EA046341B1 - METHODS AND SYSTEMS FOR DETERMINING RELATIONSHIP - Google Patents

Description

Link to related applications

This application claims priority to U.S. Provisional Patent Application No. 62/936,879, filed November 18, 2019, which is incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

Small differences in DNA within populations of individuals may account for much of the genetic and phenotypic variation observed between individuals. Certain identical by descent (IBD) haplotypes (eg, small sections of DNA that are inherited together) that are shared by individuals can indicate a familial relationship between those individuals. Therefore, IBD analysis can be performed on multiple individuals to determine their relationship.

Summary of the present invention

The present invention relates to methods for assessing the degree of relatedness between individuals. In an aspect, the method involves obtaining haplotype data containing genetic markers common to a population of individuals, dividing the haplotype data into segments based on genetic markers, for each population of individuals tested: (i) matching, based on genetic markers, segments of haplotype data that are identical in origin between two individuals, (ii) for each of the matched segments: dividing the matched segment into discrete genomic intervals, estimating each of the discrete genomic intervals based on the degree of match within or between individuals, adjusting the scores for concordance, and (iii) calculating a weighted sum for the discrete genomic matched segment intervals based on the adjusted scores and assigned weights, and (d) an estimate of the degree of relatedness between individuals based on the weighted sums of the matched segments.

In an aspect, the present invention relates to a computerized method for assessing the degree of relatedness between two individuals in a diploid population, comprising: (a) obtaining haplotype data for a population of test individuals, wherein the haplotype data comprises a plurality of genetic markers common to the population of test individuals, (b) separating haplotype data data into segments based on multiple genetic markers, (c) for each population of test individuals: (i) matching, based on multiple genetic markers, segments of haplotype data that are identical in ancestry, between a first individual and a second individual within the population of test individuals, each of the matched segments has a first size that is at least a predetermined threshold size, and contains at least a predetermined number of genetic markers, (ii) for each of the matched segments between the first individual and the second individual: dividing the matched segment into a plurality of discrete genomic intervals, estimating each of a plurality of discrete genomic intervals based on (i) the degree of homozygosity match of a discrete genomic interval in a first individual or a second individual, or (ii) the degree of pairwise matching of a discrete genomic interval between a first individual and a second individual, thereby obtaining a plurality of estimates , adjusting the plurality of scores based on the consistency of the plurality of scores, thereby obtaining a plurality of adjusted scores, and assigning a plurality of weights to the plurality of discrete genomic intervals based on the plurality of adjusted discrete genomic bin scores, and (iii) calculating a weighted sum for the plurality of discrete genomic bins of the matched segment based on a plurality of adjusted scores and a plurality of weights, and (d) estimating the degree of relatedness between the first individual and the second individual based on the weighted sums of the matched segments.

In some embodiments, the diploid population is a mammalian population. In some embodiments, the mammal population is a canine population, a feline population, a sporting animal population, or a rodent population. In some embodiments, the mammal population is a canid population. In some embodiments, the canid population is a population of dogs. In some embodiments, the mammal population is a feline population. In some embodiments, the feline population is a population of cats. In some embodiments, the mammal population is a population of sporting animals. In some embodiments, the sporting animal population is a population of horses. In some embodiments, the dog population comprises one or more dog breeds selected from the group consisting of: Affenpinscher, Afghan Hound, Africanis, Aidi, Airedale Terrier, Akbash, Akita Inu, Bully Cutta, Spanish Alano, Alapaha Bulldog, Alaskan Klee Kai , Alaskan Malamute, Alan, Alopekis, Alpine Dachshund, American Shepherd, American Akita, American Bulldog, American Cocker Spaniel,

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American Eskimo Dog, American Foxhound, American Hairless Terrier, American Mastiff, American Pit Bull Terrier, American Staffordshire Terrier, American Water Spaniel, Anatolian Shepherd Dog, Anglo-French Miniature Hound, Appenzeller Mountain Dog, Dogo Argentino, Ariege Braque, Ariege Hound, Armant, Artois Hound , Australian Bulldog, Australian Cattle Dog, Australian Kelpie, Australian Shepherd, Australian Silky Terrier, Australian Short-tailed Cattle Dog, Australian Terrier, Austrian Hound, Austrian Pinscher, Azawakh, Baharwal Dog, Barbet, African Non-Barking Dog, Basque Shepherd Dog, Artesian-Normandy Basset , Blue Gascony Basset, Red Breton Basset, Great Vendeen Basset Griffon, Small Vendeen Basset Griffon, Bavarian Mountain Hound, Beagle, Beagle Harrier, Bearded Collie, Beauceron, Bedlington Terrier, Belgian Shepherd, Belgian Shepherd (Groenendael), Belgian Shepherd Dog (Laekenois), Belgian Shepherd Dog (Malinois), Belgian Shepherd Dog (Tervuren), Bergamasco Shepherd Dog, White Swiss Shepherd Dog, Picardy Shepherd Dog, Bernese Hound, Bernese Mountain Dog, Bichon Frize, Biyi, Bisbane, Black and Tan Coonhound, Erotic Norwegian Elk Husky, Bullenbeiser, Norwegian Black Elkhound, Black Russian Terrier, Yellow with Black Mask Southern Hound, Greater Blue Gascony Hound, Small Blue Gascony Hound, Bloodhound, Blue Lacy, Blue Pol Terrier, Blue Speckled Coonhound, Boerboel, Czech Cattle Dog, Bolognese, Border Collie, Border Terrier, Russian Greyhound, Bosnian Wirehaired Hound, Boston Terrier, Bouvier des Ardenne, Bouvier des Flanders, Boxer, Boykin Spaniel, Italian Braque, Auvergne Pointing Dog, Bourbon Braque, Braque Dupuis, French Braque, Sen -Germaine marriage, Brazilian terrier, Briard, Middle Vendean griffon, Breton epagnole, Broholmer, Jurassic hound, Bukovinian shepherd dog, Bull and terriers, Bull terrier, Bull terrier (Miniature), Bullmastiff, Bully Cutta, Cairn terrier, Canaan dog, Canadian Eskimo Dog, Canadian Pointer, Cane Corso, Portuguese Shepherd Dog, Cao de Castro Laboreiro, Azores Cattle Dog, Carolina Dog, Romanian Carpathian Shepherd Dog, Catahoula Leopard Dog, Catalan Shepherd Dog, Caucasian Shepherd Dog, Cavalier King Charles Spaniel, Central Asian Shepherd Dog, Czech Fousek , Czech Terrier, Polish Greyhound, Chesapeake Bey Retriever, Gray Blue Hound, French Black and Pinto Hound, French White and Black Dog, French Tricolor Hound, Chihuahua, Chilean Fox Terrier, Chinese Chongqing, Chinese Crested Dog, Chinese Imperial Dog, Chinook, Chippiparai, Chow Chow, Uruguayan Cimarron, Black Sheepdog, Cirneco del Etna, Clumber Spaniel, Longhaired Collie, Shorthaired Collie, Kombai, Cordoba Fighting Dog, Coton de Tulear, Cretan Hound, Croatian Shepherd Dog, Cumberland Sheepdog, Curly-Coated Retriever, Czechoslovakian Wolfdog, Dachshund, Dalmatian, Dandie Dinmont Terrier, Danish-Swedish Farm Dog, Dingo, Doberman, Dogue de Bordeaux, Dogue de Cuba, Guatemalan Bull Terrier, Miniature Dogue, Drenthe Partridge Dog, Drever, Duncker, Dutch Shepherd, Dutch Smoushond, East European Shepherd, East Siberian Laika, Elo, English Cocker Spaniel, English Coon Hound, English Foxhound, English Mastiff, Pointer, English Setter, English Shepherd, English Springer Spaniel, English Toy Terrier, English Water Spaniel, English White Terrier, Entle booker Mountain Dog, Blue Picardy Spaniel, Estonian Hound, Estrela Shepherd Dog, Eurasier, Field Spaniel, Fila Brazilian, Fido, Finnish Hound, Finnish Lapphund, Finnish Spitz, Flat-Coated Retriever, Taiwanese Dog, Smooth Fox Terrier, Wire Fox Terrier, Breton Epagnole, French Bulldog , French Spaniel, Spanish Galgo, Langhaar, German Pincher, German Shepherd, Kurzhaar, German Vakhtelhund, German Spitz, Drama, Rizenshnuszer, Glen Of Imal Terrier, Golden Retriever, Scottish Setter, Big Anglo-Franza Black Persean, Big Anglo-French. red piebald hound, Greater Anglo-French Tricolor hound, Greater Griffon Vendean, Puerto Rican Mastiff, Great Dane, Pyrenean Mountain Dog, Greater Swiss Mountain Dog, Greenland Dog, Greyhound, Blue Gascony Griffon, Belgian Griffon, Breton Griffon dun, Griffon Nivernes, Ghoul -dong, Gul-terr, Indian Hare Dog, Hamilton Hound, Hanoverian Hound, Harrier, Havanese Bichon, Hawaiian Poi Dog, Himalayan Sheepdog, Hokkaido, Hortaya Greyhound, Hovawart, Transylvanian Hound, New Zealand Sheepdog, Hugenhund, Ibicenco Podenco, Icelandic Dog , Indian spitz, Irish bulletter, Irish red-white setter, Irish red setter, Irish Staffordshire Bulterier, Irish Terrier, Irish Water Spaniel, Irish Wolfhound, Istra Short-haired Hongy, Istrian Hard Ward, Italian Levgish, Jack-Rassel-Terer, German Yaging Yag. Dterier , Swedish Elkhound, Japanese Chin, Japanese Spitz, Japanese Terrier, Jonangi, Kaikadi, Kai, Kangal, Kanny, Karakachan Dog, Karelian Bear Dog, Krash Shepherd Dog, Keeshond, Kerry Beagle, Kerry Blue Terrier, Cavalier King Charles Spaniel, Royal Shepherd, Kintamani, Kishu, Komondor, Kooikerhondje, Coolie, Korean Chindo, Korean Mastiff, Kromforlander, Kunming Shepherd, Kuri, Kuvasz, Kai Leo, Labrador Husky, Labrador Retriever, Lagotto Romagnolo, Lakeland Terrier, Lancashire Heeler, Landseer , Lapp Deer Dog, Leonberger, Lhasa Apso, Lithuanian Hound, Longhaired Whippet, Brindis Fighting Dog, Lesser Lion Dog, Hungarian Agar, Large Tree Hound, Maltese, Manchester Terrier, Maremmo-Abruzzese Shepherd, McNab, Xoloitzcuintle, Miniature Australian Shepherd , Miniature Fox Terrier,

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Miniature Pinscher, Miniature Schnauzer, Miniature Siberian Husky, Romanian Mioritic Shepherd, Molossian Dog, Montenegrin Mountain Hound, Moscow Watchdog, Moscow Diver, Mountain Hound, Kentucky Mountain Hound, Mukuchies, Mudi, Caravan Greyhound, Greater Munsterlander, Small Munsterlander, Curly-haired ny retriever with Murray River, Neapolitan Mastiff, Newfoundland, New Guinea Singing Dog, Norfolk Spaniel, Norfolk Terrier, Norbotten Husky, Northern Beagle, Northern Inuit Dog, Norwegian Buhund, Norwegian Gray Elkhound, Norwegian Lundehund, Norwich Terrier, Nova Scotia Retriever, Old Danish Pointer, Bobtail, Old English Bulldog, Black and Tan Terrier, Old German Shepherd, Old English Bulldog, Otterhound, Navarre Pachon, Paisley Terrier, Continental Toy Spaniel, Parson Russell Terrier, Paterdale Terrier, Pekingese, Dogo Canary, Ca-de-bo , Peruvian Inca Orchid, Phalene, Pharaoh Hound, Picardy Spaniel, Plott Hound, Podenco Canario, Pointer, Polish Ogar, Polish Hound, Polish Lowland Sheepdog, Polish Podgalian Sheepdog, Pomeranian Spitz, Pont-Audemer Spaniel, Poodle, Porcelain Hound, Portuguese Podengo, Portuguese Pointer, Portuguese Water Dog, Prague Rat, Poodle Pointer, Pug, Puli, Pumi, Pungsan, Iberian Mastiff, Iberian Shepherd Dog, Rafeiro do Alentejo, Rajapalayam, Rampur Greyhound, Brazilian Head Hound, Andalusian Ratonero, Rat Terrier, Red Coonhound, Rhodesian Ridgeback, Rottweiler, Russian Hunting Spaniel, Russian Toy, Russian-European Laika, Jack Russell Terrier, Saarloos Wolfdog, Spanish Hound, Persian Shepherd, Iranian Mazandarani Shepherd, Sakhalin Husky, Saluki, Samoyed, Sapsari, Sharplaninac , Chapendois, Schillerstevare, Schipperke, Old Croatian Greyhound, Giant Schnauzer, Miniature Schnauzer, Medium Schnauzer, Swiss Hound, Small Swiss Hound, Longhaired Collie, Deerhound, Scottish Terrier, Sealyham Terrier, Italian Shorthair Hound, Siberian Sled Dog, Serbian Hound, Serbian sky tricolor hound, Charley, Sheltie, Shiba Inu, Shih Tzu, Shikoku, Shiloh Shepherd Dog, Bolonka, Siberian Husky, Silky Windhound, Sinhalese Hound, Skye Terrier, Slugi, Slovakian Chuvach, Slovakian Rough-haired Stavac, Slovakian Copov, Smaland Hound, Small Greek Dog , Irish Soft Coated Wheaten Terrier, South Russian Shepherd Dog, Southern Hound, Spanish Mastiff, Spanish Water Dog, Spinone, Lucas Terrier, St. Bernard, Small Newfoundland, Stabyhoun, Staffordshire Bull Terrier, Stefan's Hound, Austrian Brutches, Sussex Spaniel, Swedish Lapphund, Swedish Vallhund, Swedish Beagle, Tahltan Bear Dog, Taigan, Tamaskan Dog, Teddy Roosevelt Terrier, Telomian, Tenterfield Terrier, Thai Bangku, Thai Ridgeback, Tibetan Mastiff, Tibetan Spaniel, Tibetan Terrier, Tornjak, Tosa Inu, Toy Bulldog, Toy Fox Terrier, Toy Manchester Terrier, Treeing Cure, Treeing Walker Coonhound, Tyrolean Hound, Utonagan, Hungarian Vizsla, Volpino Italiano, Weimaraner, Cardigan Welsh Corgi, Pembroke Welsh Corgi, Welsh Sheepdog, Welsh Springer Spaniel, Welsh Terrier , West Highland White Terrier, West Siberian Husky, Westphalian Dachshund, Wetterhun, Whippet, White English Bulldog, White Swiss Shepherd, Hungarian Wirehaired Vizsla, Griffon Cortalsa and Yorkshire Terrier. In some embodiments, the population includes one or more purebred dogs (eg, having one breed type) or one or more mixed breed dogs (eg, having multiple breed types). In some embodiments, the population is a population of mixed breed dogs having DNA from any number (eg, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10) or combinations of purebred dogs.

