0022 0.2957

British SH 169 12 0.0059 0 0017 0.0017 0 0022 0.6667 0 005 0 0022 0 001 0.0035 0.0016 0.0047 0.0016 0.0014 0 0017 0.0025 0 0023 0.2943

British SH 170 1 0.0577 0 0023 0.0047 0 0055 0.6134 0 0012 0 0064 0 0022 0.0091 0.0197 0.0064 0.0031 0.0031 0 002 0.002 0 0051 0.2561

British SH 171 0 0.005 0 0027 0.0034 0 0023 0.6278 0 0111 0 0096 0 0014 0.0015 0.0075 0.0018 0.007 0.0027 0 0015 0.0183 0 0057 0.2907

British SH 172 2 0.0017 0 0011 0.0031 0 0128 0.6267 0 004 0 0012 0 0015 0.0017 0.0013 0.0015 0.0016 0.001 0 0017 0.0487 0 0016 0.2888

British SH 173 2 0.0036 0 004 0.0074 0 0219 0.5993 0 0111 0 0018 0 0025 0.0018 0.0079 0.001 0.0045 0.0017 0 0097 0.0261 0 005 0.2907

Scottish

Fold 5655 12 0.001 0 001 0.002 0 0021 0.8314 0 144 0 001 0 0019 0.002 0.0015 0.002 0.0023 0.0015 0 0012 0.0015 0 001 0.0026

Scottish

Fold 5669 3 0.0015 0 001 0.0011 0 001 0.9817 0 001 0 001 0 001 0.001 0.001 0.0016 0.0011 0.001 0 001 0.001 0 001 0.002

Scottish

Fold 7205 21 0.0061 0 0018 0.002 0 0031 0.8988 0 002 0 0039 0 0082 0.0032 0.005 0.0122 0.0033 0.0018 0 0377 0.002 0 0037 0.0052

Scottish

Fold 7260 6 0.005 0 0027 0.004 0 0052 0.9178 0 008 0 0069 0 0035 0.0066 0.0026 0.0082 0.0019 0.0027 0 0026 0.0081 0 0111 0.0031

Scottish

Fold 8552 16 0.0026 0 0036 0.002 0 0029 0.9355 0 0029 0 0024 0 002 0.001 0.0021 0.0288 0.0018 0.0019 0 0021 0.002 0 0028 0.0037

Scottish

Fold 9823 12 0.0018 0 001 0.001 0 0049 0.9614 0 0023 0 0034 0 0016 0.001 0.0028 0.002 0.0011 0.0015 0 0013 0.0038 0 002 0.0071

Scottish

Fold 9824 4 0.0018 0 001 0.002 0 0012 0.9613 0 0024 0 0026 0 0019 0.001 0.003 0.0047 0.0019 0.0023 0 0026 0.0019 0 0039 0.0045

Scottish

Fold 9825 4 0.0028 0 0031 0.0076 0 0011 0.8719 0 0121 0 0151 0 0111 0.0023 0.0173 0.0054 0.0065 0.005 0 0183 0.0029 0 0083 0.0092

Scottish

Fold 9826 11 0.001 0 0015 0.001 0 0076 0.9714 0 0017 0 001 0 001 0.001 0.0011 0.002 0.001 0.001 0 001 0.0035 0 0013 0.0019

JTable 19 - Population clustering of each pedigreed individual in the database by SNPs at K = 17

Feline ID Missing Groups

Population No. Data 1 8 10 11 12 13 14 15 16 17

Scottish

Fold 0.0016 0.0015 0.0018 0.0044 0.919 0.0123 0.0025 0.0126 0.0041 0.0035 0.0021 0.0036 0.0016 0.0075 0.0054 0.0114 0.0052

Scottish

Fold 0.0038 0.006 0.0034 0.0025 0.9008 0.0036 0.0097 0.0178 0.002 0.0052 0.0057 0.0035 0.0025 0.016 0.0025 0.0091 0.0059

Scottish

Fold 0.0061 0.0022 0.0024 0.0012 0.9544 0.002 0.003 0.002 0.002 0.003 0.0053 0.0031 0.0017 0.0026 0.0027 0.0027 0.0036

Scottish

Fold 0.0028 0.0036 0.0064 0.0037 0.9049 0.0045 0.0033 0.0067 0.0027 0.0115 0.0072 0.0137 0.0037 0.0101 0.0041 0.0029 0.0082

Scottish

Fold 0.0036 0.0018 0.0012 0.0023 0.9601 0.0017 0.0029 0.0031 0.0048 0.002 0.0036 0.002 0.0014 0.0024 0.0016 0.0025 0.003

Scottish

Fold 0.0045 0.0027 0.0023 0.0032 0.9064 0.0127 0.0113 0.0045 0.0139 0.0087 0.0033 0.0033 0.0043 0.0023 0.003 0.008 0.0056

Scottish

Fold 0.0017 0.0013 0.0017 0.0019 0.961 0.0043 0.002 0.0025 0.001 0.0023 0.0047 0.0019 0.0017 0.0033 0.0032 0.0029 0.0026

Scottish

Fold 9965 6 0.0057 0. .0017 0.0086 0 .0066 0.9227 0 .0042 0.0075 0 .003 0.0042 0. .0075 0. .0069 0, .0026 0, .0043 0, .0028 0.001 0, .0063 0, ,0044

Chartreux 1772 8 0.0011 0, .001 0.0019 0, .0016 0.969 0, .0014 0.0025 0, .0021 0.0017 0, .0017 0, ,0026 0, .0015 0, ,001 0, .0028 0.0034 0, .002 0. 0027 Chartreux 2226 2 0.001 0, .001 0.001 0, .001 0.9828 0, .001 0.001 0, .001 0.001 0, .001 0, ,0013 0, .001 0, ,001 0, .001 0.001 0, .001 0. 0019 Chartreux 2229 4 0.0034 0, .0021 0.0021 0, .001 0.949 0, .0036 0.0027 0, .0018 0.0012 0, .0024 0, ,0036 0, .003 0, ,0019 0, .003 0.0071 0, .0031 0. 009 Chartreux 2524 6 0.0023 0, .0033 0.0012 0, .0023 0.9603 0, .0026 0.0014 0, .002 0.0013 0, .0019 0, ,0086 0, .0015 0, ,0015 0, .0021 0.0019 0, .002 0, ,0038 Chartreux 2787 0 0.0046 0. .0038 0.0031 0. .0046 0.8816 0. .0076 0.0072 0. .0091 0.0011 0. .0045 0. ,0033 0, .0035 0, ,0032 0, .0216 0.021 0, .0082 0, ,0119 Chartreux 2805 10 0.0021 0, .0066 0.003 0, .0042 0.8308 0, .045 0.0029 0, .0158 0.002 0, .0029 0, ,0089 0, .0047 0, ,002 0, .0045 0.0481 0, .0086 0, ,0078 Chartreux 2813 19 0.0256 0, .0031 0.0042 0, .0083 0.8391 0, .0049 0.0044 0, .0235 0.0171 0, .0053 0, ,0123 0, .0048 0, ,0186 0, .0099 0.0028 0, .0046 0, ,0114 Chartreux 2979 2 0.0048 0, .0132 0.0033 0, .011 0.9009 0, .0078 0.0036 0, .0093 0.0058 0, .0075 0, ,003 0, .0045 0, ,0037 0, .0058 0.003 0, .0056 0, ,0072 Chartreux 4059 7 0.0048 0, .002 0.0031 0, .0119 0.767 0, .0164 0.0069 0, .0047 0.001 0, .0041 0, ,0941 0, .0025 0, ,0021 0, .0054 0.0033 0, .0076 0, ,063 Chartreux 4063 1 0.0081 0. .0036 0.0024 0. .0024 0.7695 0. .0029 0.0024 0. .0124 0.002 0. .0026 0. ,009 0, .0033 0, ,0016 0, .0553 0.0047 0, .0021 0, , 1157 Chartreux 5606 0 0.0028 0, .0027 0.0107 0, .0281 0.8417 0, .0139 0.003 0, .0046 0.0018 0, .0044 0, ,0282 0, .0049 0, ,0044 0, .006 0.0058 0, .0054 0, ,0317 Chartreux 5609 1 0.005 0, .0154 0.003 0, .0027 0.7679 0, .003 0.0048 0, .0075 0.0036 0, .0083 0, ,0259 0, .0073 0, ,0044 0, .0048 0.0036 0, .017 0. 1158 Chartreux 5611 0 0.002 0, .0053 0.003 0, .0018 0.8149 0, .0021 0.0035 0, .002 0.0019 0, .0027 0, ,0043 0, .0027 0, ,002 0, .0029 0.0034 0, .0054 0. 1401 American

SH 143 2 0.002 0. .0019 0.0025 0 .0865 0.7379 0 .0022 0.0073 0 .0019 0.001 0. .0038 0. ,0056 0, .0019 0, .0012 0, .0095 0.0024 0, .002 0, ,1304

American

SH 144 0 0.0264 0, .0038 0.0049 0 .0021 0.682 0 .0037 0.0033 0 .005 0.001 0, .0035 0, ,0418 0, .0094 0, .0028 0, .0124 0.002 0, .0111 0, ,1848

American

SH 145 0 0.0095 0, .0018 0.0019 0 .0047 0.6966 0 .0041 0.0025 0 .0054 0.0017 0, .0028 0, ,0106 0, .0051 0, .001 0, .0033 0.0013 0, .01 0, ,2377

Feline ID Missing Groups

Population No. Data 1 8 10 11 12 13 14 15 16 17

American

SH 0.002 0.0063 0.0043 0.0029 0.8084 0.002 0.0149 0.0036 0.002 0.003 0.0077 0.0033 0.0043 0.0055 0.008 0.0035 0.1183

American

SH 0.0011 0.004 0.0019 0.0015 0.7862 0.0017 0.0052 0.0021 0.0011 0.0015 0.0231 0.0021 0.0019 0.0023 0.002 0.002 0.1603

American

SH 0.0116 0.0014 0.0101 0.0028 0.7964 0.006 0.0052 0.0071 0.007 0.0051 0.0078 0.0041 0.0051 0.003 0.0081 0.0035 0.1157

American

SH 0.0045 0.001 0.0035 0.0492 0.764 0.0014 0.0062 0.0053 0.0022 0.005 0.0123 0.0041 0.0084 0.0056 0.002 0.0014 0.1239

American

SH 0.0059 0.0082 0.0032 0.0024 0.8816 0.004 0.0032 0.0039 0.001 0.0021 0.0205 0.0037 0.0061 0.0052 0.0028 0.002 0.0442

American

SH 0.0073 0.0045 0.0036 0.0012 0.7815 0.0014 0.0028 0.0046 0.001 0.0021 0.0082 0.0043 0.0029 0.0582 0.0032 0.0055 0.1077

American

SH 0.0171 0.025 0.0048 0.0077 0.7794 0.0292 0.0055 0.0063 0.0027 0.0027 0.0329 0.0057 0.0066 0.0063 0.0342 0.007 0.0269

American

SH 0.002 0.0078 0.0022 0.0032 0.4852 0.0031 0.014 0.0038 0.2669 0.0034 0.0053 0.0055 0.0033 0.0047 0.0047 0.0056 0.1793

American

SH 0.0019 0.001 0.002 0.002 0.3294 0.0021 0.0019 0.0021 0.4475 0.0019 0.0013 0.0019 0.0012 0.0031 0.0024 0.0016 0.1967

American

SH 0.0018 0.0018 0.0015 0.0018 0.5073 0.003 0.0036 0.002 0.2659 0.0019 0.001 0.0018 0.0018 0.0024 0.0025 0.0025 0.1975

American

SH 2259 10 0.0019 0, .002 0.0019 0, .0017 0.4169 0, .0048 0, ,0023 0, .0039 0, ,3483 0, .0026 0, ,0036 0, .0015 0.0014 0, .002 0.0089 0, .0035 0. 1928

Sphynx 277 4 0.001 0, .0011 0.001 0, .001 0.3356 0, .0016 0, ,0016 0, .0018 0, 4479 0, .0013 0, ,001 0, .001 0.001 0, .0017 0.0025 0, .0011 0. 1978

Sphynx 278 5 0.001 0, .001 0.0014 0, .001 0.4198 0, .0026 0, ,0018 0, .0017 0, ,3616 0, .0013 0, ,001 0, .0012 0.0011 0, .0017 0.0025 0, .0019 0. 1973

Sphynx 279 0 0.0017 0, .0012 0.0016 0, .001 0.3979 0, .0051 0, ,0037 0, .003 0, ,3736 0, .0018 0, ,001 0, .0021 0.0026 0, .0028 0.0024 0, .0025 0. 196

Sphynx 280 6 0.001 0. .001 0.001 0. .001 0.4469 0. .0015 0. ,001 0, .0015 0, ,3394 0, .001 0, ,0012 0, .001 0.001 0, .0012 0.0034 0, .0012 0. 1956

Sphynx 281 14 0.0018 0, .0013 0.0013 0, .001 0.3006 0, .0037 0, ,0018 0, .0021 0, ,4772 0, .0017 0, ,001 0, .0012 0.0017 0, .001 0.003 0, .0014 0. 1982

Sphynx 282 8 0.0016 0, .0018 0.0021 0, .0022 0.3815 0, .001 0, ,0022 0, .0028 0, ,399 0, .0022 0, ,0021 0, .0012 0.0021 0, .003 0.0028 0, .0033 0. 1892

Sphynx 283 1 0.0016 0, .0014 0.001 0, .0015 0.4405 0, .0022 0, ,0025 0, .0023 0, ,3356 0, .0017 0, ,001 0, .0018 0.0012 0, .0027 0.0037 0, .0018 0. 1975

Sphynx 284 14 0.0024 0, .001 0.0018 0, .0018 0.3368 0, .0043 0, ,002 0, .0044 0, ,4309 0, .0021 0, ,0017 0, .0019 0.0024 0, .0025 0.0031 0, .0018 0. 1991

Sphynx 285 4 0.0018 0. .0018 0.0028 0. .0021 0.5105 0. .0021 0. ,0051 0, .0059 0, ,2202 0, .0033 0, ,0024 0, .0024 0.0023 0, .0279 0.0108 0, .0052 0. 1934

Sphynx 286 22 0.0033 0, .0035 0.0062 0, .0018 0.5472 0, .0085 0, ,0109 0, .0038 0, , 1648 0, .0035 0, ,0018 0, .0034 0.0052 0, .0033 0.0282 0, .0106 0. 194

Sphynx 287 9 0.008 0, .0073 0.0055 0, .0028 0.4222 0, .0031 0, ,0277 0, .0152 0, 2448 0, .0076 0, ,002 0, .0088 0.0084 0, .0592 0.0109 0, .009 0. 1575

Sphynx 288 2 0.001 0. .001 0.001 0. .0018 0.4424 0. .0012 0. ,002 0, .0021 0, ,3381 0, .0014 0, ,001 0, .0015 0.0011 0, .0016 0.0028 0, .0018 0. 1982

Sphynx 289 1 0.0023 0, .001 0.002 0, .0033 0.4343 0, .0022 0, ,0025 0, .0031 0, ,3368 0, .0021 0, ,0046 0, .0019 0.0018 0, .0024 0.0036 0, .0028 0. 1933

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Sphynx 290 2 0.019 0.0033 0.0051 0.0041 0.0021 0.003 0.0032 0.0052 0.9015 0.0065 0.0099 0.0064 0.0065 0.0065 0.0019 0.0064 0.0094

Sphynx 291 2 0.0034 0.008 0.011 0.1264 0.0022 0.0035 0.0056 0.1043 0.6641 0.0092 0.0028 0.0064 0.0046 0.0144 0.0095 0.0072 0.0174

Sphynx 292 1 0.0114 0.0097 0.0121 0.0037 0.0093 0.0075 0.0074 0.0032 0.8437 0.0103 0.0103 0.0093 0.0068 0.0056 0.004 0.0273 0.0183

Sphynx 293 1 0.0074 0.0929 0.0113 0.0109 0.0029 0.0046 0.0032 0.0129 0.75 0.0113 0.0047 0.0049 0.0167 0.0166 0.0061 0.0101 0.0336

Japanese

BT 1949 6 0.0045 0.0053 0.0168 0.0019 0.0016 0.0019 0.0046 0.0039 0.8907 0.005 0.0051 0.0212 0.0157 0.0053 0.0042 0.007 0.0053

Japanese

BT 1966 4 0.0064 0.0179 0.0132 0.0025 0.0053 0.0055 0.0068 0.0134 0.7273 0.0113 0.0109 0.1083 0.0215 0.0158 0.0022 0.0035 0.0282

Japanese

BT 2661 0 0.0199 0.0014 0.0037 0.0137 0.0015 0.0025 0.0029 0.0088 0.9117 0.0036 0.0016 0.0075 0.0022 0.0023 0.001 0.0023 0.0134

Japanese

BT 2663 4 0.0038 0.0013 0.0014 0.0017 0.0017 0.001 0.0024 0.0017 0.9681 0.001 0.004 0.0017 0.002 0.002 0.002 0.0021 0.0021

Japanese

BT 2666 4 0.0222 0.0022 0.0155 0.0039 0.0019 0.002 0.0043 0.0111 0.8141 0.0165 0.0028 0.0173 0.0376 0.0033 0.0075 0.0109 0.0269

Japanese

BT 2668 0 0.044 0.0029 0.0065 0.0095 0.005 0.0037 0.0064 0.0082 0.8562 0.0077 0.0027 0.0071 0.0053 0.0093 0.0055 0.006 0.0141

Japanese

BT 2973 3 0.0102 0.006 0.0298 0.0063 0.0133 0.0044 0.0068 0.0068 0.8237 0.0071 0.0017 0.0039 0.0049 0.0256 0.0061 0.0055 0.0379

Japanese

BT 3324 1 0.0518 0.0049 0.0084 0.0028 0.0024 0.0124 0.0151 0.0103 0.7868 0.0047 0.0053 0.0107 0.0076 0.0125 0.0026 0.052 0.0095

Japanese

BT 3355 2 0.0061 0.0017 0.0048 0.0013 0.001 0.0018 0.0017 0.0025 0.9616 0.0026 0.0017 0.0027 0.0028 0.0019 0.001 0.0019 0.0029

Japanese

BT 3356 3 0.0293 0.0032 0.0062 0.0035 0.0023 0.0037 0.0037 0.006 0.8994 0.0032 0.0036 0.0069 0.0082 0.0042 0.0028 0.0056 0.0083

Japanese

BT 3523 1 0.0032 0.0036 0.008 0.1776 0.0243 0.0097 0.0023 0.0055 0.694 0.009 0.0107 0.0058 0.0046 0.0114 0.0063 0.0058 0.0181

Japanese

BT 3621 12 0.001 0.001 0.002 0.9746 0.001 0.001 0.0017 0.0028 0.001 0.0017 0.001 0.0012 0.0017 0.0028 0.001 0.003 0.0015

Japanese

BT 3622 6 0.0011 0.001 0.001 0.9805 0.0011 0.001 0.001 0.0011 0.001 0.001 0.0028 0.0011 0.0017 0.0012 0.001 0.001 0.0014

Japanese

BT 3673 0 0.0088 0.0025 0.0029 0.9363 0.0047 0.0081 0.003 0.0051 0.002 0.0028 0.0039 0.0029 0.002 0.002 0.0048 0.0041 0.0041

Japanese

BT 3691 11 0.001 0.001 0.001 0.978 0.0023 0.002 0.001 0.0018 0.001 0.001 0.0011 0.0012 0.001 0.0013 0.0023 0.0015 0.0015

Japanese 3693 8 0.0043 0.001 0.0049 0.8894 0.0014 0.0027 0.0067 0.0491 0.0067 0.005 0.001 0.0062 0.0081 0.0033 0.002 0.002 0.0062

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

BT

Japanese

BT 4027 23 0.003 0. .0056 0.0127 0. .9234 0.0047 0. .0103 0. .0022 0, .003 0, ,0046 0, .0031 0, ,0056 0, .004 0.005 0, .0012 0.004 0, .0041 0.0035

Japanese

BT 4030 14 0.0019 0, .0014 0.0019 0, .9649 0.0032 0, .004 0, ,0027 0, .003 0, ,001 0, .0021 0, ,002 0, .0013 0.0015 0, .002 0.003 0, .002 0.0021

Japanese

BT 4043 14 0.0024 0, .0014 0.0028 0, .9753 0.001 0, .0011 0, ,001 0, .0016 0, ,001 0, .0016 0, ,001 0, .0027 0.002 0, .0011 0.0012 0, .001 0.0018

Cornish

Rex 2497 14 0.0042 0, .002 0.0029 0, .9274 0.0021 0, .0053 0, ,0049 0, .0094 0, ,0011 0, .0028 0, ,0079 0, .0073 0.0033 0, .0037 0.0041 0, .0084 0.0032 Cornish

Rex 2863 8 0.0068 0. .002 0.0077 0. .925 0.0018 0. .006 0. ,006 0, .0074 0, ,0027 0, .0038 0, ,0027 0, .004 0.0057 0, .0046 0.0032 0, .0062 0.0044 Cornish

Rex 2872 5 0.0026 0, .0046 0.0042 0, .9097 0.0056 0, .003 0, ,0045 0, .0052 0, ,0181 0, .0089 0, ,0021 0, .0045 0.0096 0, .0023 0.0057 0, .0047 0.0047 Cornish

Rex 3283 16 0.0126 0, .03 0.0028 0, 7524 0.0036 0, .002 0, ,0223 0, .0107 0, ,0055 0, .0402 0, ,0064 0, .0072 0.0125 0, .0416 0.0056 0, .0255 0.0191 Cornish

Rex 3306 0 0.0124 0, .0132 0.0132 0, .7913 0.0172 0, .0064 0, ,0151 0, .0077 0, ,0012 0, .0246 0, ,0109 0, .0351 0.0106 0, .0036 0.0023 0, .0094 0.0258 Cornish

Rex 3931 12 0.0301 0, .0025 0.0055 0, .008 0.5654 0, .0078 0, ,0166 0, .0059 0, ,0065 0, .0108 0, ,0034 0, .0185 0.0032 0, .0036 0.0066 0, .0223 0.2832 Cornish

Rex 3955 4 0.0039 0. .0181 0.0056 0. .8535 0.0047 0. .0041 0. ,0063 0, .0122 0, ,0055 0, .0098 0, ,0016 0, .0038 0.0084 0, .0112 0.0358 0, .0074 0.0081 Cornish

Rex 3992 2 0.0021 0, .001 0.001 0, .9741 0.0018 0, .0021 0, ,0015 0, .0017 0, ,001 0, .002 0, ,002 0, .0015 0.0015 0, .002 0.001 0, .002 0.0017 Cornish

Rex 5790 1 0.002 0, .0023 0.0022 0, .9484 0.0025 0, .002 0, ,0046 0, .0048 0, ,0032 0, .0038 0, ,0017 0, .0022 0.003 0, .0044 0.0021 0, .0075 0.0033 Cornish

Rex 7031 3 0.0021 0, .001 0.0028 0, .9665 0.0018 0, .002 0, ,0018 0, .002 0, ,0012 0, .0028 0, ,0035 0, .002 0.003 0, .0017 0.0015 0, .0022 0.0021 Cornish

Rex 7036 15 0.0053 0, .0082 0.1478 0, .0082 0.0026 0, .0031 0, ,0135 0, .008 0, ,0051 0, .0287 0, ,0032 0, .6506 0.0242 0, .0132 0.0062 0, .0663 0.0059 Cornish

Rex 8528 2 0.0024 0. .003 0.2225 0. .001 0.0011 0. .002 0. ,0092 0, .005 0, ,001 0, .0203 0, ,0025 0, .6794 0.0339 0, .005 0.0017 0, .0035 0.0065 Cornish

Rex 8529 6 0.0034 0, .0197 0.1784 0, .0015 0.0065 0, .0213 0, ,0166 0, .0034 0, ,0055 0, .0052 0, ,0136 0, .6886 0.006 0, .0051 0.009 0, .012 0.0042 Cornish

Rex 8530 2 0.003 0, .0029 0.1972 0, .0014 0.001 0, .0021 0, ,0042 0, .002 0, ,001 0, .0047 0, ,001 0, .7671 0.0038 0, .0022 0.001 0, .0022 0.0032

J able 19 - Population clustering of each pedigreed individual in the database by SNPs at K = 17

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Cornish

Rex 8543 10 0.0069 0 0021 0.0046 0 0138 0.001 0 005 0 002 0 8884 0 0044 0 037 0 0017 0 0057 0.0055 0 0034 0 0014 0 0145 0 0026

Ragdoll 7429 4 0.0035 0 0033 0.1846 0 0064 0.001 0 0034 0 0098 0 0119 0 0026 0 0084 0 002 0 7138 0.0101 0 0083 0 0034 0 0075 0 02

Ragdoll 8362 2 0.0059 0 0021 0.1897 0 0033 0.0018 0 0031 0 0024 0 0092 0 0012 0 0054 0 0043 0 7526 0.0041 0 0033 0 0013 0 0057 0 0046

Ragdoll 8366 0 0.0072 0 1174 0.0959 0 0013 0.0014 0 0023 0 003 0 0026 0 0025 0 0052 0 003 0 5522 0.1919 0 003 0 001 0 0031 0 007

Ragdoll 8367 2 0.0059 0 0028 0.1976 0 0022 0.0043 0 0029 0 0078 0 0042 0 0011 0 0267 0 0028 0 7227 0.0049 0 0049 0 001 0 0041 0 004

Ragdoll 8368 4 0.0035 0 0147 0.1672 0 0012 0.0051 0 0111 0 004 0 0029 0 0027 0 0047 0 0045 0 7554 0.0079 0 0037 0 0022 0 0037 0 0055

Ragdoll 8372 2 0.0402 0 021 0.1437 0 002 0.0033 0 0063 0 0138 0 0148 0 0028 0 0163 0 0167 0 571 0.1194 0 0059 0 0054 0 0062 0 0112

Ragdoll 8377 3 0.0132 0 0058 0.0606 0 0084 0.0039 0 018 0 008 0 0067 0 0018 0 0082 0 0054 0 5088 0.2876 0 0196 0 0061 0 0047 0 0332

Ragdoll 9292 0 0.0051 0 0028 0.1696 0 0022 0.0013 0 0053 0 0024 0 0046 0 0038 0 006 0 0025 0 7707 0.0028 0 0089 0 0024 0 0023 0 0073

Ragdoll 9295 0 0.0045 0 002 0.0046 0 0015 0.0013 0 002 0 0055 0 0069 0 001 0 0066 0 0024 0 0034 0.9344 0 0142 0 002 0 0039 0 0038

Ragdoll 9299 1 0.0027 0 0062 0.0081 0 001 0.0028 0 0017 0 004 0 002 0 0021 0 0056 0 0079 0 0064 0.9385 0 0029 0 001 0 002 0 0051

Ragdoll 9300 0 0.0039 0 0298 0.0234 0 1373 0.0021 0 0043 0 0089 0 0129 0 001 0 3176 0 0094 0 0849 0.3055 0 0124 0 0106 0 0184 0 0175

Ragdoll 9301 2 0.0226 0 1856 0.0085 0 1741 0.0082 0 003 0 0082 0 0167 0 002 0 0135 0 0061 0 0203 0.4666 0 0228 0 0091 0 0157 0 0171

Ragdoll 9302 2 0.003 0 0023 0.0148 0 001 0.001 0 0027 0 0111 0 0089 0 0021 0 1519 0 002 0 038 0.6837 0 0467 0 0013 0 0044 0 025

Ragdoll 9304 2 0.0125 0 003 0.0083 0 0011 0.003 0 0142 0 0049 0 0067 0 002 0 0268 0 002 0 0047 0.8808 0 0135 0 0038 0 0036 0 0091

Ragdoll 9305 2 0.0136 0 0032 0.0076 0 0027 0.002 0 0046 0 0143 0 0243 0 0037 0 0036 0 0025 0 0051 0.8859 0 0101 0 0021 0 0083 0 0065

Maine

Coon 2950 1 0.0041 0 0033 0.0417 0 0174 0.0048 0 0079 0 0234 0 0356 0 0061 0 0324 0 0051 0 0254 0.6881 0 0283 0 0035 0 0457 0 0271

Maine

Coon 2959 2 0.0642 0 005 0.0807 0 0107 0.0049 0 0049 0 0179 0 0069 0 0341 0 0156 0 0028 0 0715 0.6367 0 0136 0 0046 0 0105 0 0155

Maine

Coon 3304 9 0.0088 0 002 0.0151 0 0125 0.0046 0 0102 0 0305 0 0696 0 0035 0 0272 0 002 0 0108 0.7572 0 0116 0 0037 0 0158 0 0148

Maine

Coon 3311 6 0.0035 0 0027 0.0069 0 0058 0.0021 0 005 0 0027 0 0067 0 0083 0 0052 0 002 0 0046 0.9005 0 0148 0 0022 0 0039 0 0231

Coon 3495 3 0.0221 0 006 0.1272 0 0179 0.0087 0 0033 0 0572 0 0087 0 002 0 0563 0 0139 0 1512 0.4019 0 0177 0 0072 0 0685 0 0303

Maine

Coon 3925 2 0.002 0 002 0.0049 0 0401 0.0084 0 0036 0 0107 0 0037 0 0052 0 003 0 0268 0 0037 0.8522 0 0074 0 0056 0 0103 0 0105

Maine

Coon 3941 2 0.008 0 0075 0.0086 0 0021 0.0043 0 006 0 0066 0 0036 0 0027 0 0055 0 0027 0 0108 0.8893 0 0213 0 0032 0 0039 0 0139

Maine

Coon 9198 14 0.0021 0 0016 0.0082 0 002 0.001 0 002 0 0066 0 0052 0 0022 0 0591 0 001 0 0047 0.8941 0 0011 0 001 0 002 0 0061

Maine

Coon 9775 2 0.0042 0 0091 0.0186 0 028 0.0035 0 0075 0 0223 0 0052 0 001 0 0329 0 0032 0 0086 0.8173 0 0145 0 0025 0 008 0 0135

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Maine

Coon 10662 16 0.0047 0, .0044 0.002 0, .001 0.0018 0, .0017 0, ,0034 0, .0069 0, ,2196 0, .0031 0, ,0023 0, .0028 0.6859 0, .0049 0, ,002 0, .0029 0, ,0506

IvldlNc

Coon 11544 8 0.0036 0, .0041 0.0079 0, .0025 0.0026 0, .0028 0, ,0173 0, .0113 0, ,002 0, .0074 0, ,0034 0, .0044 0.9072 0, .011 0, ,0034 0, .0034 0, ,0057 aine

Coon 217 14 0.0534 0, .005 0.0128 0, .1373 0.0072 0, .002 0, ,0071 0, .0107 0, ,0036 0, .0103 0, ,0021 0, .0139 0.3961 0, .161 0, ,0107 0, .0066 0, ,1601

Maine

Coon 218 3 0.0027 0, .0258 0.0107 0, .0061 0.0018 0, .0011 0, ,0054 0, .0536 0, ,0237 0, .0096 0, ,003 0, .0071 0.5809 0, .0884 0, ,0025 0, .0064 0, ,1711

Maine

Coon 219 9 0.0044 0, .0243 0.0025 0, .0111 0.0033 0, .0057 0, ,0071 0, .0067 0, ,0027 0, .0096 0, ,0102 0, .004 0.648 0, .0881 0, ,0023 0, .0044 0, ,1657

Maine

Coon 220 1 0.062 0, .1609 0.012 0, .0244 0.0057 0, .0119 0, ,0034 0, .0882 0, ,0038 0, .0109 0, ,0313 0, .0126 0.1559 0, .2167 0, ,0036 0, .0021 0, ,1946

Maine

Coon 221 8 0.004 0, .0133 0.011 0, .1828 0.0124 0, .0075 0, ,0034 0, .0125 0, , 1474 0, .0048 0, ,0109 0, .0093 0.3585 0, .0705 0, ,002 0, .0109 0, ,1387 iv ftiaine

Coon 222 5 0.0061 0. .0081 0.0054 0. .0496 0.0206 0. .0036 0. ,0094 0, .0155 0, ,002 0, .019 0, ,0236 0, .0065 0.5996 0, .0772 0, ,0039 0, .0039 0, ,146 iviaine

Coon 223 20 0.0045 0, .0031 0.0045 0, .0054 0.0056 0, .0059 0, ,0212 0, .1216 0, ,0018 0, .5962 0, ,0023 0, .0044 0.0321 0, .0775 0, ,0037 0, .0119 0, ,0983

Maine

Coon 224 6 0.0032 0, .0275 0.0028 0, .0152 0.0035 0, .0018 0, ,0043 0, .0078 0, ,002 0, .0096 0, ,0071 0, .0069 0.6352 0, .1041 0, ,0038 0, .0034 0, ,1618

Abyssinian 110 13 0.0709 0. .01 0.041 0. .0175 0.0272 0. .0456 0. ,0126 0, .0118 0, ,0042 0, .0088 0, ,0875 0, .0571 0.1872 0, .2001 0, ,0201 0, .0051 0, , 1932

Abyssinian 111 26 0.0031 0, .0401 0.0095 0, .0534 0.0012 0, .0055 0, ,0028 0, .02 0, ,0071 0, .0184 0, ,0018 0, .0209 0.4782 0, .116 0, ,0016 0, .0026 0, ,2179

Abyssinian 112 10 0.0036 0, .0064 0.0054 0, .0068 0.0077 0, .0258 0, ,0035 0, .0057 0, ,0027 0, .007 0, ,004 0, .0936 0.4985 0, .1167 0, ,0097 0, .0027 0, ,2003

Abyssinian 113 10 0.0051 0, .0088 0.0081 0, .0027 0.0034 0, .0023 0, ,0037 0, .0137 0, ,0222 0, .0075 0, ,0017 0, .0176 0.4555 0, .2409 0, ,0036 0, .0034 0, , 1997

Abyssinian 114 1 0.0069 0, .0056 0.0041 0, .0065 0.0058 0, .0085 0, ,0046 0, .0051 0, ,0963 0, .0047 0, ,0042 0, .0076 0.5434 0, .0907 0, ,0113 0, .0039 0, , 1909

Abyssinian 115 18 0.015 0. .0279 0.0223 0. .004 0.0109 0. .0054 0. ,0042 0, .0324 0, ,003 0, .0899 0, .01 0, .0574 0.0087 0, .0077 0, ,0147 0, .6734 0, ,013

Abyssinian 116 15 0.0029 0, .0072 0.0237 0, .0019 0.0027 0, .0088 0, ,005 0, .0044 0, ,0179 0, .0079 0, ,002 0, .01 0.0065 0, .005 0, ,0063 0, .8827 0, ,0051

Abyssinian 117 5 0.0025 0, .0018 0.002 0, .0018 0.001 0, .001 0, ,002 0, .0026 0, ,0018 0, .0019 0, ,0012 0, .002 0.0018 0, .0039 0, ,001 0, .9701 0, ,0016

Abyssinian 118 4 0.0023 0, .001 0.0011 0, .0042 0.0022 0, .0068 0, ,0046 0, .0049 0, ,0011 0, .0024 0, ,0021 0, .0012 0.001 0, .0049 0, ,0531 0, .9052 0, ,0019

Abyssinian 119 5 0.0609 0, .0099 0.0263 0, .0043 0.0555 0, .0154 0, ,0129 0, .007 0, ,0033 0, .0498 0, ,0337 0, .0114 0.0106 0, .0103 0, ,0093 0, .6646 0, ,0149

Abyssinian 120 10 0.0098 0. .0054 0.0051 0. .0052 0.0104 0. .1715 0. ,0071 0, .0035 0, ,0091 0, .0044 0, ,0174 0, .0068 0.0031 0, .0036 0, ,0015 0, .73 0, ,0062

Abyssinian 121 2 0.0044 0, .002 0.0083 0, .0078 0.002 0, .002 0, ,0059 0, .0111 0, ,002 0, .0105 0, ,002 0, .0227 0.0036 0, .0038 0, ,0023 0, .9031 0, ,0064

Abyssinian 122 4 0.0048 0, .0033 0.0021 0, .0057 0.0157 0, .0728 0, ,0187 0, .0112 0, ,001 0, .0077 0, ,0218 0, .0025 0.002 0, .0097 0, ,003 0, .8142 0, ,0038

Abyssinian 123 18 0.016 0. .01 0.0047 0. .002 0.0686 0. .0075 0. ,0511 0, .0213 0, ,0053 0, .026 0, ,0051 0, .0213 0.0277 0, .0106 0, ,0097 0, .6973 0, ,0157

Abyssinian 6934 9 0.0028 0, .0032 0.0069 0, .0048 0.0035 0, .008 0, ,005 0, .0069 0, ,0016 0, .0166 0, ,0968 0, .0036 0.0062 0, .0057 0, ,0044 0, .821 0, ,003

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Siberian 3273 10 0.0026 0, .0125 0.0022 0, .0033 0, ,0058 0, .1109 0, ,0121 0, .0111 0.0076 0.0022 0.002 0.0054 0.0028 0.0035 0.0047 0.8075 0.0038

Siberian 4593 6 0.002 0, .0102 0.0053 0, .0037 0, ,0031 0, .002 0, ,0094 0, .0068 0.0021 0.0052 0.0035 0.0042 0.0032 0.004 0.003 0.9295 0.0028

Siberian 4829 6 0.0053 0. .002 0.0046 0. .0029 0. ,0017 0, .0032 0, ,0104 0, .0073 0.002 0.02 0.0127 0.011 0.0077 0.0039 0.002 0.9003 0.003

Siberian 4928 6 0.0188 0, .008 0.1093 0, .0067 0, ,0264 0, .0181 0, ,0125 0, .0057 0.0034 0.0221 0.0127 0.1924 0.0034 0.0104 0.002 0.5319 0.0161

Siberian 4930 14 0.0446 0, .006 0.0065 0, .002 0, ,0033 0, .0031 0, ,0036 0, .0058 0.0041 0.0031 0.0016 0.0034 0.0071 0.0048 0.0093 0.8871 0.0047

Siberian 5101 9 0.0019 0, .0058 0.0039 0, .002 0, ,001 0, .0019 0, ,0054 0, .0027 0.002 0.0034 0.0081 0.004 0.009 0.0032 0.0027 0.9403 0.0027

Siberian 5105 2 0.0043 0, .0077 0.0192 0, .0121 0, ,0069 0, .0091 0, ,0123 0, .0085 0.0038 0.0077 0.0114 0.0095 0.0064 0.0104 0.0272 0.8356 0.0079

Siberian 5107 6 0.0046 0, .0035 0.0037 0, .0074 0, , 1061 0, .0989 0, ,0121 0, .0067 0.0016 0.0057 0.0687 0.0022 0.002 0.0032 0.0051 0.6631 0.0055

Siberian 5110 9 0.0601 0, .0042 0.2709 0, .0029 0, ,0016 0, .0923 0, ,0033 0, .0052 0.0021 0.0294 0.002 0.2111 0.0045 0.0047 0.0056 0.2615 0.0386

Siberian 5118 6 0.0041 0, .0012 0.8928 0, .0045 0, ,003 0, .0022 0, ,0049 0, .0063 0.0043 0.0033 0.0073 0.0037 0.0023 0.0525 0.0012 0.003 0.0034

Siberian 5120 18 0.0175 0. .0125 0.8265 0. .0026 0. ,0019 0, .0044 0, ,0124 0, .0084 0.0037 0.012 0.0029 0.0488 0.0159 0.0029 0.002 0.0198 0.0059

Siberian 5632 12 0.0041 0, .0084 0.6525 0, .0155 0, ,0038 0, .0165 0, ,0047 0, .0039 0.0164 0.0346 0.0076 0.1406 0.0655 0.0034 0.0041 0.0106 0.0077

Siberian 6474 18 0.0033 0, .0037 0.8813 0, .0037 0, ,0011 0, .0021 0, ,0028 0, .0059 0.0015 0.0065 0.002 0.0108 0.0093 0.0138 0.002 0.0455 0.0046

Siberian 11562 2 0.0066 0, .0021 0.7764 0, .002 0, ,0146 0, .0046 0, ,0147 0, .0324 0.0307 0.043 0.0075 0.0171 0.0078 0.0077 0.0071 0.0073 0.0184

Siberian 11582 10 0.0083 0, .0034 0.8619 0, .0091 0, ,0021 0, .0048 0, ,0033 0, .039 0.0051 0.0078 0.0032 0.0109 0.0151 0.0062 0.0037 0.0064 0.0097

Siberian 11559 24 0.0029 0. .0021 0.9554 0. .0011 0. ,001 0, .0022 0, ,0034 0, .004 0.0026 0.0039 0.0016 0.0034 0.0031 0.0073 0.001 0.0024 0.0026

Siberian 11560 1 0.0574 0, .0134 0.5024 0, .0201 0, ,021 0, .008 0, ,0077 0, .0268 0.0747 0.1203 0.0138 0.0523 0.0145 0.009 0.0127 0.0066 0.0391

Norwegian

FC 2942 2 0.0037 0.0055 0.8857 0.0045 0.0023 0.0015 0.0051 0.011 0.004 0.0109 0.0019 0.029 0.0113 0.0034 0.0046 0.0059 0.0097

Norwegian

FC 3610 1 0.0047 0.0128 0.9192 0.0018 0.0019 0.0024 0.0137 0.0054 0.0012 0.0058 0.0025 0.0068 0.0096 0.0031 0.0012 0.0045 0.0034

Norwegian

FC 3611 1 0.0189 0.0207 0.8418 0.0025 0.0023 0.0046 0.0075 0.0036 0.0017 0.0094 0.0036 0.0248 0.0053 0.012 0.002 0.0356 0.0037

Norwegian

FC 3612 10 0.004 0.0076 0.9207 0.0052 0.0018 0.002 0.002 0.0024 0.0047 0.0065 0.001 0.0128 0.0144 0.0021 0.0048 0.0027 0.0053

Norwegian

FC 3617 1 0.0072 0.0024 0.3296 0.0089 0.0128 0.0027 0.0114 0.009 0.0075 0.3362 0.0073 0.0719 0.0134 0.0385 0.0118 0.0059 0.1235

Norwegian

FC 3661 2 0.0027 0.0022 0.9535 0.003 0.0019 0.002 0.0044 0.0028 0.0031 0.0029 0.0019 0.0029 0.0028 0.0051 0.0033 0.0025 0.003

Norwegian

FC 4815 0 0.0277 0.0148 0.0825 0.0287 0.0057 0.0475 0.0536 0.1629 0.0088 0.1034 0.0073 0.0381 0.085 0.1571 0.0171 0.0787 0.0809

Norwegian

FC 4816 0 0.0029 0.0025 0.5885 0.0041 0.002 0.013 0.0123 0.0038 0.0026 0.0809 0.0034 0.1778 0.005 0.0033 0.0048 0.0617 0.0314

Norwegian

FC 6004 2 0.1582 0.0155 0.4053 0.0133 0.0316 0.002 0.0185 0.0284 0.0547 0.0687 0.0162 0.037 0.0233 0.0176 0.0131 0.0324 0.0644

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Norwegian

FC 6932 8 0.0414 0, .0475 0.6597 0, .0015 0, ,0023 0, .0054 0, ,0117 0, .0096 0.1112 0.0152 0.002 0.0065 0.0059 0, .0342 0.0085 0, .0309 0.0066

Norwegian

FC 9321 7 0.007 0, .0068 0.7523 0, .005 0, ,0028 0, .0036 0, ,0058 0, .0099 0.013 0.1302 0.0032 0.0248 0.0086 0, .0031 0.002 0, .009 0.0128

Norwegian

FC 10367 0 0.0021 0, .0042 0.1867 0, .0035 0, ,002 0, .0011 0, ,0071 0, .003 0.0026 0.0129 0.0036 0.7461 0.0053 0, .0107 0.0025 0, .0024 0.0042

Norwegian

FC 10682 18 0.0075 0, .002 0.3517 0, .0028 0, ,0046 0, .0031 0, ,0078 0, .0359 0.0046 0.2105 0.0095 0.1456 0.0164 0, .0196 0.0081 0, .0033 0.1671

Norwegian

FC 11548 11 0.0031 0, .0014 0.003 0, .0019 0, ,001 0, .0027 0, ,002 0, .9126 0.0042 0.0426 0.0015 0.0031 0.0041 0, .0039 0.001 0, .0095 0.0024

Norwegian

FC 242 8 0.0056 0, .0137 0.0239 0, .0149 0, ,0031 0, .0072 0, ,0096 0, .396 0.0014 0.1297 0.0153 0.0735 0.147 0, .0118 0.0067 0, .0047 0.1359

Manx 2928 18 0.0041 0, .0073 0.0409 0, .002 0, ,002 0, .0254 0, ,0123 0, .23 0.0055 0.3063 0.0021 0.0588 0.1812 0, .0041 0.0028 0, .0121 0.1031

Manx 2980 9 0.0179 0, .0211 0.1271 0, .0136 0, ,0062 0, .0156 0, ,016 0, .042 0.0067 0.2808 0.0096 0.202 0.0118 0, .0233 0.007 0, .0075 0.1918

Manx 3926 19 0.0185 0, .0029 0.3123 0, .0089 0, ,001 0, .0028 0, ,0046 0, .1822 0.003 0.1788 0.001 0.0926 0.0051 0, .0099 0.001 0, .0029 0.1725

Manx 4378 1 0.0097 0. .0176 0.1723 0. .0035 0. ,0035 0, .0039 0, ,0051 0, .0076 0.0034 0.45 0.0042 0.089 0.0512 0, .0037 0.0044 0, .0028 0.1681

Manx 5757 1 0.0052 0, .0105 0.232 0, .0023 0, ,0019 0, .0066 0, ,0085 0, .0088 0.0106 0.4071 0.0034 0.1137 0.0038 0, .0103 0.0018 0, .0053 0.1682

Manx 6294 0 0.0072 0, .0036 0.0713 0, .0036 0, ,0013 0, .002 0, ,0131 0, .0049 0.003 0.5938 0.0021 0.1068 0.0116 0, .0024 0.0033 0, .0041 0.1659

Manx 6296 1 0.0071 0, .0151 0.0902 0, .0028 0, ,0023 0, .0035 0, ,033 0, .0187 0.0037 0.4694 0.0052 0.1305 0.0307 0, .0072 0.002 0, .0038 0.1748

Manx 6299 3 0.0039 0, .0081 0.2136 0, .0118 0, ,0014 0, .0084 0, ,0056 0, .2642 0.0094 0.1142 0.0024 0.1602 0.009 0, .0031 0.0025 0, .0032 0.1791

Manx 7079 3 0.0021 0. .0051 0.0863 0. .0041 0. ,0019 0, .001 0, ,0069 0, .0087 0.003 0.6472 0.0061 0.0799 0.0094 0, .0039 0.0018 0, .0065 0.1261

Manx 7082 0 0.0039 0, .0041 0.2686 0, .0016 0, ,0015 0, .0016 0, ,0041 0, .0052 0.0039 0.36 0.002 0.159 0.0097 0, .0054 0.001 0, .0056 0.1629

Manx 7083 0 0.0027 0, .0074 0.0887 0, .0019 0, ,0022 0, .0032 0, ,0256 0, .0067 0.0022 0.5248 0.0069 0.1002 0.0267 0, .0037 0.0041 0, .0202 0.1728

Manx 7105 8 0.0142 0, .0023 0.0435 0, .0106 0, ,0591 0, .0126 0, ,6295 0, .0726 0.0024 0.0362 0.0127 0.0242 0.0097 0, .0108 0.0072 0, .0229 0.0295

Manx 7108 5 0.0045 0, .006 0.0212 0, .0033 0, ,0031 0, .012 0, ,2192 0, .5462 0.0015 0.0203 0.0069 0.0344 0.003 0, .0561 0.0047 0, .0101 0.0473

Manx 7112 17 0.0146 0. .0067 0.0712 0. .0059 0. ,001 0, .003 0, ,0072 0, .0888 0.0091 0.4508 0.001 0.1166 0.0185 0, .0154 0.0039 0, .0449 0.1414

Manx 7708 0 0.0169 0, .003 0.0062 0, .0106 0, ,002 0, .002 0, ,0078 0, .0485 0.0028 0.0244 0.0022 0.0055 0.821 0, .0264 0.0039 0, .0047 0.012

Manx 9084 2 0.0229 0, .007 0.0111 0, .0202 0, ,0074 0, .0144 0, ,0327 0, .0062 0.0106 0.2454 0.0048 0.0199 0.4794 0, .0184 0.0632 0, .0158 0.0207

Manx 9091 1 0.0048 0, .331 0.0181 0, .0023 0, ,0037 0, .014 0, , 1974 0, .045 0.0041 0.0283 0.0029 0.0613 0.2251 0, .0251 0.0031 0, .003 0.0309

Egyptian

Mau 1812 0 0.1072 0. .017 0.009 0. .0031 0. ,0189 0, .003 0, ,0834 0, .0363 0.0033 0.0102 0.0073 0.0141 0.625 0, .0273 0.0061 0, .0061 0.0226

Egyptian

Mau 2431 0 0.0165 0, .0103 0.0169 0, .0099 0, ,0021 0, .0191 0, ,0094 0, .0223 0.0038 0.0368 0.003 0.0568 0.7314 0, .005 0.0037 0, .0179 0.0351

Egyptian

Mau 2433 1 0.0038 0, .0048 0.0051 0, .0011 0, ,001 0, .002 0, ,0088 0, .0115 0.0017 0.01 0.0027 0.0144 0.8833 0, .0126 0.0011 0, .0127 0.0233

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Egyptian

Mau 3331 0 0.0073 0 0708 0.0036 0 007 0.0184 0 0054 0 0382 0 0123 0.0019 0.0068 0.0084 0.0048 0.746 0.0016 0.0451 0.0071 0.0154

Egyptian

Mau 3332 0 0.0057 0 0179 0.0071 0 005 0.0256 0 0055 0 0198 0 0138 0.0032 0.0285 0.0107 0.0159 0.7932 0.0084 0.0133 0.0113 0.015

Egyptian

Mau 5545 6 0.0065 0 0175 0.0144 0 0032 0.0027 0 0026 0 0054 0 0167 0.002 0.0058 0.0019 0.0172 0.8843 0.0048 0.0029 0.0073 0.0048

Egyptian

Mau 5553 11 0.0502 0 0171 0.0193 0 0057 0.0024 0 0046 0 006 0 0092 0.0019 0.0134 0.004 0.0208 0.7785 0.0162 0.002 0.0185 0.0302

Egyptian

Mau 5567 0 0.0032 0 0046 0.0261 0 0288 0.0336 0 0037 0 0841 0 0309 0.006 0.0142 0.0035 0.0215 0.6683 0.004 0.0136 0.0196 0.0344

Egyptian

Mau 5568 8 0.0236 0 0063 0.016 0 0054 0.002 0 0183 0 1136 0 0176 0.0032 0.063 0.0054 0.0422 0.6282 0.0059 0.0023 0.0133 0.0337

Egyptian

Mau 7475 0 0.0115 0 0672 0.0046 0 0062 0.0317 0 0238 0 0303 0 0217 0.0024 0.0079 0.0203 0.0287 0.7119 0.0052 0.0051 0.0116 0.0099

Egyptian

Mau 7476 0 0.0036 0 0064 0.0037 0 0039 0.0029 0 002 0 0066 0 004 0.0023 0.0053 0.0036 0.0034 0.8813 0.0029 0.0036 0.0615 0.0031

Egyptian

Mau 7479 0 0.003 0 0076 0.0043 0 0077 0.0056 0 0029 0 2979 0 0047 0.002 0.0047 0.006 0.0035 0.6308 0.0031 0.0033 0.0098 0.0031

Egyptian

Mau 7480 2 0.005 0 0095 0.002 0 002 0.021 0 0071 0 0036 0 0029 0.0018 0.0032 0.0319 0.0024 0.8849 0.0016 0.0028 0.0041 0.0142

Egyptian

Mau 11410 5 0.002 0 0123 0.0833 0 004 0.0073 0 001 0 0036 0 003 0.0217 0.0112 0.0019 0.0195 0.8121 0.004 0.0015 0.0041 0.0076

Turk.

Angora 607 1 0.0123 0 0055 0.0131 0 0043 0.0049 0 01 0 0625 0 0808 0.002 0.033 0.0049 0.0172 0.6397 0.0066 0.0076 0.0825 0.0132

Turk.

Angora 1832 5 0.0021 0 0333 0.0048 0 0023 0.001 0 002 0 0074 0 0044 0.001 0.0079 0.002 0.0025 0.9204 0.0031 0.001 0.0027 0.0021

Turk.

Angora 1845 6 0.0101 0 015 0.0083 0 001 0.0023 0 0024 0 0059 0 0128 0.0097 0.0224 0.0024 0.0194 0.7297 0.0208 0.0021 0.1201 0.0156

Turk.

Angora 2848 0 0.0269 0 0038 0.0064 0 0018 0.0047 0 008 0 0878 0 0112 0.0239 0.0072 0.0054 0.0089 0.7786 0.0076 0.0031 0.004 0.0106

Turk.

Angora 2862 4 0.0041 0 0061 0.0045 0 001 0.0027 0 0021 0 0031 0 0023 0.0019 0.0027 0.0016 0.0082 0.9461 0.0037 0.0047 0.001 0.0041

Turk.

Angora 5552 1 0.0074 0 0031 0.0087 0 003 0.0055 0 0043 0 0085 0 0383 0.0035 0.0172 0.0021 0.0073 0.7871 0.0077 0.03 0.0581 0.0082

Turk. 5563 0 0.0063 0 0055 0.0027 0 0029 0.0031 0 0023 0 0046 0 0064 0.0031 0.0041 0.0072 0.005 0.929 0.0051 0.002 0.0044 0.0063

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Angora

Turk.

Angora 5564 10 0.0049 0. .0019 0.0028 0. .0057 0.0014 0. .002 0. .0033 0, .0057 0, ,002 0, .0042 0, ,0029 0, .0035 0.9505 0, .0024 0, ,001 0, .0022 0.0036 Turk.

Angora 6350 5 0.0318 0, .0117 0.0086 0, .004 0.0103 0, .0799 0, ,087 0, .0112 0, ,0051 0, .0229 0, ,0123 0, .007 0.6462 0, .0047 0, ,0121 0, .0087 0.0364 Turk.

Angora 9541 8 0.0064 0, .0278 0.0066 0, .0024 0.0194 0, .0021 0, ,0854 0, .0181 0, ,0046 0, .0028 0, ,0049 0, .0071 0.7741 0, .0039 0, ,019 0, .0042 0.0111 Turk.

Angora 9542 1 0.003 0, .0074 0.0029 0, .002 0.0063 0, .023 0, ,0289 0, .0351 0, ,0028 0, .0224 0, ,0055 0, .0156 0.7847 0, .0042 0, ,0066 0, .0415 0.0081 Turk.

Angora 9584 1 0.0173 0. .0346 0.0453 0. .0024 0.0032 0. .0038 0. ,0736 0, .0299 0, ,0044 0, .0689 0, ,0024 0, .0811 0.4471 0, .0129 0, ,0032 0, .1383 0.0316 Turk.

Angora 9586 2 0.0035 0, .0074 0.0163 0, .0052 0.0021 0, .0014 0, ,0244 0, .0043 0, ,0024 0, .0055 0, ,0038 0, .0671 0.837 0, .0047 0, ,003 0, .0062 0.0057 Turk.

Angora 9608 2 0.0032 0, .0648 0.0072 0, .0046 0.0039 0, .005 0, ,006 0, .0127 0, ,0016 0, .0089 0, ,0036 0, .0065 0.8319 0, .0072 0, ,0197 0, .0057 0.0075 Turk.

Angora 9609 10 0.0139 0, .0308 0.0048 0, .0022 0.0031 0, .021 0, ,0041 0, .0252 0, ,005 0, .0154 0, ,0056 0, .0068 0.8347 0, .0043 0, ,0052 0, .0115 0.0063 Turk.

Angora 9610 7 0.0034 0, .0128 0.0034 0, .001 0.0187 0, .0078 0, ,0065 0, .0096 0, ,0123 0, .006 0, ,0085 0, .0052 0.8537 0, .0102 0, ,011 0, .0033 0.0266 Turk.

Angora 9611 2 0.02 0. .0027 0.0063 0. .0136 0.0012 0. .008 0. ,0077 0, .0234 0, ,0019 0, .1127 0, ,0028 0, .0121 0.7586 0, .0039 0, ,0089 0, .011 0.0053 Turk.

Angora 9612 9 0.0049 0, .002 0.0055 0, .0422 0.0029 0, .0046 0, ,0404 0, .0304 0, ,0025 0, .7876 0, ,0061 0, .0044 0.0042 0, .0079 0, ,0031 0, .0475 0.0037 Turk.

Angora 9613 1 0.0022 0, .0145 0.0199 0, .0112 0.0122 0, .0057 0, ,0052 0, .0037 0, ,0036 0, .8433 0, ,0266 0, .0092 0.0114 0, .0062 0, ,0099 0, .0051 0.0101 Turk.

Angora 9614 0 0.0294 0, .0139 0.0048 0, .0046 0.0287 0, .0208 0, ,0258 0, .0084 0, ,0019 0, .766 0, ,0355 0, .0125 0.0057 0, .0178 0, ,0026 0, .0111 0.0105 Turk.

Angora 9615 2 0.0076 0, .0043 0.0036 0, .0051 0.0111 0, .0038 0, ,0035 0, .0033 0, ,0018 0, .8982 0, ,018 0, .0048 0.0037 0, .0033 0, ,0159 0, .0032 0.0088 Turkish

Van 1789 6 0.0059 0. .0062 0.0181 0. .0032 0.0022 0. .0025 0. ,0337 0, .0081 0, ,009 0, .8286 0, ,0034 0, .0064 0.018 0, .0032 0, .0025 0, .0406 0.0084 Turkish

Van 3013 1 0.006 0, .0121 0.0043 0, .0308 0.0044 0, .0035 0, ,0111 0, .005 0, ,0012 0, .852 0, ,0031 0, .006 0.0058 0, .0112 0, .0059 0, .0314 0.0061 Turkish

Van 3056 2 0.0049 0, .017 0.0059 0, .0028 0.0037 0, .0061 0, ,0042 0, .0042 0, ,0028 0, .8905 0, ,0165 0, .0096 0.0066 0, .0077 0, .0038 0, .0052 0.0085

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Turkish

Van 4090 7 0.0259 0.0036 0.0068 0.0089 0.0169 0.0053 0.0051 0.0084 0.0019 0.787 0.0808 0.0094 0.0064 0.0034 0.0114 0.0044 0.0144

Turkish

Van 7662 2 0.0044 0.0022 0.0397 0.0344 0.0037 0.0079 0.0075 0.0077 0.001 0.8116 0.0339 0.0197 0.0045 0.0039 0.0076 0.0066 0.0037

Turkish

Van 7663 3 0.0046 0.0051 0.0026 0.0021 0.0044 0.0074 0.0054 0.0055 0.0015 0.9341 0.003 0.0058 0.0036 0.0033 0.0024 0.0041 0.0051

Turkish

Van 7666 1 0.0023 0.005 0.0307 0.0021 0.0172 0.0031 0.014 0.01 0.0029 0.6741 0.17 0.0051 0.0039 0.0209 0.005 0.0285 0.0051

Turkish

Van 7667 17 0.0174 0.0049 0.0862 0.1776 0.0035 0.005 0.0044 0.0469 0.0017 0.6215 0.0019 0.0079 0.004 0.0037 0.0025 0.007 0.0038

Turkish

Van 9322 4 0.0055 0.0101 0.0184 0.002 0.201 0.0143 0.0041 0.004 0.0072 0.6373 0.0036 0.009 0.0439 0.0055 0.0088 0.005 0.0202

Turkish

Van 9535 6 0.002 0.0217 0.0059 0.0026 0.002 0.0017 0.0037 0.0047 0.0027 0.9194 0.0029 0.0095 0.0037 0.0038 0.002 0.0082 0.0036

Turkish

Van 9538 4 0.0105 0.0042 0.0586 0.002 0.0148 0.0033 0.0152 0.0041 0.0175 0.7686 0.0022 0.0064 0.0047 0.0035 0.0493 0.0161 0.019

Turkish

Van 9539 2 0.0061 0.002 0.0134 0.0032 0.0018 0.004 0.0092 0.0052 0.0018 0.8995 0.0021 0.0096 0.0112 0.0037 0.0029 0.0167 0.0076

Turkish

Van 9543 6 0.0047 0.0047 0.0114 0.0095 0.0154 0.0137 0.0179 0.0112 0.0022 0.8275 0.0191 0.0193 0.0097 0.004 0.0035 0.0191 0.0072

Turkish

Van 9563 12 0.0055 0.0043 0.0043 0.0745 0.1379 0.0035 0.0125 0.4148 0.0053 0.0096 0.0038 0.0043 0.0039 0.2531 0.049 0.0062 0.0075 Turkish

Van 9564 0 0.0069 0.0049 0.0764 0.01 0.0354 0.0123 0.0251 0.199 0.0048 0.3185 0.0108 0.0908 0.0052 0.0221 0.0028 0.0037 0.1713

Turkish

Van 9573 2 0.046 0.0028 0.087 0.0052 0.0023 0.0031 0.0139 0.099 0.0039 0.2677 0.0021 0.1381 0.0025 0.0822 0.0019 0.0627 0.1797

Turkish

Van 9574 6 0.0057 0.0228 0.0766 0.0064 0.0025 0.028 0.0801 0.2059 0.0027 0.0873 0.0039 0.2216 0.0119 0.0282 0.0042 0.0332 0.179

Turkish

Van 9575 10 0.0254 0.0032 0.007 0.0125 0.0047 0.0118 0.7715 0.02 0.0047 0.0294 0.0139 0.0285 0.0038 0.0253 0.0047 0.004 0.0295

Turkish

Van 9576 9 0.0093 0.0097 0.0116 0.0106 0.0037 0.0389 0.1459 0.4242 0.0014 0.0252 0.0067 0.0886 0.0074 0.0969 0.0032 0.0154 0.1013

Turkish

Van 9581 12 0.0399 0.0051 0.0998 0.0062 0.0073 0.0495 0.0086 0.2752 0.0063 0.0491 0.0023 0.0861 0.0738 0.0262 0.0172 0.1126 0.1347

Bengal 2518 10 0.0252 0.0014 0.0094 0.0101 0.0029 0.0186 0.033 0.4085 0.0023 0.2062 0.002 0.0851 0.0119 0.0084 0.0054 0.0157 0.1539

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Bengal 3455 3 0.0111 0, .0068 0.0243 0.002 0.0082 0, .0024 0, ,3067 0, .1636 0, , 1962 0.0546 0.004 0.0082 0.0207 0.1638 0.0072 0, .0052 0.015

Bengal 3478 18 0.0132 0, .0051 0.0248 0.0701 0.0048 0, .026 0, ,0113 0, .6078 0, ,0202 0.0643 0.0057 0.0446 0.0064 0.0052 0.0059 0, .0153 0.0694

Bengal 3522 12 0.0073 0. .0028 0.1736 0.0035 0.0017 0. .0092 0. ,0042 0, .012 0, ,0025 0.4492 0.002 0.1066 0.0034 0.0287 0.002 0, .0046 0.1867

Bengal 3541 1 0.0224 0, .0054 0.0121 0.0429 0.0397 0, .0037 0, ,2309 0, .263 0, ,0212 0.0592 0.0197 0.0701 0.0095 0.0098 0.0459 0, .0221 0.1224

Bengal 3550 0 0.0182 0, .0194 0.0483 0.0036 0.0095 0, .0048 0, ,0395 0, .1681 0, ,0178 0.0715 0.0513 0.2959 0.01 0.0168 0.0046 0, .0594 0.1613

Bengal 6678 2 0.0182 0, .0042 0.1058 0.0067 0.001 0, .0029 0, ,022 0, .2968 0, ,0027 0.1273 0.0011 0.1697 0.0131 0.0093 0.0024 0, .0113 0.2055

Bengal 6899 2 0.0139 0, .0085 0.0159 0.0037 0.0039 0, .0031 0, ,5038 0, .1083 0, , 1169 0.0155 0.0033 0.0669 0.0119 0.0273 0.0017 0, .0222 0.0733

Bengal 6902 12 0.6799 0, .0038 0.0087 0.0053 0.0054 0, .0143 0, ,0251 0, .0144 0, ,0033 0.0067 0.0416 0.0672 0.0059 0.0104 0.0026 0, .0389 0.0665

Bengal 6907 6 0.6325 0, .0039 0.0069 0.0069 0.0055 0, .0014 0, ,0071 0, .0155 0, ,0481 0.0168 0.0042 0.1132 0.0162 0.0068 0.011 0, .0088 0.0953

Bengal 8399 6 0.6618 0, .0053 0.0467 0.002 0.0022 0, .0061 0, ,0064 0, .023 0, ,0029 0.0086 0.0031 0.0979 0.0116 0.0296 0.0012 0, .0074 0.0842

Bengal 8400 6 0.7636 0. .002 0.0027 0.0015 0.001 0. .0019 0. ,0046 0, .0028 0, ,0031 0.0034 0.003 0.0959 0.0073 0.0055 0.0012 0, .0035 0.097

Bengal 8766 14 0.7295 0, .0012 0.004 0.0135 0.0061 0, .0028 0, ,0079 0, .0058 0, ,001 0.0056 0.0125 0.0926 0.0032 0.0126 0.0067 0, .0025 0.0925

Bengal 9053 15 0.7742 0, .002 0.0032 0.0019 0.0015 0, .0012 0, ,0022 0, .0036 0, ,001 0.0026 0.001 0.0982 0.0029 0.0034 0.001 0, .002 0.0981

Bengal 9800 6 0.6652 0, .0093 0.0164 0.064 0.0024 0, .0052 0, ,0044 0, .0064 0, ,0118 0.0054 0.0058 0.099 0.006 0.0042 0.003 0, .0093 0.0822

Bengal 10344 12 0.7778 0, .0024 0.0033 0.001 0.001 0, .0034 0, ,006 0, .0035 0, ,004 0.0051 0.0022 0.087 0.0079 0.0013 0.0012 0, .0078 0.0851

Bengal 10946 12 0.6542 0. .0026 0.0033 0.0117 0.0036 0. .0076 0. ,0052 0, .0471 0, ,0562 0.008 0.002 0.0893 0.0019 0.0063 0.002 0, .0053 0.0937

Bengal 11194 1 0.776 0, .002 0.002 0.002 0.0018 0, .0022 0, ,002 0, .0025 0, ,0015 0.0018 0.0022 0.0976 0.0022 0.003 0.0012 0, .002 0.098

Sokoke 1890 2 0.6563 0 .004 0.0092 0.006 0.001 0, .0013 0, ,0073 0, .0195 0, ,0059 0.0141 0.0022 0.0971 0.0255 0.0373 0.0019 0, .0087 0.1028

Sokoke 1898 4 0.8122 0 .0028 0.0028 0.0024 0.0022 0, .0032 0, ,0042 0, .0022 0, ,0037 0.004 0.0012 0.074 0.0019 0.0032 0.004 0, .0043 0.0717

Sokoke 2054 2 0.7688 0 .002 0.0021 0.0025 0.0025 0, .002 0, ,002 0, .0033 0, ,0041 0.0023 0.0017 0.0979 0.0029 0.0023 0.0021 0, .003 0.0985

Sokoke 2061 2 0.7072 0 .0022 0.0148 0.0454 0.0013 0. .005 0. ,0025 0, .0071 0, ,0037 0.0039 0.002 0.0923 0.005 0.0116 0.0025 0, .0026 0.0909

Sokoke 2063 18 0.0038 0 .1292 0.0061 0.0023 0.0025 0, .7543 0, ,0054 0, .0099 0, ,0048 0.0169 0.0036 0.0122 0.0049 0.0225 0.003 0, .0115 0.0071

Sokoke 2067 16 0.0024 0 .003 0.0011 0.001 0.0026 0, .9686 0, ,002 0, .002 0, ,0018 0.0014 0.0021 0.0019 0.0023 0.0026 0.002 0, .001 0.0022

Sokoke 6615 6 0.001 0 .001 0.001 0.0011 0.002 0, .9815 0, ,001 0, .001 0, ,001 0.001 0.002 0.001 0.001 0.0012 0.001 0, .0012 0.001

Ocicat 2933 13 0.0103 0, .0046 0.0028 0.0058 0.0073 0, .9242 0, ,003 0, .003 0, ,0011 0.0025 0.0101 0.0036 0.0042 0.0048 0.0042 0, .0036 0.0049

Ocicat 2951 3 0.0018 0. .0067 0.002 0.0023 0.0244 0. .9116 0. ,002 0, .0053 0, ,0021 0.0025 0.0043 0.0019 0.002 0.002 0.0211 0, .0037 0.0043

Ocicat 2954 6 0.0022 0, .0022 0.0014 0.0012 0.005 0, .968 0, ,002 0, .0021 0, ,001 0.0021 0.002 0.0012 0.001 0.0018 0.0025 0, .0019 0.0024

Ocicat 3514 10 0.0078 0, .008 0.0027 0.0059 0.0936 0, .6685 0, ,0088 0, .0083 0, ,002 0.0063 0.1174 0.0051 0.0053 0.003 0.0044 0, .0161 0.0368

Ocicat 5744 0 0.0041 0, .0061 0.0121 0.0033 0.0161 0, .0044 0, ,0141 0, .5178 0, ,0057 0.0152 0.0075 0.0101 0.0153 0.002 0.2625 0, .0963 0.0074

Ocicat 9966 12 0.0174 0, .0085 0.003 0.0021 0.0021 0, .0084 0, ,011 0, .8611 0, ,0072 0.0044 0.0027 0.0052 0.017 0.0201 0.001 0, .024 0.0048

Ocicat 9967 14 0.002 0. .0027 0.0023 0.0067 0.0169 0. .0037 0. ,0099 0, .8953 0, ,0022 0.0071 0.0153 0.0044 0.0022 0.0096 0.004 0, .011 0.0047

Ocicat 10400 17 0.1411 0, .0104 0.0184 0.0047 0.031 0, .0028 0, ,0073 0, .3582 0, ,0197 0.0468 0.0862 0.1401 0.0405 0.0201 0.0032 0, .0154 0.0542

Ocicat 10652 20 0.011 0, .003 0.0038 0.0113 0.0035 0, .0062 0, ,006 0, .8187 0, ,0022 0.0073 0.0036 0.0051 0.0056 0.0716 0.0272 0, .0065 0.0074

Ocicat 10654 11 0.0038 0. .0012 0.0039 0.0018 0.001 0. .0043 0. ,003 0, .9572 0, ,0062 0.0031 0.001 0.0019 0.0013 0.0041 0.001 0, .0032 0.002

Russian 1834 14 0.005 0, .0036 0.0099 0.0051 0.002 0, .0077 0, , 1095 0, .735 0, ,0043 0.0376 0.0025 0.0115 0.0104 0.0185 0.0133 0, .0052 0.019

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Blue

Russian

Blue 1835 13 0.0052 0.0272 0.1582 0.0055 0.0064 0.001 0.0117 0.6653 0.0218 0.0157 0.0026 0.0107 0.0128 0.0183 0.02 0.0067 0.0109

Russian

Blue 2505 6 0.0047 0.0123 0.0034 0.0309 0.0564 0.0114 0.0051 0.7558 0.0163 0.0085 0.0024 0.0058 0.003 0.0351 0.0056 0.0115 0.0318

Russian

Blue 4068 8 0.0097 0.012 0.028 0.0026 0.0051 0.0072 0.0724 0.1258 0.0045 0.0752 0.0203 0.0113 0.0098 0.1044 0.0172 0.4776 0.017

Russian

Blue 4072 2 0.0012 0.002 0.0023 0.0028 0.002 0.0043 0.0051 0.0977 0.002 0.0064 0.0032 0.0057 0.002 0.8561 0.0012 0.0029 0.0031

Russian

Blue 4074 14 0.0703 0.0027 0.4326 0.0085 0.0043 0.04 0.1037 0.0966 0.0033 0.0339 0.0881 0.0213 0.0079 0.0074 0.0113 0.0599 0.0081

Russian

Blue 4076 9 0.03 0.01 0.039 0.0039 0.002 0.0059 0.0035 0.4242 0.0048 0.0651 0.0013 0.2547 0.0022 0.014 0.0029 0.0839 0.0526

Russian

Blue 4077 2 0.0558 0.0157 0.0121 0.0078 0.0048 0.0026 0.045 0.7749 0.0073 0.0074 0.0071 0.0075 0.0136 0.0156 0.0021 0.0104 0.0103

Russian

Blue 4078 21 0.0112 0.0114 0.0105 0.0169 0.0311 0.0213 0.0115 0.6287 0.0112 0.0548 0.0114 0.0171 0.0288 0.0841 0.0187 0.0102 0.0211

Russian

Blue 4302 14 0.0029 0.0029 0.0025 0.0022 0.0042 0.0047 0.003 0.9139 0.0015 0.0038 0.0157 0.0032 0.0042 0.0205 0.0063 0.0055 0.003

Russian

Blue 4867 6 0.0039 0.0048 0.0045 0.0151 0.0014 0.0027 0.0088 0.9054 0.0025 0.0071 0.0026 0.0063 0.0167 0.0059 0.0025 0.0044 0.0054

Russian

Blue 4869 9 0.0314 0.0097 0.0185 0.0058 0.0122 0.0034 0.0122 0.7102 0.0122 0.0151 0.0104 0.005 0.0068 0.1176 0.0032 0.0169 0.0095

Russian

Blue 5216 0 0.0045 0.0818 0.0424 0.0021 0.0026 0.0017 0.005 0.5951 0.0028 0.0128 0.0011 0.0194 0.1819 0.0145 0.0047 0.0187 0.0089

Russian

Blue 5630 2 0.0036 0.0054 0.0039 0.0275 0.004 0.0053 0.0136 0.8921 0.0023 0.0052 0.0045 0.0029 0.0066 0.0034 0.0092 0.0064 0.0041

Russian

Blue 5631 6 0.0075 0.0156 0.0049 0.003 0.003 0.0196 0.0095 0.7978 0.0109 0.0076 0.0034 0.012 0.0724 0.0114 0.004 0.004 0.0134

Russian

Blue 5704 12 0.0048 0.005 0.005 0.0354 0.004 0.0435 0.031 0.0949 0.0018 0.0071 0.0023 0.0043 0.003 0.7369 0.0139 0.0022 0.0049

Russian

Blue 5709 5 0.0048 0. ,0023 0.0025 0, .009 0, ,0211 0, .001 0, ,0056 0, .0858 0, ,0058 0, .002 0, ,0066 0, .0025 0, ,0016 0, .7793 0, ,0461 0, .0148 0.0092

Aust. Mist 4716 10 0.0013 0. .004 0.0035 0. .002 0. ,001 0, .002 0, ,0069 0, .0865 0, ,002 0, .0043 0, ,0018 0, .0057 0, ,0865 0, .7681 0, ,003 0, .0021 0.0194 Aust. Mist 4718 20 0.0085 0, .0031 0.0161 0, .0039 0, ,0021 0, .0031 0, ,0043 0, .0923 0, ,0018 0, .0182 0, ,0012 0, .0104 0, ,0043 0, .8184 0, ,002 0, .0037 0.0066

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Aust. Mist 4720 1 0.0189 0 .0035 0.0129 0, .0155 0, ,0078 0, .0051 0, ,029 0, .1144 0, ,0077 0.0256 0.0113 0.0064 0.006 0, .7032 0.0058 0, .0141 0.0129

Aust. Mist 4722 2 0.0153 0 .0041 0.0118 0, .0031 0, ,003 0, .0035 0, ,0047 0, .3428 0, ,0097 0.0089 0.0021 0.0078 0.005 0, .4904 0.0061 0, .0756 0.0062

Aust. Mist 4723 1 0.0047 0 .0044 0.0082 0. .0028 0. ,0158 0, .0073 0, ,0142 0, .0881 0, ,0016 0.0291 0.0054 0.007 0.0043 0, .753 0.0404 0, .0031 0.0106

Aust. Mist 4724 6 0.0023 0 .0117 0.0025 0, .0028 0, ,003 0, .0053 0, ,0058 0, .0995 0, ,0013 0.0043 0.0031 0.0076 0.0036 0, .827 0.003 0, .0142 0.003

Aust. Mist 4725 8 0.0079 0 .0026 0.1771 0, .0096 0, ,0046 0, .002 0, ,002 0, .0801 0, ,0035 0.0058 0.0162 0.0072 0.0021 0, .6691 0.0012 0, .0031 0.0059

Aust. Mist 4727 2 0.012 0 .0021 0.0015 0, .0041 0, ,0024 0, .0019 0, ,0044 0, .0973 0, ,002 0.0028 0.0072 0.0023 0.0019 0, .8403 0.0116 0, .0038 0.0025

Aust. Mist 4731 3 0.0019 0 .0038 0.0039 0, .0023 0, ,0046 0, .0015 0, ,0054 0, .1014 0, ,0027 0.0037 0.0019 0.0034 0.0021 0, .8452 0.0059 0, .0049 0.0054

Aust. Mist 4736 16 0.0058 0 .1341 0.0457 0, .001 0, ,001 0, .002 0, ,005 0, .1049 0, ,0036 0.0319 0.0015 0.0547 0.0105 0, .5603 0.001 0, .0164 0.0205

Aust. Mist 4739 2 0.0326 0 .0087 0.0589 0, .03 0, ,0061 0, .0041 0, ,0336 0, .4825 0, ,0077 0.0347 0.008 0.013 0.0267 0, .1074 0.0047 0, .1187 0.0226

Aust. Mist 6184 0 0.0074 0 .0031 0.0042 0, .0031 0, ,0032 0, .0019 0, ,0127 0, .1207 0, ,0025 0.0176 0.0064 0.0162 0.0066 0, .7254 0.0017 0, .0606 0.0067

Aust. Mist 6187 1 0.0079 0 .002 0.0185 0. .002 0. ,001 0, .002 0, ,0043 0, .103 0, ,0077 0.0059 0.0028 0.0094 0.0045 0, .813 0.001 0, .008 0.007

Aust. Mist 6188 5 0.0024 0 .0022 0.0016 0, .0018 0, ,0019 0, .0025 0, ,0021 0, .1 0, ,001 0.002 0.002 0.0021 0.002 0, .8705 0.002 0, .0021 0.0018

Aust. Mist 6189 3 0.0052 0 .002 0.0222 0, .0031 0, ,0022 0, .0038 0, ,0215 0, .1087 0, ,0056 0.0094 0.002 0.0048 0.0036 0, .7811 0.003 0, .0173 0.0045

Burmese 21 5 0.1445 0, .02 0.005 0, .0155 0, ,018 0, .0308 0, , 121 0, .1126 0, ,0265 0.3899 0.0135 0.0204 0.0182 0, .0136 0.0149 0, .0162 0.0194

Burmese 22 6 0.0123 0, .0035 0.0092 0, .0028 0, ,0039 0, .0031 0, ,0539 0, .2275 0, ,0053 0.4045 0.018 0.0349 0.0026 0, .0225 0.007 0, .1359 0.053

Burmese 23 7 0.0179 0. .1323 0.0207 0. .036 0. ,011 0, .0099 0, ,0057 0, .1692 0, ,0074 0.2078 0.0129 0.1253 0.0081 0, .0841 0.0125 0, .0334 0.1056

Burmese 24 2 0.0012 0, .9257 0.001 0, .0035 0, ,0322 0, .0079 0, ,002 0, .001 0, ,001 0.001 0.0048 0.0011 0.0017 0, .001 0.0073 0, .0019 0.0056

Burmese 25 0 0.003 0, .948 0.0022 0, .002 0, ,005 0, .0024 0, ,0032 0, .002 0, ,001 0.0041 0.002 0.0041 0.003 0, .002 0.0069 0, .006 0.0031

Burmese 26 3 0.0039 0, .9451 0.002 0, .0012 0, ,0019 0, .0011 0, ,0075 0, .0014 0, ,0024 0.0021 0.002 0.0023 0.002 0, .0016 0.0028 0, .019 0.0017

Burmese 27 2 0.0024 0, .9565 0.002 0, .0013 0, ,0029 0, .0042 0, ,0029 0, .0015 0, ,001 0.0026 0.0059 0.0027 0.003 0, .0032 0.0029 0, .0028 0.0022

Burmese 28 1 0.0041 0. .8728 0.0133 0. .002 0. ,0039 0, .003 0, ,0032 0, .0021 0, ,0017 0.0053 0.004 0.0388 0.0239 0, .0028 0.0074 0, .0059 0.0058

Burmese 29 3 0.0021 0, .9592 0.0028 0, .002 0, ,0024 0, .0012 0, ,002 0, .0029 0, ,0025 0.0028 0.0011 0.0025 0.0038 0, .0035 0.003 0, .0036 0.0026

Burmese 4401 12 0.0018 0, .9677 0.0019 0, .001 0, ,0025 0, .0039 0, ,0029 0, .0013 0, ,001 0.0015 0.0044 0.0016 0.0023 0, .0011 0.0016 0, .002 0.0015

Burmese 4691 12 0.0083 0, .8251 0.0059 0, .0067 0, ,0059 0, .0182 0, ,004 0, .0139 0, ,002 0.0186 0.0062 0.0282 0.0163 0, .0106 0.002 0, .0063 0.0219

Burmese 4781 1 0.0035 0, .8873 0.0034 0, .0025 0, ,0149 0, .0018 0, ,0082 0, .0071 0, ,0071 0.0093 0.0034 0.0145 0.0061 0, .0122 0.0028 0, .0023 0.0135

Burmese 4782 1 0.0509 0. .7588 0.0131 0. .0314 0. ,0036 0, .0072 0, ,0059 0, .0083 0, ,0033 0.0314 0.0015 0.0124 0.0223 0, .0081 0.0074 0, .0141 0.0203

Burmese 5425 1 0.0193 0, .7378 0.0057 0, .0555 0, ,0079 0, .0133 0, ,0036 0, .0186 0, ,0021 0.0288 0.0276 0.007 0.0109 0, .0242 0.0017 0, .0046 0.0315

Burmese 5800 11 0.0028 0, .9277 0.0024 0, .0025 0, ,0031 0, .0017 0, ,0055 0, .0055 0, ,0021 0.0024 0.0093 0.0018 0.004 0, .0042 0.0174 0, .003 0.0046

Burmese 6182 2 0.0018 0, .9522 0.0026 0, .002 0, ,0028 0, .002 0, ,0035 0, .0022 0, ,002 0.0038 0.005 0.003 0.0044 0, .0043 0.0026 0, .0031 0.0027

Burmese 6471 2 0.0019 0, .9607 0.002 0, .0016 0, ,002 0, .003 0, ,0022 0, .0036 0, ,0028 0.0026 0.001 0.0024 0.0036 0, .0022 0.0013 0, .0048 0.0023

Burmese 6962 2 0.0045 0. .9231 0.002 0. .002 0. ,0057 0, .0071 0, ,0022 0, .0033 0, ,003 0.0041 0.0182 0.0039 0.0025 0, .0084 0.0027 0, .0031 0.0042

Burmese 6964 13 0.0119 0, .0088 0.1073 0, .0061 0, ,0071 0, .0168 0, ,0166 0, .1634 0, ,004 0.1736 0.0031 0.241 0.0035 0, .0411 0.0095 0, .0263 0.16

Birman 1760 14 0.0349 0, .0083 0.0799 0, .0857 0, ,002 0, .007 0, ,0052 0, .0726 0, ,012 0.0503 0.002 0.438 0.0198 0, .0105 0.002 0, .0172 0.1526

Birman 2917 8 0.0197 0. .0069 0.3869 0. .0079 0. ,0034 0, .0037 0, ,0162 0, .0165 0, ,0039 0.0934 0.0056 0.2611 0.0075 0, .0111 0.0107 0, .0135 0.1321

Birman 3910 2 0.0404 0, .0101 0.147 0, .0234 0, ,001 0, .002 0, ,013 0, .0452 0, ,0134 0.0424 0.0018 0.0313 0.5564 0, .0088 0.0036 0, .0025 0.0578

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Birman 5029 1 0.0236 0, .0052 0.159 0.0025 0.0054 0, .0032 0, .03 0, .0177 0.0279 0.1649 0.0023 0.1807 0.1692 0.0112 0.0053 0, .0196 0.1724

Birman 5033 1 0.0039 0, .0091 0.0568 0.0144 0.0015 0, .0257 0, , 1181 0, .0605 0.0024 0.0854 0.0023 0.1599 0.0906 0.0072 0.033 0, .1732 0.1559

Birman 5034 0 0.0032 0. .0051 0.1896 0.0028 0.0245 0. .0045 0. .006 0, .1924 0.0124 0.0835 0.0155 0.2938 0.0187 0.0057 0.0058 0, .0037 0.1328

Birman 5038 1 0.0039 0, .0045 0.161 0.0744 0.0017 0, .0028 0, ,0235 0, .0091 0.0041 0.1452 0.0032 0.3542 0.0358 0.0071 0.008 0, .0066 0.1549

Birman 5151 1 0.0156 0, .0036 0.0887 0.0097 0.0019 0, .0034 0, ,0045 0, .215 0.0108 0.0848 0.002 0.3419 0.0219 0.0066 0.0034 0, .0096 0.1766

Birman 5576 0 0.004 0, .0123 0.1089 0.0063 0.001 0, .0014 0, ,0049 0, .0209 0.0027 0.4963 0.0026 0.0974 0.0227 0.0468 0.0017 0, .0039 0.1662

Birman 5578 0 0.0087 0, .0058 0.1156 0.0134 0.0036 0, .0034 0, ,0106 0, .0085 0.0025 0.4583 0.0049 0.0961 0.0632 0.0129 0.0107 0, .0045 0.1772

Birman 6448 2 0.0033 0, .0086 0.0945 0.0034 0.0014 0, .0031 0, ,0149 0, .2019 0.0057 0.0963 0.003 0.3349 0.0413 0.0036 0.0013 0, .0033 0.1795

Birman 6450 6 0.0115 0, .003 0.1261 0.0034 0.002 0, .0014 0, ,0092 0, .0937 0.0045 0.1289 0.002 0.3749 0.0069 0.0205 0.0023 0, .0194 0.1902

Birman 6526 5 0.0045 0, .0078 0.0858 0.0017 0.0014 0, .0021 0, ,0056 0, .0891 0.0034 0.1287 0.0027 0.4452 0.0161 0.0036 0.001 0, .0169 0.1843

Birman 6527 5 0.0395 0. .0055 0.019 0.0078 0.0045 0. .0168 0. ,052 0, .0581 0.0155 0.0424 0.0042 0.1022 0.4828 0.0618 0.0038 0, .0152 0.069

Birman 6528 0 0.0127 0, .053 0.0894 0.0117 0.001 0, .001 0, ,0062 0, .1642 0.0012 0.1261 0.0036 0.2747 0.0704 0.0051 0.0018 0, .0081 0.1698

Birman 6529 2 0.0093 0, .0208 0.0605 0.0035 0.0032 0, .0139 0, ,2812 0, .0861 0.0068 0.0755 0.0061 0.2131 0.0426 0.0195 0.0052 0, .0154 0.1372

Birman 6604 2 0.0078 0, .0031 0.0086 0.0082 0.0033 0, .0161 0, ,8459 0, .0342 0.0075 0.0195 0.0036 0.0098 0.0052 0.0062 0.0071 0, .008 0.0059

Birman 6607 0 0.002 0, .0043 0.0027 0.0029 0.0081 0, .0038 0, ,9448 0, .0029 0.003 0.0036 0.0021 0.0016 0.002 0.0032 0.0054 0, .0038 0.0038

Birman 6608 0 0.0066 0. .002 0.0107 0.004 0.0035 0. .0041 0. ,8995 0, .0021 0.0021 0.0052 0.0157 0.007 0.0196 0.0029 0.0029 0, .0059 0.0062

Birman 6609 1 0.0044 0, .0062 0.0031 0.002 0.002 0, .0021 0, ,8791 0, .0237 0.002 0.0088 0.0015 0.003 0.008 0.0438 0.002 0, .0056 0.0027

Havana

Brn. 787 2 0.0022 0, .0056 0.0019 0.0025 0.0038 0, .0049 0, ,9431 0, .0021 0.001 0.0028 0.0132 0.0022 0.0029 0.0039 0.0027 0, .0027 0.0025

Havana

Brn. 2415 0 0.0061 0. .0968 0.007 0.0097 0.006 0. .0081 0. 7292 0, .0063 0.0145 0.0058 0.0198 0.0092 0.022 0.0061 0.0065 0, .0316 0.0153

Havana

Brn. 2500 0 0.0049 0, .0062 0.0061 0.0026 0.0265 0, .0026 0, ,8498 0, .0043 0.0059 0.0039 0.0062 0.0076 0.0048 0.0141 0.0314 0, .0122 0.011

Havana

Brn. 2501 0 0.0032 0, .0031 0.006 0.002 0.002 0, .002 0, 9278 0, .0025 0.001 0.005 0.0027 0.0066 0.0033 0.0164 0.005 0, .0091 0.0023

Havana

Brn. 2502 3 0.006 0, .0075 0.0081 0.002 0.147 0, .0077 0, ,7075 0, .0047 0.0014 0.0136 0.023 0.0151 0.0116 0.0067 0.0163 0, .0037 0.0181

Havana

Brn. 3312 1 0.0034 0, .0051 0.0036 0.0023 0.0014 0, .002 0, ,9295 0, .0046 0.001 0.0143 0.0063 0.0031 0.0082 0.0045 0.002 0, .0052 0.0034

Havana

Brn. 3404 1 0.0199 0. .0134 0.0033 0.0036 0.0024 0. .0108 0. ,8823 0, .0065 0.0031 0.0038 0.002 0.0116 0.0103 0.0034 0.0037 0, .0122 0.0077

Havana

Brn. 3513 0 0.0027 0, .0019 0.0037 0.0028 0.0168 0, .0049 0, ,8938 0, .0032 0.0064 0.0062 0.0192 0.0044 0.0051 0.003 0.0102 0, .0035 0.0123

Havana

Brn. 5707 0 0.0034 0, .002 0.004 0.0013 0.0025 0, .0026 0, ,9396 0, .0053 0.002 0.0052 0.003 0.0043 0.0069 0.005 0.0027 0, .0078 0.0024

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Havana

Brn. 5708 2 0.0044 0, .0042 0.0032 0, .003 0.003 0, .002 0, ,9191 0, .0051 0, ,0021 0, .0032 0, ,0066 0, .0039 0.0051 0, .0054 0, ,0028 0, .0231 0.0038

Brn. 6972 0 0.0136 0, .0053 0.0025 0, .002 0.0041 0, .0082 0, ,8921 0, .0073 0, ,0223 0, .0039 0, ,0028 0, .0034 0.0021 0, .0078 0, ,0025 0, .0127 0.0074 navana

Brn. 6973 0 0.0267 0, .0151 0.1473 0, .0027 0.001 0, .002 0, ,034 0, .0032 0, ,0082 0, .4883 0, ,002 0, .0601 0.1698 0, .0119 0, ,0053 0, .0097 0.0126

Havana

Brn. 10350 4 0.0066 0, .0033 0.1676 0, .0017 0.001 0, .0047 0, ,0068 0, .0096 0, ,0527 0, .0097 0, ,0013 0, .0571 0.6417 0, .0088 0, ,002 0, .0095 0.016 navana

Brn. 5554 0 0.0042 0, .0078 0.1527 0, .0044 0.001 0, .0018 0, ,0071 0, .0053 0, ,0063 0, .044 0, ,002 0, .0116 0.7164 0, .0213 0, ,0016 0, .0087 0.0038

Korat 4708 2 0.0046 0. .0024 0.0972 0. .0019 0.001 0. .002 0. ,0165 0, .0152 0, ,0023 0, .0781 0, ,0011 0, .2501 0.3313 0, .0255 0, ,0012 0, .0103 0.1593

Korat 4711 1 0.0044 0, .0075 0.0304 0, .001 0.002 0, .0022 0, ,0037 0, .0028 0, ,0021 0, .0053 0, ,0024 0, .0299 0.8859 0, .0036 0, ,0092 0, .0035 0.004

Korat 5059 4 0.0019 0, .002 0.0053 0, .0019 0.001 0, .0018 0, ,002 0, .002 0, ,0012 0, .0031 0, ,0025 0, .0019 0.9649 0, .0015 0, ,001 0, .0043 0.0017

Korat 5069 2 0.0014 0, .0022 0.0037 0, .002 0.001 0, .001 0, ,0021 0, .0021 0, ,0025 0, .0032 0, ,0018 0, .0026 0.9658 0, .0027 0, ,001 0, .002 0.0029

Korat 5098 3 0.0019 0, .0033 0.0025 0, .001 0.001 0, .001 0, ,0013 0, .0013 0, ,0011 0, .0021 0, ,0012 0, .0019 0.9746 0, .0012 0, ,001 0, .0019 0.0017

Korat 5175 2 0.0018 0. .0019 0.0053 0. .001 0.001 0. .0013 0. ,0021 0, .0017 0, ,0022 0, .002 0, ,0015 0, .0021 0.9696 0, .002 0, ,001 0, .002 0.0015

Korat 5176 1 0.0031 0, .002 0.0044 0, .0018 0.001 0, .0032 0, ,0024 0, .0025 0, ,0018 0, .0064 0, ,0031 0, .0038 0.9548 0, .0027 0, ,0018 0, .0029 0.0023

Korat 5177 2 0.002 0, .0029 0.0027 0, .0019 0.0012 0, .0014 0, ,004 0, .0022 0, ,0012 0, .0032 0, ,0048 0, .002 0.9616 0, .0031 0, ,0011 0, .0029 0.0018

Korat 5178 4 0.0025 0, .0032 0.0039 0, .001 0.001 0, .0018 0, ,0029 0, .0019 0, ,0014 0, .002 0, ,002 0, .0023 0.9675 0, .0021 0, ,001 0, .0017 0.0018

Korat 5224 1 0.0096 0, .0038 0.0353 0, .1421 0.0034 0, .004 0, ,021 0, .0186 0, ,0076 0, .038 0, ,0049 0, .0131 0.6445 0, .0266 0, ,0094 0, .0076 0.0105

Korat 5237 0 0.0047 0. .0022 0.0044 0. .0377 0.0045 0. .0098 0. ,0036 0, .017 0, ,0028 0, .0045 0, ,0035 0, .0049 0.8773 0, .0084 0, ,0033 0, .0055 0.0059

Korat 5240 2 0.0075 0, .0035 0.011 0, .0576 0.0122 0, .0039 0, , 1226 0, .0067 0, ,0449 0, .0147 0, ,002 0, .0291 0.633 0, .0061 0, ,0093 0, .0218 0.0141

Korat 5242 4 0.014 0, .0026 0.0042 0, .0101 0.0791 0, .002 0, ,0092 0, .0028 0, ,0605 0, .0034 0, ,006 0, .0034 0.7478 0, .0023 0, ,0074 0, .0052 0.04

Korat 5244 3 0.0057 0, .012 0.0171 0, .0095 0.0021 0, .002 0, ,0041 0, .0054 0, ,0021 0, .0232 0, ,0042 0, .0087 0.8595 0, .0055 0, ,003 0, .028 0.0079

Korat 5284 2 0.0043 0, .0044 0.0063 0, .0038 0.0124 0, .0132 0, ,0103 0, .0081 0, ,0055 0, .011 0, ,01 0, .0145 0.0033 0, .0027 0, ,8711 0, .0075 0.0116

Korat 5344 3 0.0051 0. .0018 0.0137 0. .0036 0.0035 0. .0091 0. ,0147 0, .0064 0, ,0061 0, .0094 0, ,002 0, .0108 0.0029 0, .0097 0, ,8823 0, .0144 0.0045

Korat 5481 2 0.0049 0, .0012 0.0053 0, .0152 0.0048 0, .0035 0, ,0043 0, .0267 0, ,0557 0, .0037 0, ,0057 0, .0034 0.002 0, .0064 0, ,8313 0, .0206 0.0052

Korat 5512 3 0.0192 0, .001 0.0035 0, .0015 0.026 0, .0035 0, ,0069 0, .002 0, ,0407 0, .0029 0, ,0027 0, .0018 0.0042 0, .0028 0, ,8697 0, .0063 0.0053

Korat 5514 0 0.0027 0, .0026 0.0078 0, .1579 0.003 0, .004 0, ,0048 0, .007 0, ,0024 0, .012 0, ,002 0, .0157 0.0092 0, .0023 0, ,7572 0, .0049 0.0045

Korat 5595 22 0.004 0, .1207 0.2523 0, .0749 0.004 0, .0033 0, ,0338 0, .0049 0, ,0145 0, .1431 0, ,0036 0, .0701 0.0237 0, .003 0, , 1927 0, .0245 0.0271

Korat 5597 5 0.0042 0. .0251 0.0273 0. .0033 0.0063 0. .0069 0. ,0213 0, .0036 0, ,0075 0, .0118 0, ,0034 0, .0529 0.0051 0, .0049 0, ,7622 0, .0379 0.0162

Korat 6375 2 0.0038 0, .007 0.0217 0, .0071 0.0032 0, .0196 0, ,0103 0, .0063 0, ,0021 0, .0058 0, ,0043 0, .0485 0.0101 0, .0108 0, ,8213 0, .0107 0.0074

Korat 6376 20 0.0068 0, .003 0.0032 0, .0056 0.0129 0, .0288 0, ,0054 0, .006 0, ,0087 0, .0056 0, ,0247 0, .003 0.0044 0, .0026 0, ,8672 0, .0077 0.0044

Korat 6377 15 0.0029 0. .0039 0.0025 0. .0044 0.0071 0. .0051 0. ,0027 0, .0036 0, ,0017 0, .0029 0, ,0031 0, .0026 0.003 0, .003 0, ,9445 0, .0027 0.0043

Korat 6378 7 0.0031 0, .0251 0.0222 0, .002 0.0022 0, .0024 0, ,0206 0, .0113 0, ,0433 0, .012 0, ,0089 0, .0165 0.0235 0, .0075 0, ,7825 0, .0094 0.0075

Feline ID Missing Groups

Population No. Data 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Siamese 2868 0 0.0406 0 .0044 0.0239 0, .0091 0, ,044 0, .0031 0, ,0097 0, .0034 0, ,0048 0.0125 0.0148 0.0166 0.0155 0.005 0.7727 0, .0028 0.0171

Siamese 6686 2 0.002 0 .0056 0.0049 0, .0029 0, ,004 0, .0712 0, ,0112 0, .0035 0, ,0027 0.0044 0.002 0.0039 0.0083 0.005 0.8617 0, .0028 0.0039

Siamese 6688 4 0.013 0 .0143 0.0047 0. .0061 0. ,0027 0, .0063 0, ,0042 0, .1075 0, ,0282 0.0915 0.0068 0.0103 0.0535 0.0236 0.6112 0, .0045 0.0117

Siamese 6690 2 0.018 0 .0032 0.0023 0, .002 0, ,0029 0, .0051 0, ,0067 0, .0153 0, ,0164 0.0045 0.0041 0.0043 0.0055 0.0157 0.8787 0, .0075 0.0078

Siamese 6696 18 0.5406 0 .0018 0.0035 0, .0026 0, ,002 0, .0397 0, ,0032 0, .0037 0, ,0011 0.0043 0.0017 0.0029 0.0026 0.002 0.3829 0, .0023 0.0031

Siamese 7839 14 0.8253 0 .0174 0.0091 0, .001 0, ,0061 0, .0205 0, ,004 0, .0035 0, ,0028 0.0038 0.0643 0.0094 0.0117 0.0044 0.0064 0, .0064 0.0039

Siamese 8181 18 0.5983 0 .0058 0.0172 0, .001 0, ,0028 0, .006 0, ,005 0, .0021 0, ,0078 0.0035 0.0022 0.015 0.3146 0.0028 0.0029 0, .0087 0.0043

Siamese 8182 8 0.915 0 .0055 0.0102 0, .0032 0, ,0023 0, .0147 0, ,0064 0, .0046 0, ,002 0.0069 0.0017 0.0061 0.0036 0.0032 0.0055 0, .0049 0.0042

Siamese 8184 6 0.821 0 .0218 0.0064 0, .0124 0, ,0037 0, .0023 0, ,0033 0, .0085 0, ,0043 0.0171 0.0033 0.007 0.0049 0.002 0.0625 0, .0123 0.0073

Siamese 8185 8 0.3645 0 .007 0.0472 0, .0054 0, ,0118 0, .0068 0, ,0061 0, .0083 0, ,0025 0.0264 0.003 0.128 0.0069 0.0113 0.0832 0, .2143 0.0674

Siamese 8187 2 0.8651 0 .0037 0.0104 0. .0035 0. ,0033 0, .0037 0, ,0165 0, .015 0, ,0075 0.0067 0.0023 0.0049 0.0325 0.0066 0.0055 0, .0051 0.0077

Siamese 8251 1 0.7731 0 .0072 0.0046 0, .004 0, ,0167 0, .002 0, ,0071 0, .0175 0, ,0816 0.009 0.0053 0.0051 0.004 0.0129 0.0107 0, .0168 0.0224

Siamese 8253 3 0.8016 0 .002 0.012 0, .0994 0, ,0063 0, .0012 0, ,0057 0, .0176 0, ,0052 0.0052 0.0069 0.0037 0.0051 0.0114 0.0042 0, .0061 0.0063

Siamese 8258 4 0.8358 0 .008 0.052 0, .0013 0, ,0024 0, .002 0, ,0183 0, .0028 0, ,0077 0.0097 0.0123 0.0089 0.0165 0.0055 0.002 0, .0081 0.0067

Siamese 8259 0 0.6547 0 .0047 0.0057 0, .0282 0, ,0404 0, .0071 0, ,008 0, .0118 0, ,0075 0.0068 0.0051 0.01 0.0036 0.0049 0.1246 0, .0137 0.0632

Singapura 3428 2 0.9 0. .0079 0.006 0. .0019 0. ,0027 0, .004 0, ,0034 0, .0057 0, ,0022 0.0064 0.0162 0.0083 0.0077 0.0152 0.0018 0, .0056 0.005

Singapura 3919 8 0.7495 0, .002 0.0025 0, .0048 0, ,0088 0, .0153 0, ,0231 0, .004 0, ,0026 0.0032 0.0055 0.0019 0.0031 0.0025 0.1614 0, .0066 0.0031

Singapura 4464 3 0.864 0, .0053 0.0027 0, .0103 0, ,0087 0, .0101 0, ,0076 0, .0067 0, ,002 0.0085 0.0166 0.0057 0.0067 0.0036 0.0277 0, .0078 0.0059

Singapura 4467 0 0.8436 0, .0086 0.0114 0, .0032 0, ,0023 0, .0043 0, ,0059 0, .0041 0, ,0037 0.0791 0.0039 0.0066 0.0045 0.0024 0.0031 0, .0073 0.006

Singapura 4468 0 0.8299 0, .0052 0.0074 0, .0012 0, ,0126 0, .0518 0, ,0036 0, .0051 0, ,0031 0.0063 0.0077 0.0147 0.017 0.0048 0.0147 0, .0039 0.0109

Singapura 4469 1 0.8894 0. .0037 0.0024 0. .0133 0. ,0057 0, .002 0, ,0065 0, .0101 0, ,005 0.004 0.0154 0.0028 0.0029 0.0042 0.0149 0, .0123 0.0054

Singapura 4470 17 0.8528 0, .0193 0.0055 0, .0221 0, ,0041 0, .0104 0, ,0061 0, .0083 0, ,0034 0.0127 0.0018 0.0045 0.0115 0.0053 0.0127 0, .0097 0.0098

Singapura 4471 16 0.0649 0, .0063 0.0545 0, .033 0, ,2708 0, .0044 0, ,0181 0, .0261 0, , 1831 0.0267 0.0136 0.0098 0.0073 0.023 0.0071 0, .0798 0.1715

Singapura 4472 6 0.0051 0, .1693 0.047 0, .0032 0, ,4249 0, .0062 0, ,0704 0, .041 0, ,0874 0.0046 0.0051 0.012 0.0094 0.0187 0.0051 0, .0045 0.086

Singapura 4473 10 0.0193 0, .0049 0.0063 0, .0138 0, ,2706 0, .0159 0, ,347 0, .0294 0, ,0044 0.0444 0.0077 0.0088 0.033 0.0392 0.0043 0, .0204 0.1305

Singapura 4474 18 0.0402 0. .0065 0.02 0. .0023 0. 2262 0, .0572 0, ,0754 0, .0045 0, ,0029 0.0156 0.0042 0.0505 0.3651 0.0042 0.0075 0, .0097 0.1081

Singapura 4485 1 0.0143 0, .0627 0.0307 0, .0033 0, ,3816 0, .0049 0, ,0231 0, .0138 0, ,0794 0.0061 0.0031 0.018 0.2459 0.0281 0.0122 0, .0058 0.067

Singapura 4486 13 0.0046 0, .0032 0.005 0, .0079 0, , 1946 0, .0229 0, ,208 0, .006 0, ,0055 0.016 0.0417 0.0164 0.1886 0.0155 0.1575 0, .0033 0.1032

Singapura 4487 2 0.0064 0, .012 0.0231 0, .0121 0, ,3331 0, .0038 0, ,0161 0, .0542 0, ,0221 0.0358 0.0042 0.0331 0.0119 0.2805 0.0028 0, .0041 0.1445

Singapura 4488 0 0.0044 0, .018 0.1542 0, .0027 0, ,3062 0, .1154 0, ,0068 0, .0076 0, , 1604 0.0059 0.0023 0.0518 0.0184 0.005 0.009 0, .0046 0.1272

Singapura 6597 0 0.0069 0. .003 0.2742 0. .0039 0. ,271 0, .0051 0, ,0774 0, .0194 0, ,0071 0.0199 0.0051 0.0762 0.0514 0.034 0.0111 0, .0151 0.1191

Singapura 6975 2 0.0134 0, .073 0.0316 0, .0221 0, 1489 0, .0314 0, ,0889 0, .0072 0, , 1075 0.0161 0.023 0.0426 0.214 0.0108 0.0101 0, .0117 0.1476

Table 20 - Population clustering of each random bred Table 20 - Population clustering of each random bred individual in the database by SNPs at K = 5 individual in the database by SNPs at K = 5 Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

USA-NY 2547 1 0.821 0.021 0.037 0.08 0.041 Brazil 7966 1 0.858 0.024 0.085 0.012 0.021

USA-NY 2559 0 0.556 0.088 0.156 0.187 0.012 Brazil 7968 22 0.775 0.026 0.044 0.12 0.035

USA-NY 2568 0 0.964 0.01 0.011 0.006 0.009 Brazil 7969 0 0.953 0.008 0.016 0.011 0.012

USA-NY 2569 1 0.921 0.01 0.017 0.027 0.025 Brazil 7970 0 0.934 0.013 0.015 0.023 0.015

USA-NY 2572 0 0.903 0.01 0.046 0.02 0.022 Brazil 7971 0 0.924 0.012 0.025 0.015 0.023

USA-NY 2578 20 0.768 0.017 0.084 0.05 0.082 Brazil 7972 0 0.865 0.012 0.096 0.014 0.013

USA-NY 2590 0 0.901 0.023 0.043 0.022 0.012 Brazil 7973 0 0.915 0.015 0.026 0.028 0.016

USA-NY 2591 1 0.929 0.01 0.022 0.01 0.029 Brazil 7974 3 0.869 0.022 0.073 0.022 0.014

USA-NY 2597 1 0.864 0.018 0.032 0.024 0.062 Brazil 7975 3 0.888 0.013 0.036 0.04 0.022

USA-MS 9971 2 0.896 0.039 0.023 0.026 0.016 Brazil 7976 0 0.902 0.019 0.034 0.025 0.02

USA-MS 9972 2 0.778 0.027 0.104 0.019 0.072 Brazil 7977 1 0.895 0.006 0.059 0.029 0.011

USA-MS 9974 2 0.687 0.259 0.03 0.011 0.014 Brazil 7978 1 0.804 0.02 0.052 0.101 0.023

USA-MS 9977 4 0.941 0.007 0.024 0.018 0.011 Brazil 7979 1 0.861 0.043 0.041 0.034 0.021

USA-MS 9980 1 0.878 0.034 0.034 0.023 0.032 Brazil 7980 2 0.64 0.014 0.252 0.058 0.036

USA-MS 9983 1 0.924 0.007 0.024 0.017 0.029 Brazil 7981 2 0.768 0.009 0.025 0.188 0.009

USA-MS 9985 2 0.706 0.226 0.021 0.013 0.034 Brazil 7982 15 0.707 0.141 0.037 0.093 0.022

USA-MS 9987 2 0.641 0.084 0.187 0.071 0.018 Brazil 7983 1 0.659 0.074 0.15 0.062 0.054

USA-MS 9989 2 0.933 0.025 0.017 0.013 0.012 Brazil 7984 6 0.614 0.25 0.054 0.07 0.013

USA-MS 9992 2 0.883 0.013 0.051 0.017 0.036 Brazil 7985 2 0.705 0.054 0.145 0.084 0.012

USA-HI 5366 1 0.751 0.026 0.042 0.156 0.025 Brazil 7986 2 0.755 0.136 0.073 0.026 0.01

USA-HI 5367 0 0.775 0.054 0.024 0.08 0.068 Brazil 7987 1 0.854 0.025 0.091 0.02 0.01

USA-HI 5371 0 0.898 0.023 0.047 0.012 0.019 Brazil 7988 0 0.747 0.073 0.041 0.126 0.013

USA-HI 5372 0 0.564 0.043 0.263 0.031 0.099 Brazil 7989 0 0.568 0.376 0.023 0.018 0.015

USA-HI 5379 0 0.858 0.014 0.064 0.032 0.032 Brazil 7990 0 0.717 0.222 0.028 0.019 0.014

USA-HI 5380 1 0.941 0.005 0.032 0.014 0.007 Finland 8077 8 0.86 0.011 0.075 0.024 0.03

USA-HI 5383 1 0.39 0.054 0.039 0.101 0.416 Finland 8084 6 0.824 0.008 0.049 0.047 0.073

USA-HI 5384 2 0.879 0.012 0.044 0.032 0.034 Finland 8086 4 0.829 0.035 0.072 0.039 0.024

USA-HI 5401 1 0.938 0.016 0.021 0.013 0.013 Finland 8089 4 0.688 0.029 0.097 0.075 0.111

USA-HI 5402 0 0.838 0.073 0.025 0.047 0.017 Finland 8093 16 0.863 0.013 0.032 0.055 0.037

Brazil 7961 0 0.861 0.022 0.067 0.029 0.021 Finland 8094 3 0.904 0.01 0.028 0.018 0.04

Brazil 7962 2 0.763 0.06 0.095 0.057 0.026 Finland 8096 3 0.941 0.005 0.017 0.008 0.028

Brazil 7963 15 0.544 0.3 0.095 0.047 0.014 Finland 8107 22 0.953 0.006 0.014 0.011 0.016

Brazil 7964 1 0.871 0.009 0.077 0.027 0.016 Finland 8110 5 0.96 0.009 0.015 0.007 0.01

Brazil 7965 18 0.934 0.015 0.013 0.024 0.014 Finland 8116 8 0.857 0.04 0.031 0.045 0.028

Table 20 - Population clustering of each random bred Table 20 - Population clustering of each random bred individual in the database by SNPs at K = 5 individual in the database by SNPs at K = 5

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Finland 8120 19 0.933 0.015 0.02 0.023 0.008 Italy-Milan 8062 1 0.39 0.034 0.525 0.011 0.04

Germany 8711 2 0.884 0.009 0.052 0.038 0.016 Italy-Milan 8065 4 0.625 0.013 0.309 0.015 0.039

Germany 8712 3 0.931 0.008 0.023 0.013 0.026 Italy-Milan 8066 0 0.329 0.072 0.489 0.018 0.092

Germany 8713 2 0.681 0.012 0.264 0.027 0.016 Italy-Milan 8067 2 0.553 0.06 0.304 0.027 0.055

Germany 8714 2 0.744 0.021 0.128 0.03 0.077 Italy-Milan 8068 0 0.599 0.017 0.311 0.03 0.042

Germany 8715 4 0.914 0.006 0.033 0.012 0.034 Italy-Milan 8069 0 0.692 0.09 0.132 0.046 0.04

Germany 8716 4 0.898 0.021 0.027 0.033 0.021 Italy-Milan 8071 2 0.732 0.026 0.161 0.05 0.03

Germany 8717 12 0.899 0.015 0.037 0.019 0.03 Italy-Milan 8072 2 0.315 0.275 0.273 0.117 0.02

Germany 8720 2 0.271 0.657 0.046 0.01 0.017 Italy-Milan 8073 0 0.753 0.011 0.183 0.023 0.031

Germany 8721 10 0.909 0.014 0.052 0.013 0.013 Italy-Milan 8074 1 0.71 0.167 0.091 0.018 0.013

Germany 8727 0 0.937 0.01 0.02 0.022 0.01 Italy-Rome 8586 1 0.475 0.012 0.439 0.031 0.042

Germany 8728 10 0.857 0.018 0.045 0.025 0.054 Italy-Rome 8589 2 0.903 0.016 0.046 0.017 0.019

Germany 8729 1 0.948 0.011 0.013 0.013 0.015 Italy-Rome 8592 1 0.54 0.026 0.123 0.127 0.185

Germany 8730 22 0.767 0.022 0.015 0.173 0.023 Italy-Rome 8594 1 0.783 0.033 0.116 0.051 0.017

Germany 8731 12 0.953 0.005 0.014 0.015 0.013 Italy-Rome 8595 0 0.705 0.064 0.147 0.033 0.051

Germany 8732 3 0.892 0.006 0.073 0.01 0.019 Italy-Rome 8596 1 0.353 0.02 0.585 0.017 0.025

Germany 8733 14 0.833 0.034 0.047 0.027 0.059 Italy-Rome 8597 0 0.779 0.077 0.07 0.041 0.033

Germany 8734 0 0.938 0.007 0.033 0.008 0.016 Italy-Rome 8599 0 0.925 0.023 0.02 0.017 0.015

Germany 8735 0 0.851 0.021 0.082 0.027 0.017 Italy-Rome 8601 2 0.667 0.034 0.198 0.074 0.026

Germany 8736 0 0.933 0.008 0.041 0.011 0.007 Italy-Rome 8602 2 0.807 0.045 0.087 0.024 0.037

Germany 8737 2 0.944 0.01 0.016 0.012 0.019 Italy-Rome 8603 0 0.864 0.03 0.049 0.042 0.016

Germany 8738 10 0.866 0.008 0.097 0.012 0.018 Italy-Rome 8604 2 0.241 0.01 0.591 0.139 0.018

Germany 8739 0 0.913 0.005 0.065 0.008 0.009 Italy-Rome 8609 1 0.898 0.011 0.059 0.017 0.014

Germany 8741 12 0.147 0.008 0.813 0.012 0.02 Italy-Rome 8610 1 0.529 0.051 0.245 0.1 0.075

Germany 8742 10 0.902 0.025 0.047 0.011 0.014 Italy-Rome 8611 2 0.711 0.049 0.173 0.021 0.045

Germany 8744 6 0.555 0.088 0.324 0.024 0.008 Turkey 6477 7 0.206 0.007 0.754 0.008 0.024

Germany 8745 22 0.918 0.008 0.045 0.013 0.016 Turkey 6478 12 0.065 0.055 0.673 0.087 0.12

Germany 8746 0 0.896 0.025 0.035 0.016 0.028 Turkey 6480 11 0.039 0.012 0.917 0.011 0.022

Germany 8747 12 0.959 0.01 0.014 0.008 0.009 Turkey 6481 12 0.067 0.647 0.064 0.08 0.142

Germany 8749 0 0.939 0.012 0.016 0.016 0.017 Turkey 6482 14 0.047 0.008 0.893 0.04 0.012

Italy-Milan 8050 0 0.712 0.04 0.139 0.017 0.092 Turkey 6484 7 0.087 0.019 0.855 0.021 0.019

Italy-Milan 8057 1 0.619 0.116 0.092 0.047 0.127 Turkey 6486 8 0.729 0.025 0.193 0.019 0.035

Italy-Milan 8060 0 0.657 0.074 0.037 0.018 0.213 Turkey 6487 15 0.279 0.012 0.648 0.035 0.025

Italy-Milan 8061 2 0.653 0.231 0.083 0.016 0.017 Turkey 6488 12 0.078 0.011 0.778 0.059 0.075

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Turkey 6491 15 0.018 0.482 0.022 0.04 0.439 Turkey 6750 3 0.398 0.025 0.522 0.025 0.03

Turkey 6494 11 0.227 0.098 0.551 0.044 0.08 Turkey 6753 0 0.333 0.014 0.565 0.029 0.059

Turkey 6496 12 0.062 0.038 0.793 0.058 0.049 Turkey 6754 2 0.335 0.018 0.466 0.059 0.122

Turkey 6499 12 0.625 0.026 0.282 0.037 0.031 Turkey 6755 1 0.271 0.007 0.656 0.019 0.046

Turkey 6500 15 0.261 0.01 0.576 0.066 0.088 Turkey 6756 0 0.16 0.012 0.786 0.023 0.019

Turkey 6502 6 0.142 0.024 0.647 0.083 0.104 Turkey 6758 1 0.082 0.03 0.826 0.046 0.016

Turkey 6503 1 0.161 0.012 0.78 0.018 0.03 Turkey 6759 2 0.424 0.015 0.46 0.034 0.067

Turkey 6507 14 0.03 0.016 0.891 0.04 0.023 Turkey 6760 1 0.654 0.009 0.201 0.023 0.113

Turkey 6510 10 0.035 0.187 0.676 0.068 0.033 Cyprus 10128 3 0.061 0.036 0.805 0.07 0.027

Turkey 6512 15 0.2 0.01 0.381 0.034 0.374 Cyprus 10129 0 0.638 0.065 0.112 0.074 0.112

Turkey 6513 14 0.731 0.038 0.13 0.044 0.057 Cyprus 10130 2 0.06 0.009 0.909 0.011 0.011

Turkey 6514 9 0.012 0.95 0.014 0.008 0.016 Cyprus 10131 1 0.326 0.039 0.29 0.312 0.033

Turkey 6516 12 0.94 0.012 0.023 0.013 0.012 Cyprus 10132 0 0.009 0.096 0.773 0.042 0.08

Turkey 6519 7 0.574 0.02 0.183 0.11 0.112 Cyprus 10133 0 0.162 0.125 0.653 0.044 0.017

Turkey 6520 15 0.229 0.012 0.658 0.025 0.076 Cyprus 10134 0 0.279 0.02 0.638 0.029 0.034

Turkey 6521 12 0.707 0.018 0.198 0.049 0.029 Cyprus 10135 2 0.2 0.019 0.562 0.032 0.186

Turkey 6729 1 0.101 0.029 0.05 0.115 0.704 Cyprus 10136 2 0.437 0.01 0.283 0.031 0.24

Turkey 6730 1 0.039 0.009 0.928 0.009 0.015 Cyprus 10137 1 0.049 0.01 0.908 0.013 0.02

Turkey 6731 2 0.363 0.023 0.475 0.035 0.104 Cyprus 10138 2 0.017 0.053 0.883 0.022 0.025

Turkey 6732 0 0.024 0.077 0.803 0.055 0.041 Cyprus 10139 3 0.08 0.045 0.749 0.098 0.028

Turkey 6733 3 0.051 0.022 0.845 0.038 0.044 Cyprus 10140 0 0.067 0.025 0.844 0.05 0.015

Turkey 6734 0 0.023 0.026 0.808 0.015 0.128 Cyprus 10141 0 0.035 0.01 0.929 0.014 0.012

Turkey 6735 2 0.024 0.006 0.948 0.009 0.013 Cyprus 10142 1 0.042 0.06 0.789 0.02 0.089

Turkey 6736 1 0.288 0.011 0.634 0.018 0.048 Cyprus 10143 0 0.016 0.019 0.871 0.076 0.017

Turkey 6738 2 0.183 0.004 0.798 0.006 0.008 Cyprus 10144 2 0.04 0.039 0.566 0.041 0.313

Turkey 6739 2 0.327 0.02 0.563 0.018 0.073 Cyprus 10145 1 0.107 0.022 0.794 0.047 0.029

Turkey 6740 0 0.961 0.006 0.013 0.01 0.01 Cyprus 10146 0 0.032 0.008 0.69 0.024 0.246

Turkey 6741 0 0.094 0.839 0.019 0.024 0.023 Cyprus 10147 1 0.129 0.014 0.766 0.052 0.039

Turkey 6742 2 0.273 0.025 0.479 0.032 0.191 Cyprus 10148 0 0.054 0.033 0.843 0.025 0.045

Turkey 6743 1 0.193 0.014 0.771 0.009 0.012 Cyprus 10149 2 0.039 0.046 0.833 0.02 0.063

Turkey 6745 0 0.068 0.009 0.824 0.037 0.063 Cyprus 10150 0 0.196 0.029 0.667 0.071 0.037

Turkey 6746 1 0.233 0.259 0.127 0.22 0.161 Cyprus 10151 2 0.022 0.024 0.9 0.031 0.022

Turkey 6748 1 0.186 0.054 0.455 0.046 0.258 Cyprus 10152 2 0.375 0.008 0.525 0.02 0.073

Turkey 6749 0 0.042 0.026 0.887 0.02 0.027 Cyprus 10153 1 0.031 0.008 0.732 0.186 0.043

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Cyprus 10154 2 0.035 0.048 0.869 0.027 0.02 Lebanon 10265 10 0.035 0.022 0.884 0.028 0.031

Cyprus 10155 2 0.026 0.038 0.888 0.027 0.021 Lebanon 10266 5 0.013 0.06 0.852 0.054 0.022

Cyprus 10156 5 0.018 0.025 0.84 0.017 0.099 Lebanon 10267 12 0.865 0.016 0.038 0.017 0.065

Cyprus 10157 2 0.106 0.023 0.529 0.019 0.323 Lebanon 10268 17 0.049 0.082 0.716 0.116 0.037

Lebanon 10235 21 0.039 0.084 0.688 0.098 0.091 Lebanon 10270 10 0.236 0.424 0.276 0.034 0.03

Lebanon 10236 25 0.018 0.021 0.786 0.132 0.043 Lebanon 10271 1 0.021 0.154 0.637 0.045 0.143

Lebanon 10237 19 0.031 0.06 0.583 0.266 0.06 Lebanon 10273 4 0.076 0.064 0.59 0.23 0.041

Lebanon 10238 2 0.353 0.055 0.459 0.055 0.077 Lebanon 10274 3 0.145 0.019 0.782 0.041 0.014

Lebanon 10239 16 0.056 0.014 0.821 0.025 0.084 Lebanon 10276 4 0.029 0.011 0.829 0.023 0.107

Lebanon 10240 20 0.049 0.025 0.825 0.031 0.069 Lebanon 10277 4 0.256 0.08 0.518 0.107 0.039

Lebanon 10241 5 0.038 0.033 0.621 0.148 0.16 Lebanon 10278 3 0.029 0.012 0.713 0.044 0.202

Lebanon 10242 0 0.192 0.159 0.567 0.042 0.04 Lebanon 10279 2 0.077 0.02 0.653 0.037 0.213

Lebanon 10243 1 0.057 0.028 0.742 0.043 0.131 Lebanon 10280 2 0.051 0.054 0.767 0.089 0.04

Lebanon 10244 18 0.04 0.032 0.819 0.058 0.051 Lebanon 10281 7 0.09 0.015 0.825 0.038 0.032

Lebanon 10245 6 0.058 0.03 0.715 0.02 0.178 Lebanon 10282 2 0.062 0.104 0.694 0.108 0.031

Lebanon 10246 18 0.201 0.078 0.564 0.035 0.122 Lebanon 10283 16 0.019 0.019 0.869 0.031 0.063

Lebanon 10247 26 0.017 0.04 0.764 0.048 0.131 Lebanon 10284 14 0.091 0.041 0.581 0.062 0.225

Lebanon 10248 22 0.031 0.053 0.709 0.11 0.097 Lebanon 10285 1 0.027 0.028 0.901 0.007 0.036

Lebanon 10249 18 0.378 0.044 0.506 0.031 0.04 Lebanon 10286 3 0.253 0.212 0.232 0.2 0.104

Lebanon 10250 27 0.051 0.014 0.881 0.022 0.032 Lebanon 10287 1 0.334 0.026 0.499 0.064 0.078

Lebanon 10251 4 0.029 0.025 0.814 0.038 0.094 Lebanon 10288 25 0.34 0.037 0.277 0.283 0.063

Lebanon 10252 14 0.261 0.061 0.193 0.031 0.455 Lebanon 10289 2 0.021 0.024 0.812 0.048 0.094

Lebanon 10253 4 0.047 0.163 0.108 0.027 0.655 Lebanon 10290 3 0.044 0.037 0.772 0.05 0.097

Lebanon 10254 13 0.043 0.015 0.821 0.096 0.025 Lebanon 10291 8 0.097 0.053 0.611 0.09 0.149

Lebanon 10255 7 0.124 0.125 0.564 0.043 0.144 Lebanon 10292 6 0.096 0.097 0.664 0.024 0.119

Lebanon 10256 10 0.191 0.032 0.336 0.036 0.405 Lebanon 10294 9 0.045 0.111 0.751 0.07 0.024

Lebanon 10257 3 0.434 0.169 0.352 0.028 0.018 Lebanon 10295 6 0.117 0.175 0.512 0.112 0.083

Lebanon 10258 19 0.017 0.037 0.616 0.227 0.103 Lebanon 10297 5 0.093 0.04 0.57 0.043 0.254

Lebanon 10259 0 0.043 0.5 0.339 0.104 0.014 Lebanon 10298 7 0.036 0.026 0.786 0.045 0.107

Lebanon 10260 17 0.166 0.023 0.756 0.023 0.033 Lebanon 10299 20 0.144 0.047 0.66 0.035 0.113

Lebanon 10261 14 0.02 0.118 0.776 0.058 0.027 Lebanon 10300 25 0.06 0.041 0.764 0.045 0.091

Lebanon 10262 22 0.015 0.013 0.87 0.024 0.077 Israel 4962 0 0.259 0.016 0.414 0.097 0.213

Lebanon 10263 2 0.078 0.025 0.707 0.035 0.154 Israel 4963 0 0.122 0.019 0.758 0.059 0.041

Lebanon 10264 3 0.188 0.035 0.583 0.09 0.105 Israel 4964 0 0.018 0.042 0.68 0.028 0.232

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Israel 4966 2 0.046 0.025 0.878 0.026 0.025 Israel 5003 1 0.307 0.259 0.151 0.101 0.182

Israel 4967 1 0.302 0.029 0.316 0.018 0.335 Israel 5004 0 0.023 0.156 0.611 0.052 0.158

Israel 4968 2 0.016 0.029 0.876 0.046 0.034 Israel 5005 0 0.218 0.012 0.707 0.035 0.028

Israel 4969 1 0.661 0.162 0.045 0.098 0.033 Israel 5006 1 0.089 0.053 0.72 0.024 0.113

Israel 4970 0 0.074 0.032 0.816 0.047 0.031 Israel 5007 1 0.033 0.103 0.711 0.117 0.036

Israel 4971 0 0.026 0.011 0.919 0.012 0.032 Israel 5008 0 0.356 0.115 0.374 0.097 0.058

Israel 4972 0 0.11 0.027 0.61 0.14 0.113 Israel 5009 0 0.03 0.292 0.534 0.028 0.116

Israel 4973 2 0.063 0.034 0.787 0.064 0.053 Israel 5010 1 0.042 0.01 0.886 0.011 0.051

Israel 4974 1 0.05 0.027 0.822 0.053 0.048 Israel 5011 0 0.016 0.024 0.881 0.05 0.03

Israel 4975 0 0.103 0.026 0.793 0.031 0.047 Egypt-Cairo 8190 0 0.058 0.054 0.795 0.048 0.045

Israel 4976 0 0.136 0.054 0.511 0.061 0.239 Egypt-Cairo 8192 2 0.142 0.014 0.796 0.017 0.031

Israel 4977 0 0.378 0.029 0.29 0.022 0.281 Egypt-Cairo 8193 0 0.384 0.049 0.429 0.044 0.094

Israel 4978 1 0.037 0.031 0.695 0.096 0.141 Egypt-Cairo 8196 1 0.029 0.039 0.774 0.065 0.094

Israel 4979 1 0.058 0.033 0.583 0.078 0.248 Egypt-Cairo 8203 1 0.029 0.012 0.922 0.012 0.025

Israel 4980 1 0.31 0.019 0.578 0.021 0.071 Egypt-Cairo 8215 1 0.085 0.044 0.698 0.064 0.109

Israel 4981 2 0.109 0.07 0.192 0.565 0.063 Egypt-Cairo 8198 2 0.028 0.047 0.867 0.021 0.038

Israel 4982 3 0.039 0.057 0.819 0.023 0.061 Egypt-Cairo 8194 0 0.106 0.059 0.738 0.035 0.062

Israel 4983 0 0.109 0.138 0.549 0.076 0.129 Egypt-Cairo 8211 10 0.025 0.008 0.938 0.012 0.017

Israel 4984 2 0.238 0.072 0.37 0.027 0.292 Egypt-Cairo 8216 1 0.039 0.136 0.793 0.01 0.022

Israel 4985 16 0.099 0.021 0.783 0.075 0.022 Egypt-Cairo 8195 1 0.165 0.013 0.772 0.022 0.029

Israel 4986 1 0.057 0.162 0.689 0.027 0.065 Egypt-Cairo 8199 9 0.051 0.015 0.863 0.045 0.026

Israel 4988 0 0.092 0.107 0.705 0.065 0.03 Egypt-Cairo 8200 16 0.038 0.029 0.885 0.022 0.026

Israel 4989 4 0.091 0.104 0.604 0.18 0.02 Egypt-Cairo 8201 0 0.205 0.123 0.6 0.018 0.053

Israel 4990 2 0.051 0.249 0.341 0.331 0.029 Egypt-Cairo 8202 2 0.033 0.679 0.111 0.055 0.122

Israel 4992 6 0.063 0.011 0.519 0.045 0.362 Egypt-Cairo 8204 2 0.023 0.012 0.93 0.023 0.012

Israel 4993 10 0.051 0.03 0.827 0.036 0.055 Egypt-Cairo 8208 0 0.011 0.027 0.839 0.012 0.11

Israel 4994 2 0.016 0.013 0.94 0.013 0.018 Egypt-Cairo 8210 3 0.032 0.014 0.731 0.148 0.074

Israel 4995 1 0.129 0.147 0.468 0.242 0.015 Egypt-Cairo 8214 2 0.061 0.225 0.599 0.032 0.083

Israel 4996 0 0.029 0.014 0.742 0.193 0.022 Egypt-Cairo 8191 1 0.024 0.014 0.928 0.017 0.018

Israel 4997 0 0.045 0.17 0.563 0.191 0.031 Egypt-Cairo 8197 0 0.032 0.043 0.897 0.012 0.015

Israel 4998 0 0.21 0.021 0.404 0.032 0.333 Egypt-Cairo 8205 0 0.011 0.011 0.766 0.06 0.151

Israel 5000 1 0.025 0.013 0.907 0.025 0.03 Egypt-Cairo 8206 1 0.01 0.044 0.856 0.05 0.04

Israel 5001 1 0.376 0.051 0.478 0.076 0.02 Egypt-Cairo 8207 2 0.023 0.1 0.813 0.033 0.031

Israel 5002 0 0.072 0.117 0.748 0.047 0.015 Egypt-Cairo 8209 1 0.013 0.018 0.883 0.027 0.059

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Egypt-Cairo 8212 0 0.04 0.095 0.787 0.016 0.062 Egypt-Cairo 10030 1 0.013 0.012 0.946 0.014 0.015

Egypt-Cairo 8213 0 0.043 0.053 0.832 0.023 0.049 Egypt-Cairo 10031 3 0.016 0.015 0.93 0.027 0.014

Egypt-Cairo 9942 3 0.033 0.02 0.866 0.051 0.031 Egypt-Cairo 10032 0 0.03 0.015 0.903 0.016 0.036

Egypt-Cairo 9943 0 0.182 0.418 0.375 0.011 0.014 Egypt-Cairo 10033 1 0.014 0.016 0.881 0.011 0.078

Egypt-Cairo 9944 4 0.583 0.053 0.049 0.053 0.263 Egypt-Cairo 10034 1 0.059 0.016 0.879 0.022 0.024

Egypt-Cairo 9945 0 0.009 0.044 0.864 0.024 0.059 Egypt-Cairo 10035 4 0.033 0.024 0.762 0.053 0.128

Egypt-Cairo 9946 0 0.029 0.024 0.898 0.033 0.016 Egypt-Cairo 10037 5 0.372 0.016 0.532 0.043 0.037

Egypt-Cairo 9947 2 0.017 0.038 0.786 0.023 0.137 Egypt-Cairo 10042 1 0.116 0.026 0.784 0.058 0.017

Egypt-Cairo 9948 0 0.157 0.017 0.795 0.011 0.02 Egypt-Cairo 10043 1 0.059 0.02 0.822 0.015 0.084

Egypt-Cairo 9949 0 0.029 0.2 0.708 0.045 0.018 Egypt-Cairo 10044 4 0.036 0.019 0.819 0.091 0.034

Egypt-Cairo 9950 1 0.046 0.009 0.921 0.012 0.012 Egypt-Cairo 10045 1 0.099 0.008 0.865 0.013 0.015

Egypt-Cairo 9951 2 0.024 0.095 0.756 0.026 0.099 Egypt-Cairo 10046 0 0.021 0.023 0.891 0.024 0.04

Egypt-Cairo 9952 1 0.01 0.013 0.667 0.021 0.289 Egypt-Cairo 10047 0 0.016 0.019 0.921 0.016 0.029

Egypt-Cairo 9953 4 0.659 0.087 0.143 0.063 0.048 Egypt-Cairo 10048 1 0.018 0.059 0.775 0.099 0.049

Egypt-Cairo 9954 0 0.028 0.52 0.405 0.032 0.015 Egypt-Cairo 10083 25 0.054 0.012 0.792 0.095 0.047

Egypt-Cairo 9955 3 0.187 0.047 0.665 0.029 0.072 Egypt-Cairo 10040 3 0.024 0.02 0.89 0.032 0.034

Egypt-Cairo 9956 5 0.021 0.252 0.605 0.106 0.016 Egypt-Cairo 10041 1 0.012 0.016 0.733 0.047 0.192

Egypt-Cairo 9957 1 0.241 0.025 0.39 0.061 0.282 Egypt-Cairo 10049 10 0.028 0.059 0.838 0.045 0.029

Egypt-Cairo 9958 1 0.02 0.141 0.784 0.023 0.032 Egypt-Cairo 10084 8 0.109 0.034 0.799 0.039 0.019

Egypt-Cairo 9959 2 0.075 0.224 0.376 0.073 0.252 Egypt-Cairo 10085 18 0.056 0.038 0.884 0.01 0.012

Egypt-Cairo 9960 1 0.015 0.044 0.881 0.026 0.035 Egypt-Cairo 10087 4 0.133 0.009 0.799 0.023 0.035

Egypt-Cairo 9961 5 0.024 0.457 0.362 0.12 0.037 Egypt-Cairo 10090 3 0.105 0.042 0.77 0.022 0.062

Egypt-Cairo 9962 2 0.154 0.065 0.744 0.023 0.014 Egypt-Cairo 9968 0 0.031 0.017 0.822 0.095 0.034

Egypt-Cairo 9963 4 0.032 0.021 0.886 0.036 0.024 Egypt-Asuit 10091 1 0.025 0.015 0.918 0.025 0.017

Egypt-Cairo 9964 2 0.048 0.118 0.726 0.033 0.075 Egypt-Asuit 10093 4 0.061 0.01 0.904 0.015 0.011

Egypt-Cairo 10021 8 0.024 0.021 0.926 0.01 0.018 Egypt-Asuit 10094 4 0.059 0.017 0.878 0.03 0.016

Egypt-Cairo 10022 1 0.015 0.016 0.914 0.021 0.035 Egypt-Asuit 10095 16 0.159 0.022 0.78 0.025 0.015

Egypt-Cairo 10023 0 0.013 0.014 0.891 0.011 0.071 Egypt-Asuit 10096 2 0.037 0.026 0.821 0.103 0.013

Egypt-Cairo 10024 2 0.014 0.006 0.966 0.007 0.007 Egypt-Asuit 10098 2 0.091 0.01 0.878 0.012 0.009

Egypt-Cairo 10025 2 0.024 0.026 0.784 0.055 0.111 Egypt-Asuit 10099 0 0.03 0.008 0.922 0.022 0.018

Egypt-Cairo 10026 21 0.099 0.023 0.803 0.06 0.015 Egypt-Asuit 10100 2 0.164 0.064 0.635 0.103 0.033

Egypt-Cairo 10027 1 0.021 0.016 0.928 0.017 0.018 Egypt-Asuit 10101 2 0.018 0.026 0.914 0.02 0.021

Egypt-Cairo 10028 1 0.069 0.01 0.877 0.018 0.026 Egypt-Asuit 10102 2 0.017 0.015 0.936 0.017 0.015

Egypt-Cairo 10029 4 0.037 0.014 0.91 0.014 0.025 Egypt- Luxor 10038 1 0.02 0.075 0.858 0.027 0.021

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Egypt-Luxor 10039 6 0.012 0.035 0.904 0.035 0.013 Simbel

Egypt-Luxor 10050 2 0.012 0.042 0.895 0.032 0.02 Egypt-Abu

Egypt-Luxor 10051 1 0.013 0.018 0.856 0.093 0.019 Simbel 10092 2 0.048 0.008 0.908 0.02 0.016

Egypt-Luxor 10052 14 0.026 0.027 0.885 0.05 0.011 Iraq-West 9587 12 0.148 0.012 0.159 0.031 0.65

Egypt-Luxor 10053 3 0.015 0.071 0.612 0.157 0.144 Iraq-West 10202 2 0.063 0.013 0.391 0.018 0.515

Egypt-Luxor 10054 2 0.014 0.01 0.903 0.024 0.049 Iraq-West 10204 13 0.086 0.013 0.271 0.022 0.608

Egypt-Luxor 10055 2 0.011 0.062 0.863 0.044 0.021 Iraq-West 11854 23 0.058 0.064 0.141 0.044 0.692

Egypt-Luxor 10056 2 0.072 0.069 0.794 0.033 0.032 Iraq-West 11860 8 0.048 0.014 0.196 0.071 0.671

Egypt-Luxor 10057 13 0.029 0.011 0.91 0.035 0.015 Iraq-West 11861 2 0.031 0.056 0.061 0.033 0.819

Egypt-Luxor 10058 8 0.031 0.016 0.873 0.036 0.044 Iraq-West 11863 2 0.066 0.011 0.174 0.012 0.737

Egypt-Luxor 10060 5 0.033 0.022 0.912 0.02 0.012 Iraq-West 11864 1 0.098 0.021 0.144 0.015 0.722

Egypt-Luxor 10061 0 0.055 0.046 0.857 0.029 0.014 Iraq-West 11888 2 0.043 0.301 0.062 0.038 0.556

Egypt-Luxor 10062 3 0.028 0.013 0.885 0.015 0.058 Iraq-West 11889 3 0.029 0.009 0.102 0.013 0.848

Egypt-Luxor 10063 0 0.293 0.049 0.435 0.12 0.103 Iraq-West 11890 20 0.05 0.038 0.126 0.048 0.738

Egypt-Luxor 10064 1 0.138 0.016 0.814 0.015 0.017 Iraq-West 11891 18 0.028 0.041 0.078 0.029 0.824

Egypt-Luxor 10065 0 0.031 0.035 0.8 0.052 0.082 Iraq-Baghdad 11847 0 0.026 0.009 0.126 0.017 0.823

Egypt-Luxor 10066 4 0.048 0.066 0.827 0.039 0.02 Iraq-Baghdad 11848 1 0.04 0.03 0.071 0.017 0.842

Egypt-Luxor 10067 4 0.027 0.013 0.875 0.033 0.052 Iraq-Baghdad 11849 0 0.078 0.006 0.282 0.013 0.621

Egypt-Luxor 10068 9 0.104 0.019 0.832 0.026 0.019 Iraq-Baghdad 11850 1 0.072 0.061 0.077 0.033 0.757

Egypt-Luxor 10069 3 0.025 0.009 0.774 0.036 0.157 Iraq-Baghdad 11852 18 0.015 0.022 0.021 0.018 0.924

Egypt-Luxor 10070 2 0.058 0.044 0.856 0.028 0.014 Iraq-Baghdad 11853 9 0.02 0.096 0.338 0.031 0.515

Egypt-Luxor 10071 4 0.019 0.014 0.919 0.012 0.036 Iraq-Baghdad 11855 22 0.024 0.027 0.078 0.023 0.849

Egypt-Luxor 10072 2 0.032 0.021 0.872 0.034 0.04 Iraq-Baghdad 11856 1 0.063 0.015 0.132 0.01 0.78

Egypt-Luxor 10073 0 0.928 0.006 0.019 0.024 0.023 Iraq-Baghdad 11857 2 0.114 0.01 0.127 0.021 0.728

Egypt-Luxor 10074 0 0.021 0.008 0.945 0.008 0.019 Iraq-Baghdad 11858 0 0.016 0.085 0.243 0.059 0.598

Egypt-Luxor 10079 6 0.073 0.018 0.873 0.019 0.017 Iraq-Baghdad 11859 3 0.055 0.013 0.091 0.023 0.818

Egypt-Luxor 10080 6 0.012 0.023 0.931 0.022 0.013 Iraq-Baghdad 11862 1 0.048 0.009 0.333 0.012 0.599

Egypt-Abu Iraq-Baghdad 11865 2 0.036 0.048 0.313 0.082 0.521

Simbel 10076 11 0.057 0.03 0.813 0.06 0.04 Iraq-Baghdad 11868 10 0.018 0.123 0.058 0.048 0.752

Egypt-Abu Iraq-Baghdad 11869 8 0.054 0.014 0.117 0.023 0.793

Simbel 10077 18 0.02 0.009 0.925 0.024 0.021 Iraq-Baghdad 11870 1 0.26 0.03 0.36 0.018 0.333

Egypt-Abu Iraq-Baghdad 11871 2 0.138 0.012 0.184 0.089 0.577

Simbel 10081 19 0.047 0.023 0.841 0.017 0.072 Iraq-Baghdad 11872 4 0.021 0.011 0.023 0.011 0.934

Egypt-Abu 10089 2 0.039 0.019 0.899 0.024 0.019 Iraq-Baghdad 11873 2 0.05 0.03 0.367 0.027 0.526

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5 raq-Baghdad 11874 0 0.042 0.018 0.215 0.102 0.624 Iran 9440 1 0.013 0.007 0.016 0.011 0.953 raq-Baghdad 11875 1 0.025 0.018 0.205 0.011 0.742 Iran 9441 1 0.008 0.007 0.009 0.015 0.961 raq-Baghdad 11876 1 0.017 0.064 0.078 0.012 0.83 Iran 9442 2 0.008 0.018 0.024 0.029 0.922 raq-Baghdad 11877 2 0.021 0.057 0.429 0.027 0.466 Iran 9443 0 0.015 0.027 0.019 0.018 0.921 raq-Baghdad 11878 2 0.181 0.017 0.11 0.01 0.682 Iran 9444 0 0.01 0.056 0.018 0.026 0.891 raq-Baghdad 11879 0 0.024 0.213 0.027 0.022 0.715 Iran 9445 1 0.01 0.015 0.016 0.04 0.92 raq-Baghdad 11880 3 0.082 0.028 0.224 0.079 0.586 Iran 9446 2 0.012 0.028 0.012 0.02 0.928 raq-Baghdad 11881 2 0.19 0.009 0.179 0.037 0.584 Iran 9447 22 0.014 0.013 0.017 0.02 0.937 raq-Baghdad 11882 0 0.027 0.07 0.137 0.033 0.733 Iran 9448 0 0.01 0.009 0.019 0.037 0.925 raq-Baghdad 11883 2 0.123 0.018 0.28 0.049 0.53 Iran 9449 0 0.008 0.023 0.013 0.122 0.834 raq-Baghdad 11884 17 0.058 0.027 0.164 0.048 0.704 Iran 9450 0 0.01 0.011 0.014 0.016 0.949 raq-Baghdad 11885 0 0.009 0.282 0.294 0.012 0.404 Iran 9451 2 0.007 0.019 0.008 0.019 0.947 raq-Baghdad 11886 2 0.032 0.049 0.191 0.066 0.661 Iran 9452 0 0.014 0.011 0.01 0.013 0.952 raq-Baghdad 11887 16 0.051 0.069 0.14 0.016 0.724 Iran 9453 2 0.007 0.014 0.017 0.01 0.953 ran 9419 22 0.018 0.031 0.036 0.019 0.896 Iran 9454 0 0.006 0.012 0.007 0.013 0.962 ran 9420 17 0.053 0.049 0.175 0.088 0.636 Iran 9455 1 0.025 0.037 0.049 0.013 0.875 ran 9421 20 0.018 0.011 0.1 0.07 0.801 Iran 9456 4 0.006 0.015 0.009 0.01 0.96 ran 9422 18 0.017 0.012 0.079 0.07 0.822 Iran 9457 2 0.01 0.022 0.012 0.012 0.945 ran 9424 25 0.011 0.022 0.019 0.021 0.928 Iran 9458 0 0.005 0.006 0.007 0.017 0.965 ran 9425 4 0.014 0.038 0.134 0.039 0.774 Iran 9459 2 0.01 0.018 0.018 0.012 0.942 ran 9426 6 0.023 0.024 0.032 0.205 0.717 Iran 9460 0 0.012 0.031 0.026 0.065 0.866 ran 9427 3 0.014 0.157 0.029 0.031 0.769 Iran 9461 1 0.036 0.011 0.037 0.018 0.898 ran 9428 2 0.025 0.073 0.303 0.11 0.489 Iran 9462 0 0.013 0.009 0.011 0.024 0.942 ran 9429 0 0.024 0.147 0.067 0.082 0.68 Iran 9463 0 0.007 0.015 0.009 0.025 0.944 ran 9430 10 0.043 0.015 0.398 0.025 0.52 Iran 9464 0 0.012 0.037 0.013 0.051 0.886 ran 9431 0 0.014 0.035 0.052 0.027 0.872 Iran 9465 1 0.012 0.01 0.02 0.013 0.945 ran 9432 1 0.01 0.182 0.024 0.029 0.754 Iran 9466 0 0.007 0.013 0.008 0.031 0.941 ran 9433 1 0.073 0.023 0.085 0.12 0.698 Iran 9468 1 0.007 0.026 0.01 0.039 0.918 ran 9434 0 0.022 0.013 0.044 0.011 0.91 Iran 9469 0 0.017 0.011 0.017 0.079 0.875 ran 9435 2 0.012 0.027 0.02 0.028 0.913 Iran 9470 1 0.008 0.017 0.014 0.059 0.901 ran 9436 18 0.014 0.017 0.019 0.025 0.925 Iran 9471 0 0.04 0.015 0.048 0.05 0.847 ran 9437 1 0.011 0.015 0.018 0.024 0.932 Iran 9472 0 0.008 0.102 0.014 0.076 0.8 ran 9438 1 0.009 0.026 0.019 0.06 0.886 Iran 9473 0 0.02 0.011 0.012 0.018 0.94 ran 9439 0 0.008 0.007 0.009 0.015 0.96 Iran 9474 0 0.045 0.028 0.036 0.053 0.839

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Iran 9475 0 0.014 0.015 0.018 0.059 0.894 Iran 9510 2 0.009 0.014 0.011 0.025 0.94

Iran 9476 2 0.038 0.017 0.03 0.016 0.9 Iran 9511 0 0.011 0.032 0.024 0.049 0.884

Iran 9477 1 0.019 0.054 0.023 0.01 0.894 Iran 9512 1 0.007 0.008 0.011 0.021 0.953

Iran 9478 0 0.018 0.015 0.024 0.049 0.893 Iran 9513 2 0.013 0.016 0.02 0.024 0.927

Iran 9479 1 0.016 0.009 0.018 0.007 0.95 Iran 9514 2 0.027 0.023 0.062 0.025 0.864

Iran 9480 1 0.007 0.021 0.016 0.012 0.945 Iran 9515 2 0.005 0.015 0.007 0.014 0.959

Iran 9481 1 0.017 0.049 0.007 0.091 0.836 Iran 9516 5 0.009 0.012 0.015 0.026 0.938

Iran 9482 1 0.01 0.013 0.007 0.022 0.948 Iran 9517 0 0.01 0.01 0.01 0.02 0.95

Iran 9483 2 0.029 0.014 0.1 0.172 0.685 Iran 9518 1 0.007 0.026 0.009 0.011 0.947

Iran 9484 2 0.029 0.012 0.125 0.013 0.822 Iran 9519 0 0.01 0.012 0.017 0.03 0.932

Iran 9485 0 0.012 0.01 0.018 0.069 0.891 Iran 9520 1 0.006 0.009 0.008 0.01 0.966

Iran 9486 1 0.008 0.008 0.009 0.015 0.959 Iran 9521 4 0.011 0.012 0.009 0.058 0.911

Iran 9487 1 0.018 0.013 0.026 0.025 0.918 Iran 9522 7 0.006 0.009 0.011 0.01 0.965

Iran 9488 1 0.022 0.172 0.048 0.011 0.748 Iran 9523 0 0.027 0.016 0.017 0.014 0.925

Iran 9489 0 0.04 0.017 0.046 0.03 0.867 Iran 9524 0 0.006 0.009 0.009 0.023 0.953

Iran 9490 0 0.026 0.007 0.014 0.039 0.915 Iran 9526 2 0.005 0.007 0.006 0.016 0.966

Iran 9491 0 0.008 0.035 0.013 0.02 0.924 Iran 9527 1 0.009 0.046 0.015 0.026 0.904

Iran 9492 2 0.017 0.026 0.028 0.011 0.918 Iran 9528 3 0.01 0.016 0.013 0.069 0.893

Iran 9493 1 0.016 0.014 0.014 0.032 0.924 Iran 9529 1 0.012 0.017 0.016 0.009 0.945

Iran 9494 1 0.008 0.011 0.011 0.018 0.952 Iran 9530 4 0.008 0.007 0.017 0.016 0.952

Iran 9495 0 0.012 0.014 0.013 0.013 0.948 Iran 9531 2 0.01 0.048 0.017 0.028 0.897

Iran 9497 1 0.008 0.013 0.011 0.043 0.926 Iran 9532 16 0.192 0.211 0.184 0.011 0.402

Iran 9498 0 0.009 0.011 0.01 0.023 0.947 Dubai 10104 0 0.228 0.013 0.22 0.025 0.514

Iran 9499 2 0.013 0.007 0.016 0.042 0.922 Dubai 10105 2 0.069 0.113 0.169 0.022 0.627

Iran 9500 0 0.01 0.012 0.01 0.014 0.954 Dubai 10106 0 0.051 0.169 0.08 0.05 0.65

Iran 9501 1 0.023 0.014 0.016 0.01 0.938 Dubai 10107 0 0.055 0.037 0.059 0.154 0.695

Iran 9502 4 0.011 0.016 0.016 0.007 0.95 Dubai 10108 2 0.076 0.145 0.295 0.074 0.41

Iran 9503 2 0.107 0.016 0.046 0.022 0.809 Dubai 10109 1 0.034 0.083 0.048 0.332 0.503

Iran 9504 1 0.009 0.008 0.01 0.01 0.964 Dubai 10110 2 0.084 0.095 0.299 0.027 0.495

Iran 9505 2 0.033 0.013 0.026 0.01 0.918 Dubai 10111 2 0.082 0.014 0.089 0.015 0.8

Iran 9506 6 0.052 0.05 0.024 0.035 0.839 Dubai 10112 0 0.035 0.11 0.098 0.043 0.715

Iran 9507 0 0.083 0.015 0.042 0.036 0.824 Dubai 10120 0 0.032 0.028 0.063 0.193 0.685

Iran 9508 2 0.01 0.008 0.009 0.012 0.961 Kenya-Nairobi 9833 2 0.771 0.027 0.052 0.022 0.127

Iran 9509 2 0.016 0.012 0.013 0.056 0.903 Kenya-Nairobi 9834 0 0.186 0.038 0.415 0.29 0.071

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Kenya- Nairobi 9835 0 0.016 0.069 0.818 0.022 0.076 Kenya-Pate 2000 0 0.04 0.039 0.047 0.306 0.568

Kenya- Nairobi 9836 2 0.61 0.182 0.133 0.051 0.023 Kenya-Pate 2001 0 0.076 0.067 0.015 0.098 0.744

Kenya- Nairobi 9837 3 0.738 0.178 0.043 0.022 0.019 Kenya-Pate 2002 0 0.084 0.059 0.016 0.087 0.754

Kenya- Nairobi 9838 4 0.532 0.079 0.325 0.019 0.045 Kenya-Pate 2003 0 0.219 0.013 0.036 0.061 0.672

Kenya- Nairobi 9839 1 0.76 0.035 0.068 0.039 0.097 Kenya-Pate 2004 1 0.029 0.201 0.26 0.115 0.395

Kenya- Nairobi 9840 4 0.757 0.072 0.043 0.022 0.105 Kenya-Pate 2006 2 0.227 0.015 0.051 0.408 0.3

Kenya- Nairobi 9841 4 0.757 0.058 0.033 0.048 0.104 Kenya-Pate 2007 3 0.14 0.011 0.201 0.319 0.329

Kenya- Nairobi 9842 2 0.532 0.039 0.309 0.057 0.064 Kenya-Pate 2009 0 0.087 0.05 0.147 0.122 0.594

Kenya- Nairobi 9843 6 0.568 0.197 0.112 0.037 0.085 Kenya-Pate 2011 0 0.049 0.043 0.132 0.324 0.453

Kenya- Nairobi 9844 4 0.639 0.201 0.058 0.045 0.058 Kenya-Lamu 1848 14 0.07 0.028 0.292 0.389 0.222

Kenya- Nairobi 9845 2 0.737 0.147 0.062 0.022 0.033 Kenya-Lamu 2014 4 0.112 0.016 0.048 0.186 0.639

Kenya- Nairobi 9846 1 0.612 0.054 0.076 0.014 0.243 Kenya-Lamu 2015 0 0.264 0.224 0.023 0.118 0.371

Kenya- Nairobi 9847 4 0.665 0.103 0.047 0.092 0.093 Kenya-Lamu 2016 0 0.019 0.22 0.034 0.151 0.576

Kenya-Nairobi 9848 4 0.679 0.049 0.167 0.042 0.062 Kenya-Lamu 2018 0 0.484 0.056 0.013 0.073 0.373

Kenya- Nairobi 9849 2 0.478 0.148 0.089 0.255 0.03 Kenya-Lamu 2019 0 0.156 0.047 0.454 0.082 0.261

Kenya- Nairobi 9850 2 0.771 0.052 0.054 0.02 0.103 Kenya-Lamu 2021 1 0.198 0.061 0.095 0.282 0.364

Kenya- Nairobi 9851 2 0.321 0.26 0.156 0.072 0.19 Kenya-Lamu 2023 1 0.049 0.042 0.083 0.232 0.593

Kenya- Nairobi 9852 4 0.394 0.21 0.136 0.174 0.087 Kenya-Lamu 2024 2 0.087 0.024 0.105 0.189 0.595

Kenya- Nairobi 9853 4 0.583 0.17 0.088 0.077 0.082 Kenya-Lamu 2025 3 0.155 0.012 0.056 0.3 0.477

Kenya- Nairobi 9854 2 0.515 0.281 0.121 0.047 0.036 Kenya-Lamu 2026 1 0.138 0.022 0.208 0.091 0.542

Kenya- Nairobi 9855 2 0.523 0.103 0.071 0.153 0.151 Kenya-Lamu 2027 2 0.06 0.01 0.033 0.558 0.339

Kenya- Nairobi 9856 3 0.564 0.118 0.086 0.037 0.195 Kenya-Lamu 2029 1 0.09 0.03 0.11 0.211 0.56

Kenya- Nairobi 9857 2 0.789 0.047 0.099 0.018 0.047 Kenya-Lamu 2030 0 0.118 0.01 0.072 0.038 0.761

Kenya- Nairobi 9858 0 0.579 0.037 0.051 0.105 0.227 Kenya-Lamu 2031 0 0.067 0.025 0.041 0.23 0.636

Kenya-Nairobi 9859 1 0.761 0.045 0.061 0.109 0.024 Kenya-Lamu 2032 0 0.039 0.023 0.1 0.22 0.619

Kenya- Nairobi 9860 2 0.726 0.108 0.032 0.103 0.03 Kenya-Lamu 2033 0 0.112 0.022 0.071 0.048 0.746

Kenya- Nairobi 9861 1 0.666 0.054 0.096 0.098 0.086 Kenya-Lamu 3241 0 0.115 0.017 0.235 0.032 0.601

Kenya- Nairobi 9862 1 0.717 0.158 0.06 0.024 0.041 Kenya-Lamu 3246 0 0.3 0.192 0.049 0.081 0.378

Kenya- Nairobi 9863 1 0.379 0.18 0.132 0.271 0.039 Kenya-Lamu 3247 0 0.141 0.031 0.119 0.488 0.221

Kenya- Nairobi 9864 1 0.368 0.226 0.22 0.076 0.11 India-Udaipur 11835 7 0.023 0.368 0.021 0.12 0.469

Kenya- Nairobi 9865 2 0.859 0.021 0.042 0.055 0.023 India-Udaipur 11836 3 0.163 0.076 0.088 0.187 0.487

Kenya- Nairobi 9866 2 0.623 0.216 0.031 0.054 0.075 India-Udaipur 11837 1 0.014 0.099 0.051 0.408 0.429

Kenya- Nairobi 9867 0 0.752 0.028 0.058 0.11 0.052 India-Agra 11823 2 0.029 0.258 0.189 0.243 0.281

Kenya- Nairobi 9868 5 0.585 0.312 0.03 0.025 0.048 India-Agra 11824 2 0.036 0.023 0.371 0.038 0.533

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

India-Agra 11825 2 0.012 0.031 0.022 0.356 0.58 India-

India-Agra 11826 6 0.015 0.023 0.058 0.569 0.335 Hyderbad 11815 2 0.023 0.273 0.629 0.027 0.048

India-Agra 11827 25 0.046 0.105 0.111 0.104 0.634 India-

India-Agra 11828 2 0.036 0.042 0.72 0.07 0.132 Hyderbad 11816 4 0.027 0.032 0.054 0.226 0.661

India-Agra 11829 4 0.02 0.06 0.041 0.281 0.598 India-

India-Agra 11830 8 0.042 0.024 0.093 0.193 0.648 Hyderbad 11817 2 0.017 0.493 0.264 0.044 0.182

India-Agra 11831 2 0.015 0.029 0.027 0.205 0.724 India-

India-Agra 11832 2 0.017 0.096 0.024 0.175 0.687 Hyderbad 11818 1 0.534 0.372 0.04 0.012 0.042

India-Agra 11833 20 0.037 0.364 0.196 0.126 0.276 India-

India-Agra 11834 2 0.019 0.023 0.051 0.563 0.344 Hyderbad 11819 6 0.031 0.033 0.086 0.233 0.617

India- India-

Hyderbad 11802 14 0.018 0.609 0.244 0.048 0.081 Hyderbad 11820 6 0.01 0.379 0.044 0.074 0.494

India- India-

Hyderbad 11803 7 0.018 0.207 0.074 0.045 0.657 Hyderbad 11821 17 0.028 0.572 0.228 0.033 0.139

India- India-

Hyderbad 11804 4 0.01 0.385 0.033 0.242 0.33 Hyderbad 11822 2 0.042 0.235 0.167 0.076 0.48

India- India-Andhra 10159 1 0.069 0.286 0.097 0.065 0.484

Hyderbad 11805 11 0.023 0.284 0.308 0.035 0.351 India-Andhra 10160 1 0.017 0.605 0.023 0.307 0.048

India- India-Andhra 10161 2 0.105 0.382 0.086 0.054 0.373

Hyderbad 11807 13 0.141 0.369 0.175 0.201 0.113 India-Andhra 10162 5 0.062 0.599 0.041 0.04 0.258

India- India-Andhra 10163 0 0.039 0.452 0.043 0.041 0.424

Hyderbad 11808 10 0.158 0.156 0.284 0.261 0.141 India-Andhra 10164 2 0.396 0.382 0.066 0.043 0.114

India- India-Andhra 10165 1 0.079 0.752 0.095 0.02 0.054

Hyderbad 11809 4 0.201 0.224 0.085 0.1 0.39 India-Andhra 10166 2 0.038 0.477 0.112 0.031 0.342

India- India-Andhra 10167 0 0.025 0.717 0.033 0.087 0.138

Hyderbad 11810 0 0.033 0.041 0.054 0.434 0.438 India-Andhra 10168 1 0.141 0.632 0.133 0.048 0.045

India- India-Andhra 10169 7 0.061 0.512 0.111 0.065 0.251

Hyderbad 11811 0 0.173 0.064 0.458 0.179 0.126 India-Andhra 10170 6 0.02 0.49 0.349 0.071 0.069

India- India-Andhra 10171 1 0.393 0.389 0.069 0.041 0.107

Hyderbad 11812 0 0.021 0.253 0.143 0.137 0.447 India-Andhra 10172 2 0.046 0.572 0.098 0.038 0.247

India- India-Andhra 10173 2 0.075 0.647 0.085 0.018 0.175

Hyderbad 11813 2 0.068 0.5 0.333 0.034 0.064 India-Andhra 10174 3 0.043 0.4 0.104 0.367 0.086

India- India-Andhra 10175 2 0.061 0.296 0.234 0.358 0.051

Hyderbad 11814 2 0.021 0.272 0.164 0.016 0.527 India-Andhra 10176 2 0.065 0.303 0.29 0.214 0.127

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

India-Andhra 10177 0 0.327 0.448 0.017 0.122 0.086 Sri Lanka 8802 2 0.359 0.129 0.126 0.316 0.07