In some embodiments, haplotype data is obtained by processing genotypic data from a population of test individuals using a haplotype phasing algorithm. In some embodiments, the haplotype phasing algorithm comprises a reference-based haplotype phasing algorithm. In some embodiments, the reference-based haplotype phasing algorithm comprises a hidden Markov model search. In some embodiments, the reference-based haplotype phasing algorithm comprises the Eagle1 algorithm, the Eagle2 algorithm, the PHASE algorithm, the fastPHASE algorithm, the BEAGLE algorithm, the Findhap algorithm, the Impute algorithm, the Flmpute algorithm, the Alphalmpute algorithm, the IMPUTE2 algorithm, the MaCH algorithm, the SHAPEIT1 algorithm, the SHAPEIT2 algorithm, SHAPEIT3 algorithm, SHAPEIT4 algorithm or a combination of both. In some embodiments, the haplotype phasing algorithm comprises a cohort-based haplotype phasing algorithm.

In some embodiments, genotypic data is obtained by evaluating biological samples taken from a population of test individuals. In some embodiments, the biological samples comprise blood samples, saliva samples, swab samples, cell samples, or tissue samples. In some embodiments, the assessment involves sequencing biological samples or derivatives thereof.

In some embodiments, the plurality of genetic markers comprises at least about 500, at least about 1000, at least about 2000, at least about 3000, at least about 4000, at least about 5000, at least about 6000, at least about 7,000, at least about 8,000, at least about 9,000, or at least about 10,000 different genetic markers.

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In some embodiments, matching segments of haplotype data that are identical by ancestry involves using the GERMLINE algorithm, the PLINK algorithm, the PREST algorithm, the Random Projection Algorithm for IBD Detection (RaPID), the Fast IBD Shared Haplotype Detection (FISHR) algorithm, the Advanced Identity by Ancestry algorithm (IBD), fastIBD algorithm, KING algorithm, HaploScore algorithm, TRUFFLE algorithm or a combination thereof. In some embodiments, the predetermined threshold size is about 100 thousand base pairs (kb), about 200 kb, about 300 kb, about 400 kb, about 500 kb, about 600 kb, about 700 kb, about 800 kb, about 900 kb. or about 1000 kb. In some embodiments, the predetermined number of genetic markers is about 30, about 40, about 50, about 60, about 70, about 80, about 90, or about 100 different genetic markers.

In some embodiments, the method further comprises dividing the matched segments such that the discrete genomic intervals of the plurality of discrete genomic intervals are of equal size. In some embodiments, the method further comprises dividing the matched segments such that the discrete genomic intervals of the plurality of discrete genomic intervals have a variable size. In some embodiments, the variable size of a given discrete genomic interval of a plurality of discrete genomic intervals is determined, at least in part, based on the starting position and ending position of IBD hits proximal to the given discrete genomic interval, the density of genomic markers in the given discrete genomic interval, the maximum number of markers for a given discrete genomic interval, the maximum length of a given discrete genomic interval, or a combination thereof.

In some embodiments, the method further comprises estimating each of the plurality of discrete genomic intervals based on (i) the degree of homozygosity match of the discrete genomic interval in the first individual or the second individual, and (ii) the degree of pairwise matching of the discrete genomic interval between the first individual and the second individual, with thereby obtaining a plurality of homozygosity match scores and a plurality of pairwise match scores. In some embodiments, the method further includes adjusting the plurality of pairwise match scores based on the consistency of the pairwise match score with the corresponding homozygosity match score, thereby obtaining a plurality of adjusted pairwise match scores.

In some embodiments, the method further comprises assigning a plurality of weights to a plurality of discrete genomic intervals based on the plurality of identity states for two alleles in two diploid individuals. In some embodiments, the plurality of identity states comprises identity states selected from a table, and a plurality of weights are assigned based on the plurality of relatedness contributions rxy, as listed in the table. In some embodiments, the degree of relatedness comprises a relatedness coefficient. In some embodiments, the method further comprises calculating a weighted sum for a plurality of discrete genomic intervals of a matched

Δ1 +217 + (0.75X213) + (0.5X218) ^g xy - ~ segment, where the weighted sum is expressed by:

In some embodiments, the degree of relatedness comprises a relatedness coefficient. In some embodiments, the method further comprises calculating a weighted sum for the plurality of discrete genomic intervals of the matched segments, _ 211 + (0.5 X (213+217)) + (0.25 X 218) ______________ „ ____ ____________ . ^xy ~ I ' is where the weighted sum is expressed by:

In some embodiments, assessing the degree of relatedness between the first individual and the second individual involves determining the degree of inbreeding of the first individual or the second individual. In some embodiments, assessing the degree of relatedness between the first individual and the second individual involves determining the degree of inbreeding between the first individual and the second individual. In some embodiments, the method further comprises determining a familial relationship between the first individual and the second individual based at least in part on the degree of inbreeding of the first individual and the second individual. In some embodiments, the family relationship is a parent-child relationship, a sibling relationship, an aunt/uncle-nephew/niece relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent-great-grandchild relationship. great-granddaughter In some embodiments, a family relationship is defined as a relationship between a pair of people such that the degree of relatedness between a first individual and a second individual is comparable to the degree of relatedness expected between a pair of people. In some embodiments, the relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle relationship

- 4 046341 nephew/niece, relationship at the level of cousin/cousin, relationship grandmother/grandfather-grandson/granddaughter or relationship great-grandmother/great-grandfather-great-grandson/great-granddaughter.

In some embodiments, the method further comprises determining the expected degree of inbreeding between the potential offspring of the first individual and the second individual. In some embodiments, the method further comprises determining a recommendation indicating whether the first individual and the second individual should be bred together based on the expected degree of inbreeding of the potential offspring of the first individual and the second individual. In some embodiments, the method further comprises defining a recommendation indicating that the first individual and the second individual should be bred together when the expected degree of inbreeding of the potential offspring of the first individual and the second individual does not exceed a predetermined threshold degree of inbreeding. In some embodiments, the method further comprises defining a recommendation indicating that the first individual and the second individual should not be bred together when the expected degree of inbreeding of the potential offspring of the first individual and the second individual exceeds a predetermined threshold degree of inbreeding. In some embodiments, the predetermined threshold degree of inbreeding is about 0.10, about 0.15, about 0.20, about 0.25, about 0.30, about 0.35, about 0.40, about 0.45, or about 0.50.

In some embodiments, the method further comprises obtaining a social relationship between a first entity associated with the first individual and a second entity associated with the second individual based at least in part on an estimated degree of relatedness between the first individual and the second individual. In some embodiments, a social connection is obtained when the estimated degree of relatedness between a first individual and a second individual exceeds a predetermined threshold value. In some embodiments, the predetermined threshold value is about 0.00001, about 0.0001, about 0.001, about 0.01, about 0.02, about 0.04, about 0.06, about 0.08, about 0. 1, about 0.12, about 0.14, about 0.16, about 0.18, about 0.2, about 0.25, about 0.3, about 0.35, about 0.4, about 0, 45 or about 0.5. In some embodiments, the social connection is obtained through a social media network. In some embodiments, the first subject owns the first individual as a pet, and the second subject owns the second individual as a pet. In some embodiments, the same entity owns the first individual and the second individual as a pet. In some embodiments, obtaining a social connection between a first subject and a second subject involves providing the location of the first subject or the second subject. In some embodiments, obtaining a social connection between a first subject and a second subject involves providing a communication link between the first subject and a second subject.

In some embodiments, the method further comprises identifying a familial relationship between the first individual and the second individual based at least in part on the degree of relationship. In some embodiments, the familial relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle-nephew/niece relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent-great-grandchild relationship. /great-granddaughter. In some embodiments, a family relationship is defined as a relationship between a pair of people such that the degree of relatedness between a first individual and a second individual is comparable to the degree of relatedness expected between a pair of people. In some embodiments, the relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle-nephew/niece relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent-great-grandchild relationship. /great-granddaughter.

In some embodiments, the method further comprises identifying, for each of one or more individuals in a population of test individuals, the presence, absence, risk, or carrier status of a genetic or health condition in the individual. In some embodiments, the genetic or health condition is selected from the group consisting of Thrombopathy (RASGRP2 Exon 8), Pyruvate Kinase Deficiency (PKLR Exon 7 Pug Variant), Factor IX Deficiency, Hemophilia B (F9 Exon 7 Terrier Variant), Pyruvate Kinase Deficiency ( PKLR Exon 7 Beagle Variant), P2Y12 Platelet Receptor Disorders (P2RY12), Glanzmann Thrombasthenia Type I (ITGA2B Exon 12), Von Willebrand Disease Type II, VWD Type II (VWF), May-Hegglin Anomalies (MYH9), Thrombopathy (RASGRP2 Exon 5, American Eskimo Dog Variant), Factor IX Deficiency, Hemophilia B (F9 Exon 7, Rhodesian Ridgeback Variant), Von Willebrand Disease Type I (VWF), Canine Elliptocytosis (SPTB Exon 30), Canine Leukocyte Adhesion Disorder Type III, LAD3 ( FERMT3), Prokallikrein deficiency (KLKB1 Exon 8), Diseases

- 5 046341 von Willebrand type III, VWD type III (VWF Exon 4), Factor VIII deficiency, Hemophilia A (F8 Exon 10, Boxer variant), Trapped neutrophil syndrome (VPS13B), Pyruvate kinase deficiency (PKLR Exon 7 Labrador variant), Thrombopathy (RASGRP2 Exon 5, Basset Hound Variant), Pyruvate Kinase Deficiency (PKLR Exon 5), Factor VIII Deficiency, Hemophilia A (F8 Exon 1, Shepherd Variant 2), Factor VII Deficiency (F7 Exon 5), Congenital Macrothrombocytopenia (TUBB1 Exon 1 , Cavalier King Charles Spaniel Variant), Ligneous Membranitis, LM (PLG), Pyruvate Kinase Deficiency (PKLR Exon 10), Factor VIII Deficiency, Hemophilia A (F8 Exon 11, Shepherd Variant 1), Other Systems, Shar Pei Autoinflammatory Disease, SPAID , Shar Pei Fever (SRF), Autosomal Recessive Enamel Dysplasia, Hereditary Dental Hypoplasia (Italian Italian Greyhound Variant), Persistent Müllerian Duct Syndrome, PMDS (AMHR2), Vestibular Syndrome and Dobermann Deafness, DVDob, DINGS, Eye Disorders, Progressive Retinal Atrophy, crd2 (IQCB1), Primary lens luxation (ADAMTS17), Glaucoma primary open angle glaucoma (ADAMTS17 Exon 2), Progressive retinal atrophy, crd1 (PDE6B), Progressive retinal atrophy, rcdl Rod cone dysplasia, rcdl (PDE6B Exon 21 Irish Red Setter variant ), Collie eye anomalies, Uveal hypoplasia, CEA (NHEJ1), Progressive retinal atrophy (SAG), Achromatopsia (CNGA3 Exon 7 German Shepherd variant), Canine multifocal retinopathy cmr2 (BEST1 Exon 5), Primary open-angle glaucoma (ADAMTS17 Exon 11), Progressive retinal atrophy, prcd Progressive rod-cone degeneration (PRCD Exon 1), Hereditary cataracts, Early cataracts, Juvenile cataracts (HSF4 Exon 9 Shepherd variant), Autosomal dominant progressive retinal atrophy (RHO), Canine multifocal retinopathy cmr3 (BEST1 Exon 10 SNP), Achromatopsia (CNGA3 Exon 7 Labrador Retriever Variant), Canine Multifocal Retinopathy cmr1 (BEST1 Exon 2), Progressive Retinal Atrophy, rcd3 Rod Cone Dysplasia, rcd3 (PDE6A), Progressive Retinal Atrophy (CNGB1), Progressive Atrophy Golden Retriever Retina 2, GR-PRA2 (TTC8), Progressive Retinal Atrophy, CNGA (CNGA1 Exon 9),

Golden Retriever Progressive Retinal Atrophy 1, GR-PRA1 (SLC4A3), Progressive Retinal Atrophy - crd4/cord1 (RPGRIP1), Congenital Stationary Night Blindness (RPE65), Fehr's Corneal Dystrophy, MCD (CHST6), Primary Open Angle Glaucoma (ADAMTS10 Exon 9 ), Canine Multifocal Retinopathy cmr3 (BEST1 Exon 10 Deletion), Glaucoma Primary Open Angle Glaucoma (ADAMTS10 Exon 17), Muscle Disorders, Centronuclear Myopathy (PTPLA), Thomsen's Disease (CLCN1 Exon 7), Hereditary Great Dane Myopathy (BIN1), Thomsen's Disease (CLCN1 Exon 23), Muscular Dystrophy (DMD Pembroke Welsh Corgi Variant), Exercise-Induced Collapse (DNM1), Muscular Dystrophy (DMD Golden Retriever Variant), Myostatin Deficiency, Bully-Whippet Syndrome (MSTN), Myotubular Myopathy 1 Associated X-chromosome myotubular myopathy, XL-MTM (MTM1, Labrador variant), Muscular dystrophy (Cavalier King Charles Spaniel variant 1), Multisystem disorders, Primary ciliary dyskinesia, PCD (CCDC39 Exon 3), GM1 Gangliosidosis (GLB1 Exon 2) , Hurler syndrome type IIIA, Sanfilippo disease type A, MPS IIIA (SGSH Exon 6 Variant 1), Acquired neuronal ceroid lipofuscinosis (ATP13A2), GM1 Gangliosidosis (GLB1 Exon 15 Shiba Inu Variant), Neuronal ceroid lipofuscinosis 2, NCL 2 (TPP1 Exon 4), Hurler Syndrome Type VII, Sly Syndrome, MPS VII (GUSB Exon 3), Canine Fucosidosis (FUCA1), GM1 Gangliosidosis (GLB1 Exon 15 Alaskan Husky Variant), Lagotto Storage Disease (ATG4D), Congenital Keratoconjunctivitis Sicca and Ichthyosiform Dermatosis, Curly Coat and Dry Eye Syndrome, CKCSID (FAM83H Exon 5), Glycogen Disorder Type VII, Phosphohexokinase Deficiency, PFK Deficiency (PFKM Whippet and English Springer Spaniel Variant), Glycogen Disorder Type IA, Gierke's Disease, GSD IA (G6PC ), Glycogen disease type VII, Phosphohexokinase deficiency, PFK deficiency (PFKM Wachtelhund Variant), Neuronal ceroid lipofuscinosis 1, NCL 1 (CLN5 Border Collie Variant), Neuronal ceroid lipofuscinosis 1, Cerebellar ataxia, NCL-A (ARSG Exon 2), Neuronal ceroid lipofuscinosis 6, NCL 6 (CLN6 Exon 7), Hurler syndrome type I, MPS I (IDUA), Renal cystadenocarcinoma and nodular dermatofibrosis, RCND (FLCN Exon 7), Neuronal ceroid lipofuscinosis 10, NCL 10 (CTSD Exon 5 ), Globoid cell leukodystrophy, Krabbe disease (GALC Exon 5), Glycogen storage disease type IIIA, GSD IIIA (AGL), Neuronal ceroid lipofuscinosis (MFSD8), GM2 Gangliosidosis (NEGV, Poodle Variant), X-linked congenital ectodermal dysplasia , Anhidrotic ectodermal dysplasia (EDA Intron 8), Neuronal ceroid lipofuscinosis (CLN8 Australian Shepherd Variant), Neuronal ceroid lipofuscinosis 8, NCL 8 (CLN8 English Setter Variant), Neuronal ceroid lipofuscinosis 1, NCL 1 (PPT1 Exon 8), Neuronal Ceroid Lipofuscinosis (CLN5 Golden Retriever Variant), Hurler Syndrome Type VII, Sly Syndrome, MPS VII (GUSB Exon 5), Glycogen Storage Disease Type II, Pompe Disease, GSDII (GAA), GM2 Gangliosidosis (HECA), Hurler Syndrome Type IIIA , Sanfilippo disease type A, MPS IIIA (SGSH Exon 6 Option 2), Skin and connective tissue diseases, Ichthyosis (PNPLA1), Ichthyosis (SLC27A4), Dystrophic epidermosis bullosa (COL7A1), Ichthyosis, Epidermolytic ichthyosis (KRT10), Ectodermal dysplasia, Skin fragility syndrome (PKP1), Ichthyosis (NIPAL4), Musladin-Luquet syndrome