India-Andhra 10178 2 0.061 0.66 0.075 0.05 0.155 Sri Lanka 8803 4 0.151 0.403 0.128 0.277 0.041

India-Andhra 10179 5 0.036 0.547 0.024 0.042 0.351 Thailand 11688 6 0.03 0.94 0.011 0.012 0.007

India-Andhra 10180 12 0.081 0.287 0.081 0.216 0.335 Thailand 11689 13 0.009 0.942 0.008 0.032 0.009

India-Andhra 10181 4 0.058 0.537 0.056 0.103 0.246 Thailand 11691 20 0.026 0.921 0.016 0.023 0.013

India-Kolkata 10113 0 0.019 0.019 0.062 0.573 0.326 Thailand 11698 17 0.006 0.972 0.009 0.007 0.006

India-Kolkata 10114 1 0.038 0.588 0.115 0.088 0.171 Thailand 11702 10 0.006 0.95 0.006 0.03 0.008

India-Kolkata 10115 1 0.021 0.222 0.504 0.191 0.062 Thailand 11703 12 0.007 0.958 0.009 0.018 0.007

India-Kolkata 10116 1 0.008 0.538 0.022 0.054 0.377 Thailand 11705 21 0.014 0.873 0.034 0.026 0.053

India-Kolkata 10117 2 0.029 0.415 0.407 0.06 0.089 Thailand 11707 3 0.005 0.96 0.007 0.018 0.01

India-Kolkata 10118 0 0.137 0.423 0.253 0.11 0.076 Thailand 11708 22 0.009 0.95 0.009 0.022 0.01

India-Kolkata 10119 2 0.175 0.202 0.231 0.076 0.316 Thailand 11709 12 0.006 0.958 0.008 0.013 0.014

Sri Lanka 8780 2 0.173 0.057 0.098 0.563 0.11 Thailand 11710 8 0.016 0.921 0.019 0.029 0.015

Sri Lanka 8781 0 0.256 0.312 0.08 0.296 0.056 Thailand 11711 7 0.011 0.951 0.01 0.016 0.012

Sri Lanka 8782 0 0.093 0.207 0.431 0.202 0.067 Thailand 11714 5 0.015 0.944 0.012 0.012 0.017

Sri Lanka 8783 0 0.082 0.224 0.64 0.022 0.033 Thailand 11715 16 0.016 0.8 0.015 0.02 0.148

Sri Lanka 8784 0 0.482 0.348 0.074 0.074 0.023 Thailand 11717 15 0.005 0.951 0.008 0.014 0.022

Sri Lanka 8785 11 0.332 0.321 0.03 0.133 0.183 Thailand 11718 0 0.004 0.973 0.004 0.01 0.008

Sri Lanka 8786 0 0.121 0.134 0.094 0.449 0.202 Thailand 11720 3 0.004 0.98 0.004 0.006 0.006

Sri Lanka 8787 0 0.529 0.18 0.082 0.099 0.11 Vietnam 8844 4 0.023 0.93 0.009 0.02 0.018

Sri Lanka 8788 4 0.078 0.234 0.534 0.065 0.088 Vietnam 8845 1 0.025 0.788 0.053 0.029 0.104

Sri Lanka 8789 2 0.164 0.333 0.384 0.085 0.034 Vietnam 8846 0 0.021 0.92 0.033 0.013 0.014

Sri Lanka 8790 1 0.279 0.568 0.095 0.012 0.047 Vietnam 8847 9 0.01 0.957 0.009 0.018 0.006

Sri Lanka 8791 0 0.159 0.316 0.429 0.058 0.038 Vietnam 8848 6 0.009 0.945 0.014 0.02 0.011

Sri Lanka 8792 0 0.029 0.054 0.461 0.193 0.264 Vietnam 8849 2 0.026 0.87 0.048 0.036 0.02

Sri Lanka 8793 1 0.146 0.083 0.333 0.101 0.337 Vietnam 8850 0 0.085 0.795 0.042 0.038 0.041

Sri Lanka 8794 1 0.117 0.075 0.257 0.211 0.339 Vietnam 8851 0 0.032 0.774 0.094 0.013 0.087

Sri Lanka 8795 1 0.399 0.138 0.162 0.207 0.094 Vietnam 8852 13 0.046 0.813 0.06 0.063 0.019

Sri Lanka 8796 3 0.049 0.189 0.198 0.384 0.18 Vietnam 8853 1 0.13 0.713 0.102 0.036 0.019

Sri Lanka 8797 0 0.293 0.437 0.069 0.035 0.165 Vietnam 8854 5 0.02 0.832 0.04 0.056 0.051

Sri Lanka 8798 0 0.055 0.077 0.51 0.303 0.054 Vietnam 8855 0 0.277 0.639 0.06 0.015 0.009

Sri Lanka 8799 1 0.364 0.205 0.132 0.089 0.21 Vietnam 8856 0 0.03 0.857 0.038 0.034 0.041

Sri Lanka 8800 0 0.55 0.254 0.08 0.048 0.068 Vietnam 8857 2 0.058 0.664 0.228 0.036 0.014

Sri Lanka 8801 4 0.306 0.149 0.254 0.165 0.125 Vietnam 8858 0 0.098 0.625 0.25 0.01 0.018

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Vietnam 8859 6 0.049 0.774 0.093 0.059 0.025 Japan-Oita 11967 4 0.04 0.679 0.054 0.195 0.032

Vietnam 8860 0 0.017 0.701 0.049 0.146 0.087 Japan-Oita 11968 5 0.319 0.298 0.031 0.329 0.022

Vietnam 8861 0 0.026 0.66 0.133 0.136 0.045 Japan-Oita 11969 5 0.135 0.292 0.108 0.447 0.018

Vietnam 8862 0 0.087 0.507 0.112 0.038 0.257 Japan-Oita 11970 16 0.031 0.847 0.077 0.029 0.016

Vietnam 8863 0 0.2 0.61 0.146 0.017 0.027 Japan-Oita 11971 8 0.032 0.538 0.156 0.122 0.152

Taiwan 8681 3 0.593 0.043 0.015 0.333 0.015 Japan-Oita 11972 3 0.067 0.468 0.252 0.182 0.032

Taiwan 8682 0 0.204 0.461 0.057 0.171 0.107 Japan-Oita 11973 2 0.398 0.043 0.014 0.53 0.014

Taiwan 8683 2 0.06 0.125 0.409 0.276 0.13 Japan-Oita 11974 20 0.015 0.45 0.023 0.485 0.027

Taiwan 8684 2 0.6 0.06 0.019 0.281 0.039 Japan-Oita 11975 22 0.275 0.545 0.052 0.116 0.013

Taiwan 8685 2 0.026 0.355 0.022 0.562 0.036 Japan-Oita 11976 16 0.033 0.602 0.015 0.316 0.034

Taiwan 8686 6 0.158 0.096 0.068 0.572 0.106 Japan-Oita 11977 4 0.014 0.629 0.01 0.295 0.052

Taiwan 8687 6 0.019 0.585 0.098 0.262 0.035 Japan-Oita 11979 12 0.147 0.667 0.024 0.113 0.049

Taiwan 8688 21 0.098 0.335 0.047 0.382 0.138 Japan-Oita 11980 21 0.013 0.694 0.03 0.148 0.115

Taiwan 8689 5 0.027 0.056 0.034 0.537 0.346 Japan-Oita 11981 18 0.012 0.164 0.012 0.798 0.014

Taiwan 8690 0 0.016 0.215 0.017 0.693 0.059 Japan-Oita 11982 4 0.025 0.427 0.018 0.472 0.058

Taiwan 8691 14 0.068 0.254 0.036 0.56 0.081 Japan-Oita 11985 11 0.033 0.255 0.043 0.639 0.03

Taiwan 8692 3 0.127 0.5 0.063 0.215 0.094 Japan-Oita 11986 6 0.364 0.431 0.036 0.146 0.023

Taiwan 8693 0 0.174 0.196 0.033 0.489 0.108 Japan-

Taiwan 8694 0 0.28 0.228 0.086 0.299 0.107 Kanazawa 11929 6 0.933 0.008 0.015 0.02 0.024

Taiwan 8695 0 0.089 0.297 0.091 0.509 0.014 Japan-

Taiwan 8696 8 0.968 0.005 0.013 0.008 0.006 Kanazawa 11931 20 0.033 0.12 0.014 0.794 0.038

Taiwan 8697 0 0.723 0.034 0.019 0.193 0.031 Japan-

Taiwan 8698 4 0.948 0.008 0.014 0.019 0.011 Kanazawa 11932 6 0.123 0.124 0.028 0.701 0.024

Taiwan 8699 6 0.057 0.239 0.077 0.428 0.199 Japan-

Taiwan 8700 1 0.337 0.2 0.048 0.38 0.036 Kanazawa 11933 14 0.132 0.042 0.048 0.767 0.011

Taiwan 8701 2 0.099 0.191 0.015 0.67 0.026 Japan-

Taiwan 8702 1 0.33 0.101 0.036 0.509 0.023 Kanazawa 11934 3 0.145 0.042 0.023 0.783 0.007

Taiwan 8703 1 0.8 0.083 0.042 0.041 0.034 Japan-

Taiwan 8704 0 0.016 0.162 0.154 0.654 0.013 Kanazawa 11936 8 0.065 0.419 0.029 0.459 0.027

Taiwan 8705 1 0.025 0.26 0.043 0.631 0.041 Japan-

Taiwan 8706 0 0.111 0.13 0.199 0.288 0.273 Kanazawa 11937 27 0.035 0.033 0.019 0.889 0.025

Taiwan 8707 4 0.031 0.177 0.088 0.522 0.182 Japan-

Taiwan 8708 0 0.93 0.027 0.014 0.013 0.017 Kanazawa 11939 20 0.022 0.321 0.011 0.637 0.009

Taiwan 8709 2 0.021 0.212 0.022 0.72 0.025 Japan- 11940 12 0.018 0.116 0.014 0.838 0.014

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Kanazawa Sapporo

Japan- Japan-

Kanazawa 11941 6 0.029 0.334 0.016 0.607 0.014 Sapporo 11913 16 0.046 0.345 0.129 0.455 0.025

Japan- Japan-

Kanazawa 11942 9 0.011 0.04 0.009 0.93 0.01 Sapporo 11914 4 0.033 0.315 0.194 0.412 0.046

Japan- Japan-

Kanazawa 11943 17 0.018 0.467 0.02 0.45 0.045 Sapporo 11915 7 0.012 0.819 0.01 0.148 0.011

Japan- Japan-

Kanazawa 11944 7 0.508 0.119 0.256 0.049 0.069 Sapporo 11916 7 0.926 0.021 0.034 0.007 0.012

Japan- Japan-

Kanazawa 11945 18 0.048 0.132 0.025 0.781 0.014 Sapporo 11917 4 0.905 0.011 0.017 0.018 0.049

Japan- Japan-

Kanazawa 11946 16 0.006 0.029 0.009 0.949 0.006 Sapporo 11918 13 0.087 0.207 0.045 0.613 0.048

Japan-Ohmiya 11947 4 0.085 0.12 0.106 0.672 0.017 Japan-

Japan-Ohmiya 11948 17 0.009 0.031 0.013 0.924 0.024 Sapporo 11921 5 0.012 0.378 0.038 0.526 0.046

Japan-Ohmiya 11951 22 0.06 0.049 0.064 0.731 0.096 Japan-

Japan-Ohmiya 11953 2 0.011 0.267 0.015 0.693 0.014 Sapporo 11922 6 0.016 0.058 0.015 0.507 0.404

Japan-Ohmiya 11954 2 0.064 0.066 0.029 0.814 0.026 Japan-

Japan-Ohmiya 11955 5 0.237 0.019 0.014 0.707 0.022 Sapporo 11923 7 0.005 0.408 0.007 0.57 0.009

Japan-Ohmiya 11956 2 0.009 0.722 0.025 0.233 0.011 Japan-

Japan-Ohmiya 11957 3 0.063 0.077 0.086 0.755 0.019 Sapporo 11924 6 0.58 0.192 0.056 0.158 0.014

Japan-Ohmiya 11959 4 0.02 0.196 0.016 0.757 0.011 Japan-

Japan-Ohmiya 11960 3 0.014 0.551 0.011 0.413 0.011 Sapporo 11925 9 0.011 0.9 0.017 0.04 0.032

Japan-Ohmiya 11961 3 0.033 0.17 0.02 0.76 0.017 Japan-

Japan-Ohmiya 11962 4 0.328 0.13 0.047 0.469 0.027 Sapporo 11926 11 0.083 0.032 0.318 0.513 0.054

Japan-Ohmiya 11963 3 0.026 0.155 0.03 0.699 0.091 China-Henan 8869 2 0.007 0.014 0.022 0.851 0.106

Japan-Ohmiya 11964 4 0.053 0.025 0.02 0.879 0.024 China-Henan 8870 0 0.01 0.016 0.017 0.932 0.025

Japan-Ohmiya 11965 4 0.046 0.141 0.16 0.557 0.096 China-Henan 8871 0 0.013 0.012 0.021 0.926 0.028

Japan-Ohmiya 11966 5 0.089 0.179 0.077 0.565 0.09 China-Henan 8872 1 0.021 0.027 0.046 0.885 0.021

Japan- China-Henan 8873 1 0.006 0.011 0.013 0.925 0.045

Sapporo 11907 7 0.059 0.118 0.026 0.675 0.122 China-Henan 8874 8 0.008 0.069 0.016 0.793 0.114

Japan- China-Henan 8875 1 0.008 0.008 0.012 0.953 0.02

Sapporo 11909 9 0.955 0.011 0.01 0.011 0.012 China-Henan 8876 0 0.007 0.012 0.011 0.953 0.018

Japan- 11911 17 0.304 0.053 0.029 0.593 0.02 China-Henan 8877 0 0.062 0.01 0.019 0.882 0.027

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

China-Henan 8878 2 0.012 0.01 0.026 0.904 0.049 South Korea 7686 2 0.03 0.008 0.037 0.888 0.036

China-Henan 8879 2 0.015 0.009 0.016 0.944 0.016 South Korea 7687 4 0.121 0.133 0.049 0.676 0.021

China-Henan 8880 0 0.078 0.009 0.03 0.857 0.026 South Korea 7688 13 0.326 0.024 0.014 0.622 0.015

China-Henan 8881 2 0.01 0.013 0.018 0.912 0.047 South Korea 7689 0 0.01 0.008 0.01 0.877 0.095

China-Henan 8882 2 0.015 0.012 0.02 0.913 0.04 South Korea 7690 0 0.061 0.027 0.115 0.757 0.04

China-Henan 8883 0 0.013 0.014 0.01 0.853 0.11 South Korea 7691 2 0.01 0.179 0.013 0.786 0.013

China-Henan 8884 0 0.008 0.007 0.009 0.949 0.027 South Korea 7692 0 0.057 0.026 0.073 0.767 0.077

China-Henan 8885 0 0.024 0.012 0.048 0.773 0.144 South Korea 7693 0 0.021 0.049 0.03 0.86 0.04

China-Henan 8886 0 0.007 0.009 0.013 0.913 0.059 South Korea 7694 1 0.007 0.056 0.016 0.905 0.016

China-Henan 8887 1 0.009 0.042 0.021 0.886 0.043 South Korea 7695 9 0.081 0.009 0.121 0.741 0.049

China-Henan 8888 1 0.01 0.007 0.009 0.963 0.011 South Korea 7696 2 0.015 0.01 0.018 0.939 0.018

South Korea 2769 4 0.018 0.018 0.061 0.812 0.091 South Korea 7697 2 0.061 0.014 0.045 0.832 0.048

South Korea 2772 12 0.01 0.01 0.008 0.961 0.011 South Korea 7698 2 0.023 0.363 0.111 0.489 0.013

South Korea 2775 8 0.015 0.075 0.025 0.828 0.057 South Korea 7699 0 0.043 0.031 0.065 0.846 0.014

South Korea 2776 25 0.008 0.038 0.011 0.908 0.035 South Korea 7700 6 0.012 0.035 0.018 0.916 0.019

South Korea 2779 0 0.03 0.046 0.023 0.849 0.052

South Korea 2784 2 0.018 0.016 0.056 0.817 0.094

South Korea 2785 1 0.011 0.033 0.018 0.909 0.029

South Korea 2786 2 0.011 0.042 0.021 0.918 0.009

South Korea 7671 1 0.121 0.014 0.064 0.775 0.025

South Korea 7672 0 0.009 0.102 0.019 0.843 0.026

South Korea 7673 0 0.014 0.015 0.047 0.892 0.033

South Korea 7674 1 0.12 0.015 0.114 0.581 0.171

South Korea 7675 2 0.009 0.021 0.011 0.943 0.016

South Korea 7676 2 0.154 0.028 0.05 0.753 0.014

South Korea 7677 14 0.835 0.041 0.054 0.054 0.016

South Korea 7678 0 0.024 0.018 0.014 0.928 0.016

South Korea 7679 2 0.015 0.023 0.036 0.905 0.021

South Korea 7680 8 0.168 0.012 0.045 0.744 0.031

South Korea 7681 2 0.017 0.063 0.018 0.888 0.014

South Korea 7682 0 0.008 0.051 0.01 0.921 0.01

South Korea 7683 0 0.008 0.012 0.008 0.952 0.021

South Korea 7684 1 0.561 0.014 0.025 0.291 0.11

South Korea 7685 6 0.03 0.134 0.098 0.628 0.109

Table 21 - Population clustering of each random bred Table 21 - Population clustering of each random bred individual in the database by STRs at K = 5 individual in the database by STRs at K = 5 Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

USA-NY 2547 2 0.006 0.013 0.111 0.821 0.049 Brazil 7966 5 0.005 0.015 0.011 0.96 0.009

USA-NY 2559 13 0.004 0.006 0.176 0.803 0.011 Brazil 7968 23 0.009 0.008 0.018 0.953 0.011

USA-NY 2568 2 0.017 0.004 0.01 0.963 0.006 Brazil 7969 2 0.006 0.005 0.01 0.971 0.009

USA-NY 2569 21 0.022 0.015 0.028 0.923 0.013 Brazil 7970 2 0.005 0.005 0.016 0.96 0.014

USA-NY 2572 5 0.012 0.007 0.016 0.863 0.102 Brazil 7971 2 0.013 0.006 0.007 0.968 0.007

USA-NY 2578 5 0.005 0.008 0.01 0.962 0.016 Brazil 7972 2 0.007 0.009 0.011 0.968 0.006

USA-NY 2590 5 0.005 0.003 0.037 0.935 0.019 Brazil 7973 2 0.015 0.006 0.015 0.958 0.007

USA-NY 2591 7 0.031 0.01 0.017 0.929 0.012 Brazil 7974 2 0.009 0.008 0.014 0.955 0.014

USA-NY 2597 15 0.011 0.014 0.018 0.865 0.092 Brazil 7975 13 0.019 0.005 0.017 0.943 0.017

USA-MS 9971 5 0.011 0.011 0.018 0.888 0.072 Brazil 7976 2 0.008 0.004 0.007 0.976 0.005

USA-MS 9972 0 0.013 0.019 0.021 0.857 0.089 Brazil 7977 7 0.009 0.004 0.016 0.967 0.005

USA-MS 9974 2 0.011 0.01 0.023 0.908 0.048 Brazil 7978 2 0.007 0.006 0.011 0.962 0.015

USA-MS 9977 10 0.015 0.012 0.017 0.936 0.02 Brazil 7979 5 0.022 0.007 0.014 0.943 0.014

USA-MS 9980 7 0.007 0.017 0.043 0.922 0.012 Brazil 7980 2 0.026 0.025 0.262 0.675 0.013

USA-MS 9983 7 0.006 0.008 0.011 0.951 0.024 Brazil 7981 5 0.145 0.009 0.02 0.791 0.036

USA-MS 9985 5 0.133 0.004 0.051 0.795 0.017 Brazil 7982 42 0.038 0.014 0.055 0.832 0.062

USA-MS 9987 10 0.01 0.015 0.025 0.92 0.031 Brazil 7983 15 0.007 0.009 0.03 0.768 0.186

USA-MS 9989 2 0.05 0.009 0.042 0.875 0.024 Brazil 7984 50 0.136 0.024 0.137 0.6 0.103

USA-MS 9992 7 0.016 0.009 0.136 0.754 0.085 Brazil 7985 21 0.006 0.006 0.012 0.964 0.011

USA-HI 5366 10 0.161 0.005 0.057 0.681 0.097 Brazil 7986 10 0.033 0.01 0.032 0.905 0.021

USA-HI 5367 7 0.017 0.016 0.092 0.8 0.074 Brazil 7987 5 0.009 0.006 0.017 0.886 0.081

USA-HI 5371 5 0.064 0.024 0.319 0.448 0.144 Brazil 7988 2 0.015 0.027 0.049 0.843 0.066

USA-HI 5372 7 0.02 0.011 0.047 0.762 0.161 Brazil 7989 5 0.02 0.011 0.018 0.893 0.057

USA-HI 5379 2 0.051 0.021 0.03 0.878 0.019 Brazil 7990 5 0.005 0.004 0.007 0.973 0.012

USA-HI 5380 2 0.016 0.007 0.019 0.948 0.01 Finland 8077 23 0.004 0.007 0.008 0.976 0.005

USA-HI 5383 10 0.025 0.122 0.386 0.43 0.037 Finland 8084 31 0.037 0.008 0.024 0.88 0.051

USA-HI 5384 18 0.005 0.007 0.175 0.805 0.008 Finland 8086 13 0.005 0.007 0.028 0.954 0.006

USA-HI 5401 5 0.063 0.005 0.442 0.466 0.024 Finland 8089 26 0.016 0.017 0.02 0.914 0.033

USA-HI 5402 7 0.117 0.057 0.066 0.741 0.018 Finland 8093 18 0.004 0.005 0.373 0.612 0.005

Brazil 7961 2 0.011 0.005 0.02 0.959 0.005 Finland 8094 34 0.013 0.072 0.104 0.78 0.03

Brazil 7962 2 0.004 0.003 0.077 0.907 0.008 Finland 8096 23 0.006 0.013 0.138 0.834 0.009

Brazil 7963 23 0.008 0.006 0.014 0.791 0.181 Finland 8107 31 0.004 0.004 0.008 0.979 0.005

Brazil 7964 5 0.013 0.005 0.009 0.958 0.014 Finland 8110 28 0.013 0.021 0.238 0.606 0.123

Brazil 7965 65 0.007 0.008 0.014 0.958 0.014 Finland 8116 44 0.015 0.02 0.034 0.914 0.017

Table 21 - Population clustering of each random bred Table 21 - Population clustering of each random bred individual in the database by STRs at K = 5 individual in the database by STRs at K = 5

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Finland 8120 28 0.015 0.014 0.087 0.878 0.006 Italy-Milan 8062 10 0.017 0.01 0.441 0.511 0.021

Germany 8711 13 0.004 0.004 0.007 0.981 0.004 Italy-Milan 8065 10 0.02 0.007 0.576 0.384 0.013

Germany 8712 2 0.012 0.019 0.007 0.946 0.016 Italy-Milan 8066 2 0.006 0.006 0.011 0.927 0.05

Germany 8713 7 0.039 0.034 0.022 0.885 0.02 Italy-Milan 8067 2 0.006 0.016 0.37 0.555 0.053

Germany 8714 5 0.007 0.014 0.017 0.952 0.011 Italy-Milan 8068 7 0.01 0.008 0.627 0.337 0.019

Germany 8715 5 0.005 0.006 0.011 0.972 0.007 Italy-Milan 8069 2 0.021 0.024 0.251 0.529 0.175

Germany 8716 2 0.005 0.006 0.012 0.972 0.006 Italy-Milan 8071 5 0.01 0.008 0.017 0.95 0.015

Germany 8717 7 0.006 0.006 0.013 0.968 0.007 Italy-Milan 8072 7 0.084 0.054 0.135 0.698 0.03

Germany 8720 5 0.038 0.007 0.012 0.493 0.45 Italy-Milan 8073 5 0.027 0.033 0.164 0.739 0.037

Germany 8721 10 0.005 0.014 0.063 0.911 0.006 Italy-Milan 8074 5 0.026 0.004 0.015 0.603 0.352

Germany 8727 7 0.006 0.006 0.133 0.84 0.015 Italy-Rome 8586 2 0.08 0.021 0.294 0.583 0.022

Germany 8728 10 0.005 0.004 0.006 0.977 0.007 Italy-Rome 8589 7 0.079 0.007 0.154 0.742 0.018

Germany 8729 7 0.003 0.003 0.011 0.98 0.004 Italy-Rome 8592 7 0.013 0.119 0.219 0.628 0.021

Germany 8730 21 0.011 0.004 0.132 0.843 0.01 Italy-Rome 8594 2 0.01 0.095 0.037 0.836 0.021

Germany 8731 13 0.008 0.006 0.022 0.957 0.008 Italy-Rome 8595 5 0.031 0.173 0.149 0.548 0.098

Germany 8732 7 0.006 0.004 0.012 0.961 0.016 Italy-Rome 8596 2 0.05 0.012 0.415 0.507 0.017

Germany 8733 23 0.015 0.011 0.013 0.944 0.016 Italy-Rome 8597 2 0.021 0.039 0.199 0.721 0.019

Germany 8734 7 0.004 0.002 0.011 0.978 0.004 Italy-Rome 8599 5 0.074 0.007 0.258 0.647 0.014

Germany 8735 7 0.005 0.004 0.01 0.973 0.007 Italy-Rome 8601 2 0.034 0.242 0.03 0.676 0.017

Germany 8736 5 0.015 0.116 0.022 0.836 0.011 Italy-Rome 8602 5 0.011 0.015 0.24 0.716 0.018

Germany 8737 10 0.021 0.024 0.068 0.848 0.039 Italy-Rome 8603 5 0.007 0.026 0.078 0.863 0.026

Germany 8738 10 0.009 0.145 0.023 0.814 0.01 Italy-Rome 8604 7 0.232 0.027 0.029 0.684 0.028

Germany 8739 7 0.005 0.005 0.01 0.966 0.013 Italy-Rome 8609 5 0.018 0.014 0.515 0.438 0.015

Germany 8741 7 0.005 0.003 0.007 0.978 0.006 Italy-Rome 8610 7 0.008 0.005 0.247 0.731 0.008

Germany 8742 7 0.009 0.005 0.007 0.975 0.004 Italy-Rome 8611 2 0.01 0.094 0.155 0.732 0.009

Germany 8744 7 0.163 0.026 0.1 0.672 0.039 Turkey 6477 5 0.009 0.052 0.866 0.067 0.006

Germany 8745 26 0.008 0.01 0.049 0.917 0.015 Turkey 6478 10 0.006 0.018 0.944 0.023 0.009

Germany 8746 5 0.004 0.004 0.008 0.977 0.007 Turkey 6480 7 0.014 0.01 0.779 0.129 0.069

Germany 8747 18 0.012 0.004 0.014 0.959 0.01 Turkey 6481 5 0.034 0.005 0.026 0.069 0.866

Germany 8749 7 0.004 0.005 0.006 0.98 0.004 Turkey 6482 7 0.02 0.118 0.378 0.44 0.045

Italy-Milan 8050 5 0.303 0.027 0.04 0.597 0.032 Turkey 6484 5 0.008 0.01 0.894 0.067 0.022

Italy-Milan 8057 5 0.009 0.039 0.23 0.578 0.145 Turkey 6486 5 0.007 0.031 0.766 0.156 0.04

Italy-Milan 8060 2 0.008 0.007 0.224 0.717 0.044 Turkey 6487 7 0.011 0.011 0.938 0.033 0.006

Italy-Milan 8061 5 0.04 0.055 0.521 0.298 0.085 Turkey 6488 7 0.026 0.031 0.774 0.154 0.015

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Turkey 6491 13 0.556 0.005 0.036 0.304 0.1 Turkey 6750 5 0.005 0.005 0.314 0.669 0.007

Turkey 6494 7 0.031 0.063 0.445 0.437 0.024 Turkey 6753 10 0.115 0.083 0.612 0.129 0.062

Turkey 6496 5 0.019 0.01 0.622 0.199 0.15 Turkey 6754 10 0.005 0.22 0.525 0.242 0.008

Turkey 6499 7 0.019 0.015 0.406 0.548 0.012 Turkey 6755 10 0.042 0.018 0.566 0.361 0.012

Turkey 6500 15 0.005 0.006 0.941 0.043 0.005 Turkey 6756 10 0.005 0.015 0.421 0.551 0.008

Turkey 6502 7 0.02 0.006 0.89 0.071 0.013 Turkey 6758 13 0.073 0.059 0.718 0.136 0.015

Turkey 6503 10 0.035 0.013 0.605 0.296 0.051 Turkey 6759 13 0.013 0.006 0.892 0.071 0.018

Turkey 6507 5 0.015 0.008 0.853 0.118 0.006 Turkey 6760 7 0.008 0.019 0.261 0.665 0.048

Turkey 6510 5 0.012 0.004 0.159 0.819 0.006 Cyprus 10128 2 0.041 0.009 0.875 0.061 0.014

Turkey 6512 7 0.025 0.012 0.894 0.049 0.019 Cyprus 10129 5 0.005 0.008 0.705 0.274 0.008

Turkey 6513 5 0.008 0.053 0.775 0.086 0.078 Cyprus 10130 7 0.013 0.01 0.519 0.408 0.05

Turkey 6514 5 0.378 0.009 0.009 0.02 0.584 Cyprus 10131 2 0.024 0.017 0.788 0.021 0.15

Turkey 6516 5 0.003 0.011 0.019 0.953 0.013 Cyprus 10132 2 0.01 0.019 0.888 0.059 0.024

Turkey 6519 5 0.007 0.015 0.625 0.338 0.015 Cyprus 10133 15 0.009 0.027 0.856 0.089 0.019

Turkey 6520 5 0.008 0.006 0.335 0.632 0.02 Cyprus 10134 5 0.006 0.027 0.934 0.013 0.019

Turkey 6521 7 0.049 0.029 0.747 0.161 0.013 Cyprus 10135 5 0.017 0.005 0.802 0.03 0.146

Turkey 6729 5 0.055 0.336 0.555 0.029 0.025 Cyprus 10136 5 0.027 0.091 0.486 0.372 0.024

Turkey 6730 5 0.026 0.011 0.504 0.451 0.008 Cyprus 10137 2 0.04 0.269 0.603 0.079 0.009

Turkey 6731 7 0.005 0.049 0.422 0.507 0.017 Cyprus 10138 2 0.012 0.036 0.92 0.015 0.016

Turkey 6732 7 0.062 0.166 0.354 0.256 0.162 Cyprus 10139 2 0.02 0.047 0.584 0.017 0.331

Turkey 6733 7 0.055 0.021 0.499 0.374 0.05 Cyprus 10140 0 0.006 0.027 0.882 0.07 0.014

Turkey 6734 5 0.044 0.01 0.568 0.346 0.032 Cyprus 10141 5 0.019 0.034 0.394 0.54 0.013

Turkey 6735 7 0.006 0.021 0.951 0.012 0.01 Cyprus 10142 2 0.043 0.022 0.871 0.014 0.049

Turkey 6736 2 0.006 0.017 0.881 0.087 0.009 Cyprus 10143 5 0.012 0.015 0.953 0.014 0.006

Turkey 6738 7 0.016 0.032 0.847 0.076 0.029 Cyprus 10144 7 0.008 0.006 0.931 0.039 0.016

Turkey 6739 13 0.011 0.06 0.42 0.491 0.018 Cyprus 10145 5 0.005 0.008 0.906 0.076 0.005

Turkey 6740 5 0.004 0.004 0.004 0.982 0.006 Cyprus 10146 0 0.012 0.008 0.922 0.01 0.048

Turkey 6741 7 0.041 0.006 0.011 0.021 0.921 Cyprus 10147 5 0.004 0.006 0.899 0.085 0.007

Turkey 6742 13 0.036 0.031 0.444 0.34 0.15 Cyprus 10148 7 0.036 0.029 0.791 0.06 0.084

Turkey 6743 5 0.013 0.063 0.51 0.368 0.046 Cyprus 10149 7 0.021 0.2 0.436 0.321 0.023

Turkey 6745 7 0.04 0.018 0.093 0.841 0.008 Cyprus 10150 2 0.011 0.019 0.925 0.019 0.026

Turkey 6746 5 0.073 0.028 0.067 0.468 0.363 Cyprus 10151 2 0.019 0.042 0.88 0.04 0.018

Turkey 6748 10 0.008 0.012 0.859 0.103 0.018 Cyprus 10152 5 0.048 0.015 0.887 0.016 0.035

Turkey 6749 10 0.012 0.067 0.036 0.878 0.006 Cyprus 10153 0 0.015 0.02 0.924 0.026 0.014

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Cyprus 10154 0 0.011 0.129 0.709 0.038 0.112 Lebanon 10265 7 0.009 0.12 0.841 0.02 0.01

Cyprus 10155 5 0.016 0.008 0.851 0.018 0.107 Lebanon 10266 0 0.013 0.014 0.822 0.037 0.114

Cyprus 10156 7 0.125 0.017 0.752 0.09 0.016 Lebanon 10267 18 0.008 0.007 0.893 0.085 0.007

Cyprus 10157 2 0.009 0.042 0.931 0.01 0.009 Lebanon 10268 5 0.018 0.005 0.914 0.028 0.035

Lebanon 10235 10 0.045 0.046 0.769 0.069 0.07 Lebanon 10270 13 0.107 0.117 0.637 0.027 0.113

Lebanon 10236 13 0.045 0.024 0.843 0.047 0.04 Lebanon 10271 18 0.032 0.22 0.088 0.524 0.136

Lebanon 10237 5 0.044 0.01 0.56 0.024 0.363 Lebanon 10273 10 0.053 0.068 0.517 0.309 0.052

Lebanon 10238 5 0.011 0.042 0.905 0.013 0.029 Lebanon 10274 13 0.065 0.009 0.307 0.029 0.591

Lebanon 10239 5 0.01 0.011 0.953 0.015 0.011 Lebanon 10276 10 0.005 0.014 0.931 0.015 0.035

Lebanon 10240 15 0.029 0.033 0.778 0.037 0.122 Lebanon 10277 2 0.18 0.045 0.669 0.087 0.019

Lebanon 10241 13 0.105 0.053 0.53 0.265 0.047 Lebanon 10278 15 0.039 0.02 0.89 0.026 0.025

Lebanon 10242 7 0.007 0.031 0.883 0.066 0.014 Lebanon 10279 0 0.01 0.051 0.835 0.015 0.089

Lebanon 10243 10 0.058 0.05 0.76 0.039 0.093 Lebanon 10280 0 0.021 0.046 0.497 0.056 0.381

Lebanon 10244 5 0.049 0.104 0.786 0.02 0.042 Lebanon 10281 10 0.009 0.046 0.925 0.01 0.01

Lebanon 10245 13 0.01 0.268 0.592 0.072 0.058 Lebanon 10282 10 0.011 0.022 0.906 0.012 0.049

Lebanon 10246 2 0.004 0.024 0.94 0.013 0.018 Lebanon 10283 23 0.008 0.01 0.945 0.021 0.016

Lebanon 10247 18 0.012 0.289 0.671 0.015 0.014 Lebanon 10284 23 0.04 0.105 0.607 0.051 0.197

Lebanon 10248 7 0.007 0.01 0.884 0.089 0.01 Lebanon 10285 2 0.168 0.017 0.727 0.063 0.025

Lebanon 10249 13 0.013 0.016 0.181 0.75 0.04 Lebanon 10286 15 0.02 0.305 0.551 0.015 0.108

Lebanon 10250 2 0.023 0.138 0.748 0.081 0.01 Lebanon 10287 5 0.074 0.055 0.321 0.38 0.171

Lebanon 10251 2 0.006 0.013 0.872 0.097 0.012 Lebanon 10288 13 0.01 0.004 0.023 0.958 0.005

Lebanon 10252 5 0.019 0.09 0.579 0.3 0.011 Lebanon 10289 13 0.009 0.029 0.931 0.01 0.021

Lebanon 10253 15 0.006 0.008 0.938 0.042 0.005 Lebanon 10290 5 0.095 0.033 0.59 0.011 0.271

Lebanon 10254 13 0.023 0.04 0.867 0.016 0.053 Lebanon 10291 15 0.066 0.147 0.732 0.023 0.033

Lebanon 10255 7 0.026 0.174 0.702 0.078 0.02 Lebanon 10292 13 0.054 0.077 0.386 0.032 0.45

Lebanon 10256 18 0.007 0.008 0.969 0.01 0.006 Lebanon 10294 2 0.033 0.056 0.74 0.062 0.109

Lebanon 10257 5 0.026 0.071 0.84 0.007 0.056 Lebanon 10295 7 0.023 0.08 0.781 0.05 0.066

Lebanon 10258 7 0.008 0.015 0.933 0.024 0.02 Lebanon 10297 5 0.03 0.128 0.798 0.025 0.02

Lebanon 10259 10 0.014 0.01 0.058 0.085 0.833 Lebanon 10298 10 0.029 0.495 0.38 0.018 0.079

Lebanon 10260 2 0.011 0.021 0.939 0.011 0.018 Lebanon 10299 2 0.114 0.085 0.247 0.388 0.166

Lebanon 10261 10 0.015 0.077 0.824 0.069 0.015 Lebanon 10300 2 0.021 0.083 0.835 0.018 0.042

Lebanon 10262 5 0.068 0.062 0.678 0.139 0.052 Israel 4962 7 0.007 0.152 0.79 0.033 0.019

Lebanon 10263 7 0.008 0.011 0.949 0.011 0.021 Israel 4963 5 0.017 0.031 0.893 0.028 0.031

Lebanon 10264 5 0.017 0.015 0.808 0.092 0.067 Israel 4964 7 0.013 0.102 0.333 0.531 0.02

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Israel 4966 5 0.008 0.009 0.898 0.03 0.054 Israel 5003 7 0.028 0.01 0.883 0.071 0.008

Israel 4967 5 0.108 0.021 0.519 0.317 0.036 Israel 5004 15 0.015 0.028 0.313 0.513 0.131

Israel 4968 7 0.028 0.041 0.877 0.024 0.03 Israel 5005 7 0.008 0.018 0.837 0.116 0.021

Israel 4969 2 0.065 0.018 0.557 0.307 0.053 Israel 5006 10 0.006 0.012 0.95 0.027 0.005

Israel 4970 7 0.022 0.054 0.691 0.138 0.096 Israel 5007 5 0.269 0.01 0.145 0.464 0.112

Israel 4971 2 0.065 0.077 0.75 0.038 0.069 Israel 5008 5 0.011 0.035 0.912 0.025 0.017

Israel 4972 10 0.03 0.016 0.828 0.089 0.037 Israel 5009 13 0.055 0.101 0.376 0.381 0.087

Israel 4973 5 0.01 0.011 0.951 0.017 0.012 Israel 5010 15 0.008 0.009 0.927 0.048 0.008

Israel 4974 5 0.009 0.029 0.888 0.052 0.023 Israel 5011 13 0.016 0.043 0.792 0.119 0.029

Israel 4975 2 0.007 0.015 0.832 0.132 0.014 Egypt-Cairo 8190 15 0.01 0.01 0.895 0.065 0.022

Israel 4976 5 0.007 0.016 0.629 0.302 0.046 Egypt-Cairo 8192 18 0.015 0.033 0.668 0.099 0.186

Israel 4977 15 0.01 0.035 0.625 0.316 0.014 Egypt-Cairo 8193 18 0.005 0.019 0.957 0.009 0.01

Israel 4978 10 0.019 0.107 0.634 0.179 0.061 Egypt-Cairo 8196 15 0.038 0.015 0.85 0.021 0.076

Israel 4979 7 0.01 0.024 0.91 0.048 0.007 Egypt-Cairo 8203 15 0.024 0.008 0.927 0.022 0.02

Israel 4980 2 0.157 0.161 0.421 0.113 0.148 Egypt-Cairo 8215 10 0.008 0.007 0.97 0.008 0.008

Israel 4981 15 0.158 0.039 0.778 0.013 0.012 Egypt-Cairo 8198 10 0.021 0.015 0.889 0.057 0.017

Israel 4982 10 0.012 0.008 0.676 0.269 0.035 Egypt-Cairo 8194 7 0.019 0.014 0.849 0.099 0.019

Israel 4983 7 0.109 0.03 0.027 0.797 0.036 Egypt-Cairo 8211 5 0.011 0.03 0.928 0.022 0.009

Israel 4984 2 0.033 0.006 0.009 0.926 0.025 Egypt-Cairo 8216 7 0.013 0.011 0.72 0.246 0.011

Israel 4985 21 0.034 0.012 0.865 0.022 0.067 Egypt-Cairo 8195 5 0.035 0.02 0.853 0.013 0.079

Israel 4986 5 0.015 0.013 0.947 0.016 0.01 Egypt-Cairo 8199 2 0.006 0.01 0.943 0.008 0.033

Israel 4988 13 0.027 0.03 0.896 0.02 0.028 Egypt-Cairo 8200 5 0.01 0.018 0.915 0.051 0.006

Israel 4989 5 0.016 0.059 0.825 0.01 0.091 Egypt-Cairo 8201 5 0.102 0.101 0.748 0.024 0.025

Israel 4990 5 0.034 0.011 0.88 0.039 0.036 Egypt-Cairo 8202 5 0.038 0.02 0.042 0.034 0.866

Israel 4992 7 0.007 0.007 0.763 0.213 0.01 Egypt-Cairo 8204 5 0.022 0.009 0.917 0.02 0.032

Israel 4993 7 0.008 0.011 0.95 0.02 0.011 Egypt-Cairo 8208 2 0.08 0.016 0.78 0.099 0.025

Israel 4994 5 0.007 0.006 0.97 0.012 0.005 Egypt-Cairo 8210 5 0.007 0.05 0.891 0.013 0.04

Israel 4995 5 0.026 0.063 0.85 0.02 0.041 Egypt-Cairo 8214 2 0.006 0.006 0.977 0.007 0.004

Israel 4996 2 0.02 0.026 0.933 0.012 0.009 Egypt-Cairo 8191 5 0.074 0.045 0.784 0.051 0.046

Israel 4997 15 0.012 0.086 0.853 0.017 0.032 Egypt-Cairo 8197 2 0.01 0.02 0.915 0.008 0.047

Israel 4998 10 0.019 0.037 0.583 0.322 0.039 Egypt-Cairo 8205 2 0.023 0.011 0.83 0.076 0.059

Israel 5000 7 0.025 0.021 0.752 0.017 0.185 Egypt-Cairo 8206 2 0.017 0.008 0.841 0.091 0.043

Israel 5001 5 0.013 0.051 0.791 0.128 0.018 Egypt-Cairo 8207 2 0.027 0.013 0.894 0.023 0.044

Israel 5002 7 0.012 0.012 0.855 0.098 0.024 Egypt-Cairo 8209 2 0.008 0.006 0.939 0.041 0.005

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Egypt-Cairo 8212 2 0.009 0.009 0.969 0.008 0.005 Egypt-Cairo 10030 0 0.03 0.009 0.902 0.048 0.011

Egypt-Cairo 8213 2 0.008 0.011 0.964 0.011 0.006 Egypt-Cairo 10031 2 0.005 0.018 0.964 0.005 0.008

Egypt-Cairo 9942 0 0.035 0.223 0.7 0.011 0.031 Egypt-Cairo 10032 0 0.006 0.027 0.956 0.005 0.006

Egypt-Cairo 9943 15 0.336 0.014 0.01 0.252 0.388 Egypt-Cairo 10033 2 0.014 0.005 0.956 0.006 0.019

Egypt-Cairo 9944 7 0.004 0.028 0.907 0.052 0.01 Egypt-Cairo 10034 0 0.008 0.008 0.965 0.01 0.009

Egypt-Cairo 9945 2 0.012 0.015 0.797 0.161 0.015 Egypt-Cairo 10035 0 0.175 0.012 0.765 0.01 0.038

Egypt-Cairo 9946 13 0.048 0.03 0.803 0.018 0.1 Egypt-Cairo 10037 5 0.011 0.014 0.843 0.081 0.052

Egypt-Cairo 9947 7 0.01 0.038 0.917 0.021 0.014 Egypt-Cairo 10042 0 0.026 0.031 0.834 0.029 0.08

Egypt-Cairo 9948 0 0.02 0.007 0.901 0.051 0.019 Egypt-Cairo 10043 7 0.005 0.009 0.924 0.047 0.016

Egypt-Cairo 9949 0 0.022 0.019 0.894 0.05 0.016 Egypt-Cairo 10044 2 0.009 0.007 0.954 0.016 0.014

Egypt-Cairo 9950 0 0.019 0.015 0.807 0.096 0.062 Egypt-Cairo 10045 2 0.017 0.012 0.933 0.017 0.021

Egypt-Cairo 9951 5 0.012 0.004 0.952 0.02 0.012 Egypt-Cairo 10046 0 0.039 0.007 0.891 0.025 0.038

Egypt-Cairo 9952 15 0.01 0.01 0.689 0.277 0.013 Egypt-Cairo 10047 0 0.022 0.025 0.922 0.012 0.019

Egypt-Cairo 9953 21 0.009 0.085 0.731 0.158 0.017 Egypt-Cairo 10048 2 0.02 0.004 0.909 0.05 0.018

Egypt-Cairo 9954 2 0.14 0.007 0.816 0.019 0.019 Egypt-Cairo 10083 21 0.01 0.006 0.907 0.062 0.014

Egypt-Cairo 9955 0 0.061 0.007 0.214 0.566 0.152 Egypt-Cairo 10040 0 0.045 0.019 0.463 0.437 0.036

Egypt-Cairo 9956 18 0.043 0.019 0.899 0.027 0.012 Egypt-Cairo 10041 0 0.031 0.01 0.487 0.439 0.033

Egypt-Cairo 9957 5 0.09 0.009 0.868 0.02 0.013 Egypt-Cairo 10049 5 0.028 0.028 0.89 0.024 0.03

Egypt-Cairo 9958 15 0.038 0.007 0.876 0.044 0.035 Egypt-Cairo 10084 2 0.015 0.012 0.93 0.017 0.027

Egypt-Cairo 9959 5 0.03 0.027 0.687 0.214 0.043 Egypt-Cairo 10085 23 0.012 0.011 0.861 0.066 0.049

Egypt-Cairo 9960 2 0.03 0.053 0.882 0.024 0.011 Egypt-Cairo 10087 5 0.007 0.004 0.973 0.008 0.008

Egypt-Cairo 9961 5 0.074 0.014 0.205 0.15 0.557 Egypt-Cairo 10090 7 0.067 0.009 0.871 0.02 0.033

Egypt-Cairo 9962 7 0.01 0.069 0.882 0.019 0.02 Egypt-Cairo 9968 0 0.006 0.027 0.911 0.039 0.017

Egypt-Cairo 9963 2 0.021 0.013 0.925 0.025 0.016 Egypt-Asuit 10091 2 0.007 0.007 0.956 0.021 0.009

Egypt-Cairo 9964 0 0.009 0.006 0.942 0.023 0.02 Egypt-Asuit 10093 0 0.006 0.005 0.962 0.016 0.011

Egypt-Cairo 10021 0 0.006 0.009 0.971 0.008 0.007 Egypt-Asuit 10094 5 0.008 0.007 0.959 0.012 0.015

Egypt-Cairo 10022 0 0.03 0.006 0.924 0.023 0.016 Egypt-Asuit 10095 2 0.005 0.009 0.959 0.012 0.016

Egypt-Cairo 10023 0 0.01 0.004 0.958 0.006 0.021 Egypt-Asuit 10096 0 0.015 0.005 0.96 0.011 0.009

Egypt-Cairo 10024 0 0.015 0.009 0.911 0.057 0.009 Egypt-Asuit 10098 0 0.006 0.004 0.975 0.007 0.007

Egypt-Cairo 10025 0 0.005 0.014 0.819 0.149 0.013 Egypt-Asuit 10099 5 0.007 0.005 0.974 0.006 0.008

Egypt-Cairo 10026 2 0.045 0.009 0.914 0.022 0.011 Egypt-Asuit 10100 2 0.093 0.042 0.721 0.081 0.063

Egypt-Cairo 10027 0 0.019 0.023 0.639 0.089 0.229 Egypt-Asuit 10101 2 0.021 0.017 0.919 0.015 0.028

Egypt-Cairo 10028 0 0.013 0.026 0.941 0.006 0.015 Egypt-Asuit 10102 0 0.023 0.014 0.772 0.009 0.182

Egypt-Cairo 10029 0 0.008 0.021 0.959 0.006 0.006 Egypt- Luxor 10038 0 0.044 0.008 0.915 0.01 0.023

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Egypt-Luxor 10039 2 0.005 0.051 0.932 0.008 0.005 Simbel

Egypt-Luxor 10050 2 0.048 0.015 0.897 0.009 0.031 Egypt-Abu

Egypt-Luxor 10051 0 0.005 0.005 0.969 0.012 0.009 Simbel 10092 0 0.013 0.01 0.962 0.006 0.009

Egypt-Luxor 10052 0 0.018 0.015 0.81 0.023 0.134 Iraq-West 9587 0 0.02 0.725 0.091 0.026 0.138

Egypt-Luxor 10053 0 0.006 0.074 0.863 0.027 0.03 Iraq-West 10202 23 0.015 0.746 0.191 0.031 0.017

Egypt-Luxor 10054 0 0.065 0.02 0.877 0.021 0.018 Iraq-West 10204 23 0.006 0.763 0.166 0.051 0.013

Egypt-Luxor 10055 0 0.008 0.01 0.956 0.014 0.012 Iraq-West 11854 0 0.037 0.879 0.06 0.013 0.009

Egypt-Luxor 10056 0 0.049 0.024 0.91 0.009 0.008 Iraq-West 11860 21 0.008 0.881 0.017 0.006 0.088

Egypt-Luxor 10057 7 0.026 0.079 0.858 0.016 0.02 Iraq-West 11861 7 0.257 0.549 0.157 0.02 0.017

Egypt-Luxor 10058 0 0.01 0.008 0.962 0.014 0.006 Iraq-West 11863 21 0.016 0.94 0.009 0.022 0.012

Egypt-Luxor 10060 0 0.011 0.011 0.922 0.047 0.009 Iraq-West 11864 2 0.033 0.901 0.026 0.007 0.032

Egypt-Luxor 10061 0 0.443 0.008 0.449 0.017 0.084 Iraq-West 11888 5 0.015 0.946 0.007 0.004 0.028

Egypt-Luxor 10062 5 0.023 0.01 0.893 0.063 0.011 Iraq-West 11889 7 0.016 0.7 0.03 0.224 0.029

Egypt-Luxor 10063 2 0.024 0.071 0.841 0.026 0.039 Iraq-West 11890 2 0.103 0.695 0.157 0.015 0.031

Egypt-Luxor 10064 0 0.029 0.013 0.88 0.021 0.057 Iraq-West 11891 0 0.009 0.632 0.308 0.037 0.014

Egypt-Luxor 10065 0 0.008 0.015 0.893 0.03 0.054 Iraq-Baghdad 11847 5 0.017 0.858 0.081 0.015 0.029

Egypt-Luxor 10066 2 0.05 0.015 0.841 0.067 0.026 Iraq-Baghdad 11848 0 0.081 0.773 0.017 0.013 0.116

Egypt-Luxor 10067 2 0.005 0.018 0.961 0.008 0.008 Iraq-Baghdad 11849 5 0.055 0.782 0.108 0.014 0.042

Egypt-Luxor 10068 0 0.119 0.004 0.812 0.014 0.05 Iraq-Baghdad 11850 0 0.021 0.902 0.017 0.045 0.015

Egypt-Luxor 10069 2 0.007 0.009 0.958 0.012 0.014 Iraq-Baghdad 11852 0 0.02 0.868 0.039 0.016 0.057

Egypt-Luxor 10070 0 0.422 0.007 0.465 0.021 0.085 Iraq-Baghdad 11853 5 0.029 0.908 0.02 0.009 0.035

Egypt-Luxor 10071 2 0.143 0.023 0.772 0.017 0.045 Iraq-Baghdad 11855 5 0.021 0.874 0.049 0.01 0.047

Egypt-Luxor 10072 2 0.03 0.008 0.946 0.008 0.008 Iraq-Baghdad 11856 2 0.008 0.842 0.064 0.052 0.034

Egypt-Luxor 10073 5 0.008 0.006 0.04 0.935 0.011 Iraq-Baghdad 11857 0 0.016 0.578 0.158 0.233 0.014

Egypt-Luxor 10074 0 0.008 0.005 0.972 0.008 0.007 Iraq-Baghdad 11858 2 0.008 0.956 0.013 0.012 0.011

Egypt-Luxor 10079 0 0.007 0.014 0.778 0.085 0.116 Iraq-Baghdad 11859 5 0.017 0.85 0.056 0.026 0.051

Egypt-Luxor 10080 0 0.021 0.017 0.591 0.059 0.311 Iraq-Baghdad 11862 2 0.274 0.562 0.06 0.083 0.02

Egypt-Abu Iraq-Baghdad 11865 5 0.197 0.624 0.107 0.011 0.061

Simbel 10076 5 0.088 0.007 0.857 0.019 0.03 Iraq-Baghdad 11868 2 0.009 0.965 0.011 0.007 0.009

Egypt-Abu Iraq-Baghdad 11869 2 0.027 0.652 0.083 0.224 0.014

Simbel 10077 13 0.019 0.007 0.888 0.009 0.077 Iraq-Baghdad 11870 0 0.102 0.707 0.111 0.057 0.022

Egypt-Abu Iraq-Baghdad 11871 5 0.011 0.507 0.416 0.009 0.056

Simbel 10081 7 0.02 0.01 0.927 0.015 0.028 Iraq-Baghdad 11872 2 0.005 0.711 0.25 0.021 0.012

Egypt-Abu 10089 0 0.004 0.004 0.881 0.102 0.008 Iraq-Baghdad 11873 7 0.042 0.551 0.379 0.008 0.02

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5 raq-Baghdad 11874 2 0.062 0.859 0.054 0.016 0.01 Iran 9440 2 0.005 0.963 0.014 0.004 0.014 raq-Baghdad 11875 15 0.186 0.587 0.013 0.011 0.204 Iran 9441 7 0.006 0.974 0.005 0.005 0.009 raq-Baghdad 11876 21 0.029 0.806 0.082 0.013 0.07 Iran 9442 5 0.014 0.873 0.019 0.023 0.071 raq-Baghdad 11877 7 0.068 0.848 0.055 0.012 0.017 Iran 9443 10 0.013 0.953 0.009 0.013 0.013 raq-Baghdad 11878 7 0.015 0.535 0.374 0.055 0.02 Iran 9444 5 0.006 0.981 0.005 0.004 0.004 raq-Baghdad 11879 2 0.021 0.771 0.029 0.01 0.168 Iran 9445 5 0.004 0.983 0.003 0.003 0.006 raq-Baghdad 11880 10 0.01 0.906 0.053 0.012 0.018 Iran 9446 10 0.014 0.962 0.006 0.005 0.013 raq-Baghdad 11881 0 0.115 0.634 0.155 0.069 0.027 Iran 9447 7 0.007 0.967 0.005 0.009 0.012 raq-Baghdad 11882 15 0.017 0.826 0.122 0.018 0.017 Iran 9448 13 0.005 0.977 0.005 0.005 0.008 raq-Baghdad 11883 0 0.013 0.67 0.108 0.182 0.026 Iran 9449 5 0.003 0.985 0.004 0.004 0.004 raq-Baghdad 11884 0 0.015 0.961 0.006 0.005 0.012 Iran 9450 7 0.025 0.947 0.016 0.005 0.006 raq-Baghdad 11885 0 0.011 0.949 0.021 0.005 0.013 Iran 9451 10 0.098 0.867 0.011 0.007 0.016 raq-Baghdad 11886 0 0.088 0.605 0.236 0.014 0.058 Iran 9452 2 0.01 0.952 0.008 0.009 0.022 raq-Baghdad 11887 13 0.05 0.814 0.066 0.016 0.054 Iran 9453 7 0.004 0.978 0.006 0.007 0.005 ran 9419 2 0.043 0.769 0.047 0.128 0.012 Iran 9454 7 0.005 0.98 0.006 0.005 0.005 ran 9420 2 0.131 0.822 0.015 0.008 0.023 Iran 9455 13 0.008 0.958 0.011 0.008 0.014 ran 9421 5 0.013 0.95 0.018 0.012 0.006 Iran 9456 2 0.021 0.926 0.015 0.017 0.02 ran 9422 5 0.13 0.751 0.088 0.02 0.011 Iran 9457 5 0.004 0.975 0.01 0.006 0.005 ran 9424 7 0.125 0.853 0.006 0.005 0.011 Iran 9458 2 0.014 0.958 0.01 0.005 0.012 ran 9425 7 0.017 0.965 0.005 0.004 0.008 Iran 9459 5 0.006 0.978 0.004 0.004 0.007 ran 9426 2 0.012 0.957 0.008 0.013 0.01 Iran 9460 10 0.014 0.925 0.018 0.023 0.02 ran 9427 2 0.006 0.963 0.01 0.012 0.009 Iran 9461 10 0.005 0.973 0.006 0.006 0.01 ran 9428 5 0.01 0.968 0.007 0.007 0.008 Iran 9462 10 0.008 0.941 0.008 0.019 0.024 ran 9429 15 0.095 0.821 0.029 0.017 0.039 Iran 9463 15 0.008 0.965 0.012 0.006 0.009 ran 9430 7 0.05 0.865 0.031 0.021 0.033 Iran 9464 7 0.011 0.972 0.006 0.004 0.007 ran 9431 7 0.064 0.747 0.038 0.021 0.129 Iran 9465 5 0.005 0.968 0.009 0.008 0.01 ran 9432 13 0.008 0.957 0.006 0.006 0.023 Iran 9466 13 0.006 0.931 0.027 0.014 0.022 ran 9433 5 0.034 0.931 0.013 0.01 0.013 Iran 9468 10 0.029 0.916 0.016 0.029 0.01 ran 9434 7 0.127 0.754 0.013 0.041 0.065 Iran 9469 10 0.038 0.861 0.029 0.028 0.043 ran 9435 5 0.017 0.923 0.007 0.009 0.043 Iran 9470 13 0.022 0.921 0.017 0.021 0.018 ran 9436 2 0.008 0.962 0.012 0.01 0.008 Iran 9471 7 0.005 0.969 0.008 0.01 0.008 ran 9437 2 0.009 0.952 0.013 0.01 0.017 Iran 9472 10 0.01 0.958 0.015 0.006 0.01 ran 9438 2 0.015 0.956 0.005 0.007 0.017 Iran 9473 5 0.01 0.969 0.006 0.006 0.01 ran 9439 5 0.155 0.729 0.043 0.028 0.044 Iran 9474 7 0.006 0.959 0.014 0.011 0.01

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Iran 9475 7 0.004 0.962 0.015 0.009 0.009 Iran 9510 15 0.069 0.908 0.007 0.007 0.009

Iran 9476 7 0.012 0.975 0.006 0.004 0.004 Iran 9511 13 0.007 0.959 0.008 0.02 0.006

Iran 9477 7 0.068 0.902 0.009 0.012 0.008 Iran 9512 28 0.015 0.812 0.03 0.117 0.026

Iran 9478 5 0.031 0.796 0.017 0.012 0.144 Iran 9513 21 0.007 0.952 0.013 0.013 0.015

Iran 9479 7 0.012 0.975 0.004 0.004 0.005 Iran 9514 15 0.015 0.773 0.123 0.053 0.036

Iran 9480 13 0.017 0.939 0.008 0.013 0.022 Iran 9515 13 0.008 0.791 0.01 0.165 0.025

Iran 9481 10 0.008 0.958 0.018 0.009 0.007 Iran 9516 15 0.01 0.922 0.015 0.016 0.037

Iran 9482 5 0.039 0.878 0.033 0.021 0.03 Iran 9517 13 0.059 0.823 0.019 0.04 0.058

Iran 9483 13 0.195 0.73 0.013 0.014 0.047 Iran 9518 13 0.026 0.712 0.04 0.199 0.024

Iran 9484 2 0.007 0.713 0.187 0.079 0.015 Iran 9519 18 0.008 0.887 0.016 0.074 0.015

Iran 9485 10 0.101 0.862 0.013 0.006 0.019 Iran 9520 15 0.006 0.86 0.023 0.086 0.025

Iran 9486 10 0.007 0.944 0.012 0.027 0.009 Iran 9521 21 0.01 0.874 0.017 0.02 0.078

Iran 9487 5 0.011 0.951 0.008 0.006 0.024 Iran 9522 15 0.075 0.642 0.191 0.05 0.042

Iran 9488 10 0.007 0.967 0.01 0.009 0.007 Iran 9523 13 0.011 0.835 0.104 0.037 0.014

Iran 9489 15 0.009 0.922 0.01 0.007 0.052 Iran 9524 15 0.054 0.923 0.007 0.005 0.01

Iran 9490 13 0.014 0.961 0.005 0.007 0.014 Iran 9526 15 0.12 0.853 0.009 0.005 0.012

Iran 9491 7 0.006 0.981 0.005 0.004 0.005 Iran 9527 13 0.028 0.852 0.023 0.024 0.073

Iran 9492 10 0.005 0.969 0.006 0.007 0.014 Iran 9528 15 0.007 0.977 0.004 0.005 0.006

Iran 9493 2 0.023 0.952 0.008 0.008 0.01 Iran 9529 15 0.03 0.935 0.005 0.003 0.027

Iran 9494 7 0.004 0.972 0.012 0.006 0.006 Iran 9530 10 0.047 0.926 0.009 0.009 0.01

Iran 9495 5 0.007 0.971 0.005 0.006 0.011 Iran 9531 15 0.033 0.784 0.05 0.045 0.088

Iran 9497 10 0.005 0.971 0.005 0.006 0.012 Iran 9532 5 0.006 0.974 0.006 0.006 0.008

Iran 9498 10 0.013 0.965 0.008 0.006 0.008 Dubai 10104 2 0.004 0.298 0.178 0.038 0.482

Iran 9499 5 0.102 0.868 0.009 0.006 0.015 Dubai 10105 2 0.024 0.189 0.018 0.018 0.751

Iran 9500 7 0.006 0.97 0.004 0.005 0.015 Dubai 10106 18 0.007 0.023 0.015 0.007 0.947

Iran 9501 2 0.009 0.854 0.035 0.047 0.056 Dubai 10107 5 0.165 0.384 0.039 0.045 0.367

Iran 9502 5 0.279 0.693 0.012 0.009 0.007 Dubai 10108 2 0.034 0.029 0.026 0.011 0.9

Iran 9503 7 0.008 0.973 0.004 0.004 0.011 Dubai 10109 18 0.194 0.53 0.023 0.029 0.224

Iran 9504 5 0.004 0.984 0.005 0.004 0.003 Dubai 10110 7 0.016 0.364 0.025 0.045 0.55

Iran 9505 5 0.007 0.985 0.003 0.002 0.003 Dubai 10111 13 0.009 0.019 0.017 0.006 0.949

Iran 9506 7 0.01 0.919 0.009 0.019 0.043 Dubai 10112 7 0.037 0.024 0.098 0.046 0.794

Iran 9507 31 0.044 0.885 0.028 0.015 0.028 Dubai 10120 15 0.008 0.059 0.016 0.017 0.9

Iran 9508 10 0.121 0.844 0.009 0.006 0.02 Kenya-Nairobi 9833 13 0.047 0.202 0.023 0.593 0.135

Iran 9509 18 0.043 0.929 0.01 0.009 0.01 Kenya-Nairobi 9834 0 0.02 0.009 0.017 0.709 0.245

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Kenya- Nairobi 9835 0 0.208 0.009 0.075 0.534 0.173 Kenya-Pate 2000 7 0.005 0.007 0.042 0.175 0.77

Kenya- Nairobi 9836 2 0.033 0.013 0.01 0.673 0.27 Kenya-Pate 2001 13 0.005 0.166 0.016 0.006 0.807

Kenya- Nairobi 9837 0 0.116 0.056 0.028 0.759 0.041 Kenya-Pate 2002 2 0.01 0.174 0.013 0.005 0.798

Kenya-Nairobi 9838 2 0.024 0.239 0.087 0.614 0.036 Kenya-Pate 2003 2 0.024 0.017 0.014 0.016 0.928

Kenya-Nairobi 9839 0 0.03 0.02 0.036 0.221 0.694 Kenya-Pate 2004 10 0.007 0.031 0.058 0.024 0.88

Kenya- Nairobi 9840 0 0.012 0.017 0.325 0.466 0.18 Kenya-Pate 2006 7 0.075 0.064 0.044 0.014 0.802

Kenya- Nairobi 9841 2 0.013 0.01 0.016 0.805 0.157 Kenya-Pate 2007 7 0.01 0.036 0.032 0.033 0.888

Kenya- Nairobi 9842 2 0.127 0.053 0.041 0.71 0.069 Kenya-Pate 2009 28 0.007 0.017 0.313 0.053 0.61

Kenya- Nairobi 9843 0 0.014 0.011 0.017 0.752 0.206 Kenya-Pate 2011 13 0.006 0.004 0.026 0.012 0.953

Kenya- Nairobi 9844 2 0.062 0.01 0.018 0.821 0.089 Kenya-Lamu 1848 10 0.015 0.014 0.023 0.013 0.935

Kenya- Nairobi 9845 0 0.056 0.024 0.188 0.69 0.042 Kenya-Lamu 2014 5 0.005 0.01 0.042 0.011 0.932

Kenya- Nairobi 9846 2 0.157 0.016 0.033 0.579 0.216 Kenya-Lamu 2015 7 0.023 0.019 0.775 0.023 0.16

Kenya-Nairobi 9847 0 0.022 0.02 0.016 0.839 0.103 Kenya-Lamu 2016 7 0.007 0.009 0.066 0.07 0.848

Kenya-Nairobi 9848 0 0.112 0.033 0.214 0.583 0.058 Kenya-Lamu 2018 2 0.01 0.036 0.373 0.055 0.526

Kenya- Nairobi 9849 5 0.024 0.009 0.062 0.19 0.715 Kenya-Lamu 2019 7 0.007 0.009 0.055 0.04 0.889

Kenya- Nairobi 9850 7 0.018 0.022 0.067 0.784 0.109 Kenya-Lamu 2021 7 0.005 0.187 0.033 0.022 0.753

Kenya- Nairobi 9851 2 0.008 0.008 0.008 0.518 0.457 Kenya-Lamu 2023 13 0.008 0.008 0.074 0.042 0.867

Kenya- Nairobi 9852 7 0.005 0.008 0.092 0.52 0.375 Kenya-Lamu 2024 2 0.006 0.042 0.017 0.009 0.927

Kenya- Nairobi 9853 2 0.029 0.083 0.016 0.766 0.107 Kenya-Lamu 2025 5 0.02 0.039 0.106 0.012 0.823

Kenya- Nairobi 9854 0 0.007 0.088 0.035 0.824 0.046 Kenya-Lamu 2026 18 0.006 0.008 0.032 0.006 0.949

Kenya- Nairobi 9855 2 0.009 0.023 0.166 0.517 0.285 Kenya-Lamu 2027 26 0.014 0.092 0.047 0.053 0.794

Kenya- Nairobi 9856 2 0.013 0.008 0.024 0.665 0.29 Kenya-Lamu 2029 5 0.006 0.033 0.016 0.008 0.938

Kenya-Nairobi 9857 23 0.13 0.014 0.043 0.505 0.309 Kenya-Lamu 2030 5 0.006 0.011 0.024 0.132 0.827

Kenya-Nairobi 9858 15 0.057 0.009 0.015 0.837 0.081 Kenya-Lamu 2031 18 0.038 0.079 0.163 0.301 0.419

Kenya- Nairobi 9859 15 0.105 0.021 0.007 0.817 0.05 Kenya-Lamu 2032 10 0.01 0.251 0.023 0.02 0.696

Kenya- Nairobi 9860 5 0.071 0.016 0.012 0.839 0.062 Kenya-Lamu 2033 13 0.008 0.02 0.13 0.023 0.818

Kenya- Nairobi 9861 2 0.007 0.013 0.048 0.897 0.035 Kenya-Lamu 3241 18 0.045 0.011 0.073 0.061 0.81

Kenya- Nairobi 9862 7 0.028 0.006 0.094 0.505 0.367 Kenya-Lamu 3246 7 0.015 0.021 0.832 0.017 0.114

Kenya- Nairobi 9863 2 0.02 0.011 0.008 0.612 0.35 Kenya-Lamu 3247 13 0.01 0.024 0.073 0.127 0.765

Kenya- Nairobi 9864 7 0.005 0.087 0.022 0.683 0.204 India-Udaipur 11835 10 0.237 0.215 0.16 0.034 0.354

Kenya- Nairobi 9865 10 0.06 0.065 0.079 0.36 0.436 India-Udaipur 11836 5 0.027 0.34 0.026 0.01 0.596

Kenya- Nairobi 9866 7 0.032 0.007 0.177 0.641 0.143 India-Udaipur 11837 5 0.038 0.32 0.017 0.007 0.618

Kenya-Nairobi 9867 0 0.044 0.052 0.085 0.361 0.458 India-Agra 11823 0 0.008 0.447 0.015 0.008 0.522

Kenya- Nairobi 9868 5 0.008 0.014 0.286 0.523 0.169 India-Agra 11824 0 0.007 0.223 0.016 0.006 0.747

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

India-Agra 11825 2 0.013 0.494 0.012 0.013 0.468 India-

India-Agra 11826 7 0.007 0.473 0.007 0.007 0.506 Hyderbad 11815 0 0.024 0.048 0.008 0.007 0.913

India-Agra 11827 0 0.008 0.407 0.015 0.005 0.566 India-

India-Agra 11828 5 0.019 0.35 0.033 0.007 0.591 Hyderbad 11816 0 0.007 0.023 0.019 0.007 0.944

India-Agra 11829 5 0.012 0.166 0.035 0.023 0.764 India-

India-Agra 11830 5 0.009 0.37 0.014 0.005 0.602 Hyderbad 11817 5 0.016 0.012 0.009 0.008 0.956

India-Agra 11831 5 0.01 0.442 0.041 0.013 0.495 India-

India-Agra 11832 0 0.009 0.509 0.012 0.006 0.464 Hyderbad 11818 2 0.048 0.01 0.007 0.005 0.929

India-Agra 11833 2 0.022 0.47 0.015 0.006 0.487 India-

India-Agra 11834 5 0.007 0.48 0.007 0.007 0.499 Hyderbad 11819 0 0.007 0.022 0.02 0.007 0.943

India- India-

Hyderbad 11802 7 0.047 0.013 0.443 0.044 0.452 Hyderbad 11820 5 0.075 0.022 0.008 0.006 0.888

India- India-

Hyderbad 11803 7 0.014 0.04 0.134 0.011 0.8 Hyderbad 11821 0 0.046 0.035 0.04 0.012 0.867

India- India-

Hyderbad 11804 10 0.408 0.092 0.012 0.011 0.476 Hyderbad 11822 0 0.065 0.012 0.044 0.017 0.862

India- India-Andhra 10159 5 0.087 0.005 0.019 0.014 0.875

Hyderbad 11805 5 0.038 0.016 0.02 0.025 0.901 India-Andhra 10160 2 0.232 0.01 0.01 0.011 0.736

India- India-Andhra 10161 0 0.012 0.014 0.007 0.011 0.956

Hyderbad 11807 2 0.019 0.03 0.011 0.006 0.934 India-Andhra 10162 5 0.016 0.011 0.011 0.011 0.952

India- India-Andhra 10163 0 0.076 0.013 0.008 0.006 0.898

Hyderbad 11808 2 0.125 0.006 0.009 0.012 0.847 India-Andhra 10164 2 0.186 0.072 0.008 0.006 0.728

India- India-Andhra 10165 0 0.015 0.018 0.01 0.009 0.947

Hyderbad 11809 2 0.005 0.076 0.105 0.125 0.689 India-Andhra 10166 5 0.021 0.009 0.035 0.039 0.897

India- India-Andhra 10167 2 0.026 0.009 0.24 0.045 0.68

Hyderbad 11810 0 0.047 0.02 0.077 0.016 0.84 India-Andhra 10168 2 0.026 0.037 0.024 0.009 0.904

India- India-Andhra 10169 5 0.077 0.007 0.025 0.034 0.857

Hyderbad 11811 0 0.019 0.018 0.015 0.007 0.941 India-Andhra 10170 0 0.097 0.008 0.032 0.034 0.829

India- India-Andhra 10171 2 0.188 0.073 0.009 0.006 0.725

Hyderbad 11812 13 0.072 0.244 0.013 0.005 0.666 India-Andhra 10172 5 0.079 0.045 0.027 0.085 0.764

India- India-Andhra 10173 5 0.021 0.005 0.013 0.006 0.955

Hyderbad 11813 10 0.319 0.127 0.032 0.01 0.512 India-Andhra 10174 5 0.013 0.004 0.021 0.018 0.944

India- India-Andhra 10175 5 0.018 0.017 0.018 0.091 0.856

Hyderbad 11814 0 0.013 0.014 0.01 0.007 0.956 India-Andhra 10176 0 0.237 0.029 0.019 0.015 0.699

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

India-Andhra 10177 0 0.102 0.014 0.021 0.013 0.85 Sri Lanka 8802 2 0.005 0.035 0.007 0.522 0.43

India-Andhra 10178 5 0.02 0.025 0.011 0.014 0.93 Sri Lanka 8803 15 0.015 0.011 0.033 0.051 0.89

India-Andhra 10179 2 0.017 0.006 0.016 0.042 0.919 Thailand 11688 7 0.036 0.005 0.004 0.006 0.949

India-Andhra 10180 2 0.009 0.016 0.167 0.075 0.732 Thailand 11689 15 0.033 0.022 0.007 0.008 0.93

India-Andhra 10181 7 0.228 0.007 0.019 0.015 0.731 Thailand 11691 55 0.639 0.022 0.017 0.021 0.301

India-Kolkata 10113 7 0.019 0.013 0.018 0.019 0.931 Thailand 11698 23 0.459 0.009 0.014 0.017 0.502

India-Kolkata 10114 2 0.018 0.017 0.014 0.008 0.943 Thailand 11702 10 0.026 0.005 0.006 0.016 0.947

India-Kolkata 10115 0 0.02 0.012 0.071 0.028 0.869 Thailand 11703 13 0.084 0.008 0.005 0.007 0.896

India-Kolkata 10116 7 0.023 0.009 0.011 0.009 0.949 Thailand 11705 21 0.022 0.006 0.013 0.007 0.952

India-Kolkata 10117 5 0.359 0.043 0.087 0.018 0.494 Thailand 11707 10 0.086 0.007 0.007 0.006 0.893

India-Kolkata 10118 13 0.042 0.07 0.078 0.015 0.794 Thailand 11708 2 0.091 0.008 0.022 0.01 0.87

India-Kolkata 10119 7 0.008 0.016 0.091 0.059 0.825 Thailand 11709 15 0.049 0.006 0.012 0.01 0.923

Sri Lanka 8780 0 0.065 0.022 0.048 0.054 0.812 Thailand 11710 15 0.105 0.005 0.009 0.012 0.868

Sri Lanka 8781 5 0.022 0.023 0.106 0.1 0.75 Thailand 11711 2 0.294 0.006 0.004 0.006 0.689

Sri Lanka 8782 15 0.023 0.035 0.097 0.12 0.726 Thailand 11714 13 0.029 0.007 0.01 0.014 0.94

Sri Lanka 8783 0 0.02 0.026 0.053 0.03 0.871 Thailand 11715 21 0.108 0.013 0.006 0.015 0.858

Sri Lanka 8784 5 0.099 0.026 0.02 0.03 0.825 Thailand 11717 13 0.036 0.009 0.011 0.012 0.932

Sri Lanka 8785 7 0.025 0.162 0.09 0.107 0.616 Thailand 11718 7 0.097 0.009 0.019 0.03 0.845

Sri Lanka 8786 0 0.018 0.065 0.268 0.131 0.517 Thailand 11720 7 0.103 0.006 0.009 0.007 0.875

Sri Lanka 8787 0 0.008 0.023 0.027 0.251 0.691 Vietnam 8844 5 0.03 0.006 0.007 0.007 0.95

Sri Lanka 8788 5 0.015 0.164 0.025 0.054 0.742 Vietnam 8845 13 0.011 0.01 0.007 0.008 0.964

Sri Lanka 8789 2 0.012 0.041 0.049 0.515 0.383 Vietnam 8846 13 0.053 0.007 0.024 0.06 0.856

Sri Lanka 8790 0 0.011 0.009 0.037 0.172 0.772 Vietnam 8847 15 0.05 0.021 0.011 0.252 0.666

Sri Lanka 8791 5 0.01 0.047 0.01 0.006 0.926 Vietnam 8848 10 0.012 0.009 0.014 0.105 0.859

Sri Lanka 8792 2 0.007 0.012 0.032 0.2 0.749 Vietnam 8849 2 0.041 0.005 0.026 0.012 0.916

Sri Lanka 8793 5 0.006 0.022 0.027 0.351 0.595 Vietnam 8850 5 0.247 0.007 0.02 0.463 0.262

Sri Lanka 8794 5 0.015 0.02 0.069 0.295 0.602 Vietnam 8851 10 0.029 0.029 0.015 0.017 0.91

Sri Lanka 8795 0 0.03 0.01 0.1 0.041 0.82 Vietnam 8852 13 0.119 0.017 0.095 0.195 0.574

Sri Lanka 8796 7 0.063 0.007 0.126 0.084 0.72 Vietnam 8853 10 0.018 0.011 0.262 0.062 0.647

Sri Lanka 8797 2 0.059 0.089 0.013 0.45 0.389 Vietnam 8854 7 0.19 0.131 0.021 0.4 0.259

Sri Lanka 8798 2 0.028 0.042 0.065 0.068 0.797 Vietnam 8855 10 0.278 0.008 0.008 0.008 0.699

Sri Lanka 8799 7 0.004 0.017 0.007 0.235 0.737 Vietnam 8856 10 0.015 0.007 0.141 0.018 0.82

Sri Lanka 8800 10 0.292 0.031 0.044 0.329 0.304 Vietnam 8857 7 0.012 0.04 0.014 0.028 0.905

Sri Lanka 8801 10 0.008 0.159 0.022 0.098 0.713 Vietnam 8858 13 0.227 0.005 0.008 0.011 0.748

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Vietnam 8859 13 0.249 0.015 0.011 0.019 0.707 Japan-Oita 11967 5 0.909 0.013 0.027 0.021 0.029

Vietnam 8860 10 0.116 0.015 0.175 0.318 0.376 Japan-Oita 11968 7 0.41 0.008 0.02 0.023 0.538

Vietnam 8861 10 0.048 0.04 0.04 0.096 0.776 Japan-Oita 11969 7 0.899 0.011 0.025 0.04 0.025

Vietnam 8862 7 0.087 0.005 0.013 0.01 0.885 Japan-Oita 11970 7 0.738 0.044 0.047 0.037 0.134

Vietnam 8863 15 0.232 0.01 0.024 0.038 0.695 Japan-Oita 11971 7 0.879 0.011 0.008 0.057 0.046

Taiwan 8681 7 0.248 0.018 0.013 0.71 0.012 Japan-Oita 11972 5 0.962 0.005 0.006 0.009 0.017

Taiwan 8682 2 0.862 0.027 0.007 0.006 0.098 Japan-Oita 11973 7 0.823 0.039 0.024 0.059 0.056

Taiwan 8683 7 0.803 0.015 0.015 0.104 0.063 Japan-Oita 11974 21 0.768 0.01 0.025 0.026 0.171

Taiwan 8684 7 0.214 0.013 0.028 0.709 0.036 Japan-Oita 11975 10 0.668 0.004 0.028 0.035 0.264

Taiwan 8685 2 0.694 0.005 0.012 0.154 0.135 Japan-Oita 11976 13 0.616 0.009 0.021 0.338 0.017

Taiwan 8686 7 0.589 0.011 0.021 0.353 0.027 Japan-Oita 11977 7 0.564 0.006 0.012 0.398 0.02

Taiwan 8687 2 0.437 0.049 0.066 0.086 0.362 Japan-Oita 11979 10 0.888 0.011 0.025 0.013 0.063

Taiwan 8688 47 0.702 0.011 0.025 0.025 0.238 Japan-Oita 11980 7 0.92 0.009 0.012 0.029 0.03

Taiwan 8689 5 0.584 0.035 0.046 0.138 0.197 Japan-Oita 11981 7 0.957 0.006 0.005 0.004 0.028

Taiwan 8690 7 0.684 0.075 0.015 0.027 0.2 Japan-Oita 11982 13 0.764 0.006 0.016 0.111 0.103

Taiwan 8691 26 0.39 0.008 0.035 0.023 0.544 Japan-Oita 11985 10 0.741 0.007 0.036 0.007 0.209

Taiwan 8692 5 0.907 0.008 0.01 0.017 0.058 Japan-Oita 11986 10 0.608 0.021 0.206 0.126 0.039