- 6 046341 (ADAMTSL2), Focal non-epidermolytic palmoplantar keratoderma, Congenital pachyonychia (KRT16), Hereditary nasal parakeratosis (SUV39H2), Hereditary footpad hyperkeratosis (FAM83G), Brain and spinal cord disorders, Juvenile polyneuropathy, Leonberger polyneuropathy 1, LPN1 ( LPN1, ARHGEF10), Cerebellar trophism disorders, Neonatal degeneration of the cerebellar cortex, NCCD (SPTBN2), Narcolepsy (HCRTR2 Intron 6), L-2 Hydroxyglutaric aciduria, L2HGA (L2HGDH), Spongiform degeneration with cerebellar ataxia 2, SDCA2 (ATP1B2), Progressive neuronal abi otrophy , Canine multiple system degeneration, CMSD (SERAC1 Exon 15), Neonatal fetal neuroaxonal dystrophy (MFN2), Neonatal encephalopathy with seizures, NEWS (ATF2), Benign familial juvenile epilepsy, Relapsing-remitting focal epilepsy (LGI2), Juvenile laryngeal palsy and polyneuropathy, Polyneuropathy with ocular abnormalities and neuronal vacuolation, POANV (RAB3GAP1, Rottweiler Variant), Progressive neuronal abiotrophy, Canine multiple system degeneration, CMSD (SERAC1 Exon 4), Cerebellar ataxia, Progressive early cerebellar ataxia (SEL1L), Hereditary sensory autonomic neuropathy, Acral syndrome disfigurement, AMS (GDNF-AS), Shaken Puppy Syndrome, X-linked Generalized Tremor Syndrome (PLP), Hypomyelination and Tremor (FNIP2), Spinocerebellar Ataxia, Late Onset Ataxia, LoSCA (CAPN1), Polyneuropathy, NDRG1 Greyhound Variant ( NDRG1 Exon 15), Polyneuropathy, NDRG1 Alaska Malamute Variant (NDRG1 Exon 4), Cerebellar hypoplasia (VLDLR), Spongiform degeneration with cerebellar ataxia 1, SDCA1, SeSAME/EAST syndrome (KCNJ10), Spinocerebellar ataxia with myokymia and/or seizures ( KCNJ10), Alaskan Husky Encephalopathy, Subacute Leigh's Disease (SLC19A3), Degenerative Myelopathy, DM (SOD1A), Alexander Disease (GFAP), Heart Disorders, Dilated Cardiomyopathy, DCM1 (PDK4), Long QT Syndrome (KCNQ1), Dilated Cardiomyopathy cardiomyopathy, DCM2 (TTN), skeletal disorders, Hereditary vitamin E-resistant rickets (VDR), Osteogenesis incomplete, Crystal disease (COLIAI), Osteogenesis incomplete, Crystal disease (SERPINH1), Chondrodystrophy and intervertebral disc disease, CDDY/IVDD, Type I IVDD (FGF4 retrogen - CFA12), Osteogenesis incomplete, Crystalline disease (COL1A2), Craniomandibular osteopathy, SMO (SLC37A2), Skeletal dysplasia 2, SD2 (COL11A2), Cleft lip and/or cleft palate (ADAMTS20), Skeletal oculodysplasia 1, Dwarfism-Retinal Dysplasia, OSD1 (COL9A3, Labrador Retriever), Osteochondrodysplasia, Skeletal Dwarfism (SLC13A1), Metabolic Disorders, Malignant Hyperthermia (RYR1), Hypocatalasia, Acatalasemia (CAT), Pyruvate Dehydrogenase Deficiency (PDP1), Kidney and Bladder Disorders, Hyperuricosuria and hyperuricemia or urolithiasis, HUU (SLC2A9), Polycystic kidney disease, PKD (PKD1), Protein-losing nephropathy, PLN (NPHS1), Cystinuria Type II-A (SLC3A1), Primary hyperoxaluria (AGXT), Cystinuria Type I-A ( SLC3A1), Autosomal recessive hereditary nephropathy, Hereditary nephropathy, ARHN (COL4A4 Exon 3), X-linked hereditary nephropathy, XLHN (COL4A5 Exon 35, Samoyed Variant 2), Cystinuria Type II-B (SLC7A9), urolithiasis, 2,8-dihydroxyadenine-related, 2,8-DHA Urolithiasis (APRT), Neuromuscular disorders, Episodic falling syndrome (BCAN), Congenital myasthenic syndrome (COLQ), Congenital myasthenic syndrome (CHAT), Immune system disorders, Severe combined immunodeficiency (RAG1) , X-linked severe combined immunodeficiency (IL2RG Variant 1), Severe combined immunodeficiency (PRKDC), X-linked severe combined immunodeficiency (IL2RG Variant 2), Complement deficiency 3, C3 deficiency (C3), Gastrointestinal disorders , Imerslund-Gresbeck Syndrome, Selective Cobalamin Malabsorption (CUBN Exon 53), Imerslund-Gresbeck Syndrome, Selective Cobalamin Malabsorption (CUBN Exon 8), Clinical Disorders, MDR1 Drug Sensitivity (MDR1), Alanine Aminotransferase (GPT) Activity, Hormonal Disorders and congenital hypothyroidism (TPO, Tenterfield Terrier Variant). In some embodiments, risk is expressed as the probability or relative risk of an individual having a genetic or health condition. In some embodiments, the relative risk is a numerical value (eg, a relative frequency ratio) or a categorical value (eg, at risk, not at risk, or not at risk). In some embodiments, carrier status is defined as a positive or negative indication that an individual is a carrier of a genetic or health condition.

In some embodiments, the method further comprises identifying, for each of the one or more individuals in the population of test individuals, one or more dog breeds from the individual. In some embodiments, the method further comprises identifying, for each of the one or more individuals in the population of test individuals, one or more portions of one or more dog breeds of the individual. In some embodiments, the method further comprises creating a family tree of a plurality of individuals in a population of test individuals. In some embodiments, the method further comprises identifying

- 7 046341 for each of one or more individuals in a population of test individuals for the presence, absence or risk of a phenotype or trait in the individual. In some embodiments, the phenotype or trait is selected from the group consisting of: base coat color (e.g., dark or light coat color, pigment color, and color dilution), coat color modifiers (e.g., latent pattern, body pattern, and muzzle pattern), coat traits (e.g. color, coat length, shedding, coat texture, lack of hair (Xolo type), lack of hair (Terrier type), and albinism), body features (e.g. muzzle length, tail length, hind claws, back musculature and volume, eye color), body size (eg, small, medium, and large), behavior (eg, adaptation to altitude), genetic diversity (eg, degree of inbreeding and diversity of immune response).

In some embodiments, the method further comprises generating a report that includes one or more of: degree of relationship, familial relationship, status of presence, absence, risk or carrier of a genetic or health condition, one or more dog breeds, one or more shares of one or more multiple dog breeds, family tree, status of presence, absence, risk or carrier of a phenotype or trait, and any combination thereof. In some embodiments, the method further comprises transmitting the report to the veterinarian.

According to another aspect, the present invention relates to a computing system for estimating the degree of relatedness between two individuals of a diploid population, comprising: a database that is configured to store haplotype data for a population of test individuals, wherein the haplotype data comprises a plurality of genetic markers common to the population of test individuals , and one or more computer processors operably coupled to a database, wherein the one or more computer processors are individually or collectively programmed to: (a) partition the haplotype data into segments based on a plurality of genetic markers, (b) for each population of test individuals: (i) matching, based on the plurality of genetic markers, segments of haplotype data that are identical in ancestry between a first individual and a second individual among the population of test individuals, each of the matched segments having a first size that is at least a predetermined threshold size, and contains at least a predetermined number of genetic markers, (ii) for each of the matched segments between the first individual and the second individual: dividing the matched segment into a plurality of discrete genomic intervals, evaluating each plurality of discrete genomic intervals based on (i) the degree of homozygosity match of the discrete genomic interval in the first individual or the second individual, or (ii) the degree of pairwise matching of the discrete genomic interval between the first individual and the second individual, thereby obtaining a plurality of estimates, and adjusting the plurality of estimates based on the agreement of the plurality of estimates, thereby obtaining a plurality of adjusted estimates, and assigning a plurality of weights to the plurality of discrete genomic intervals based on the plurality of adjusted discrete genomic interval scores, and (iii) calculating a weighted sum for the plurality of discrete genomic intervals of the matched segment based on the plurality of adjusted scores and the plurality of weights, and (c) estimating the degree of relatedness between the first individual and a second individual based on the weighted sums of the matched segments.

In some embodiments, the diploid population is a mammalian population. In some embodiments, the mammal population is a canine population, a feline population, a sporting animal population, or a rodent population. In some embodiments, the mammal population is a canid population. In some embodiments, the canid population is a population of dogs. In some embodiments, the mammal population is a feline population. In some embodiments, the feline population is a population of cats. In some embodiments, the mammal population is a population of sporting animals. In some embodiments, the sporting animal population is a population of horses. In some embodiments, the dog population comprises one or more dog breeds selected from the group consisting of: Affenpinscher, Afghan Hound, Africanis, Aidi, Airedale Terrier, Akbash, Akita Inu, Bully Cutta, Spanish Alano, Alapaha Bulldog, Alaskan Klee Kai , Alaskan Malamute, Alan, Alopekis, Alpine Dachshound, American Shepherd, American Akita, American Bulldog, American Cocker Spaniel, American Eskimo Dog, American Foxhound, American Hairless Terrier, American Mastiff, American Pit Bull Terrier, American Staffordshire Terrier, American Water Spaniel , Anatolian Shepherd Dog, Anglo-French Lesser Hound, Appenzeller Mountain Dog, Dogo Argentino, Ariège Braque, Ariège Hound, Armant, Artois Hound, Australian Bulldog, Australian Cattle Dog, Australian Kelpie, Australian Shepherd, Australian

- 8 046341 Silky Terrier, Australian Short-Tailed Cattle Dog, Australian Terrier, Austrian Hound, Austrian Pinscher, Azawakh, Baharwal Dog, Barbet, African Non-Barking Dog, Basque Shepherd Dog, Artesian-Normandy Basset, Blue Gascony Basset, Red Breton Basset, Great Vendeen Basset -griffon, Lesser Vendeen Basset Griffon, Bavarian Mountain Hound, Beagle, Beagle Harrier, Bearded Collie, Beauceron, Bedlington Terrier, Belgian Shepherd, Belgian Shepherd (Groenendael), Belgian Shepherd (Laekenois), Belgian Shepherd (Malinois), Belgian Shepherd Dog (Tervuren), Bergamasco Shepherd Dog, White Swiss Shepherd Dog, Picardy Shepherd Dog, Bernese Hound, Bernese Mountain Dog, Bichon Frize, Biyi, Bisben, Black and Tan Coonhound, Erotic Norwegian Elk Husky, Bullenbeiser, Norwegian Black Elkhound, Black Russian Terrier, Yellow S Black Masked Southern Hound, Greater Blue Gascony Hound, Lesser Blue Gascony Hound, Bloodhound, Blue Lacy, Blue Half Terrier, Mottled Blue Coonhound, Boerboel, Czech Cattle Dog, Bolognese, Border Collie, Border Terrier, Russian Canine Greyhound, Bosnian Wirehaired Hound, Boston Terrier, Bouvier de Ardennes, Bouvier des Flandres, Boxer, Boykin Spaniel, Italian Braque, Auvergne Pointing Dog, Bourbon Braque, Braque Dupuis, French Braque, Saint-Germain Braque, Brazilian Terrier, Briard, Middle Vendeen Griffon , Breton Epagnole, Broholmer, Jurassic Hound, Bukovinian Shepherd Dog, Bull and Terriers, Bull Terrier, Bull Terrier (Miniature), Bullmastiff, Bully Cutta, Cairn Terrier, Canaan Dog, Canadian Eskimo Dog, Canadian Pointer, Cane Corso, Portuguese Shepherd Dog , Cao de Castro Laboreiro, Azores Cattle Dog, Carolina Dog, Romanian Carpathian Shepherd Dog, Catahoula Leopard Dog, Catalan Shepherd Dog, Caucasian Shepherd Dog, Cavalier King Charles Spaniel, Central Asian Shepherd Dog, Czech Fousek, Czech Terrier, Polish Greyhound, Chesapeake Bay Retriever, Gray -blue hound, French black and piebald hound, French white and black dog, French tricolor hound, Chihuahua, Chilean Fox Terrier, Chinese Chongqing, Chinese Crested Dog, Chinese Imperial Dog, Chinook, Chippiparai, Chow Chow, Uruguayan Cimarron, Black Shepherd Dog, Cirneco del Etna, Clumber Spaniel, Longhaired Collie, Shorthaired Collie, Combay, Cordoba Fighting Dog, Coton de Tulear, Cretan Hound, Croatian Shepherd Dog, Cumberland Shepherd Dog, Curly-Coated Retriever, Czechoslovakian Wolfdog, Dachshund, Dalmatian, Dandie Dinmont- Terrier, Danish-Swedish Farm Dog, Dingo, Doberman, Dogue de Bordeaux, Dogue de Cuba, Guatemalan Bull Terrier, Miniature Dogo, Drenthe Partridge Dog, Drever, Dunker, Dutch Shepherd, Dutch Smoushond, East European Shepherd, East Siberian Laika, Elo, English Cocker Spaniel , English Coonhound, English Foxhound, English Mastiff, Pointer, English Setter, English Shepherd, English Springer Spaniel, English Toy Terrier, English Water Spaniel, English White Terrier, Entlebucher Mountain Dog, Blue Picardy Spaniel, Estonian Hound, Esht relais shepherd dog, Eurasier, Field Spaniel, Brazilian Fila, Fido, Finnish Hound, Finnish Lapphund, Finnish Spitz, Flat-Coated Retriever, Taiwanese Dog, Smooth Fox Terrier, Wire Fox Terrier, Breton Epagnole, French Bulldog, French Spaniel, Spanish Galgo, Langhaar, German Pinscher, German Shepherd Dog, Shorthaired Pointer, German Wachtelhund, German Spitz, Drathaar, Giant Schnauzer, Glen of Imaal Terrier, Golden Retriever, Scottish Setter, Large Anglo-French Black and Pied Hound, Large Anglo-French Red and Pied Hound, Large Anglo-French Tricolor Hound, Great Vendean Griffon, Puerto Rican Mastiff, Great Dane, Pyrenees Mountain Dog, Greater Swiss Mountain Dog, Greenland Dog, Greyhound, Blue Gascony Griffon, Belgian Griffon, Breton Tawny Griffon, Nivernes Griffon, Gul-dong, Gul-terr, Indian Hare Dog, Hamilton Hound, Hanoverian Hound, Harrier, Havanese Bichon, Hawaiian Poi Dog, Himalayan Sheepdog, Hokkaido, Hortaya Greyhound, Hovawart, Transylvanian Hound, New Zealand Sheepdog, Hugenhund, Podenco Ibicenco, Icelandic Dog, Indian Spitz, Irish Bull Terrier, Irish Red and White Setter , Irish Red Setter, Irish Staffordshire Bull Terrier, Irish Terrier, Irish Water Spaniel, Irish Wolfhound, Istrian Shorthaired Hound, Istrian Wirehaired Hound, Italian Italian Greyhound, Jack Russell Terrier, German Jagdterrier, Swedish Elkhound, Japanese Chin, Japanese Spitz, Japanese sky terrier , Jonangi, Kaikadi, Kai, Kangal, Kanny, Karakachan dog, Karelian Bear Dog, Krash Shepherd Dog, Keeshond, Kerry Beagle, Kerry Blue Terrier, Cavalier King Charles Spaniel, Royal Shepherd Dog, Kintamani, Kishu, Komondor, Kooikerhondje , Coolie, Korean Chindo, Korean Mastiff, Cromforlander, Kunming Shepherd Dog, Kuri, Kuvasz, Kai Leo, Labrador Husky, Labrador Retriever, Lagotto Romagnolo, Lakeland Terrier, Lancashire Heeler, Landseer, Lapper Deer Dog, Leonberger, Lhasa Apso, Lithuanian Hound , Longhaired Whippet, Brindiz Fighting Dog, Lesser Lion Dog, Hungarian Agar, Large Tree Hound, Maltese, Manchester Terrier, Maremmo-Abruzzese Sheepdog, McNab, Xoloitzcuintle, Miniature Australian Shepherd, Miniature Fox Terrier, Miniature Pinscher, Miniature Schnauzer, Miniature Siberian skiy Husky, Romanian myoritic shepherd, Molossian dog, Montenegrin Mountain Hound, Moscow Watchtower, Moscow Divers, Mountain Hound, Kentuk Mountain Hound, Mukuchies, Moody, Caravan Borza, Big Munsterlander, Small Münsterland, Causage Retriever from the Murray River, Neapolitan Mastifa, Nufandlandland , New Guinea Singing Dog, Norfolk Spaniel, Norfolk Terrier, Norbotten

- 9 046341 likes, Northern Beagle, Northern Inuit Dog, Norwegian Buhund, Norwegian Gray Elkhound, Norwegian Lundehund, Norwich Terrier, Nova Scotia Retriever, Old Danish Pointer, Bobtail, Old English Bulldog, Black and Tan Terrier, Old German Shepherd, Old English Bulldog, Otterhound , Navarre Pachon, Paisley Terrier, Continental Toy Spaniel, Parson Russell Terrier, Paterdale Terrier, Pekingese, Dogo Canario, Ca de Beau, Peruvian Inca Orchid, Phalene, Pharaoh Hound, Picardy Spaniel, Plott Hound, Podenco Canario, Pointer, Polish Ogar, Polish Hound, Polish Lowland Sheepdog, Polish Podgalian Sheepdog, Pomeranian Spitz, Pont Oedmer Spaniel, Poodle, Porcelain Hound, Portuguese Podengo, Portuguese Pointer, Portuguese Water Dog, Prague Ratdog, Poodle Pointer, Pug, Puli, Pumi, Pungsan, Iberian Mastiff, Iberian Shepherd Dog, Rafeiro do Alentejo, Rajapalayam, Rampur Greyhound, Brazilian Oblavna Hound, Andalusian Ratonero, Rat Terrier, Red Coonhound, Rhodesian Ridgeback, Rottweiler, Russian Hunting Spaniel, Russian Toy, Russian-European Laika, Jack Russell Terrier, Saarloos Wolfdog, Spanish Hound, Persian Shepherd, Iranian Mazandarani Shepherd, Sakhalin Husky, Saluki, Samoyed, Sapsari, Sharplaninats, Chapendois, Schillerstevare, Schipperke, Old Croatian Greyhound, Giant Schnauzer, Miniature Schnauzer, Medium Schnauzer, Swiss Hound, Lesser Swiss Hound, Longhaired Collie, Deerhound, Scottish Terrier, Sealyham Terrier, Italian Shorthair Hound, Siberian Sled Dog, Serbian Hound, Serbian Tricolor Hound, Charley, Sheltie, Shiba Inu, Shih Tzu, Shikoku, Shiloh Shepherd Dog , Bolonka, Siberian Husky, Silky Windhound, Sinhala Hound, Skye Terrier, Slugi, Slovak Chuvach, Slovak Rough-haired Stavac, Slovak Copov, Smaland Hound, Small Greek Dog, Irish Soft Coated Wheaten Terrier, South Russian Shepherd Dog, Southern Hound, Spanish Mastiff, Spanish Water Dog , Spinone, Lucas Terrier, St. Bernard, Small Newfoundland, Stabyhune, Staffordshire Bull Terrier, Stefan's Beagle, Austrian Brutches, Sussex Spaniel, Swedish Lapphund, Swedish Vallhund, Swedish Beagle, Tahltan Bear Dog, Taigan, Tamaskan Dog, Teddy Roosevelt Terrier , Telomian, Tenterfield Terrier, Thai Bangku, Thai Ridgeback, Tibetan Mastiff, Tibetan Spaniel, Tibetan Terrier, Tornjak, Tosa Inu, Toy Bulldog, Toy Fox Terrier, Toy Manchester Terrier, Treeing Cure, Treeing Walker Coonhound, Tyrolean Hound, Utonagan, Hungarian Vizsla, Volpino Italiano, Weimaraner, Cardigan Welsh Corgi, Pembroke Welsh Corgi, Welsh Shepherd, Welsh Springer Spaniel, Welsh Terrier, West Highland White Terrier, West Siberian Laika, Westphalian Dachshund Hound, Wetterhun, Whippet, White English Bulldog, White Swiss Shepherd, Hungarian Wirehaired Vizsla, Griffon Cortalsa and Yorkshire Terrier. In some embodiments, the population includes one or more purebred dogs (eg, having one breed type) or one or more mixed breed dogs (eg, having multiple breed types). In some embodiments, the population is a population of mixed breed dogs having DNA from any number (eg, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10) or combinations of purebred dogs.

In some embodiments, haplotype data is obtained by processing genotypic data from a population of test individuals using a haplotype phasing algorithm. In some embodiments, the haplotype phasing algorithm comprises a reference-based haplotype phasing algorithm. In some embodiments, the reference-based haplotype phasing algorithm comprises a hidden Markov model search. In some embodiments, the reference-based haplotype phasing algorithm comprises the Eagle1 algorithm, the Eagle2 algorithm, the PHASE algorithm, the fastPHASE algorithm, the BEAGLE algorithm, the Findhap algorithm, the Impute algorithm, the Flmpute algorithm, the Alphalmpute algorithm, the IMPUTE2 algorithm, the MaCH algorithm, the SHAPEIT1 algorithm, the SHAPEIT2 algorithm, SHAPEIT3 algorithm, SHAPEIT4 algorithm or a combination of both. In some embodiments, the haplotype phasing algorithm comprises a cohort-based haplotype phasing algorithm.

In some embodiments, genotypic data is obtained by evaluating biological samples taken from a population of test individuals. In some embodiments, the biological samples comprise blood samples, saliva samples, swab samples, cell samples, or tissue samples. In some embodiments, the assessment involves sequencing biological samples or derivatives thereof.

In some embodiments, the plurality of genetic markers comprises at least about 500, at least about 1000, at least about 2000, at least about 3000, at least about 4000, at least about 5000, at least about 6000, at least about 7,000, at least about 8,000, at least about 9,000, or at least about 10,000 different genetic markers.

In some embodiments, matching segments of haplotype data that are identical by ancestry involves using the GERMLINE algorithm, the PLINK algorithm, the PREST algorithm, the Random Projection Algorithm for IBD Detection (RaPID), the Fast IBD Shared Haplotype Detection (FISHR) algorithm, the Advanced Identity by Ancestry algorithm (IBD), fastIBD algorithm, KING algorithm, HaploScore algorithm, TRUFFLE algorithm

- 10 046341 or combinations thereof. In some embodiments, the predetermined threshold size is about 100 thousand base pairs (kb), about 200 kb, about 300 kb, about 400 kb, about 500 kb, about 600 kb, about 700 kb, about 800 kb, about 900 kb. or about 1000 kb. In some embodiments, the predetermined number of genetic markers is about 30, about 40, about 50, about 60, about 70, about 80, about 90, or about 100 different genetic markers.

In some embodiments, one or more computer processors are individually or collectively programmed to further partition the matched segments such that the discrete genomic intervals of the plurality of discrete genomic intervals are of equal size. In some embodiments, one or more computer processors are individually or collectively programmed to further partition the matched segments such that the discrete genomic intervals of the plurality of discrete genomic intervals have a variable size. In some embodiments, the variable size of a given discrete genomic interval of a plurality of discrete genomic intervals is determined, at least in part, based on the starting position and ending position of IBD hits proximal to the given discrete genomic interval, the density of genomic markers in the given discrete genomic interval, the maximum number of markers for a given discrete genomic interval, the maximum length of a given discrete genomic interval, or a combination thereof.

In some embodiments, one or more computer processors are individually or collectively programmed to further evaluate each plurality of discrete genomic intervals based on (i) the degree of homozygosity match of the discrete genomic interval in the first individual or the second individual and (ii) the degree of pairwise matching of the discrete genomic interval between the first individual and the second individual, thereby obtaining a plurality of homozygosity match scores and a plurality of pairwise match scores. In some embodiments, one or more computer processors are individually or collectively programmed to further adjust the plurality of pairwise match scores based on the consistency of a given pairwise match score with a corresponding homozygosity match score, thereby obtaining a plurality of adjusted pairwise match scores.

In some embodiments, one or more computer processors are individually or collectively programmed to further assign multiple weights to multiple discrete genomic intervals based on multiple identity states for two alleles in two diploid individuals. In some embodiments, the plurality of identity states comprises identity states selected from a table, and a plurality of weights are assigned based on a plurality of relatedness contributions r _xy as listed in the table. In some embodiments, the degree of relationship comprises a relationship coefficient. In some embodiments, one or more computer processors are individually or collectively programmed to further calculate a weighted sum for a plurality of discrete genomic intervals of a matched segment, where the weighted sum is expressed by:

+ Δ7 + (0.75x43) + (0.5x48) ^g xy - £ ·

In some embodiments, the degree of relatedness comprises a relatedness coefficient. In some embodiments, one or more computer processors are individually or collectively programmed to further calculate a weighted sum for a plurality of discrete genomic intervals of a matched segment, where the weighted sum is expressed by _41 + (0.5 X (43 +47)) + (0.25x48 ) by means of: ^xy '

In some embodiments, assessing the degree of relatedness between the first individual and the second individual involves determining the degree of inbreeding of the first individual or the second individual. In some embodiments, assessing the degree of relatedness between the first individual and the second individual involves determining the degree of inbreeding between the first individual and the second individual. In some embodiments, one or more computer processors are individually or collectively programmed to further determine the familial relationship between the first individual and the second individual based at least in part on the degree of inbreeding of the first individual and the second individual. In some embodiments, the family relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle/niece relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent-great-grandchild relationship. great-granddaughter In some embodiments, a family relationship is defined as a relationship between a pair of people such that the degree of relatedness between a first individual and a second individual is comparable to the degree of relatedness expected between a pair of people. According to

- 11 046341 In some embodiments, the relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle-nephew/niece relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent relationship. -great-grandson/great-granddaughter.

In some embodiments, one or more computer processors are individually or collectively programmed to further determine the expected degree of inbreeding between the potential offspring of the first individual and the second individual. In some embodiments, one or more computer processors are individually or collectively programmed to further determine a recommendation indicating whether the first individual and the second individual should be bred together based on the expected degree of inbreeding of the potential offspring of the first individual and the second individual. In some embodiments, one or more computer processors are individually or collectively programmed to further determine a recommendation indicating that the first individual and the second individual should be bred together when the expected degree of inbreeding of the potential offspring of the first individual and the second individual does not exceed a predetermined threshold degree of inbreeding. In some embodiments, one or more computer processors are individually or collectively programmed to further determine a recommendation indicating that the first individual and the second individual should not be bred together when the expected degree of inbreeding of the potential offspring of the first individual and the second individual exceeds a predetermined threshold degree of inbreeding. In some embodiments, the predetermined threshold degree of inbreeding is about 0.10, about 0.15, about 0.20, about 0.25, about 0.30, about 0.35, about 0.40, about 0.45, or about 0.50.