Taiwan 8693 7 0.912 0.009 0.021 0.032 0.026 Japan-

Taiwan 8694 5 0.189 0.004 0.007 0.473 0.327 Kanazawa 11929 10 0.005 0.004 0.007 0.979 0.005

Taiwan 8695 2 0.854 0.009 0.073 0.036 0.029 Japan-

Taiwan 8696 23 0.037 0.005 0.013 0.929 0.015 Kanazawa 11931 10 0.828 0.01 0.01 0.069 0.083

Taiwan 8697 5 0.009 0.01 0.025 0.95 0.006 Japan-

Taiwan 8698 7 0.007 0.004 0.009 0.974 0.006 Kanazawa 11932 7 0.714 0.013 0.013 0.227 0.032

Taiwan 8699 10 0.432 0.08 0.075 0.25 0.164 Japan-

Taiwan 8700 7 0.562 0.018 0.119 0.098 0.203 Kanazawa 11933 15 0.748 0.006 0.011 0.014 0.221

Taiwan 8701 2 0.691 0.014 0.027 0.214 0.055 Japan-

Taiwan 8702 5 0.599 0.019 0.249 0.083 0.05 Kanazawa 11934 7 0.922 0.004 0.012 0.045 0.018

Taiwan 8703 7 0.009 0.005 0.02 0.959 0.006 Japan-

Taiwan 8704 2 0.733 0.017 0.036 0.076 0.139 Kanazawa 11936 7 0.903 0.006 0.015 0.021 0.056

Taiwan 8705 5 0.624 0.025 0.023 0.02 0.308 Japan-

Taiwan 8706 7 0.855 0.01 0.019 0.017 0.099 Kanazawa 11937 10 0.958 0.005 0.009 0.02 0.008

Taiwan 8707 7 0.865 0.044 0.01 0.032 0.05 Japan-

Taiwan 8708 15 0.022 0.006 0.009 0.952 0.012 Kanazawa 11939 10 0.881 0.023 0.011 0.029 0.056

Taiwan 8709 13 0.703 0.036 0.076 0.063 0.122 Japan- 11940 10 0.949 0.022 0.011 0.008 0.011

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Kanazawa Sapporo

Japan- Japan-

Kanazawa 11941 10 0.925 0.031 0.02 0.011 0.013 Sapporo 11913 7 0.543 0.004 0.009 0.433 0.01

Japan- Japan-

Kanazawa 11942 7 0.875 0.008 0.018 0.009 0.091 Sapporo 11914 13 0.736 0.006 0.008 0.236 0.013

Japan- Japan-

Kanazawa 11943 28 0.634 0.065 0.056 0.06 0.186 Sapporo 11915 10 0.562 0.022 0.032 0.029 0.355

Japan- Japan-

Kanazawa 11944 39 0.019 0.014 0.027 0.92 0.02 Sapporo 11916 5 0.036 0.004 0.13 0.782 0.048

Japan- Japan-

Kanazawa 11945 10 0.855 0.011 0.01 0.048 0.077 Sapporo 11917 5 0.04 0.013 0.053 0.873 0.021

Japan- Japan-

Kanazawa 11946 10 0.954 0.006 0.015 0.013 0.013 Sapporo 11918 7 0.528 0.016 0.024 0.342 0.09

Japan-Ohmiya 11947 5 0.842 0.008 0.043 0.067 0.04 Japan-

Japan-Ohmiya 11948 10 0.691 0.039 0.149 0.105 0.016 Sapporo 11921 7 0.9 0.01 0.009 0.009 0.072

Japan-Ohmiya 11951 5 0.76 0.041 0.036 0.065 0.098 Japan-

Japan-Ohmiya 11953 7 0.94 0.006 0.015 0.015 0.024 Sapporo 11922 5 0.909 0.004 0.01 0.06 0.017

Japan-Ohmiya 11954 5 0.628 0.014 0.033 0.2 0.125 Japan-

Japan-Ohmiya 11955 7 0.523 0.006 0.032 0.194 0.246 Sapporo 11923 7 0.665 0.009 0.023 0.221 0.082

Japan-Ohmiya 11956 7 0.813 0.012 0.015 0.053 0.107 Japan-

Japan-Ohmiya 11957 2 0.7 0.027 0.056 0.204 0.013 Sapporo 11924 5 0.121 0.01 0.012 0.602 0.254

Japan-Ohmiya 11959 18 0.687 0.01 0.211 0.079 0.013 Japan-

Japan-Ohmiya 11960 2 0.924 0.023 0.007 0.015 0.031 Sapporo 11925 7 0.887 0.005 0.009 0.007 0.093

Japan-Ohmiya 11961 5 0.964 0.008 0.015 0.006 0.007 Japan-

Japan-Ohmiya 11962 13 0.839 0.013 0.011 0.013 0.124 Sapporo 11926 5 0.528 0.007 0.039 0.408 0.017

Japan-Ohmiya 11963 2 0.712 0.007 0.009 0.252 0.021 China-Henan 8869 2 0.847 0.012 0.071 0.062 0.009

Japan-Ohmiya 11964 5 0.646 0.034 0.031 0.167 0.121 China-Henan 8870 5 0.977 0.009 0.004 0.004 0.007

Japan-Ohmiya 11965 7 0.925 0.005 0.012 0.008 0.05 China-Henan 8871 5 0.81 0.158 0.014 0.008 0.01

Japan-Ohmiya 11966 2 0.8 0.014 0.025 0.111 0.05 China-Henan 8872 2 0.954 0.019 0.006 0.008 0.013

Japan- China-Henan 8873 5 0.95 0.01 0.018 0.013 0.008

Sapporo 11907 2 0.915 0.01 0.021 0.032 0.023 China-Henan 8874 5 0.91 0.043 0.014 0.005 0.029

Japan- China-Henan 8875 5 0.986 0.003 0.004 0.004 0.004

Sapporo 11909 7 0.015 0.007 0.014 0.951 0.013 China-Henan 8876 5 0.985 0.003 0.003 0.004 0.005

Japan- 11911 5 0.533 0.015 0.018 0.422 0.013 China-Henan 8877 7 0.874 0.026 0.018 0.006 0.076

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

China-Henan 8878 5 0.977 0.004 0.011 0.004 0.004 South Korea 7686 10 0.934 0.007 0.014 0.037 0.008

China-Henan 8879 2 0.96 0.016 0.005 0.004 0.016 South Korea 7687 10 0.699 0.005 0.03 0.068 0.197

China-Henan 8880 7 0.954 0.022 0.007 0.009 0.008 South Korea 7688 2 0.362 0.005 0.032 0.538 0.063

China-Henan 8881 5 0.88 0.049 0.058 0.005 0.009 South Korea 7689 10 0.966 0.007 0.008 0.011 0.009

China-Henan 8882 5 0.872 0.012 0.027 0.031 0.057 South Korea 7690 10 0.928 0.004 0.014 0.01 0.044

China-Henan 8883 2 0.973 0.006 0.008 0.005 0.007 South Korea 7691 7 0.945 0.01 0.015 0.014 0.016

China-Henan 8884 2 0.892 0.029 0.011 0.003 0.065 South Korea 7692 5 0.88 0.016 0.029 0.014 0.061

China-Henan 8885 2 0.971 0.005 0.008 0.008 0.008 South Korea 7693 2 0.739 0.017 0.118 0.086 0.04

China-Henan 8886 2 0.969 0.008 0.012 0.005 0.006 South Korea 7694 5 0.981 0.003 0.005 0.004 0.006

China-Henan 8887 2 0.872 0.071 0.029 0.021 0.007 South Korea 7695 7 0.784 0.011 0.125 0.066 0.014

China-Henan 8888 2 0.936 0.027 0.012 0.014 0.011 South Korea 7696 5 0.974 0.009 0.006 0.006 0.005

South Korea 2769 5 0.981 0.004 0.005 0.005 0.005 South Korea 7697 10 0.95 0.019 0.008 0.01 0.013

South Korea 2772 47 0.958 0.01 0.008 0.006 0.018 South Korea 7698 23 0.64 0.04 0.085 0.033 0.202

South Korea 2775 13 0.964 0.007 0.01 0.011 0.008 South Korea 7699 7 0.945 0.015 0.018 0.008 0.014

South Korea 2776 13 0.825 0.006 0.056 0.042 0.072 South Korea 7700 7 0.886 0.026 0.027 0.012 0.049

South Korea 2779 2 0.96 0.01 0.006 0.006 0.018

South Korea 2784 2 0.98 0.006 0.004 0.004 0.006

South Korea 2785 2 0.863 0.096 0.018 0.015 0.009

South Korea 2786 2 0.976 0.003 0.006 0.011 0.005

South Korea 7671 2 0.965 0.006 0.012 0.009 0.008

South Korea 7672 2 0.957 0.005 0.008 0.01 0.02

South Korea 7673 2 0.93 0.028 0.026 0.011 0.005

South Korea 7674 2 0.901 0.01 0.032 0.046 0.011

South Korea 7675 10 0.965 0.005 0.012 0.011 0.007

South Korea 7676 7 0.947 0.018 0.011 0.013 0.011

South Korea 7677 15 0.385 0.29 0.056 0.259 0.01

South Korea 7678 2 0.952 0.011 0.013 0.018 0.006

South Korea 7679 5 0.974 0.007 0.007 0.006 0.005

South Korea 7680 7 0.924 0.007 0.03 0.03 0.009

South Korea 7681 13 0.736 0.016 0.224 0.016 0.007

South Korea 7682 5 0.955 0.005 0.021 0.009 0.01

South Korea 7683 7 0.976 0.006 0.005 0.005 0.009

South Korea 7684 7 0.588 0.012 0.316 0.041 0.043

South Korea 7685 18 0.941 0.006 0.011 0.019 0.022

Table 22 - Population clustering of each random bred Table 22 - Population clustering of each random bred individual in the database by SNPs and STRs at K = 5 individual in the database by SNPs and STRs at K = 5

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

USA-NY 2547 1 0.008 0.079 0.08 0.823 0.011 Brazil 7964 2 0.009 0.009 0.012 0.965 0.006

USA-NY 2559 3 0.008 0.011 0.315 0.663 0.003 Brazil 7965 27 0.007 0.006 0.005 0.978 0.005

USA-NY 2568 0 0.004 0.004 0.004 0.986 0.002 Brazil 7966 2 0.004 0.011 0.018 0.955 0.012

USA-NY 2569 5 0.01 0.007 0.011 0.96 0.013 Brazil 7968 23 0.016 0.009 0.017 0.949 0.009

USA-NY 2572 1 0.008 0.024 0.017 0.945 0.006 Brazil 7969 1 0.003 0.004 0.005 0.985 0.003

USA-NY 2578 17 0.005 0.034 0.018 0.928 0.015 Brazil 7970 1 0.006 0.007 0.007 0.977 0.004

USA-NY 2590 1 0.006 0.008 0.028 0.956 0.002 Brazil 7971 1 0.007 0.004 0.006 0.979 0.005

USA-NY 2591 2 0.008 0.007 0.011 0.964 0.01 Brazil 7972 1 0.004 0.005 0.012 0.973 0.006

USA-NY 2597 4 0.015 0.036 0.018 0.897 0.035 Brazil 7973 0 0.012 0.004 0.009 0.97 0.005

USA-MS 9971 3 0.017 0.041 0.012 0.921 0.01 Brazil 7974 3 0.008 0.008 0.018 0.961 0.006

USA-MS 9972 2 0.011 0.119 0.026 0.822 0.023 Brazil 7975 5 0.013 0.01 0.013 0.96 0.005

USA-MS 9974 2 0.028 0.047 0.019 0.897 0.009 Brazil 7976 1 0.007 0.004 0.006 0.979 0.004

USA-MS 9977 5 0.015 0.005 0.011 0.962 0.007 Brazil 7977 2 0.006 0.004 0.02 0.968 0.003

USA-MS 9980 2 0.01 0.027 0.027 0.923 0.013 Brazil 7978 1 0.014 0.03 0.012 0.938 0.006

USA-MS 9983 2 0.007 0.012 0.009 0.961 0.011 Brazil 7979 2 0.023 0.011 0.014 0.946 0.006

USA-MS 9985 3 0.171 0.012 0.031 0.783 0.004 Brazil 7980 2 0.013 0.025 0.291 0.656 0.015

USA-MS 9987 3 0.054 0.028 0.065 0.841 0.012 Brazil 7981 2 0.157 0.008 0.013 0.818 0.004

USA-MS 9989 2 0.023 0.012 0.013 0.947 0.004 Brazil 7982 20 0.077 0.116 0.024 0.775 0.008

USA-MS 9992 3 0.016 0.012 0.133 0.826 0.012 Brazil 7983 4 0.02 0.148 0.052 0.768 0.012

USA-HI 5366 3 0.201 0.044 0.026 0.726 0.004 Brazil 7984 15 0.344 0.009 0.027 0.614 0.006

USA-HI 5367 1 0.061 0.122 0.028 0.765 0.024 Brazil 7985 5 0.014 0.019 0.021 0.942 0.005

USA-HI 5371 1 0.032 0.028 0.207 0.711 0.022 Brazil 7986 3 0.088 0.012 0.038 0.857 0.005

USA-HI 5372 2 0.019 0.193 0.099 0.674 0.016 Brazil 7987 2 0.013 0.034 0.038 0.911 0.004

USA-HI 5379 1 0.035 0.009 0.047 0.889 0.021 Brazil 7988 0 0.118 0.035 0.029 0.809 0.009

USA-HI 5380 1 0.007 0.004 0.017 0.969 0.003 Brazil 7989 1 0.151 0.1 0.013 0.73 0.006

USA-HI 5383 3 0.032 0.111 0.111 0.465 0.28 Brazil 7990 1 0.019 0.022 0.008 0.947 0.004

USA-HI 5384 5 0.003 0.011 0.097 0.88 0.008 Finland 8077 11 0.003 0.005 0.012 0.973 0.007

USA-HI 5401 2 0.037 0.025 0.05 0.883 0.005 Finland 8084 11 0.023 0.024 0.034 0.905 0.013

USA-HI 5402 2 0.128 0.01 0.028 0.81 0.023 Finland 8086 5 0.007 0.009 0.058 0.918 0.008

Brazil 7961 1 0.009 0.005 0.021 0.961 0.004 Finland 8089 8 0.019 0.104 0.018 0.827 0.032

Brazil 7962 2 0.008 0.012 0.092 0.885 0.003 Finland 8093 16 0.005 0.005 0.204 0.78 0.006

Brazil 7963 17 0.091 0.164 0.022 0.72 0.004 Finland 8094 9 0.011 0.017 0.058 0.835 0.079

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Finland 8096 7 0.003 0.004 0.021 0.959 0.014 Germany 8747 13 0.004 0.006 0.006 0.982 0.003

Finland 8107 24 0.002 0.004 0.005 0.986 0.003 Germany 8749 2 0.003 0.003 0.005 0.984 0.005

Finland 8110 10 0.005 0.029 0.042 0.915 0.008 Italy-Milan 8050 1 0.105 0.012 0.095 0.673 0.115

Finland 8116 16 0.016 0.03 0.013 0.93 0.01 Italy-Milan 8057 2 0.051 0.1 0.207 0.578 0.064

Finland 8120 21 0.012 0.004 0.025 0.954 0.005 Italy-Milan 8060 1 0.016 0.045 0.21 0.707 0.022

Germany 8711 4 0.004 0.003 0.008 0.981 0.004 Italy-Milan 8061 2 0.104 0.028 0.295 0.552 0.022

Germany 8712 3 0.006 0.013 0.006 0.955 0.02 Italy-Milan 8062 3 0.007 0.012 0.583 0.392 0.006

Germany 8713 3 0.025 0.02 0.062 0.869 0.023 Italy-Milan 8065 5 0.005 0.008 0.488 0.493 0.005

Germany 8714 2 0.008 0.01 0.026 0.928 0.028 Italy-Milan 8066 1 0.029 0.058 0.107 0.786 0.021

Germany 8715 4 0.003 0.004 0.012 0.974 0.007 Italy-Milan 8067 2 0.008 0.016 0.467 0.492 0.017

Germany 8716 3 0.005 0.005 0.009 0.975 0.005 Italy-Milan 8068 2 0.008 0.013 0.509 0.465 0.006

Germany 8717 11 0.005 0.005 0.009 0.975 0.006 Italy-Milan 8069 1 0.033 0.129 0.201 0.614 0.023

Germany 8720 3 0.49 0.093 0.015 0.396 0.005 Italy-Milan 8071 3 0.015 0.013 0.04 0.923 0.01

Germany 8721 10 0.004 0.005 0.039 0.944 0.007 Italy-Milan 8072 3 0.272 0.015 0.201 0.501 0.011

Germany 8727 1 0.005 0.01 0.027 0.954 0.004 Italy-Milan 8073 1 0.013 0.009 0.263 0.694 0.02

Germany 8728 10 0.004 0.009 0.006 0.977 0.004 Italy-Milan 8074 2 0.184 0.061 0.025 0.726 0.003

Germany 8729 2 0.002 0.003 0.005 0.987 0.002 Italy-Rome 8586 1 0.014 0.021 0.475 0.478 0.011

Germany 8730 22 0.028 0.016 0.052 0.9 0.004 Italy-Rome 8589 3 0.039 0.015 0.092 0.848 0.005

Germany 8731 12 0.003 0.005 0.007 0.981 0.003 Italy-Rome 8592 2 0.013 0.018 0.14 0.626 0.202

Germany 8732 4 0.003 0.01 0.013 0.971 0.003 Italy-Rome 8594 1 0.02 0.02 0.112 0.808 0.039

Germany 8733 16 0.021 0.014 0.013 0.938 0.015 Italy-Rome 8595 1 0.042 0.044 0.188 0.606 0.12

Germany 8734 1 0.002 0.003 0.009 0.984 0.002 Italy-Rome 8596 1 0.011 0.01 0.542 0.43 0.007

Germany 8735 2 0.007 0.004 0.009 0.977 0.004 Italy-Rome 8597 1 0.034 0.012 0.188 0.73 0.036

Germany 8736 1 0.006 0.006 0.031 0.939 0.018 Italy-Rome 8599 1 0.053 0.007 0.077 0.858 0.005

Germany 8737 3 0.007 0.013 0.016 0.95 0.013 Italy-Rome 8601 2 0.037 0.014 0.081 0.701 0.167

Germany 8738 10 0.004 0.007 0.049 0.864 0.076 Italy-Rome 8602 3 0.01 0.023 0.26 0.694 0.013

Germany 8739 2 0.003 0.005 0.011 0.978 0.003 Italy-Rome 8603 1 0.012 0.01 0.044 0.915 0.019

Germany 8741 11 0.004 0.006 0.019 0.967 0.004 Italy-Rome 8604 3 0.207 0.011 0.118 0.645 0.019

Germany 8742 9 0.006 0.004 0.008 0.979 0.003 Italy-Rome 8609 2 0.007 0.006 0.364 0.616 0.007

Germany 8744 6 0.094 0.013 0.316 0.572 0.006 Italy-Rome 8610 2 0.01 0.011 0.4 0.572 0.007

Germany 8745 23 0.004 0.009 0.027 0.953 0.007 Italy-Rome 8611 2 0.007 0.01 0.295 0.648 0.04

Germany 8746 1 0.004 0.01 0.007 0.975 0.004 Turkey 6477 6 0.004 0.003 0.856 0.109 0.028

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Turkey 6478 12 0.01 0.011 0.922 0.017 0.041 Turkey 6738 3 0.003 0.005 0.848 0.138 0.006

Turkey 6480 10 0.006 0.018 0.922 0.048 0.006 Turkey 6739 4 0.006 0.038 0.506 0.403 0.047

Turkey 6481 11 0.587 0.356 0.032 0.019 0.005 Turkey 6740 1 0.002 0.004 0.003 0.988 0.003

Turkey 6482 12 0.014 0.02 0.698 0.259 0.01 Turkey 6741 2 0.858 0.064 0.015 0.059 0.004

Turkey 6484 6 0.007 0.015 0.88 0.093 0.005 Turkey 6742 4 0.026 0.054 0.677 0.189 0.055

Turkey 6486 8 0.006 0.026 0.648 0.305 0.016 Turkey 6743 2 0.006 0.012 0.664 0.308 0.009

Turkey 6487 13 0.006 0.005 0.886 0.095 0.007 Turkey 6745 1 0.013 0.007 0.56 0.403 0.018

Turkey 6488 11 0.013 0.009 0.838 0.097 0.044 Turkey 6746 2 0.324 0.218 0.056 0.346 0.055

Turkey 6491 15 0.691 0.088 0.044 0.167 0.01 Turkey 6748 3 0.011 0.077 0.815 0.08 0.017

Turkey 6494 10 0.032 0.018 0.494 0.382 0.074 Turkey 6749 2 0.018 0.006 0.434 0.51 0.031

Turkey 6496 11 0.027 0.063 0.787 0.114 0.009 Turkey 6750 3 0.004 0.006 0.503 0.484 0.003

Turkey 6499 11 0.01 0.011 0.444 0.527 0.009 Turkey 6753 2 0.021 0.014 0.626 0.277 0.062

Turkey 6500 15 0.004 0.005 0.889 0.096 0.005 Turkey 6754 3 0.004 0.006 0.508 0.283 0.198

Turkey 6502 6 0.021 0.014 0.861 0.095 0.009 Turkey 6755 3 0.009 0.005 0.727 0.248 0.012

Turkey 6503 3 0.013 0.007 0.691 0.278 0.01 Turkey 6756 2 0.003 0.006 0.629 0.356 0.006

Turkey 6507 12 0.01 0.004 0.939 0.041 0.006 Turkey 6758 3 0.041 0.018 0.796 0.124 0.02

Turkey 6510 9 0.04 0.011 0.515 0.429 0.005 Turkey 6759 4 0.007 0.023 0.73 0.235 0.005

Turkey 6512 13 0.015 0.019 0.887 0.044 0.035 Turkey 6760 2 0.005 0.017 0.385 0.577 0.017

Turkey 6513 12 0.007 0.058 0.628 0.277 0.03 Cyprus 10128 3 0.031 0.013 0.907 0.041 0.008

Turkey 6514 8 0.951 0.018 0.011 0.008 0.013 Cyprus 10129 1 0.007 0.009 0.514 0.455 0.014

Turkey 6516 10 0.003 0.008 0.008 0.976 0.006 Cyprus 10130 3 0.004 0.012 0.866 0.114 0.004

Turkey 6519 6 0.01 0.016 0.519 0.439 0.017 Cyprus 10131 1 0.084 0.078 0.776 0.051 0.012

Turkey 6520 13 0.006 0.007 0.536 0.444 0.006 Cyprus 10132 0 0.017 0.031 0.924 0.007 0.02

Turkey 6521 11 0.017 0.012 0.579 0.381 0.012 Cyprus 10133 3 0.013 0.018 0.853 0.101 0.015

Turkey 6729 2 0.036 0.079 0.306 0.067 0.512 Cyprus 10134 1 0.005 0.041 0.915 0.027 0.013

Turkey 6730 2 0.006 0.005 0.788 0.197 0.004 Cyprus 10135 2 0.011 0.17 0.755 0.059 0.006

Turkey 6731 3 0.005 0.014 0.549 0.397 0.035 Cyprus 10136 3 0.009 0.015 0.542 0.332 0.102

Turkey 6732 2 0.086 0.116 0.656 0.055 0.087 Cyprus 10137 1 0.01 0.005 0.86 0.026 0.099

Turkey 6733 4 0.028 0.023 0.879 0.059 0.012 Cyprus 10138 2 0.01 0.015 0.953 0.007 0.015

Turkey 6734 1 0.023 0.012 0.906 0.047 0.013 Cyprus 10139 3 0.088 0.095 0.773 0.015 0.03

Turkey 6735 3 0.003 0.003 0.977 0.009 0.007 Cyprus 10140 0 0.008 0.011 0.914 0.057 0.011

Turkey 6736 1 0.004 0.011 0.765 0.21 0.01 Cyprus 10141 1 0.007 0.008 0.908 0.07 0.007

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Cyprus 10142 1 0.017 0.031 0.917 0.01 0.025 Lebanon 10251 3 0.005 0.03 0.929 0.023 0.012

Cyprus 10143 1 0.007 0.005 0.973 0.007 0.008 Lebanon 10252 12 0.014 0.016 0.385 0.332 0.254

Cyprus 10144 3 0.01 0.099 0.867 0.014 0.01 Lebanon 10253 6 0.015 0.013 0.89 0.02 0.063

Cyprus 10145 2 0.005 0.005 0.922 0.061 0.007 Lebanon 10254 13 0.032 0.021 0.919 0.012 0.016

Cyprus 10146 0 0.007 0.045 0.923 0.008 0.017 Lebanon 10255 7 0.027 0.009 0.699 0.062 0.203

Cyprus 10147 2 0.004 0.006 0.875 0.111 0.004 Lebanon 10256 12 0.008 0.007 0.958 0.011 0.016

Cyprus 10148 1 0.02 0.046 0.894 0.02 0.02 Lebanon 10257 3 0.078 0.046 0.83 0.027 0.02

Cyprus 10149 3 0.012 0.028 0.789 0.061 0.111 Lebanon 10258 17 0.018 0.029 0.917 0.008 0.028

Cyprus 10150 1 0.014 0.008 0.913 0.051 0.014 Lebanon 10259 2 0.547 0.087 0.328 0.032 0.007

Cyprus 10151 2 0.013 0.02 0.935 0.012 0.019 Lebanon 10260 14 0.008 0.008 0.957 0.015 0.012

Cyprus 10152 2 0.011 0.008 0.865 0.094 0.021 Lebanon 10261 13 0.029 0.012 0.909 0.017 0.032

Cyprus 10153 1 0.023 0.007 0.931 0.016 0.022 Lebanon 10262 18 0.025 0.014 0.875 0.017 0.069

Cyprus 10154 1 0.01 0.109 0.823 0.018 0.04 Lebanon 10263 3 0.009 0.015 0.946 0.014 0.017

Cyprus 10155 3 0.011 0.126 0.85 0.009 0.004 Lebanon 10264 3 0.032 0.052 0.773 0.119 0.024

Cyprus 10156 5 0.038 0.012 0.907 0.016 0.027 Lebanon 10265 9 0.006 0.007 0.927 0.01 0.049

Cyprus 10157 2 0.006 0.008 0.859 0.012 0.117 Lebanon 10266 4 0.018 0.077 0.886 0.01 0.008

Lebanon 10235 19 0.08 0.068 0.789 0.023 0.039 Lebanon 10267 13 0.004 0.006 0.54 0.439 0.01

Lebanon 10236 22 0.033 0.101 0.842 0.01 0.015 Lebanon 10268 15 0.07 0.011 0.897 0.017 0.005

Lebanon 10237 16 0.177 0.192 0.604 0.018 0.009 Lebanon 10270 10 0.315 0.025 0.591 0.038 0.031

Lebanon 10238 3 0.015 0.038 0.842 0.054 0.05 Lebanon 10271 4 0.041 0.271 0.514 0.057 0.117

Lebanon 10239 14 0.007 0.009 0.962 0.012 0.01 Lebanon 10273 5 0.173 0.018 0.67 0.1 0.039

Lebanon 10240 19 0.029 0.062 0.835 0.04 0.035 Lebanon 10274 5 0.057 0.082 0.801 0.054 0.005

Lebanon 10241 6 0.128 0.044 0.688 0.068 0.072 Lebanon 10276 5 0.004 0.012 0.957 0.01 0.016

Lebanon 10242 2 0.014 0.015 0.852 0.1 0.02 Lebanon 10277 4 0.174 0.011 0.677 0.113 0.025

Lebanon 10243 3 0.04 0.039 0.819 0.027 0.075 Lebanon 10278 5 0.016 0.017 0.914 0.01 0.043

Lebanon 10244 15 0.026 0.05 0.861 0.011 0.051 Lebanon 10279 1 0.008 0.104 0.787 0.013 0.088

Lebanon 10245 7 0.007 0.039 0.713 0.024 0.217 Lebanon 10280 2 0.033 0.244 0.681 0.019 0.023

Lebanon 10246 15 0.008 0.014 0.911 0.022 0.045 Lebanon 10281 8 0.007 0.008 0.954 0.013 0.019

Lebanon 10247 24 0.011 0.012 0.727 0.005 0.245 Lebanon 10282 3 0.039 0.015 0.921 0.01 0.015

Lebanon 10248 19 0.012 0.02 0.916 0.042 0.01 Lebanon 10283 18 0.005 0.037 0.943 0.007 0.009

Lebanon 10249 17 0.014 0.051 0.339 0.587 0.009 Lebanon 10284 16 0.046 0.061 0.693 0.03 0.169

Lebanon 10250 22 0.011 0.009 0.841 0.041 0.097 Lebanon 10285 1 0.021 0.028 0.92 0.016 0.015

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Lebanon 10286 5 0.166 0.053 0.583 0.02 0.178 Israel 4982 4 0.012 0.079 0.817 0.083 0.009

Lebanon 10287 2 0.043 0.039 0.519 0.334 0.065 Israel 4983 1 0.333 0.021 0.081 0.518 0.046

Lebanon 10288 22 0.042 0.01 0.087 0.856 0.006 Israel 4984 2 0.24 0.028 0.024 0.679 0.028

Lebanon 10289 4 0.009 0.024 0.922 0.007 0.038 Israel 4985 17 0.034 0.016 0.904 0.038 0.008

Lebanon 10290 3 0.071 0.167 0.722 0.008 0.032 Israel 4986 2 0.019 0.016 0.933 0.015 0.017

Lebanon 10291 9 0.038 0.13 0.71 0.016 0.107 Israel 4988 3 0.042 0.026 0.89 0.026 0.016

Lebanon 10292 7 0.017 0.401 0.528 0.027 0.028 Israel 4989 4 0.102 0.019 0.834 0.013 0.032

Lebanon 10294 8 0.105 0.018 0.835 0.022 0.02 Israel 4990 2 0.291 0.014 0.671 0.018 0.007

Lebanon 10295 6 0.057 0.053 0.771 0.033 0.087 Israel 4992 6 0.007 0.009 0.9 0.068 0.016

Lebanon 10297 5 0.018 0.014 0.729 0.021 0.217 Israel 4993 9 0.007 0.011 0.958 0.013 0.011

Lebanon 10298 8 0.014 0.071 0.687 0.008 0.22 Israel 4994 2 0.003 0.005 0.981 0.006 0.005

Lebanon 10299 17 0.054 0.07 0.488 0.276 0.111 Israel 4995 2 0.191 0.01 0.763 0.018 0.018

Lebanon 10300 20 0.021 0.024 0.866 0.012 0.078 Israel 4996 1 0.026 0.004 0.938 0.009 0.022

Israel 4962 1 0.006 0.042 0.7 0.067 0.184 Israel 4997 3 0.106 0.011 0.812 0.011 0.06

Israel 4963 1 0.017 0.039 0.898 0.029 0.017 Israel 4998 2 0.014 0.034 0.603 0.222 0.127

Israel 4964 2 0.013 0.014 0.841 0.059 0.073 Israel 5000 2 0.04 0.016 0.917 0.008 0.018

Israel 4966 2 0.008 0.036 0.929 0.021 0.006 Israel 5001 2 0.012 0.022 0.679 0.272 0.015

Israel 4967 2 0.029 0.064 0.57 0.285 0.053 Israel 5002 2 0.022 0.036 0.891 0.047 0.005

Israel 4968 3 0.027 0.017 0.91 0.012 0.035 Israel 5003 2 0.11 0.025 0.643 0.185 0.038

Israel 4969 1 0.222 0.024 0.142 0.594 0.018 Israel 5004 3 0.114 0.059 0.711 0.057 0.059

Israel 4970 1 0.023 0.017 0.814 0.115 0.03 Israel 5005 2 0.006 0.016 0.806 0.161 0.011

Israel 4971 1 0.018 0.007 0.918 0.014 0.043 Israel 5006 3 0.005 0.008 0.925 0.036 0.026

Israel 4972 2 0.036 0.018 0.853 0.069 0.024 Israel 5007 2 0.257 0.026 0.632 0.079 0.005

Israel 4973 2 0.01 0.02 0.947 0.014 0.009 Israel 5008 1 0.022 0.024 0.88 0.047 0.026

Israel 4974 2 0.009 0.016 0.919 0.03 0.026 Israel 5009 3 0.217 0.02 0.649 0.038 0.076

Israel 4975 1 0.007 0.009 0.884 0.087 0.013 Israel 5010 4 0.004 0.007 0.955 0.026 0.009

Israel 4976 1 0.015 0.049 0.735 0.152 0.049 Israel 5011 2 0.017 0.009 0.933 0.017 0.023

Israel 4977 3 0.007 0.018 0.559 0.327 0.09 Egypt-Cairo 8190 3 0.015 0.017 0.916 0.043 0.01

Israel 4978 3 0.028 0.041 0.75 0.046 0.135 Egypt-Cairo 8192 5 0.007 0.072 0.746 0.148 0.026

Israel 4979 2 0.014 0.008 0.868 0.023 0.088 Egypt-Cairo 8193 4 0.007 0.018 0.936 0.023 0.017

Israel 4980 1 0.035 0.088 0.66 0.131 0.085 Egypt-Cairo 8196 4 0.04 0.071 0.863 0.009 0.017

Israel 4981 5 0.311 0.008 0.624 0.011 0.045 Egypt-Cairo 8203 4 0.009 0.006 0.965 0.013 0.006

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Egypt-Cairo 8215 3 0.008 0.012 0.961 0.009 0.01 Egypt-Cairo 9952 4 0.006 0.018 0.942 0.009 0.024

Egypt-Cairo 8198 3 0.015 0.015 0.938 0.022 0.01 Egypt-Cairo 9953 8 0.013 0.013 0.319 0.535 0.12

Egypt-Cairo 8194 1 0.019 0.019 0.869 0.076 0.017 Egypt-Cairo 9954 1 0.324 0.014 0.651 0.007 0.004

Egypt-Cairo 8211 9 0.005 0.004 0.971 0.009 0.01 Egypt-Cairo 9955 2 0.032 0.18 0.376 0.404 0.008

Egypt-Cairo 8216 2 0.01 0.012 0.9 0.071 0.007 Egypt-Cairo 9956 8 0.171 0.018 0.789 0.011 0.01

Egypt-Cairo 8195 2 0.009 0.052 0.909 0.02 0.01 Egypt-Cairo 9957 2 0.066 0.012 0.875 0.027 0.019

Egypt-Cairo 8199 8 0.005 0.045 0.937 0.007 0.007 Egypt-Cairo 9958 4 0.053 0.015 0.914 0.012 0.006

Egypt-Cairo 8200 13 0.006 0.005 0.942 0.032 0.015 Egypt-Cairo 9959 2 0.036 0.339 0.553 0.05 0.021

Egypt-Cairo 8201 1 0.026 0.063 0.786 0.037 0.087 Egypt-Cairo 9960 1 0.023 0.007 0.924 0.013 0.033

Egypt-Cairo 8202 2 0.614 0.27 0.066 0.019 0.032 Egypt-Cairo 9961 5 0.529 0.059 0.384 0.022 0.006

Egypt-Cairo 8204 2 0.012 0.011 0.965 0.009 0.004 Egypt-Cairo 9962 3 0.009 0.033 0.914 0.031 0.013

Egypt-Cairo 8208 1 0.015 0.024 0.929 0.009 0.023 Egypt-Cairo 9963 3 0.015 0.007 0.954 0.014 0.009

Egypt-Cairo 8210 3 0.009 0.053 0.885 0.008 0.045 Egypt-Cairo 9964 1 0.034 0.047 0.892 0.019 0.008

Egypt-Cairo 8214 2 0.007 0.007 0.971 0.006 0.01 Egypt-Cairo 10021 6 0.003 0.007 0.979 0.006 0.006

Egypt-Cairo 8191 2 0.028 0.014 0.91 0.02 0.028 Egypt-Cairo 10022 1 0.011 0.013 0.962 0.008 0.006

Egypt-Cairo 8197 1 0.005 0.041 0.938 0.007 0.008 Egypt-Cairo 10023 0 0.005 0.011 0.975 0.004 0.005

Egypt-Cairo 8205 1 0.019 0.027 0.922 0.01 0.021 Egypt-Cairo 10024 1 0.004 0.003 0.978 0.012 0.003

Egypt-Cairo 8206 1 0.022 0.018 0.945 0.007 0.008 Egypt-Cairo 10025 2 0.007 0.012 0.924 0.037 0.019

Egypt-Cairo 8207 2 0.035 0.026 0.92 0.009 0.01 Egypt-Cairo 10026 17 0.022 0.006 0.941 0.026 0.005

Egypt-Cairo 8209 1 0.005 0.004 0.971 0.011 0.008 Egypt-Cairo 10027 1 0.009 0.086 0.879 0.018 0.009

Egypt-Cairo 8212 1 0.006 0.007 0.97 0.007 0.009 Egypt-Cairo 10028 1 0.007 0.008 0.964 0.007 0.013

Egypt-Cairo 8213 1 0.005 0.009 0.967 0.009 0.009 Egypt-Cairo 10029 3 0.004 0.005 0.973 0.005 0.013

Egypt-Cairo 9942 2 0.033 0.011 0.817 0.008 0.132 Egypt-Cairo 10030 1 0.009 0.005 0.971 0.01 0.005

Egypt-Cairo 9943 3 0.47 0.055 0.058 0.404 0.013 Egypt-Cairo 10031 3 0.004 0.006 0.98 0.003 0.007

Egypt-Cairo 9944 4 0.006 0.017 0.521 0.341 0.116 Egypt-Cairo 10032 0 0.004 0.006 0.964 0.005 0.021

Egypt-Cairo 9945 0 0.007 0.031 0.928 0.014 0.019 Egypt-Cairo 10033 1 0.006 0.01 0.975 0.004 0.006

Egypt-Cairo 9946 3 0.06 0.034 0.883 0.011 0.012 Egypt-Cairo 10034 1 0.005 0.01 0.97 0.009 0.005

Egypt-Cairo 9947 3 0.008 0.028 0.909 0.008 0.048 Egypt-Cairo 10035 3 0.067 0.059 0.848 0.008 0.019

Egypt-Cairo 9948 0 0.012 0.006 0.882 0.095 0.005 Egypt-Cairo 10037 5 0.006 0.035 0.731 0.219 0.008

Egypt-Cairo 9949 0 0.032 0.016 0.925 0.019 0.008 Egypt-Cairo 10042 1 0.03 0.055 0.85 0.051 0.013

Egypt-Cairo 9950 1 0.006 0.013 0.932 0.043 0.006 Egypt-Cairo 10043 2 0.004 0.014 0.935 0.037 0.01

Egypt-Cairo 9951 2 0.012 0.013 0.961 0.01 0.005 Egypt-Cairo 10044 4 0.012 0.008 0.964 0.01 0.005

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Egypt-Cairo 10045 1 0.008 0.01 0.951 0.026 0.005 Egypt- Luxor 10057 12 0.009 0.01 0.949 0.01 0.021

Egypt-Cairo 10046 0 0.022 0.015 0.946 0.01 0.007 Egypt- Luxor 10058 6 0.006 0.006 0.973 0.008 0.007

Egypt-Cairo 10047 0 0.009 0.014 0.954 0.007 0.016 Egypt- Luxor 10060 4 0.005 0.008 0.959 0.022 0.005

Egypt-Cairo 10048 1 0.049 0.017 0.918 0.012 0.003 Egypt- Luxor 10061 0 0.254 0.012 0.714 0.016 0.005

Egypt-Cairo 10083 24 0.013 0.008 0.936 0.037 0.007 Egypt- Luxor 10062 3 0.007 0.01 0.947 0.026 0.01

Egypt-Cairo 10040 2 0.017 0.025 0.912 0.035 0.011 Egypt- Luxor 10063 1 0.051 0.098 0.735 0.056 0.061

Egypt-Cairo 10041 1 0.015 0.054 0.904 0.018 0.01 Egypt- Luxor 10064 1 0.007 0.068 0.893 0.027 0.005

Egypt-Cairo 10049 9 0.019 0.055 0.901 0.012 0.013 Egypt- Luxor 10065 0 0.01 0.122 0.835 0.014 0.018

Egypt-Cairo 10084 7 0.019 0.021 0.923 0.03 0.007 Egypt- Luxor 10066 3 0.028 0.066 0.859 0.039 0.008

Egypt-Cairo 10085 19 0.007 0.03 0.89 0.068 0.004 Egypt- Luxor 10067 4 0.004 0.01 0.967 0.005 0.014

Egypt-Cairo 10087 4 0.004 0.008 0.976 0.009 0.003 Egypt- Luxor 10068 7 0.044 0.031 0.905 0.017 0.003

Egypt-Cairo 10090 4 0.021 0.107 0.84 0.023 0.009 Egypt- Luxor 10069 3 0.005 0.011 0.964 0.006 0.014

Egypt-Cairo 9968 0 0.007 0.02 0.936 0.019 0.018 Egypt- Luxor 10070 1 0.244 0.013 0.718 0.021 0.004

Egypt-Asuit 10091 1 0.004 0.007 0.974 0.01 0.005 Egypt- Luxor 10071 3 0.015 0.027 0.935 0.006 0.016

Egypt-Asuit 10093 3 0.003 0.012 0.964 0.018 0.003 Egypt- Luxor 10072 2 0.013 0.009 0.963 0.007 0.008

Egypt-Asuit 10094 4 0.005 0.015 0.961 0.014 0.004 Egypt- Luxor 10073 1 0.005 0.009 0.016 0.964 0.006

Egypt-Asuit 10095 13 0.005 0.02 0.939 0.032 0.004 Egypt- Luxor 10074 0 0.003 0.004 0.983 0.006 0.004

Egypt-Asuit 10096 1 0.012 0.01 0.967 0.008 0.003 Egypt- Luxor 10079 4 0.006 0.056 0.864 0.068 0.007

Egypt-Asuit 10098 1 0.003 0.006 0.98 0.009 0.003 Egypt- Luxor 10080 5 0.014 0.212 0.752 0.016 0.006

Egypt-Asuit 10099 1 0.004 0.008 0.979 0.006 0.004 Egypt-Abu

Egypt-Asuit 10100 2 0.082 0.107 0.694 0.097 0.02 Simbel 10076 10 0.039 0.028 0.904 0.023 0.007

Egypt-Asuit 10101 2 0.009 0.026 0.949 0.006 0.01 Egypt-Abu

Egypt-Asuit 10102 1 0.023 0.022 0.94 0.005 0.01 Simbel 10077 17 0.01 0.024 0.954 0.006 0.006

Egypt-Luxor 10038 1 0.076 0.029 0.884 0.006 0.004 Egypt-Abu

Egypt-Luxor 10039 5 0.004 0.005 0.967 0.004 0.021 Simbel 10081 17 0.011 0.022 0.944 0.013 0.009

Egypt-Luxor 10050 2 0.032 0.017 0.936 0.005 0.01 Egypt-Abu

Egypt-Luxor 10051 1 0.005 0.008 0.979 0.005 0.004 Simbel 10089 1 0.004 0.009 0.919 0.065 0.003

Egypt-Luxor 10052 11 0.022 0.053 0.908 0.011 0.005 Egypt-Abu

Egypt-Luxor 10053 2 0.013 0.042 0.827 0.007 0.111 Simbel 10092 2 0.005 0.006 0.979 0.005 0.005

Egypt-Luxor 10054 2 0.016 0.008 0.955 0.007 0.015 Iraq-West 9587 9 0.012 0.039 0.25 0.046 0.653

Egypt-Luxor 10055 1 0.012 0.012 0.963 0.005 0.007 Iraq-West 10202 6 0.007 0.006 0.331 0.023 0.634

Egypt-Luxor 10056 2 0.026 0.014 0.93 0.012 0.018 Iraq-West 10204 15 0.004 0.011 0.277 0.048 0.661

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5 raq-West 11854 18 0.03 0.014 0.14 0.012 0.804 Iraq-Baghdad 11878 3 0.006 0.01 0.307 0.071 0.605 raq-West 11860 10 0.012 0.034 0.052 0.009 0.893 Iraq-Baghdad 11879 1 0.049 0.127 0.025 0.012 0.787 raq-West 11861 3 0.088 0.068 0.151 0.018 0.674 Iraq-Baghdad 11880 4 0.015 0.014 0.176 0.017 0.779 raq-West 11863 5 0.007 0.011 0.041 0.057 0.884 Iraq-Baghdad 11881 2 0.014 0.009 0.296 0.076 0.605 raq-West 11864 1 0.011 0.033 0.112 0.027 0.818 Iraq-Baghdad 11882 3 0.013 0.006 0.219 0.008 0.755 raq-West 11888 2 0.093 0.061 0.015 0.006 0.824 Iraq-Baghdad 11883 2 0.011 0.023 0.179 0.154 0.634 raq-West 11889 4 0.006 0.024 0.06 0.119 0.791 Iraq-Baghdad 11884 13 0.026 0.016 0.034 0.015 0.909 raq-West 11890 16 0.062 0.047 0.214 0.012 0.664 Iraq-Baghdad 11885 0 0.013 0.034 0.228 0.003 0.721 raq-West 11891 15 0.007 0.025 0.333 0.014 0.621 Iraq-Baghdad 11886 2 0.057 0.088 0.232 0.008 0.615 raq-Baghdad 11847 1 0.007 0.007 0.161 0.009 0.816 Iraq-Baghdad 11887 15 0.01 0.245 0.088 0.01 0.647 raq-Baghdad 11848 1 0.036 0.157 0.024 0.013 0.77 Iran 9419 18 0.012 0.027 0.049 0.068 0.844 raq-Baghdad 11849 1 0.006 0.012 0.303 0.022 0.657 Iran 9420 14 0.122 0.044 0.038 0.013 0.784 raq-Baghdad 11850 1 0.028 0.011 0.025 0.076 0.859 Iran 9421 17 0.01 0.01 0.059 0.01 0.911 raq-Baghdad 11852 14 0.013 0.068 0.022 0.008 0.89 Iran 9422 15 0.05 0.019 0.135 0.008 0.788 raq-Baghdad 11853 8 0.039 0.047 0.099 0.01 0.805 Iran 9424 21 0.03 0.012 0.006 0.004 0.949 raq-Baghdad 11855 19 0.014 0.04 0.059 0.01 0.877 Iran 9425 4 0.018 0.009 0.01 0.003 0.959 raq-Baghdad 11856 1 0.004 0.032 0.149 0.042 0.773 Iran 9426 5 0.034 0.017 0.011 0.019 0.919 raq-Baghdad 11857 2 0.008 0.01 0.203 0.153 0.626 Iran 9427 3 0.009 0.013 0.01 0.007 0.962 raq-Baghdad 11858 1 0.021 0.016 0.113 0.013 0.837 Iran 9428 2 0.029 0.087 0.031 0.013 0.841 raq-Baghdad 11859 3 0.008 0.012 0.091 0.044 0.845 Iran 9429 3 0.116 0.103 0.023 0.011 0.746 raq-Baghdad 11862 1 0.025 0.01 0.272 0.066 0.627 Iran 9430 9 0.02 0.081 0.138 0.035 0.726 raq-Baghdad 11865 3 0.154 0.022 0.213 0.01 0.601 Iran 9431 1 0.025 0.236 0.02 0.006 0.713 raq-Baghdad 11868 8 0.02 0.022 0.036 0.007 0.916 Iran 9432 3 0.021 0.023 0.007 0.004 0.946 raq-Baghdad 11869 7 0.011 0.012 0.124 0.151 0.701 Iran 9433 2 0.052 0.016 0.027 0.032 0.872 raq-Baghdad 11870 1 0.019 0.018 0.29 0.139 0.532 Iran 9434 2 0.033 0.029 0.018 0.043 0.877 raq-Baghdad 11871 2 0.01 0.073 0.388 0.017 0.512 Iran 9435 2 0.017 0.017 0.005 0.005 0.956 raq-Baghdad 11872 3 0.003 0.017 0.159 0.02 0.8 Iran 9436 15 0.005 0.011 0.009 0.005 0.969 raq-Baghdad 11873 3 0.016 0.017 0.464 0.005 0.499 Iran 9437 1 0.006 0.018 0.008 0.005 0.962 raq-Baghdad 11874 1 0.035 0.007 0.191 0.016 0.751 Iran 9438 1 0.014 0.005 0.004 0.003 0.974 raq-Baghdad 11875 4 0.04 0.185 0.029 0.008 0.738 Iran 9439 1 0.018 0.019 0.008 0.008 0.946 raq-Baghdad 11876 5 0.011 0.133 0.104 0.005 0.747 Iran 9440 1 0.002 0.007 0.006 0.002 0.982 raq-Baghdad 11877 3 0.033 0.015 0.256 0.006 0.69 Iran 9441 2 0.003 0.005 0.003 0.003 0.987

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Iran 9442 2 0.012 0.019 0.01 0.005 0.954 Iran 9475 2 0.004 0.01 0.008 0.006 0.973

Iran 9443 2 0.007 0.008 0.005 0.005 0.975 Iran 9476 3 0.005 0.004 0.005 0.004 0.981

Iran 9444 1 0.005 0.003 0.003 0.002 0.985 Iran 9477 2 0.017 0.006 0.007 0.008 0.962

Iran 9445 2 0.004 0.003 0.003 0.002 0.988 Iran 9478 1 0.023 0.065 0.011 0.007 0.894

Iran 9446 3 0.01 0.008 0.004 0.004 0.975 Iran 9479 2 0.004 0.003 0.003 0.003 0.987

Iran 9447 19 0.004 0.015 0.004 0.005 0.972 Iran 9480 3 0.009 0.027 0.006 0.004 0.954

Iran 9448 3 0.004 0.006 0.004 0.003 0.983 Iran 9481 3 0.039 0.012 0.007 0.014 0.928

Iran 9449 1 0.005 0.003 0.003 0.002 0.987 Iran 9482 2 0.014 0.037 0.007 0.008 0.934

Iran 9450 2 0.007 0.004 0.006 0.003 0.979 Iran 9483 4 0.206 0.017 0.018 0.01 0.75

Iran 9451 3 0.018 0.009 0.004 0.003 0.967 Iran 9484 2 0.004 0.017 0.192 0.04 0.747

Iran 9452 1 0.006 0.006 0.004 0.006 0.979 Iran 9485 2 0.046 0.01 0.008 0.004 0.932

Iran 9453 3 0.002 0.003 0.004 0.002 0.989 Iran 9486 3 0.004 0.009 0.006 0.008 0.973

Iran 9454 1 0.003 0.003 0.003 0.002 0.99 Iran 9487 2 0.007 0.022 0.007 0.004 0.96

Iran 9455 3 0.006 0.016 0.013 0.007 0.957 Iran 9488 3 0.007 0.008 0.017 0.009 0.959

Iran 9456 3 0.007 0.009 0.004 0.003 0.977 Iran 9489 3 0.007 0.032 0.015 0.009 0.936

Iran 9457 3 0.003 0.004 0.005 0.004 0.985 Iran 9490 3 0.006 0.008 0.003 0.005 0.977

Iran 9458 1 0.005 0.005 0.003 0.002 0.985 Iran 9491 2 0.004 0.004 0.003 0.002 0.987

Iran 9459 2 0.004 0.008 0.003 0.003 0.983 Iran 9492 3 0.004 0.011 0.006 0.006 0.973

Iran 9460 2 0.013 0.012 0.012 0.009 0.955 Iran 9493 1 0.014 0.005 0.004 0.005 0.971

Iran 9461 3 0.004 0.009 0.005 0.007 0.975 Iran 9494 2 0.003 0.004 0.005 0.003 0.985

Iran 9462 2 0.006 0.017 0.004 0.009 0.965 Iran 9495 1 0.004 0.007 0.004 0.004 0.981

Iran 9463 3 0.006 0.006 0.004 0.003 0.981 Iran 9497 3 0.006 0.012 0.004 0.003 0.975

Iran 9464 2 0.009 0.005 0.003 0.003 0.98 Iran 9498 2 0.006 0.004 0.004 0.003 0.984

Iran 9465 2 0.003 0.007 0.008 0.004 0.978 Iran 9499 3 0.023 0.006 0.006 0.004 0.962

Iran 9466 3 0.005 0.031 0.009 0.004 0.95 Iran 9500 2 0.004 0.009 0.003 0.003 0.982

Iran 9468 3 0.023 0.007 0.007 0.007 0.956 Iran 9501 1 0.005 0.04 0.014 0.03 0.91

Iran 9469 2 0.031 0.007 0.009 0.015 0.937 Iran 9502 4 0.017 0.005 0.011 0.005 0.963

Iran 9470 3 0.025 0.009 0.009 0.007 0.95 Iran 9503 3 0.007 0.02 0.006 0.011 0.956

Iran 9471 2 0.006 0.008 0.011 0.015 0.96 Iran 9504 2 0.002 0.003 0.003 0.002 0.99

Iran 9472 2 0.021 0.012 0.008 0.003 0.956 Iran 9505 2 0.003 0.003 0.003 0.003 0.989

Iran 9473 1 0.005 0.008 0.003 0.004 0.98 Iran 9506 6 0.012 0.077 0.006 0.033 0.872

Iran 9474 1 0.006 0.009 0.013 0.013 0.959 Iran 9507 6 0.013 0.051 0.023 0.048 0.865

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Iran 9508 3 0.011 0.011 0.004 0.003 0.971 Dubai 10112 2 0.036 0.808 0.081 0.011 0.064

Iran 9509 5 0.019 0.006 0.005 0.005 0.965 Dubai 10120 3 0.006 0.871 0.008 0.006 0.109

Iran 9510 4 0.015 0.006 0.003 0.003 0.972 Kenya-Nairobi 9833 4 0.019 0.044 0.01 0.694 0.234

Iran 9511 3 0.006 0.006 0.007 0.007 0.974 Kenya-Nairobi 9834 0 0.083 0.332 0.029 0.54 0.017

Iran 9512 6 0.009 0.01 0.008 0.008 0.966 Kenya-Nairobi 9835 0 0.039 0.353 0.533 0.068 0.007

Iran 9513 5 0.006 0.021 0.008 0.007 0.958 Kenya-Nairobi 9836 2 0.044 0.322 0.009 0.619 0.005

Iran 9514 4 0.013 0.016 0.105 0.019 0.848 Kenya-Nairobi 9837 2 0.163 0.035 0.023 0.758 0.021

Iran 9515 4 0.006 0.016 0.004 0.007 0.967 Kenya-Nairobi 9838 4 0.012 0.135 0.218 0.545 0.091

Iran 9516 7 0.008 0.021 0.007 0.006 0.958 Kenya-Nairobi 9839 1 0.032 0.439 0.013 0.498 0.017

Iran 9517 3 0.029 0.009 0.006 0.009 0.947 Kenya-Nairobi 9840 3 0.013 0.306 0.082 0.579 0.02

Iran 9518 3 0.012 0.036 0.01 0.012 0.931 Kenya-Nairobi 9841 3 0.02 0.219 0.01 0.739 0.012

Iran 9519 4 0.007 0.013 0.009 0.017 0.954 Kenya-Nairobi 9842 2 0.038 0.286 0.045 0.603 0.028

Iran 9520 4 0.003 0.021 0.006 0.006 0.963 Kenya-Nairobi 9843 4 0.034 0.327 0.017 0.612 0.01

Iran 9521 8 0.008 0.114 0.006 0.008 0.864 Kenya-Nairobi 9844 3 0.096 0.155 0.017 0.722 0.01

Iran 9522 9 0.024 0.019 0.04 0.006 0.912 Kenya-Nairobi 9845 1 0.038 0.17 0.113 0.668 0.011

Iran 9523 3 0.005 0.021 0.038 0.024 0.913 Kenya-Nairobi 9846 1 0.061 0.332 0.019 0.565 0.022

Iran 9524 3 0.01 0.005 0.003 0.002 0.98 Kenya-Nairobi 9847 3 0.023 0.216 0.012 0.737 0.012

Iran 9526 4 0.009 0.005 0.003 0.002 0.98 Kenya-Nairobi 9848 3 0.069 0.173 0.141 0.604 0.013

Iran 9527 3 0.016 0.058 0.011 0.006 0.909 Kenya-Nairobi 9849 2 0.029 0.653 0.016 0.297 0.004

Iran 9528 5 0.007 0.005 0.003 0.003 0.981 Kenya-Nairobi 9850 3 0.016 0.118 0.052 0.779 0.035

Iran 9529 4 0.009 0.014 0.003 0.002 0.972 Kenya-Nairobi 9851 2 0.016 0.527 0.01 0.436 0.012

Iran 9530 5 0.007 0.006 0.005 0.003 0.979 Kenya-Nairobi 9852 4 0.012 0.534 0.033 0.415 0.006

Iran 9531 4 0.018 0.102 0.024 0.01 0.846 Kenya-Nairobi 9853 4 0.039 0.315 0.01 0.601 0.035

Iran 9532 14 0.011 0.014 0.033 0.028 0.914 Kenya-Nairobi 9854 2 0.012 0.286 0.047 0.617 0.039

Dubai 10104 1 0.002 0.796 0.055 0.02 0.127 Kenya-Nairobi 9855 2 0.015 0.383 0.048 0.534 0.02

Dubai 10105 2 0.011 0.746 0.011 0.01 0.222 Kenya-Nairobi 9856 3 0.013 0.39 0.026 0.563 0.009

Dubai 10106 4 0.006 0.941 0.006 0.004 0.042 Kenya-Nairobi 9857 6 0.108 0.163 0.031 0.683 0.015

Dubai 10107 1 0.047 0.476 0.024 0.03 0.424 Kenya-Nairobi 9858 3 0.028 0.318 0.01 0.635 0.01

Dubai 10108 2 0.048 0.843 0.047 0.009 0.054 Kenya-Nairobi 9859 4 0.199 0.018 0.006 0.766 0.011

Dubai 10109 4 0.125 0.501 0.009 0.009 0.356 Kenya-Nairobi 9860 2 0.167 0.11 0.01 0.705 0.008

Dubai 10110 3 0.006 0.791 0.013 0.014 0.176 Kenya-Nairobi 9861 1 0.011 0.178 0.048 0.744 0.02

Dubai 10111 4 0.004 0.937 0.008 0.004 0.048 Kenya-Nairobi 9862 2 0.037 0.337 0.049 0.571 0.004

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Kenya- Nairobi 9863 1 0.035 0.536 0.006 0.417 0.006 Kenya-Lamu 3241 4 0.013 0.917 0.036 0.026 0.008

Kenya- Nairobi 9864 2 0.01 0.393 0.021 0.53 0.045 Kenya-Lamu 3246 2 0.006 0.908 0.052 0.028 0.007

Kenya- Nairobi 9865 4 0.062 0.277 0.022 0.614 0.025 Kenya-Lamu 3247 3 0.013 0.865 0.017 0.091 0.013

Kenya- Nairobi 9866 3 0.075 0.222 0.06 0.635 0.008 India-Udaipur 11835 8 0.357 0.374 0.024 0.01 0.235

Kenya- Nairobi 9867 0 0.059 0.319 0.019 0.578 0.025 India-Udaipur 11836 3 0.031 0.581 0.013 0.012 0.363

Kenya-Nairobi 9868 5 0.021 0.219 0.132 0.606 0.021 India-Udaipur 11837 2 0.063 0.682 0.01 0.003 0.242

Kenya- Pate 2000 2 0.006 0.916 0.024 0.045 0.009 India-Agra 11823 1 0.04 0.553 0.014 0.005 0.389

Kenya- Pate 2001 3 0.004 0.97 0.003 0.003 0.02 India-Agra 11824 2 0.006 0.739 0.024 0.005 0.226

Kenya- Pate 2002 1 0.006 0.948 0.004 0.003 0.038 India-Agra 11825 2 0.099 0.353 0.009 0.007 0.532

Kenya- Pate 2003 0 0.006 0.974 0.005 0.009 0.007 India-Agra 11826 6 0.044 0.52 0.008 0.004 0.425

Kenya- Pate 2004 3 0.007 0.963 0.015 0.005 0.01 India-Agra 11827 19 0.012 0.555 0.012 0.004 0.417

Kenya- Pate 2006 3 0.021 0.92 0.01 0.015 0.034 India-Agra 11828 2 0.023 0.618 0.118 0.007 0.233

Kenya- Pate 2007 4 0.005 0.963 0.012 0.011 0.008 India-Agra 11829 4 0.018 0.633 0.012 0.008 0.329

Kenya- Pate 2009 6 0.005 0.86 0.083 0.034 0.018 India-Agra 11830 8 0.013 0.546 0.008 0.004 0.429

Kenya- Pate 2011 3 0.004 0.982 0.008 0.003 0.003 India-Agra 11831 3 0.061 0.39 0.034 0.007 0.507

Kenya-Lamu 1848 13 0.009 0.969 0.008 0.009 0.005 India-Agra 11832 2 0.016 0.466 0.007 0.003 0.508

Kenya-Lamu 2014 4 0.005 0.922 0.02 0.019 0.033 India-Agra 11833 16 0.049 0.634 0.018 0.004 0.294

Kenya-Lamu 2015 2 0.009 0.89 0.063 0.032 0.006 India-Agra 11834 3 0.051 0.484 0.008 0.005 0.452

Kenya-Lamu 2016 2 0.007 0.963 0.015 0.009 0.007 India-

Kenya-Lamu 2018 1 0.004 0.841 0.02 0.127 0.008 Hyderbad 11802 13 0.104 0.604 0.271 0.011 0.009

Kenya-Lamu 2019 2 0.005 0.923 0.04 0.027 0.005 India-

Kenya-Lamu 2021 2 0.006 0.866 0.018 0.027 0.083 Hyderbad 11803 7 0.021 0.883 0.05 0.004 0.043

Kenya-Lamu 2023 3 0.005 0.976 0.006 0.008 0.005 India-

Kenya-Lamu 2024 2 0.003 0.977 0.005 0.004 0.01 Hyderbad 11804 5 0.445 0.516 0.005 0.003 0.031

Kenya-Lamu 2025 3 0.009 0.946 0.016 0.009 0.02 India-

Kenya-Lamu 2026 4 0.005 0.938 0.046 0.006 0.006 Hyderbad 11805 10 0.042 0.901 0.023 0.01 0.023

Kenya-Lamu 2027 6 0.017 0.916 0.008 0.02 0.039 India-

Kenya-Lamu 2029 2 0.003 0.981 0.005 0.003 0.008 Hyderbad 11807 11 0.099 0.858 0.014 0.008 0.02

Kenya-Lamu 2030 1 0.003 0.962 0.007 0.02 0.008 India-

Kenya-Lamu 2031 4 0.009 0.929 0.017 0.017 0.029 Hyderbad 11808 9 0.251 0.72 0.01 0.014 0.005

Kenya-Lamu 2032 2 0.007 0.833 0.011 0.008 0.142 India-

Kenya-Lamu 2033 2 0.003 0.971 0.012 0.004 0.009 Hyderbad 11809 3 0.008 0.73 0.053 0.141 0.068

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

India- India-Andhra 10165 1 0.08 0.895 0.008 0.007 0.011

Hyderbad 11810 0 0.234 0.66 0.049 0.007 0.05 India-Andhra 10166 3 0.064 0.887 0.023 0.019 0.007

India- India-Andhra 10167 1 0.199 0.732 0.04 0.024 0.005

Hyderbad 11811 0 0.015 0.949 0.017 0.012 0.007 India-Andhra 10168 1 0.127 0.822 0.025 0.012 0.015

India- India-Andhra 10169 6 0.217 0.749 0.011 0.018 0.005

Hyderbad 11812 3 0.097 0.715 0.009 0.003 0.176 India-Andhra 10170 4 0.293 0.664 0.025 0.013 0.004

India- India-Andhra 10171 1 0.323 0.598 0.011 0.012 0.056

Hyderbad 11813 4 0.392 0.543 0.037 0.006 0.023 India-Andhra 10172 2 0.106 0.837 0.011 0.02 0.026

India- India-Andhra 10173 2 0.223 0.752 0.011 0.009 0.005

Hyderbad 11814 2 0.024 0.908 0.021 0.005 0.042 India-Andhra 10174 3 0.194 0.784 0.01 0.009 0.003

India- India-Andhra 10175 2 0.105 0.793 0.024 0.067 0.011

Hyderbad 11815 2 0.134 0.802 0.025 0.008 0.031 India-Andhra 10176 2 0.358 0.585 0.025 0.011 0.021

India- India-Andhra 10177 0 0.271 0.632 0.009 0.077 0.01

Hyderbad 11816 3 0.007 0.954 0.007 0.004 0.029 India-Andhra 10178 3 0.152 0.822 0.007 0.007 0.012

India- India-Andhra 10179 4 0.048 0.924 0.005 0.018 0.004

Hyderbad 11817 2 0.134 0.837 0.011 0.004 0.014 India-Andhra 10180 10 0.113 0.676 0.113 0.066 0.032

India- India-Andhra 10181 5 0.368 0.608 0.01 0.007 0.006

Hyderbad 11818 1 0.057 0.86 0.019 0.057 0.006 India-Kolkata 10113 2 0.056 0.91 0.014 0.007 0.013

India- India-Kolkata 10114 1 0.09 0.889 0.007 0.004 0.01

Hyderbad 11819 4 0.007 0.954 0.009 0.005 0.024 India-Kolkata 10115 1 0.086 0.806 0.088 0.012 0.008

India- India-Kolkata 10116 2 0.072 0.911 0.006 0.003 0.01

Hyderbad 11820 5 0.152 0.817 0.007 0.003 0.021 India-Kolkata 10117 2 0.283 0.621 0.076 0.007 0.014

India- India-Kolkata 10118 3 0.197 0.543 0.204 0.022 0.034

Hyderbad 11821 13 0.309 0.647 0.024 0.005 0.015 India-Kolkata 10119 3 0.035 0.73 0.093 0.096 0.046

India- Sri Lanka 8780 1 0.194 0.677 0.023 0.095 0.01

Hyderbad 11822 2 0.17 0.764 0.037 0.008 0.021 Sri Lanka 8781 1 0.18 0.657 0.037 0.117 0.009

India-Andhra 10159 2 0.313 0.638 0.025 0.012 0.012 Sri Lanka 8782 3 0.055 0.759 0.101 0.07 0.015

India-Andhra 10160 1 0.455 0.529 0.005 0.004 0.006 Sri Lanka 8783 0 0.014 0.827 0.108 0.041 0.01

India-Andhra 10161 2 0.051 0.922 0.005 0.008 0.014 Sri Lanka 8784 1 0.283 0.439 0.016 0.25 0.012

India-Andhra 10162 5 0.188 0.782 0.008 0.009 0.014 Sri Lanka 8785 10 0.033 0.768 0.015 0.151 0.033

India-Andhra 10163 0 0.122 0.86 0.005 0.005 0.009 Sri Lanka 8786 0 0.061 0.675 0.124 0.097 0.043

India-Andhra 10164 2 0.314 0.611 0.01 0.013 0.053 Sri Lanka 8787 0 0.006 0.747 0.011 0.229 0.007

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Sri Lanka 8788 4 0.061 0.697 0.077 0.076 0.088 Thailand 11720 4 0.982 0.008 0.004 0.002 0.004

Sri Lanka 8789 2 0.065 0.448 0.107 0.363 0.017 Vietnam 8844 4 0.845 0.137 0.004 0.007 0.007

Sri Lanka 8790 1 0.054 0.64 0.021 0.279 0.007 Vietnam 8845 3 0.694 0.269 0.014 0.007 0.016

Sri Lanka 8791 1 0.032 0.892 0.046 0.017 0.013 Vietnam 8846 3 0.545 0.408 0.031 0.012 0.004

Sri Lanka 8792 0 0.016 0.772 0.085 0.104 0.023 Vietnam 8847 10 0.933 0.021 0.009 0.029 0.008

Sri Lanka 8793 2 0.011 0.653 0.027 0.282 0.027 Vietnam 8848 6 0.778 0.187 0.017 0.013 0.005

Sri Lanka 8794 2 0.024 0.62 0.079 0.236 0.041 Vietnam 8849 2 0.87 0.054 0.057 0.015 0.005

Sri Lanka 8795 1 0.076 0.651 0.059 0.208 0.007 Vietnam 8850 1 0.735 0.022 0.014 0.222 0.007

Sri Lanka 8796 4 0.272 0.621 0.071 0.03 0.006 Vietnam 8851 2 0.713 0.088 0.067 0.031 0.1

Sri Lanka 8797 1 0.087 0.487 0.008 0.355 0.063 Vietnam 8852 13 0.721 0.067 0.157 0.043 0.011

Sri Lanka 8798 1 0.134 0.528 0.259 0.059 0.02 Vietnam 8853 3 0.605 0.134 0.201 0.055 0.006

Sri Lanka 8799 2 0.007 0.69 0.005 0.285 0.013 Vietnam 8854 5 0.739 0.048 0.039 0.05 0.125

Sri Lanka 8800 2 0.227 0.375 0.02 0.365 0.013 Vietnam 8855 2 0.738 0.021 0.046 0.19 0.005

Sri Lanka 8801 5 0.006 0.755 0.013 0.197 0.029 Vietnam 8856 2 0.634 0.164 0.185 0.01 0.008

Sri Lanka 8802 2 0.019 0.591 0.006 0.371 0.013 Vietnam 8857 3 0.35 0.608 0.018 0.015 0.009

Sri Lanka 8803 6 0.131 0.771 0.03 0.061 0.006 Vietnam 8858 2 0.673 0.084 0.175 0.062 0.006

Thailand 11688 6 0.965 0.018 0.004 0.01 0.003 Vietnam 8859 7 0.647 0.319 0.015 0.01 0.009

Thailand 11689 13 0.94 0.024 0.004 0.004 0.028 Vietnam 8860 2 0.784 0.051 0.114 0.04 0.01

Thailand 11691 27 0.959 0.019 0.008 0.008 0.005 Vietnam 8861 2 0.608 0.212 0.118 0.022 0.041

Thailand 11698 18 0.978 0.008 0.007 0.003 0.004 Vietnam 8862 2 0.414 0.55 0.016 0.012 0.009

Thailand 11702 10 0.977 0.012 0.004 0.004 0.003 Vietnam 8863 3 0.624 0.071 0.104 0.178 0.022

Thailand 11703 12 0.983 0.007 0.003 0.003 0.004 Taiwan 8681 4 0.326 0.005 0.005 0.658 0.005

Thailand 11705 21 0.853 0.107 0.027 0.005 0.009 Taiwan 8682 0 0.876 0.052 0.014 0.019 0.04

Thailand 11707 4 0.972 0.017 0.005 0.003 0.004 Taiwan 8683 3 0.7 0.03 0.071 0.152 0.047

Thailand 11708 18 0.897 0.055 0.028 0.012 0.009 Taiwan 8684 3 0.36 0.005 0.008 0.619 0.008

Thailand 11709 12 0.974 0.011 0.007 0.004 0.005 Taiwan 8685 2 0.898 0.033 0.011 0.054 0.004

Thailand 11710 9 0.974 0.01 0.008 0.004 0.004 Taiwan 8686 6 0.672 0.016 0.017 0.283 0.012

Thailand 11711 6 0.975 0.012 0.003 0.006 0.005 Taiwan 8687 5 0.801 0.035 0.1 0.026 0.038

Thailand 11714 6 0.883 0.09 0.01 0.006 0.012 Taiwan 8688 26 0.821 0.111 0.019 0.033 0.016

Thailand 11715 17 0.814 0.061 0.006 0.008 0.111 Taiwan 8689 5 0.715 0.023 0.035 0.064 0.163

Thailand 11717 15 0.877 0.1 0.005 0.003 0.015 Taiwan 8690 2 0.892 0.017 0.008 0.011 0.073

Thailand 11718 2 0.965 0.018 0.004 0.007 0.005 Taiwan 8691 16 0.776 0.137 0.023 0.053 0.011

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Taiwan 8692 3 0.922 0.018 0.012 0.034 0.013 Japan-Oita 11982 6 0.923 0.018 0.011 0.04 0.008

Taiwan 8693 2 0.807 0.033 0.015 0.134 0.011 Japan-Oita 11985 11 0.952 0.012 0.023 0.007 0.005

Taiwan 8694 1 0.541 0.02 0.005 0.428 0.007 Japan-Oita 11986 7 0.668 0.008 0.037 0.28 0.007

Taiwan 8695 1 0.837 0.01 0.063 0.086 0.005 Japan-

Taiwan 8696 11 0.01 0.005 0.005 0.976 0.003 Kanazawa 11929 7 0.003 0.004 0.004 0.984 0.004

Taiwan 8697 1 0.021 0.014 0.017 0.932 0.016 Japan-

Taiwan 8698 4 0.004 0.004 0.005 0.985 0.003 Kanazawa 11931 18 0.926 0.025 0.005 0.034 0.01

Taiwan 8699 6 0.493 0.244 0.056 0.08 0.128 Japan-

Taiwan 8700 2 0.659 0.031 0.054 0.241 0.015 Kanazawa 11932 6 0.772 0.009 0.006 0.207 0.007

Taiwan 8701 2 0.777 0.016 0.007 0.194 0.006 Japan-

Taiwan 8702 2 0.627 0.036 0.058 0.261 0.017 Kanazawa 11933 14 0.844 0.078 0.018 0.055 0.004

Taiwan 8703 2 0.012 0.012 0.014 0.958 0.005 Japan-

Taiwan 8704 1 0.843 0.032 0.104 0.016 0.005 Kanazawa 11934 4 0.88 0.004 0.006 0.108 0.002

Taiwan 8705 2 0.827 0.113 0.023 0.013 0.023 Japan-

Taiwan 8706 2 0.755 0.053 0.085 0.074 0.033 Kanazawa 11936 8 0.952 0.011 0.01 0.023 0.004

Taiwan 8707 5 0.844 0.015 0.014 0.022 0.105 Japan-

Taiwan 8708 3 0.01 0.007 0.005 0.974 0.004 Kanazawa 11937 23 0.957 0.008 0.008 0.023 0.005

Taiwan 8709 4 0.911 0.024 0.023 0.025 0.018 Japan-

Japan-Oita 11967 4 0.936 0.018 0.024 0.014 0.008 Kanazawa 11939 18 0.965 0.014 0.005 0.01 0.005

Japan-Oita 11968 5 0.741 0.045 0.022 0.188 0.005 Japan-

Japan-Oita 11969 5 0.849 0.009 0.05 0.085 0.007 Kanazawa 11940 12 0.97 0.007 0.007 0.006 0.011

Japan-Oita 11970 15 0.848 0.02 0.104 0.013 0.015 Japan-

Japan-Oita 11971 8 0.893 0.047 0.018 0.025 0.017 Kanazawa 11941 7 0.954 0.006 0.014 0.009 0.017

Japan-Oita 11972 3 0.945 0.014 0.02 0.015 0.006 Japan-

Japan-Oita 11973 3 0.716 0.009 0.008 0.26 0.008 Kanazawa 11942 9 0.956 0.031 0.005 0.004 0.004

Japan-Oita 11974 20 0.914 0.054 0.016 0.008 0.008 Japan-

Japan-Oita 11975 19 0.759 0.066 0.03 0.143 0.003 Kanazawa 11943 19 0.858 0.064 0.02 0.014 0.045

Japan-Oita 11976 16 0.843 0.007 0.01 0.133 0.006 Japan-

Japan-Oita 11977 5 0.924 0.013 0.011 0.045 0.007 Kanazawa 11944 13 0.035 0.104 0.072 0.762 0.026

Japan-Oita 11979 11 0.946 0.018 0.012 0.014 0.009 Japan-

Japan-Oita 11980 18 0.949 0.022 0.011 0.009 0.009 Kanazawa 11945 16 0.919 0.015 0.008 0.051 0.007

Japan-Oita 11981 16 0.985 0.006 0.003 0.002 0.004 Japan- 11946 15 0.98 0.007 0.006 0.005 0.003

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

Kanazawa Sapporo

Japan-Ohmiya 11947 4 0.841 0.008 0.074 0.073 0.005 Japan-

Japan-Ohmiya 11948 16 0.834 0.023 0.052 0.024 0.067 Sapporo 11918 12 0.65 0.083 0.016 0.238 0.012

Japan-Ohmiya 11951 18 0.778 0.057 0.031 0.081 0.053 Japan-

Japan-Ohmiya 11953 3 0.982 0.004 0.006 0.005 0.004 Sapporo 11921 5 0.923 0.047 0.01 0.005 0.015

Japan-Ohmiya 11954 3 0.763 0.009 0.01 0.208 0.01 Japan-

Japan-Ohmiya 11955 5 0.678 0.013 0.007 0.296 0.005 Sapporo 11922 5 0.795 0.165 0.008 0.024 0.008

Japan-Ohmiya 11956 3 0.964 0.017 0.008 0.007 0.005 Japan-

Japan-Ohmiya 11957 3 0.762 0.025 0.071 0.129 0.013 Sapporo 11923 7 0.938 0.028 0.008 0.021 0.005

Japan-Ohmiya 11959 6 0.862 0.014 0.07 0.047 0.006 Japan-

Japan-Ohmiya 11960 3 0.982 0.005 0.003 0.005 0.005 Sapporo 11924 5 0.401 0.011 0.009 0.574 0.005

Japan-Ohmiya 11961 3 0.962 0.008 0.015 0.01 0.005 Japan-

Japan-Ohmiya 11962 5 0.77 0.039 0.019 0.165 0.007 Sapporo 11925 9 0.972 0.016 0.005 0.003 0.004

Japan-Ohmiya 11963 3 0.85 0.031 0.01 0.102 0.007 Japan-

Japan-Ohmiya 11964 4 0.747 0.05 0.012 0.171 0.02 Sapporo 11926 10 0.579 0.009 0.152 0.25 0.011

Japan-Ohmiya 11965 4 0.718 0.246 0.022 0.008 0.005 China-Henan 8869 2 0.882 0.009 0.051 0.011 0.047

Japan-Ohmiya 11966 4 0.847 0.022 0.026 0.089 0.017 China-Henan 8870 1 0.981 0.005 0.004 0.003 0.008

Japan- China-Henan 8871 1 0.788 0.014 0.014 0.005 0.179

Sapporo 11907 6 0.929 0.011 0.016 0.028 0.015 China-Henan 8872 1 0.961 0.008 0.009 0.006 0.016

Japan- China-Henan 8873 2 0.953 0.005 0.011 0.004 0.027

Sapporo 11909 9 0.007 0.005 0.005 0.979 0.004 China-Henan 8874 8 0.845 0.028 0.005 0.003 0.12

Japan- China-Henan 8875 2 0.985 0.004 0.004 0.003 0.003

Sapporo 11911 15 0.582 0.008 0.008 0.392 0.009 China-Henan 8876 1 0.985 0.005 0.004 0.003 0.003

Japan- China-Henan 8877 2 0.817 0.061 0.025 0.023 0.074

Sapporo 11913 14 0.681 0.012 0.011 0.292 0.004 China-Henan 8878 3 0.952 0.007 0.03 0.004 0.007

Japan- China-Henan 8879 2 0.971 0.009 0.005 0.003 0.012

Sapporo 11914 6 0.761 0.027 0.017 0.189 0.006 China-Henan 8880 2 0.877 0.015 0.009 0.036 0.063

Japan- China-Henan 8881 2 0.836 0.013 0.053 0.003 0.095

Sapporo 11915 8 0.955 0.013 0.01 0.013 0.01 China-Henan 8882 2 0.906 0.034 0.023 0.014 0.022

Japan- China-Henan 8883 0 0.969 0.005 0.005 0.004 0.016

Sapporo 11916 6 0.011 0.013 0.046 0.926 0.003 China-Henan 8884 0 0.89 0.031 0.006 0.002 0.071

Japan- 11917 4 0.018 0.033 0.02 0.911 0.018 China-Henan 8885 1 0.92 0.019 0.021 0.012 0.028

Sampling ID Missing Population Sampling ID Missing Population

Location No. Data 1 2 3 4 5 Location No. Data 1 2 3 4 5

China-Henan 8886 1 0.97 0.004 0.009 0.004 0.013 South Korea 7692 1 0.851 0.033 0.064 0.023 0.028

China-Henan 8887 1 0.844 0.009 0.02 0.008 0.118 South Korea 7693 0 0.805 0.077 0.069 0.028 0.022

China-Henan 8888 1 0.948 0.01 0.008 0.01 0.024 South Korea 7694 2 0.981 0.008 0.005 0.002 0.004

South Korea 2769 4 0.972 0.003 0.008 0.004 0.013 South Korea 7695 9 0.695 0.009 0.229 0.051 0.015

South Korea 2772 19 0.97 0.014 0.005 0.004 0.007 South Korea 7696 2 0.974 0.004 0.008 0.006 0.008

South Korea 2775 9 0.97 0.007 0.007 0.006 0.011 South Korea 7697 3 0.899 0.009 0.016 0.039 0.037

South Korea 2776 23 0.922 0.042 0.024 0.007 0.006 South Korea 7698 6 0.82 0.013 0.143 0.01 0.014

South Korea 2779 1 0.967 0.01 0.005 0.006 0.012 South Korea 7699 2 0.952 0.004 0.025 0.01 0.009

South Korea 2784 2 0.959 0.011 0.009 0.004 0.017 South Korea 7700 6 0.957 0.01 0.01 0.006 0.017