In some embodiments, one or more computer processors are individually or collectively programmed to further obtain a social relationship between a first entity associated with a first individual and a second entity associated with a second individual based at least in part on the estimated degree of relatedness between the first individual and the second individual. In some embodiments, a social connection is obtained when the estimated degree of relatedness between a first individual and a second individual exceeds a predetermined threshold value. In some embodiments, the predetermined threshold value is about 0.00001, about 0.0001, about 0.001, about 0.01, about 0.02, about 0.04, about 0.06, about 0.08, about 0. 1, about 0.12, about 0.14, about 0.16, about 0.18, about 0.2, about 0.25, about 0.3, about 0.35, about 0.4, about 0, 45 or about 0.5. In some embodiments, the social connection is obtained through a social media network. In some embodiments, the first subject owns the first individual as a pet, and the second subject owns the second individual as a pet. In some embodiments, the same entity owns the first individual and the second individual as a pet. In some embodiments, obtaining a social connection between a first subject and a second subject involves providing the location of the first subject or the second subject.

In some embodiments, obtaining a social connection between a first subject and a second subject involves providing a communication link between the first subject and a second subject.

In some embodiments, one or more computer processors are individually or collectively programmed to further identify a familial relationship between a first individual and a second individual based at least in part on the degree of relationship. In some embodiments, the familial relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle/niece relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent-great-grandchild relationship. great-granddaughter In some embodiments, a family relationship is defined as a relationship between a pair of people such that the degree of relatedness between a first individual and a second individual is comparable to the degree of relatedness expected between a pair of people. In some embodiments, the relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle-nephew/niece relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent-great-grandchild relationship. /great-granddaughter.

In some embodiments, one or more computer processors are individually or collectively programmed to further identify, for each of one or more individuals in a population of test individuals, the presence, absence, risk, or carrier status of a genetic or health condition in the individual. In some embodiments, the genetic or health condition is selected from the group consisting

- 12 046341 cabbage soup from Thrombopathy (RASGRP2 Exon 8), Pyruvate Kinase Deficiency (PKLR Exon 7 Pug Variant), Factor IX Deficiency, Hemophilia B (F9 Exon 7, Terrier Variant), Pyruvate Kinase Deficiency (PKLR Exon 7 Beagle Variant), P2Y12 Platelet Disorders receptors (P2RY12), Glanzmann thrombasthenia type I (ITGA2B Exon 12), Von Willebrand disease type II, VWD type II (VWF), MayHegglin anomalies (MYH9), Thrombopathy (RASGRP2 Exon 5, American Eskimo Dog variant), Factor IX deficiency, Hemophilia B (F9 Exon 7, Rhodesian Ridgeback Variant), Von Willebrand Disease Type I (VWF), Canine Elliptocytosis (SPTB Exon 30), Canine Leukocyte Adhesion Disorder Type III, LAD3 (FERMT3), Prokallikrein Deficiency (KLKB1 Exon 8), Diseases von Willebrand type III, VWD type III (VWF Exon 4), Factor VIII Deficiency, Hemophilia A (F8 Exon 10, Boxer Variant), Trapped Neutrophil Syndrome (VPS13B), Pyruvate Kinase Deficiency (PKLR Exon 7 Labrador Variant), Thrombopathy (RASGRP2 Exon 5, Basset Hound Variant), Pyruvate Kinase Deficiency (PKLR Exon 5), Factor VIII Deficiency, Hemophilia A (F8 Exon 1, Shepherd Variant 2), Factor VII Deficiency (F7 Exon 5), Congenital Macrothrombocytopenia (TUBB1 Exon 1, Cavalier Variant King Charles Spaniel), Ligneous Membranitis, LM (PLG), Pyruvate Kinase Deficiency (PKLR Exon 10), Factor VIII Deficiency, Hemophilia A (F8 Exon 11, Shepherd Variant 1), Other Systems, Shar Pei Autoinflammatory Disease, SPAID, Shar Pei Fever (MTFR) ), Autosomal recessive enamel dysplasia, Hereditary dental enamel hypoplasia (Italian greyhound variant), Persistent Müllerian duct syndrome, PMDS (AMHR2), Vestibular syndrome and Doberman deafness, DVDob, DINGS, Eye disorders, Progressive retinal atrophy, crd2 (IQCB1), Primary lens luxation (ADAMTS17), Glaucoma primary open angle glaucoma (ADAMTS17 Exon 2), Progressive retinal atrophy, crd1 (PDE6B), Progressive retinal atrophy, rcd1 Rod cone dysplasia, rcd1 (PDE6B Exon 21 Irish Red Setter variant), Collie eye abnormalities , Uveal hypoplasia, CEA (NHEJ1), Progressive retinal atrophy (SAG), Achromatopsia (CNGA3 Exon 7 German Shepherd variant), Canine multifocal retinopathy cmr2 (BEST1 Exon 5), Primary open-angle glaucoma (ADAMTS17 Exon 11), Progressive atrophy retina, prcd Progressive rod-cone degeneration (PRCD Exon 1), Hereditary cataracts, Early cataracts, Juvenile cataracts (HSF4 Exon 9 Sheepdog variant), Autosomal dominant progressive retinal atrophy (RHO), Canine multifocal retinopathy cmr3 (BEST1 Exon 10 SNP ), Achromatopsia (CNGA3 Exon 7 Labrador Retriever Variant), Canine Multifocal Retinopathy cmr1 (BEST1 Exon 2), Progressive Retinal Atrophy, rcd3 Rod Cone Dysplasia, rcd3 (PDE6A), Progressive Retinal Atrophy (CNGB1), Golden Retriever Progressive Retinal Atrophy 2, GR-PRA2 (TTC8), Progressive Retinal Atrophy, CNGA (CNGA1 Exon 9), Golden Retriever Progressive Retinal Atrophy 1, GR-PRA1 (SLC4A3), Progressive Retinal Atrophy - crd4/cord1 (RPGRIP1), Congenital stationary night blindness (RPE65) , Fehr Corneal Dystrophy, MCD (CHST6), Glaucoma Primary Open Angle Glaucoma (ADAMTS10 Exon 9), Canine Multifocal Retinopathy cmr3 (BEST1 Exon 10 Deletion), Glaucoma Primary Open Angle Glaucoma (ADAMTS10 Exon 17), Muscle Disorders, Centronuclear Myopathy (PTPLA), Thomsen's Disease (CLCN1 Exon 7), Hereditary Great Dane Myopathy (BIN1), Thomsen's Disease (CLCN1 Exon 23), Muscular Dystrophy (DMD Pembroke Welsh Corgi Variant), Exercise-Induced Collapse (DNM1), Muscular Dystrophy (DMD Golden Retriever Variant) , Myostatin deficiency, Bully-Whippet syndrome (MSTN), Myotubular myopathy 1, X-linked myotubular myopathy, XL-MTM (MTM1, Labrador variant), Muscular dystrophy (Cavalier King Charles Spaniel variant 1), Multisystem disorders, Primary ciliary dyskinesia, PCD (CCDC39 Exon 3), GM1 Gangliosidosis (GLB1 Exon 2), Hurler syndrome type IIIA, Sanfilippo disease type A, MPS IIIA (SGSH Exon 6 Option 1), Acquired neuronal ceroid lipofuscinosis (ATP13A2), GM1 Gangliosidosis (GLB1 Exon 15 Sibainu Variant), Neuronal ceroid lipofuscinosis 2, NCL 2 (TPP1 Exon 4), Hurler syndrome type VII, Sly syndrome, MPS VII (GUSB Exon 3), Canine fucosidosis (FUCA1), GM1 Gangliosidosis (GLB1 Exon 15 Alaskan Husky Variant), Lagotto storage disease (ATG4D), Congenital keratoconjunctivitis Sicca and ichthyosiform dermatosis, Curly coat and dry eye syndrome, CKCSID (FAM83H Exon 5), Glycogen storage disease type VII, Phosphohexokinase deficiency, PFK deficiency (PFKM Whippet and English Springer- spaniel Variant), Glycogen disease type IA, Gierke's disease, GSD IA (G6PC), Glycogen disease type VII, Phosphohexokinase deficiency, PFK deficiency (PFKM Wachtelhund Variant), Neuronal ceroid lipofuscinosis 1, NCL 1 (CLN5 Border Collie Variant), Neuronal ceroid lipofuscinosis 1, Cerebellar ataxia, NCL-A (ARSG Exon 2), Neuronal ceroid lipofuscinosis 6, NCL 6 (CLN6 Exon 7), Hurler syndrome type I, MPS I (IDUA), Renal cystadenocarcinoma and nodular dermatofibrosis, RCND (FLCN Exon 7), Neuronal ceroidlipofuscinosis 10, NCL 10 (CTSD Exon 5), Globoid cell leukodystrophy, Krabbe disease (GALC Exon 5), Glycogen storage disease type IIIA, GSD IIIA (AGL), Neuronal ceroidlipofuscinosis (MFSD8), GM2 Gangliosidosis (HEB, Poodle Variant), X-linked congenital ectodermal dysplasia, Anhidrotic ectodermal dysplasia (EDA Intron 8), Neuronal ceroid lipofuscinosis (CLN8 Australian Shepherd Variant), Neuronal ceroid

- 13 046341 lipofuscinosis 8, NCL 8 (CLN8 English Setter Variant), Neuronal ceroid lipofuscinosis 1, NCL 1 (PPT1 Exon 8), Neuronal ceroid lipofuscinosis (CLN5 Golden Retriever Variant), Hurler Syndrome type VII, Sly Syndrome, MPS VII (GUSB Exon 5), Glycogen storage disease type II, Pompe disease, GSDII (GAA), GM2 Gangliosidosis (HEXA), Hurler syndrome type IIIA, Sanfilippo disease type A, MPS IIIA (SGSH Exon 6 Variant 2), Skin and connective tissue diseases , Ichthyosis (PNPLA1), Ichthyosis (SLC27A4), Dystrophic epidermosis bullosa (COL7A1), Ichthyosis, Epidermolytic ichthyosis (KRT10), Ectodermal dysplasia, Skin fragility syndrome (PKP1), Ichthyosis (NIPAL4), Musladin-Luquet syndrome (ADAMTSL2), Focal non-epidermolytic palmoplantar keratoderma, Congenital pachyonychia (KRT16), Hereditary nasal parakeratosis (SUV39H2), Hereditary footpad hyperkeratosis (FAM83G), Brain and spinal cord disorders, Juvenile polyneuropathy, Leonberger polyneuropathy 1, LPN1 (LPN1, ARHGEF10), Trophy disorders ics of the cerebellum , Neonatal cerebellar cortical degeneration, NCCD (SPTBN2), Narcolepsy (HCRTR2 Intron 6), L-2-Hydroxyglutaric aciduria, L2HGA (L2HGDH), Spongiform degeneration with cerebellar ataxia 2, SDCA2 (ATP1B2), Progressive neuronal abiotrophy, Canine multiple system degeneration, CMSD (SERAC1 Exon 15), Neonatal fetal neuroaxonal dystrophy (MFN2), Neonatal encephalopathy with seizures, NEWS (ATF2), Benign familial juvenile epilepsy, Relapsing-remitting focal epilepsy (LGI2), Juvenile laryngeal paralysis and polyneuropathy, Polyneuropathy with ocular abnormalities and neuronal vacuolation , POANV (RAB3GAP1, Rottweiler Variant), Progressive Neuronal Abiotrophy, Canine Multiple System Degeneration, CMSD (SERAC1 Exon 4), Cerebellar Ataxia, Progressive Early Cerebellar Ataxia (SEL1L), Hereditary Sensory Autonomic Neuropathy, Acral Mutilation Syndrome, AMS (GDNF- AS), Shaken Puppy Syndrome, X-linked Generalized Tremor Syndrome (PLP), Hypomyelination and Tremor (FNIP2), Spinocerebellar Ataxia, Late Onset Ataxia, LoSCA (CAPN1), Polyneuropathy, NDRG1 Greyhound Variant (NDRG1 Exon 15) , Polyneuropathies, NDRG1 Alaska Malamute Variant (NDRG1 Exon 4), Cerebellar hypoplasia (VLDLR), Spongiform degeneration with cerebellar ataxia 1, SDCA1, SeSAME/EAST syndrome (KCNJ10), Spinocerebellar ataxia with myokymia and/or seizures (KCNJ10), Encephalopathy Alaskan Husky, Subacute Leigh's Disease (SLC19A3), Degenerative Myelopathy, DM (SOD1A), Alexander Disease (GFAP), Heart Disorders, Dilated Cardiomyopathy, DCM1 (PDK4), Long QT Syndrome (KCNQ1), Dilated Cardiomyopathy, DCM2 ( TTN), skeletal disorders, Hereditary vitamin O-resistant rickets (VDR), Osteogenesis incomplete, Crystal disease (COLD1). Osteogenesis incomplete, Crystal disease (SERPINH1), Chondrodystrophy and intervertebral disc disease, CDDY/IVDD, Type I IVDD (FGF4 retrogen - CFA12), Osteogenesis incomplete, Crystal disease (COL1A2), Craniomandibular osteopathy, SMO (SLC37A2), Skeletal dysplasia 2, SD2 (COL11A2), Cleft Lip and/or Cleft Palate (ADAMTS20), Oculo-Skeletal Dysplasia 1, Dwarfism-Retinal Dysplasia, OSD1 (COL9A3, Labrador Retriever), Osteochondrodysplasia, Skeletal Dwarfism (SLC13A1), Metabolic Disorders, Malignant Hyperthermia ( RYR1), Hypocatalasia, Acatalasemia (CAT), Pyruvate dehydrogenase deficiency (PDP1), Kidney and bladder disorders, Hyperuricosuria and hyperuricemia or urolithiasis, HUU (SLC2A9), Polycystic kidney disease, PKD (PKD1), Protein-losing nephropathy, PLN ( NPHS1), Cystinuria Type II-A (SLC3A1), Primary hyperoxaluria (AGXT), Cystinuria Type I-A (SLC3A1), Autosomal recessive hereditary nephropathy, Hereditary nephropathy, ARHN (COL4A4 Exon 3), X-linked hereditary nephropathy, XLHN (COL4A5 Exon 35, Samoyed Variant 2), Cystinuria Type II-B (SLC7A9), 2,8-dihydroxyadenine-related urolithiasis, 2,8-DHA Urolithiasis (APRT), Neuromuscular disorders.

Episodic falling syndrome (BCAN), Congenital myasthenic syndrome (COLQ), Congenital myasthenic syndrome (CHAT), Immune system disorders, Severe combined immunodeficiency (RAG1), X-linked severe combined immunodeficiency (IL2RG Variant 1), Severe combined immunodeficiency ( PRKDC), X-linked severe combined immunodeficiency (IL2RG Variant 2), Complement deficiency 3, C3 deficiency (C3), Gastrointestinal disorders, Imerslund-Gresbeck syndrome, Selective cobalamin malabsorption (CUBN Exon 53), Imerslund-Gresbeck syndrome, Selective cobalamin malabsorption (CUBN Exon 8), Clinical disorders, MDR1 Drug sensitivity (MDR1), Alanine aminotransferase (GPT) activity, Hormonal disorders and congenital hypothyroidism (CPO, Tenterfield Terrier Variant). In some embodiments, risk is defined as the probability or relative risk of an individual having a genetic or health condition. In some embodiments, the relative risk is a numerical value (eg, a relative frequency ratio) or a categorical value (eg, at risk, not at risk, or not at risk). In some embodiments, carrier status is defined as a positive or negative indication that an individual is a carrier of a genetic or health condition.

In some embodiments, one or more computer processors

- 14 046341 individually or collectively programmed to provide additional identification for each of one or more individuals in a population of test individuals of one or more dog breeds in the individual. In some embodiments, one or more computer processors are individually or collectively programmed to further identify, for each of one or more individuals in a population of test individuals, one or more portions of one or more dog breeds of the individual. In some embodiments, one or more computer processors are individually or collectively programmed to further create a family tree of a plurality of individuals in a population of test individuals. In some embodiments, one or more computer processors are individually or collectively programmed to further identify, for each of one or more individuals in a population of test individuals, the presence, absence, or risk of a phenotype or trait in the individual. In some embodiments, the phenotype or trait is selected from the group consisting of: base coat color (e.g., dark or light coat color, pigment color, and color dilution), coat color modifiers (e.g., latent pattern, body pattern, and muzzle pattern), coat traits (e.g. color, coat length, shedding, coat texture, lack of hair (Xolo type), lack of hair (Terrier type), and albinism), body features (e.g. muzzle length, tail length, hind claws, back musculature and volume, eye color), body size (eg small, medium and large), behavior (eg adaptation to altitude), genetic diversity (eg degree of inbreeding and diversity of immune response).

In some embodiments, one or more computer processors are individually or collectively programmed to further generate a report indicating one or more of: degree of relationship, familial relationship, status of presence, absence, risk or carrier of a genetic or health condition, one or multiple dog breeds, one or more shares of one or more dog breeds, family tree, status of presence, absence, risk or carrier of a phenotype or trait, and any combination thereof.

In some embodiments, one or more computer processors are individually or collectively programmed to further transmit the report to the veterinarian.

In another aspect, the present invention relates to a non-transitory computer-readable medium comprising a computer-executable program that, when executed by one or more computer processors, implements a method for estimating the degree of relatedness between two individuals of a diploid population, the method comprising: (a) obtaining haplotype data for a population of test individuals wherein the haplotype data comprises a plurality of genetic markers common to a population of test individuals, (b) dividing the haplotype data into segments based on a plurality of genetic markers, (c) for each population of test individuals: (i) matching, based on a plurality of genetic markers, segments of the haplotype data that are identical in ancestry, between a first individual and a second individual within a population of test individuals, wherein each of the matched segments has a first size that is at least a predetermined threshold size, and contains at least a predetermined number of genetic markers, (ii ) for each of the matched segments between the first individual and the second individual: dividing the matched segment into a plurality of discrete genomic intervals, estimating each of the plurality of discrete genomic intervals based on (i) the degree of homozygosity match of the discrete genomic interval in the first individual or the second individual, or (ii) ) the degree of pairwise matching of a discrete genomic interval between a first individual and a second individual, thereby obtaining a plurality of scores, adjusting the plurality of scores based on the consistency of the plurality of scores, thereby obtaining a plurality of adjusted scores, and assigning a plurality of weights to the plurality of discrete genomic intervals based on the plurality of adjusted scores discrete genomic intervals, and (iii) calculating a weighted sum for the plurality of discrete genomic intervals of the matched segment based on the plurality of adjusted scores and the plurality of weights, and (d) estimating the degree of relatedness between the first individual and the second individual based on the weighted sums of the matched segments.

In some embodiments, the diploid population is a mammalian population. In some embodiments, the mammal population is a canine population, a feline population, a sporting animal population, or a rodent population. In some embodiments, the mammal population is a canid population. In some embodiments, the canid population is a population of dogs. In some embodiments, the mammal population is a feline population. In some embodiments, the feline population is a population of cats. In some embodiments, the mammal population is a population of sporting animals. In some embodiments, the population of sporting animals is

- 15 046341 is a population of horses. In some embodiments, the dog population comprises one or more dog breeds selected from the group consisting of: Affenpinscher, Afghan Hound, Africanis, Aidi, Airedale Terrier, Akbash, Akita Inu, Bully Cutta, Spanish Alano, Alapaha Bulldog, Alaskan Klee Kai , Alaskan Malamute, Alan, Alopekis, Alpine Dachshound, American Shepherd, American Akita, American Bulldog, American Cocker Spaniel, American Eskimo Dog, American Foxhound, American Hairless Terrier, American Mastiff, American Pit Bull Terrier, American Staffordshire Terrier, American Water Spaniel , Anatolian Shepherd, Anglo-French Lesser Hound, Appenzeller Mountain Dog, Dogo Argentino, Ariege Braque, Ariege Hound, Armant, Artois Hound, Australian Bulldog, Australian Cattle Dog, Australian Kelpie, Australian Shepherd, Australian Silky Terrier, Australian Short-tailed Cattle Dog, Australian Terrier, Austrian Hound, Austrian Pinscher, Azawakh, Bakharwal Dog, Barbet, African Non-Barking Dog, Basque Shepherd Dog, Artesian-Normandy Basset, Blue Gascon Basset, Red Breton Basset, Greater Vendean Basset Griffon, Small Vendean Basset Griffon, Bavarian Mountain Hound , Beagle, Beagle Harrier, Bearded Collie, Beauceron, Bedlington Terrier, Belgian Shepherd, Belgian Shepherd (Groenendael), Belgian Shepherd (Laekenois), Belgian Shepherd (Malinois), Belgian Shepherd (Tervuren), Bergamasco Shepherd, White Swiss Shepherd, Picardy Shepherd, Bernese Hound, Bernese Mountain Dog, Bichon Frize, Biyi, Bisben, Black and Tan Coonhound, Ruffed Norwegian Elk Husky, Bullenbeiser, Norwegian Black Elkhound, Black Russian Terrier, Yellow with Black Mask Southern Hound, Great Blue Gascon Hound, Little Blue Gascony Hound, Bloodhound, Blue Lacy, Blue Pol Terrier, Blue Spotted Coonhound, Boerboel, Czech Cattle Dog, Bolognese, Border Collie, Border Terrier, Russian Hound, Bosnian Wirehaired Hound, Boston Terrier, Bouvier de Ardennes , Bouvier des Flandres, Boxer, Boykin Spaniel, Italian Braque, Auvergne Pointer, Bourbon Braque, Dupuis Braque, French Braque, Saint-Germain Braque, Brazilian Terrier, Briard, Middle Vendeen Griffon, Breton Epagnole, Broholmer, Jurassic Hound, Bukovinian Shepherd Dog, Bull and Terriers, Bull Terrier, Bull Terrier (Miniature), Bullmastiff, Bully Cutta, Cairn Terrier, Canaan Dog, Canadian Eskimo Dog, Canadian Pointer, Cane Corso, Portuguese Shepherd, Cao de Castro Laboreiro, Azores Cattle Dog, Carolina dog, Romanian Carpathian Shepherd Dog, Catahoula Leopard Dog, Catalan Shepherd Dog, Caucasian Shepherd Dog, Cavalier King Charles Spaniel, Central Asian Shepherd Dog, Czech Fousek, Czech Terrier, Polish Greyhound, Chesapeake Bayretriever, Gray-blue hound, French black and piebald hound, French white -black dog, French Tricolor Hound, Chihuahua, Chilean Fox Terrier, Chinese Chongqing, Chinese Crested Dog, Chinese Imperial Dog, Chinook, Chippiparai, Chow Chow, Uruguayan Cimarron, Black Sheepdog, Cirneco del Etna, Clumber Spaniel, Longhaired Collie, Collie Shorthair, Combay, Cordoba Fighting Dog, Coton de Tulear, Cretan Hound, Croatian Shepherd Dog, Cumberland Shepherd Dog, Curly Haired Retriever, Czechoslovakian Wolfdog, Dachshund, Dalmatian, Dandie Dinmont Terrier, Danish-Swedish Farm Dog, Dingo, Doberman Pinscher, Bordeaid Dog, Kubinsky Dog, Guatemensky Bulterer, dwarf dog, Drown resource dog, old, dunketer, Dutch shepherd, Dutch Smoushond, East European Shepherd, East Siberian Laika, Elo, English Coker-Panel, English Highway, English Foxhand, English Mastiff, Pointer, Pointer , English Setter, English Shepherd, English Springer Spaniel, English Toy Terrier, English Water Spaniel, English White Terrier, Entlebucher Mountain Dog, Blue Picardy Spaniel, Estonian Hound, Estrela Shepherd Dog, Eurasier, Field Spaniel, Fila Brazilian, Fido, Finnish Hound, Finnish Lapphund, Finnish Spitz, Flat-Coated Retriever, Taiwanese Dog, Smooth Fox Terrier, Wire Fox Terrier, Breton Spaniel, French Bulldog, French Spaniel, Spanish Galgo, Langhaar, German Pinscher, German Shepherd, Kurzhaar, German Wachtelhund, German Spitz, Drathaar, Giant Schnauzer, Glen of Imaal Terrier, Golden Retriever, Scottish Setter, Great Anglo-French Black and Pinto Hound, Greater Anglo-French Red and Pinto Hound, Greater Anglo-French Tri-Colored Hound, Greater Griffon Vendeen, Puerto Rican Mastiff, Great Dane, Pyrenees Mountain dog, Greater Swiss Mountain Dog, Greenland dog, Greyhound, Blue Gascony Griffon, Belgian Griffon, Breton Tawny Griffon, Nivernine Griffon, Gul Dong, Gul Terr, Indian Hare Dog, Hamilton Hound, Hanoverian Hound, Harrier, Havanese Bichon, Hawaiian poi dog, Himalayan Shepherd, Hokkaido, Hortaya Greyhound, Hovawart, Transylvanian Hound, New Zealand Shepherd, Hugenhund, Podenco Ibicenco, Icelandic dog, Indian Spitz, Irish Bull Terrier, Irish Red and White Setter, Irish Red Setter, Irish Staffordshire Bull Terrier, Irish Terrier, Irish Water Spaniel, Irish Wolfhound, Istrian Shorthaired Hound, Istrian Wirehaired Hound, Italian Greyhound, Jack Russell Terrier, German Jagd Terrier, Swedish Elkhound, Japanese Chin, Japanese Spitz, Japanese Terrier, Jonangi, Kaikadi, Kai, Kangal, Kanny, Karakachan dog, Karelian Bear Dog, Krash Shepherd Dog, Keeshond, Kerry Beagle, Kerry Blue Terrier, Cavalier King Charles Spaniel, Royal Shepherd Dog, Kintamani, Kishu, Komondor, Kooikerhondje, Kuli, Korean Chindo, Korean Mastiff, Kromforlander, Kunming

- 16 046341 Shepherd Dog, Kuri, Kuvasz, Kai Leo, Labrador Husky, Labrador Retriever, Lagotto Romagnolo, Lakeland Terrier, Lancashire Heeler, Landseer, Lapp Reindeer Dog, Leonberger, Lhasa Apso, Lithuanian Hound, Longhaired Whippet, Brindis Fighting Dog, Small Lion Dog, Hungarian Agar, Large Tree Hound, Maltese, Manchester Terrier, Maremmo-Abruzzese Shepherd, McNab, Xoloitzcuintle, Miniature Australian Shepherd, Miniature Fox Terrier, Miniature Pinscher, Miniature Schnauzer, Miniature Siberian Husky, Romanian Mioritic Shepherd, Molossian Dog, Montenegrin Mountain Goncha, Moscow Watchtower, Moscow Diver, Mountain Hound, Kentuk Mountain Hound, Mukuchies, Moody, Caravan Borzaa, Big Munsterlander, Small Münsterland, Curly-haired Retriever from the Murray River, Neapolitan mastiff, Newfoundland, Novogvinean singing, Norfolk-Spaniel, Norfolk-Spaniel, Norfolk-Panniel, Norfolk-Panniel, Norfolk-Panniel, Norfolk-Panniel, Norfolk-Panniel Folk -Terrier, Norbotten Husky, Northern Beagle, Northern Inuit Dog, Norwegian Buhund, Norwegian Gray Elkhound, Norwegian Lundehund, Norwich Terrier, Nova Scotia Retriever, Old Danish Pointer, Bobtail, Old English Bulldog, Black and Tan Terrier, Old German Shepherd, Old English Bulldog, Otterhound , Navarre Puchon, Paisley Terrier, Continental Toy Spaniel, Parson Russell Terrier, Paterdale Terrier, Pekingese, Dogo Canary, Ca-de-bo, Peruvian Inca Orchid, Phalene, Pharaoh Hound, Picardy Spaniel, Plott Hound, Podenco Canario, Pointer, Polish Ogar, Polish Hound, Polish Lowland Sheepdog, Polish Podgalian Sheepdog, Pomeranian Spitz, Pont-Audemer Spaniel, Poodle, Porcelain Hound, Portuguese Podengo, Portuguese Pointer, Portuguese Water Dog, Prague Ratdog, Poodle Pointer, Pug , Puli, Pumi, Pungsan, Iberian Mastiff, Iberian Shepherd Dog, Rafeiro do Alentejo, Rajapalayam, Rampur Greyhound, Brazilian Oblavna Hound, Andalusian Ratonero, Rat Terrier, Red Coonhound, Rhodesian Ridgeback, Rottweiler, Russian Hunting Spaniel, Russian Toy, Russian- European Husky, Jack Russell Terrier, Saarloos Wolfdog, Spanish Hound, Persian Shepherd, Iranian Mazandarani Shepherd, Sakhalin Husky, Saluki, Samoyed, Sapsari, Sharplaninats, Chapendois, Schillerstevare, Schipperke, Old Croatian Greyhound, Giant Schnauzer, Miniature Schnauzer, Medium Schnauzer , Swiss Hound, Lesser Swiss Hound, Longhaired Collie, Deerhound, Scottish Terrier, Sealyham Terrier, Italian Shorthair Hound, Siberian Sled Dog, Serbian Hound, Serbian Tricolor Hound, Charley, Sheltie, Shiba Inu, Shih Tzu, Shikoku, Shilon Shepherd Dog, Bolonka, Siberian Husky, Silky Windhound, Sinhala Hound, Skye Terrier, Slugi, Slovak Dude, Slovak Coarse-haired Stavach, Slovak Copov, Smaland Hound, Small Greek Dog, Irish Soft Coated Wheaten Terrier, South Russian Shepherd Dog, Southern Hound, Spanish Mastiff, Spanish Water Dog dog, Spinone, Lucas Terrier, St. Bernard, Small Newfoundland, Stabyhoun, Staffordshire Bull Terrier, Stefan's Beagle, Austrian Brutches, Sussex Spaniel, Swedish Lapphund, Swedish Vallhund, Swedish Beagle, Tahltan Bear Dog, Taigan, Tamaskan Dog, Teddy Roosevelt Terrier, Telomian, Tenterfield Terrier, Thai Bangku, Thai Ridgeback, Tibetan Mastiff, Tibetan Spaniel, Tibetan Terrier, Tornjak, Tosa Inu, Toy Bulldog, Toy Fox Terrier, Toy Manchester Terrier, Treeing Cure, Treeing Walker Coonhound , Tyrolean Hound, Utonagan, Hungarian Vizsla, Volpino Italiano, Weimaraner, Cardigan Welsh Corgi, Pembroke Welsh Corgi, Welsh Sheepdog, Welsh Springer Spaniel, Welsh Terrier, West Highland White Terrier, West Siberian Laika, Westphalian Dachshund Hound, Wetterhun , Whippet, White English Bulldog, White Swiss Shepherd, Hungarian Wirehaired Vizsla, Griffon Cortalsa and Yorkshire Terrier. In some embodiments, the population includes one or more purebred dogs (eg, having one breed type) or one or more mixed breed dogs (eg, having multiple breed types). In some embodiments, the population is a population of mixed breed dogs having DNA from any number (eg, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10) or combinations of purebred dogs.