South Korea 2785 1 0.892 0.007 0.009 0.007 0.085

South Korea 2786 2 0.986 0.003 0.005 0.004 0.002

South Korea 7671 1 0.897 0.004 0.038 0.053 0.007

South Korea 7672 0 0.954 0.027 0.009 0.004 0.006

South Korea 7673 1 0.862 0.004 0.066 0.009 0.059

South Korea 7674 1 0.727 0.028 0.086 0.114 0.045

South Korea 7675 3 0.981 0.004 0.006 0.005 0.004

South Korea 7676 3 0.907 0.009 0.019 0.053 0.012

South Korea 7677 14 0.162 0.011 0.034 0.613 0.18

South Korea 7678 1 0.938 0.006 0.01 0.036 0.01

South Korea 7679 2 0.969 0.005 0.011 0.005 0.011

South Korea 7680 8 0.799 0.008 0.061 0.118 0.013

South Korea 7681 4 0.909 0.004 0.067 0.009 0.012

South Korea 7682 1 0.977 0.006 0.009 0.004 0.004

South Korea 7683 2 0.975 0.009 0.004 0.003 0.009

South Korea 7684 2 0.436 0.019 0.083 0.436 0.026

South Korea 7685 8 0.901 0.039 0.029 0.017 0.013

South Korea 7686 3 0.903 0.007 0.023 0.056 0.011

South Korea 7687 5 0.846 0.017 0.02 0.113 0.004

South Korea 7688 11 0.535 0.005 0.006 0.45 0.003

South Korea 7689 2 0.957 0.006 0.007 0.008 0.022

South Korea 7690 2 0.902 0.027 0.041 0.023 0.007

South Korea 7691 3 0.973 0.009 0.007 0.005 0.005

Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

USA-NY 2547 1 0.8149 0.02 0.0204 0.0175 0.0471 0.0288 0.0087 0.0426

USA-NY 2559 0 0.5437 0.0368 0.1514 0.0059 0.0184 0.0713 0.05 0.1224

USA-NY 2568 0 0.965 0.0055 0.0054 0.0042 0.0058 0.007 0.0041 0.003

USA-NY 2569 1 0.9214 0.0148 0.0084 0.0146 0.0116 0.0116 0.0043 0.0133

USA-NY 2572 0 0.9093 0.0225 0.0219 0.0134 0.0058 0.0128 0.0043 0.01

USA-NY 2578 20 0.7478 0.076 0.0405 0.0482 0.0264 0.0316 0.0068 0.0227

USA-NY 2590 0 0.9116 0.0158 0.0259 0.0058 0.0066 0.0107 0.0132 0.0104

USA-NY 2591 1 0.9247 0.0181 0.0102 0.017 0.0111 0.009 0.0048 0.0051

USA-NY 2597 1 0.8567 0.0364 0.0148 0.0483 0.013 0.0119 0.008 0.0109

USA-MS 9971 2 0.899 0.0149 0.0137 0.0077 0.0168 0.0113 0.0212 0.0154

USA-MS 9972 2 0.7381 0.0906 0.039 0.0276 0.044 0.0422 0.01 0.0085

USA-MS 9974 2 0.6857 0.0156 0.0155 0.0066 0.0062 0.009 0.2562 0.0052

USA-MS 9977 4 0.9489 0.0096 0.0125 0.0053 0.0063 0.0054 0.0031 0.0089

USA-MS 9980 1 0.8555 0.0296 0.0143 0.0142 0.0263 0.0319 0.0177 0.0105

USA-MS 9983 1 0.9283 0.0123 0.0146 0.0164 0.01 0.0067 0.0031 0.0086

USA-MS 9985 2 0.683 0.0179 0.0106 0.0162 0.024 0.0111 0.2318 0.0054

USA-MS 9987 2 0.5845 0.1533 0.104 0.0077 0.0244 0.0302 0.0569 0.039

USA-MS 9989 2 0.9404 0.008 0.0105 0.0061 0.0053 0.0112 0.0113 0.0072

USA-MS 9992 2 0.8732 0.0381 0.0277 0.0305 0.0064 0.0104 0.0052 0.0085

USA-HI 5366 1 0.7618 0.0147 0.0289 0.0152 0.0118 0.0569 0.013 0.0977

USA-HI 5367 0 0.7171 0.0163 0.0098 0.0367 0.0951 0.0654 0.0198 0.0398

USA-HI 5371 0 0.8947 0.0277 0.0266 0.0112 0.0058 0.0148 0.0128 0.0064

USA-HI 5372 0 0.4323 0.3105 0.0857 0.0461 0.0248 0.0755 0.0139 0.0111

USA-HI 5379 0 0.8471 0.0419 0.032 0.0197 0.0097 0.0275 0.0062 0.0159

USA-HI 5380 1 0.952 0.0093 0.018 0.0031 0.0032 0.0054 0.0024 0.0066

USA-HI 5383 1 0.3502 0.054 0.0307 0.2709 0.1737 0.0285 0.0282 0.0639

USA-HI 5384 2 0.87 0.0266 0.0253 0.0211 0.0215 0.016 0.0051 0.0144

USA-HI 5401 1 0.9419 0.0095 0.0105 0.0071 0.0087 0.0091 0.0071 0.0061

USA-HI 5402 0 0.8481 0.0151 0.0136 0.008 0.0124 0.0436 0.0356 0.0236

Brazil 7961 0 0.8658 0.0254 0.0433 0.0113 0.0071 0.0212 0.0099 0.016

Brazil 7962 2 0.7395 0.0339 0.0842 0.0109 0.0262 0.0436 0.0336 0.028

Brazil 7963 15 0.5211 0.0423 0.1078 0.0069 0.0148 0.0649 0.213 0.0291

Brazil 7964 1 0.874 0.0134 0.0684 0.0095 0.0065 0.0122 0.0041 0.0119

Brazil 7965 18 0.9426 0.0072 0.0058 0.0064 0.0115 0.0075 0.0079 0.0111

Brazil 7966 1 0.8629 0.0244 0.0471 0.0104 0.0092 0.0298 0.0105 0.0057

Brazil 7968 22 0.7997 0.0217 0.0308 0.0207 0.0208 0.0175 0.0144 0.0744

Brazil 7969 0 0.9541 0.0086 0.0081 0.0058 0.0079 0.0069 0.0041 0.0045

Brazil 7970 0 0.9429 0.0073 0.0083 0.0071 0.0103 0.0077 0.0057 0.0107

Brazil 7971 0 0.9351 0.0138 0.0123 0.0134 0.0059 0.0056 0.006 0.0079

Brazil 7972 0 0.8774 0.037 0.0451 0.0063 0.0071 0.0157 0.0049 0.0065

Brazil 7973 0 0.921 0.0144 0.0116 0.0092 0.0076 0.0119 0.0076 0.0167

Brazil 7974 3 0.858 0.0185 0.0765 0.0068 0.0058 0.0124 0.0107 0.0113

Brazil 7975 3 0.907 0.012 0.0212 0.0134 0.0058 0.0132 0.0061 0.0213

Brazil 7976 0 0.9157 0.0133 0.0197 0.0105 0.0056 0.014 0.0076 0.0136

Brazil 7977 1 0.8937 0.0299 0.0357 0.0067 0.0053 0.0082 0.0027 0.0178

Brazil 7978 1 0.8036 0.0277 0.0283 0.0107 0.0443 0.019 0.0094 0.0569

Brazil 7979 1 0.8596 0.0299 0.0212 0.0118 0.012 0.0164 0.0305 0.0186

Brazil 7980 2 0.6096 0.0259 0.1856 0.0116 0.0943 0.0485 0.0048 0.0198

Brazil 7981 2 0.7936 0.0137 0.0128 0.0045 0.011 0.0078 0.0051 0.1515

Brazil 7982 15 0.6989 0.0203 0.0196 0.0111 0.0546 0.0793 0.0716 0.0446

Brazil 7983 1 0.6454 0.0622 0.1221 0.0347 0.0236 0.0401 0.0362 0.0356

Brazil 7984 6 0.6041 0.0144 0.0595 0.0065 0.0114 0.0157 0.2475 0.0409

Brazil 7985 2 0.7088 0.0774 0.0858 0.005 0.0175 0.0212 0.0299 0.0545

Brazil 7986 2 0.7468 0.0334 0.0551 0.0052 0.0135 0.0204 0.1129 0.0127

Brazil 7987 1 0.8607 0.0225 0.07 0.005 0.0063 0.0125 0.0127 0.0103

Brazil 7988 0 0.7416 0.0334 0.0174 0.0057 0.0215 0.0439 0.0439 0.0927 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Brazil 7989 0 0.5809 0.0098 0.0116 0.0082 0.0067 0.0126 0.3619 0.0083

Brazil 7990 0 0.7188 0.0135 0.013 0.0067 0.0065 0.012 0.22 0.0096

Finland 8077 8 0.8471 0.06 0.0289 0.017 0.0118 0.0194 0.0046 0.0111

Finland 8084 6 0.8304 0.0319 0.0287 0.0471 0.0208 0.0151 0.0035 0.0225

Finland 8086 4 0.8111 0.0661 0.0384 0.0153 0.0093 0.0213 0.019 0.0195

Finland 8089 4 0.5705 0.1316 0.0331 0.0403 0.0242 0.166 0.0085 0.0259

Finland 8093 16 0.8786 0.0208 0.0151 0.0226 0.0116 0.0188 0.0055 0.027

Finland 8094 3 0.8916 0.0249 0.0131 0.0211 0.0106 0.026 0.0041 0.0086

Finland 8096 3 0.9442 0.0119 0.0073 0.0187 0.0069 0.0045 0.0021 0.0044

Finland 8107 22 0.9514 0.0083 0.0062 0.0071 0.0119 0.0084 0.0021 0.0046

Finland 8110 5 0.9633 0.0077 0.007 0.0043 0.0047 0.0068 0.0031 0.0031

Finland 8116 8 0.7999 0.012 0.0255 0.0115 0.0848 0.0334 0.0146 0.0183

Finland 8120 19 0.9402 0.0122 0.0102 0.004 0.0083 0.0069 0.0071 0.0111

Germany 8711 2 0.891 0.0204 0.0373 0.0088 0.009 0.008 0.0044 0.0211

Germany 8712 3 0.9323 0.0166 0.0095 0.0157 0.0076 0.0089 0.0038 0.0056

Germany 8713 2 0.6576 0.0447 0.2334 0.0079 0.018 0.0198 0.0046 0.014

Germany 8714 2 0.6758 0.1335 0.0649 0.0423 0.0232 0.0372 0.0092 0.0138

Germany 8715 4 0.9043 0.0384 0.0117 0.0192 0.011 0.006 0.0031 0.0063

Germany 8716 4 0.9015 0.0213 0.0147 0.0122 0.0088 0.0135 0.0085 0.0195

Germany 8717 12 0.9087 0.0177 0.0199 0.0174 0.0061 0.0137 0.0061 0.0104

Germany 8720 2 0.2269 0.0804 0.04 0.0094 0.0096 0.0335 0.5953 0.0049

Germany 8721 10 0.9045 0.0397 0.023 0.0062 0.0066 0.0082 0.0059 0.0059

Germany 8727 0 0.9441 0.0077 0.0097 0.0057 0.008 0.0092 0.0043 0.0113

Germany 8728 10 0.8226 0.0286 0.026 0.0247 0.0617 0.0168 0.009 0.0107

Germany 8729 1 0.953 0.0086 0.0064 0.0076 0.007 0.0064 0.0045 0.0065

Germany 8730 22 0.7621 0.0083 0.0079 0.0101 0.0613 0.0139 0.0096 0.1268

Germany 8731 12 0.9541 0.007 0.007 0.0054 0.0124 0.0058 0.0021 0.0062

Germany 8732 3 0.8954 0.0342 0.0309 0.0086 0.0158 0.0076 0.0029 0.0046

Germany 8733 14 0.8288 0.033 0.0282 0.0301 0.0323 0.0167 0.0173 0.0136

Germany 8734 0 0.9398 0.0174 0.0135 0.0084 0.0072 0.0073 0.0027 0.0037

Germany 8735 0 0.853 0.0417 0.0469 0.0111 0.0048 0.0155 0.0112 0.0158

Germany 8736 0 0.9326 0.019 0.0249 0.004 0.0043 0.006 0.004 0.0052

Germany 8737 2 0.9447 0.01 0.0089 0.0093 0.0085 0.0086 0.0042 0.0058

Germany 8738 10 0.8424 0.0646 0.0574 0.0086 0.0088 0.0097 0.0031 0.0054

Germany 8739 0 0.9201 0.0206 0.0394 0.0049 0.0039 0.0055 0.0021 0.0035

Germany 8741 12 0.0993 0.1491 0.7016 0.0089 0.0123 0.0205 0.0031 0.0052

Germany 8742 10 0.8989 0.0282 0.0222 0.0063 0.0079 0.0193 0.0117 0.0055

Germany 8744 6 0.5045 0.1384 0.2209 0.0039 0.01 0.0523 0.0603 0.0098

Germany 8745 22 0.9016 0.0364 0.0197 0.0071 0.0116 0.0142 0.0032 0.0062

Germany 8746 0 0.8691 0.0171 0.0171 0.0118 0.0437 0.025 0.0095 0.0067

Germany 8747 12 0.9586 0.0065 0.0069 0.004 0.0084 0.0081 0.0041 0.0034

Germany 8749 0 0.9421 0.0094 0.0083 0.0098 0.0077 0.0103 0.005 0.0074

Italy-Milan 8050 0 0.602 0.203 0.0706 0.0627 0.0118 0.0239 0.0174 0.0087

Italy-Milan 8057 1 0.5543 0.14 0.0371 0.0592 0.0589 0.0641 0.0616 0.0247

Italy-Milan 8060 0 0.6467 0.053 0.0189 0.1859 0.0132 0.0147 0.0584 0.0092

Italy-Milan 8061 2 0.6295 0.0815 0.0415 0.0086 0.0065 0.0403 0.1821 0.01

Italy-Milan 8062 1 0.2173 0.6024 0.1243 0.0111 0.0142 0.0139 0.0121 0.0047

Italy-Milan 8065 4 0.2969 0.6124 0.0434 0.0103 0.0101 0.0181 0.0037 0.0051

Italy-Milan 8066 0 0.1381 0.6217 0.1306 0.0407 0.0104 0.019 0.0315 0.008

Italy-Milan 8067 2 0.4805 0.2641 0.133 0.0413 0.007 0.0244 0.0376 0.0122

Italy-Milan 8068 0 0.3707 0.4037 0.1242 0.0138 0.0367 0.0319 0.0078 0.0111

Italy-Milan 8069 0 0.5977 0.1327 0.0543 0.0159 0.0155 0.1351 0.0308 0.0179

Italy-Milan 8071 2 0.6873 0.1343 0.0781 0.0153 0.0114 0.0309 0.0108 0.0318

Italy-Milan 8072 2 0.2072 0.2507 0.1594 0.0083 0.0245 0.0491 0.199 0.1017

Italy-Milan 8073 0 0.6913 0.0349 0.1821 0.0146 0.0145 0.048 0.0041 0.0106

Italy-Milan 8074 1 0.7195 0.0239 0.0693 0.0058 0.0104 0.0327 0.1294 0.009 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Italy-Rome 8586 1 0.4396 0.1849 0.2332 0.0171 0.0406 0.0685 0.0044 0.0118

Italy-Rome 8589 2 0.9124 0.0137 0.0244 0.0094 0.0088 0.0145 0.0083 0.0085

Italy-Rome 8592 1 0.3862 0.2207 0.0434 0.0751 0.0496 0.1617 0.0073 0.056

Italy-Rome 8594 1 0.7447 0.1067 0.0561 0.0071 0.0201 0.021 0.0161 0.0281

Italy-Rome 8595 0 0.6873 0.051 0.1204 0.0247 0.0268 0.0388 0.0332 0.0177

Italy-Rome 8596 1 0.2942 0.1279 0.481 0.0122 0.0055 0.063 0.0075 0.0088

Italy-Rome 8597 0 0.7674 0.0273 0.0603 0.0181 0.0133 0.0584 0.0332 0.0219

Italy-Rome 8599 0 0.9274 0.0083 0.0134 0.0072 0.0111 0.0111 0.013 0.0085

Italy-Rome 8601 2 0.5432 0.2255 0.1248 0.0126 0.0187 0.0143 0.0186 0.0423

Italy-Rome 8602 2 0.7002 0.0579 0.0404 0.0131 0.012 0.1561 0.0116 0.0087

Italy-Rome 8603 0 0.8695 0.0332 0.0264 0.0085 0.0066 0.0116 0.0165 0.0277

Italy-Rome 8604 2 0.1576 0.2738 0.4345 0.0077 0.0203 0.0174 0.0043 0.0843

Italy-Rome 8609 1 0.8883 0.0327 0.0297 0.0066 0.0104 0.0189 0.0048 0.0085

Italy-Rome 8610 1 0.385 0.2904 0.1399 0.0329 0.0272 0.0516 0.0195 0.0536

Italy-Rome 8611 2 0.6267 0.1049 0.1002 0.0186 0.0145 0.1139 0.0129 0.0083

Turkey 6477 2 0.7494 0.0419 0.0268 0.1058 0.035 0.0163 0.0152 0.0095

Turkey 6478 0 0.0803 0.4344 0.1166 0.0208 0.0151 0.1532 0.0122 0.1676

Turkey 6480 0 0.0088 0.1331 0.6852 0.0481 0.0197 0.0672 0.0294 0.0086

Turkey 6481 2 0.5551 0.0603 0.0425 0.006 0.1769 0.0823 0.0593 0.0175

Turkey 6482 3 0.7276 0.018 0.0275 0.0091 0.0089 0.0662 0.1312 0.0116

Turkey 6484 4 0.3921 0.3362 0.1122 0.0164 0.039 0.0732 0.0233 0.0076

Turkey 6486 1 0.7103 0.0467 0.0446 0.0456 0.0815 0.0353 0.0171 0.0189

Turkey 6487 4 0.6456 0.0329 0.0191 0.034 0.1001 0.1411 0.0187 0.0085

Turkey 6488 4 0.7409 0.0323 0.0184 0.0645 0.0394 0.0483 0.0281 0.0281

Turkey 6491 2 0.35 0.1655 0.105 0.0115 0.3306 0.0154 0.0094 0.0126

Turkey 6494 6 0.4972 0.1157 0.0744 0.0423 0.0358 0.0921 0.1229 0.0194

Turkey 6496 4 0.6051 0.0426 0.0421 0.0345 0.0257 0.0234 0.2017 0.0249

Turkey 6499 2 0.6808 0.0265 0.0377 0.0148 0.0379 0.124 0.0683 0.0099

Turkey 6500 1 0.4518 0.0472 0.0307 0.0451 0.3319 0.0737 0.0139 0.0057

Turkey 6502 4 0.5497 0.0364 0.0171 0.0339 0.0523 0.2541 0.0238 0.0327

Turkey 6503 4 0.5737 0.0299 0.031 0.0083 0.3192 0.0205 0.0097 0.0077

Turkey 6507 2 0.2732 0.0622 0.0209 0.007 0.0667 0.4754 0.0265 0.068

Turkey 6510 2 0.7601 0.0349 0.039 0.063 0.0315 0.0368 0.0253 0.0093

Turkey 6512 2 0.202 0.1712 0.0851 0.0909 0.0248 0.2593 0.1215 0.0451

Turkey 6513 4 0.2724 0.0715 0.0486 0.0246 0.0906 0.3703 0.0736 0.0485

Turkey 6514 4 0.4091 0.0938 0.0246 0.0141 0.316 0.0498 0.0698 0.0229

Turkey 6516 2 0.3466 0.1908 0.0483 0.0129 0.0244 0.2886 0.0674 0.0211

Turkey 6519 2 0.3946 0.0957 0.0172 0.0326 0.0557 0.3361 0.0216 0.0464

Turkey 6520 3 0.4668 0.1244 0.0515 0.0792 0.1229 0.0814 0.0576 0.0161

Turkey 6521 2 0.7308 0.105 0.0422 0.02 0.019 0.0549 0.019 0.0091

Turkey 6729 0 0.4579 0.0228 0.0212 0.0354 0.3831 0.0485 0.0119 0.0192

Turkey 6730 1 0.7362 0.0366 0.0376 0.0105 0.0759 0.0175 0.0251 0.0606

Turkey 6731 2 0.6791 0.0206 0.0183 0.0125 0.0566 0.1118 0.0396 0.0614

Turkey 6732 1 0.6 0.0921 0.0549 0.0515 0.0191 0.1021 0.0248 0.0556

Turkey 6733 1 0.5933 0.0173 0.0308 0.0119 0.0382 0.262 0.0391 0.0074

Turkey 6734 1 0.1613 0.0897 0.0354 0.0074 0.1713 0.4542 0.0357 0.045

Turkey 6735 1 0.2061 0.058 0.0414 0.0124 0.0153 0.6383 0.0141 0.0144

Turkey 6736 2 0.7785 0.0174 0.022 0.0098 0.1306 0.0143 0.0091 0.0183

Turkey 6738 2 0.6074 0.0168 0.021 0.0468 0.0848 0.0218 0.1739 0.0275

Turkey 6739 0 0.6749 0.0327 0.0206 0.0164 0.1628 0.0481 0.0092 0.0353

Turkey 6740 5 0.5768 0.025 0.0172 0.0248 0.0369 0.0204 0.2859 0.013

Turkey 6741 7 0.1037 0.4046 0.4554 0.0117 0.0072 0.0101 0.003 0.0043

Turkey 6742 12 0.0268 0.1655 0.5709 0.0336 0.1129 0.04 0.0145 0.0359

Turkey 6743 11 0.0125 0.7056 0.2448 0.009 0.0066 0.0131 0.0041 0.0043

Turkey 6745 12 0.0191 0.036 0.023 0.0271 0.1568 0.2601 0.4483 0.0297

Turkey 6746 14 0.0255 0.3924 0.5028 0.0046 0.0463 0.0074 0.0033 0.0176 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Turkey 6748 7 0.0347 0.637 0.2716 0.0067 0.0156 0.0175 0.0082 0.0086

Turkey 6749 8 0.5575 0.2392 0.0994 0.0114 0.0476 0.027 0.0099 0.008

Turkey 6750 15 0.1611 0.4095 0.3553 0.0091 0.0127 0.0304 0.0048 0.0171

Turkey 6753 12 0.0288 0.405 0.4555 0.0392 0.0101 0.0286 0.0041 0.0287

Turkey 6754 15 0.0058 0.0079 0.0078 0.0338 0.5464 0.0173 0.3679 0.0131

Turkey 6755 11 0.1046 0.4561 0.2677 0.0321 0.0123 0.0745 0.0308 0.0219

Turkey 6756 12 0.0218 0.6947 0.193 0.0139 0.0191 0.0177 0.0153 0.0245

Turkey 6758 12 0.4593 0.3022 0.1461 0.0142 0.009 0.043 0.0089 0.0172

Turkey 6759 15 0.0881 0.6066 0.154 0.0211 0.0945 0.0106 0.004 0.0211

Turkey 6760 6 0.0683 0.3367 0.3902 0.0476 0.0468 0.0593 0.0115 0.0396

Cyprus 10128 1 0.0529 0.6925 0.2097 0.011 0.0054 0.017 0.0047 0.0068

Cyprus 10129 14 0.0154 0.7548 0.1735 0.0081 0.0113 0.0143 0.0067 0.016

Cyprus 10130 10 0.0153 0.1691 0.5096 0.012 0.1362 0.0238 0.1032 0.0309

Cyprus 10131 15 0.0505 0.7148 0.0468 0.1358 0.0149 0.0193 0.004 0.0139

Cyprus 10132 14 0.6346 0.1209 0.1012 0.0248 0.0249 0.061 0.0129 0.0195

Cyprus 10133 9 0.008 0.0136 0.0101 0.0093 0.0109 0.0109 0.9332 0.004

Cyprus 10134 12 0.9429 0.0101 0.0127 0.0065 0.0077 0.0083 0.0057 0.0061

Cyprus 10135 7 0.2713 0.4963 0.069 0.0471 0.0326 0.0256 0.0073 0.0508

Cyprus 10136 15 0.1118 0.3276 0.4577 0.0424 0.015 0.0291 0.0044 0.0119

Cyprus 10137 12 0.4979 0.375 0.0541 0.0105 0.0115 0.0244 0.0055 0.0211

Cyprus 10138 1 0.0484 0.0878 0.0213 0.5443 0.0482 0.1812 0.011 0.0578

Cyprus 10139 1 0.0171 0.5866 0.3635 0.0056 0.0112 0.0087 0.0032 0.0041

Cyprus 10140 2 0.1454 0.6382 0.1156 0.0224 0.0343 0.0245 0.0077 0.0119

Cyprus 10141 0 0.011 0.3811 0.4365 0.0197 0.0261 0.0685 0.0329 0.0243

Cyprus 10142 3 0.0155 0.69 0.21 0.014 0.0278 0.0197 0.0083 0.0147

Cyprus 10143 0 0.0095 0.6003 0.2972 0.0532 0.0097 0.0131 0.0102 0.0069

Cyprus 10144 2 0.011 0.5562 0.4086 0.0064 0.0047 0.0066 0.0027 0.0038

Cyprus 10145 1 0.1119 0.6666 0.1659 0.012 0.019 0.0147 0.0039 0.006

Cyprus 10146 2 0.0626 0.714 0.2033 0.0031 0.0062 0.0061 0.0018 0.0029

Cyprus 10147 2 0.2089 0.411 0.1097 0.0156 0.0224 0.2223 0.0046 0.0055

Cyprus 10148 0 0.9578 0.0103 0.0061 0.0056 0.0073 0.0055 0.003 0.0044

Cyprus 10149 0 0.0991 0.0105 0.0218 0.0139 0.0333 0.0126 0.7939 0.0149

Cyprus 10150 2 0.0999 0.7136 0.0788 0.0547 0.0123 0.0206 0.0083 0.0119

Cyprus 10151 1 0.1075 0.3023 0.5569 0.0056 0.0078 0.0101 0.0058 0.0041

Cyprus 10152 0 0.0256 0.4862 0.407 0.0229 0.0314 0.0089 0.0034 0.0147

Cyprus 10153 1 0.0887 0.1397 0.0314 0.0372 0.0548 0.4654 0.0749 0.1077

Cyprus 10154 1 0.0771 0.1913 0.1746 0.0214 0.3335 0.1737 0.0149 0.0134

Cyprus 10155 0 0.0231 0.1207 0.7555 0.0115 0.0318 0.0397 0.0085 0.0092

Cyprus 10156 3 0.2038 0.6156 0.1279 0.0083 0.0096 0.0162 0.009 0.0097

Cyprus 10157 0 0.2116 0.3524 0.3623 0.0318 0.0071 0.0152 0.0056 0.014

Lebanon 10235 2 0.1141 0.7295 0.0705 0.0221 0.0221 0.0106 0.0072 0.0239

Lebanon 10236 1 0.131 0.3684 0.4463 0.018 0.0122 0.0119 0.003 0.0092

Lebanon 10237 0 0.0692 0.6794 0.2001 0.0059 0.0161 0.0161 0.0044 0.0088

Lebanon 10238 1 0.0431 0.5284 0.3114 0.0057 0.0118 0.0724 0.0113 0.0159

Lebanon 10239 2 0.3051 0.3118 0.2237 0.0234 0.0639 0.0542 0.0051 0.0129

Lebanon 10240 1 0.3692 0.5016 0.0435 0.0372 0.0281 0.0076 0.0033 0.0095

Lebanon 10241 3 0.029 0.1906 0.5794 0.0148 0.0082 0.1375 0.0108 0.0297

Lebanon 10242 0 0.5443 0.1975 0.0467 0.0775 0.0139 0.0505 0.0271 0.0425

Lebanon 10243 2 0.0346 0.2644 0.6365 0.0048 0.0071 0.0436 0.0039 0.0052

Lebanon 10244 1 0.2191 0.2947 0.1575 0.018 0.0127 0.0572 0.0227 0.218

Lebanon 10245 0 0.0041 0.3287 0.4552 0.0401 0.0217 0.0702 0.0564 0.0237

Lebanon 10246 0 0.0761 0.1966 0.5194 0.0081 0.0108 0.0996 0.0665 0.0229

Lebanon 10247 0 0.1617 0.422 0.3287 0.0142 0.0243 0.0291 0.0079 0.0121

Lebanon 10248 2 0.0695 0.3312 0.3294 0.0517 0.129 0.0701 0.0078 0.0113

Lebanon 10249 2 0.2897 0.351 0.136 0.1631 0.0205 0.0223 0.0041 0.0133

Lebanon 10250 1 0.0175 0.6093 0.3216 0.0079 0.0056 0.0276 0.0046 0.0059 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Lebanon 10251 2 0.0074 0.4529 0.4552 0.0124 0.0128 0.022 0.0275 0.0098

Lebanon 10252 3 0.0315 0.531 0.3055 0.0125 0.0184 0.0215 0.022 0.0576

Lebanon 10253 0 0.0272 0.4327 0.4627 0.0071 0.0093 0.022 0.0125 0.0265

Lebanon 10254 0 0.013 0.4075 0.5161 0.0055 0.0076 0.0395 0.0041 0.0067

Lebanon 10255 1 0.0163 0.7811 0.1209 0.0242 0.0105 0.0153 0.0235 0.0082

Lebanon 10256 0 0.0067 0.6663 0.2554 0.007 0.008 0.0226 0.0066 0.0274

Lebanon 10257 2 0.0156 0.3061 0.2404 0.0953 0.1756 0.1349 0.0158 0.0162

Lebanon 10258 1 0.0441 0.2025 0.5791 0.0117 0.0319 0.1051 0.0089 0.0167

Lebanon 10259 0 0.0136 0.1684 0.5525 0.1331 0.0783 0.0418 0.0031 0.0092

Lebanon 10260 1 0.0325 0.7087 0.1834 0.0113 0.0249 0.0138 0.005 0.0204

Lebanon 10261 0 0.0256 0.4343 0.4421 0.0176 0.0225 0.0318 0.0139 0.0121

Lebanon 10262 2 0.0174 0.1123 0.6791 0.0286 0.0205 0.1162 0.0173 0.0086

Lebanon 10263 0 0.0822 0.5951 0.2266 0.0146 0.0099 0.0179 0.0154 0.0383

Lebanon 10264 2 0.0098 0.1393 0.6933 0.0094 0.0194 0.1066 0.0096 0.0126

Lebanon 10265 2 0.2518 0.5535 0.1258 0.0343 0.0118 0.0121 0.0031 0.0075

Lebanon 10266 1 0.0121 0.6189 0.2274 0.0184 0.0179 0.0114 0.0036 0.0903

Lebanon 10267 2 0.0145 0.0925 0.7665 0.01 0.008 0.0768 0.02 0.0117

Lebanon 10268 2 0.0086 0.7006 0.2164 0.007 0.0246 0.0201 0.0127 0.01

Lebanon 10270 5 0.0078 0.2784 0.5489 0.0671 0.0104 0.0696 0.0105 0.0073

Lebanon 10271 2 0.0592 0.1565 0.4237 0.2265 0.0317 0.0822 0.0117 0.0085

Lebanon 10273 21 0.0179 0.2355 0.3902 0.0298 0.0393 0.2248 0.0227 0.0397

Lebanon 10274 25 0.0083 0.1596 0.3479 0.011 0.1331 0.2895 0.0055 0.0451

Lebanon 10276 19 0.0122 0.4411 0.2162 0.0271 0.0447 0.0813 0.0266 0.1508

Lebanon 10277 2 0.2434 0.1635 0.3474 0.0265 0.0294 0.1519 0.016 0.0219

Lebanon 10278 16 0.0202 0.5363 0.2975 0.0299 0.0435 0.057 0.0053 0.0102

Lebanon 10279 20 0.0225 0.4778 0.3676 0.0301 0.0219 0.0557 0.0103 0.0141

Lebanon 10280 5 0.0196 0.3098 0.3519 0.1155 0.0146 0.0971 0.0159 0.0755

Lebanon 10281 0 0.0866 0.43 0.3027 0.0157 0.0121 0.0371 0.0903 0.0254

Lebanon 10282 1 0.0189 0.6503 0.1991 0.0636 0.0129 0.0302 0.01 0.0149

Lebanon 10283 18 0.0201 0.2162 0.4903 0.0206 0.0243 0.1927 0.0096 0.0263

Lebanon 10284 6 0.0176 0.6474 0.1881 0.0504 0.0498 0.0254 0.0124 0.0089

Lebanon 10285 18 0.1226 0.3423 0.3331 0.0904 0.009 0.0365 0.0479 0.0183

Lebanon 10286 26 0.0077 0.2736 0.5223 0.0707 0.0479 0.0337 0.019 0.0251

Lebanon 10287 22 0.0129 0.2714 0.2604 0.02 0.334 0.0452 0.0188 0.0372

Lebanon 10288 18 0.2136 0.5667 0.1054 0.0121 0.0206 0.0586 0.0122 0.0108

Lebanon 10289 27 0.0223 0.6432 0.267 0.0126 0.0128 0.0268 0.0061 0.0093

Lebanon 10290 4 0.0102 0.6603 0.1905 0.0261 0.0335 0.0558 0.0079 0.0156

Lebanon 10291 14 0.1626 0.2685 0.1163 0.2976 0.0265 0.0856 0.027 0.0158

Lebanon 10292 4 0.0066 0.0454 0.0192 0.1971 0.65 0.0187 0.0473 0.0157

Lebanon 10294 13 0.0168 0.5621 0.2948 0.0095 0.0255 0.0324 0.0068 0.0521

Lebanon 10295 7 0.0554 0.2153 0.4258 0.1033 0.0102 0.096 0.0685 0.0255

Lebanon 10297 10 0.0786 0.3293 0.1791 0.156 0.1776 0.0507 0.0119 0.0168

Lebanon 10298 3 0.3815 0.0979 0.3208 0.0101 0.0094 0.0492 0.1168 0.0143

Lebanon 10299 19 0.0086 0.2198 0.4538 0.0589 0.0289 0.0341 0.0202 0.1758

Lebanon 10300 0 0.0235 0.0561 0.3501 0.0066 0.012 0.021 0.4725 0.0582

Israel 4962 17 0.1057 0.4387 0.3541 0.0149 0.0081 0.0578 0.0113 0.0094

Israel 4963 14 0.0091 0.4248 0.4223 0.014 0.0071 0.0206 0.0623 0.0398

Israel 4964 22 0.0062 0.6755 0.2503 0.0322 0.009 0.0094 0.0061 0.0113

Israel 4966 2 0.0324 0.2986 0.4524 0.0784 0.0601 0.051 0.0117 0.0153

Israel 4967 3 0.0829 0.5028 0.2313 0.0468 0.0219 0.0598 0.0133 0.0413

Israel 4968 10 0.0151 0.0844 0.7741 0.0154 0.0114 0.0789 0.0096 0.0111

Israel 4969 5 0.0059 0.2667 0.5593 0.0108 0.0091 0.0928 0.0261 0.0293

Israel 4970 12 0.8446 0.0281 0.0221 0.0303 0.0512 0.009 0.0076 0.0071

Israel 4971 17 0.0191 0.1511 0.6555 0.0163 0.0361 0.0249 0.0421 0.0549

Israel 4972 10 0.1452 0.183 0.2077 0.015 0.0226 0.0246 0.3872 0.0148

Israel 4973 1 0.0109 0.1992 0.4756 0.0889 0.0718 0.0411 0.0899 0.0226 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Israel 4974 4 0.0353 0.3558 0.3084 0.0231 0.0086 0.0229 0.045 0.2008

Israel 4975 3 0.0846 0.1692 0.6612 0.0056 0.0122 0.0406 0.0077 0.019

Israel 4976 4 0.0101 0.6943 0.2099 0.0369 0.0266 0.0095 0.0043 0.0085

Israel 4977 4 0.1199 0.4947 0.1893 0.0186 0.0077 0.0857 0.0299 0.0542

Israel 4978 3 0.0138 0.1679 0.6089 0.1524 0.0127 0.0187 0.0057 0.0198

Israel 4979 2 0.0249 0.6712 0.1467 0.0868 0.0192 0.0267 0.0078 0.0167

Israel 4980 2 0.0254 0.1236 0.6685 0.0217 0.0292 0.0681 0.0209 0.0426

Israel 4981 7 0.0324 0.6631 0.2448 0.0095 0.0177 0.0128 0.0064 0.0133

Israel 4982 2 0.0311 0.1899 0.5462 0.0155 0.0111 0.0664 0.066 0.0738

Israel 4983 16 0.0086 0.0816 0.7355 0.02 0.063 0.0747 0.0054 0.0113

Israel 4984 14 0.0372 0.0939 0.6094 0.1373 0.0102 0.0654 0.0164 0.0303

Israel 4985 1 0.0111 0.0972 0.7576 0.0152 0.0359 0.0681 0.0118 0.0031

Israel 4986 3 0.1134 0.2036 0.1339 0.0376 0.0307 0.2596 0.086 0.1351

Israel 4988 1 0.2149 0.0893 0.5612 0.0422 0.0088 0.0399 0.0102 0.0336

Israel 4989 25 0.2301 0.2447 0.1459 0.0268 0.0329 0.1 0.0166 0.2031

Israel 4990 2 0.0083 0.6561 0.2341 0.0357 0.0149 0.021 0.0095 0.0204

Israel 4992 3 0.0203 0.422 0.3707 0.039 0.0174 0.0941 0.0133 0.0232

Israel 4993 8 0.0377 0.3123 0.3383 0.038 0.1205 0.1062 0.0146 0.0323

Israel 4994 6 0.0371 0.4967 0.1587 0.0202 0.14 0.1128 0.0268 0.0076

Israel 4995 9 0.0151 0.4571 0.364 0.0107 0.0117 0.0213 0.0821 0.038

Israel 4996 6 0.0611 0.1476 0.4386 0.0629 0.013 0.1088 0.092 0.076

Israel 4997 5 0.0482 0.1031 0.4904 0.1692 0.0275 0.1271 0.0174 0.0171

Israel 4998 7 0.0124 0.0487 0.7184 0.0374 0.0448 0.1138 0.0077 0.0169

Israel 5000 20 0.0857 0.1338 0.5661 0.0637 0.0303 0.0858 0.0204 0.0142

Israel 5001 25 0.0256 0.4044 0.3805 0.0392 0.0425 0.0704 0.0173 0.0202

Israel 5002 0 0.1693 0.1751 0.2846 0.0633 0.2199 0.0534 0.0054 0.029

Israel 5003 0 0.0578 0.2144 0.3998 0.011 0.0883 0.2096 0.005 0.0141

Israel 5004 0 0.0088 0.2362 0.4943 0.1704 0.0097 0.0501 0.0172 0.0132

Israel 5005 2 0.0187 0.1387 0.7404 0.0098 0.0382 0.0349 0.0084 0.0109

Israel 5006 1 0.1279 0.4641 0.1387 0.0929 0.1334 0.0243 0.0116 0.0072

Israel 5007 2 0.0073 0.0527 0.7963 0.0172 0.0085 0.086 0.012 0.02

Israel 5008 1 0.6522 0.0283 0.0259 0.019 0.0133 0.0824 0.102 0.0769

Israel 5009 0 0.0301 0.3734 0.505 0.0155 0.0111 0.0265 0.0166 0.0218

Israel 5010 0 0.0104 0.3837 0.5676 0.0137 0.0068 0.0068 0.005 0.006

Israel 5011 0 0.0442 0.2644 0.4944 0.0695 0.0179 0.0141 0.0126 0.083

Egypt-Cairo 8190 2 0.0229 0.3802 0.3973 0.018 0.1106 0.0319 0.0131 0.026

Egypt-Cairo 8192 1 0.0234 0.2141 0.5805 0.0239 0.0155 0.1101 0.0097 0.0228

Egypt-Cairo 8193 0 0.0402 0.5017 0.3644 0.0209 0.0121 0.035 0.012 0.0137

Egypt-Cairo 8196 0 0.0628 0.1656 0.4636 0.1446 0.0721 0.0291 0.0314 0.0307

Egypt-Cairo 8203 0 0.2673 0.175 0.2104 0.169 0.0268 0.132 0.0099 0.0095

Egypt-Cairo 8215 1 0.0156 0.3212 0.3801 0.0625 0.1323 0.0383 0.0126 0.0373

Egypt-Cairo 8198 1 0.0215 0.3737 0.2784 0.1419 0.1073 0.026 0.017 0.0341

Egypt-Cairo 8194 1 0.1308 0.3771 0.3105 0.015 0.1322 0.0195 0.0063 0.0086

Egypt-Cairo 8211 2 0.0612 0.0647 0.1698 0.0381 0.0099 0.0956 0.0255 0.5353

Egypt-Cairo 8216 3 0.0196 0.058 0.5911 0.0189 0.0144 0.2778 0.0117 0.0085

Egypt-Cairo 8195 0 0.0451 0.3903 0.2232 0.0341 0.116 0.0949 0.06 0.0363

Egypt-Cairo 8199 2 0.083 0.5569 0.1061 0.0628 0.1049 0.0495 0.0243 0.0126

Egypt-Cairo 8200 16 0.0533 0.1033 0.7252 0.0127 0.0073 0.0533 0.0084 0.0364

Egypt-Cairo 8201 1 0.0246 0.3423 0.352 0.0326 0.0172 0.1288 0.0899 0.0126

Egypt-Cairo 8202 0 0.0434 0.1732 0.5444 0.0133 0.0163 0.1345 0.0449 0.03

Egypt-Cairo 8204 4 0.0525 0.2221 0.4149 0.0127 0.0049 0.0999 0.0801 0.1129

Egypt-Cairo 8208 2 0.0207 0.0595 0.321 0.0153 0.015 0.139 0.1903 0.2391

Egypt-Cairo 8210 6 0.0249 0.4847 0.185 0.1692 0.0968 0.0153 0.0043 0.0199

Egypt-Cairo 8214 10 0.0194 0.3837 0.4908 0.0224 0.0316 0.0164 0.0181 0.0176

Egypt-Cairo 8191 2 0.0068 0.6047 0.3252 0.0076 0.0098 0.0347 0.0052 0.006

Egypt-Cairo 8197 1 0.0602 0.1212 0.4084 0.0076 0.0053 0.0931 0.113 0.1912 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Egypt-Cairo 8205 0 0.0127 0.2425 0.5066 0.0131 0.0063 0.0902 0.007 0.1216

Egypt-Cairo 8206 0 0.0198 0.1813 0.4052 0.0195 0.0065 0.1075 0.1409 0.1194

Egypt-Cairo 8207 0 0.1309 0.2393 0.2543 0.2481 0.0459 0.0593 0.0089 0.0132

Egypt-Cairo 8209 1 0.0111 0.5614 0.3539 0.0156 0.0103 0.03 0.0065 0.0112

Egypt-Cairo 8212 1 0.2888 0.1058 0.4111 0.0079 0.0376 0.0954 0.02 0.0334

Egypt-Cairo 8213 0 0.0286 0.4116 0.3308 0.0053 0.0328 0.1381 0.0377 0.0151

Egypt-Cairo 9942 1 0.2027 0.1753 0.0885 0.1089 0.0375 0.1801 0.1496 0.0574

Egypt-Cairo 9943 0 0.0094 0.078 0.567 0.1313 0.0198 0.0831 0.0815 0.03

Egypt-Cairo 9944 0 0.0965 0.5944 0.225 0.0099 0.0247 0.0307 0.0045 0.0143

Egypt-Cairo 9945 1 0.0506 0.0897 0.655 0.0677 0.0131 0.0945 0.0199 0.0094

Egypt-Cairo 9946 1 0.0132 0.3816 0.3723 0.0148 0.0542 0.0428 0.0532 0.0678

Egypt-Cairo 9947 0 0.1948 0.2128 0.3182 0.0204 0.1216 0.0341 0.063 0.0351

Egypt-Cairo 9948 0 0.0145 0.3067 0.2668 0.0735 0.0161 0.0392 0.2709 0.0124

Egypt-Cairo 9949 1 0.0135 0.7171 0.2183 0.0157 0.0112 0.016 0.0035 0.0047

Egypt-Cairo 9950 0 0.0068 0.2971 0.5737 0.013 0.008 0.064 0.0095 0.0279

Egypt-Cairo 9951 0 0.0175 0.0427 0.7607 0.0215 0.006 0.1108 0.0208 0.02

Egypt-Cairo 9952 2 0.066 0.611 0.2515 0.0089 0.0253 0.0236 0.0067 0.007

Egypt-Cairo 9953 0 0.2966 0.0623 0.485 0.0529 0.0145 0.0446 0.0209 0.0233

Egypt-Cairo 9954 1 0.0129 0.3399 0.4646 0.0399 0.0519 0.0433 0.0196 0.028

Egypt-Cairo 9955 1 0.0111 0.6656 0.28 0.0086 0.0068 0.0179 0.0048 0.0052

Egypt-Cairo 9956 1 0.0357 0.4529 0.1909 0.03 0.0188 0.2412 0.0107 0.0198

Egypt-Cairo 9957 2 0.0117 0.1107 0.7133 0.0175 0.0103 0.1064 0.0205 0.0096

Egypt-Cairo 9958 0 0.0325 0.0562 0.7217 0.0261 0.0109 0.1188 0.0214 0.0124

Egypt-Cairo 9959 10 0.0144 0.157 0.7767 0.0081 0.0081 0.0258 0.0034 0.0065

Egypt-Cairo 9960 1 0.0165 0.0877 0.7731 0.0106 0.0113 0.0271 0.0687 0.0049

Egypt-Cairo 9961 1 0.0943 0.0716 0.7391 0.0127 0.0355 0.0323 0.0051 0.0095

Egypt-Cairo 9962 9 0.0259 0.465 0.3561 0.0105 0.0193 0.0979 0.0056 0.0197

Egypt-Cairo 9963 16 0.0193 0.0765 0.8057 0.0117 0.0081 0.0601 0.0094 0.0091

Egypt-Cairo 9964 0 0.1236 0.1321 0.5508 0.0213 0.0188 0.0839 0.0618 0.0077

Egypt-Cairo 10021 2 0.0135 0.1514 0.0451 0.0527 0.0406 0.1013 0.57 0.0253

Egypt-Cairo 10022 2 0.0122 0.1959 0.6576 0.005 0.0091 0.1049 0.0041 0.0112

Egypt-Cairo 10023 0 0.005 0.0735 0.778 0.0616 0.0101 0.0563 0.01 0.0054

Egypt-Cairo 10024 3 0.0169 0.4188 0.3592 0.0376 0.0317 0.0431 0.0067 0.0859

Egypt-Cairo 10025 2 0.0198 0.5359 0.1328 0.035 0.0093 0.1445 0.1099 0.0128

Egypt-Cairo 10026 1 0.009 0.6837 0.2687 0.0071 0.0049 0.0138 0.0061 0.0067

Egypt-Cairo 10027 0 0.0147 0.2904 0.6285 0.0065 0.0196 0.0159 0.0194 0.0051

Egypt-Cairo 10028 0 0.0051 0.232 0.5443 0.1234 0.0096 0.0457 0.0042 0.0357

Egypt-Cairo 10029 1 0.0041 0.3348 0.5404 0.0218 0.0103 0.0487 0.0144 0.0256

Egypt-Cairo 10030 2 0.0092 0.0484 0.7438 0.0126 0.0088 0.1259 0.0372 0.0141

Egypt-Cairo 10031 1 0.0057 0.3257 0.5443 0.037 0.0064 0.0623 0.0064 0.0122

Egypt-Cairo 10032 0 0.0187 0.4441 0.3494 0.0245 0.0217 0.0899 0.045 0.0068

Egypt-Cairo 10033 0 0.0185 0.4404 0.3175 0.0151 0.0204 0.16 0.0194 0.0088

Egypt-Cairo 10034 3 0.0137 0.0783 0.8012 0.013 0.0476 0.0211 0.0067 0.0184

Egypt-Cairo 10035 0 0.1083 0.0724 0.4031 0.0059 0.008 0.0392 0.3585 0.0047

Egypt-Cairo 10037 4 0.5388 0.026 0.0306 0.1271 0.0983 0.1358 0.0206 0.0228

Egypt-Cairo 10042 0 0.0041 0.0523 0.7491 0.0206 0.0284 0.1233 0.0133 0.0089

Egypt-Cairo 10043 0 0.0141 0.268 0.65 0.008 0.0083 0.0268 0.0099 0.0148

Egypt-Cairo 10044 2 0.0069 0.2254 0.5655 0.0438 0.1039 0.0325 0.0138 0.0082

Egypt-Cairo 10045 0 0.0585 0.5482 0.3471 0.0091 0.005 0.0189 0.0084 0.0049

Egypt-Cairo 10046 0 0.0134 0.1061 0.591 0.009 0.0083 0.1383 0.1093 0.0246

Egypt-Cairo 10047 1 0.0273 0.0925 0.8261 0.0058 0.0048 0.0342 0.0033 0.0061

Egypt-Cairo 10048 2 0.01 0.1433 0.6051 0.0463 0.0112 0.1426 0.0303 0.0112

Egypt-Cairo 10083 1 0.0043 0.2661 0.478 0.1238 0.0798 0.0342 0.0049 0.0089

Egypt-Cairo 10040 4 0.6381 0.0808 0.0771 0.0312 0.0114 0.0909 0.0394 0.031

Egypt-Cairo 10041 0 0.0085 0.0254 0.4525 0.0067 0.0123 0.045 0.4357 0.014

Egypt-Cairo 10049 3 0.1062 0.4291 0.2275 0.0175 0.0533 0.1448 0.0119 0.0097 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Egypt-Cairo 10084 5 0.0095 0.0359 0.6399 0.0065 0.0182 0.0409 0.1638 0.0853

Egypt-Cairo 10085 1 0.1073 0.3931 0.2318 0.1681 0.024 0.0328 0.0105 0.0324

Egypt-Cairo 10087 1 0.0108 0.1416 0.6616 0.0217 0.0101 0.0382 0.1055 0.0105

Egypt-Cairo 10090 2 0.0123 0.0163 0.0292 0.0152 0.0378 0.8679 0.0111 0.0102

Egypt-Cairo 9968 1 0.0063 0.0512 0.8014 0.018 0.0099 0.0823 0.0203 0.0106

Egypt-Asuit 10091 5 0.0129 0.1401 0.2491 0.0189 0.0152 0.0851 0.388 0.0908

Egypt-Asuit 10093 2 0.0676 0.021 0.8179 0.0051 0.0199 0.0263 0.0338 0.0085

Egypt-Asuit 10094 4 0.015 0.0678 0.8376 0.0111 0.0079 0.0307 0.0109 0.0191

Egypt-Asuit 10095 2 0.0176 0.491 0.2695 0.0278 0.0364 0.0882 0.0542 0.0152

Egypt-Asuit 10096 8 0.0125 0.0991 0.8423 0.0092 0.012 0.0125 0.0078 0.0045

Egypt-Asuit 10098 1 0.0062 0.0283 0.9124 0.0137 0.0096 0.0158 0.0062 0.0078

Egypt-Asuit 10099 0 0.0057 0.5676 0.3607 0.0357 0.0065 0.0125 0.0063 0.005

Egypt-Asuit 10100 2 0.0065 0.0379 0.9349 0.0037 0.0032 0.0082 0.0024 0.0032

Egypt-Asuit 10101 2 0.011 0.4772 0.3538 0.0563 0.0174 0.0473 0.0131 0.024

Egypt-Asuit 10102 21 0.0412 0.0342 0.8202 0.0075 0.0062 0.0494 0.0104 0.0309

Egypt-Luxor 10038 1 0.0095 0.1167 0.8346 0.0083 0.0055 0.0114 0.0069 0.0071

Egypt-Luxor 10039 1 0.0339 0.5228 0.4006 0.01 0.0098 0.0112 0.0041 0.0076

Egypt-Luxor 10050 4 0.0158 0.3732 0.5702 0.011 0.0076 0.0102 0.0057 0.0063

Egypt-Luxor 10051 1 0.0052 0.0362 0.9234 0.0064 0.0043 0.0137 0.0048 0.006

Egypt-Luxor 10052 3 0.0067 0.0409 0.9124 0.0063 0.0075 0.0106 0.0054 0.0102

Egypt-Luxor 10053 0 0.0124 0.3867 0.5491 0.0155 0.0093 0.0133 0.0065 0.0072

Egypt-Luxor 10054 1 0.0059 0.568 0.358 0.0358 0.0066 0.0136 0.007 0.0051

Egypt-Luxor 10055 1 0.0269 0.451 0.4258 0.01 0.0132 0.0582 0.0057 0.0092

Egypt-Luxor 10056 4 0.0147 0.3087 0.4245 0.0779 0.012 0.1329 0.0075 0.0218

Egypt-Luxor 10057 5 0.2909 0.0629 0.5261 0.0169 0.0193 0.061 0.0059 0.017

Egypt-Luxor 10058 1 0.0606 0.0554 0.7784 0.0078 0.0368 0.0273 0.0109 0.0228

Egypt-Luxor 10060 1 0.0241 0.13 0.7326 0.0325 0.0454 0.021 0.0072 0.0072

Egypt-Luxor 10061 4 0.016 0.2821 0.5926 0.0196 0.0094 0.0149 0.0101 0.0553

Egypt-Luxor 10062 1 0.0528 0.5586 0.3523 0.0058 0.0063 0.0149 0.0037 0.0055

Egypt-Luxor 10063 0 0.0102 0.3006 0.6085 0.0177 0.0096 0.0312 0.0099 0.0123

Egypt-Luxor 10064 0 0.0064 0.0237 0.9192 0.013 0.0083 0.0162 0.0068 0.0064

Egypt-Luxor 10065 1 0.0072 0.4304 0.3769 0.0263 0.0203 0.0625 0.0287 0.0477

Egypt-Luxor 10066 25 0.0228 0.1488 0.7166 0.0218 0.0294 0.0103 0.0055 0.0449

Egypt-Luxor 10067 3 0.0104 0.0919 0.7963 0.02 0.0112 0.0471 0.0078 0.0154

Egypt-Luxor 10068 1 0.0053 0.3155 0.4241 0.0822 0.0534 0.0914 0.0058 0.0223

Egypt-Luxor 10069 10 0.0136 0.394 0.2434 0.0086 0.0143 0.3002 0.0133 0.0126

Egypt-Luxor 10070 8 0.0386 0.0718 0.8112 0.0063 0.0337 0.0091 0.0164 0.0129

Egypt-Luxor 10071 18 0.0218 0.2886 0.6104 0.0046 0.0379 0.0169 0.0153 0.0046

Egypt-Luxor 10072 4 0.057 0.115 0.7388 0.0104 0.0559 0.0116 0.0031 0.0082

Egypt-Luxor 10073 3 0.046 0.0773 0.6397 0.0144 0.1639 0.0411 0.0107 0.007

Egypt-Luxor 10074 0 0.0141 0.0676 0.6902 0.0105 0.0886 0.0962 0.0052 0.0276

Egypt-Luxor 10079 1 0.01 0.0313 0.8109 0.0064 0.0078 0.1212 0.0041 0.0083

Egypt-Luxor 10080 4 0.0202 0.0154 0.9187 0.0041 0.0043 0.0279 0.0035 0.0059

Egypt-Abu Simbel 10076 4 0.0226 0.0287 0.8049 0.0065 0.0137 0.1095 0.0052 0.0089

Egypt-Abu Simbel 10077 16 0.0834 0.0704 0.7314 0.0072 0.0141 0.0732 0.0097 0.0107

Egypt-Abu Simbel 10081 2 0.0147 0.0333 0.7654 0.0052 0.0132 0.1181 0.0074 0.0427

Egypt-Abu Simbel 10089 2 0.0313 0.0115 0.9013 0.0041 0.0061 0.0382 0.0033 0.0041

Egypt-Abu Simbel 10092 0 0.0109 0.0178 0.9023 0.0066 0.0115 0.0394 0.003 0.0085

Iraq-West 9587 2 0.0806 0.076 0.6246 0.0115 0.091 0.0491 0.0236 0.0435

Iraq-West 10202 2 0.0064 0.0166 0.8825 0.0103 0.0111 0.0563 0.0083 0.0084

Iraq-West 10204 2 0.0082 0.0809 0.8137 0.0069 0.0067 0.0688 0.0054 0.0094

Iraq-West 11854 1 0.0081 0.0965 0.6933 0.0072 0.0674 0.0967 0.0215 0.0094

Iraq-West 11860 6 0.0051 0.0147 0.9229 0.0051 0.0043 0.0223 0.0129 0.0127

Iraq-West 11861 2 0.0059 0.0491 0.8123 0.0101 0.0055 0.0885 0.0143 0.0143

Iraq-West 11863 1 0.0063 0.0338 0.8286 0.0084 0.0142 0.0583 0.0079 0.0425

Iraq-West 11864 14 0.0117 0.045 0.8229 0.0047 0.0194 0.0662 0.011 0.0191 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Iraq-West 11888 3 0.0058 0.0677 0.5868 0.0691 0.0575 0.0915 0.0301 0.0914

Iraq-West 11889 2 0.0063 0.2702 0.653 0.0228 0.0179 0.0152 0.004 0.0106

Iraq-West 11890 2 0.0044 0.0586 0.8357 0.0095 0.0112 0.028 0.0301 0.0225

Iraq-West 11891 2 0.0343 0.1488 0.6589 0.0119 0.0251 0.0838 0.0228 0.0143

Iraq-Baghdad 11847 13 0.0152 0.216 0.6415 0.0071 0.0071 0.0929 0.0037 0.0165

Iraq-Baghdad 11848 8 0.0152 0.1072 0.7644 0.024 0.0099 0.0587 0.0072 0.0134

Iraq-Baghdad 11849 5 0.0203 0.1896 0.7205 0.0054 0.0185 0.0287 0.0085 0.0085

Iraq-Baghdad 11850 0 0.0202 0.0986 0.8189 0.0056 0.0128 0.0116 0.0198 0.0125

Iraq-Baghdad 11852 3 0.0117 0.0731 0.803 0.0288 0.0166 0.0554 0.0051 0.0063

Iraq-Baghdad 11853 0 0.199 0.2937 0.1206 0.026 0.0482 0.2737 0.0104 0.0283

Iraq-Baghdad 11855 1 0.082 0.2489 0.5995 0.0068 0.0125 0.038 0.0061 0.0062

Iraq-Baghdad 11856 0 0.0126 0.0754 0.6803 0.0336 0.0318 0.134 0.0121 0.0202

Iraq-Baghdad 11857 4 0.0202 0.2615 0.5334 0.0073 0.0176 0.1268 0.0181 0.0151

Iraq-Baghdad 11858 4 0.0096 0.0304 0.8845 0.0187 0.0192 0.0205 0.0045 0.0126

Iraq-Baghdad 11859 9 0.0446 0.0971 0.7963 0.0086 0.0129 0.0235 0.0071 0.0099

Iraq-Baghdad 11862 3 0.0129 0.1648 0.5964 0.0929 0.053 0.0618 0.0031 0.0151

Iraq-Baghdad 11865 2 0.0211 0.0974 0.8228 0.0055 0.0122 0.0105 0.0189 0.0116

Iraq-Baghdad 11868 4 0.009 0.081 0.8525 0.0166 0.0127 0.0184 0.0048 0.0051

Iraq-Baghdad 11869 2 0.0199 0.2129 0.6427 0.0188 0.0295 0.0543 0.0075 0.0144

Iraq-Baghdad 11870 0 0.9272 0.0089 0.0112 0.0127 0.0128 0.0128 0.0025 0.0119

Iraq-Baghdad 11871 0 0.01 0.0489 0.8995 0.0107 0.005 0.0187 0.0039 0.0033

Iraq-Baghdad 11872 6 0.0302 0.3878 0.5101 0.0081 0.0112 0.0372 0.0066 0.0088

Iraq-Baghdad 11873 6 0.0059 0.124 0.7839 0.0056 0.0387 0.0225 0.0103 0.0091

Iraq-Baghdad 11874 11 0.0248 0.0922 0.7799 0.0168 0.0186 0.0324 0.0102 0.0251

Iraq-Baghdad 11875 18 0.0083 0.0441 0.8968 0.01 0.0079 0.0188 0.0037 0.0104

Iraq-Baghdad 11876 19 0.0152 0.4873 0.3901 0.0252 0.045 0.0205 0.01 0.0067

Iraq-Baghdad 11877 2 0.0156 0.3172 0.6022 0.0081 0.0261 0.0108 0.0093 0.0107

Iraq-Baghdad 11878 2 0.0182 0.0563 0.8797 0.0061 0.0202 0.0089 0.0032 0.0074

Iraq-Baghdad 11879 12 0.0828 0.2126 0.122 0.4716 0.0587 0.0331 0.0049 0.0143

Iraq-Baghdad 11880 2 0.0275 0.4851 0.0963 0.3449 0.0129 0.0176 0.0063 0.0094

Iraq-Baghdad 11881 13 0.0309 0.4225 0.073 0.435 0.0096 0.0137 0.0046 0.0107

Iraq-Baghdad 11882 23 0.0226 0.1957 0.0664 0.3916 0.1907 0.0843 0.0275 0.0212

Iraq-Baghdad 11883 8 0.0227 0.2335 0.0854 0.5987 0.0055 0.0132 0.0072 0.0339

Iraq-Baghdad 11884 2 0.0106 0.0849 0.0228 0.5902 0.119 0.1462 0.0144 0.0119

Iraq-Baghdad 11885 2 0.0346 0.2034 0.0962 0.6267 0.0175 0.0112 0.0049 0.0056

Iraq-Baghdad 11886 1 0.0367 0.3376 0.0432 0.5355 0.0084 0.0246 0.0076 0.0064

Iraq-Baghdad 11887 2 0.0189 0.1157 0.0338 0.5075 0.0081 0.0447 0.2487 0.0226

Iran 9419 3 0.0118 0.2221 0.0463 0.6745 0.0189 0.0167 0.0034 0.0063

Iran 9420 20 0.0173 0.3386 0.0368 0.5055 0.0381 0.0247 0.0152 0.0238

Iran 9421 18 0.0121 0.2742 0.0283 0.5848 0.0359 0.035 0.0163 0.0134

Iran 9422 0 0.0098 0.2846 0.0268 0.6544 0.0071 0.0061 0.0034 0.0078

Iran 9424 1 0.0183 0.0989 0.0361 0.6938 0.0204 0.1165 0.0089 0.0071

Iran 9425 0 0.0289 0.4359 0.0573 0.4261 0.027 0.0169 0.0021 0.0057

Iran 9426 1 0.0299 0.2645 0.0334 0.572 0.0308 0.0186 0.0344 0.0164

Iran 9427 18 0.0069 0.1064 0.0097 0.8224 0.0231 0.0126 0.0102 0.0086

Iran 9428 9 0.0065 0.4848 0.0573 0.3387 0.021 0.0312 0.0457 0.0147

Iran 9429 22 0.01 0.2284 0.0242 0.673 0.0131 0.0325 0.0093 0.0094

Iran 9430 1 0.0315 0.2226 0.0834 0.6039 0.0343 0.0135 0.0066 0.0042

Iran 9431 2 0.0462 0.3501 0.0301 0.5301 0.0149 0.0159 0.0038 0.0089

Iran 9432 0 0.0056 0.3826 0.049 0.4477 0.0148 0.0268 0.0476 0.0259

Iran 9433 3 0.0282 0.2226 0.0533 0.6615 0.0105 0.0076 0.0056 0.0106

Iran 9434 1 0.0122 0.5052 0.0315 0.4262 0.0089 0.0068 0.004 0.0052

Iran 9435 2 0.015 0.3905 0.0836 0.4113 0.0086 0.0234 0.0253 0.0424

Iran 9436 10 0.0093 0.1247 0.0347 0.6786 0.0129 0.0502 0.061 0.0286

Iran 9437 8 0.0226 0.2728 0.0449 0.6013 0.0141 0.029 0.0059 0.0095

Iran 9438 1 0.082 0.5305 0.1228 0.0737 0.1182 0.0565 0.0093 0.0069 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Iran 9439 2 0.066 0.3368 0.0813 0.3698 0.05 0.0421 0.0042 0.0497

Iran 9440 4 0.0101 0.202 0.0088 0.7326 0.0247 0.011 0.0053 0.0055

Iran 9441 2 0.0153 0.4654 0.0932 0.3348 0.043 0.0231 0.0139 0.0114

Iran 9442 0 0.0157 0.3089 0.0769 0.5043 0.0103 0.014 0.0079 0.062

Iran 9443 1 0.0095 0.3381 0.0434 0.5637 0.0087 0.0238 0.0073 0.0055

Iran 9444 1 0.007 0.1664 0.0319 0.6336 0.0796 0.0469 0.0296 0.005

Iran 9445 2 0.0077 0.474 0.1321 0.3021 0.0109 0.0286 0.0294 0.0152

Iran 9446 2 0.0959 0.3054 0.0517 0.5078 0.0122 0.0154 0.0065 0.005

Iran 9447 0 0.0145 0.1241 0.0154 0.6357 0.0138 0.0262 0.1564 0.0139

Iran 9448 3 0.0419 0.3079 0.0755 0.4535 0.0086 0.0624 0.0104 0.0398

Iran 9449 2 0.0425 0.537 0.0263 0.3502 0.0149 0.0102 0.0034 0.0155

Iran 9450 0 0.0094 0.2644 0.0351 0.6021 0.008 0.0116 0.0544 0.015

Iran 9451 2 0.0455 0.3321 0.1815 0.3716 0.0184 0.0191 0.0075 0.0244

Iran 9452 17 0.0271 0.3026 0.0492 0.5503 0.0107 0.0259 0.0108 0.0234

Iran 9453 0 0.0038 0.3402 0.1015 0.2946 0.0099 0.0701 0.1736 0.0064

Iran 9454 2 0.0112 0.2873 0.038 0.4008 0.0475 0.1819 0.014 0.0193

Iran 9455 16 0.0133 0.1809 0.0621 0.4158 0.0566 0.2518 0.0135 0.006

Iran 9456 22 0.0085 0.1605 0.0176 0.6798 0.0626 0.0489 0.0133 0.0089

Iran 9457 17 0.0212 0.2918 0.0719 0.3444 0.0938 0.116 0.0175 0.0434

Iran 9458 20 0.0081 0.1583 0.048 0.5994 0.1141 0.029 0.0042 0.0389

Iran 9459 18 0.0073 0.1305 0.0341 0.6046 0.1608 0.0264 0.0041 0.0322

Iran 9460 25 0.0046 0.0706 0.0094 0.4993 0.2799 0.1181 0.0077 0.0104

Iran 9461 4 0.0059 0.1872 0.0737 0.6339 0.024 0.0375 0.0151 0.0227

Iran 9462 6 0.0132 0.0195 0.0181 0.7147 0.0383 0.0161 0.0127 0.1674

Iran 9463 3 0.0069 0.0243 0.0162 0.7772 0.0141 0.0512 0.0941 0.0159

Iran 9464 2 0.0079 0.2171 0.0972 0.1407 0.4055 0.0609 0.031 0.0396

Iran 9465 0 0.0121 0.1648 0.0351 0.485 0.0664 0.1019 0.0756 0.0591

Iran 9466 10 0.0164 0.2698 0.157 0.273 0.1512 0.1163 0.0046 0.0117

Iran 9468 0 0.0059 0.1664 0.0221 0.6426 0.1134 0.0238 0.014 0.0118

Iran 9469 1 0.0058 0.0784 0.0186 0.7109 0.007 0.0373 0.1248 0.0172

Iran 9470 1 0.0397 0.1706 0.0461 0.5045 0.023 0.1513 0.0076 0.0572

Iran 9471 0 0.0102 0.2077 0.0195 0.7203 0.0103 0.0221 0.0048 0.0051

Iran 9472 2 0.0058 0.0849 0.0109 0.8451 0.0097 0.0164 0.0122 0.015

Iran 9473 18 0.0068 0.14 0.0127 0.7593 0.0315 0.0286 0.007 0.0141

Iran 9474 1 0.0056 0.0115 0.0107 0.8495 0.0875 0.0189 0.0059 0.0104

Iran 9475 1 0.0038 0.0082 0.0086 0.9267 0.006 0.0065 0.0138 0.0264

Iran 9476 0 0.0037 0.0054 0.0047 0.948 0.0216 0.0058 0.0032 0.0076

Iran 9477 1 0.006 0.0109 0.0061 0.9563 0.0061 0.0073 0.0028 0.0045

Iran 9478 1 0.004 0.0072 0.0043 0.9626 0.0063 0.006 0.0024 0.0072

Iran 9479 2 0.0033 0.019 0.0111 0.9292 0.0109 0.0066 0.0074 0.0125

Iran 9480 0 0.0069 0.0158 0.0086 0.9184 0.0103 0.0195 0.0129 0.0076

Iran 9481 0 0.0044 0.0135 0.0077 0.9079 0.0079 0.012 0.0341 0.0125

Iran 9482 1 0.0043 0.0101 0.0074 0.9378 0.0085 0.0056 0.0067 0.0196

Iran 9483 2 0.0055 0.007 0.0069 0.9349 0.0168 0.0066 0.0136 0.0087

Iran 9484 22 0.007 0.0352 0.0097 0.8954 0.0236 0.0134 0.0052 0.0105

Iran 9485 0 0.0051 0.0096 0.0118 0.9042 0.0398 0.0092 0.004 0.0163

Iran 9486 0 0.0038 0.0059 0.0082 0.8812 0.0084 0.0106 0.0097 0.0722

Iran 9487 0 0.0056 0.0093 0.0075 0.9456 0.0078 0.0127 0.0041 0.0074

Iran 9488 2 0.0031 0.0053 0.004 0.9468 0.0098 0.0135 0.0087 0.0088

Iran 9489 0 0.0063 0.0065 0.0049 0.9617 0.005 0.0051 0.0049 0.0056

Iran 9490 2 0.003 0.0087 0.0062 0.9607 0.0051 0.0056 0.0066 0.0041

Iran 9491 0 0.0028 0.0048 0.0031 0.9666 0.0062 0.0049 0.0057 0.0059

Iran 9492 1 0.0137 0.0342 0.0268 0.8536 0.0123 0.0378 0.0146 0.007

Iran 9493 4 0.0027 0.0048 0.0041 0.9608 0.0092 0.0073 0.0061 0.005

Iran 9494 2 0.0048 0.007 0.0061 0.944 0.013 0.0082 0.0118 0.0051

Iran 9495 0 0.0021 0.0036 0.0031 0.9698 0.007 0.0045 0.003 0.0069 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Iran 9497 2 0.0058 0.0265 0.0102 0.9101 0.0211 0.0127 0.0073 0.0063

Iran 9498 0 0.0058 0.0115 0.0152 0.8991 0.0074 0.0151 0.0125 0.0334

Iran 9499 1 0.0197 0.0259 0.0198 0.8715 0.0261 0.024 0.0048 0.0082

Iran 9500 0 0.0061 0.0063 0.0055 0.9417 0.0149 0.0108 0.0039 0.0108

Iran 9501 0 0.0035 0.0077 0.0051 0.94 0.0138 0.0095 0.007 0.0134

Iran 9502 0 0.0061 0.007 0.0071 0.9027 0.0069 0.0279 0.0154 0.0269

Iran 9503 1 0.0055 0.011 0.0103 0.9454 0.0105 0.0072 0.0048 0.0053

Iran 9504 0 0.0033 0.005 0.0043 0.905 0.0524 0.0089 0.0062 0.0149

Iran 9505 1 0.0041 0.0066 0.0042 0.926 0.0092 0.02 0.0111 0.0187

Iran 9506 0 0.0093 0.0318 0.0091 0.8752 0.0058 0.0117 0.0045 0.0527

Iran 9507 1 0.0041 0.0173 0.0071 0.9004 0.0108 0.0147 0.0069 0.0387

Iran 9508 0 0.0182 0.1299 0.0174 0.7366 0.0197 0.0467 0.0059 0.0256

Iran 9509 0 0.0041 0.0124 0.0084 0.8008 0.0204 0.0493 0.0524 0.0522

Iran 9510 0 0.0095 0.0162 0.0065 0.8724 0.0642 0.0183 0.0045 0.0084

Iran 9511 0 0.0251 0.0262 0.0174 0.8654 0.0076 0.015 0.0139 0.0294

Iran 9512 0 0.0074 0.0227 0.0094 0.862 0.0339 0.0316 0.0062 0.0267

Iran 9513 2 0.0196 0.0121 0.0201 0.8927 0.0239 0.018 0.0073 0.0063

Iran 9514 1 0.0101 0.0183 0.014 0.8726 0.024 0.0258 0.0306 0.0046

Iran 9515 0 0.01 0.016 0.021 0.8647 0.0391 0.0183 0.0069 0.024

Iran 9516 1 0.0082 0.0147 0.0107 0.946 0.0077 0.0047 0.0049 0.0031

Iran 9517 1 0.0039 0.0446 0.0078 0.8479 0.0578 0.0214 0.011 0.0056

Iran 9518 1 0.0101 0.0059 0.0039 0.8436 0.0062 0.0149 0.0374 0.078

Iran 9519 1 0.0057 0.0058 0.0041 0.8319 0.1185 0.0174 0.0057 0.0109

Iran 9520 2 0.0143 0.2207 0.0603 0.5458 0.0131 0.0214 0.0057 0.1186

Iran 9521 2 0.0159 0.0991 0.0646 0.7526 0.0178 0.0398 0.0041 0.0061

Iran 9522 0 0.0066 0.0185 0.0081 0.8807 0.0171 0.0163 0.0047 0.048

Iran 9523 1 0.0046 0.0062 0.0051 0.8773 0.085 0.0116 0.0036 0.0066

Iran 9524 1 0.0102 0.0173 0.0179 0.899 0.0108 0.0253 0.0057 0.0138

Iran 9526 1 0.0105 0.0321 0.0265 0.784 0.007 0.0246 0.1105 0.0048

Iran 9527 0 0.0208 0.0141 0.0281 0.894 0.0083 0.014 0.0069 0.0138

Iran 9528 0 0.0121 0.0072 0.0069 0.9274 0.0182 0.0071 0.0028 0.0183

Iran 9529 0 0.0039 0.0099 0.0064 0.9197 0.0118 0.0199 0.0188 0.0096

Iran 9530 2 0.0089 0.0229 0.0142 0.9044 0.0167 0.0134 0.0144 0.0051

Iran 9531 1 0.0072 0.0079 0.0072 0.938 0.009 0.008 0.0074 0.0153

Iran 9532 1 0.0032 0.0052 0.0046 0.9605 0.0068 0.006 0.005 0.0087

Dubai 10104 0 0.006 0.0063 0.0064 0.9482 0.0142 0.0072 0.0057 0.006

Dubai 10105 1 0.0043 0.0069 0.006 0.9264 0.0161 0.008 0.0063 0.026

Dubai 10106 0 0.004 0.0054 0.0049 0.9584 0.0066 0.0057 0.0053 0.0097

Dubai 10107 2 0.0062 0.0075 0.0083 0.9342 0.016 0.0061 0.0032 0.0185

Dubai 10108 0 0.0046 0.011 0.0054 0.9408 0.0177 0.0077 0.0057 0.0071

Dubai 10109 1 0.0113 0.0345 0.0079 0.9148 0.0113 0.0097 0.0062 0.0043

Dubai 10110 4 0.0052 0.0131 0.0087 0.9452 0.0066 0.0115 0.0066 0.0031

Dubai 10111 2 0.0726 0.0257 0.0311 0.7494 0.0628 0.0435 0.0059 0.0089

Dubai 10112 1 0.0043 0.0079 0.0053 0.959 0.0094 0.0068 0.0032 0.0041

Dubai 10120 2 0.016 0.0147 0.0155 0.9253 0.0098 0.0096 0.0052 0.0039

Kenya-Nairobi 9833 6 0.0363 0.0507 0.0138 0.7907 0.0288 0.0335 0.0288 0.0174

Kenya-Nairobi 9834 0 0.0397 0.0263 0.0246 0.7146 0.0821 0.0918 0.0053 0.0155

Kenya-Nairobi 9835 2 0.0043 0.0137 0.0046 0.9266 0.0341 0.0078 0.0039 0.005

Kenya-Nairobi 9836 2 0.0079 0.0173 0.0068 0.8738 0.0434 0.0155 0.0053 0.03

Kenya-Nairobi 9837 2 0.004 0.006 0.0052 0.9521 0.008 0.0074 0.0066 0.0107

Kenya-Nairobi 9838 0 0.0052 0.011 0.011 0.8914 0.0118 0.0332 0.0137 0.0227

Kenya-Nairobi 9839 1 0.003 0.0058 0.005 0.9573 0.0102 0.0049 0.0037 0.0101

Kenya-Nairobi 9840 2 0.0064 0.0133 0.0119 0.8319 0.0985 0.0209 0.0061 0.011

Kenya-Nairobi 9841 2 0.0116 0.0353 0.0295 0.8798 0.0083 0.0124 0.0111 0.012

Kenya-Nairobi 9842 2 0.0024 0.0066 0.0033 0.9578 0.0084 0.008 0.0067 0.0068

Kenya-Nairobi 9843 5 0.0042 0.0105 0.0078 0.9335 0.0169 0.008 0.006 0.0131 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Kenya-Nairobi 9844 0 0.0055 0.0074 0.0049 0.9529 0.0074 0.0071 0.0047 0.0101

Kenya-Nairobi 9845 1 0.0031 0.0194 0.0044 0.9285 0.0109 0.0163 0.0122 0.0052

Kenya-Nairobi 9846 0 0.0047 0.0131 0.0085 0.9261 0.0112 0.015 0.0054 0.016

Kenya-Nairobi 9847 1 0.003 0.0073 0.0045 0.9465 0.0233 0.0072 0.0039 0.0043

Kenya-Nairobi 9848 4 0.0053 0.0071 0.0049 0.8729 0.0509 0.0216 0.0053 0.032

Kenya-Nairobi 9849 7 0.003 0.0134 0.0056 0.9533 0.0066 0.0102 0.0033 0.0046

Kenya-Nairobi 9850 0 0.0162 0.0123 0.01 0.9057 0.0265 0.0168 0.0055 0.0069

Kenya-Nairobi 9851 0 0.0029 0.0044 0.0049 0.9612 0.0069 0.0053 0.004 0.0104

Kenya-Nairobi 9852 2 0.002 0.0035 0.0031 0.9699 0.0072 0.0044 0.003 0.0069

Kenya-Nairobi 9853 1 0.0046 0.0126 0.009 0.9132 0.0077 0.0213 0.0196 0.012

Kenya-Nairobi 9854 3 0.0054 0.0093 0.0073 0.8978 0.0259 0.0081 0.008 0.0382

Kenya-Nairobi 9855 1 0.0053 0.0114 0.0074 0.9514 0.0044 0.0091 0.0066 0.0044

Kenya-Nairobi 9856 4 0.0038 0.0079 0.0083 0.9519 0.0094 0.0094 0.003 0.0063

Kenya-Nairobi 9857 2 0.005 0.0137 0.0107 0.9051 0.0079 0.0235 0.0202 0.0139

Kenya-Nairobi 9858 16 0.0856 0.2827 0.1344 0.2372 0.045 0.0749 0.1346 0.0057

Kenya-Nairobi 9859 0 0.0443 0.0577 0.0392 0.0221 0.7812 0.0465 0.0033 0.0056

Kenya-Nairobi 9860 2 0.0223 0.065 0.0999 0.2842 0.4256 0.0516 0.0438 0.0076

Kenya-Nairobi 9861 0 0.0059 0.0143 0.0108 0.0936 0.414 0.4349 0.0157 0.0109

Kenya-Nairobi 9862 0 0.0221 0.0878 0.0293 0.4245 0.2995 0.0601 0.015 0.0617

Kenya-Nairobi 9863 2 0.018 0.1869 0.0748 0.0481 0.572 0.05 0.0301 0.0201

Kenya-Nairobi 9864 1 0.0098 0.0196 0.015 0.0986 0.3897 0.2668 0.0234 0.1771

Kenya-Nairobi 9865 2 0.0151 0.0434 0.0495 0.0477 0.7614 0.0467 0.0295 0.0068

Kenya-Nairobi 9866 2 0.0123 0.032 0.0193 0.3694 0.4188 0.1399 0.0035 0.0048

Kenya-Nairobi 9867 0 0.0072 0.1017 0.0197 0.2399 0.5035 0.0807 0.0314 0.0159

Kenya-Nairobi 9868 0 0.0093 0.0227 0.0255 0.1019 0.7605 0.0376 0.0059 0.0366

Kenya-Pate 2000 0 0.0052 0.0117 0.009 0.0424 0.8919 0.0098 0.0058 0.0242

Kenya-Pate 2001 0 0.0043 0.004 0.0031 0.0101 0.9608 0.0053 0.0061 0.0063

Kenya-Pate 2002 0 0.0049 0.0045 0.0034 0.012 0.9574 0.0052 0.0058 0.0068

Kenya-Pate 2003 0 0.0048 0.0055 0.0046 0.0064 0.9661 0.0057 0.0022 0.0047

Kenya-Pate 2004 1 0.0048 0.0155 0.0154 0.0097 0.9015 0.0212 0.0234 0.0085

Kenya-Pate 2006 2 0.0161 0.0081 0.0064 0.0094 0.933 0.0054 0.0034 0.0182

Kenya-Pate 2007 3 0.009 0.0132 0.0127 0.008 0.9199 0.0113 0.0031 0.0228

Kenya-Pate 2009 0 0.0115 0.0371 0.0233 0.0391 0.8493 0.0197 0.0096 0.0103

Kenya-Pate 2011 0 0.0068 0.013 0.0124 0.0193 0.8722 0.0444 0.0103 0.0216

Kenya-Lamu 1848 14 0.0132 0.0251 0.0246 0.0071 0.8378 0.0481 0.007 0.0371

Kenya-Lamu 2014 4 0.0284 0.0363 0.0118 0.2268 0.6639 0.0102 0.0049 0.0177

Kenya-Lamu 2015 0 0.0078 0.0051 0.004 0.0044 0.92 0.0186 0.0303 0.0098

Kenya-Lamu 2016 0 0.0038 0.0098 0.0077 0.0241 0.8601 0.024 0.0562 0.0143

Kenya-Lamu 2018 0 0.0457 0.0063 0.0046 0.0064 0.9204 0.0063 0.0052 0.0051

Kenya-Lamu 2019 0 0.0312 0.058 0.0697 0.0084 0.7664 0.05 0.0081 0.0083

Kenya-Lamu 2021 1 0.0608 0.0443 0.0317 0.0414 0.7045 0.0266 0.0148 0.0759

Kenya-Lamu 2023 1 0.0036 0.0043 0.0048 0.0074 0.9602 0.0059 0.0063 0.0075

Kenya-Lamu 2024 2 0.0042 0.0076 0.0055 0.0098 0.9542 0.0078 0.005 0.0059

Kenya-Lamu 2025 3 0.0085 0.0059 0.0051 0.0072 0.9553 0.0055 0.003 0.0095

Kenya-Lamu 2026 1 0.0107 0.0203 0.0361 0.0128 0.8933 0.0115 0.005 0.0103

Kenya-Lamu 2027 2 0.0073 0.0069 0.0056 0.0089 0.8997 0.0111 0.0056 0.0549

Kenya-Lamu 2029 1 0.0041 0.0066 0.0055 0.0081 0.9574 0.0078 0.0051 0.0054

Kenya-Lamu 2030 0 0.0075 0.0091 0.0084 0.0295 0.924 0.0103 0.0031 0.0081

Kenya-Lamu 2031 0 0.0079 0.0112 0.0109 0.068 0.845 0.0258 0.0064 0.0248

Kenya-Lamu 2032 0 0.0067 0.0152 0.0182 0.0572 0.8627 0.0123 0.0067 0.0211

Kenya-Lamu 2033 0 0.0031 0.0057 0.0052 0.0077 0.9657 0.0063 0.0031 0.0032

Kenya-Lamu 3241 0 0.0157 0.0341 0.0258 0.0169 0.8764 0.0174 0.0059 0.0078

Kenya-Lamu 3246 0 0.0153 0.0057 0.0054 0.0051 0.8894 0.0239 0.0425 0.0127

Kenya-Lamu 3247 0 0.0115 0.0075 0.0128 0.0071 0.9151 0.0131 0.0103 0.0226

India-Udaipur 11835 7 0.0061 0.0114 0.0059 0.1045 0.0078 0.7642 0.0528 0.0473

India-Udaipur 11836 3 0.042 0.0331 0.0254 0.145 0.0099 0.6785 0.01 0.056 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Missing Population