In some embodiments, haplotype data is obtained by processing genotypic data from a population of test individuals using a haplotype phasing algorithm. In some embodiments, the haplotype phasing algorithm comprises a reference-based haplotype phasing algorithm. In some embodiments, the reference-based haplotype phasing algorithm comprises a hidden Markov model search. In some embodiments, the reference-based haplotype phasing algorithm comprises the Eagle1 algorithm, the Eagle2 algorithm, the PHASE algorithm, the fastPHASE algorithm, the BEAGLE algorithm, the Findhap algorithm, the Impute algorithm, the Flmpute algorithm, the Alphalmpute algorithm, the IMPUTE2 algorithm, the MaCH algorithm, the SHAPEIT1 algorithm, the SHAPEIT2 algorithm, SHAPEIT3 algorithm, SHAPEIT4 algorithm or a combination of both. In some embodiments, the haplotype phasing algorithm comprises a cohort-based haplotype phasing algorithm.

In some embodiments, genotypic data is obtained by evaluating biological samples taken from a population of test individuals. In some embodiments, the biological samples comprise blood samples, saliva samples, swab samples, cell samples, or tissue samples. In some embodiments, the assessment involves sequencing biological samples or derivatives thereof.

- 17 046341

In some embodiments, the plurality of genetic markers comprises at least about 500, at least about 1000, at least about 2000, at least about 3000, at least about 4000, at least about 5000, at least about 6000, at least about 7,000, at least about 8,000, at least about 9,000, or at least about 10,000 different genetic markers.

In some embodiments, matching segments of haplotype data that are identical by ancestry involves using the GERMLINE algorithm, the PLINK algorithm, the PREST algorithm, the Random Projection Algorithm for IBD Detection (RaPID), the Fast IBD Shared Haplotype Detection (FISHR) algorithm, the Advanced Identity by Ancestry algorithm (IBD), fastIBD algorithm, KING algorithm, HaploScore algorithm, TRUFFLE algorithm or a combination thereof. In some embodiments, the predetermined threshold size is about 100 thousand base pairs (kb), about 200 kb, about 300 kb, about 400 kb, about 500 kb, about 600 kb, about 700 kb, about 800 kb, about 900 kb. or about 1000 kb. In some embodiments, the predetermined number of genetic markers is about 30, about 40, about 50, about 60, about 70, about 80, about 90, or about 100 different genetic markers.

In some embodiments, the method further comprises dividing the matched segments such that the discrete genomic intervals of the plurality of discrete genomic intervals are of equal size. In some embodiments, the method further comprises dividing the matched segments such that the discrete genomic intervals of the plurality of discrete genomic intervals have a variable size. In some embodiments, the variable size of a given discrete genomic interval of a plurality of discrete genomic intervals is determined, at least in part, based on the starting position and ending position of IBD hits proximal to the given discrete genomic interval, the density of genomic markers in the given discrete genomic interval, the maximum number of markers for a given discrete genomic interval, the maximum length of a given discrete genomic interval, or a combination thereof.

In some embodiments, the method further comprises estimating each of the plurality of discrete genomic intervals based on (i) the degree of homozygosity match of the discrete genomic interval in the first individual or the second individual, and (ii) the degree of pairwise matching of the discrete genomic interval between the first individual and the second individual, with thereby obtaining a plurality of homozygosity match scores and a plurality of pairwise match scores. In some embodiments, the method further includes adjusting the plurality of pairwise match scores based on the consistency of the pairwise match score with the corresponding homozygosity match score, thereby obtaining a plurality of adjusted pairwise match scores.

In some embodiments, the method further comprises assigning a plurality of weights to a plurality of discrete genomic intervals based on the plurality of identity states for two alleles in two diploid individuals. In some embodiments, the plurality of identity states comprises identity states selected from a table, and a plurality of weights are assigned based on a plurality of relatedness contributions r _xy as listed in the table. In some embodiments, the degree of relationship comprises a relationship coefficient. In some embodiments, the method further comprises calculating a weighted sum for the plurality of discrete genomic intervals of the matched 41 +47 + (0.75x43) + (0.5x48) ^r xv ⁼ ---------------- -------------segment where the weighted sum is expressed by: ^L

In some embodiments, the degree of relatedness comprises a relatedness coefficient. In some embodiments, the method further comprises calculating a weighted sum for the plurality of discrete genomic intervals of the matched segments, _ 41 + (0.5 X (43 +47)) + (0.25 X 48)

-xy ^- : · where the weighted sum is expressed by: ^{L L}

In some embodiments, assessing the degree of relatedness between the first individual and the second individual involves determining the degree of inbreeding of the first individual or the second individual. In some embodiments, assessing the degree of relatedness between the first individual and the second individual involves determining the degree of inbreeding between the first individual and the second individual. In some embodiments, the method further comprises determining a familial relationship between the first individual and the second individual based at least in part on the degree of inbreeding of the first individual and the second individual. In some embodiments, the family relationship is a parent-child relationship, a sibling relationship, an aunt/uncle-nephew relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent relationship.

- 18 046341 ka/great-grandfather-great-grandson/great-granddaughter. In some embodiments, a family relationship is defined as a relationship between a pair of people such that the degree of relatedness between a first individual and a second individual is comparable to the degree of relatedness expected between a pair of people. In some embodiments, the relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle-nephew/niece relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent-great-grandchild relationship. .

In some embodiments, the method further comprises determining the expected degree of inbreeding between the potential offspring of the first individual and the second individual. In some embodiments, the method further comprises determining a recommendation indicating whether the first individual and the second individual should be bred together based on the expected degree of inbreeding of the potential offspring of the first individual and the second individual. In some embodiments, the method further comprises defining a recommendation indicating that the first individual and the second individual should be bred together when the expected degree of inbreeding of the potential offspring of the first individual and the second individual does not exceed a predetermined threshold degree of inbreeding. In some embodiments, the method further comprises defining a recommendation indicating that the first individual and the second individual should not be bred together when the expected degree of inbreeding of the potential offspring of the first individual and the second individual exceeds a predetermined threshold degree of inbreeding. In some embodiments, the predetermined threshold degree of inbreeding is about 0.10, about 0.15, about 0.20, about 0.25, about 0.30, about 0.35, about 0.40, about 0.45, or about 0.50.

In some embodiments, the method further comprises obtaining a social relationship between a first entity associated with the first individual and a second entity associated with the second individual based at least in part on an estimated degree of relatedness between the first individual and the second individual. In some embodiments, a social connection is obtained when the estimated degree of relatedness between a first individual and a second individual exceeds a predetermined threshold value. In some embodiments, the predetermined threshold value is about 0.00001, about 0.0001, about 0.001, about 0.01, about 0.02, about 0.04, about 0.06, about 0.08, about 0. 1, about 0.12, about 0.14, about 0.16, about 0.18, about 0.2, about 0.25, about 0.3, about 0.35, about 0.4, about 0, 45 or about 0.5. In some embodiments, the social connection is obtained through a social media network. In some embodiments, the first subject owns the first individual as a pet, and the second subject owns the second individual as a pet. In some embodiments, the same entity owns the first individual and the second individual as a pet. In some embodiments, obtaining a social connection between a first subject and a second subject involves providing the location of the first subject or the second subject. In some embodiments, obtaining a social connection between a first subject and a second subject involves providing a communication link between the first subject and a second subject.

In some embodiments, the method further comprises identifying a familial relationship between the first individual and the second individual based at least in part on the degree of relationship. In some embodiments, the family relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle-nephew/niece relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent/great-grandparent relationship. great-granddaughter In some embodiments, a family relationship is defined as a relationship between a pair of people such that the degree of relatedness between a first individual and a second individual is comparable to the degree of relatedness expected between a pair of people. In some embodiments, the relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle-nephew/niece relationship, a cousin-level relationship, a grandparent-grandchild relationship, or a great-grandparent-great-grandchild relationship. /great-granddaughter.

In some embodiments, the method further comprises identifying, for each of one or more individuals in a population of test individuals, the presence, absence, risk, or carrier status of a genetic or health condition in the individual. In some embodiments, the genetic or health condition is selected from the group consisting of Thrombopathy (RASGRP2 Exon 8), Pyruvate Kinase Deficiency (PKLR Exon 7 Pug Variant), Factor IX Deficiency, Hemophilia B (F9 Exon 7 Terrier Variant), Pyruvate Kinase Deficiency ( PKLR Exon 7 Beagle Variant), P2Y12 Platelet Receptor Disorders

- 19 046341 (P2RY12), Glanzmann thrombasthenia type I (ITGA2B Exon 12), Von Willebrand disease type II, VWD type II (VWF), May-Hegglin anomalies (MYH9), Thrombopathy (RASGRP2 Exon 5, American Eskimo Dog Variant), Factor IX deficiency, Hemophilia B (F9 Exon 7, Rhodesian Ridgeback variant), Von Willebrand disease type I (VWF), Canine elliptocytosis (SPTB Exon 30), Canine leukocyte adhesion disorder type III, LAD3 (FERMT3), Prokallikrein deficiency (KLKB1 Exon 8), Von Willebrand disease type III, VWD type III (VWF Exon 4), Factor VIII deficiency, Hemophilia A (F8 Exon 10, Boxer variant), Captured neutrophil syndrome (VPS13B), Pyruvate kinase deficiency (PKLR Exon 7 Labrador variant), Thrombopathy (RASGRP2 Exon 5, Basset Hound Variant), Pyruvate Kinase Deficiency (PKLR Exon 5), Factor VIII Deficiency, Hemophilia A (F8 Exon 1, Shepherd Variant 2), Factor VII Deficiency (F7 Exon 5), Congenital Macrothrombocytopenia (TUBB1 Exon 1, Cavalier King Charles Spaniel Variant), Ligneous Membranitis, LM (PLG), Pyruvate Kinase Deficiency (PKLR Exon 10), Factor VIII Deficiency, Hemophilia A (F8 Exon 11, Shepherd Variant 1), Other Systems, Shar Pei Autoinflammatory Disease, SPAID, Shar Pei fever (SRF), Autosomal recessive enamel dysplasia,