Data 1 2 3 4 5 6 7 8

India-Udaipur 11837 1 0.004 0.0141 0.0095 0.051 0.261 0.541 0.0213 0.0981

India-Agra 11823 2 0.0092 0.0847 0.0329 0.0969 0.0133 0.5361 0.066 0.1609

India-Agra 11824 2 0.0125 0.0799 0.165 0.2201 0.2554 0.2475 0.0063 0.0133

India-Agra 11825 2 0.005 0.0962 0.0116 0.3637 0.0219 0.2722 0.0092 0.2202

India-Agra 11826 6 0.0057 0.0421 0.0194 0.1086 0.0279 0.3254 0.0062 0.4647

India-Agra 11827 25 0.0189 0.1981 0.0543 0.3265 0.0422 0.2944 0.0284 0.0371

India-Agra 11828 2 0.0145 0.458 0.1678 0.0208 0.1538 0.1515 0.0116 0.022

India-Agra 11829 4 0.0081 0.0272 0.0146 0.4481 0.0288 0.3092 0.0176 0.1465

India-Agra 11830 8 0.0147 0.1537 0.034 0.3953 0.025 0.3117 0.0069 0.0586

India-Agra 11831 2 0.0064 0.1822 0.0122 0.5332 0.024 0.1034 0.0118 0.1268

India-Agra 11832 2 0.0063 0.0759 0.0089 0.3934 0.0181 0.416 0.0196 0.0617

India-Agra 11833 20 0.0075 0.0314 0.0226 0.019 0.2423 0.5895 0.0604 0.0273

India-Agra 11834 2 0.0069 0.0522 0.0185 0.1321 0.0223 0.2839 0.0065 0.4776

India-Hyderbad 11802 14 0.0042 0.0218 0.0173 0.012 0.0117 0.8432 0.0773 0.0125

India-Hyderbad 11803 7 0.0034 0.0178 0.0084 0.0692 0.0576 0.8204 0.0126 0.0106

India-Hyderbad 11804 4 0.0032 0.0077 0.006 0.0273 0.037 0.8421 0.0376 0.0391

India-Hyderbad 11805 11 0.0047 0.014 0.0223 0.0195 0.0653 0.8498 0.0174 0.007

India-Hyderbad 11807 13 0.033 0.0199 0.0322 0.0125 0.0627 0.7689 0.0353 0.0356

India-Hyderbad 11808 10 0.0344 0.0211 0.0303 0.0106 0.1872 0.6834 0.0105 0.0224

India-Hyderbad 11809 4 0.0459 0.0738 0.0135 0.0425 0.0543 0.7282 0.0195 0.0224

India-Hyderbad 11810 0 0.0079 0.0155 0.0185 0.0845 0.2349 0.4859 0.0097 0.1431

India-Hyderbad 11811 0 0.0567 0.0178 0.0748 0.0102 0.0569 0.7598 0.0056 0.0182

India-Hyderbad 11812 0 0.0038 0.0081 0.0104 0.0168 0.0676 0.8639 0.0144 0.015

India-Hyderbad 11813 2 0.0076 0.014 0.0161 0.0071 0.029 0.8419 0.0732 0.0112

India-Hyderbad 11814 2 0.0047 0.0216 0.0166 0.0558 0.0214 0.8582 0.0173 0.0044

India-Hyderbad 11815 2 0.007 0.0477 0.0329 0.0081 0.0133 0.8657 0.0187 0.0066

India-Hyderbad 11816 4 0.0065 0.0184 0.0085 0.1971 0.0851 0.6486 0.0055 0.0303

India-Hyderbad 11817 2 0.0034 0.0134 0.0134 0.014 0.0219 0.8772 0.0483 0.0084

India-Hyderbad 11818 1 0.4405 0.0183 0.0304 0.0141 0.1267 0.1626 0.2022 0.0052

India-Hyderbad 11819 6 0.0084 0.0344 0.013 0.1712 0.113 0.6199 0.0056 0.0345

India-Hyderbad 11820 6 0.0031 0.0106 0.0103 0.0557 0.0429 0.8148 0.044 0.0185

India-Hyderbad 11821 17 0.0083 0.0393 0.0389 0.0207 0.0645 0.6513 0.164 0.0131

India-Hyderbad 11822 2 0.0053 0.0107 0.0154 0.0874 0.0654 0.781 0.0207 0.0141

India-Andhra 10159 1 0.0178 0.0229 0.0272 0.1705 0.0107 0.678 0.0476 0.0253

India-Andhra 10160 1 0.0053 0.0081 0.0078 0.0122 0.0113 0.6141 0.2167 0.1245

India-Andhra 10161 2 0.017 0.0228 0.0131 0.0369 0.081 0.7808 0.0375 0.0109

India-Andhra 10162 5 0.011 0.0102 0.0109 0.0441 0.0105 0.7626 0.14 0.0107

India-Andhra 10163 0 0.0082 0.0122 0.0107 0.0217 0.0695 0.8088 0.0566 0.0123

India-Andhra 10164 2 0.0944 0.0255 0.0181 0.0228 0.01 0.7638 0.0512 0.0143

India-Andhra 10165 1 0.0257 0.0258 0.0469 0.0194 0.0456 0.3363 0.491 0.0092

India-Andhra 10166 2 0.0093 0.0764 0.0299 0.089 0.0732 0.4868 0.2196 0.0158

India-Andhra 10167 0 0.0064 0.0069 0.0082 0.0168 0.0345 0.5586 0.3478 0.0208

India-Andhra 10168 1 0.0344 0.0209 0.0539 0.0102 0.0223 0.6305 0.2121 0.0157

India-Andhra 10169 7 0.0054 0.0086 0.0069 0.0091 0.0682 0.8535 0.0407 0.0076

India-Andhra 10170 6 0.007 0.0348 0.0499 0.0104 0.0949 0.6735 0.1065 0.0231

India-Andhra 10171 1 0.0923 0.0251 0.017 0.0237 0.01 0.7657 0.0524 0.0139

India-Andhra 10172 2 0.0062 0.0085 0.0097 0.0117 0.0988 0.7613 0.0956 0.0082

India-Andhra 10173 2 0.0118 0.0172 0.0179 0.0146 0.0337 0.7665 0.1328 0.0054

India-Andhra 10174 3 0.0069 0.008 0.0119 0.0069 0.048 0.7983 0.0757 0.0443

India-Andhra 10175 2 0.0237 0.0391 0.0716 0.0133 0.0384 0.6239 0.0584 0.1315

India-Andhra 10176 2 0.009 0.0312 0.0162 0.0138 0.0198 0.8628 0.0219 0.0253

India-Andhra 10177 0 0.197 0.0094 0.0075 0.0358 0.0224 0.5453 0.1257 0.0569

India-Andhra 10178 2 0.0072 0.0068 0.0073 0.0083 0.0467 0.7306 0.1842 0.0089

India-Andhra 10179 5 0.0069 0.0074 0.0051 0.0096 0.4515 0.3078 0.2017 0.01

India-Andhra 10180 12 0.0208 0.0108 0.0204 0.0853 0.017 0.7135 0.0499 0.0823

India-Andhra 10181 4 0.0082 0.0122 0.0088 0.0236 0.0517 0.7971 0.0831 0.0153 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

India-Kolkata 10113 0 0.0052 0.0125 0.0102 0.029 0.1619 0.6324 0.0043 0.1446

India-Kolkata 10114 1 0.0061 0.0247 0.0096 0.0138 0.0528 0.6832 0.1886 0.0212

India-Kolkata 10115 1 0.0086 0.0669 0.0544 0.0112 0.0392 0.7438 0.036 0.0399

India-Kolkata 10116 1 0.0029 0.0415 0.0078 0.0999 0.0282 0.5887 0.2034 0.0275

India-Kolkata 10117 2 0.0071 0.065 0.0279 0.0109 0.0467 0.752 0.0788 0.0117

India-Kolkata 10118 0 0.0614 0.1603 0.0992 0.0215 0.0198 0.4194 0.1579 0.0606

India-Kolkata 10119 2 0.0806 0.1532 0.093 0.0886 0.0581 0.4495 0.0433 0.0338

Sri Lanka 8780 2 0.0483 0.0296 0.0217 0.0139 0.2366 0.4524 0.0142 0.1833

Sri Lanka 8781 0 0.0532 0.041 0.0196 0.0108 0.1335 0.5079 0.0895 0.1444

Sri Lanka 8782 0 0.0157 0.0255 0.0527 0.0088 0.1014 0.7458 0.0217 0.0284

Sri Lanka 8783 0 0.0374 0.1252 0.3072 0.01 0.0178 0.4467 0.0479 0.0078

Sri Lanka 8784 0 0.325 0.0999 0.0303 0.0077 0.0251 0.3352 0.145 0.0318

Sri Lanka 8785 11 0.1125 0.0236 0.0124 0.0241 0.0657 0.6425 0.07 0.0493

Sri Lanka 8786 0 0.023 0.0245 0.0169 0.0199 0.1936 0.534 0.0352 0.1528

Sri Lanka 8787 0 0.0798 0.0175 0.0111 0.0058 0.0817 0.7888 0.0069 0.0084

Sri Lanka 8788 4 0.028 0.0329 0.1215 0.0099 0.1415 0.6453 0.0127 0.0082

Sri Lanka 8789 2 0.0987 0.0681 0.3622 0.0137 0.0776 0.0823 0.2557 0.0415

Sri Lanka 8790 1 0.1667 0.0812 0.0367 0.0161 0.0178 0.293 0.3834 0.0051

Sri Lanka 8791 0 0.0654 0.2282 0.1887 0.0127 0.0226 0.3205 0.1378 0.0241

Sri Lanka 8792 0 0.0088 0.0218 0.0532 0.0387 0.0712 0.7818 0.0059 0.0185

Sri Lanka 8793 1 0.0555 0.0196 0.0741 0.0453 0.1449 0.6362 0.0115 0.0128

Sri Lanka 8794 1 0.0398 0.0214 0.0567 0.108 0.0686 0.6686 0.0094 0.0276

Sri Lanka 8795 1 0.2526 0.037 0.0715 0.015 0.2086 0.3528 0.0308 0.0317

Sri Lanka 8796 3 0.0161 0.0396 0.0499 0.0333 0.0951 0.5129 0.0376 0.2155

Sri Lanka 8797 0 0.1746 0.0544 0.033 0.0399 0.0406 0.4824 0.1572 0.0179

Sri Lanka 8798 0 0.0309 0.143 0.1005 0.0175 0.0219 0.5893 0.0142 0.0827

Sri Lanka 8799 1 0.1612 0.0172 0.0354 0.0121 0.0628 0.6906 0.0111 0.0096

Sri Lanka 8800 0 0.3314 0.044 0.0257 0.0131 0.0691 0.4522 0.0502 0.0143

Sri Lanka 8801 4 0.0764 0.0262 0.0279 0.0087 0.0666 0.7749 0.008 0.0113

Sri Lanka 8802 2 0.1278 0.0192 0.0323 0.0063 0.1581 0.618 0.0112 0.0271

Sri Lanka 8803 4 0.0681 0.0233 0.0951 0.0138 0.0774 0.2957 0.2675 0.1592

Thailand 11688 6 0.0257 0.0075 0.0062 0.0031 0.0135 0.0056 0.9335 0.0049

Thailand 11689 13 0.0048 0.0061 0.0044 0.0048 0.0034 0.0059 0.9535 0.0171

Thailand 11691 20 0.0251 0.0114 0.015 0.0095 0.0087 0.0359 0.8786 0.0158

Thailand 11698 17 0.0053 0.0053 0.0049 0.003 0.0021 0.0074 0.9678 0.0042

Thailand 11702 10 0.0028 0.0021 0.0028 0.0039 0.0036 0.004 0.9671 0.0137

Thailand 11703 12 0.0043 0.0051 0.0068 0.0038 0.0051 0.0089 0.9549 0.0111

Thailand 11705 21 0.0079 0.0217 0.0207 0.0319 0.0452 0.0343 0.825 0.0133

Thailand 11707 3 0.0027 0.0041 0.0038 0.0058 0.0045 0.0084 0.9625 0.0082

Thailand 11708 22 0.0041 0.0043 0.0043 0.0048 0.0069 0.0076 0.958 0.01

Thailand 11709 12 0.0042 0.0067 0.0054 0.0113 0.007 0.0126 0.9452 0.0076

Thailand 11710 8 0.0103 0.0109 0.0105 0.0108 0.0054 0.0085 0.9252 0.0184

Thailand 11711 7 0.007 0.005 0.0062 0.0071 0.007 0.0056 0.9535 0.0086

Thailand 11714 5 0.008 0.0072 0.0071 0.0102 0.0046 0.0073 0.9496 0.006

Thailand 11715 16 0.0084 0.0247 0.0085 0.1675 0.0371 0.0166 0.7256 0.0116

Thailand 11717 15 0.0029 0.0047 0.0046 0.0135 0.0117 0.0113 0.944 0.0073

Thailand 11718 0 0.002 0.0027 0.0026 0.0045 0.0031 0.0047 0.9753 0.0051

Thailand 11720 3 0.0021 0.0022 0.002 0.0027 0.0033 0.0035 0.9812 0.003

Vietnam 8844 4 0.0242 0.0111 0.0055 0.0109 0.0094 0.0157 0.9109 0.0123

Vietnam 8845 1 0.0198 0.0318 0.0363 0.1173 0.0139 0.0218 0.7387 0.0205

Vietnam 8846 0 0.011 0.0185 0.0225 0.0079 0.007 0.1624 0.7642 0.0065

Vietnam 8847 9 0.0054 0.0045 0.0043 0.0031 0.0042 0.0056 0.9658 0.0071

Vietnam 8848 6 0.0056 0.0154 0.011 0.0091 0.0184 0.0285 0.8964 0.0156

Vietnam 8849 2 0.02 0.0343 0.0484 0.0152 0.0065 0.0387 0.8094 0.0275

Vietnam 8850 0 0.049 0.0845 0.0215 0.0315 0.0215 0.0365 0.7385 0.0171

Vietnam 8851 0 0.0213 0.0755 0.0617 0.0779 0.0063 0.0309 0.7192 0.0073 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Vietnam 8852 13 0.0309 0.0609 0.0264 0.0089 0.0479 0.0573 0.7288 0.0388

Vietnam 8853 1 0.0866 0.0949 0.0549 0.0089 0.0181 0.1195 0.5948 0.0222

Vietnam 8854 5 0.0106 0.032 0.0214 0.0422 0.0305 0.0814 0.7395 0.0424

Vietnam 8855 0 0.2697 0.0221 0.0583 0.0046 0.006 0.011 0.6205 0.0078

Vietnam 8856 0 0.019 0.02 0.0443 0.0305 0.0078 0.0911 0.7695 0.0178

Vietnam 8857 2 0.0242 0.0345 0.1661 0.0048 0.0235 0.2018 0.5294 0.0157

Vietnam 8858 0 0.0484 0.0271 0.2905 0.0087 0.0084 0.0247 0.5874 0.0048

Vietnam 8859 6 0.0192 0.0786 0.0386 0.0108 0.1204 0.0308 0.6787 0.023

Vietnam 8860 0 0.0082 0.0541 0.0207 0.0565 0.052 0.0793 0.633 0.0961

Vietnam 8861 0 0.0109 0.0996 0.0894 0.0327 0.0294 0.04 0.6182 0.08

Vietnam 8862 0 0.0316 0.2645 0.0297 0.1359 0.0257 0.0767 0.4134 0.0226

Vietnam 8863 0 0.1561 0.0374 0.1777 0.0192 0.0061 0.0226 0.5736 0.0073

Taiwan 8681 3 0.5988 0.01 0.0074 0.0077 0.0137 0.0123 0.0294 0.3207

Taiwan 8682 0 0.1407 0.0589 0.0403 0.0813 0.0511 0.1265 0.3831 0.1182

Taiwan 8683 2 0.0227 0.2842 0.1142 0.0669 0.0188 0.2839 0.031 0.1783

Taiwan 8684 2 0.5912 0.0131 0.0096 0.0279 0.0045 0.0134 0.028 0.3123

Taiwan 8685 2 0.0113 0.0172 0.01 0.0116 0.099 0.049 0.2604 0.5414

Taiwan 8686 6 0.069 0.0477 0.0286 0.0421 0.1558 0.122 0.0335 0.5014

Taiwan 8687 6 0.0088 0.0926 0.0465 0.0204 0.0105 0.0898 0.4907 0.2408

Taiwan 8688 21 0.0626 0.0533 0.0285 0.0877 0.0447 0.1327 0.2007 0.3898

Taiwan 8689 5 0.0135 0.0156 0.0206 0.3609 0.0099 0.0198 0.0249 0.5348

Taiwan 8690 0 0.0077 0.0124 0.0098 0.0463 0.011 0.0484 0.1458 0.7187

Taiwan 8691 14 0.0466 0.0359 0.0193 0.0622 0.0321 0.0544 0.1959 0.5536

Taiwan 8692 3 0.0917 0.0914 0.0458 0.0772 0.0417 0.0507 0.4872 0.1143

Taiwan 8693 0 0.0435 0.0138 0.0129 0.0251 0.0746 0.5199 0.0429 0.2673

Taiwan 8694 0 0.1651 0.2053 0.0361 0.0669 0.061 0.0144 0.1864 0.2647

Taiwan 8695 0 0.0535 0.0809 0.0499 0.0088 0.0067 0.0792 0.2074 0.5136

Taiwan 8696 8 0.9708 0.0054 0.0067 0.003 0.0048 0.0035 0.0021 0.0037

Taiwan 8697 0 0.7175 0.0103 0.0097 0.016 0.0197 0.0769 0.0123 0.1375

Taiwan 8698 4 0.9531 0.007 0.0075 0.0066 0.0069 0.0061 0.0038 0.009

Taiwan 8699 6 0.0158 0.0156 0.0465 0.052 0.1473 0.4495 0.0796 0.1938

Taiwan 8700 1 0.3523 0.0295 0.0328 0.0258 0.0187 0.0195 0.2145 0.3069

Taiwan 8701 2 0.0741 0.0103 0.0087 0.0117 0.0542 0.0285 0.1495 0.663

Taiwan 8702 1 0.296 0.0349 0.0211 0.01 0.0348 0.0598 0.0466 0.4967

Taiwan 8703 1 0.7562 0.0119 0.0362 0.0128 0.0408 0.0954 0.0288 0.0179

Taiwan 8704 0 0.0066 0.0228 0.1598 0.0054 0.0339 0.0218 0.1195 0.6302

Taiwan 8705 1 0.0115 0.0213 0.0286 0.0238 0.0267 0.0476 0.1907 0.6498

Taiwan 8706 0 0.07 0.0952 0.1384 0.2491 0.0243 0.1176 0.0711 0.2342

Taiwan 8707 4 0.0125 0.0975 0.0375 0.1074 0.0341 0.1234 0.0811 0.5065

Taiwan 8708 0 0.9342 0.0096 0.0068 0.008 0.0091 0.0137 0.0125 0.0061

Taiwan 8709 2 0.0098 0.0188 0.0101 0.0127 0.0232 0.0157 0.1617 0.748

Japan-Oita 11967 4 0.021 0.0383 0.0357 0.0133 0.1053 0.0625 0.5731 0.1507

Japan-Oita 11968 5 0.274 0.0194 0.0228 0.009 0.1373 0.0239 0.2583 0.2554

Japan-Oita 11969 5 0.0936 0.0207 0.1672 0.0084 0.0136 0.037 0.2395 0.4199

Japan-Oita 11970 16 0.019 0.0622 0.0634 0.0084 0.0164 0.0263 0.789 0.0153

Japan-Oita 11971 8 0.0111 0.0531 0.0573 0.0524 0.0256 0.4899 0.2553 0.0552

Japan-Oita 11972 3 0.0272 0.1819 0.1543 0.0177 0.0168 0.0868 0.3903 0.1249

Japan-Oita 11973 2 0.4089 0.007 0.0087 0.0072 0.0083 0.0138 0.0185 0.5276

Japan-Oita 11974 20 0.0084 0.012 0.0207 0.0139 0.0286 0.0498 0.3617 0.5049

Japan-Oita 11975 22 0.2289 0.0883 0.0279 0.006 0.021 0.078 0.4869 0.0631

Japan-Oita 11976 16 0.02 0.0092 0.0093 0.0171 0.0179 0.0433 0.5176 0.3656

Japan-Oita 11977 4 0.0072 0.0064 0.0048 0.0393 0.0498 0.0245 0.5591 0.3089

Japan-Oita 11979 12 0.1335 0.0164 0.0181 0.0454 0.0164 0.0077 0.6835 0.079

Japan-Oita 11980 21 0.0073 0.0185 0.0206 0.1152 0.0311 0.0344 0.6555 0.1173

Japan-Oita 11981 18 0.0059 0.0057 0.0063 0.0067 0.0131 0.0065 0.2067 0.749

Japan-Oita 11982 4 0.0151 0.02 0.0122 0.0359 0.0315 0.0153 0.3762 0.4938 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 8

Japan-Oita 11985 11 0.0182 0.0207 0.0232 0.0194 0.0077 0.016 0.2148 0.6799

Japan-Oita 11986 6 0.373 0.0219 0.0269 0.0133 0.0142 0.0311 0.4005 0.1191

Japan-Kanazawa 11929 6 0.9291 0.0104 0.0082 0.0119 0.0167 0.0109 0.0034 0.0094

Japan-Kanazawa 11931 20 0.0207 0.0098 0.0082 0.0283 0.0133 0.0323 0.0707 0.8167

Japan-Kanazawa 11932 6 0.0855 0.028 0.0167 0.0122 0.0159 0.0493 0.0933 0.6991

Japan-Kanazawa 11933 14 0.1065 0.0226 0.0316 0.005 0.0118 0.023 0.0168 0.7827

Japan-Kanazawa 11934 3 0.1539 0.0144 0.014 0.0031 0.01 0.0092 0.0249 0.7705

Japan-Kanazawa 11936 8 0.0408 0.0238 0.0173 0.0188 0.0136 0.09 0.3327 0.463

Japan-Kanazawa 11937 27 0.0204 0.0126 0.0089 0.0128 0.0235 0.0146 0.0137 0.8935

Japan-Kanazawa 11939 20 0.016 0.0055 0.0072 0.004 0.0344 0.0396 0.2948 0.5985

Japan-Kanazawa 11940 12 0.0119 0.0091 0.0074 0.0067 0.0188 0.0229 0.0872 0.836

Japan-Kanazawa 11941 6 0.0181 0.0114 0.0093 0.0071 0.0103 0.0489 0.2794 0.6155

Japan-Kanazawa 11942 9 0.0061 0.0037 0.0048 0.0045 0.0074 0.0064 0.0203 0.9468

Japan-Kanazawa 11943 17 0.0137 0.0175 0.0185 0.0315 0.0386 0.0523 0.4918 0.3361

Japan-Kanazawa 11944 7 0.3983 0.1001 0.1915 0.0224 0.0393 0.2042 0.0291 0.0151

Japan-Kanazawa 11945 18 0.0354 0.0111 0.0169 0.0069 0.0137 0.0272 0.0745 0.8143

Japan-Kanazawa 11946 16 0.0031 0.0031 0.0055 0.0031 0.0048 0.0068 0.0146 0.959

Japan-Ohmiya 11947 4 0.0618 0.0347 0.094 0.0089 0.006 0.0278 0.0664 0.7005

Japan-Ohmiya 11948 17 0.0042 0.0069 0.0065 0.0134 0.0081 0.0145 0.0152 0.9312

Japan-Ohmiya 11951 22 0.0305 0.0252 0.0508 0.0404 0.0717 0.0682 0.0182 0.6948

Japan-Ohmiya 11953 2 0.0057 0.0087 0.007 0.0075 0.0057 0.0103 0.2061 0.749

Japan-Ohmiya 11954 2 0.0462 0.0192 0.0192 0.0149 0.0076 0.0294 0.0259 0.8376

Japan-Ohmiya 11955 5 0.2035 0.015 0.0058 0.0133 0.0102 0.0115 0.0075 0.7332

Japan-Ohmiya 11956 2 0.0055 0.009 0.0343 0.0063 0.0117 0.0239 0.6507 0.2586

Japan-Ohmiya 11957 3 0.0187 0.0116 0.04 0.0058 0.2075 0.0208 0.0324 0.6631

Japan-Ohmiya 11959 4 0.011 0.0112 0.0096 0.0054 0.0107 0.0139 0.1684 0.7698

Japan-Ohmiya 11960 3 0.0066 0.0058 0.0055 0.0061 0.0093 0.0075 0.5186 0.4406

Japan-Ohmiya 11961 3 0.022 0.0123 0.0126 0.0081 0.0424 0.0181 0.1068 0.7777

Japan-Ohmiya 11962 4 0.2858 0.0341 0.0282 0.0099 0.1244 0.0417 0.067 0.4088

Japan-Ohmiya 11963 3 0.0125 0.0174 0.0152 0.0425 0.0909 0.056 0.0778 0.6876

Japan-Ohmiya 11964 4 0.0329 0.0136 0.0096 0.0109 0.0526 0.0321 0.0095 0.8388

Japan-Ohmiya 11965 4 0.0088 0.0207 0.0208 0.0111 0.381 0.2777 0.0351 0.2449

Japan-Ohmiya 11966 5 0.0452 0.053 0.0528 0.0525 0.0226 0.0939 0.0674 0.6125

Japan-Sapporo 11907 7 0.0364 0.0416 0.0118 0.0787 0.0528 0.0316 0.0609 0.6862

Japan-Sapporo 11909 9 0.9613 0.0058 0.0044 0.0057 0.0069 0.0056 0.005 0.0053

Japan-Sapporo 11911 17 0.2459 0.029 0.0119 0.0083 0.0166 0.1486 0.0251 0.5147

Japan-Sapporo 11913 16 0.0174 0.058 0.0467 0.0098 0.019 0.4068 0.157 0.2853

Japan-Sapporo 11914 4 0.012 0.0358 0.0505 0.0105 0.1369 0.3908 0.1625 0.201

Japan-Sapporo 11915 7 0.01 0.0066 0.006 0.0059 0.0088 0.0141 0.757 0.1915

Japan-Sapporo 11916 7 0.9309 0.0148 0.0163 0.0063 0.0064 0.0111 0.0109 0.0033

Japan-Sapporo 11917 4 0.9094 0.0145 0.0079 0.0298 0.0155 0.0086 0.005 0.0093

Japan-Sapporo 11918 13 0.0492 0.0158 0.0276 0.0232 0.0258 0.2314 0.0766 0.5504

Japan-Sapporo 11921 5 0.0053 0.0176 0.0244 0.0277 0.0676 0.0419 0.3003 0.5152

Japan-Sapporo 11922 6 0.0045 0.0057 0.0085 0.1235 0.4193 0.1359 0.0249 0.2776

Japan-Sapporo 11923 7 0.002 0.0037 0.0038 0.0046 0.0079 0.0055 0.3865 0.586

Japan-Sapporo 11924 6 0.5659 0.0661 0.0311 0.0065 0.013 0.0333 0.1571 0.127

Japan-Sapporo 11925 9 0.0071 0.0156 0.0112 0.0244 0.0368 0.0272 0.8529 0.0249

Japan-Sapporo 11926 11 0.041 0.3068 0.1245 0.0288 0.0075 0.017 0.0207 0.4536

China-Henan 8869 2 0.0031 0.0173 0.0141 0.0728 0.0267 0.0176 0.0062 0.8422

China-Henan 8870 0 0.0051 0.0107 0.0083 0.0139 0.0095 0.0124 0.007 0.9331

China-Henan 8871 0 0.0072 0.0145 0.0107 0.0127 0.0149 0.0183 0.0049 0.9168

China-Henan 8872 1 0.0096 0.0233 0.0223 0.0089 0.0087 0.0596 0.0081 0.8595

China-Henan 8873 1 0.0027 0.0065 0.006 0.0255 0.0043 0.0095 0.0043 0.9412

China-Henan 8874 8 0.004 0.0203 0.0096 0.0693 0.0166 0.0775 0.026 0.7768

China-Henan 8875 1 0.0031 0.0061 0.005 0.0099 0.0079 0.0068 0.0036 0.9576

China-Henan 8876 0 0.0032 0.0059 0.0058 0.0096 0.0074 0.0075 0.0046 0.956 Table 23 - Population clustering of each random bred individual in the database by

SNPs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

China-Henan 8877 0 0.0398 0.0179 0.0083 0.0151 0.0095 0.0141 0.0043 0.8911

China-Henan 8878 2 0.0057 0.0168 0.0133 0.024 0.0532 0.0121 0.004 0.8709

China-Henan 8879 2 0.0076 0.0089 0.0085 0.0067 0.0202 0.0226 0.0033 0.9222

China-Henan 8880 0 0.0492 0.0263 0.0167 0.0134 0.0133 0.0187 0.0034 0.859

China-Henan 8881 2 0.0046 0.0139 0.0091 0.0203 0.0713 0.0113 0.0052 0.8643

China-Henan 8882 2 0.0067 0.0125 0.009 0.0276 0.0057 0.0142 0.0052 0.9191

China-Henan 8883 0 0.0079 0.0073 0.0051 0.099 0.0061 0.0108 0.0071 0.8567

China-Henan 8884 0 0.0042 0.0055 0.0044 0.0183 0.0065 0.0054 0.0032 0.9525

China-Henan 8885 0 0.011 0.0548 0.0223 0.1045 0.0156 0.0245 0.0042 0.7631

China-Henan 8886 0 0.0036 0.0093 0.0065 0.0363 0.0093 0.0095 0.004 0.9215

China-Henan 8887 1 0.0041 0.0114 0.0114 0.0233 0.0122 0.0477 0.0186 0.8714

China-Henan 8888 1 0.0056 0.0048 0.0048 0.0061 0.009 0.0107 0.0029 0.9561

South Korea 2769 4 0.0072 0.0421 0.0367 0.0598 0.0161 0.0142 0.0083 0.8156

South Korea 2772 12 0.0053 0.0043 0.0043 0.0058 0.0074 0.0105 0.0049 0.9575

South Korea 2775 8 0.0077 0.0171 0.015 0.0299 0.0179 0.0225 0.0434 0.8465

South Korea 2776 25 0.0037 0.0062 0.0066 0.0162 0.0219 0.0106 0.0263 0.9085

South Korea 2779 0 0.017 0.0281 0.0133 0.0242 0.0734 0.0106 0.0255 0.8078

South Korea 2784 2 0.0074 0.029 0.0334 0.0394 0.0898 0.0494 0.0057 0.746

South Korea 2785 1 0.005 0.0068 0.0125 0.0145 0.0842 0.014 0.014 0.849

South Korea 2786 2 0.0055 0.0089 0.0108 0.0042 0.0064 0.0083 0.0226 0.9333

South Korea 7671 1 0.0629 0.0954 0.0266 0.0128 0.0074 0.0095 0.0066 0.7788

South Korea 7672 0 0.0043 0.0185 0.0101 0.0149 0.0122 0.0343 0.0522 0.8535

South Korea 7673 0 0.0078 0.0278 0.0259 0.0187 0.0102 0.0132 0.0056 0.8907

South Korea 7674 1 0.0524 0.0798 0.049 0.056 0.2326 0.0283 0.0061 0.4958

South Korea 7675 2 0.0045 0.0081 0.0056 0.008 0.0075 0.0069 0.0098 0.9496

South Korea 7676 2 0.1262 0.0274 0.0318 0.0066 0.0068 0.0227 0.0105 0.7681

South Korea 7677 14 0.8348 0.0273 0.0359 0.008 0.0105 0.0363 0.018 0.0291

South Korea 7678 0 0.0134 0.0081 0.0064 0.0084 0.0091 0.0166 0.0062 0.9318

South Korea 7679 2 0.0076 0.026 0.0171 0.0121 0.0113 0.0112 0.0104 0.9043

South Korea 7680 8 0.1295 0.0645 0.0252 0.0164 0.0099 0.0178 0.0049 0.7318

South Korea 7681 2 0.0096 0.0111 0.0106 0.0068 0.0053 0.0199 0.0238 0.9129

South Korea 7682 0 0.0037 0.005 0.0046 0.0051 0.0069 0.0084 0.0318 0.9345

South Korea 7683 0 0.0041 0.0054 0.0045 0.012 0.0167 0.0196 0.0045 0.9332

South Korea 7684 1 0.5642 0.0237 0.0143 0.0745 0.0554 0.0179 0.0073 0.2427

South Korea 7685 6 0.0109 0.0423 0.0363 0.0278 0.242 0.0438 0.0953 0.5016

South Korea 7686 2 0.0171 0.0165 0.0213 0.0202 0.0356 0.0095 0.0032 0.8766

South Korea 7687 4 0.0938 0.0308 0.0355 0.0087 0.0375 0.0152 0.0728 0.7057

South Korea 7688 13 0.3179 0.0088 0.0074 0.007 0.0088 0.0065 0.0112 0.6324

South Korea 7689 0 0.0041 0.0105 0.0046 0.0754 0.0105 0.0058 0.0035 0.8856

South Korea 7690 0 0.0275 0.092 0.0544 0.0206 0.0092 0.028 0.0088 0.7595

South Korea 7691 2 0.0045 0.0057 0.0069 0.0072 0.0065 0.0199 0.1218 0.8275

South Korea 7692 0 0.0355 0.0478 0.0443 0.0599 0.0246 0.0423 0.0105 0.7351

South Korea 7693 0 0.0089 0.0127 0.0161 0.0138 0.0918 0.0349 0.0177 0.8041

South Korea 7694 1 0.0032 0.0161 0.0076 0.0076 0.0396 0.0335 0.0269 0.8655

South Korea 7695 9 0.0515 0.0557 0.0928 0.0239 0.0143 0.0252 0.0032 0.7335

South Korea 7696 2 0.0073 0.0103 0.0095 0.0084 0.0103 0.0142 0.0041 0.9359

South Korea 7697 2 0.0357 0.0343 0.0234 0.0288 0.0502 0.0116 0.006 0.81

South Korea 7698 2 0.0112 0.0263 0.1216 0.007 0.0041 0.0243 0.3231 0.4825

South Korea 7699 0 0.0235 0.0274 0.0444 0.008 0.0058 0.0109 0.0186 0.8614

South Korea 7700 6 0.0052 0.0078 0.0104 0.0093 0.0059 0.0098 0.0161 0.9355 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

USA-NY 2547 2 0.72 0.045 0.006 0.044 0.173 0.004 0.008

USA-NY 2559 13 0.738 0.229 0.005 0.006 0.016 0.003 0.004

USA-NY 2568 2 0.96 0.007 0.005 0.005 0.01 0.012 0.003

USA-NY 2569 21 0.902 0.014 0.005 0.011 0.042 0.015 0.011

USA-NY 2572 5 0.876 0.014 0.047 0.042 0.009 0.008 0.005

USA-NY 2578 5 0.937 0.008 0.004 0.019 0.025 0.003 0.005

USA-NY 2590 5 0.929 0.025 0.006 0.014 0.02 0.004 0.002

USA-NY 2591 7 0.916 0.011 0.014 0.007 0.021 0.023 0.007

USA-NY 2597 15 0.845 0.011 0.006 0.11 0.011 0.006 0.01

USA-MS 9971 5 0.875 0.017 0.005 0.077 0.011 0.007 0.007

USA-MS 9972 0 0.785 0.01 0.01 0.096 0.078 0.008 0.013

USA-MS 9974 2 0.907 0.021 0.024 0.024 0.01 0.008 0.007

USA-MS 9977 10 0.916 0.01 0.004 0.029 0.018 0.013 0.01

USA-MS 9980 7 0.904 0.04 0.017 0.005 0.017 0.004 0.011

USA-MS 9983 7 0.946 0.008 0.011 0.018 0.008 0.004 0.005

USA-MS 9985 5 0.786 0.062 0.004 0.03 0.02 0.095 0.002

USA-MS 9987 10 0.897 0.026 0.006 0.031 0.023 0.006 0.011

USA-MS 9989 2 0.865 0.019 0.007 0.021 0.053 0.03 0.005

USA-MS 9992 7 0.687 0.097 0.012 0.075 0.114 0.009 0.006

USA-HI 5366 10 0.628 0.057 0.071 0.04 0.063 0.137 0.003

USA-HI 5367 7 0.574 0.011 0.011 0.055 0.333 0.008 0.008

USA-HI 5371 5 0.46 0.329 0.01 0.119 0.024 0.037 0.021

USA-HI 5372 7 0.74 0.041 0.012 0.165 0.023 0.012 0.007

USA-HI 5379 2 0.875 0.024 0.005 0.019 0.026 0.036 0.014

USA-HI 5380 2 0.915 0.009 0.016 0.005 0.038 0.012 0.005

USA-HI 5383 10 0.366 0.266 0.113 0.012 0.154 0.014 0.075

USA-HI 5384 18 0.753 0.13 0.016 0.004 0.088 0.003 0.005

USA-HI 5401 5 0.274 0.09 0.041 0.009 0.548 0.035 0.003

USA-HI 5402 7 0.714 0.064 0.008 0.018 0.063 0.086 0.046

Brazil 7961 2 0.944 0.009 0.004 0.003 0.028 0.008 0.003

Brazil 7962 2 0.885 0.089 0.006 0.005 0.01 0.003 0.002

Brazil 7963 23 0.805 0.012 0.026 0.137 0.01 0.005 0.004

Brazil 7964 5 0.945 0.007 0.021 0.006 0.007 0.009 0.004

Brazil 7965 65 0.954 0.008 0.008 0.009 0.011 0.005 0.006

Brazil 7966 5 0.955 0.006 0.004 0.007 0.013 0.004 0.011

Brazil 7968 23 0.952 0.013 0.005 0.007 0.011 0.006 0.006

Brazil 7969 2 0.968 0.007 0.004 0.008 0.006 0.004 0.003

Brazil 7970 2 0.956 0.01 0.01 0.009 0.008 0.003 0.003

Brazil 7971 2 0.967 0.004 0.005 0.004 0.007 0.008 0.004

Brazil 7972 2 0.962 0.005 0.008 0.003 0.011 0.004 0.006

Brazil 7973 2 0.921 0.007 0.009 0.004 0.045 0.01 0.004

Brazil 7974 2 0.952 0.009 0.009 0.007 0.011 0.006 0.005

Brazil 7975 13 0.94 0.007 0.009 0.01 0.017 0.013 0.003

Brazil 7976 2 0.975 0.004 0.004 0.003 0.005 0.005 0.003

Brazil 7977 7 0.956 0.009 0.005 0.003 0.017 0.007 0.003

Brazil 7978 2 0.942 0.007 0.032 0.005 0.006 0.004 0.003

Brazil 7979 5 0.933 0.009 0.017 0.007 0.013 0.017 0.005

Brazil 7980 2 0.63 0.065 0.005 0.009 0.252 0.02 0.019

Brazil 7981 5 0.798 0.023 0.01 0.023 0.015 0.124 0.007

Brazil 7982 42 0.843 0.039 0.02 0.037 0.026 0.026 0.009

Brazil 7983 15 0.74 0.022 0.007 0.195 0.026 0.005 0.006

Brazil 7984 50 0.588 0.061 0.011 0.106 0.118 0.097 0.019

Brazil 7985 21 0.956 0.009 0.012 0.006 0.008 0.004 0.004

Brazil 7986 10 0.911 0.033 0.006 0.017 0.009 0.018 0.007

Brazil 7987 5 0.84 0.01 0.012 0.096 0.032 0.006 0.004

Brazil 7988 2 0.821 0.045 0.013 0.071 0.02 0.01 0.021 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Brazil 7989 5 0.889 0.011 0.03 0.033 0.014 0.014 0.008

Brazil 7990 5 0.967 0.005 0.008 0.008 0.006 0.003 0.003

Finland 8077 23 0.961 0.006 0.004 0.003 0.017 0.003 0.006

Finland 8084 31 0.612 0.007 0.006 0.052 0.301 0.018 0.005

Finland 8086 13 0.914 0.027 0.006 0.004 0.04 0.004 0.005

Finland 8089 26 0.915 0.013 0.024 0.013 0.015 0.011 0.009

Finland 8093 18 0.441 0.046 0.006 0.003 0.499 0.003 0.003

Finland 8094 34 0.732 0.067 0.019 0.026 0.091 0.01 0.056

Finland 8096 23 0.555 0.019 0.007 0.005 0.402 0.004 0.008

Finland 8107 31 0.975 0.006 0.003 0.004 0.007 0.003 0.003

Finland 8110 28 0.213 0.029 0.006 0.109 0.622 0.009 0.011

Finland 8116 44 0.906 0.015 0.015 0.01 0.03 0.011 0.013

Finland 8120 28 0.685 0.033 0.004 0.005 0.254 0.01 0.01

Germany 8711 13 0.977 0.004 0.003 0.003 0.008 0.003 0.003

Germany 8712 2 0.946 0.004 0.008 0.011 0.007 0.01 0.014

Germany 8713 7 0.867 0.013 0.015 0.013 0.034 0.029 0.029

Germany 8714 5 0.933 0.009 0.004 0.01 0.028 0.005 0.012

Germany 8715 5 0.963 0.008 0.004 0.005 0.012 0.004 0.005

Germany 8716 2 0.966 0.008 0.005 0.004 0.011 0.003 0.004

Germany 8717 7 0.962 0.008 0.005 0.005 0.012 0.004 0.004

Germany 8720 5 0.398 0.007 0.004 0.569 0.007 0.01 0.004

Germany 8721 10 0.85 0.023 0.014 0.003 0.096 0.004 0.009

Germany 8727 7 0.719 0.162 0.011 0.008 0.091 0.004 0.005

Germany 8728 10 0.974 0.004 0.005 0.005 0.006 0.004 0.003

Germany 8729 7 0.975 0.007 0.003 0.003 0.008 0.002 0.002

Germany 8730 21 0.811 0.036 0.005 0.008 0.13 0.008 0.003

Germany 8731 13 0.933 0.016 0.004 0.005 0.032 0.006 0.004

Germany 8732 7 0.954 0.009 0.007 0.011 0.011 0.004 0.003

Germany 8733 23 0.907 0.007 0.048 0.007 0.015 0.008 0.007

Germany 8734 7 0.972 0.007 0.003 0.003 0.01 0.003 0.002

Germany 8735 7 0.936 0.006 0.002 0.01 0.038 0.004 0.003

Germany 8736 5 0.835 0.011 0.006 0.006 0.031 0.011 0.099

Germany 8737 10 0.709 0.016 0.025 0.019 0.201 0.013 0.016

Germany 8738 10 0.716 0.007 0.005 0.008 0.177 0.006 0.081

Germany 8739 7 0.952 0.005 0.015 0.006 0.015 0.004 0.004

Germany 8741 7 0.969 0.005 0.003 0.005 0.011 0.004 0.003

Germany 8742 7 0.968 0.004 0.002 0.004 0.012 0.007 0.003

Germany 8744 7 0.635 0.042 0.01 0.031 0.119 0.144 0.019

Germany 8745 26 0.779 0.025 0.01 0.01 0.163 0.005 0.007

Germany 8746 5 0.968 0.005 0.009 0.004 0.007 0.003 0.003

Germany 8747 18 0.957 0.009 0.004 0.008 0.012 0.008 0.003

Germany 8749 7 0.975 0.005 0.004 0.003 0.007 0.003 0.004

Italy-Milan 8050 5 0.469 0.016 0.005 0.053 0.166 0.271 0.019

Italy-Milan 8057 5 0.098 0.011 0.006 0.035 0.837 0.005 0.008

Italy-Milan 8060 2 0.65 0.074 0.03 0.021 0.215 0.006 0.005

Italy-Milan 8061 5 0.179 0.077 0.088 0.047 0.556 0.027 0.026

Italy-Milan 8062 10 0.412 0.195 0.013 0.012 0.349 0.013 0.006

Italy-Milan 8065 10 0.397 0.489 0.016 0.007 0.073 0.013 0.005

Italy-Milan 8066 2 0.873 0.005 0.008 0.068 0.037 0.005 0.004

Italy-Milan 8067 2 0.471 0.24 0.121 0.017 0.137 0.005 0.01

Italy-Milan 8068 7 0.126 0.061 0.012 0.015 0.773 0.008 0.006

Italy-Milan 8069 2 0.109 0.008 0.011 0.111 0.74 0.013 0.008

Italy-Milan 8071 5 0.933 0.011 0.02 0.007 0.017 0.007 0.005

Italy-Milan 8072 7 0.21 0.024 0.004 0.021 0.694 0.03 0.017

Italy-Milan 8073 5 0.612 0.081 0.003 0.064 0.205 0.014 0.021

Italy-Milan 8074 5 0.603 0.012 0.011 0.347 0.008 0.016 0.003 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Italy-Rome 8586 2 0.431 0.148 0.043 0.009 0.311 0.045 0.013

Italy-Rome 8589 7 0.633 0.103 0.007 0.012 0.187 0.053 0.005

Italy-Rome 8592 7 0.349 0.064 0.005 0.022 0.493 0.008 0.059

Italy-Rome 8594 2 0.728 0.017 0.007 0.014 0.165 0.006 0.064

Italy-Rome 8595 5 0.361 0.092 0.011 0.08 0.31 0.021 0.125

Italy-Rome 8596 2 0.481 0.135 0.016 0.011 0.31 0.039 0.009

Italy-Rome 8597 2 0.635 0.188 0.006 0.014 0.124 0.012 0.022

Italy-Rome 8599 5 0.609 0.27 0.003 0.016 0.045 0.052 0.004

Italy-Rome 8601 2 0.645 0.02 0.009 0.01 0.043 0.026 0.247

Italy-Rome 8602 5 0.616 0.104 0.007 0.011 0.243 0.008 0.01

Italy-Rome 8603 5 0.82 0.096 0.005 0.021 0.035 0.004 0.019

Italy-Rome 8604 7 0.602 0.012 0.01 0.019 0.107 0.23 0.02

Italy-Rome 8609 5 0.365 0.473 0.006 0.015 0.121 0.011 0.009

Italy-Rome 8610 7 0.672 0.037 0.008 0.005 0.27 0.005 0.004

Italy-Rome 8611 2 0.604 0.097 0.011 0.005 0.215 0.008 0.06

Turkey 6477 5 0.013 0.109 0.003 0.004 0.843 0.007 0.021

Turkey 6478 10 0.016 0.532 0.005 0.007 0.424 0.004 0.012

Turkey 6480 7 0.101 0.461 0.028 0.024 0.369 0.009 0.008

Turkey 6481 5 0.027 0.009 0.007 0.891 0.054 0.009 0.004

Turkey 6482 7 0.165 0.017 0.015 0.033 0.731 0.014 0.025

Turkey 6484 5 0.021 0.024 0.024 0.006 0.913 0.006 0.005

Turkey 6486 5 0.014 0.01 0.011 0.014 0.931 0.005 0.016

Turkey 6487 7 0.01 0.025 0.002 0.004 0.95 0.005 0.004

Turkey 6488 7 0.048 0.361 0.004 0.02 0.532 0.016 0.018

Turkey 6491 13 0.3 0.043 0.011 0.289 0.012 0.342 0.003

Turkey 6494 7 0.17 0.069 0.005 0.009 0.694 0.017 0.037

Turkey 6496 5 0.08 0.037 0.019 0.09 0.756 0.013 0.006

Turkey 6499 7 0.277 0.032 0.081 0.005 0.582 0.015 0.008

Turkey 6500 15 0.029 0.037 0.004 0.003 0.92 0.004 0.004

Turkey 6502 7 0.058 0.209 0.032 0.007 0.674 0.016 0.006

Turkey 6503 10 0.054 0.013 0.005 0.034 0.879 0.011 0.004

Turkey 6507 5 0.038 0.05 0.003 0.005 0.887 0.011 0.006

Turkey 6510 5 0.616 0.017 0.003 0.004 0.35 0.008 0.002

Turkey 6512 7 0.012 0.031 0.015 0.009 0.898 0.026 0.008

Turkey 6513 5 0.059 0.338 0.064 0.029 0.457 0.007 0.046

Turkey 6514 5 0.015 0.006 0.005 0.87 0.014 0.084 0.006

Turkey 6516 5 0.935 0.009 0.017 0.007 0.021 0.002 0.008

Turkey 6519 5 0.081 0.071 0.006 0.007 0.824 0.005 0.007

Turkey 6520 5 0.105 0.041 0.003 0.009 0.834 0.004 0.003

Turkey 6521 7 0.027 0.015 0.01 0.006 0.916 0.019 0.008

Turkey 6729 5 0.01 0.026 0.038 0.01 0.513 0.04 0.364

Turkey 6730 5 0.14 0.039 0.012 0.005 0.772 0.027 0.005

Turkey 6731 7 0.278 0.022 0.005 0.007 0.664 0.003 0.02

Turkey 6732 7 0.076 0.034 0.02 0.087 0.632 0.055 0.096

Turkey 6733 7 0.022 0.016 0.005 0.015 0.92 0.016 0.006

Turkey 6734 5 0.067 0.022 0.009 0.025 0.837 0.036 0.004

Turkey 6735 7 0.004 0.011 0.005 0.004 0.965 0.004 0.006

Turkey 6736 2 0.057 0.494 0.004 0.007 0.419 0.005 0.013

Turkey 6738 7 0.015 0.062 0.013 0.011 0.871 0.013 0.015

Turkey 6739 13 0.129 0.031 0.012 0.011 0.794 0.006 0.017

Turkey 6740 5 0.979 0.003 0.005 0.004 0.004 0.003 0.003

Turkey 6741 7 0.012 0.006 0.007 0.948 0.013 0.011 0.003

Turkey 6742 13 0.017 0.012 0.004 0.049 0.895 0.014 0.008

Turkey 6743 5 0.122 0.037 0.014 0.022 0.778 0.008 0.02

Turkey 6745 7 0.647 0.028 0.006 0.005 0.275 0.028 0.012

Turkey 6746 5 0.21 0.019 0.009 0.361 0.348 0.035 0.017 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Turkey 6748 10 0.081 0.213 0.011 0.012 0.668 0.005 0.009

Turkey 6749 10 0.737 0.016 0.002 0.006 0.183 0.008 0.048

Turkey 6750 5 0.156 0.018 0.007 0.003 0.811 0.003 0.002

Turkey 6753 10 0.041 0.031 0.007 0.043 0.768 0.076 0.033

Turkey 6754 10 0.016 0.007 0.004 0.005 0.93 0.003 0.034

Turkey 6755 10 0.265 0.167 0.004 0.012 0.515 0.027 0.011

Turkey 6756 10 0.214 0.021 0.005 0.004 0.748 0.003 0.005

Turkey 6758 13 0.045 0.054 0.007 0.009 0.83 0.03 0.025

Turkey 6759 13 0.021 0.108 0.012 0.012 0.833 0.01 0.004

Turkey 6760 7 0.121 0.014 0.023 0.006 0.826 0.005 0.007

Cyprus 10128 2 0.024 0.018 0.011 0.008 0.905 0.028 0.006

Cyprus 10129 5 0.176 0.174 0.012 0.005 0.624 0.003 0.005

Cyprus 10130 7 0.097 0.017 0.013 0.013 0.85 0.006 0.004

Cyprus 10131 2 0.016 0.258 0.006 0.175 0.516 0.013 0.015

Cyprus 10132 2 0.034 0.176 0.031 0.019 0.717 0.008 0.014

Cyprus 10133 15 0.046 0.032 0.015 0.009 0.88 0.005 0.014

Cyprus 10134 5 0.01 0.301 0.046 0.008 0.61 0.005 0.021

Cyprus 10135 5 0.028 0.565 0.062 0.075 0.251 0.015 0.004

Cyprus 10136 5 0.171 0.069 0.043 0.01 0.651 0.023 0.034

Cyprus 10137 2 0.034 0.061 0.007 0.005 0.592 0.023 0.278

Cyprus 10138 2 0.015 0.521 0.013 0.012 0.394 0.01 0.035

Cyprus 10139 2 0.011 0.524 0.031 0.295 0.08 0.018 0.041

Cyprus 10140 0 0.02 0.04 0.011 0.007 0.904 0.004 0.014

Cyprus 10141 5 0.469 0.128 0.013 0.008 0.343 0.013 0.025

Cyprus 10142 2 0.006 0.056 0.055 0.011 0.831 0.027 0.013

Cyprus 10143 5 0.015 0.596 0.005 0.004 0.352 0.011 0.016

Cyprus 10144 7 0.023 0.111 0.012 0.014 0.831 0.005 0.004

Cyprus 10145 5 0.021 0.018 0.003 0.003 0.946 0.004 0.005

Cyprus 10146 0 0.007 0.786 0.025 0.028 0.138 0.01 0.006

Cyprus 10147 5 0.013 0.016 0.004 0.004 0.957 0.002 0.003

Cyprus 10148 7 0.03 0.775 0.015 0.05 0.075 0.032 0.023

Cyprus 10149 7 0.191 0.066 0.044 0.009 0.502 0.011 0.178

Cyprus 10150 2 0.015 0.693 0.006 0.028 0.237 0.007 0.014

Cyprus 10151 2 0.012 0.029 0.004 0.012 0.909 0.011 0.022

Cyprus 10152 5 0.014 0.709 0.006 0.044 0.175 0.039 0.013

Cyprus 10153 0 0.007 0.013 0.004 0.007 0.951 0.008 0.009

Cyprus 10154 0 0.033 0.467 0.011 0.087 0.272 0.009 0.121

Cyprus 10155 5 0.009 0.034 0.086 0.029 0.826 0.011 0.005

Cyprus 10156 7 0.107 0.373 0.005 0.018 0.39 0.089 0.018

Cyprus 10157 2 0.008 0.908 0.006 0.006 0.045 0.006 0.021

Lebanon 10235 10 0.073 0.732 0.016 0.049 0.064 0.03 0.036

Lebanon 10236 13 0.019 0.036 0.014 0.035 0.84 0.041 0.014

Lebanon 10237 5 0.021 0.4 0.072 0.226 0.22 0.053 0.009

Lebanon 10238 5 0.01 0.82 0.012 0.029 0.073 0.009 0.047

Lebanon 10239 5 0.012 0.348 0.006 0.009 0.611 0.008 0.007

Lebanon 10240 15 0.02 0.216 0.009 0.089 0.615 0.024 0.027

Lebanon 10241 13 0.04 0.018 0.015 0.028 0.81 0.064 0.025

Lebanon 10242 7 0.049 0.281 0.024 0.009 0.607 0.005 0.026

Lebanon 10243 10 0.021 0.082 0.022 0.043 0.732 0.032 0.067

Lebanon 10244 5 0.013 0.065 0.012 0.035 0.76 0.035 0.08

Lebanon 10245 13 0.047 0.167 0.034 0.027 0.459 0.008 0.258

Lebanon 10246 2 0.01 0.905 0.009 0.011 0.045 0.003 0.016

Lebanon 10247 18 0.012 0.598 0.013 0.009 0.067 0.008 0.293

Lebanon 10248 7 0.1 0.804 0.024 0.005 0.055 0.005 0.007

Lebanon 10249 13 0.538 0.035 0.01 0.018 0.384 0.008 0.007

Lebanon 10250 2 0.026 0.046 0.007 0.005 0.786 0.017 0.113 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Lebanon 10251 2 0.094 0.793 0.012 0.007 0.08 0.004 0.01

Lebanon 10252 5 0.086 0.027 0.011 0.006 0.83 0.009 0.031

Lebanon 10253 15 0.041 0.854 0.005 0.003 0.088 0.004 0.006

Lebanon 10254 13 0.008 0.053 0.011 0.031 0.865 0.015 0.017

Lebanon 10255 7 0.05 0.648 0.01 0.015 0.1 0.018 0.16

Lebanon 10256 18 0.008 0.827 0.006 0.005 0.14 0.007 0.008

Lebanon 10257 5 0.004 0.114 0.048 0.02 0.74 0.013 0.062

Lebanon 10258 7 0.024 0.408 0.022 0.01 0.518 0.007 0.012

Lebanon 10259 10 0.078 0.066 0.017 0.791 0.032 0.008 0.008

Lebanon 10260 2 0.005 0.053 0.02 0.006 0.891 0.007 0.018

Lebanon 10261 10 0.047 0.225 0.008 0.011 0.634 0.011 0.064

Lebanon 10262 5 0.087 0.169 0.015 0.034 0.612 0.041 0.041

Lebanon 10263 7 0.008 0.424 0.005 0.022 0.528 0.005 0.008

Lebanon 10264 5 0.018 0.015 0.009 0.03 0.908 0.01 0.01

Lebanon 10265 7 0.014 0.428 0.007 0.007 0.428 0.007 0.109

Lebanon 10266 0 0.032 0.736 0.03 0.105 0.073 0.012 0.012

Lebanon 10267 18 0.099 0.575 0.004 0.007 0.303 0.006 0.006

Lebanon 10268 5 0.025 0.781 0.01 0.042 0.128 0.011 0.003

Lebanon 10270 13 0.023 0.58 0.012 0.184 0.049 0.065 0.088

Lebanon 10271 18 0.18 0.011 0.049 0.082 0.545 0.026 0.107

Lebanon 10273 10 0.186 0.116 0.019 0.029 0.567 0.039 0.044

Lebanon 10274 13 0.027 0.164 0.037 0.53 0.204 0.032 0.007

Lebanon 10276 10 0.011 0.887 0.007 0.022 0.062 0.003 0.008

Lebanon 10277 2 0.072 0.27 0.005 0.02 0.43 0.162 0.04

Lebanon 10278 15 0.023 0.208 0.067 0.011 0.645 0.035 0.011

Lebanon 10279 0 0.008 0.066 0.026 0.031 0.837 0.007 0.025

Lebanon 10280 0 0.021 0.061 0.044 0.232 0.589 0.015 0.038

Lebanon 10281 10 0.007 0.424 0.007 0.007 0.509 0.008 0.038

Lebanon 10282 10 0.01 0.753 0.011 0.062 0.142 0.007 0.016

Lebanon 10283 23 0.007 0.012 0.077 0.005 0.888 0.005 0.006

Lebanon 10284 23 0.032 0.125 0.213 0.036 0.514 0.022 0.057

Lebanon 10285 2 0.035 0.131 0.017 0.019 0.676 0.111 0.011

Lebanon 10286 15 0.009 0.088 0.007 0.169 0.446 0.013 0.268

Lebanon 10287 5 0.142 0.04 0.036 0.08 0.629 0.043 0.03

Lebanon 10288 13 0.949 0.017 0.003 0.005 0.018 0.007 0.002

Lebanon 10289 13 0.008 0.547 0.008 0.019 0.389 0.007 0.022

Lebanon 10290 5 0.008 0.077 0.236 0.082 0.484 0.089 0.023

Lebanon 10291 15 0.011 0.089 0.03 0.013 0.69 0.045 0.122

Lebanon 10292 13 0.013 0.028 0.032 0.161 0.681 0.028 0.057

Lebanon 10294 2 0.066 0.289 0.023 0.095 0.455 0.025 0.047

Lebanon 10295 7 0.055 0.594 0.066 0.033 0.167 0.018 0.067

Lebanon 10297 5 0.01 0.027 0.007 0.015 0.813 0.021 0.108

Lebanon 10298 10 0.013 0.108 0.061 0.036 0.249 0.021 0.51

Lebanon 10299 2 0.246 0.057 0.208 0.034 0.342 0.083 0.03

Lebanon 10300 2 0.008 0.03 0.005 0.028 0.893 0.009 0.027

Israel 4962 7 0.009 0.029 0.013 0.008 0.904 0.004 0.034

Israel 4963 5 0.007 0.021 0.006 0.02 0.92 0.014 0.012

Israel 4964 7 0.201 0.042 0.006 0.016 0.693 0.009 0.033

Israel 4966 5 0.017 0.139 0.025 0.022 0.785 0.005 0.006

Israel 4967 5 0.052 0.021 0.019 0.015 0.784 0.097 0.012

Israel 4968 7 0.009 0.011 0.008 0.025 0.912 0.022 0.013

Israel 4969 2 0.076 0.014 0.015 0.022 0.831 0.032 0.01

Israel 4970 7 0.026 0.018 0.013 0.045 0.867 0.012 0.02

Israel 4971 2 0.006 0.005 0.006 0.014 0.929 0.022 0.019

Israel 4972 10 0.013 0.009 0.046 0.007 0.903 0.015 0.006

Israel 4973 5 0.006 0.009 0.026 0.005 0.942 0.006 0.005 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Israel 4974 5 0.015 0.009 0.006 0.012 0.945 0.005 0.009

Israel 4975 2 0.015 0.01 0.004 0.011 0.949 0.004 0.006

Israel 4976 5 0.072 0.037 0.04 0.021 0.817 0.004 0.009

Israel 4977 15 0.067 0.016 0.006 0.006 0.89 0.004 0.012

Israel 4978 10 0.017 0.005 0.009 0.013 0.928 0.007 0.021

Israel 4979 7 0.012 0.023 0.005 0.005 0.941 0.007 0.007

Israel 4980 2 0.084 0.093 0.011 0.118 0.458 0.106 0.129

Israel 4981 15 0.009 0.22 0.006 0.009 0.588 0.136 0.032

Israel 4982 10 0.066 0.008 0.014 0.019 0.882 0.006 0.005

Israel 4983 7 0.784 0.019 0.005 0.072 0.035 0.06 0.025

Israel 4984 2 0.924 0.006 0.005 0.029 0.009 0.022 0.005

Israel 4985 21 0.009 0.028 0.005 0.084 0.851 0.014 0.009

Israel 4986 5 0.004 0.008 0.007 0.005 0.959 0.009 0.008

Israel 4988 13 0.009 0.013 0.015 0.013 0.919 0.012 0.019

Israel 4989 5 0.006 0.141 0.005 0.137 0.647 0.009 0.055

Israel 4990 5 0.015 0.06 0.003 0.058 0.839 0.016 0.008

Israel 4992 7 0.018 0.009 0.005 0.005 0.956 0.004 0.004

Israel 4993 7 0.006 0.009 0.028 0.004 0.941 0.005 0.006

Israel 4994 5 0.004 0.008 0.003 0.003 0.974 0.004 0.003

Israel 4995 5 0.009 0.031 0.003 0.063 0.849 0.01 0.034

Israel 4996 2 0.009 0.149 0.003 0.01 0.803 0.014 0.013

Israel 4997 15 0.006 0.036 0.005 0.023 0.873 0.007 0.049

Israel 4998 10 0.034 0.011 0.005 0.018 0.916 0.007 0.01

Israel 5000 7 0.011 0.039 0.01 0.16 0.758 0.011 0.011

Israel 5001 5 0.041 0.049 0.006 0.014 0.856 0.007 0.028

Israel 5002 7 0.028 0.068 0.025 0.01 0.855 0.007 0.007

Israel 5003 7 0.023 0.019 0.012 0.005 0.914 0.019 0.007

Israel 5004 15 0.172 0.035 0.014 0.11 0.651 0.008 0.011

Israel 5005 7 0.022 0.017 0.011 0.01 0.927 0.004 0.008

Israel 5006 10 0.014 0.057 0.005 0.004 0.908 0.005 0.007

Israel 5007 5 0.208 0.034 0.027 0.071 0.463 0.192 0.005

Israel 5008 5 0.018 0.223 0.037 0.008 0.682 0.008 0.024

Israel 5009 13 0.277 0.138 0.006 0.106 0.371 0.034 0.067

Israel 5010 15 0.01 0.023 0.004 0.004 0.951 0.004 0.004

Israel 5011 13 0.019 0.011 0.009 0.012 0.926 0.006 0.016

Egypt-Cairo 8190 15 0.045 0.903 0.022 0.009 0.011 0.005 0.006

Egypt-Cairo 8192 18 0.1 0.682 0.066 0.077 0.027 0.012 0.037

Egypt-Cairo 8193 18 0.007 0.895 0.013 0.005 0.054 0.004 0.023

Egypt-Cairo 8196 15 0.009 0.875 0.008 0.038 0.041 0.021 0.009

Egypt-Cairo 8203 15 0.014 0.886 0.004 0.031 0.037 0.022 0.006

Egypt-Cairo 8215 10 0.005 0.955 0.003 0.007 0.021 0.004 0.005

Egypt-Cairo 8198 10 0.055 0.872 0.026 0.009 0.017 0.011 0.01

Egypt-Cairo 8194 7 0.101 0.82 0.025 0.008 0.023 0.013 0.01

Egypt-Cairo 8211 5 0.014 0.384 0.005 0.008 0.555 0.008 0.026

Egypt-Cairo 8216 7 0.312 0.633 0.01 0.006 0.023 0.008 0.009

Egypt-Cairo 8195 5 0.011 0.708 0.028 0.039 0.15 0.04 0.024

Egypt-Cairo 8199 2 0.005 0.917 0.041 0.007 0.022 0.004 0.005

Egypt-Cairo 8200 5 0.04 0.351 0.003 0.004 0.581 0.007 0.013

Egypt-Cairo 8201 5 0.014 0.036 0.042 0.009 0.729 0.095 0.076

Egypt-Cairo 8202 5 0.018 0.045 0.008 0.886 0.018 0.011 0.015

Egypt-Cairo 8204 5 0.019 0.781 0.009 0.031 0.139 0.016 0.006

Egypt-Cairo 8208 2 0.111 0.69 0.011 0.017 0.09 0.069 0.012

Egypt-Cairo 8210 5 0.007 0.86 0.019 0.02 0.053 0.004 0.037

Egypt-Cairo 8214 2 0.004 0.976 0.003 0.003 0.006 0.004 0.004

Egypt-Cairo 8191 5 0.034 0.534 0.018 0.032 0.254 0.083 0.046

Egypt-Cairo 8197 2 0.006 0.698 0.108 0.017 0.14 0.007 0.024 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Egypt-Cairo 8205 2 0.088 0.597 0.009 0.055 0.224 0.018 0.008

Egypt-Cairo 8206 2 0.068 0.744 0.008 0.031 0.134 0.01 0.006

Egypt-Cairo 8207 2 0.014 0.913 0.034 0.012 0.008 0.014 0.006

Egypt-Cairo 8209 2 0.075 0.824 0.003 0.004 0.082 0.007 0.005

Egypt-Cairo 8212 2 0.004 0.976 0.002 0.004 0.005 0.005 0.005

Egypt-Cairo 8213 2 0.007 0.968 0.003 0.004 0.007 0.005 0.006

Egypt-Cairo 9942 0 0.009 0.678 0.006 0.017 0.03 0.022 0.239

Egypt-Cairo 9943 15 0.2 0.006 0.003 0.669 0.006 0.109 0.007

Egypt-Cairo 9944 7 0.051 0.753 0.006 0.007 0.151 0.003 0.03

Egypt-Cairo 9945 2 0.107 0.845 0.007 0.009 0.015 0.006 0.01

Egypt-Cairo 9946 13 0.017 0.514 0.061 0.06 0.283 0.039 0.027

Egypt-Cairo 9947 7 0.018 0.868 0.019 0.007 0.05 0.007 0.03

Egypt-Cairo 9948 0 0.035 0.908 0.005 0.012 0.026 0.008 0.006

Egypt-Cairo 9949 0 0.029 0.917 0.007 0.01 0.018 0.011 0.008

Egypt-Cairo 9950 0 0.085 0.578 0.046 0.026 0.24 0.015 0.01

Egypt-Cairo 9951 5 0.009 0.055 0.012 0.005 0.907 0.008 0.003

Egypt-Cairo 9952 15 0.159 0.104 0.01 0.008 0.705 0.007 0.008

Egypt-Cairo 9953 21 0.06 0.048 0.008 0.01 0.828 0.005 0.041

Egypt-Cairo 9954 2 0.013 0.132 0.028 0.008 0.715 0.099 0.005

Egypt-Cairo 9955 0 0.506 0.166 0.006 0.212 0.077 0.027 0.005

Egypt-Cairo 9956 18 0.018 0.141 0.007 0.007 0.783 0.034 0.01

Egypt-Cairo 9957 5 0.014 0.886 0.005 0.011 0.011 0.066 0.006

Egypt-Cairo 9958 15 0.04 0.808 0.045 0.015 0.056 0.032 0.005

Egypt-Cairo 9959 5 0.137 0.093 0.006 0.065 0.663 0.017 0.017

Egypt-Cairo 9960 2 0.025 0.807 0.004 0.009 0.064 0.03 0.061

Egypt-Cairo 9961 5 0.066 0.319 0.006 0.556 0.027 0.018 0.008

Egypt-Cairo 9962 7 0.017 0.724 0.106 0.008 0.071 0.009 0.065

Egypt-Cairo 9963 2 0.031 0.808 0.032 0.01 0.096 0.014 0.008

Egypt-Cairo 9964 0 0.021 0.913 0.004 0.022 0.027 0.007 0.005

Egypt-Cairo 10021 0 0.005 0.96 0.01 0.004 0.009 0.004 0.007

Egypt-Cairo 10022 0 0.016 0.939 0.005 0.008 0.014 0.015 0.004

Egypt-Cairo 10023 0 0.005 0.947 0.007 0.012 0.019 0.006 0.003

Egypt-Cairo 10024 0 0.064 0.875 0.006 0.005 0.035 0.009 0.007

Egypt-Cairo 10025 0 0.04 0.035 0.006 0.009 0.891 0.003 0.015

Egypt-Cairo 10026 2 0.02 0.603 0.019 0.005 0.309 0.038 0.007

Egypt-Cairo 10027 0 0.063 0.649 0.223 0.022 0.02 0.01 0.012

Egypt-Cairo 10028 0 0.004 0.952 0.006 0.006 0.01 0.007 0.016

Egypt-Cairo 10029 0 0.004 0.968 0.006 0.003 0.006 0.004 0.01

Egypt-Cairo 10030 0 0.046 0.879 0.004 0.01 0.04 0.014 0.006

Egypt-Cairo 10031 2 0.003 0.961 0.01 0.004 0.007 0.003 0.012

Egypt-Cairo 10032 0 0.004 0.964 0.005 0.004 0.005 0.003 0.014

Egypt-Cairo 10033 2 0.004 0.95 0.008 0.01 0.016 0.009 0.004

Egypt-Cairo 10034 0 0.006 0.967 0.008 0.004 0.007 0.004 0.004

Egypt-Cairo 10035 0 0.007 0.763 0.086 0.009 0.021 0.107 0.007

Egypt-Cairo 10037 5 0.054 0.842 0.068 0.009 0.012 0.006 0.008

Egypt-Cairo 10042 0 0.031 0.747 0.035 0.034 0.108 0.018 0.027

Egypt-Cairo 10043 7 0.032 0.892 0.005 0.012 0.047 0.003 0.008

Egypt-Cairo 10044 2 0.014 0.934 0.005 0.008 0.027 0.006 0.005

Egypt-Cairo 10045 2 0.011 0.929 0.02 0.012 0.013 0.008 0.008

Egypt-Cairo 10046 0 0.017 0.912 0.006 0.022 0.018 0.021 0.004

Egypt-Cairo 10047 0 0.009 0.938 0.004 0.015 0.01 0.012 0.013

Egypt-Cairo 10048 2 0.031 0.927 0.005 0.013 0.012 0.011 0.002

Egypt-Cairo 10083 21 0.057 0.684 0.006 0.013 0.228 0.007 0.005

Egypt-Cairo 10040 0 0.301 0.252 0.024 0.019 0.365 0.027 0.011

Egypt-Cairo 10041 0 0.4 0.331 0.02 0.018 0.203 0.022 0.007

Egypt-Cairo 10049 5 0.015 0.901 0.011 0.016 0.02 0.015 0.022 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Egypt-Cairo 10084 2 0.012 0.456 0.037 0.015 0.461 0.01 0.009

Egypt-Cairo 10085 23 0.047 0.321 0.052 0.02 0.544 0.009 0.008

Egypt-Cairo 10087 5 0.007 0.936 0.005 0.005 0.04 0.005 0.002

Egypt-Cairo 10090 7 0.017 0.667 0.029 0.02 0.214 0.046 0.008

Egypt-Cairo 9968 0 0.022 0.929 0.01 0.007 0.014 0.004 0.014

Egypt-Asuit 10091 2 0.015 0.955 0.003 0.006 0.012 0.005 0.004

Egypt-Asuit 10093 0 0.008 0.966 0.012 0.003 0.005 0.004 0.003

Egypt-Asuit 10094 5 0.007 0.963 0.008 0.007 0.007 0.005 0.004

Egypt-Asuit 10095 2 0.009 0.927 0.009 0.008 0.033 0.003 0.01

Egypt-Asuit 10096 0 0.007 0.967 0.004 0.005 0.006 0.008 0.003

Egypt-Asuit 10098 0 0.005 0.974 0.006 0.003 0.005 0.004 0.003

Egypt-Asuit 10099 5 0.004 0.968 0.011 0.003 0.006 0.005 0.003

Egypt-Asuit 10100 2 0.069 0.321 0.182 0.015 0.315 0.069 0.03

Egypt-Asuit 10101 2 0.011 0.901 0.01 0.012 0.025 0.02 0.021

Egypt-Asuit 10102 0 0.005 0.742 0.007 0.215 0.013 0.011 0.007

Egypt-Luxor 10038 0 0.008 0.928 0.006 0.017 0.011 0.026 0.005

Egypt-Luxor 10039 2 0.005 0.96 0.004 0.003 0.007 0.003 0.019

Egypt-Luxor 10050 2 0.009 0.887 0.019 0.014 0.017 0.043 0.01

Egypt-Luxor 10051 0 0.008 0.934 0.011 0.005 0.036 0.003 0.004

Egypt-Luxor 10052 0 0.012 0.882 0.016 0.049 0.023 0.009 0.009

Egypt-Luxor 10053 0 0.023 0.419 0.016 0.023 0.449 0.004 0.066

Egypt-Luxor 10054 0 0.015 0.858 0.003 0.013 0.022 0.081 0.008

Egypt-Luxor 10055 0 0.01 0.917 0.025 0.006 0.029 0.005 0.008

Egypt-Luxor 10056 0 0.006 0.919 0.01 0.006 0.014 0.03 0.015

Egypt-Luxor 10057 7 0.016 0.632 0.007 0.019 0.223 0.033 0.07

Egypt-Luxor 10058 0 0.01 0.949 0.01 0.003 0.016 0.006 0.006

Egypt-Luxor 10060 0 0.033 0.195 0.01 0.005 0.741 0.009 0.007

Egypt-Luxor 10061 0 0.012 0.479 0.03 0.044 0.013 0.417 0.005

Egypt-Luxor 10062 5 0.036 0.901 0.007 0.007 0.028 0.016 0.007

Egypt-Luxor 10063 2 0.022 0.642 0.028 0.023 0.188 0.016 0.082

Egypt-Luxor 10064 0 0.013 0.72 0.206 0.01 0.031 0.013 0.007

Egypt-Luxor 10065 0 0.024 0.729 0.204 0.013 0.015 0.005 0.009

Egypt-Luxor 10066 2 0.032 0.856 0.038 0.014 0.015 0.037 0.008

Egypt-Luxor 10067 2 0.006 0.931 0.006 0.005 0.032 0.003 0.016

Egypt-Luxor 10068 0 0.011 0.806 0.006 0.038 0.041 0.095 0.003

Egypt-Luxor 10069 2 0.007 0.936 0.018 0.008 0.018 0.005 0.008

Egypt-Luxor 10070 0 0.015 0.5 0.027 0.041 0.012 0.4 0.005

Egypt-Luxor 10071 2 0.012 0.575 0.015 0.034 0.223 0.123 0.018

Egypt-Luxor 10072 2 0.008 0.867 0.009 0.005 0.073 0.032 0.006

Egypt-Luxor 10073 5 0.926 0.038 0.008 0.008 0.01 0.006 0.005

Egypt-Luxor 10074 0 0.007 0.785 0.008 0.004 0.185 0.007 0.004

Egypt-Luxor 10079 0 0.088 0.607 0.011 0.191 0.09 0.004 0.009

Egypt-Luxor 10080 0 0.044 0.55 0.314 0.033 0.038 0.012 0.009

Egypt-Abu Simbel 10076 5 0.015 0.81 0.027 0.015 0.074 0.054 0.006

Egypt-Abu Simbel 10077 13 0.005 0.906 0.017 0.036 0.017 0.013 0.005

Egypt-Abu Simbel 10081 7 0.013 0.655 0.023 0.016 0.268 0.018 0.007

Egypt-Abu Simbel 10089 0 0.116 0.761 0.027 0.004 0.086 0.003 0.003

Egypt-Abu Simbel 10092 0 0.005 0.957 0.01 0.004 0.011 0.008 0.004

Iraq-West 9587 0 0.02 0.056 0.017 0.096 0.062 0.012 0.736

Iraq-West 10202 23 0.01 0.012 0.006 0.012 0.316 0.009 0.636

Iraq-West 10204 23 0.037 0.102 0.009 0.008 0.086 0.004 0.754

Iraq-West 11854 0 0.007 0.027 0.003 0.009 0.041 0.025 0.888

Iraq-West 11860 21 0.004 0.012 0.012 0.06 0.013 0.005 0.892

Iraq-West 11861 7 0.011 0.082 0.012 0.009 0.107 0.246 0.534

Iraq-West 11863 21 0.016 0.006 0.006 0.01 0.009 0.011 0.943

Iraq-West 11864 2 0.005 0.018 0.006 0.026 0.022 0.023 0.9 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Iraq-West 11888 5 0.003 0.004 0.122 0.007 0.005 0.01 0.85

Iraq-West 11889 7 0.187 0.014 0.012 0.017 0.058 0.012 0.7

Iraq-West 11890 2 0.008 0.231 0.041 0.008 0.016 0.068 0.629

Iraq-West 11891 0 0.019 0.06 0.01 0.007 0.282 0.007 0.616

Iraq-Baghdad 11847 5 0.01 0.038 0.006 0.022 0.082 0.01 0.832

Iraq-Baghdad 11848 0 0.01 0.013 0.011 0.111 0.015 0.059 0.78

Iraq-Baghdad 11849 5 0.01 0.127 0.004 0.047 0.017 0.033 0.761

Iraq-Baghdad 11850 0 0.039 0.016 0.004 0.015 0.011 0.016 0.899

Iraq-Baghdad 11852 0 0.01 0.02 0.057 0.021 0.025 0.013 0.854

Iraq-Baghdad 11853 5 0.006 0.015 0.008 0.026 0.014 0.017 0.913

Iraq-Baghdad 11855 5 0.006 0.013 0.067 0.015 0.062 0.013 0.824

Iraq-Baghdad 11856 2 0.027 0.026 0.016 0.012 0.072 0.005 0.844

Iraq-Baghdad 11857 0 0.173 0.156 0.007 0.011 0.084 0.011 0.558

Iraq-Baghdad 11858 2 0.008 0.008 0.004 0.01 0.011 0.005 0.954

Iraq-Baghdad 11859 5 0.02 0.035 0.014 0.036 0.038 0.011 0.846

Iraq-Baghdad 11862 2 0.059 0.033 0.013 0.011 0.062 0.262 0.561

Iraq-Baghdad 11865 5 0.007 0.07 0.008 0.08 0.067 0.147 0.62

Iraq-Baghdad 11868 2 0.005 0.008 0.008 0.006 0.009 0.006 0.958

Iraq-Baghdad 11869 2 0.116 0.024 0.007 0.009 0.228 0.018 0.598

Iraq-Baghdad 11870 0 0.031 0.146 0.004 0.025 0.039 0.077 0.679

Iraq-Baghdad 11871 5 0.006 0.361 0.015 0.031 0.067 0.008 0.513

Iraq-Baghdad 11872 2 0.016 0.169 0.014 0.007 0.082 0.004 0.707

Iraq-Baghdad 11873 7 0.005 0.369 0.016 0.01 0.045 0.028 0.526

Iraq-Baghdad 11874 2 0.01 0.029 0.008 0.007 0.053 0.048 0.847

Iraq-Baghdad 11875 15 0.008 0.007 0.062 0.124 0.016 0.175 0.608

Iraq-Baghdad 11876 21 0.009 0.06 0.038 0.033 0.035 0.018 0.808

Iraq-Baghdad 11877 7 0.008 0.048 0.054 0.006 0.017 0.047 0.82

Iraq-Baghdad 11878 7 0.027 0.064 0.005 0.015 0.373 0.009 0.508

Iraq-Baghdad 11879 2 0.007 0.022 0.012 0.187 0.018 0.015 0.74

Iraq-Baghdad 11880 10 0.008 0.043 0.015 0.01 0.03 0.007 0.886

Iraq-Baghdad 11881 0 0.046 0.071 0.011 0.016 0.158 0.079 0.62

Iraq-Baghdad 11882 15 0.012 0.075 0.007 0.012 0.067 0.013 0.815

Iraq-Baghdad 11883 0 0.041 0.006 0.011 0.008 0.327 0.008 0.598

Iraq-Baghdad 11884 0 0.004 0.004 0.006 0.009 0.005 0.01 0.962

Iraq-Baghdad 11885 0 0.003 0.017 0.004 0.012 0.012 0.008 0.943

Iraq-Baghdad 11886 0 0.008 0.131 0.01 0.041 0.126 0.075 0.609

Iraq-Baghdad 11887 13 0.012 0.092 0.027 0.021 0.014 0.035 0.799

Iran 9419 2 0.091 0.034 0.009 0.008 0.046 0.032 0.779

Iran 9420 2 0.005 0.013 0.01 0.013 0.008 0.094 0.855

Iran 9421 5 0.01 0.013 0.005 0.005 0.012 0.01 0.945

Iran 9422 5 0.013 0.065 0.005 0.01 0.054 0.11 0.742

Iran 9424 7 0.004 0.005 0.002 0.013 0.004 0.092 0.88

Iran 9425 7 0.002 0.004 0.005 0.006 0.004 0.011 0.968

Iran 9426 2 0.009 0.006 0.041 0.004 0.005 0.008 0.927

Iran 9427 2 0.008 0.006 0.012 0.005 0.008 0.004 0.958

Iran 9428 5 0.006 0.006 0.003 0.007 0.005 0.007 0.967

Iran 9429 15 0.012 0.025 0.05 0.022 0.015 0.065 0.811

Iran 9430 7 0.012 0.009 0.01 0.027 0.064 0.034 0.843

Iran 9431 7 0.012 0.062 0.013 0.106 0.017 0.053 0.738

Iran 9432 13 0.004 0.004 0.048 0.01 0.005 0.005 0.924

Iran 9433 5 0.007 0.007 0.006 0.01 0.011 0.025 0.933

Iran 9434 7 0.033 0.01 0.006 0.096 0.014 0.084 0.757

Iran 9435 5 0.007 0.006 0.017 0.028 0.005 0.011 0.925

Iran 9436 2 0.006 0.007 0.005 0.006 0.013 0.006 0.958

Iran 9437 2 0.007 0.009 0.008 0.011 0.012 0.006 0.948

Iran 9438 2 0.005 0.004 0.011 0.013 0.004 0.01 0.953 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Iran 9439 5 0.02 0.023 0.016 0.034 0.049 0.115 0.744

Iran 9440 2 0.003 0.009 0.014 0.006 0.008 0.003 0.958

Iran 9441 7 0.003 0.004 0.009 0.005 0.004 0.004 0.971

Iran 9442 5 0.019 0.013 0.067 0.033 0.012 0.009 0.846

Iran 9443 10 0.009 0.006 0.008 0.01 0.008 0.009 0.951

Iran 9444 5 0.002 0.003 0.002 0.003 0.004 0.004 0.981

Iran 9445 5 0.002 0.002 0.003 0.004 0.002 0.003 0.983

Iran 9446 10 0.004 0.004 0.007 0.007 0.003 0.009 0.965

Iran 9447 7 0.007 0.005 0.011 0.006 0.003 0.005 0.963

Iran 9448 13 0.003 0.003 0.005 0.005 0.004 0.003 0.977

Iran 9449 5 0.003 0.003 0.002 0.003 0.003 0.002 0.985

Iran 9450 7 0.004 0.012 0.005 0.005 0.007 0.017 0.95

Iran 9451 10 0.005 0.008 0.004 0.02 0.009 0.061 0.893

Iran 9452 2 0.006 0.005 0.005 0.023 0.006 0.006 0.949

Iran 9453 7 0.005 0.005 0.004 0.003 0.004 0.003 0.977

Iran 9454 7 0.003 0.005 0.003 0.004 0.004 0.003 0.979

Iran 9455 13 0.006 0.008 0.011 0.01 0.006 0.006 0.952

Iran 9456 2 0.011 0.009 0.006 0.018 0.012 0.013 0.932

Iran 9457 5 0.004 0.007 0.004 0.004 0.005 0.003 0.973

Iran 9458 2 0.004 0.007 0.006 0.009 0.006 0.01 0.958

Iran 9459 5 0.003 0.003 0.009 0.004 0.003 0.004 0.974

Iran 9460 10 0.016 0.01 0.047 0.008 0.012 0.009 0.899

Iran 9461 10 0.004 0.003 0.004 0.008 0.004 0.004 0.973

Iran 9462 10 0.012 0.006 0.014 0.017 0.007 0.006 0.938

Iran 9463 15 0.004 0.007 0.007 0.006 0.01 0.006 0.959

Iran 9464 7 0.003 0.005 0.004 0.005 0.004 0.008 0.972

Iran 9465 5 0.006 0.007 0.004 0.009 0.005 0.003 0.966

Iran 9466 13 0.01 0.017 0.048 0.008 0.013 0.004 0.901

Iran 9468 10 0.02 0.009 0.006 0.009 0.02 0.021 0.914

Iran 9469 10 0.02 0.021 0.02 0.035 0.017 0.024 0.863

Iran 9470 13 0.015 0.013 0.006 0.018 0.011 0.014 0.923

Iran 9471 7 0.007 0.005 0.004 0.007 0.009 0.004 0.964

Iran 9472 10 0.004 0.01 0.006 0.007 0.008 0.007 0.957

Iran 9473 5 0.004 0.004 0.008 0.006 0.005 0.007 0.967

Iran 9474 7 0.007 0.01 0.035 0.005 0.008 0.004 0.931

Iran 9475 7 0.006 0.008 0.008 0.005 0.01 0.003 0.96

Iran 9476 7 0.003 0.003 0.003 0.002 0.004 0.008 0.976

Iran 9477 7 0.009 0.006 0.003 0.009 0.008 0.043 0.922

Iran 9478 5 0.008 0.008 0.065 0.058 0.026 0.021 0.815

Iran 9479 7 0.002 0.003 0.003 0.004 0.003 0.009 0.977

Iran 9480 13 0.008 0.005 0.005 0.019 0.008 0.012 0.942

Iran 9481 10 0.005 0.015 0.01 0.004 0.006 0.006 0.954

Iran 9482 5 0.013 0.023 0.007 0.024 0.023 0.03 0.88

Iran 9483 13 0.009 0.008 0.006 0.056 0.015 0.175 0.732

Iran 9484 2 0.033 0.128 0.005 0.01 0.132 0.005 0.688

Iran 9485 10 0.004 0.007 0.008 0.011 0.01 0.081 0.877

Iran 9486 10 0.02 0.008 0.006 0.007 0.01 0.005 0.944

Iran 9487 5 0.004 0.006 0.013 0.015 0.006 0.008 0.948

Iran 9488 10 0.006 0.008 0.003 0.006 0.007 0.005 0.964

Iran 9489 15 0.005 0.007 0.035 0.022 0.008 0.006 0.916

Iran 9490 13 0.005 0.003 0.006 0.01 0.005 0.009 0.962

Iran 9491 7 0.003 0.003 0.004 0.003 0.004 0.005 0.979

Iran 9492 10 0.005 0.005 0.005 0.01 0.004 0.003 0.968

Iran 9493 2 0.005 0.006 0.005 0.008 0.004 0.015 0.956

Iran 9494 7 0.004 0.007 0.003 0.004 0.011 0.003 0.968

Iran 9495 5 0.004 0.003 0.013 0.006 0.004 0.005 0.964 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Iran 9497 10 0.004 0.004 0.056 0.004 0.004 0.004 0.924

Iran 9498 10 0.004 0.005 0.005 0.005 0.006 0.008 0.967

Iran 9499 5 0.005 0.007 0.011 0.012 0.007 0.071 0.888

Iran 9500 7 0.003 0.003 0.012 0.008 0.003 0.004 0.968

Iran 9501 2 0.036 0.027 0.034 0.031 0.023 0.006 0.842

Iran 9502 5 0.006 0.009 0.003 0.007 0.011 0.228 0.736

Iran 9503 7 0.003 0.003 0.007 0.009 0.003 0.005 0.971

Iran 9504 5 0.003 0.003 0.002 0.002 0.004 0.003 0.983

Iran 9505 5 0.002 0.002 0.002 0.002 0.002 0.005 0.985

Iran 9506 7 0.015 0.006 0.027 0.027 0.008 0.008 0.91

Iran 9507 31 0.01 0.038 0.039 0.015 0.011 0.036 0.852

Iran 9508 10 0.004 0.006 0.02 0.012 0.008 0.092 0.858

Iran 9509 18 0.006 0.005 0.005 0.008 0.01 0.031 0.934

Iran 9510 15 0.005 0.004 0.01 0.005 0.006 0.049 0.921

Iran 9511 13 0.014 0.005 0.005 0.004 0.007 0.005 0.96

Iran 9512 28 0.069 0.014 0.099 0.009 0.031 0.011 0.768

Iran 9513 21 0.008 0.006 0.012 0.007 0.019 0.005 0.943

Iran 9514 15 0.024 0.057 0.033 0.021 0.106 0.012 0.748

Iran 9515 13 0.167 0.007 0.018 0.016 0.007 0.006 0.78

Iran 9516 15 0.009 0.008 0.081 0.011 0.01 0.006 0.876

Iran 9517 13 0.027 0.012 0.009 0.053 0.028 0.056 0.814

Iran 9518 13 0.172 0.022 0.02 0.02 0.041 0.018 0.708

Iran 9519 18 0.05 0.01 0.009 0.011 0.016 0.005 0.9

Iran 9520 15 0.063 0.014 0.085 0.009 0.013 0.004 0.812

Iran 9521 21 0.009 0.009 0.132 0.02 0.008 0.006 0.816

Iran 9522 15 0.024 0.069 0.006 0.036 0.191 0.048 0.626

Iran 9523 13 0.021 0.081 0.025 0.008 0.043 0.008 0.814

Iran 9524 15 0.004 0.006 0.004 0.008 0.005 0.046 0.928

Iran 9526 15 0.004 0.006 0.006 0.01 0.006 0.091 0.877

Iran 9527 13 0.015 0.014 0.024 0.042 0.025 0.022 0.859

Iran 9528 15 0.004 0.003 0.004 0.005 0.003 0.005 0.977

Iran 9529 15 0.002 0.003 0.005 0.022 0.003 0.025 0.938

Iran 9530 10 0.006 0.006 0.006 0.007 0.006 0.032 0.936

Iran 9531 15 0.025 0.038 0.05 0.031 0.032 0.02 0.804

Iran 9532 5 0.004 0.004 0.004 0.005 0.006 0.004 0.973

Dubai 10104 2 0.009 0.053 0.676 0.017 0.021 0.003 0.222

Dubai 10105 2 0.011 0.007 0.762 0.021 0.01 0.074 0.115

Dubai 10106 18 0.009 0.011 0.661 0.19 0.064 0.01 0.057

Dubai 10107 5 0.044 0.026 0.293 0.075 0.033 0.155 0.375

Dubai 10108 2 0.008 0.02 0.309 0.574 0.02 0.044 0.026

Dubai 10109 18 0.013 0.008 0.424 0.022 0.015 0.102 0.415

Dubai 10110 7 0.015 0.006 0.821 0.006 0.009 0.011 0.132

Dubai 10111 13 0.004 0.015 0.791 0.141 0.02 0.009 0.02

Dubai 10112 7 0.021 0.019 0.521 0.077 0.283 0.053 0.027

Dubai 10120 15 0.01 0.009 0.759 0.073 0.017 0.011 0.12

Kenya-Nairobi 9833 13 0.551 0.011 0.027 0.123 0.059 0.029 0.2

Kenya-Nairobi 9834 0 0.732 0.015 0.019 0.202 0.011 0.014 0.007

Kenya-Nairobi 9835 0 0.449 0.088 0.241 0.025 0.012 0.18 0.006

Kenya-Nairobi 9836 2 0.724 0.008 0.027 0.19 0.009 0.029 0.013

Kenya-Nairobi 9837 0 0.762 0.048 0.014 0.032 0.011 0.078 0.055

Kenya-Nairobi 9838 2 0.559 0.048 0.087 0.01 0.078 0.016 0.202

Kenya-Nairobi 9839 0 0.173 0.028 0.388 0.341 0.02 0.038 0.012

Kenya-Nairobi 9840 0 0.475 0.207 0.013 0.175 0.108 0.008 0.014

Kenya-Nairobi 9841 2 0.821 0.021 0.078 0.05 0.011 0.011 0.008

Kenya-Nairobi 9842 2 0.686 0.03 0.113 0.019 0.022 0.091 0.04

Kenya-Nairobi 9843 0 0.833 0.013 0.051 0.07 0.012 0.013 0.009 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Kenya-Nairobi 9844 2 0.753 0.01 0.182 0.012 0.009 0.029 0.005

Kenya-Nairobi 9845 0 0.67 0.08 0.056 0.013 0.118 0.046 0.018

Kenya-Nairobi 9846 2 0.567 0.013 0.006 0.295 0.027 0.08 0.011

Kenya-Nairobi 9847 0 0.81 0.008 0.117 0.017 0.022 0.016 0.01

Kenya-Nairobi 9848 0 0.553 0.102 0.02 0.027 0.182 0.092 0.024

Kenya-Nairobi 9849 5 0.151 0.054 0.024 0.664 0.084 0.016 0.008

Kenya-Nairobi 9850 7 0.637 0.017 0.035 0.045 0.244 0.01 0.013

Kenya-Nairobi 9851 2 0.535 0.006 0.043 0.389 0.014 0.006 0.007

Kenya-Nairobi 9852 7 0.473 0.063 0.407 0.009 0.039 0.003 0.005

Kenya-Nairobi 9853 2 0.752 0.014 0.107 0.039 0.011 0.02 0.058

Kenya-Nairobi 9854 0 0.785 0.024 0.045 0.023 0.044 0.005 0.075

Kenya-Nairobi 9855 2 0.461 0.281 0.125 0.074 0.038 0.007 0.015

Kenya-Nairobi 9856 2 0.685 0.018 0.192 0.075 0.015 0.01 0.005

Kenya-Nairobi 9857 23 0.434 0.033 0.007 0.449 0.03 0.039 0.009

Kenya-Nairobi 9858 15 0.854 0.013 0.032 0.042 0.011 0.042 0.006

Kenya-Nairobi 9859 15 0.87 0.005 0.029 0.029 0.005 0.047 0.015

Kenya-Nairobi 9860 5 0.864 0.014 0.036 0.025 0.006 0.043 0.012

Kenya-Nairobi 9861 2 0.764 0.028 0.183 0.005 0.009 0.004 0.007

Kenya-Nairobi 9862 7 0.456 0.037 0.03 0.336 0.119 0.016 0.004

Kenya-Nairobi 9863 2 0.666 0.007 0.09 0.202 0.005 0.019 0.011

Kenya-Nairobi 9864 7 0.7 0.02 0.044 0.123 0.018 0.004 0.091

Kenya-Nairobi 9865 10 0.37 0.103 0.033 0.334 0.029 0.054 0.076

Kenya-Nairobi 9866 7 0.532 0.191 0.058 0.061 0.134 0.019 0.005

Kenya-Nairobi 9867 0 0.313 0.031 0.02 0.454 0.123 0.023 0.037

Kenya-Nairobi 9868 5 0.454 0.373 0.059 0.047 0.05 0.005 0.011

Kenya-Pate 2000 7 0.076 0.02 0.844 0.008 0.045 0.004 0.003

Kenya-Pate 2001 13 0.002 0.003 0.983 0.002 0.003 0.002 0.004

Kenya-Pate 2002 2 0.002 0.003 0.97 0.003 0.003 0.008 0.011

Kenya-Pate 2003 2 0.005 0.005 0.919 0.009 0.003 0.054 0.005

Kenya-Pate 2004 10 0.007 0.016 0.901 0.023 0.025 0.016 0.011

Kenya-Pate 2006 7 0.004 0.007 0.774 0.01 0.006 0.08 0.12

Kenya-Pate 2007 7 0.004 0.004 0.977 0.004 0.004 0.004 0.004

Kenya-Pate 2009 28 0.014 0.022 0.941 0.003 0.013 0.003 0.004

Kenya-Pate 2011 13 0.01 0.015 0.895 0.043 0.026 0.008 0.004

Kenya-Lamu 1848 10 0.01 0.004 0.865 0.034 0.01 0.068 0.009

Kenya-Lamu 2014 5 0.006 0.02 0.934 0.019 0.012 0.003 0.006

Kenya-Lamu 2015 7 0.01 0.051 0.655 0.006 0.261 0.01 0.007

Kenya-Lamu 2016 7 0.032 0.014 0.907 0.009 0.026 0.006 0.004

Kenya-Lamu 2018 2 0.029 0.067 0.863 0.005 0.027 0.004 0.004

Kenya-Lamu 2019 7 0.034 0.048 0.823 0.057 0.021 0.011 0.007

Kenya-Lamu 2021 7 0.008 0.006 0.936 0.011 0.006 0.005 0.028

Kenya-Lamu 2023 13 0.006 0.004 0.975 0.004 0.004 0.005 0.002

Kenya-Lamu 2024 2 0.003 0.003 0.984 0.003 0.003 0.002 0.003

Kenya-Lamu 2025 5 0.005 0.008 0.95 0.004 0.01 0.009 0.015

Kenya-Lamu 2026 18 0.003 0.007 0.963 0.006 0.012 0.005 0.004

Kenya-Lamu 2027 26 0.088 0.016 0.773 0.047 0.021 0.021 0.033

Kenya-Lamu 2029 5 0.003 0.003 0.984 0.003 0.003 0.003 0.003

Kenya-Lamu 2030 5 0.022 0.005 0.939 0.013 0.013 0.004 0.004

Kenya-Lamu 2031 18 0.045 0.02 0.896 0.007 0.015 0.008 0.009

Kenya-Lamu 2032 10 0.006 0.007 0.952 0.006 0.006 0.006 0.017

Kenya-Lamu 2033 13 0.011 0.028 0.908 0.017 0.018 0.007 0.011

Kenya-Lamu 3241 18 0.018 0.029 0.808 0.039 0.013 0.084 0.008

Kenya-Lamu 3246 7 0.034 0.131 0.682 0.011 0.119 0.014 0.008

Kenya-Lamu 3247 13 0.188 0.011 0.714 0.029 0.037 0.009 0.012

India-Udaipur 11835 10 0.024 0.075 0.112 0.159 0.137 0.268 0.225

India-Udaipur 11836 5 0.009 0.02 0.075 0.422 0.044 0.023 0.407 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 ndia-Udaipur 11837 5 0.005 0.02 0.053 0.551 0.009 0.021 0.341 ndia-Agra 11823 0 0.006 0.018 0.014 0.452 0.007 0.006 0.497 ndia-Agra 11824 0 0.005 0.016 0.108 0.585 0.016 0.007 0.263 ndia-Agra 11825 2 0.009 0.01 0.006 0.475 0.007 0.007 0.487 ndia-Agra 11826 7 0.004 0.005 0.003 0.536 0.004 0.004 0.444 ndia-Agra 11827 0 0.003 0.006 0.005 0.565 0.012 0.004 0.404 ndia-Agra 11828 5 0.006 0.024 0.034 0.517 0.044 0.01 0.365 ndia-Agra 11829 5 0.016 0.032 0.012 0.746 0.045 0.007 0.141 ndia-Agra 11830 5 0.003 0.007 0.005 0.593 0.012 0.005 0.376 ndia-Agra 11831 5 0.01 0.031 0.013 0.442 0.027 0.007 0.469 ndia-Agra 11832 0 0.004 0.009 0.015 0.416 0.007 0.006 0.544 ndia-Agra 11833 2 0.005 0.029 0.058 0.379 0.01 0.017 0.502 ndia-Agra 11834 5 0.004 0.005 0.004 0.518 0.004 0.004 0.462 ndia-Hyderbad 11802 7 0.032 0.463 0.016 0.353 0.086 0.038 0.012 ndia-Hyderbad 11803 7 0.007 0.066 0.031 0.708 0.133 0.008 0.047 ndia-Hyderbad 11804 10 0.007 0.008 0.213 0.23 0.011 0.464 0.067 ndia-Hyderbad 11805 5 0.021 0.017 0.037 0.858 0.019 0.033 0.015 ndia-Hyderbad 11807 2 0.004 0.013 0.014 0.91 0.007 0.009 0.042 ndia-Hyderbad 11808 2 0.011 0.008 0.012 0.88 0.009 0.075 0.004 ndia-Hyderbad 11809 2 0.111 0.065 0.007 0.655 0.085 0.003 0.073 ndia-Hyderbad 11810 0 0.015 0.134 0.024 0.766 0.026 0.021 0.014 ndia-Hyderbad 11811 0 0.005 0.008 0.435 0.516 0.013 0.012 0.011 ndia-Hyderbad 11812 13 0.004 0.009 0.012 0.663 0.013 0.041 0.259 ndia-Hyderbad 11813 10 0.006 0.01 0.007 0.712 0.063 0.122 0.08 ndia-Hyderbad 11814 0 0.007 0.013 0.041 0.904 0.008 0.012 0.014 ndia-Hyderbad 11815 0 0.007 0.011 0.153 0.756 0.006 0.019 0.049 ndia-Hyderbad 11816 0 0.005 0.031 0.011 0.917 0.015 0.004 0.017 ndia-Hyderbad 11817 5 0.006 0.009 0.013 0.947 0.006 0.009 0.01 ndia-Hyderbad 11818 2 0.004 0.005 0.023 0.916 0.007 0.022 0.025 ndia-Hyderbad 11819 0 0.006 0.035 0.01 0.913 0.016 0.004 0.017 ndia-Hyderbad 11820 5 0.006 0.011 0.137 0.724 0.006 0.087 0.03 ndia-Hyderbad 11821 0 0.017 0.125 0.046 0.748 0.019 0.028 0.018 ndia-Hyderbad 11822 0 0.008 0.015 0.071 0.75 0.077 0.068 0.01 ndia-Andhra 10159 5 0.01 0.015 0.006 0.92 0.008 0.038 0.004 ndia-Andhra 10160 2 0.009 0.012 0.068 0.635 0.005 0.259 0.013 ndia-Andhra 10161 0 0.008 0.005 0.006 0.959 0.006 0.006 0.009 ndia-Andhra 10162 5 0.006 0.006 0.01 0.953 0.008 0.007 0.009 ndia-Andhra 10163 0 0.005 0.007 0.104 0.784 0.01 0.075 0.015 ndia-Andhra 10164 2 0.004 0.006 0.01 0.712 0.009 0.174 0.086 ndia-Andhra 10165 0 0.008 0.015 0.114 0.821 0.008 0.009 0.025 ndia-Andhra 10166 5 0.023 0.017 0.33 0.577 0.032 0.015 0.006 ndia-Andhra 10167 2 0.029 0.197 0.064 0.597 0.085 0.022 0.006 ndia-Andhra 10168 2 0.005 0.021 0.006 0.907 0.015 0.016 0.03 ndia-Andhra 10169 5 0.017 0.024 0.007 0.91 0.01 0.027 0.004 ndia-Andhra 10170 0 0.02 0.031 0.017 0.824 0.031 0.068 0.008 ndia-Andhra 10171 2 0.004 0.006 0.01 0.713 0.009 0.173 0.085 ndia-Andhra 10172 5 0.072 0.02 0.007 0.808 0.015 0.034 0.045 ndia-Andhra 10173 5 0.004 0.013 0.015 0.946 0.007 0.012 0.003 ndia-Andhra 10174 5 0.01 0.013 0.003 0.957 0.009 0.006 0.002 ndia-Andhra 10175 5 0.087 0.015 0.012 0.851 0.012 0.009 0.014 ndia-Andhra 10176 0 0.009 0.013 0.013 0.713 0.021 0.191 0.04 ndia-Andhra 10177 0 0.01 0.015 0.01 0.863 0.019 0.072 0.011 ndia-Andhra 10178 5 0.008 0.006 0.007 0.942 0.009 0.01 0.018 ndia-Andhra 10179 2 0.045 0.031 0.021 0.882 0.008 0.007 0.005 ndia-Andhra 10180 2 0.024 0.074 0.005 0.758 0.124 0.005 0.01 ndia-Andhra 10181 7 0.009 0.015 0.013 0.767 0.011 0.18 0.005 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

India-Kolkata 10113 7 0.014 0.019 0.075 0.833 0.025 0.019 0.014

India-Kolkata 10114 2 0.006 0.015 0.014 0.933 0.01 0.01 0.013

India-Kolkata 10115 0 0.029 0.115 0.024 0.769 0.037 0.018 0.008

India-Kolkata 10116 7 0.007 0.012 0.023 0.926 0.01 0.014 0.008

India-Kolkata 10117 5 0.011 0.045 0.262 0.138 0.045 0.48 0.019

India-Kolkata 10118 13 0.011 0.162 0.043 0.652 0.012 0.018 0.101

India-Kolkata 10119 7 0.021 0.028 0.013 0.834 0.088 0.005 0.012

Sri Lanka 8780 0 0.048 0.027 0.016 0.796 0.072 0.026 0.014

Sri Lanka 8781 5 0.046 0.043 0.011 0.796 0.083 0.009 0.012

Sri Lanka 8782 15 0.094 0.096 0.026 0.702 0.044 0.016 0.022

Sri Lanka 8783 0 0.031 0.051 0.06 0.718 0.078 0.016 0.046

Sri Lanka 8784 5 0.02 0.01 0.036 0.852 0.02 0.043 0.02

Sri Lanka 8785 7 0.077 0.09 0.024 0.545 0.064 0.015 0.185

Sri Lanka 8786 0 0.066 0.096 0.017 0.52 0.236 0.013 0.052

Sri Lanka 8787 0 0.242 0.025 0.009 0.68 0.017 0.005 0.022

Sri Lanka 8788 5 0.03 0.012 0.005 0.776 0.024 0.006 0.146

Sri Lanka 8789 2 0.419 0.025 0.007 0.429 0.089 0.006 0.024

Sri Lanka 8790 0 0.123 0.03 0.004 0.806 0.026 0.006 0.005

Sri Lanka 8791 5 0.007 0.01 0.07 0.811 0.011 0.009 0.082

Sri Lanka 8792 2 0.105 0.016 0.01 0.824 0.034 0.004 0.007

Sri Lanka 8793 5 0.211 0.01 0.015 0.693 0.054 0.003 0.013

Sri Lanka 8794 5 0.124 0.043 0.005 0.744 0.064 0.008 0.012

Sri Lanka 8795 0 0.021 0.092 0.032 0.807 0.025 0.016 0.007

Sri Lanka 8796 7 0.06 0.092 0.013 0.76 0.05 0.02 0.005

Sri Lanka 8797 2 0.416 0.009 0.018 0.381 0.042 0.037 0.097

Sri Lanka 8798 2 0.033 0.029 0.006 0.787 0.098 0.014 0.034

Sri Lanka 8799 7 0.183 0.006 0.117 0.666 0.007 0.003 0.018

Sri Lanka 8800 10 0.314 0.034 0.05 0.318 0.035 0.226 0.023

Sri Lanka 8801 10 0.125 0.03 0.334 0.278 0.019 0.008 0.205

Sri Lanka 8802 2 0.527 0.004 0.009 0.425 0.007 0.004 0.024

Sri Lanka 8803 15 0.044 0.023 0.174 0.671 0.066 0.011 0.012

Thailand 11688 7 0.003 0.003 0.003 0.976 0.003 0.009 0.003

Thailand 11689 15 0.004 0.005 0.003 0.964 0.004 0.009 0.011

Thailand 11691 55 0.012 0.01 0.01 0.71 0.013 0.232 0.013

Thailand 11698 23 0.015 0.008 0.006 0.641 0.01 0.315 0.005

Thailand 11702 10 0.009 0.004 0.006 0.965 0.005 0.009 0.003

Thailand 11703 13 0.004 0.003 0.003 0.971 0.003 0.013 0.004

Thailand 11705 21 0.003 0.005 0.004 0.969 0.009 0.006 0.003

Thailand 11707 10 0.004 0.004 0.005 0.966 0.005 0.013 0.003

Thailand 11708 2 0.008 0.025 0.025 0.893 0.013 0.029 0.006

Thailand 11709 15 0.005 0.005 0.004 0.968 0.005 0.009 0.003

Thailand 11710 15 0.006 0.006 0.004 0.957 0.005 0.018 0.004

Thailand 11711 2 0.004 0.003 0.007 0.925 0.003 0.054 0.004

Thailand 11714 13 0.01 0.005 0.021 0.931 0.012 0.016 0.005

Thailand 11715 21 0.007 0.004 0.004 0.95 0.005 0.023 0.007

Thailand 11717 13 0.006 0.006 0.003 0.966 0.005 0.008 0.005

Thailand 11718 7 0.022 0.018 0.012 0.895 0.011 0.036 0.006

Thailand 11720 7 0.003 0.003 0.002 0.967 0.006 0.016 0.003

Vietnam 8844 5 0.004 0.005 0.007 0.959 0.005 0.015 0.005

Vietnam 8845 13 0.005 0.006 0.01 0.961 0.005 0.006 0.007

Vietnam 8846 13 0.083 0.024 0.027 0.791 0.033 0.038 0.005

Vietnam 8847 15 0.182 0.006 0.005 0.765 0.013 0.016 0.013

Vietnam 8848 10 0.096 0.013 0.01 0.858 0.013 0.005 0.005

Vietnam 8849 2 0.009 0.013 0.006 0.937 0.018 0.013 0.004

Vietnam 8850 5 0.423 0.013 0.011 0.378 0.026 0.144 0.005

Vietnam 8851 10 0.009 0.009 0.004 0.95 0.008 0.009 0.011 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Vietnam 8852 13 0.074 0.011 0.015 0.561 0.269 0.064 0.007

Vietnam 8853 10 0.021 0.028 0.005 0.591 0.341 0.008 0.006

Vietnam 8854 7 0.209 0.007 0.004 0.6 0.069 0.055 0.055

Vietnam 8855 10 0.004 0.005 0.004 0.905 0.005 0.072 0.005

Vietnam 8856 10 0.006 0.03 0.003 0.869 0.083 0.005 0.003

Vietnam 8857 7 0.023 0.011 0.011 0.908 0.014 0.006 0.028

Vietnam 8858 13 0.006 0.006 0.004 0.915 0.005 0.061 0.003

Vietnam 8859 13 0.015 0.006 0.008 0.728 0.013 0.22 0.01

Vietnam 8860 10 0.195 0.132 0.031 0.409 0.164 0.059 0.009

Vietnam 8861 10 0.08 0.016 0.016 0.711 0.137 0.021 0.02

Vietnam 8862 7 0.011 0.015 0.046 0.844 0.009 0.072 0.004

Vietnam 8863 15 0.022 0.02 0.011 0.831 0.017 0.092 0.007

Taiwan 8681 7 0.737 0.01 0.008 0.009 0.011 0.21 0.015

Taiwan 8682 2 0.004 0.004 0.006 0.243 0.006 0.72 0.017

Taiwan 8683 7 0.077 0.012 0.012 0.069 0.019 0.8 0.011

Taiwan 8684 7 0.735 0.03 0.008 0.035 0.011 0.17 0.011

Taiwan 8685 2 0.094 0.006 0.006 0.283 0.013 0.595 0.003

Taiwan 8686 7 0.332 0.008 0.012 0.029 0.044 0.567 0.008

Taiwan 8687 2 0.056 0.024 0.034 0.349 0.094 0.406 0.038

Taiwan 8688 47 0.021 0.023 0.035 0.199 0.012 0.702 0.008

Taiwan 8689 5 0.098 0.014 0.165 0.047 0.051 0.596 0.029

Taiwan 8690 7 0.015 0.006 0.007 0.421 0.017 0.495 0.039

Taiwan 8691 26 0.018 0.032 0.02 0.579 0.025 0.319 0.007

Taiwan 8692 5 0.011 0.007 0.011 0.118 0.007 0.841 0.005

Taiwan 8693 7 0.027 0.012 0.006 0.046 0.022 0.881 0.006

Taiwan 8694 5 0.379 0.006 0.003 0.564 0.005 0.041 0.003

Taiwan 8695 2 0.025 0.089 0.043 0.014 0.012 0.812 0.005

Taiwan 8696 23 0.929 0.012 0.006 0.014 0.009 0.027 0.004

Taiwan 8697 5 0.914 0.011 0.005 0.004 0.052 0.006 0.008

Taiwan 8698 7 0.972 0.006 0.004 0.004 0.006 0.004 0.003

Taiwan 8699 10 0.18 0.029 0.007 0.213 0.114 0.384 0.073

Taiwan 8700 7 0.051 0.055 0.006 0.425 0.049 0.399 0.015

Taiwan 8701 2 0.166 0.018 0.004 0.173 0.019 0.611 0.01

Taiwan 8702 5 0.058 0.273 0.02 0.029 0.039 0.568 0.013

Taiwan 8703 7 0.95 0.018 0.003 0.006 0.013 0.007 0.003

Taiwan 8704 2 0.02 0.008 0.027 0.058 0.145 0.731 0.011

Taiwan 8705 5 0.013 0.022 0.019 0.332 0.013 0.585 0.016

Taiwan 8706 7 0.011 0.013 0.023 0.088 0.014 0.844 0.008

Taiwan 8707 7 0.029 0.016 0.004 0.125 0.004 0.784 0.037

Taiwan 8708 15 0.952 0.006 0.007 0.009 0.008 0.015 0.004

Taiwan 8709 13 0.034 0.024 0.013 0.118 0.119 0.669 0.024

Japan-Oita 11967 5 0.015 0.015 0.011 0.038 0.017 0.894 0.009

Japan-Oita 11968 7 0.017 0.024 0.011 0.644 0.023 0.274 0.007

Japan-Oita 11969 7 0.034 0.02 0.01 0.027 0.014 0.888 0.007

Japan-Oita 11970 7 0.024 0.03 0.013 0.209 0.035 0.663 0.027

Japan-Oita 11971 7 0.043 0.004 0.024 0.041 0.009 0.871 0.007

Japan-Oita 11972 5 0.007 0.004 0.008 0.015 0.005 0.957 0.004

Japan-Oita 11973 7 0.037 0.016 0.092 0.025 0.012 0.797 0.021

Japan-Oita 11974 21 0.02 0.017 0.021 0.156 0.021 0.757 0.008

Japan-Oita 11975 10 0.027 0.01 0.007 0.376 0.017 0.559 0.003

Japan-Oita 11976 13 0.324 0.014 0.004 0.031 0.022 0.597 0.007

Japan-Oita 11977 7 0.342 0.008 0.005 0.028 0.045 0.568 0.004

Japan-Oita 11979 10 0.009 0.015 0.036 0.023 0.017 0.894 0.007

Japan-Oita 11980 7 0.021 0.007 0.017 0.022 0.011 0.916 0.007

Japan-Oita 11981 7 0.003 0.003 0.01 0.021 0.003 0.955 0.004

Japan-Oita 11982 13 0.061 0.006 0.007 0.173 0.028 0.721 0.004 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

Japan-Oita 11985 10 0.005 0.018 0.007 0.295 0.015 0.656 0.004

Japan-Oita 11986 10 0.121 0.123 0.007 0.06 0.074 0.599 0.016

Japan-Kanazawa 11929 10 0.976 0.004 0.003 0.003 0.007 0.003 0.003

Japan-Kanazawa 11931 10 0.059 0.011 0.009 0.107 0.006 0.8 0.008

Japan-Kanazawa 11932 7 0.227 0.012 0.005 0.046 0.009 0.691 0.01

Japan-Kanazawa 11933 15 0.005 0.004 0.247 0.017 0.004 0.72 0.003

Japan-Kanazawa 11934 7 0.027 0.008 0.009 0.015 0.012 0.927 0.003

Japan-Kanazawa 11936 7 0.012 0.008 0.006 0.107 0.012 0.852 0.004

Japan-Kanazawa 11937 10 0.014 0.005 0.003 0.011 0.008 0.956 0.003

Japan-Kanazawa 11939 10 0.022 0.006 0.007 0.093 0.012 0.846 0.014

Japan-Kanazawa 11940 10 0.006 0.007 0.007 0.008 0.007 0.95 0.015

Japan-Kanazawa 11941 10 0.008 0.014 0.004 0.019 0.014 0.921 0.021

Japan-Kanazawa 11942 7 0.006 0.011 0.053 0.048 0.011 0.865 0.006

Japan-Kanazawa 11943 28 0.039 0.024 0.009 0.309 0.034 0.546 0.039

Japan-Kanazawa 11944 39 0.888 0.032 0.04 0.011 0.009 0.011 0.008

Japan-Kanazawa 11945 10 0.038 0.006 0.025 0.045 0.008 0.869 0.009

Japan-Kanazawa 11946 10 0.01 0.029 0.012 0.009 0.004 0.932 0.004

Japan-Ohmiya 11947 5 0.065 0.046 0.014 0.038 0.014 0.817 0.007

Japan-Ohmiya 11948 10 0.071 0.121 0.015 0.01 0.059 0.693 0.03

Japan-Ohmiya 11951 5 0.052 0.031 0.017 0.113 0.019 0.739 0.028

Japan-Ohmiya 11953 7 0.009 0.009 0.005 0.04 0.014 0.918 0.004

Japan-Ohmiya 11954 5 0.177 0.044 0.008 0.194 0.015 0.552 0.01

Japan-Ohmiya 11955 7 0.108 0.023 0.193 0.09 0.017 0.564 0.004

Japan-Ohmiya 11956 7 0.042 0.011 0.015 0.137 0.016 0.77 0.009

Japan-Ohmiya 11957 2 0.189 0.028 0.012 0.008 0.048 0.696 0.019

Japan-Ohmiya 11959 18 0.049 0.253 0.008 0.01 0.012 0.662 0.005

Japan-Ohmiya 11960 2 0.013 0.006 0.004 0.065 0.004 0.887 0.021

Japan-Ohmiya 11961 5 0.004 0.014 0.003 0.007 0.008 0.959 0.005

Japan-Ohmiya 11962 13 0.01 0.007 0.014 0.192 0.006 0.761 0.01

Japan-Ohmiya 11963 2 0.248 0.005 0.007 0.021 0.01 0.703 0.005

Japan-Ohmiya 11964 5 0.161 0.022 0.019 0.142 0.021 0.606 0.029

Japan-Ohmiya 11965 7 0.006 0.009 0.021 0.023 0.006 0.932 0.003

Japan-Ohmiya 11966 2 0.103 0.026 0.017 0.035 0.014 0.795 0.01

Japan-Sapporo 11907 2 0.016 0.015 0.011 0.026 0.015 0.908 0.008

Japan-Sapporo 11909 7 0.941 0.008 0.004 0.014 0.017 0.012 0.005

Japan-Sapporo 11911 5 0.329 0.005 0.006 0.011 0.107 0.531 0.011

Japan-Sapporo 11913 7 0.437 0.005 0.009 0.006 0.008 0.532 0.003

Japan-Sapporo 11914 13 0.254 0.004 0.004 0.014 0.008 0.711 0.004

Japan-Sapporo 11915 10 0.017 0.028 0.011 0.625 0.011 0.294 0.013

Japan-Sapporo 11916 5 0.738 0.051 0.005 0.06 0.122 0.02 0.003

Japan-Sapporo 11917 5 0.838 0.04 0.036 0.007 0.034 0.035 0.009

Japan-Sapporo 11918 7 0.343 0.025 0.019 0.056 0.02 0.522 0.014

Japan-Sapporo 11921 7 0.006 0.006 0.016 0.05 0.007 0.907 0.008

Japan-Sapporo 11922 5 0.042 0.006 0.004 0.029 0.013 0.904 0.003

Japan-Sapporo 11923 7 0.192 0.009 0.015 0.086 0.037 0.655 0.006

Japan-Sapporo 11924 5 0.505 0.006 0.014 0.371 0.041 0.056 0.008

Japan-Sapporo 11925 7 0.005 0.006 0.01 0.086 0.005 0.883 0.004

Japan-Sapporo 11926 5 0.391 0.059 0.005 0.014 0.011 0.515 0.005

China-Henan 8869 2 0.047 0.076 0.003 0.009 0.028 0.828 0.009

China-Henan 8870 5 0.003 0.002 0.002 0.006 0.003 0.978 0.006

China-Henan 8871 5 0.006 0.017 0.019 0.005 0.008 0.843 0.103

China-Henan 8872 2 0.006 0.005 0.008 0.011 0.004 0.953 0.014

China-Henan 8873 5 0.009 0.008 0.005 0.006 0.018 0.948 0.007

China-Henan 8874 5 0.004 0.013 0.012 0.018 0.007 0.913 0.033

China-Henan 8875 5 0.003 0.003 0.003 0.002 0.002 0.985 0.002

China-Henan 8876 5 0.003 0.002 0.004 0.003 0.002 0.983 0.002 Table 24 - Population clustering of each random bred individual in the database by

STRs at K = 7

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7

China-Henan 8877 7 0.005 0.017 0.021 0.03 0.007 0.903 0.018

China-Henan 8878 5 0.003 0.007 0.003 0.003 0.006 0.976 0.002

China-Henan 8879 2 0.003 0.004 0.005 0.018 0.004 0.955 0.012

China-Henan 8880 7 0.006 0.005 0.014 0.004 0.004 0.954 0.013

China-Henan 8881 5 0.003 0.039 0.005 0.006 0.026 0.885 0.036

China-Henan 8882 5 0.022 0.016 0.019 0.04 0.02 0.874 0.009

China-Henan 8883 2 0.003 0.005 0.004 0.005 0.006 0.971 0.005

China-Henan 8884 2 0.002 0.011 0.036 0.021 0.004 0.907 0.02

China-Henan 8885 2 0.005 0.005 0.009 0.004 0.005 0.968 0.003

China-Henan 8886 2 0.003 0.009 0.005 0.004 0.006 0.967 0.006

China-Henan 8887 2 0.016 0.016 0.014 0.004 0.017 0.876 0.057

China-Henan 8888 2 0.01 0.012 0.003 0.014 0.008 0.933 0.02

South Korea 2769 5 0.004 0.003 0.002 0.005 0.004 0.979 0.003

South Korea 2772 47 0.004 0.005 0.011 0.012 0.005 0.955 0.007

South Korea 2775 13 0.007 0.006 0.006 0.005 0.013 0.959 0.005

South Korea 2776 13 0.026 0.015 0.014 0.047 0.071 0.824 0.004

South Korea 2779 2 0.004 0.004 0.007 0.016 0.005 0.957 0.007

South Korea 2784 2 0.003 0.003 0.002 0.007 0.003 0.978 0.004

South Korea 2785 2 0.011 0.011 0.005 0.009 0.015 0.865 0.084

South Korea 2786 2 0.007 0.004 0.002 0.005 0.005 0.975 0.002

South Korea 7671 2 0.007 0.007 0.004 0.008 0.011 0.96 0.004

South Korea 7672 2 0.007 0.006 0.019 0.008 0.006 0.952 0.004

South Korea 7673 2 0.006 0.01 0.003 0.003 0.027 0.93 0.019

South Korea 7674 2 0.021 0.007 0.006 0.008 0.079 0.874 0.006

South Korea 7675 10 0.008 0.007 0.003 0.006 0.012 0.961 0.003

South Korea 7676 7 0.009 0.007 0.013 0.008 0.009 0.941 0.013

South Korea 7677 15 0.232 0.023 0.006 0.006 0.084 0.365 0.284

South Korea 7678 2 0.014 0.008 0.005 0.005 0.009 0.952 0.008

South Korea 7679 5 0.004 0.006 0.004 0.004 0.005 0.973 0.005

South Korea 7680 7 0.019 0.014 0.011 0.005 0.021 0.925 0.005

South Korea 7681 13 0.013 0.154 0.003 0.007 0.053 0.758 0.013

South Korea 7682 5 0.006 0.016 0.005 0.008 0.011 0.951 0.003

South Korea 7683 7 0.003 0.003 0.004 0.008 0.004 0.974 0.004

South Korea 7684 7 0.029 0.177 0.009 0.048 0.157 0.57 0.009

South Korea 7685 18 0.014 0.008 0.006 0.022 0.008 0.937 0.005

South Korea 7686 10 0.027 0.01 0.004 0.007 0.013 0.934 0.005

South Korea 7687 10 0.062 0.049 0.056 0.125 0.01 0.695 0.004

South Korea 7688 2 0.501 0.03 0.025 0.055 0.03 0.356 0.004

South Korea 7689 10 0.007 0.006 0.006 0.006 0.007 0.963 0.005

South Korea 7690 10 0.006 0.01 0.011 0.025 0.008 0.938 0.003

South Korea 7691 7 0.008 0.008 0.016 0.008 0.015 0.938 0.007

South Korea 7692 5 0.008 0.015 0.01 0.058 0.026 0.87 0.012

South Korea 7693 2 0.052 0.047 0.03 0.021 0.106 0.734 0.011

South Korea 7694 5 0.003 0.003 0.003 0.005 0.003 0.98 0.002

South Korea 7695 7 0.039 0.04 0.063 0.004 0.075 0.774 0.006

South Korea 7696 5 0.004 0.003 0.003 0.004 0.006 0.972 0.007

South Korea 7697 10 0.007 0.005 0.005 0.012 0.008 0.949 0.014

South Korea 7698 23 0.027 0.042 0.017 0.213 0.055 0.612 0.034

South Korea 7699 7 0.005 0.01 0.004 0.014 0.014 0.941 0.011

South Korea 7700 7 0.007 0.013 0.005 0.061 0.028 0.865 0.02 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

USA-NY 2547 1 0.086 0.006 0.006 0.775 0.077 0.037 0.004 0.009

USA-NY 2559 3 0.009 0.002 0.008 0.618 0.006 0.348 0.004 0.004

USA-NY 2568 0 0.003 0.002 0.003 0.98 0.003 0.003 0.003 0.003

USA-NY 2569 5 0.015 0.008 0.004 0.942 0.009 0.005 0.011 0.005

USA-NY 2572 1 0.009 0.005 0.007 0.931 0.02 0.016 0.005 0.009

USA-NY 2578 17 0.041 0.008 0.003 0.883 0.048 0.009 0.003 0.005

USA-NY 2590 1 0.013 0.002 0.008 0.942 0.007 0.022 0.003 0.004

USA-NY 2591 2 0.015 0.009 0.004 0.944 0.004 0.007 0.007 0.011

USA-NY 2597 4 0.023 0.028 0.03 0.863 0.035 0.01 0.005 0.006

USA-MS 9971 3 0.009 0.007 0.058 0.885 0.024 0.007 0.005 0.006

USA-MS 9972 2 0.058 0.017 0.019 0.787 0.087 0.014 0.003 0.014

USA-MS 9974 2 0.01 0.005 0.119 0.823 0.022 0.011 0.003 0.007

USA-MS 9977 5 0.013 0.005 0.013 0.946 0.006 0.006 0.008 0.002

USA-MS 9980 2 0.01 0.01 0.011 0.904 0.013 0.028 0.004 0.019

USA-MS 9983 2 0.006 0.008 0.011 0.942 0.007 0.009 0.003 0.013

USA-MS 9985 3 0.009 0.002 0.228 0.719 0.005 0.028 0.005 0.004

USA-MS 9987 3 0.055 0.006 0.081 0.778 0.02 0.043 0.008 0.008

USA-MS 9989 2 0.015 0.003 0.03 0.914 0.019 0.007 0.008 0.003

USA-MS 9992 3 0.068 0.009 0.062 0.785 0.008 0.06 0.003 0.006

USA-HI 5366 3 0.023 0.003 0.018 0.712 0.021 0.019 0.176 0.028

USA-HI 5367 1 0.089 0.016 0.057 0.735 0.057 0.007 0.01 0.029

USA-HI 5371 1 0.026 0.015 0.018 0.696 0.047 0.185 0.01 0.004

USA-HI 5372 2 0.065 0.008 0.013 0.625 0.253 0.025 0.005 0.006

USA-HI 5379 1 0.067 0.016 0.016 0.84 0.01 0.023 0.026 0.004

USA-HI 5380 1 0.018 0.002 0.002 0.954 0.002 0.009 0.009 0.004

USA-HI 5383 3 0.028 0.199 0.011 0.401 0.015 0.147 0.017 0.182

USA-HI 5384 5 0.025 0.005 0.002 0.847 0.004 0.098 0.003 0.015

USA-HI 5401 2 0.039 0.004 0.02 0.848 0.007 0.026 0.024 0.033

USA-HI 5402 2 0.017 0.021 0.045 0.809 0.016 0.024 0.063 0.005

Brazil 7961 1 0.017 0.003 0.002 0.949 0.003 0.013 0.009 0.003

Brazil 7962 2 0.009 0.002 0.007 0.816 0.008 0.149 0.003 0.005

Brazil 7963 17 0.008 0.002 0.336 0.625 0.008 0.01 0.003 0.009

Brazil 7964 2 0.006 0.004 0.004 0.95 0.004 0.015 0.009 0.009

Brazil 7965 27 0.005 0.003 0.008 0.968 0.003 0.004 0.003 0.006

Brazil 7966 2 0.02 0.011 0.007 0.93 0.014 0.013 0.002 0.003

Brazil 7968 23 0.012 0.006 0.007 0.935 0.007 0.013 0.014 0.005

Brazil 7969 1 0.004 0.002 0.004 0.978 0.003 0.004 0.002 0.002

Brazil 7970 1 0.004 0.002 0.008 0.969 0.003 0.005 0.003 0.006

Brazil 7971 1 0.006 0.004 0.004 0.972 0.003 0.004 0.005 0.003

Brazil 7972 1 0.012 0.004 0.001 0.965 0.003 0.007 0.004 0.005

Brazil 7973 0 0.014 0.003 0.004 0.956 0.004 0.006 0.01 0.004

Brazil 7974 3 0.012 0.004 0.007 0.946 0.006 0.016 0.004 0.004

Brazil 7975 5 0.01 0.003 0.004 0.946 0.011 0.007 0.015 0.004

Brazil 7976 1 0.004 0.003 0.004 0.973 0.003 0.005 0.006 0.003

Brazil 7977 2 0.008 0.002 0.001 0.956 0.002 0.018 0.009 0.003

Brazil 7978 1 0.009 0.003 0.005 0.876 0.006 0.007 0.009 0.084

Brazil 7979 2 0.014 0.004 0.011 0.929 0.007 0.008 0.016 0.011

Brazil 7980 2 0.029 0.009 0.002 0.643 0.015 0.268 0.022 0.01

Brazil 7981 2 0.004 0.002 0.005 0.766 0.003 0.006 0.209 0.005

Brazil 7982 20 0.017 0.006 0.011 0.755 0.14 0.01 0.042 0.019

Brazil 7983 4 0.04 0.007 0.108 0.713 0.094 0.025 0.004 0.007

Brazil 7984 15 0.017 0.005 0.277 0.606 0.011 0.035 0.042 0.006

Brazil 7985 5 0.015 0.003 0.007 0.922 0.012 0.015 0.01 0.015

Brazil 7986 3 0.007 0.004 0.104 0.813 0.008 0.048 0.012 0.005

Brazil 7987 2 0.028 0.003 0.08 0.835 0.025 0.021 0.003 0.005

Brazil 7988 0 0.014 0.006 0.092 0.807 0.035 0.023 0.01 0.012 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Brazil 7989 1 0.008 0.004 0.263 0.69 0.015 0.008 0.005 0.008

Brazil 7990 1 0.005 0.002 0.105 0.867 0.008 0.004 0.003 0.005

Finland 8077 11 0.047 0.008 0.002 0.928 0.003 0.006 0.003 0.004

Finland 8084 11 0.263 0.006 0.005 0.69 0.014 0.004 0.013 0.004

Finland 8086 5 0.061 0.006 0.003 0.881 0.008 0.033 0.004 0.004

Finland 8089 8 0.014 0.015 0.005 0.798 0.116 0.012 0.018 0.022

Finland 8093 16 0.175 0.004 0.002 0.749 0.004 0.058 0.004 0.005

Finland 8094 9 0.04 0.076 0.015 0.808 0.019 0.032 0.003 0.008

Finland 8096 7 0.046 0.01 0.002 0.927 0.002 0.007 0.002 0.004

Finland 8107 24 0.004 0.002 0.001 0.982 0.003 0.003 0.002 0.003

Finland 8110 10 0.064 0.006 0.004 0.859 0.047 0.013 0.003 0.004

Finland 8116 16 0.01 0.006 0.009 0.899 0.012 0.011 0.009 0.044

Finland 8120 21 0.03 0.004 0.003 0.929 0.003 0.015 0.012 0.004

Germany 8711 4 0.006 0.002 0.002 0.976 0.002 0.005 0.003 0.003

Germany 8712 3 0.008 0.015 0.003 0.951 0.011 0.003 0.004 0.005

Germany 8713 3 0.073 0.015 0.004 0.822 0.008 0.03 0.031 0.016

Germany 8714 2 0.061 0.024 0.007 0.878 0.009 0.013 0.004 0.004

Germany 8715 4 0.014 0.005 0.002 0.964 0.003 0.006 0.002 0.003

Germany 8716 3 0.008 0.004 0.003 0.968 0.005 0.006 0.004 0.003

Germany 8717 11 0.008 0.005 0.003 0.968 0.006 0.005 0.003 0.002

Germany 8720 3 0.009 0.004 0.61 0.348 0.017 0.007 0.003 0.003

Germany 8721 10 0.101 0.004 0.002 0.87 0.002 0.012 0.002 0.006

Germany 8727 1 0.021 0.004 0.002 0.931 0.007 0.024 0.004 0.007

Germany 8728 10 0.006 0.003 0.003 0.964 0.005 0.005 0.003 0.01

Germany 8729 2 0.005 0.002 0.002 0.983 0.002 0.003 0.002 0.002

Germany 8730 22 0.048 0.002 0.007 0.869 0.007 0.015 0.038 0.014

Germany 8731 12 0.007 0.002 0.002 0.974 0.003 0.005 0.004 0.004

Germany 8732 4 0.015 0.003 0.002 0.956 0.006 0.009 0.003 0.007

Germany 8733 16 0.018 0.01 0.041 0.855 0.004 0.007 0.005 0.06

Germany 8734 1 0.008 0.002 0.001 0.976 0.002 0.007 0.002 0.002

Germany 8735 2 0.022 0.003 0.007 0.952 0.005 0.005 0.004 0.002

Germany 8736 1 0.026 0.015 0.002 0.919 0.005 0.023 0.007 0.003

Germany 8737 3 0.037 0.011 0.005 0.916 0.006 0.007 0.005 0.014

Germany 8738 10 0.191 0.038 0.002 0.751 0.004 0.008 0.002 0.003

Germany 8739 2 0.012 0.002 0.002 0.967 0.002 0.008 0.002 0.005

Germany 8741 11 0.04 0.003 0.002 0.935 0.006 0.008 0.003 0.003

Germany 8742 9 0.022 0.003 0.006 0.956 0.004 0.004 0.004 0.002

Germany 8744 6 0.23 0.004 0.085 0.539 0.008 0.108 0.02 0.006

Germany 8745 23 0.084 0.005 0.002 0.888 0.004 0.01 0.003 0.006

Germany 8746 1 0.007 0.003 0.004 0.958 0.004 0.005 0.002 0.016

Germany 8747 13 0.004 0.002 0.003 0.978 0.004 0.004 0.003 0.003

Germany 8749 2 0.007 0.004 0.002 0.976 0.003 0.003 0.002 0.003

Italy-Milan 8050 1 0.353 0.055 0.02 0.483 0.016 0.01 0.058 0.005

Italy-Milan 8057 2 0.633 0.011 0.058 0.265 0.018 0.005 0.004 0.006

Italy-Milan 8060 1 0.353 0.011 0.046 0.551 0.009 0.015 0.003 0.012

Italy-Milan 8061 2 0.327 0.012 0.154 0.464 0.009 0.019 0.006 0.009

Italy-Milan 8062 3 0.644 0.004 0.005 0.273 0.007 0.056 0.004 0.006

Italy-Milan 8065 5 0.032 0.004 0.004 0.508 0.004 0.434 0.004 0.009

Italy-Milan 8066 1 0.349 0.007 0.082 0.529 0.018 0.006 0.003 0.005

Italy-Milan 8067 2 0.065 0.016 0.041 0.513 0.006 0.338 0.003 0.019

Italy-Milan 8068 2 0.543 0.004 0.007 0.353 0.007 0.075 0.004 0.009

Italy-Milan 8069 1 0.412 0.008 0.039 0.429 0.083 0.018 0.005 0.006

Italy-Milan 8071 3 0.045 0.006 0.006 0.894 0.007 0.016 0.01 0.016

Italy-Milan 8072 3 0.507 0.006 0.083 0.259 0.029 0.042 0.07 0.004

Italy-Milan 8073 1 0.124 0.015 0.01 0.66 0.013 0.169 0.006 0.003

Italy-Milan 8074 2 0.008 0.002 0.292 0.66 0.013 0.017 0.003 0.005 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Italy-Rome 8586 1 0.061 0.009 0.003 0.517 0.009 0.359 0.014 0.027

Italy-Rome 8589 3 0.088 0.003 0.005 0.788 0.015 0.072 0.025 0.005

Italy-Rome 8592 2 0.458 0.075 0.005 0.394 0.034 0.018 0.011 0.005

Italy-Rome 8594 1 0.241 0.017 0.016 0.679 0.011 0.024 0.006 0.007

Italy-Rome 8595 1 0.122 0.091 0.016 0.565 0.093 0.09 0.015 0.008

Italy-Rome 8596 1 0.161 0.006 0.007 0.459 0.009 0.345 0.008 0.005

Italy-Rome 8597 1 0.053 0.026 0.016 0.704 0.012 0.174 0.011 0.004

Italy-Rome 8599 1 0.024 0.004 0.015 0.854 0.009 0.061 0.029 0.003

Italy-Rome 8601 2 0.112 0.144 0.006 0.645 0.01 0.036 0.038 0.009

Italy-Rome 8602 3 0.237 0.006 0.006 0.635 0.036 0.071 0.005 0.004

Italy-Rome 8603 1 0.021 0.012 0.015 0.882 0.012 0.05 0.005 0.003

Italy-Rome 8604 3 0.142 0.007 0.003 0.547 0.004 0.025 0.263 0.008

Italy-Rome 8609 2 0.149 0.006 0.005 0.612 0.007 0.211 0.006 0.004

Italy-Rome 8610 2 0.235 0.005 0.005 0.59 0.007 0.139 0.01 0.01

Italy-Rome 8611 2 0.421 0.021 0.004 0.504 0.007 0.032 0.004 0.007

Turkey 6477 3 0.924 0.003 0.006 0.044 0.007 0.008 0.005 0.003

Turkey 6478 12 0.927 0.004 0.003 0.013 0.005 0.032 0.014 0.003

Turkey 6480 9 0.703 0.003 0.017 0.227 0.005 0.018 0.02 0.008

Turkey 6481 13 0.93 0.015 0.003 0.008 0.007 0.01 0.018 0.009

Turkey 6482 8 0.012 0.005 0.959 0.006 0.004 0.005 0.005 0.004

Turkey 6484 10 0.01 0.005 0.003 0.965 0.005 0.004 0.002 0.006

Turkey 6486 6 0.896 0.005 0.004 0.067 0.006 0.01 0.006 0.007

Turkey 6487 13 0.906 0.002 0.003 0.052 0.005 0.026 0.003 0.003

Turkey 6488 11 0.748 0.005 0.003 0.203 0.006 0.015 0.014 0.007

Turkey 6491 6 0.781 0.014 0.002 0.039 0.003 0.157 0.003 0.002

Turkey 6494 12 0.241 0.026 0.01 0.014 0.008 0.688 0.006 0.007

Turkey 6496 10 0.875 0.004 0.004 0.011 0.008 0.086 0.003 0.009

Turkey 6499 11 0.096 0.008 0.679 0.034 0.088 0.028 0.008 0.059

Turkey 6500 12 0.839 0.004 0.007 0.098 0.006 0.015 0.007 0.023

Turkey 6502 6 0.904 0.004 0.004 0.029 0.01 0.035 0.004 0.01

Turkey 6503 8 0.932 0.005 0.006 0.029 0.008 0.007 0.003 0.012

Turkey 6507 13 0.928 0.003 0.002 0.031 0.004 0.025 0.005 0.002

Turkey 6510 11 0.763 0.016 0.003 0.024 0.018 0.162 0.012 0.003

Turkey 6512 12 0.569 0.021 0.004 0.221 0.039 0.11 0.005 0.03

Turkey 6513 15 0.026 0.009 0.534 0.13 0.008 0.025 0.026 0.242

Turkey 6514 10 0.516 0.05 0.006 0.293 0.021 0.088 0.022 0.004

Turkey 6516 11 0.893 0.004 0.021 0.028 0.025 0.01 0.009 0.011

Turkey 6519 11 0.59 0.005 0.003 0.358 0.004 0.016 0.009 0.014

Turkey 6520 15 0.859 0.004 0.002 0.076 0.003 0.045 0.005 0.008

Turkey 6521 6 0.724 0.007 0.006 0.068 0.006 0.136 0.021 0.034

Turkey 6729 2 0.374 0.456 0.004 0.023 0.024 0.012 0.072 0.035

Turkey 6730 2 0.767 0.003 0.002 0.099 0.003 0.11 0.007 0.01

Turkey 6731 3 0.794 0.012 0.004 0.161 0.006 0.016 0.002 0.004

Turkey 6732 2 0.7 0.038 0.018 0.024 0.124 0.04 0.038 0.018

Turkey 6733 4 0.93 0.005 0.008 0.013 0.009 0.018 0.01 0.007

Turkey 6734 1 0.891 0.006 0.013 0.019 0.011 0.038 0.013 0.009

Turkey 6735 3 0.974 0.003 0.002 0.003 0.002 0.011 0.002 0.003

Turkey 6736 1 0.338 0.008 0.002 0.198 0.01 0.436 0.003 0.005

Turkey 6738 3 0.936 0.003 0.001 0.021 0.003 0.029 0.002 0.004

Turkey 6739 4 0.62 0.017 0.004 0.3 0.025 0.021 0.003 0.01

Turkey 6740 1 0.003 0.002 0.001 0.984 0.002 0.002 0.002 0.003

Turkey 6741 2 0.021 0.007 0.824 0.066 0.016 0.008 0.021 0.036

Turkey 6742 4 0.906 0.01 0.007 0.03 0.027 0.007 0.008 0.004

Turkey 6743 2 0.813 0.004 0.007 0.139 0.007 0.019 0.003 0.008

Turkey 6745 1 0.791 0.007 0.002 0.159 0.004 0.014 0.017 0.006

Turkey 6746 2 0.435 0.018 0.208 0.119 0.173 0.01 0.026 0.011 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Turkey 6748 3 0.829 0.01 0.017 0.033 0.026 0.047 0.004 0.035

Turkey 6749 2 0.824 0.007 0.013 0.135 0.004 0.01 0.004 0.002

Turkey 6750 3 0.571 0.002 0.002 0.371 0.004 0.043 0.003 0.005

Turkey 6753 2 0.706 0.028 0.005 0.167 0.02 0.042 0.028 0.004

Turkey 6754 3 0.914 0.033 0.004 0.032 0.004 0.007 0.003 0.003

Turkey 6755 3 0.918 0.005 0.003 0.05 0.003 0.011 0.007 0.003

Turkey 6756 2 0.782 0.003 0.002 0.187 0.003 0.018 0.002 0.004

Turkey 6758 3 0.755 0.009 0.005 0.066 0.017 0.118 0.025 0.005

Turkey 6759 4 0.832 0.004 0.003 0.085 0.011 0.045 0.008 0.013

Turkey 6760 2 0.77 0.005 0.002 0.189 0.003 0.006 0.003 0.021

Cyprus 10128 3 0.908 0.004 0.003 0.018 0.017 0.015 0.031 0.004

Cyprus 10129 1 0.478 0.008 0.004 0.354 0.006 0.136 0.005 0.008

Cyprus 10130 3 0.797 0.002 0.003 0.096 0.012 0.083 0.003 0.005

Cyprus 10131 1 0.735 0.008 0.022 0.027 0.116 0.061 0.026 0.005

Cyprus 10132 0 0.667 0.014 0.017 0.005 0.02 0.245 0.008 0.024

Cyprus 10133 3 0.892 0.008 0.01 0.035 0.009 0.032 0.006 0.008

Cyprus 10134 1 0.793 0.01 0.003 0.017 0.012 0.131 0.005 0.03

Cyprus 10135 2 0.576 0.004 0.004 0.042 0.033 0.245 0.01 0.085

Cyprus 10136 3 0.697 0.039 0.002 0.206 0.009 0.017 0.015 0.015

Cyprus 10137 1 0.886 0.061 0.002 0.007 0.006 0.026 0.01 0.003

Cyprus 10138 2 0.551 0.013 0.007 0.006 0.012 0.397 0.006 0.007

Cyprus 10139 3 0.386 0.026 0.027 0.014 0.082 0.389 0.044 0.032

Cyprus 10140 0 0.939 0.006 0.004 0.011 0.006 0.023 0.005 0.006

Cyprus 10141 1 0.828 0.004 0.003 0.04 0.007 0.106 0.006 0.007

Cyprus 10142 1 0.879 0.011 0.007 0.005 0.017 0.05 0.009 0.021

Cyprus 10143 1 0.858 0.006 0.002 0.005 0.005 0.115 0.007 0.003

Cyprus 10144 3 0.83 0.007 0.005 0.008 0.048 0.057 0.005 0.039

Cyprus 10145 2 0.942 0.004 0.002 0.027 0.003 0.015 0.004 0.003

Cyprus 10146 0 0.12 0.014 0.003 0.007 0.019 0.793 0.006 0.038

Cyprus 10147 2 0.968 0.002 0.002 0.012 0.003 0.007 0.002 0.004

Cyprus 10148 1 0.158 0.016 0.018 0.016 0.04 0.729 0.01 0.013

Cyprus 10149 3 0.772 0.093 0.006 0.024 0.025 0.039 0.007 0.033

Cyprus 10150 1 0.819 0.006 0.013 0.017 0.008 0.126 0.008 0.003

Cyprus 10151 2 0.913 0.009 0.005 0.005 0.014 0.041 0.009 0.004

Cyprus 10152 2 0.762 0.013 0.006 0.06 0.007 0.138 0.01 0.004

Cyprus 10153 1 0.945 0.008 0.002 0.005 0.004 0.011 0.021 0.004

Cyprus 10154 1 0.144 0.018 0.005 0.025 0.163 0.635 0.006 0.004

Cyprus 10155 3 0.885 0.003 0.009 0.004 0.016 0.022 0.005 0.056

Cyprus 10156 5 0.856 0.02 0.007 0.006 0.01 0.074 0.023 0.005

Cyprus 10157 2 0.239 0.127 0.005 0.011 0.005 0.6 0.005 0.007

Lebanon 10235 9 0.226 0.643 0.008 0.031 0.016 0.056 0.007 0.012

Lebanon 10236 6 0.411 0.555 0.006 0.005 0.006 0.009 0.004 0.003

Lebanon 10237 15 0.071 0.642 0.002 0.026 0.008 0.242 0.004 0.004

Lebanon 10238 18 0.083 0.787 0.007 0.01 0.018 0.065 0.026 0.005

Lebanon 10239 10 0.03 0.88 0.005 0.006 0.031 0.036 0.007 0.005

Lebanon 10240 3 0.108 0.645 0.005 0.008 0.046 0.074 0.098 0.017

Lebanon 10241 5 0.038 0.878 0.005 0.037 0.006 0.022 0.005 0.008

Lebanon 10242 1 0.12 0.778 0.005 0.012 0.037 0.037 0.007 0.004

Lebanon 10243 2 0.009 0.806 0.097 0.004 0.012 0.009 0.019 0.043

Lebanon 10244 4 0.084 0.777 0.003 0.08 0.015 0.027 0.006 0.009

Lebanon 10245 16 0.036 0.65 0.006 0.008 0.012 0.188 0.062 0.038

Lebanon 10246 15 0.314 0.593 0.003 0.007 0.016 0.054 0.005 0.009

Lebanon 10247 1 0.133 0.795 0.004 0.004 0.004 0.051 0.004 0.004

Lebanon 10248 1 0.016 0.738 0.024 0.008 0.183 0.015 0.01 0.006

Lebanon 10249 1 0.04 0.646 0.002 0.018 0.01 0.274 0.007 0.004

Lebanon 10250 1 0.021 0.837 0.057 0.046 0.007 0.02 0.008 0.004 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Lebanon 10251 14 0.012 0.878 0.007 0.006 0.025 0.014 0.008 0.052

Lebanon 10252 8 0.037 0.774 0.014 0.006 0.045 0.098 0.019 0.007

Lebanon 10253 19 0.08 0.836 0.004 0.005 0.015 0.021 0.011 0.027

Lebanon 10254 1 0.124 0.767 0.003 0.029 0.008 0.049 0.002 0.016

Lebanon 10255 2 0.168 0.618 0.004 0.118 0.005 0.076 0.005 0.006

Lebanon 10256 1 0.054 0.822 0.041 0.009 0.01 0.053 0.008 0.005

Lebanon 10257 3 0.027 0.833 0.006 0.025 0.009 0.085 0.007 0.009

Lebanon 10258 1 0.291 0.591 0.004 0.032 0.007 0.037 0.033 0.005

Lebanon 10259 3 0.19 0.585 0.036 0.006 0.035 0.075 0.069 0.004

Lebanon 10260 8 0.02 0.897 0.014 0.005 0.019 0.026 0.011 0.007

Lebanon 10261 7 0.202 0.66 0.007 0.078 0.006 0.033 0.007 0.006

Lebanon 10262 1 0.187 0.504 0.007 0.086 0.018 0.181 0.011 0.005

Lebanon 10263 2 0.021 0.503 0.003 0.008 0.025 0.41 0.01 0.02

Lebanon 10264 3 0.079 0.788 0.003 0.016 0.009 0.086 0.002 0.018

Lebanon 10265 3 0.017 0.485 0.007 0.003 0.005 0.46 0.01 0.012

Lebanon 10266 1 0.121 0.715 0.003 0.007 0.004 0.061 0.085 0.005

Lebanon 10267 4 0.052 0.742 0.046 0.005 0.088 0.02 0.015 0.031

Lebanon 10268 5 0.033 0.749 0.023 0.004 0.036 0.109 0.004 0.042

Lebanon 10270 3 0.031 0.67 0.007 0.004 0.006 0.252 0.02 0.01

Lebanon 10271 3 0.225 0.578 0.004 0.042 0.01 0.133 0.005 0.003

Lebanon 10273 1 0.021 0.71 0.053 0.006 0.188 0.009 0.007 0.006

Lebanon 10274 4 0.121 0.754 0.005 0.009 0.009 0.085 0.01 0.007

Lebanon 10276 2 0.193 0.585 0.003 0.044 0.007 0.13 0.032 0.006

Lebanon 10277 3 0.084 0.757 0.013 0.005 0.005 0.125 0.007 0.004

Lebanon 10278 2 0.298 0.593 0.004 0.057 0.009 0.021 0.011 0.007

Lebanon 10279 13 0.034 0.894 0.015 0.01 0.01 0.014 0.017 0.007

Lebanon 10280 0 0.048 0.682 0.029 0.002 0.114 0.119 0.004 0.003

Lebanon 10281 2 0.172 0.609 0.011 0.005 0.069 0.08 0.045 0.009

Lebanon 10282 15 0.021 0.668 0.007 0.008 0.13 0.136 0.008 0.022

Lebanon 10283 18 0.06 0.826 0.005 0.042 0.017 0.028 0.011 0.011

Lebanon 10284 14 0.02 0.742 0.007 0.008 0.026 0.03 0.153 0.013

Lebanon 10285 17 0.03 0.892 0.003 0.007 0.005 0.034 0.018 0.011

Lebanon 10286 15 0.036 0.752 0.002 0.006 0.008 0.062 0.125 0.008

Lebanon 10287 21 0.003 0.927 0.007 0.003 0.024 0.004 0.028 0.003

Lebanon 10288 4 0.006 0.949 0.006 0.002 0.007 0.008 0.016 0.006

Lebanon 10289 5 0.006 0.848 0.004 0.008 0.004 0.006 0.037 0.087

Lebanon 10290 3 0.006 0.949 0.013 0.004 0.007 0.007 0.004 0.01

Lebanon 10291 2 0.018 0.827 0.008 0.01 0.072 0.023 0.027 0.015

Lebanon 10292 3 0.022 0.73 0.019 0.008 0.066 0.021 0.093 0.041

Lebanon 10294 9 0.215 0.684 0.005 0.011 0.036 0.018 0.014 0.016

Lebanon 10295 1 0.012 0.7 0.006 0.004 0.23 0.017 0.016 0.014

Lebanon 10297 3 0.004 0.905 0.055 0.003 0.013 0.005 0.006 0.009

Lebanon 10298 2 0.022 0.841 0.007 0.023 0.02 0.014 0.067 0.006

Lebanon 10299 2 0.021 0.846 0.023 0.027 0.052 0.011 0.015 0.005

Lebanon 10300 2 0.003 0.943 0.015 0.003 0.012 0.005 0.009 0.01

Israel 4962 15 0.008 0.96 0.003 0.004 0.009 0.006 0.005 0.006

Israel 4963 1 0.006 0.953 0.004 0.003 0.008 0.004 0.004 0.017

Israel 4964 1 0.003 0.966 0.011 0.002 0.004 0.002 0.008 0.004

Israel 4966 1 0.007 0.924 0.004 0.006 0.008 0.005 0.025 0.022

Israel 4967 1 0.005 0.974 0.001 0.002 0.006 0.004 0.002 0.007

Israel 4968 2 0.002 0.982 0.001 0.002 0.004 0.002 0.003 0.004

Israel 4969 2 0.007 0.928 0.01 0.004 0.008 0.01 0.006 0.027

Israel 4970 2 0.004 0.965 0.008 0.004 0.007 0.003 0.004 0.005

Israel 4971 1 0.003 0.981 0.004 0.001 0.003 0.002 0.003 0.002

Israel 4972 2 0.002 0.985 0.002 0.001 0.002 0.002 0.003 0.002

Israel 4973 3 0.002 0.969 0.004 0.002 0.005 0.003 0.007 0.007 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Israel 4974 19 0.002 0.961 0.002 0.004 0.01 0.004 0.004 0.012

Israel 4975 3 0.003 0.978 0.002 0.002 0.003 0.003 0.003 0.005

Israel 4976 1 0.002 0.984 0.002 0.001 0.002 0.002 0.004 0.002

Israel 4977 2 0.004 0.971 0.002 0.003 0.003 0.005 0.008 0.003

Israel 4978 3 0.003 0.957 0.005 0.002 0.014 0.003 0.013 0.003

Israel 4979 1 0.003 0.973 0.008 0.004 0.005 0.002 0.003 0.003

Israel 4980 3 0.002 0.986 0.002 0.002 0.002 0.002 0.002 0.002

Israel 4981 1 0.002 0.986 0.002 0.001 0.002 0.002 0.002 0.002

Israel 4982 3 0.011 0.946 0.008 0.005 0.013 0.008 0.004 0.006

Israel 4983 3 0.003 0.972 0.004 0.003 0.007 0.003 0.004 0.004

Israel 4984 3 0.004 0.98 0.002 0.003 0.003 0.003 0.002 0.003

Israel 4985 1 0.002 0.981 0.002 0.001 0.003 0.002 0.005 0.004

Israel 4986 2 0.002 0.974 0.003 0.002 0.004 0.002 0.003 0.011

Israel 4988 2 0.007 0.941 0.007 0.006 0.007 0.007 0.013 0.013

Israel 4989 3 0.005 0.97 0.004 0.004 0.007 0.003 0.002 0.005

Israel 4990 2 0.004 0.956 0.004 0.006 0.011 0.002 0.004 0.012

Israel 4992 3 0.004 0.973 0.004 0.002 0.004 0.003 0.004 0.006

Israel 4993 2 0.002 0.972 0.005 0.002 0.005 0.003 0.008 0.003

Israel 4994 2 0.005 0.972 0.002 0.003 0.006 0.007 0.002 0.003

Israel 4995 3 0.004 0.904 0.003 0.003 0.006 0.008 0.003 0.069

Israel 4996 3 0.006 0.944 0.008 0.006 0.007 0.005 0.02 0.003

Israel 4997 2 0.007 0.923 0.008 0.01 0.007 0.009 0.031 0.005

Israel 4998 3 0.008 0.932 0.008 0.004 0.013 0.005 0.025 0.004

Israel 5000 2 0.014 0.95 0.003 0.012 0.007 0.005 0.005 0.005

Israel 5001 2 0.004 0.944 0.011 0.002 0.014 0.005 0.015 0.005

Israel 5002 1 0.002 0.971 0.002 0.003 0.004 0.002 0.004 0.011

Israel 5003 1 0.014 0.943 0.005 0.01 0.004 0.007 0.005 0.012

Israel 5004 2 0.007 0.964 0.002 0.004 0.006 0.005 0.004 0.009

Israel 5005 3 0.003 0.976 0.002 0.003 0.002 0.004 0.006 0.004

Israel 5006 2 0.006 0.957 0.012 0.005 0.006 0.005 0.007 0.002

Israel 5007 1 0.009 0.862 0.008 0.005 0.018 0.007 0.02 0.073

Israel 5008 2 0.002 0.984 0.002 0.002 0.002 0.003 0.003 0.002

Israel 5009 3 0.005 0.938 0.011 0.003 0.028 0.004 0.004 0.006

Israel 5010 3 0.004 0.925 0.014 0.01 0.012 0.005 0.027 0.005

Israel 5011 2 0.005 0.921 0.005 0.006 0.029 0.005 0.017 0.013

Egypt-Cairo 8190 4 0.016 0.714 0.008 0.007 0.013 0.009 0.23 0.004

Egypt-Cairo 8192 2 0.128 0.712 0.003 0.017 0.031 0.103 0.003 0.004

Egypt-Cairo 8193 2 0.006 0.886 0.004 0.002 0.007 0.004 0.086 0.005

Egypt-Cairo 8196 3 0.004 0.965 0.002 0.007 0.005 0.003 0.003 0.01

Egypt-Cairo 8203 2 0.005 0.947 0.003 0.003 0.021 0.006 0.005 0.009

Egypt-Cairo 8215 3 0.022 0.934 0.015 0.007 0.011 0.007 0.003 0.002

Egypt-Cairo 8198 3 0.012 0.913 0.004 0.006 0.032 0.011 0.004 0.016

Egypt-Cairo 8194 3 0.003 0.968 0.002 0.004 0.006 0.002 0.009 0.005

Egypt-Cairo 8211 2 0.002 0.981 0.003 0.002 0.003 0.002 0.004 0.003

Egypt-Cairo 8216 3 0.005 0.965 0.005 0.004 0.008 0.005 0.002 0.005

Egypt-Cairo 8195 1 0.004 0.963 0.005 0.004 0.005 0.003 0.012 0.004

Egypt-Cairo 8199 2 0.003 0.981 0.002 0.002 0.003 0.003 0.002 0.003

Egypt-Cairo 8200 1 0.002 0.967 0.004 0.003 0.003 0.002 0.003 0.016

Egypt-Cairo 8201 3 0.003 0.938 0.002 0.002 0.003 0.003 0.005 0.045

Egypt-Cairo 8202 2 0.003 0.978 0.002 0.002 0.003 0.002 0.006 0.003

Egypt-Cairo 8204 3 0.004 0.926 0.004 0.003 0.004 0.004 0.046 0.01

Egypt-Cairo 8208 2 0.002 0.973 0.004 0.002 0.004 0.002 0.003 0.011

Egypt-Cairo 8210 1 0.007 0.907 0.005 0.024 0.025 0.012 0.003 0.017

Egypt-Cairo 8214 4 0.007 0.951 0.006 0.003 0.006 0.008 0.016 0.002

Egypt-Cairo 8191 3 0.004 0.945 0.006 0.008 0.014 0.004 0.004 0.014

Egypt-Cairo 8197 2 0.002 0.988 0.001 0.002 0.002 0.002 0.002 0.002 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Egypt-Cairo 8205 2 0.002 0.986 0.002 0.002 0.002 0.002 0.003 0.002

Egypt-Cairo 8206 6 0.006 0.872 0.013 0.027 0.037 0.005 0.005 0.034

Egypt-Cairo 8207 6 0.017 0.828 0.003 0.037 0.042 0.018 0.021 0.035

Egypt-Cairo 8209 3 0.003 0.947 0.004 0.002 0.004 0.003 0.013 0.023

Egypt-Cairo 8212 5 0.005 0.946 0.004 0.004 0.004 0.003 0.031 0.005

Egypt-Cairo 8213 4 0.003 0.947 0.003 0.002 0.004 0.002 0.034 0.005

Egypt-Cairo 9942 3 0.006 0.967 0.003 0.005 0.006 0.004 0.005 0.003

Egypt-Cairo 9943 6 0.009 0.92 0.003 0.005 0.003 0.004 0.007 0.049

Egypt-Cairo 9944 5 0.01 0.944 0.003 0.004 0.009 0.004 0.004 0.023

Egypt-Cairo 9945 4 0.12 0.814 0.01 0.008 0.01 0.019 0.007 0.012

Egypt-Cairo 9946 4 0.004 0.951 0.011 0.005 0.01 0.003 0.003 0.013

Egypt-Cairo 9947 7 0.005 0.925 0.005 0.004 0.005 0.004 0.004 0.048

Egypt-Cairo 9948 3 0.007 0.934 0.006 0.006 0.006 0.004 0.031 0.006

Egypt-Cairo 9949 3 0.012 0.914 0.012 0.009 0.026 0.006 0.005 0.017

Egypt-Cairo 9950 4 0.01 0.945 0.005 0.013 0.009 0.005 0.004 0.009

Egypt-Cairo 9951 4 0.005 0.909 0.002 0.005 0.007 0.004 0.002 0.067

Egypt-Cairo 9952 8 0.004 0.827 0.003 0.004 0.027 0.003 0.007 0.124

Egypt-Cairo 9953 9 0.043 0.889 0.009 0.003 0.018 0.018 0.015 0.005

Egypt-Cairo 9954 3 0.019 0.891 0.002 0.018 0.009 0.024 0.005 0.033

Egypt-Cairo 9955 3 0.002 0.963 0.002 0.001 0.004 0.002 0.022 0.003

Egypt-Cairo 9956 4 0.002 0.963 0.002 0.001 0.004 0.002 0.022 0.004

Egypt-Cairo 9957 3 0.008 0.877 0.006 0.005 0.076 0.008 0.014 0.006

Egypt-Cairo 9958 5 0.002 0.976 0.004 0.002 0.003 0.002 0.005 0.005

Egypt-Cairo 9959 4 0.002 0.966 0.005 0.002 0.013 0.002 0.007 0.002

Egypt-Cairo 9960 5 0.003 0.972 0.002 0.003 0.004 0.004 0.009 0.003

Egypt-Cairo 9961 4 0.017 0.84 0.007 0.007 0.061 0.027 0.018 0.023

Egypt-Cairo 9962 14 0.044 0.872 0.041 0.016 0.009 0.011 0.003 0.005

Egypt-Cairo 9963 1 0.084 0.107 0.002 0.012 0.144 0.025 0.002 0.625

Egypt-Cairo 9964 2 0.011 0.259 0.021 0.013 0.039 0.025 0.014 0.617

Egypt-Cairo 10021 4 0.007 0.044 0.005 0.003 0.488 0.004 0.004 0.444

Egypt-Cairo 10022 1 0.014 0.431 0.01 0.032 0.062 0.034 0.099 0.318

Egypt-Cairo 10023 2 0.022 0.033 0.016 0.007 0.487 0.042 0.028 0.364

Egypt-Cairo 10024 4 0.009 0.33 0.012 0.006 0.041 0.005 0.178 0.42

Egypt-Cairo 10025 3 0.015 0.18 0.005 0.015 0.012 0.01 0.008 0.755

Egypt-Cairo 10026 4 0.006 0.042 0.003 0.003 0.395 0.007 0.003 0.542

Egypt-Cairo 10027 2 0.169 0.077 0.025 0.006 0.221 0.013 0.026 0.463

Egypt-Cairo 10028 3 0.01 0.18 0.004 0.005 0.083 0.008 0.011 0.699

Egypt-Cairo 10029 19 0.078 0.04 0.04 0.022 0.068 0.716 0.023 0.014

Egypt-Cairo 10030 22 0.797 0.008 0.007 0.005 0.067 0.053 0.029 0.035

Egypt-Cairo 10031 16 0.668 0.005 0.046 0.01 0.116 0.032 0.077 0.045

Egypt-Cairo 10032 3 0.324 0.053 0.016 0.051 0.03 0.51 0.006 0.01

Egypt-Cairo 10033 14 0.908 0.007 0.004 0.009 0.007 0.057 0.004 0.006

Egypt-Cairo 10034 19 0.87 0.014 0.016 0.012 0.045 0.029 0.008 0.006

Egypt-Cairo 10035 6 0.772 0.02 0.01 0.015 0.038 0.015 0.123 0.008

Egypt-Cairo 10037 2 0.806 0.014 0.016 0.05 0.011 0.084 0.006 0.013

Egypt-Cairo 10042 3 0.867 0.053 0.011 0.008 0.021 0.02 0.012 0.009

Egypt-Cairo 10043 15 0.809 0.026 0.004 0.007 0.063 0.054 0.027 0.01

Egypt-Cairo 10044 7 0.717 0.164 0.01 0.014 0.018 0.05 0.005 0.021

Egypt-Cairo 10045 15 0.658 0.036 0.014 0.019 0.014 0.251 0.003 0.005

Egypt-Cairo 10046 24 0.199 0.252 0.005 0.004 0.011 0.505 0.009 0.015

Egypt-Cairo 10047 19 0.6 0.008 0.006 0.02 0.005 0.251 0.008 0.104

Egypt-Cairo 10048 17 0.595 0.005 0.007 0.359 0.01 0.013 0.004 0.006

Egypt-Cairo 10083 22 0.887 0.051 0.003 0.014 0.005 0.024 0.011 0.006

Egypt-Cairo 10040 3 0.92 0.007 0.004 0.007 0.012 0.022 0.004 0.025

Egypt-Cairo 10041 12 0.802 0.065 0.007 0.079 0.012 0.016 0.008 0.011

Egypt-Cairo 10049 6 0.741 0.051 0.012 0.009 0.004 0.089 0.011 0.083 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Egypt-Cairo 10084 13 0.925 0.006 0.008 0.005 0.01 0.019 0.017 0.01

Egypt-Cairo 10085 7 0.4 0.177 0.022 0.047 0.011 0.328 0.011 0.005

Egypt-Cairo 10087 12 0.661 0.012 0.004 0.009 0.006 0.295 0.006 0.008

Egypt-Cairo 10090 3 0.88 0.009 0.032 0.008 0.026 0.027 0.011 0.006

Egypt-Cairo 9968 17 0.196 0.023 0.008 0.007 0.016 0.702 0.021 0.028

Egypt-Asuit 10091 2 0.037 0.005 0.553 0.021 0.014 0.36 0.006 0.005

Egypt-Asuit 10093 14 0.953 0.008 0.004 0.005 0.004 0.017 0.004 0.006

Egypt-Asuit 10094 13 0.865 0.018 0.016 0.008 0.015 0.061 0.012 0.003

Egypt-Asuit 10095 18 0.898 0.019 0.009 0.009 0.013 0.032 0.013 0.006

Egypt-Asuit 10096 3 0.893 0.009 0.006 0.007 0.008 0.064 0.004 0.008

Egypt-Asuit 10098 3 0.894 0.009 0.009 0.03 0.025 0.014 0.011 0.008

Egypt-Asuit 10099 9 0.889 0.022 0.004 0.005 0.007 0.065 0.004 0.004

Egypt-Asuit 10100 4 0.082 0.006 0.026 0.006 0.135 0.728 0.008 0.009

Egypt-Asuit 10101 13 0.519 0.007 0.003 0.364 0.003 0.094 0.004 0.005

Egypt-Asuit 10102 15 0.046 0.003 0.133 0.015 0.004 0.782 0.007 0.009

Egypt-Luxor 10038 10 0.168 0.023 0.341 0.038 0.009 0.399 0.009 0.012

Egypt-Luxor 10039 4 0.685 0.068 0.035 0.014 0.114 0.01 0.013 0.062

Egypt-Luxor 10050 5 0.685 0.022 0.032 0.071 0.02 0.065 0.096 0.009

Egypt-Luxor 10051 5 0.365 0.004 0.065 0.062 0.051 0.416 0.015 0.021

Egypt-Luxor 10052 5 0.369 0.017 0.003 0.008 0.007 0.582 0.003 0.011

Egypt-Luxor 10053 4 0.605 0.018 0.011 0.091 0.032 0.1 0.14 0.003

Egypt-Luxor 10054 5 0.382 0.043 0.003 0.012 0.012 0.489 0.032 0.027

Egypt-Luxor 10055 1 0.897 0.03 0.004 0.005 0.032 0.01 0.004 0.018

Egypt-Luxor 10056 2 0.725 0.011 0.018 0.011 0.173 0.027 0.012 0.021

Egypt-Luxor 10057 8 0.911 0.012 0.003 0.007 0.005 0.048 0.007 0.006

Egypt-Luxor 10058 3 0.395 0.014 0.04 0.008 0.018 0.505 0.015 0.006

Egypt-Luxor 10060 18 0.847 0.005 0.002 0.004 0.009 0.033 0.004 0.095

Egypt-Luxor 10061 16 0.254 0.159 0.038 0.026 0.042 0.428 0.017 0.037

Egypt-Luxor 10062 1 0.246 0.012 0.024 0.014 0.009 0.642 0.008 0.045

Egypt-Luxor 10063 5 0.614 0.097 0.14 0.008 0.1 0.018 0.015 0.007

Egypt-Luxor 10064 2 0.413 0.041 0.012 0.287 0.073 0.132 0.033 0.008

Egypt-Luxor 10065 22 0.081 0.004 0.006 0.81 0.009 0.031 0.055 0.005

Egypt-Luxor 10066 4 0.875 0.025 0.004 0.004 0.022 0.057 0.008 0.006

Egypt-Luxor 10067 3 0.687 0.02 0.018 0.006 0.108 0.065 0.051 0.045

Egypt-Luxor 10068 9 0.589 0.088 0.004 0.011 0.058 0.147 0.056 0.047

Egypt-Luxor 10069 7 0.62 0.032 0.033 0.016 0.187 0.043 0.006 0.064

Egypt-Luxor 10070 8 0.781 0.009 0.159 0.007 0.007 0.022 0.006 0.008

Egypt-Luxor 10071 6 0.37 0.08 0.025 0.045 0.034 0.398 0.03 0.019

Egypt-Luxor 10072 5 0.697 0.203 0.008 0.014 0.019 0.04 0.014 0.005

Egypt-Luxor 10073 8 0.516 0.219 0.005 0.005 0.053 0.164 0.015 0.022

Egypt-Luxor 10074 17 0.371 0.074 0.024 0.241 0.03 0.146 0.024 0.089

Egypt-Luxor 10079 20 0.927 0.015 0.017 0.005 0.016 0.01 0.006 0.005

Egypt-Luxor 10080 1 0.831 0.062 0.002 0.021 0.017 0.035 0.006 0.027

Egypt-Abu Simbel 10076 1 0.923 0.005 0.006 0.007 0.024 0.012 0.012 0.01

Egypt-Abu Simbel 10077 2 0.824 0.048 0.01 0.025 0.016 0.068 0.006 0.003

Egypt-Abu Simbel 10081 2 0.91 0.004 0.008 0.007 0.011 0.042 0.003 0.015

Egypt-Abu Simbel 10089 2 0.771 0.024 0.006 0.064 0.023 0.03 0.024 0.057

Egypt-Abu Simbel 10092 3 0.87 0.012 0.011 0.005 0.033 0.031 0.033 0.005

Iraq-West 9587 1 0.218 0.013 0.107 0.541 0.027 0.024 0.056 0.013

Iraq-West 10202 1 0.931 0.009 0.013 0.015 0.007 0.012 0.007 0.006

Iraq-West 10204 1 0.961 0.01 0.003 0.004 0.004 0.007 0.006 0.003

Iraq-West 11854 2 0.914 0.006 0.005 0.01 0.004 0.011 0.024 0.026

Iraq-West 11860 2 0.913 0.004 0.004 0.004 0.005 0.007 0.008 0.053

Iraq-West 11861 2 0.95 0.009 0.005 0.01 0.008 0.008 0.005 0.005

Iraq-West 11863 1 0.95 0.004 0.006 0.017 0.007 0.011 0.003 0.003

Iraq-West 11864 1 0.814 0.013 0.034 0.042 0.014 0.035 0.004 0.043 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Iraq-West 11888 3 0.854 0.022 0.004 0.095 0.008 0.008 0.003 0.006

Iraq-West 11889 3 0.915 0.031 0.011 0.007 0.009 0.005 0.009 0.014

Iraq-West 11890 2 0.925 0.025 0.005 0.007 0.004 0.016 0.009 0.01

Iraq-West 11891 1 0.794 0.03 0.005 0.069 0.039 0.019 0.024 0.021

Iraq-Baghdad 11847 5 0.402 0.024 0.005 0.008 0.01 0.196 0.352 0.003

Iraq-Baghdad 11848 4 0.861 0.005 0.011 0.023 0.069 0.018 0.004 0.009

Iraq-Baghdad 11849 1 0.183 0.055 0.242 0.425 0.017 0.037 0.032 0.009

Iraq-Baghdad 11850 2 0.052 0.015 0.25 0.636 0.011 0.013 0.01 0.012

Iraq-Baghdad 11852 17 0.861 0.005 0.007 0.013 0.029 0.065 0.016 0.003

Iraq-Baghdad 11853 2 0.952 0.008 0.007 0.003 0.01 0.007 0.007 0.005

Iraq-Baghdad 11855 3 0.901 0.01 0.015 0.009 0.021 0.016 0.017 0.01

Iraq-Baghdad 11856 4 0.749 0.023 0.062 0.007 0.061 0.079 0.016 0.002

Iraq-Baghdad 11857 2 0.695 0.005 0.126 0.008 0.031 0.054 0.078 0.003

Iraq-Baghdad 11858 6 0.955 0.007 0.003 0.009 0.004 0.008 0.005 0.009

Iraq-Baghdad 11859 9 0.927 0.005 0.004 0.005 0.004 0.012 0.005 0.04

Iraq-Baghdad 11862 2 0.976 0.002 0.001 0.003 0.004 0.009 0.003 0.003

Iraq-Baghdad 11865 2 0.837 0.01 0.084 0.008 0.021 0.016 0.021 0.002

Iraq-Baghdad 11868 1 0.76 0.009 0.005 0.005 0.004 0.178 0.037 0.002

Iraq-Baghdad 11869 3 0.808 0.038 0.089 0.006 0.013 0.033 0.01 0.003

Iraq-Baghdad 11870 2 0.879 0.028 0.006 0.037 0.025 0.011 0.006 0.007

Iraq-Baghdad 11871 2 0.923 0.007 0.033 0.005 0.009 0.013 0.005 0.005

Iraq-Baghdad 11872 2 0.755 0.006 0.006 0.169 0.02 0.032 0.008 0.005

Iraq-Baghdad 11873 2 0.915 0.003 0.016 0.009 0.01 0.022 0.005 0.02

Iraq-Baghdad 11874 2 0.782 0.014 0.009 0.044 0.018 0.024 0.092 0.016

Iraq-Baghdad 11875 3 0.674 0.013 0.25 0.011 0.01 1 0.03 0.004 0.007

Iraq-Baghdad 11876 2 0.896 0.005 0.004 0.05 0.005 0.026 0.003 0.011

Iraq-Baghdad 11877 3 0.654 0.013 0.003 0.018 0.007 0.297 0.004 0.004

Iraq-Baghdad 11878 2 0.706 0.004 0.059 0.022 0.023 0.037 0.124 0.024

Iraq-Baghdad 11879 1 0.701 0.012 0.028 0.022 0.006 0.167 0.007 0.058

Iraq-Baghdad 11880 3 0.53 0.041 0.275 0.015 0.007 0.12 0.007 0.004

Iraq-Baghdad 11881 4 0.964 0.003 0.002 0.006 0.004 0.015 0.002 0.003

Iraq-Baghdad 11882 2 0.955 0.007 0.009 0.005 0.006 0.009 0.005 0.004

Iraq-Baghdad 11883 3 0.012 0.007 0.013 0.032 0.01 0.914 0.006 0.007

Iraq-Baghdad 11884 5 0.057 0.023 0.01 0.143 0.017 0.667 0.005 0.078

Iraq-Baghdad 11885 4 0.03 0.027 0.003 0.018 0.019 0.891 0.005 0.007

Iraq-Baghdad 11886 4 0.034 0.012 0.015 0.004 0.04 0.864 0.017 0.014

Iraq-Baghdad 11887 4 0.039 0.005 0.017 0.012 0.008 0.908 0.007 0.003

Iran 9419 3 0.03 0.008 0.004 0.007 0.025 0.917 0.006 0.004

Iran 9420 3 0.016 0.01 0.01 0.026 0.009 0.91 0.006 0.012

Iran 9421 1 0.022 0.015 0.015 0.105 0.008 0.814 0.009 0.012

Iran 9422 9 0.673 0.007 0.003 0.006 0.003 0.301 0.004 0.003

Iran 9424 2 0.039 0.008 0.025 0.12 0.006 0.79 0.004 0.008

Iran 9425 2 0.325 0.01 0.004 0.022 0.014 0.58 0.012 0.032

Iran 9426 8 0.025 0.005 0.002 0.005 0.016 0.923 0.005 0.018

Iran 9427 13 0.273 0.015 0.003 0.042 0.005 0.654 0.006 0.002

Iran 9428 1 0.491 0.077 0.007 0.037 0.069 0.27 0.029 0.019

Iran 9429 2 0.035 0.047 0.616 0.011 0.112 0.157 0.01 0.013

Iran 9430 2 0.133 0.003 0.006 0.01 0.013 0.822 0.008 0.005

Iran 9431 1 0.081 0.022 0.005 0.012 0.027 0.829 0.019 0.006

Iran 9432 3 0.174 0.065 0.004 0.007 0.042 0.681 0.008 0.021

Iran 9433 2 0.01 0.009 0.004 0.005 0.006 0.957 0.006 0.003

Iran 9434 2 0.373 0.025 0.011 0.016 0.011 0.537 0.02 0.007

Iran 9435 1 0.064 0.008 0.009 0.006 0.01 0.851 0.003 0.05

Iran 9436 1 0.08 0.023 0.004 0.012 0.057 0.788 0.032 0.005

Iran 9437 1 0.034 0.006 0.012 0.005 0.026 0.904 0.007 0.004

Iran 9438 2 0.008 0.006 0.028 0.009 0.009 0.92 0.01 0.011 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Iran 9439 1 0.175 0.007 0.002 0.016 0.007 0.784 0.006 0.002

Iran 9440 1 0.004 0.007 0.003 0.005 0.007 0.968 0.004 0.002

Iran 9441 1 0.007 0.007 0.003 0.008 0.007 0.961 0.005 0.003

Iran 9442 2 0.071 0.169 0.015 0.006 0.006 0.709 0.015 0.008

Iran 9443 3 0.013 0.007 0.513 0.355 0.07 0.034 0.005 0.002

Iran 9444 4 0.023 0.139 0.005 0.344 0.007 0.47 0.003 0.009

Iran 9445 0 0.022 0.016 0.007 0.014 0.027 0.897 0.005 0.012

Iran 9446 3 0.409 0.008 0.034 0.009 0.023 0.486 0.015 0.016

Iran 9447 3 0.041 0.053 0.004 0.007 0.014 0.821 0.006 0.054

Iran 9448 0 0.127 0.005 0.014 0.076 0.006 0.767 0.003 0.003

Iran 9449 0 0.027 0.006 0.027 0.016 0.024 0.886 0.009 0.004

Iran 9450 1 0.117 0.004 0.002 0.056 0.008 0.802 0.005 0.006

Iran 9451 2 0.036 0.004 0.009 0.009 0.014 0.91 0.007 0.012

Iran 9452 4 0.468 0.015 0.003 0.007 0.008 0.476 0.004 0.02

Iran 9453 8 0.449 0.071 0.01 0.426 0.013 0.022 0.005 0.004

Iran 9454 1 0.005 0.002 0.186 0.004 0.007 0.725 0.052 0.017

Iran 9455 2 0.015 0.005 0.039 0.381 0.185 0.361 0.006 0.007

Iran 9456 8 0.016 0.007 0.035 0.01 0.011 0.825 0.089 0.008

Iran 9457 2 0.047 0.016 0.006 0.032 0.014 0.751 0.129 0.005

Iran 9458 4 0.031 0.006 0.108 0.011 0.005 0.82 0.008 0.01

Iran 9459 2 0.271 0.009 0.008 0.071 0.49 0.136 0.008 0.007

Iran 9460 1 0.054 0.046 0.013 0.011 0.007 0.854 0.011 0.003

Iran 9461 5 0.029 0.006 0.393 0.012 0.054 0.491 0.012 0.004

Iran 9462 3 0.032 0.013 0.009 0.027 0.007 0.849 0.005 0.058

Iran 9463 3 0.066 0.007 0.009 0.016 0.004 0.871 0.012 0.015

Iran 9464 1 0.093 0.007 0.066 0.021 0.032 0.772 0.004 0.006

Iran 9465 6 0.01 0.005 0.002 0.004 0.003 0.964 0.003 0.009

Iran 9466 1 0.005 0.003 0.003 0.005 0.006 0.97 0.005 0.003

Iran 9468 0 0.119 0.005 0.003 0.003 0.01 0.851 0.004 0.005

Iran 9469 1 0.009 0.002 0.002 0.009 0.003 0.971 0.002 0.002

Iran 9470 2 0.933 0.015 0.004 0.01 0.01 0.019 0.004 0.006

Iran 9471 17 0.095 0.004 0.003 0.031 0.006 0.821 0.036 0.005

Iran 9472 1 0.009 0.007 0.004 0.011 0.023 0.91 0.005 0.031

Iran 9473 1 0.012 0.011 0.003 0.004 0.005 0.957 0.004 0.004

Iran 9474 3 0.006 0.008 0.001 0.004 0.003 0.971 0.003 0.004

Iran 9475 1 0.008 0.003 0.004 0.007 0.004 0.969 0.004 0.002

Iran 9476 3 0.009 0.006 0.002 0.002 0.003 0.97 0.003 0.005

Iran 9477 0 0.008 0.012 0.002 0.003 0.004 0.966 0.002 0.003

Iran 9478 1 0.077 0.006 0.003 0.003 0.007 0.895 0.005 0.005

Iran 9479 1 0.012 0.003 0.003 0.008 0.004 0.962 0.003 0.006

Iran 9480 3 0.031 0.011 0.003 0.006 0.017 0.789 0.106 0.037

Iran 9481 5 0.011 0.007 0.003 0.243 0.007 0.694 0.005 0.031

Iran 9482 1 0.06 0.015 0.014 0.05 0.014 0.804 0.01 0.033

Iran 9483 2 0.286 0.008 0.005 0.043 0.013 0.636 0.002 0.008

Iran 9484 4 0.011 0.005 0.004 0.008 0.006 0.953 0.008 0.004

Iran 9485 1 0.058 0.004 0.006 0.05 0.006 0.865 0.003 0.007

Iran 9486 0 0.014 0.004 0.007 0.008 0.011 0.941 0.01 0.004

Iran 9487 0 0.005 0.007 0.004 0.004 0.012 0.961 0.004 0.003

Iran 9488 1 0.021 0.003 0.014 0.009 0.018 0.904 0.025 0.006

Iran 9489 24 0.069 0.005 0.007 0.052 0.006 0.843 0.009 0.009

Iran 9490 2 0.149 0.007 0.005 0.047 0.025 0.741 0.014 0.011

Iran 9491 1 0.372 0.008 0.004 0.022 0.03 0.515 0.018 0.031

Iran 9492 9 0.061 0.016 0.007 0.009 0.052 0.827 0.014 0.014

Iran 9493 7 0.019 0.004 0.024 0.019 0.006 0.89 0.006 0.032

Iran 9494 19 0.362 0.003 0.024 0.062 0.009 0.486 0.003 0.051

Iran 9495 4 0.059 0.002 0.003 0.015 0.003 0.903 0.003 0.012 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Iran 9497 4 0.049 0.007 0.01 0.023 0.02 0.784 0.008 0.098

Iran 9498 0 0.027 0.015 0.004 0.015 0.007 0.905 0.005 0.022

Iran 9499 1 0.005 0.003 0.002 0.007 0.006 0.973 0.003 0.002

Iran 9500 3 0.005 0.002 0.001 0.008 0.004 0.974 0.003 0.004

Iran 9501 4 0.006 0.002 0.002 0.008 0.006 0.967 0.003 0.005

Iran 9502 13 0.024 0.005 0.005 0.019 0.006 0.933 0.003 0.006

Iran 9503 1 0.006 0.002 0.002 0.006 0.005 0.962 0.013 0.003

Iran 9504 1 0.004 0.002 0.001 0.006 0.003 0.978 0.003 0.004

Iran 9505 1 0.005 0.003 0.001 0.003 0.003 0.975 0.003 0.007

Iran 9506 2 0.107 0.013 0.014 0.079 0.015 0.536 0.059 0.177

Iran 9507 2 0.016 0.013 0.004 0.004 0.016 0.93 0.01 0.006

Iran 9508 1 0.031 0.006 0.028 0.004 0.023 0.893 0.008 0.006

Iran 9509 1 0.008 0.003 0.074 0.004 0.009 0.885 0.009 0.007

Iran 9510 5 0.004 0.006 0.002 0.003 0.003 0.978 0.002 0.002

Iran 9511 2 0.014 0.006 0.006 0.004 0.016 0.928 0.021 0.005

Iran 9512 1 0.022 0.002 0.003 0.004 0.004 0.95 0.004 0.011

Iran 9513 11 0.014 0.004 0.011 0.008 0.024 0.921 0.007 0.011

Iran 9514 2 0.038 0.091 0.019 0.006 0.018 0.798 0.007 0.024

Iran 9515 2 0.015 0.007 0.003 0.005 0.006 0.945 0.018 0.002

Iran 9516 1 0.024 0.007 0.01 0.004 0.006 0.932 0.005 0.013

Iran 9517 2 0.017 0.011 0.009 0.009 0.01 0.917 0.018 0.01

Iran 9518 12 0.167 0.021 0.003 0.009 0.018 0.762 0.016 0.004

Iran 9519 6 0.008 0.004 0.002 0.006 0.003 0.966 0.005 0.006

Iran 9520 4 0.108 0.004 0.003 0.027 0.005 0.836 0.005 0.013

Iran 9521 0 0.007 0.003 0.1 0.01 0.007 0.823 0.039 0.012

Iran 9522 3 0.058 0.008 0.003 0.019 0.006 0.893 0.005 0.008

Iran 9523 1 0.153 0.089 0.018 0.07 0.079 0.552 0.015 0.024

Iran 9524 1 0.034 0.004 0.002 0.044 0.017 0.851 0.005 0.043

Iran 9526 0 0.015 0.011 0.01 0.012 0.018 0.772 0.005 0.158

Iran 9527 3 0.008 0.005 0.011 0.015 0.019 0.906 0.015 0.022

Iran 9528 4 0.028 0.014 0.002 0.004 0.008 0.935 0.003 0.006

Iran 9529 7 0.013 0.002 0.006 0.013 0.039 0.899 0.023 0.004

Iran 9530 3 0.034 0.017 0.003 0.004 0.007 0.907 0.004 0.023

Iran 9531 1 0.007 0.003 0.105 0.013 0.006 0.823 0.033 0.01

Iran 9532 3 0.025 0.009 0.003 0.004 0.048 0.898 0.007 0.005

Dubai 10104 2 0.102 0.007 0.004 0.009 0.005 0.842 0.018 0.013

Dubai 10105 1 0.004 0.004 0.002 0.948 0.007 0.024 0.006 0.006

Dubai 10106 0 0.018 0.004 0.002 0.004 0.002 0.964 0.002 0.003

Dubai 10107 4 0.072 0.004 0.007 0.098 0.172 0.64 0.003 0.005

Dubai 10108 5 0.017 0.004 0.022 0.011 0.015 0.697 0.005 0.23

Dubai 10109 10 0.051 0.006 0.009 0.013 0.023 0.859 0.026 0.014

Dubai 10110 17 0.014 0.004 0.005 0.004 0.011 0.948 0.008 0.006

Dubai 10111 17 0.163 0.007 0.011 0.011 0.008 0.752 0.007 0.041

Dubai 10112 1 0.021 0.002 0.003 0.062 0.004 0.885 0.002 0.022

Dubai 10120 2 0.008 0.002 0.002 0.004 0.003 0.968 0.004 0.009

Kenya-Nairobi 9833 4 0.012 0.229 0.031 0.678 0.022 0.005 0.008 0.016

Kenya-Nairobi 9834 0 0.013 0.008 0.008 0.516 0.376 0.029 0.039 0.012

Kenya-Nairobi 9835 0 0.015 0.007 0.02 0.043 0.093 0.708 0.022 0.093

Kenya-Nairobi 9836 2 0.009 0.004 0.138 0.637 0.09 0.008 0.01 0.104

Kenya-Nairobi 9837 2 0.008 0.024 0.062 0.74 0.092 0.036 0.032 0.006

Kenya-Nairobi 9838 4 0.208 0.082 0.005 0.521 0.035 0.07 0.008 0.071

Kenya-Nairobi 9839 1 0.011 0.015 0.061 0.509 0.062 0.015 0.033 0.295

Kenya-Nairobi 9840 3 0.058 0.018 0.045 0.592 0.193 0.065 0.004 0.026

Kenya-Nairobi 9841 3 0.012 0.011 0.026 0.777 0.057 0.012 0.012 0.093

Kenya-Nairobi 9842 2 0.015 0.016 0.009 0.567 0.02 0.061 0.035 0.277

Kenya-Nairobi 9843 4 0.009 0.014 0.149 0.62 0.093 0.028 0.008 0.078 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Kenya-Nairobi 9844 3 0.006 0.006 0.064 0.681 0.009 0.012 0.026 0.195

Kenya-Nairobi 9845 1 0.042 0.011 0.018 0.657 0.068 0.121 0.018 0.065

Kenya-Nairobi 9846 1 0.086 0.035 0.184 0.558 0.08 0.012 0.01 0.037

Kenya-Nairobi 9847 3 0.018 0.01 0.005 0.739 0.106 0.006 0.018 0.098

Kenya-Nairobi 9848 3 0.051 0.011 0.008 0.594 0.032 0.115 0.048 0.143

Kenya-Nairobi 9849 2 0.018 0.003 0.013 0.311 0.607 0.014 0.014 0.021

Kenya-Nairobi 9850 3 0.136 0.03 0.016 0.722 0.056 0.011 0.006 0.024

Kenya-Nairobi 9851 2 0.016 0.01 0.033 0.428 0.474 0.007 0.006 0.026

Kenya-Nairobi 9852 4 0.034 0.006 0.074 0.44 0.039 0.049 0.004 0.355

Kenya-Nairobi 9853 4 0.008 0.037 0.044 0.614 0.123 0.011 0.012 0.151

Kenya-Nairobi 9854 2 0.117 0.055 0.028 0.606 0.098 0.044 0.005 0.048

Kenya-Nairobi 9855 2 0.047 0.025 0.033 0.552 0.121 0.042 0.009 0.17

Kenya-Nairobi 9856 3 0.018 0.006 0.087 0.573 0.066 0.028 0.004 0.217

Kenya-Nairobi 9857 6 0.018 0.01 0.123 0.668 0.14 0.021 0.012 0.008

Kenya-Nairobi 9858 3 0.006 0.009 0.009 0.651 0.096 0.008 0.035 0.185

Kenya-Nairobi 9859 4 0.005 0.008 0.182 0.699 0.004 0.005 0.013 0.085

Kenya-Nairobi 9860 2 0.004 0.008 0.132 0.694 0.024 0.012 0.031 0.094

Kenya-Nairobi 9861 1 0.015 0.013 0.008 0.722 0.014 0.055 0.006 0.168

Kenya-Nairobi 9862 2 0.018 0.004 0.126 0.571 0.174 0.068 0.005 0.035

Kenya-Nairobi 9863 1 0.005 0.005 0.105 0.459 0.262 0.005 0.011 0.148

Kenya-Nairobi 9864 2 0.028 0.042 0.016 0.512 0.35 0.022 0.003 0.026

Kenya-Nairobi 9865 4 0.012 0.027 0.008 0.643 0.166 0.029 0.069 0.046

Kenya-Nairobi 9866 3 0.028 0.008 0.108 0.629 0.041 0.076 0.016 0.093

Kenya-Nairobi 9867 0 0.023 0.021 0.009 0.58 0.302 0.009 0.029 0.027

Kenya-Nairobi 9868 5 0.048 0.018 0.126 0.571 0.047 0.13 0.005 0.055

Kenya-Pate 2000 2 0.043 0.005 0.003 0.018 0.009 0.014 0.006 0.902

Kenya-Pate 2001 3 0.002 0.003 0.002 0.001 0.002 0.001 0.002 0.987

Kenya-Pate 2002 1 0.002 0.009 0.002 0.001 0.002 0.002 0.007 0.975

Kenya-Pate 2003 0 0.002 0.004 0.003 0.003 0.006 0.004 0.024 0.953

Kenya-Pate 2004 3 0.013 0.008 0.016 0.004 0.034 0.033 0.011 0.881

Kenya-Pate 2006 3 0.006 0.049 0.002 0.004 0.007 0.003 0.11 0.82

Kenya-Pate 2007 4 0.005 0.005 0.002 0.005 0.005 0.006 0.007 0.965

Kenya-Pate 2009 6 0.03 0.005 0.003 0.012 0.005 0.023 0.003 0.918

Kenya-Pate 2011 3 0.012 0.003 0.008 0.005 0.092 0.01 0.006 0.864

Kenya-Lamu 1848 13 0.01 0.005 0.007 0.009 0.048 0.005 0.083 0.834

Kenya-Lamu 2014 4 0.038 0.055 0.006 0.019 0.01 0.01 0.004 0.859

Kenya-Lamu 2015 2 0.055 0.004 0.03 0.01 0.009 0.012 0.011 0.87

Kenya-Lamu 2016 2 0.014 0.005 0.043 0.006 0.008 0.008 0.005 0.91

Kenya-Lamu 2018 1 0.009 0.003 0.002 0.055 0.005 0.013 0.002 0.912

Kenya-Lamu 2019 2 0.054 0.005 0.018 0.023 0.04 0.069 0.005 0.785

Kenya-Lamu 2021 2 0.012 0.061 0.014 0.019 0.024 0.013 0.012 0.845

Kenya-Lamu 2023 3 0.002 0.002 0.002 0.002 0.002 0.002 0.003 0.983

Kenya-Lamu 2024 2 0.002 0.003 0.002 0.001 0.002 0.002 0.002 0.986

Kenya-Lamu 2025 3 0.004 0.008 0.002 0.003 0.003 0.003 0.007 0.971

Kenya-Lamu 2026 4 0.015 0.003 0.004 0.003 0.003 0.017 0.004 0.95

Kenya-Lamu 2027 6 0.007 0.018 0.012 0.029 0.034 0.006 0.053 0.841

Kenya-Lamu 2029 2 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.985

Kenya-Lamu 2030 1 0.01 0.006 0.002 0.01 0.047 0.005 0.003 0.918

Kenya-Lamu 2031 4 0.009 0.016 0.004 0.013 0.013 0.014 0.011 0.92

Kenya-Lamu 2032 2 0.01 0.035 0.004 0.004 0.006 0.008 0.007 0.926

Kenya-Lamu 2033 2 0.005 0.007 0.004 0.003 0.011 0.007 0.002 0.961

Kenya-Lamu 3241 4 0.016 0.007 0.004 0.016 0.059 0.033 0.029 0.836

Kenya-Lamu 3246 2 0.025 0.005 0.043 0.032 0.028 0.014 0.014 0.838

Kenya-Lamu 3247 3 0.035 0.005 0.023 0.089 0.02 0.007 0.01 0.812

India-Udaipur 11835 8 0.009 0.11 0.035 0.006 0.758 0.008 0.059 0.013

India-Udaipur 11836 3 0.008 0.125 0.002 0.007 0.831 0.006 0.014 0.006 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8 ndia-Udaipur 11837 2 0.006 0.226 0.01 0.002 0.651 0.007 0.024 0.075 ndia-Agra 11823 1 0.007 0.29 0.007 0.003 0.667 0.01 0.009 0.007 ndia-Agra 11824 2 0.03 0.157 0.002 0.004 0.72 0.012 0.004 0.071 ndia-Agra 11825 2 0.004 0.422 0.01 0.004 0.539 0.006 0.011 0.004 ndia-Agra 11826 6 0.004 0.297 0.005 0.002 0.671 0.003 0.014 0.003 ndia-Agra 11827 19 0.008 0.304 0.004 0.002 0.67 0.004 0.004 0.003 ndia-Agra 11828 2 0.346 0.091 0.004 0.003 0.52 0.009 0.007 0.018 ndia-Agra 11829 4 0.012 0.199 0.004 0.004 0.754 0.007 0.011 0.009 ndia-Agra 11830 8 0.006 0.312 0.003 0.002 0.665 0.003 0.005 0.003 ndia-Agra 11831 3 0.013 0.462 0.027 0.005 0.463 0.016 0.008 0.005 ndia-Agra 11832 2 0.003 0.417 0.004 0.002 0.56 0.004 0.006 0.004 ndia-Agra 11833 16 0.015 0.28 0.022 0.003 0.57 0.02 0.016 0.074 ndia-Agra 11834 3 0.004 0.342 0.006 0.002 0.627 0.004 0.012 0.003 ndia-Hyderbad 11802 13 0.016 0.005 0.01 0.008 0.826 0.119 0.01 0.005 ndia-Hyderbad 11803 7 0.018 0.023 0.004 0.003 0.928 0.008 0.005 0.012 ndia-Hyderbad 11804 5 0.004 0.029 0.005 0.002 0.678 0.003 0.264 0.014 ndia-Hyderbad 11805 10 0.011 0.009 0.01 0.005 0.926 0.015 0.009 0.014 ndia-Hyderbad 11807 11 0.006 0.008 0.022 0.005 0.936 0.008 0.007 0.008 ndia-Hyderbad 11808 9 0.016 0.003 0.036 0.016 0.834 0.008 0.061 0.026 ndia-Hyderbad 11809 3 0.067 0.032 0.007 0.101 0.773 0.011 0.003 0.006 ndia-Hyderbad 11810 0 0.01 0.045 0.017 0.005 0.697 0.082 0.062 0.081 ndia-Hyderbad 11811 0 0.009 0.004 0.003 0.009 0.757 0.011 0.009 0.198 ndia-Hyderbad 11812 3 0.007 0.086 0.013 0.002 0.859 0.005 0.015 0.014 ndia-Hyderbad 11813 4 0.023 0.016 0.126 0.003 0.793 0.007 0.029 0.004 ndia-Hyderbad 11814 2 0.006 0.019 0.009 0.003 0.938 0.01 0.004 0.01 ndia-Hyderbad 11815 2 0.011 0.024 0.053 0.005 0.844 0.027 0.007 0.029 ndia-Hyderbad 11816 3 0.007 0.016 0.002 0.003 0.952 0.006 0.005 0.009 ndia-Hyderbad 11817 2 0.003 0.005 0.035 0.002 0.941 0.005 0.004 0.005 ndia-Hyderbad 11818 1 0.008 0.007 0.107 0.069 0.76 0.014 0.005 0.03 ndia-Hyderbad 11819 4 0.009 0.016 0.002 0.003 0.948 0.008 0.005 0.009 ndia-Hyderbad 11820 5 0.003 0.022 0.009 0.002 0.865 0.007 0.048 0.044 ndia-Hyderbad 11821 13 0.021 0.01 0.192 0.006 0.662 0.057 0.014 0.037 ndia-Hyderbad 11822 2 0.015 0.012 0.009 0.004 0.902 0.008 0.037 0.014 ndia-Andhra 10159 2 0.007 0.006 0.01 0.008 0.919 0.014 0.032 0.003 ndia-Andhra 10160 1 0.002 0.003 0.008 0.002 0.752 0.004 0.225 0.004 ndia-Andhra 10161 2 0.004 0.006 0.021 0.006 0.948 0.004 0.005 0.007 ndia-Andhra 10162 5 0.004 0.004 0.016 0.003 0.964 0.003 0.004 0.003 ndia-Andhra 10163 0 0.004 0.007 0.009 0.004 0.937 0.003 0.018 0.017 ndia-Andhra 10164 2 0.006 0.02 0.012 0.007 0.917 0.005 0.03 0.003 ndia-Andhra 10165 1 0.006 0.01 0.073 0.004 0.87 0.007 0.004 0.026 ndia-Andhra 10166 3 0.025 0.006 0.08 0.013 0.71 0.009 0.008 0.15 ndia-Andhra 10167 1 0.013 0.003 0.064 0.013 0.85 0.036 0.012 0.009 ndia-Andhra 10168 1 0.008 0.007 0.027 0.006 0.922 0.02 0.006 0.003 ndia-Andhra 10169 6 0.004 0.003 0.112 0.008 0.848 0.012 0.007 0.006 ndia-Andhra 10170 4 0.037 0.004 0.146 0.007 0.702 0.036 0.045 0.022 ndia-Andhra 10171 1 0.006 0.021 0.013 0.006 0.91 0.004 0.036 0.003 ndia-Andhra 10172 2 0.007 0.02 0.017 0.012 0.916 0.006 0.01 0.013 ndia-Andhra 10173 2 0.005 0.003 0.321 0.004 0.648 0.008 0.003 0.009 ndia-Andhra 10174 3 0.003 0.002 0.021 0.004 0.958 0.004 0.006 0.002 ndia-Andhra 10175 2 0.009 0.005 0.007 0.044 0.891 0.019 0.018 0.006 ndia-Andhra 10176 2 0.013 0.011 0.005 0.005 0.885 0.008 0.071 0.003 ndia-Andhra 10177 0 0.008 0.006 0.016 0.09 0.833 0.005 0.034 0.008 ndia-Andhra 10178 3 0.003 0.005 0.024 0.004 0.953 0.003 0.005 0.004 ndia-Andhra 10179 4 0.005 0.004 0.018 0.016 0.901 0.006 0.007 0.044 ndia-Andhra 10180 10 0.029 0.015 0.017 0.026 0.849 0.051 0.009 0.004 ndia-Andhra 10181 5 0.004 0.003 0.023 0.004 0.898 0.005 0.057 0.006 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

India-Kolkata 10113 2 0.008 0.009 0.003 0.004 0.899 0.005 0.035 0.037

India-Kolkata 10114 1 0.005 0.006 0.026 0.003 0.942 0.004 0.009 0.006

India-Kolkata 10115 1 0.029 0.005 0.016 0.01 0.88 0.036 0.015 0.01

India-Kolkata 10116 2 0.004 0.006 0.022 0.002 0.941 0.003 0.015 0.008

India-Kolkata 10117 2 0.059 0.018 0.049 0.005 0.683 0.036 0.071 0.079

India-Kolkata 10118 3 0.009 0.025 0.09 0.012 0.558 0.267 0.018 0.021

India-Kolkata 10119 3 0.055 0.022 0.052 0.061 0.723 0.062 0.004 0.021

Sri Lanka 8780 1 0.025 0.007 0.006 0.088 0.767 0.011 0.079 0.018

Sri Lanka 8781 1 0.031 0.006 0.025 0.098 0.788 0.011 0.032 0.01

Sri Lanka 8782 3 0.024 0.005 0.006 0.056 0.852 0.035 0.011 0.011

Sri Lanka 8783 0 0.109 0.006 0.007 0.028 0.816 0.023 0.004 0.008

Sri Lanka 8784 1 0.017 0.007 0.072 0.25 0.607 0.007 0.033 0.008

Sri Lanka 8785 10 0.013 0.021 0.007 0.089 0.825 0.014 0.014 0.017

Sri Lanka 8786 0 0.188 0.025 0.013 0.046 0.625 0.024 0.031 0.048

Sri Lanka 8787 0 0.007 0.005 0.005 0.169 0.799 0.007 0.003 0.005

Sri Lanka 8788 4 0.058 0.082 0.026 0.069 0.724 0.028 0.004 0.009

Sri Lanka 8789 2 0.139 0.01 0.127 0.298 0.374 0.039 0.004 0.008

Sri Lanka 8790 1 0.015 0.003 0.07 0.184 0.718 0.005 0.003 0.002

Sri Lanka 8791 1 0.03 0.01 0.021 0.017 0.892 0.01 0.007 0.013

Sri Lanka 8792 0 0.05 0.011 0.005 0.042 0.855 0.026 0.004 0.007

Sri Lanka 8793 2 0.042 0.016 0.008 0.215 0.685 0.009 0.004 0.02

Sri Lanka 8794 2 0.033 0.019 0.005 0.173 0.742 0.018 0.007 0.004

Sri Lanka 8795 1 0.042 0.005 0.016 0.161 0.709 0.033 0.014 0.02

Sri Lanka 8796 4 0.068 0.004 0.007 0.021 0.746 0.034 0.111 0.009

Sri Lanka 8797 1 0.011 0.061 0.031 0.325 0.545 0.005 0.013 0.009

Sri Lanka 8798 1 0.107 0.011 0.005 0.061 0.763 0.028 0.022 0.003

Sri Lanka 8799 2 0.003 0.008 0.009 0.263 0.664 0.004 0.002 0.047

Sri Lanka 8800 2 0.014 0.01 0.068 0.37 0.465 0.011 0.041 0.02

Sri Lanka 8801 5 0.011 0.019 0.004 0.201 0.709 0.007 0.004 0.044

Sri Lanka 8802 2 0.006 0.007 0.024 0.4 0.54 0.004 0.004 0.015

Sri Lanka 8803 6 0.036 0.005 0.103 0.055 0.7 0.019 0.015 0.067

Thailand 11688 6 0.002 0.001 0.984 0.003 0.003 0.002 0.002 0.003

Thailand 11689 13 0.003 0.022 0.937 0.004 0.009 0.008 0.014 0.003

Thailand 11691 27 0.012 0.007 0.909 0.018 0.026 0.009 0.014 0.005

Thailand 11698 18 0.005 0.003 0.937 0.007 0.014 0.007 0.026 0.002

Thailand 11702 10 0.002 0.002 0.973 0.003 0.008 0.003 0.005 0.003

Thailand 11703 12 0.002 0.002 0.983 0.002 0.004 0.002 0.004 0.002

Thailand 11705 21 0.035 0.009 0.824 0.003 0.104 0.008 0.01 0.007

Thailand 11707 4 0.004 0.002 0.976 0.003 0.006 0.003 0.004 0.003

Thailand 11708 18 0.006 0.003 0.954 0.003 0.005 0.007 0.009 0.013

Thailand 11709 12 0.003 0.003 0.979 0.002 0.005 0.003 0.003 0.004

Thailand 11710 9 0.006 0.004 0.959 0.006 0.007 0.007 0.006 0.003

Thailand 11711 6 0.002 0.006 0.948 0.004 0.008 0.003 0.024 0.006

Thailand 11714 6 0.008 0.01 0.95 0.004 0.011 0.003 0.008 0.006

Thailand 11715 17 0.009 0.089 0.753 0.006 0.094 0.004 0.035 0.009

Thailand 11717 15 0.003 0.005 0.969 0.003 0.008 0.003 0.006 0.003

Thailand 11718 2 0.004 0.005 0.969 0.004 0.003 0.006 0.007 0.003

Thailand 11720 4 0.001 0.001 0.99 0.001 0.002 0.001 0.001 0.001

Vietnam 8844 4 0.008 0.01 0.806 0.007 0.123 0.004 0.035 0.008

Vietnam 8845 3 0.02 0.079 0.748 0.007 0.099 0.026 0.01 0.01

Vietnam 8846 3 0.022 0.003 0.65 0.014 0.265 0.038 0.004 0.005

Vietnam 8847 10 0.005 0.004 0.966 0.01 0.005 0.003 0.005 0.003

Vietnam 8848 6 0.03 0.006 0.883 0.01 0.031 0.015 0.004 0.02

Vietnam 8849 2 0.032 0.007 0.829 0.01 0.024 0.067 0.028 0.003

Vietnam 8850 1 0.114 0.007 0.659 0.149 0.01 0.009 0.042 0.009

Vietnam 8851 2 0.03 0.126 0.767 0.012 0.015 0.043 0.004 0.002 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Vietnam 8852 13 0.201 0.004 0.701 0.025 0.018 0.024 0.009 0.017

Vietnam 8853 3 0.32 0.004 0.601 0.012 0.027 0.027 0.005 0.005

Vietnam 8854 5 0.056 0.163 0.636 0.037 0.069 0.008 0.028 0.004

Vietnam 8855 2 0.029 0.003 0.743 0.134 0.009 0.045 0.033 0.003

Vietnam 8856 2 0.077 0.005 0.693 0.005 0.13 0.085 0.004 0.002

Vietnam 8857 3 0.016 0.006 0.472 0.015 0.442 0.035 0.005 0.008

Vietnam 8858 2 0.077 0.005 0.709 0.06 0.017 0.112 0.015 0.004

Vietnam 8859 7 0.048 0.014 0.658 0.015 0.069 0.015 0.147 0.035

Vietnam 8860 2 0.19 0.01 0.619 0.011 0.009 0.083 0.055 0.023

Vietnam 8861 2 0.245 0.026 0.576 0.01 0.07 0.021 0.044 0.009

Vietnam 8862 2 0.04 0.009 0.44 0.02 0.39 0.023 0.04 0.039

Vietnam 8863 3 0.093 0.012 0.613 0.048 0.015 0.197 0.015 0.006

Taiwan 8681 4 0.005 0.005 0.014 0.654 0.004 0.004 0.309 0.005

Taiwan 8682 0 0.011 0.034 0.381 0.01 0.105 0.008 0.443 0.007

Taiwan 8683 3 0.058 0.023 0.103 0.119 0.086 0.04 0.562 0.009

Taiwan 8684 3 0.007 0.008 0.06 0.65 0.005 0.006 0.261 0.003

Taiwan 8685 2 0.012 0.003 0.352 0.037 0.035 0.007 0.544 0.01

Taiwan 8686 6 0.035 0.008 0.19 0.258 0.014 0.007 0.464 0.024

Taiwan 8687 5 0.087 0.028 0.44 0.011 0.044 0.054 0.329 0.007

Taiwan 8688 26 0.016 0.014 0.288 0.03 0.111 0.013 0.499 0.028

Taiwan 8689 5 0.039 0.15 0.143 0.035 0.009 0.014 0.525 0.086

Taiwan 8690 2 0.012 0.067 0.312 0.009 0.036 0.005 0.553 0.006

Taiwan 8691 16 0.023 0.008 0.324 0.046 0.111 0.019 0.436 0.033

Taiwan 8692 3 0.014 0.009 0.478 0.025 0.01 0.006 0.45 0.009

Taiwan 8693 2 0.018 0.007 0.212 0.105 0.092 0.008 0.547 0.01

Taiwan 8694 1 0.005 0.004 0.443 0.437 0.017 0.004 0.087 0.003

Taiwan 8695 1 0.019 0.003 0.21 0.056 0.009 0.064 0.629 0.011

Taiwan 8696 11 0.004 0.002 0.003 0.972 0.004 0.004 0.009 0.003

Taiwan 8697 1 0.025 0.011 0.005 0.898 0.02 0.008 0.029 0.005

Taiwan 8698 4 0.003 0.002 0.002 0.981 0.002 0.003 0.004 0.002

Taiwan 8699 6 0.036 0.081 0.04 0.045 0.507 0.05 0.231 0.011

Taiwan 8700 2 0.087 0.014 0.278 0.213 0.025 0.022 0.355 0.006

Taiwan 8701 2 0.004 0.005 0.159 0.205 0.045 0.005 0.574 0.004

Taiwan 8702 2 0.059 0.011 0.182 0.238 0.018 0.024 0.448 0.021

Taiwan 8703 2 0.005 0.004 0.008 0.94 0.012 0.017 0.009 0.005

Taiwan 8704 1 0.086 0.003 0.263 0.007 0.007 0.018 0.574 0.042

Taiwan 8705 2 0.01 0.015 0.268 0.008 0.082 0.026 0.572 0.019

Taiwan 8706 2 0.061 0.026 0.208 0.045 0.049 0.062 0.523 0.026

Taiwan 8707 5 0.004 0.088 0.29 0.017 0.035 0.026 0.536 0.004

Taiwan 8708 3 0.004 0.003 0.007 0.967 0.005 0.003 0.006 0.005

Taiwan 8709 4 0.024 0.011 0.285 0.014 0.015 0.009 0.63 0.013

Japan-Oita 11967 2 0.039 0.025 0.004 0.01 0.019 0.04 0.857 0.005

Japan-Oita 11968 1 0.004 0.006 0.004 0.002 0.007 0.002 0.972 0.003

Japan-Oita 11969 1 0.006 0.053 0.003 0.004 0.009 0.008 0.892 0.025

Japan-Oita 11970 1 0.004 0.01 0.031 0.008 0.036 0.01 0.894 0.006

Japan-Oita 11971 2 0.009 0.008 0.004 0.003 0.006 0.006 0.961 0.003

Japan-Oita 11972 8 0.004 0.073 0.012 0.002 0.075 0.007 0.819 0.008

Japan-Oita 11973 2 0.002 0.001 0.002 0.001 0.002 0.002 0.987 0.003

Japan-Oita 11974 1 0.002 0.002 0.004 0.002 0.002 0.002 0.983 0.003

Japan-Oita 11975 2 0.007 0.009 0.002 0.008 0.04 0.008 0.914 0.012

Japan-Oita 11976 3 0.006 0.003 0.002 0.002 0.003 0.006 0.975 0.005

Japan-Oita 11977 2 0.003 0.007 0.016 0.002 0.024 0.003 0.939 0.006

Japan-Oita 11979 2 0.005 0.009 0.002 0.015 0.005 0.006 0.948 0.012

Japan-Oita 11980 2 0.017 0.03 0.003 0.002 0.007 0.023 0.908 0.007

Japan-Oita 11981 2 0.016 0.01 0.004 0.012 0.042 0.014 0.889 0.012

Japan-Oita 11982 0 0.005 0.021 0.013 0.004 0.006 0.003 0.944 0.004 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

Japan-Oita 11985 0 0.003 0.023 0.003 0.002 0.014 0.005 0.928 0.022

Japan-Oita 11986 1 0.008 0.008 0.003 0.006 0.005 0.009 0.952 0.009

Japan-Kanazawa 11929 1 0.005 0.011 0.003 0.002 0.003 0.005 0.967 0.005

Japan-Kanazawa 11931 1 0.01 0.054 0.007 0.005 0.013 0.012 0.889 0.01

Japan-Kanazawa 11932 1 0.005 0.007 0.003 0.006 0.018 0.006 0.952 0.002

Japan-Kanazawa 11933 4 0.005 0.006 0.006 0.003 0.004 0.006 0.968 0.002

Japan-Kanazawa 11934 19 0.003 0.003 0.009 0.003 0.012 0.003 0.961 0.007

Japan-Kanazawa 11936 9 0.012 0.009 0.017 0.007 0.01 0.005 0.933 0.007

Japan-Kanazawa 11937 23 0.016 0.004 0.016 0.005 0.087 0.007 0.856 0.008

Japan-Kanazawa 11939 1 0.005 0.009 0.038 0.004 0.007 0.004 0.919 0.016

Japan-Kanazawa 11940 2 0.005 0.006 0.007 0.002 0.013 0.003 0.959 0.004

Japan-Kanazawa 11941 1 0.011 0.084 0.01 0.004 0.007 0.006 0.863 0.015

Japan-Kanazawa 11942 2 0.004 0.001 0.014 0.005 0.003 0.004 0.967 0.001

Japan-Kanazawa 11943 1 0.032 0.004 0.004 0.023 0.005 0.01 0.921 0.003

Japan-Kanazawa 11944 0 0.005 0.003 0.009 0.003 0.017 0.005 0.943 0.015

Japan-Kanazawa 11945 1 0.066 0.014 0.006 0.005 0.004 0.013 0.891 0.003

Japan-Kanazawa 11946 1 0.203 0.014 0.004 0.03 0.008 0.006 0.721 0.015

Japan-Ohmiya 11947 3 0.005 0.003 0.007 0.003 0.004 0.003 0.972 0.002

Japan-Ohmiya 11948 3 0.011 0.005 0.006 0.052 0.01 0.015 0.896 0.006

Japan-Ohmiya 11951 14 0.02 0.087 0.003 0.572 0.015 0.022 0.278 0.003

Japan-Ohmiya 11953 1 0.007 0.005 0.004 0.016 0.005 0.004 0.953 0.005

Japan-Ohmiya 11954 2 0.005 0.004 0.003 0.004 0.003 0.006 0.971 0.003

Japan-Ohmiya 11955 8 0.013 0.004 0.002 0.123 0.005 0.01 0.837 0.006

Japan-Ohmiya 11956 4 0.03 0.007 0.007 0.007 0.007 0.059 0.882 0.002

Japan-Ohmiya 11957 1 0.004 0.002 0.012 0.002 0.01 0.005 0.963 0.003

Japan-Ohmiya 11959 2 0.002 0.004 0.003 0.002 0.008 0.002 0.973 0.006

Japan-Ohmiya 11960 2 0.065 0.028 0.007 0.392 0.024 0.04 0.433 0.011

Japan-Ohmiya 11961 8 0.018 0.006 0.047 0.015 0.059 0.016 0.806 0.033

Japan-Ohmiya 11962 3 0.01 0.004 0.003 0.02 0.004 0.009 0.944 0.005

Japan-Ohmiya 11963 5 0.01 0.003 0.309 0.085 0.005 0.026 0.523 0.039

Japan-Ohmiya 11964 11 0.006 0.002 0.139 0.447 0.003 0.005 0.385 0.013

Japan-Ohmiya 11965 2 0.003 0.008 0.002 0.003 0.003 0.002 0.973 0.005

Japan-Ohmiya 11966 2 0.019 0.004 0.006 0.01 0.046 0.022 0.885 0.008

Japan-Sapporo 11907 3 0.009 0.005 0.04 0.004 0.015 0.005 0.912 0.009

Japan-Sapporo 11909 1 0.024 0.014 0.007 0.011 0.131 0.013 0.792 0.009

Japan-Sapporo 11911 0 0.07 0.007 0.024 0.019 0.021 0.013 0.75 0.096

Japan-Sapporo 11913 2 0.004 0.003 0.024 0.002 0.007 0.003 0.952 0.006

Japan-Sapporo 11914 9 0.11 0.007 0.003 0.051 0.003 0.055 0.739 0.031

Japan-Sapporo 11915 2 0.005 0.006 0.002 0.003 0.003 0.003 0.977 0.002

Japan-Sapporo 11916 3 0.011 0.023 0.007 0.016 0.009 0.007 0.915 0.012

Japan-Sapporo 11917 6 0.046 0.007 0.349 0.006 0.011 0.122 0.455 0.004

Japan-Sapporo 11918 2 0.041 0.006 0.024 0.009 0.006 0.013 0.897 0.003

Japan-Sapporo 11921 6 0.016 0.012 0.017 0.005 0.025 0.008 0.915 0.003

Japan-Sapporo 11922 4 0.02 0.006 0.369 0.008 0.009 0.011 0.567 0.011

Japan-Sapporo 11923 5 0.021 0.004 0.425 0.164 0.021 0.04 0.307 0.018

Japan-Sapporo 11924 5 0.008 0.005 0.347 0.051 0.005 0.077 0.501 0.006

Japan-Sapporo 11925 15 0.039 0.01 0.591 0.01 0.011 0.101 0.233 0.005

Japan-Sapporo 11926 8 0.027 0.015 0.406 0.017 0.045 0.01 0.47 0.01

China-Henan 8869 3 0.018 0.004 0.257 0.009 0.037 0.007 0.661 0.007

China-Henan 8870 3 0.005 0.007 0.105 0.263 0.01 0.005 0.595 0.011

China-Henan 8871 20 0.011 0.006 0.314 0.007 0.073 0.013 0.56 0.017

China-Henan 8872 19 0.022 0.002 0.432 0.131 0.119 0.013 0.276 0.005

China-Henan 8873 16 0.016 0.005 0.418 0.138 0.014 0.008 0.399 0.003

China-Henan 8874 5 0.014 0.007 0.535 0.03 0.005 0.006 0.394 0.008

China-Henan 8875 11 0.007 0.007 0.435 0.012 0.006 0.011 0.512 0.01

China-Henan 8876 18 0.006 0.007 0.4 0.007 0.011 0.008 0.545 0.017 Table 25 - Population clustering of each random bred individual in the database by

SNPs and STRs at K = 8

Sampling ID Missing Population

Location No. Data 1 2 3 4 5 6 7 8

China-Henan 8877 16 0.002 0.003 0.317 0.002 0.004 0.002 0.665 0.005

China-Henan 8878 6 0.023 0.005 0.399 0.025 0.014 0.004 0.525 0.006

China-Henan 8879 11 0.01 0.004 0.338 0.006 0.028 0.018 0.594 0.003

China-Henan 8880 7 0.017 0.005 0.249 0.273 0.01 0.03 0.412 0.004

China-Henan 8881 7 0.003 0.003 0.001 0.981 0.003 0.003 0.003 0.003

China-Henan 8882 18 0.003 0.007 0.163 0.04 0.045 0.005 0.729 0.007

China-Henan 8883 6 0.006 0.006 0.111 0.205 0.028 0.005 0.634 0.004

China-Henan 8884 14 0.004 0.002 0.118 0.021 0.013 0.009 0.667 0.166

China-Henan 8885 4 0.004 0.001 0.09 0.156 0.004 0.003 0.737 0.005

China-Henan 8886 8 0.007 0.003 0.429 0.014 0.01 0.007 0.527 0.003

China-Henan 8887 23 0.007 0.005 0.013 0.025 0.01 0.004 0.933 0.004

China-Henan 8888 18 0.004 0.004 0.324 0.007 0.013 0.003 0.639 0.006

South Korea 2769 12 0.005 0.007 0.136 0.006 0.009 0.006 0.823 0.008

South Korea 2772 7 0.009 0.012 0.218 0.007 0.01 0.008 0.734 0.003

South Korea 2775 9 0.003 0.003 0.077 0.005 0.048 0.005 0.836 0.022

South Korea 2776 19 0.015 0.026 0.379 0.011 0.095 0.014 0.45 0.009

South Korea 2779 13 0.029 0.013 0.079 0.708 0.026 0.086 0.006 0.052

South Korea 2784 16 0.005 0.005 0.264 0.038 0.011 0.005 0.656 0.015

South Korea 2785 15 0.002 0.002 0.043 0.004 0.006 0.014 0.923 0.006

South Korea 2786 4 0.023 0.004 0.144 0.083 0.011 0.06 0.672 0.004

South Korea 7671 16 0.032 0.029 0.009 0.017 0.011 0.019 0.861 0.021

South Korea 7672 18 0.008 0.025 0.05 0.064 0.119 0.06 0.653 0.022

South Korea 7673 3 0.005 0.003 0.294 0.003 0.004 0.004 0.684 0.002

South Korea 7674 3 0.008 0.009 0.217 0.198 0.019 0.008 0.538 0.003

South Korea 7675 5 0.004 0.003 0.096 0.273 0.008 0.006 0.581 0.029

South Korea 7676 3 0.007 0.004 0.519 0.005 0.01 0.009 0.439 0.007

South Korea 7677 3 0.05 0.007 0.016 0.102 0.009 0.03 0.724 0.063

South Korea 7678 6 0.018 0.003 0.034 0.039 0.009 0.091 0.797 0.009

South Korea 7679 3 0.002 0.004 0.465 0.004 0.004 0.002 0.517 0.002

South Korea 7680 3 0.013 0.004 0.072 0.005 0.011 0.009 0.88 0.006

South Korea 7681 5 0.009 0.005 0.221 0.157 0.06 0.012 0.521 0.014

South Korea 7682 3 0.012 0.007 0.147 0.11 0.042 0.005 0.656 0.021

South Korea 7683 4 0.01 0.018 0.059 0.157 0.115 0.006 0.614 0.021

South Korea 7684 4 0.008 0.003 0.12 0.006 0.084 0.013 0.602 0.163

South Korea 7685 4 0.012 0.011 0.149 0.104 0.048 0.028 0.637 0.012

South Korea 7686 6 0.014 0.014 0.204 0.02 0.016 0.008 0.716 0.01

South Korea 7687 9 0.005 0.003 0.005 0.971 0.005 0.003 0.005 0.003

South Korea 7688 15 0.014 0.008 0.033 0.403 0.013 0.003 0.522 0.004

South Korea 7689 14 0.013 0.003 0.166 0.291 0.014 0.008 0.496 0.009

South Korea 7690 6 0.014 0.005 0.198 0.174 0.075 0.007 0.521 0.005

South Korea 7691 8 0.004 0.009 0.664 0.009 0.009 0.016 0.286 0.003

South Korea 7692 6 0.034 0.002 0.013 0.896 0.018 0.028 0.006 0.003

South Korea 7693 4 0.012 0.01 0.003 0.892 0.006 0.009 0.023 0.045

South Korea 7694 12 0.013 0.01 0.179 0.223 0.087 0.02 0.448 0.02

South Korea 7695 5 0.006 0.009 0.316 0.003 0.035 0.006 0.602 0.022

South Korea 7696 5 0.006 0.004 0.073 0.016 0.226 0.004 0.642 0.028

South Korea 7697 7 0.012 0.003 0.352 0.016 0.025 0.005 0.568 0.019

South Korea 7698 5 0.038 0.005 0.341 0.557 0.009 0.006 0.037 0.007

South Korea 7699 9 0.004 0.003 0.583 0.002 0.014 0.004 0.379 0.011

South Korea 7700 10 0.031 0.008 0.025 0.288 0.016 0.108 0.52 0.003 [0183] It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. All publications, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.

Claims

CLAIMS What is claimed is:

1. A computer implemented method for determining the contributions of feline populations to a feline genome, comprising:

2. The method of claim 1 , wherein the plurality of SNPs comprises at least about 100 SNPs listed in Table 1.

3. The method of claim 1 , wherein the plurality of SNPs comprises all 148 SNPs listed in Table 1.

4. The method of any one of claims 1 to 3, wherein the set of markers further comprises one or more microsatellite markers.

5. The method of claim 4, wherein the set of markers further comprises one or more short tandem repeats (STRs) selected from the group consisting of FCA005, FCA008, FCA023, FCA026, FCA035, FCA043, FCA045, FCA058, FCA069, FCA075, FCA077, FCA080B, FCA088, FCA090, FCA094, FCA096, FCA097, FCA105, FCA123, FCA126, FCA132, FCA149, FCA211, FCA220, FCA223, FCA224, FCA229, FCA262, FCA293, FCA305, FCA310, FCA391, FCA441, FCA453, FCA628, FCA649, FCA678 and FCA698.

6. The method of any one of claims 1 to 5, wherein the set of markers further comprises one or more phenotypic markers.

7. The method of claim 6, wherein the one or more phenotypic markers are selected from the group consisting of Phen_CMAH_G139A, Phen ASIP del,

Phen_MLPH_T83del, Phen_MClR_G250A, Phen_TYRPl_C298T, Phen_TYRPl_5IVS6, Phen_TYR_del975C, Phen_TYR_G715T, Phen_TYR_G940A, Phen_KIT_G1035C_BI, Phen_FGF5_475, Phen_FGF5_474, phen_FGF5_406, Phen_FGF5_356,

Phen_GBLl_G1457C_SIA_KOR, Phen HEXB Dellntr BUR,

Phen_HEXB_del39C_KOR, Phen GBE l lns NFC, Phen_KRT7 l_G/Aintro4_SPX, Phen_MYBPC_G93C_MCC, Phen_MYBPC_C2460T_RAG, phen MPO ALC,

Phen PLAU AG ALC, Phen FCAT ALC, Phen_PKLR_13delE6_Aby,

Phen PKD 1_C 10063 A PER, Phen_SHH_A479G_Hw, Phen_CEP290_PRA_Aby, Phen_CRX_546_Aby, Phen CMAH del, Phen_HEXB_C667T_DSH,

Phen_GM2A_Del_DSH, Phen GRHPR DSH, Phen_LPL_G1234A_DSH,

Phen LAMAN del PER, Phen lDUA del DSH, Phen_ARSB_G1558A_SIA,

Phen ARSB T 1427C_Sia, Phen GUSB A 1052G DSH, Phen_MYBPC_A74T_Poly, Phen_NPCl_G2864C_PER, Phen_SHH_G257C_UKl, Phen_SHH_A481T_UK2, Phen_HMBS_del842_SIA, Phen-HMBS_189TT_SIA, Phen_CYP21Bl,

Phen TAS 1 R2 CAT, Phen_TASlR2_G8224A_CAT, Phen_CYP27Bl_Rob, Phen ZFX, KRT71 -Del Drex, P2RY5_CRex, WNK4_Burm_HKL and CARTl del Burm.

8. The method of any one of claims 1 to 7, wherein the genotype information in each feline population profile comprises identities of one or both alleles of each marker of the set of markers.

9. The method of any one of claims 1 to 8, wherein the genotype information in each feline population profile comprises allele frequencies for one or both alleles of each marker of the set of markers.

10. The method of any one of claims 1 to 9, wherein the database of feline population profiles comprises a plurality of feline population profiles.

11. The method of any one of claims 1 to 10, wherein the database of feline populations profiles comprises profiles for at least one feline breed.

12. The method of any one of claims 1 to 11, wherein the set of markers comprises a subset of the 148 SNP markers listed in Table 1 and wherein the method determines the contributions of one or more feline populations to the test feline genome.

13. The method of any one of claims 1 to 12, wherein step (a) comprises amplifying genomic DNA of the test feline using primers specific for each of the set of markers and determining the size of the amplification product.

14. The method of any one of claims 1 to 12, wherein step (a) comprises amplifying genomic DNA of the test feline using primers specific for each of the set of markers and sequencing the amplification product.

15. The method of any one of claims 1 to 14, wherein the algorithm according to step (b) comprises a genotype clustering program.

16. The method of any one of claims 1 to 15, wherein the algorithm according to step (b) comprises an assignment algorithm.

17. The method of any one of claims 1 to 16, wherein step (b) comprises discriminating between the contributions of two or more genetically related feline populations to the test feline genome by comparing the alleles in the test feline genome to a database comprising profiles of the two or more genetically related feline populations.

18. The method of claim 17, wherein the two or more genetically related feline populations are selected from the group consisting of:

(i) Persian and Exotic Shorthair (SH);

(ii) British SH and Scottish Fold;

(iii) Australian Mist and Burmese;

(iv) Singapura and Burmese;

(v) Birman and Korat; and

(vi) Siamese and Havana Brown.

19. The method of any one of claims 1 to 18, further comprising the step of providing a document displaying the contributions of one or more feline populations to the genome of the test feline genome.

20. The method of claim 19, wherein the document provides additional information regarding the one or more feline populations that contributed to the genome of the test feline.

21. The method of claim 20, wherein the additional information is health- related information.

22. The method of claim 20, wherein the document provides a certification of the contributions of one or more feline populations to the genome of the test feline.

23. The method of claim 20, wherein the document provides a representation of the one or more feline populations that contributed to the genome of the test feline.

24. A method for defining one or more feline populations, comprising: (a) determining the identity of one or both alleles for each marker of a set of markers in a test feline genome, wherein the set of markers comprises a plurality of single nucleotide polymorphisms (SNPs) listed in Table 1 ; and

(b) applying a computer-implemented statistical model to define one or more distinct feline populations, wherein one or more distinct feline populations are characterized by a set of allele frequencies for each marker in the set of markers comprising a plurality of SNPs listed in Table 1.

25. One or more computer-readable media comprising:

(a) a data structure stored thereon for use in distinguishing feline populations, the data structure comprising:

(i) marker data, wherein the marker data identifies one or both alleles of each marker of a set of markers in one or more feline population profiles, wherein the set of markers comprises a plurality of single nucleotide polymorphisms (SNPs) listed in Table 1; and

(i) identify one or both alleles in a test feline genome for each marker of the set of markers; and (ii) determine the contributions of one or more feline populations to the test feline genome by comparing the identified alleles in the test feline genome to the database comprising one or more feline population profiles, wherein each feline population profile comprises genotype information for the set of markers in the feline population.

26. One or more computer-readable media comprising a data structure stored thereon for use in distinguishing feline populations, the data structure comprising:

27. The computer readable media of any one of claims 25 to 26, wherein the plurality of SNPs comprises at least about 100 SNPs listed in Table 1.

28. The computer readable media of any one of claims 25 to 26, wherein the plurality of SNPs comprises all 148 SNPs listed in Table 1.

29. The computer readable media of any one of claims 25 to 28, wherein the set of markers further comprises one or more microsatellite markers.

30. The computer readable media of claim 29, wherein the set of markers further comprises one or more short tandem repeats (STRs) selected from the group consisting of FCA005, FCA008, FCA023, FCA026, FCA035, FCA043, FCA045, FCA058, FCA069, FCA075, FCA077, FCA080B, FCA088, FCA090, FCA094, FCA096, FCA097, FCA105, FCA123, FCA126, FCA132, FCA149, FCA211, FCA220, FCA223, FCA224, FCA229, FCA262, FCA293, FCA305, FCA310, FCA391, FCA441, FCA453, FCA628, FCA649, FCA678 and FCA698.

31. The computer readable media of any one of claims 25 to 30, wherein the set of markers further comprises one or more phenotypic markers.

32. The computer readable media of claim 31 , wherein the one or more phenotypic markers are selected from the group consisting of Phen_CMAH_G139A, Phen ASIP del, Phen_MLPH_T83del, Phen_MClR_G250A, Phen_TYRPl_C298T, Phen_TYRPl_5IVS6, Phen_TYR_del975C, Phen_TYR_G715T, Phen_TYR_G940A, Phen_KIT_G1035C_BI, Phen_FGF5_475, Phen_FGF5_474, phen_FGF5_406,

Phen_FGF5_356, Phen_GBLl_G1457C_SIA_KOR, Phen HEXB Dellntr BUR,

Phen PLAU AG ALC, Phen FCAT ALC, Phen_PKLR_13delE6_Aby,

Phen_GM2A_Del_DSH, Phen GRHPR DSH, Phen_LPL_G1234A_DSH,

Phen LAMAN del PER, Phen lDUA del DSH, Phen_ARSB_G1558A_SIA,

Phen ARSB T 1427C_Sia, Phen GUSB A 1052G DSH, Phen_MYBPC_A74T_Poly, Phen_NPCl_G2864C_PER, Phen_SHH_G257C_UKl, Phen_SHH_A481T_UK2,

Phen_HMBS_del842_SIA, Phen-HMBS_189TT_SIA, Phen_CYP21Bl,

33. A method for determining the contributions of feline populations to a feline genome, comprising performing a genotyping assay on a sample comprising genomic

DNA obtained from a test feline to determine the identity of one or both alleles present in the test feline genome for each marker of a set of markers, wherein the set of markers is indicative of the contribution of feline populations to the genome of the test feline, wherein the set of markers comprises a plurality of single nucleotide polymorphisms (SNPs) listed in Table 1.

34. A method of assigning a feline individual to a population of origin, which comprises:

(a) genotyping the feline individual to identify one or both alleles of each marker of a set of markers to thereby identify the individual's genotype, wherein the set of markers comprises a plurality of single nucleotide polymorphisms (SNPs) listed in Table 1;

(b) applying a computer-implemented statistical model to assign the feline individual to one or more feline populations in a database, wherein the one or more feline populations are characterized by a set of allele frequencies for each marker in the set of markers; and

(c) assigning the feline individual to the one or more most likely populations identified in step (b).

35. The method of claim 34, wherein the individual is assigned to the one or more most likely feline populations if the population genotype probability for the most likely feline populations exceed the value of assignment to any other feline populations of the database.

36. The method of any one of claims 34 to 35, wherein the set of markers further comprises one or more STRs selected from the group consisting of FCA005,

FCA008, FCA023, FCA026, FCA035, FCA043, FCA045, FCA058, FCA069, FCA075, FCA077, FCA080B, FCA088, FCA090, FCA094, FCA096, FCA097, FCA105, FCA123, FCA126, FCA132, FCA149, FCA211, FCA220, FCA223, FCA224, FCA229, FCA262, FCA293, FCA305, FCA310, FCA391, FCA441, FCA453, FCA628, FCA649, FCA678 and FCA698.

37. The method of any one of claims 34 to 36, wherein the set of markers further comprises one or more of the phenotypic markers selected from the group consisting of Phen CMAH G 139 A, Phen ASIP del, Phen_MLPH_T83del, Phen_MClR_G250A, Phen_TYRPl_C298T, Phen_TYRPl_5IVS6, Phen_TYR_del975C, Phen_TYR_G715T, Phen_TYR_G940A, Phen_KIT_G1035C_BI, Phen_FGF5_475, Phen_FGF5_474, phen_FGF5_406, Phen_FGF5_356, Phen_GBLl_G1457C_SIA_KOR,

Phen HEXB Dellntr BUR, Phen_HEXB_del39C_KOR, Phen GBE 1 Ins NFC ,

Phen_KRT7 l_G/Aintro4_SPX, Phen_MYBPC_G93C_MCC,

Phen_MYBPC_C2460T_RAG, phen MPO ALC, Phen PLAU AG ALC,

Phen FC AT ALC , Phen PKLR l 3delE6_Aby, Phen PKD 1_C 10063 A PER,

Phen_SHH_A479G_Hw, Phen_CEP290_PRA_Aby, Phen_CRX_546_Aby,

Phen CMAH del, Phen_HEXB_C667T_DSH, Phen_GM2A_Del_DSH,

Phen GRHPR DSH, Phen_LPL_G1234A_DSH, Phen LAMAN del PER,

Phen lDUA del DSH, Phen_ARSB_G1558A_SIA, Phen_ARSB_T1427C_Sia,

Phen GUSB Al 052G DSH, Phen_MYBPC_A74T_Poly, Phen NPC 1 G2864C PER, Phen_SHH_G257C_UKl, Phen_SHH_A481T_UK2, Phen_HMBS_del842_SIA, Phen- HMBS l 89TT SIA, Phen_CYP21Bl, Phen TAS 1 R2 CAT, Phen_TASlR2_G8224A_CAT, Phen_CYP27Bl_Rob, Phen ZFX, KRT71 -Del Drex, P2RY5 CRex, WNK4_Burm_HKL and CARTl del Burm.

38. The method of any one of claims 34 to 37, wherein prior to genotyping, a most likely population of origin is based on one or more morphological features of the individual.

39. The method of any one of claims 34 to 37, wherein prior to genotyping, one or more morphological features of the individual allow the exclusion of one or more of the candidate populations of origin.

40. The method of any one of claims 34 to 39, wherein marker locus genotypes for said each candidate population are in Hardy- Weinberg Equilibrium and Gametic Phase Equilibrium.