Hereditary hypoplasia of tooth enamel (Italian greyhound variant), Persistent Müllerian duct syndrome, PMDS (AMHR2), Vestibular syndrome and Doberman deafness, DVDob, DINGS, Eye disorders, Progressive retinal atrophy, crd2 (IQCB1), Primary lens luxation (ADAMTS17), Glaucoma primary open-angle glaucoma (ADAMTS17 Exon 2), Progressive retinal atrophy, crd1 (PDE6B), Progressive retinal atrophy, rcd1 Rod cone dysplasia, rcd1 (PDE6B Exon 21 Irish Red Setter variant), Collie eye anomalies, Uveal hypoplasia, CEA (NHEJ1) , Progressive retinal atrophy (SAG), Achromatopsia (CNGA3 Exon 7 German Shepherd Variant), Canine multifocal retinopathy cmr2 (BEST1 Exon 5), Primary open-angle glaucoma (ADAMTS17 Exon 11), Progressive retinal atrophy, prcd Progressive rod-cone degeneration ( PRCD Exon 1), Hereditary cataracts, Early cataracts, Juvenile cataracts (HSF4 Exon 9 Sheepdog Variant), Autosomal dominant progressive retinal atrophy (RHO), Canine multifocal retinopathy cmr3 (BEST1 Exon 10 SNP), Achromatopsia (CNGA3 Exon 7 Labrador Retriever Variant) , Canine multifocal retinopathy cmr1 (BEST1 Exon 2), Progressive retinal atrophy, rcd3 Rod cone dysplasia, rcd3 (PDE6A), Progressive retinal atrophy (CNGB1), Golden retriever progressive retinal atrophy 2, GR-PRA2 (TTC8), Progressive retinal atrophy , CNGA (CNGA1 Exon 9), Golden Retriever Progressive Retinal Atrophy 1, GR-PRA1 (SLC4A3), Progressive Retinal Atrophy - crd4/cord1 (RPGRIP1), Congenital Stationary Night Blindness (RPE65), Fehr's Corneal Dystrophy, MCD (CHST6), Glaucoma primary open angle glaucoma (ADAMTS10 Exon 9), Canine multifocal retinopathy cmr3 (BEST1 Exon 10 Deletion), Glaucoma primary open angle glaucoma (ADAMTS10 Exon 17), Muscle disorders, Centronuclear myopathy (PTPLA), Thomsen's disease (CLCN1 Exon 7), Hereditary myopathy Great Dane (BIN1), Thomsen's Disease (CLCN1 Exon 23), Muscular Dystrophy (DMD Pembroke Welsh Corgi Variant), Exercise-Induced Collapse (DNM1), Muscular Dystrophy (DMD Golden Retriever Variant), Myostatin Deficiency, Bullywippet Syndrome (MSTN), Myotubular Myopathy 1, X-Linked Myotubular Myopathy, XL-MTM (MTM1, Labrador Variant), Muscular Dystrophy (Cavalier King Charles Spaniel Variant 1), Multisystem Disorders, Primary Ciliary Dyskinesia, PCD (CCDC39 Exon 3), GM1 Gangliosidosis (GLB1 Exon 2), Hurler's syndrome type IIIA, Sanfilippo disease type A, MPS IIIA (SGSH Exon 6 Variant 1), Acquired neuronal ceroid lipofuscinosis (ATP13A2), GM1 Gangliosidosis (GLB1 Exon 15 Shiba Inu Variant), Neuronal ceroid -lipofuscinosis 2, NCL 2 (TPP1 Exon 4), Hurler syndrome type VII, Sly syndrome, MPS VII (GUSB Exon 3), Canine fucosidosis (FUCA1), GM1 Gangliosidosis (GLB1 Exon 15 Alaskan Husky Variant), Lagotto storage disease (ATG4D ), Congenital keratoconjunctivitis Sicca and ichthyosiform dermatosis, Curly coat and dry eye syndrome, CKCSID (FAM83H Exon 5), Glycogen storage disease type VII, Phosphohexokinase deficiency, PFK deficiency (PFKM Whippet and English Springer Spaniel Variant), Glycogen storage disease type IA, Diseases Gierke, GSD IA (G6PC), Glycogen disease type VII, Phosphohexokinase deficiency, PFK deficiency (PFKM Wachtelhund Variant), Neuronal ceroid lipofuscinosis 1, NCL 1 (CLN5 Border Collie Variant), Neuronal ceroid lipofuscinosis 1, Cerebellar ataxia, NCL -A (ARSG Exon 2), Neuronal ceroid lipofuscinosis 6, NCL 6 (CLN6 Exon 7), Hurler syndrome type I, MPS I (IDUA), Renal cystadenocarcinoma and nodular dermatofibrosis, RCND (FLCN Exon 7), Neuronal ceroid lipofuscinosis 10, NCL 10 (CTSD Exon 5), Globoid cell leukodystrophy, Krabbe disease (GALC Exon 5), Glycogen storage disease type IIIA, GSD IIIA (AGL), Neuronal ceroid lipofuscinosis (MFSD8), GM2 Gangliosidosis (NECV, Poodle Variant) , X chromosome-linked congenital ectodermal dysplasia, Anhidrotic ectodermal dysplasia (EDA Intron 8), Neuronal ceroid lipofuscinosis (CLN8 Australian Shepherd Variant), Neuronal ceroid lipofuscinosis 8, NCL 8 (CLN8 English Setter Variant), Neuronal ceroid lipofuscinosis 1, NCL 1 (PPT1 Exon 8), Neuronal ceroid lipofuscinosis (CLN5 Gold variant

- 20 046341 hundred retriever), Hurler syndrome type VII, Sly syndrome, MPS VII (GUSB Exon 5), Glycogen storage disease type II, Pompe disease, GSDII (GAA), GM2 Gangliosidosis (HECA), Hurler syndrome type IIIA, Sanfilippo disease type A, MPS IIIA (SGSH Exon 6 Variant 2), Skin and connective tissue diseases, Ichthyosis (PNPLA1), Ichthyosis (SLC27A4), Dystrophic epidermosis bullosa (COL7A1), Ichthyosis, Epidermolytic ichthyosis (KRT10), Ectodermal dysplasia, Fragile skin syndrome ( PKR1), Ichthyosis (NIPAL4), Musladin-Luquet Syndrome (ADAMTSL2), Focal non-epidermolytic palmoplantar keratoderma, Congenital pachyonychia (KRT16), Hereditary nasal parakeratosis (SUV39H2), Hereditary footpad hyperkeratosis (FAM83G), Brain and spinal cord disorders, Juvenile polyneuropathy, Leonberger polyneuropathy 1, LPN1 (LPN1, ARHGEF10), Cerebellar trophic disorders, Neonatal cerebellar cortical degeneration, NCCD (SPTBN2), Narcolepsy (HCRTR2 Intron 6), L-2-Hydroxyglutaric aciduria, L2HGA (L2HGDH), Spongiform degeneration of the cerebellum checkpoint ataxia 2, SDCA2 (ATP1B2), Progressive neuronal abiotrophy, Canine multiple system degeneration, CMSD (SERAC1 Exon 15), Neonatal fetal neuroaxonal dystrophy (MFN2), Neonatal encephalopathy with seizures, NEWS (ATF2), Benign familial juvenile epilepsy, Relapsing-remitting focal epilepsy (LGI2), Juvenile laryngeal paralysis and polyneuropathy, Polyneuropathy with ocular abnormalities and neuronal vacuolation, POANV (RAB3GAP1, Rottweiler Variant), Progressive neuronal abiotrophy, Canine multiple system degeneration, CMSD (SERAC1 Exon 4), Cerebellar ataxia, Progressive early cerebellar ataxia ( SEL1L), Hereditary sensory-autonomic neuropathy, Acral mutilation syndrome, AMS (GDNF-AS), Shaken puppy syndrome, X-linked generalized tremor syndrome (PLP), Hypomyelination and tremor (FNIP2), Spinocerebellar ataxia, Ataxia with tardive onset, LoSCA (CAPN1), Polyneuropathy, NDRG1 Greyhound Variant (NDRG1 Exon 15), Polyneuropathy, NDRG1 Alaska Malamute Variant (NDRG1 Exon 4), Cerebellar hypoplasia (VLDLR), Spongiform degeneration with cerebellar ataxia 1, SDCA1, SeSAME/EAST syndrome (KCNJ10), Spinocerebellar ataxia with myokymia and/or seizures (KCNJ10), Alaskan Husky Encephalopathy, Subacute Leigh's Disease (SLC19A3), Degenerative Myelopathy, DM (SOD1A), Alexander Disease (GFAP), Heart Disorders, Dilated Cardiomyopathy, DCM1 ( PDK4), Long QT syndrome (KCNQ1), Dilated cardiomyopathy, DCM2 (TTN), skeletal disorders, Hereditary vitamin E-resistant rickets (VDR), Osteogenesis incomplete, Crystal disease (COL^O, Osteogenesis incomplete, Crystal disease ( SERPINH1), Chondrodystrophy and intervertebral disc diseases, CDDY/IVDD, Type I IVDD (FGF4 retrogen - CFA12), Incomplete osteogenesis, Crystalline disease (COL1A2), Craniomandibular osteopathy, SMO (SLC37A2), Skeletal dysplasia 2, SD2 (COL11A2), Clefts lip and/or cleft palate (ADAMTS20), Oculo-skeletal dysplasia 1, Dwarfism-retinal dysplasia, OSD1 (COL9A3, Labrador retriever), Osteochondrodysplasia, Skeletal dwarfism (SLC13A1), metabolic disorders, Malignant hyperthermia (RYR1), Hypocatalasia, Acatalasemia (CAT), Pyruvate dehydrogenase deficiency (PDP1), Kidney and bladder disorders, Hyperuricosuria and hyperuricemia or urolithiasis, HUU (SLC2A9), Polycystic kidney disease, PKD (PKD1), Protein-losing nephropathy, PLN (NPHS1), Cystinuria Type II -A (SLC3A1), Primary hyperoxaluria (AGXT), Cystinuria Type I-A (SLC3A1), Autosomal recessive hereditary nephropathy, Hereditary nephropathy, ARHN (COL4A4 Exon 3), X-linked hereditary nephropathy, XLHN (COL4A5 Exon 35, Samoyed Option 2), Cystinuria Type II-B (SLC7A9), 2,8-dihydroxyadenine-associated urolithiasis, 2,8-DHA Urolithiasis (APRT), Neuromuscular disorders, Episodic falls syndrome (BCAN), Congenital myasthenic syndrome (COLQ) , Congenital myasthenic syndrome (CHAT), Immune system disorders, Severe combined immunodeficiency (RAG1), X-linked severe combined immunodeficiency (IL2RG Variant 1), Severe combined immunodeficiency (PRKDC), X-linked severe combined immunodeficiency (IL2RG Variant 2 ), Complement 3 deficiency, C3 deficiency (C3), Gastrointestinal disorders, Imerslund-Gresbeck syndrome, Selective cobalamin malabsorption (CUBN Exon 53), Imerslund-Gresbeck syndrome, Selective cobalamin malabsorption (CUBN Exon 8), Clinical disorders, MDR1 Sensitivity to the drug (MDR1), Alanine aminotransferase activity (GPT), Hormonal disorders and congenital hypothyroidism (TPO, Tenterfield Terrier Variant). In some embodiments, risk is expressed as the probability or relative risk of an individual having a genetic or health condition. In some embodiments, the relative risk is a numerical value (eg, a relative frequency ratio) or a categorical value (eg, at risk, not at risk, or not at risk). In some embodiments, carrier status is expressed as a positive or negative indication that an individual is a carrier of a genetic or health condition.

In some embodiments, the method further comprises identifying, for each of one or more individuals in a population of test individuals, one or more dog breeds in the individual. According to some embodiments

- 21 046341 further provides for the identification, for each of one or more individuals in the population of test individuals, of one or more fractions of one or more dog breeds of the individual. In some embodiments, the method further comprises creating a family tree of a plurality of individuals in a population of test individuals. In some embodiments, the method further comprises identifying, for each of one or more individuals in a population of test individuals, the presence, absence, or risk of a phenotype or trait in the individual. In some embodiments, the phenotype or trait is selected from the group consisting of: base coat color (e.g., dark or light coat color, pigment color, and color dilution), coat color modifiers (e.g., latent pattern, body pattern, and muzzle pattern), coat traits (e.g. color, coat length, shedding, coat texture, lack of hair (Xolo type), lack of hair (Terrier type), and albinism), body features (e.g. muzzle length, tail length, hind claws, back musculature and volume, eye color), body size (eg, small, medium, and large), behavior (eg, adaptation to altitude), genetic diversity (eg, degree of inbreeding and diversity of immune response).

In some embodiments, the method further comprises generating a report that includes one or more of: degree of relationship, familial relationship, status of presence, absence, risk or carrier of a genetic or health condition, one or more dog breeds, one or more shares of one or more multiple dog breeds, family tree, status of presence, absence, risk or carrier of a phenotype or trait, and any combination thereof. In some embodiments, the method further comprises transmitting the report to the veterinarian.

In another aspect, the present invention relates to a non-transitory computer-readable medium comprising a computer-executable program that, when executed by one or more computer processors, performs any of the methods described above or elsewhere herein.

According to another aspect, the present invention relates to a system including one or more computer processors and computer memory associated therewith. Computer memory contains a computer-executable program that, when executed by one or more processors of the computer, performs any of the methods described above or elsewhere herein.

Additional aspects and advantages of the present invention will become apparent to those skilled in the art from the following detailed description, in which only illustrative embodiments of the present invention are shown and described. As will be appreciated, the present invention is capable of other and different embodiments, and certain features thereof are capable of modification in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description are to be considered illustrative in nature and not restrictive.

Incorporation by reference.

All publications, patents and patent applications mentioned herein are incorporated herein by reference to the same extent as if each individual publication, patent or patent application were specifically and individually identified as being incorporated by reference. To the extent that publications and patents or patent applications incorporated by reference are inconsistent with the disclosure contained in the specification, the specification will be deemed to govern and/or take precedence over any such conflicting material.

Brief description of drawings

The novel features of the present invention are set forth in detail in the accompanying claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description, which sets forth illustrative embodiments employing the principles of the invention, and the accompanying drawings (also figure and fig., wherein:

in fig. 1 illustrates an exemplary method for assessing the degree of relatedness between two individuals of a diploid population, according to some embodiments.

In fig. 2 illustrates a computing system that is programmed or otherwise configured to perform the methods disclosed herein.

Detailed Disclosure of the Present Invention

Although various embodiments of the present invention are shown and described herein, those skilled in the art will appreciate that such embodiments are provided by way of example only. Numerous variations, changes and substitutions will be readily apparent to those skilled in the art without departing from the present invention. It should be understood that various alternatives to the embodiments of the invention described herein can be used.

The singular forms used in the description and claims of the present invention include

- 22 046341 look for the plural form, unless the context clearly implies otherwise. For example, the term sample includes many samples, including mixtures thereof.

In the context of the present invention, the term subject generally refers to an object or environment that possesses testable or detectable genetic information. The subject may be a human, an individual, or a patient. The subject may be a vertebrate, such as a mammal. Non-limiting examples of mammals include humans, monkeys, farm animals, sporting animals, rodents, and domestic animals (eg, canids such as dogs or felids such as cats). The subject may have a normal or abnormal health condition or physiological condition or condition, or be suspected of having a normal or abnormal health condition or physiological condition or condition. A subject may exhibit symptom(s) indicative of a health condition or physiological condition or condition. Alternatively, the subject may be asymptomatic with respect to such health condition or physiological condition or condition.

In the context of the present invention, the term nucleic acid or polynucleotide generally refers to a molecule containing one or more nucleic acid subunits or nucleotides. The nucleic acid may include one or more nucleotides selected from adenosine (A), cytosine (C), guanine (G), thymine (T) and uracil (U) or variants thereof. A nucleotide generally includes a nucleoside and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more phosphate (PO3) groups. The nucleotide may include a nitrogenous base, a five-carbon sugar (either ribose or deoxyribose), and one or more phosphate groups, alone or in combination. Ribonucleotides are nucleotides whose sugar is ribose. Deoxyribonucleotides are nucleotides in which the sugar is deoxyribose. The nucleotide may be a nucleoside monophosphate or a nucleoside polyphosphate. The nucleotide may be a deoxyribonucleoside polyphosphate, such as, for example, deoxyribonucleoside triphosphate (dNTP), which may be selected from deoxyadenosine triphosphate (dATP), deoxycytidine triphosphate (dCTP), deoxyguanosine triphosphate (dGTP), uridine triphosphate (dUTP), and deoxythymidine triphosphate (dTTP), dNTPs, which include detectable labels such as luminescent labels or markers (eg, fluorophores). The nucleotide may include any subunit that can be incorporated into a growing nucleic acid chain. Such a subunit may be A, C, G, T or U or any other subunit specific for one or more complementary A, C, G, T or U, or complementary to a purine (i.e. A or G, or a variant thereof) or pyrimidine (ie C, T or U, or a variant thereof). In some examples, the nucleic acid is deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or derivatives or variants thereof. The nucleic acid may be single-stranded or double-stranded. The nucleic acid molecule may be linear, curved or circular, or any combination thereof.

In the context of the present invention, the terms nucleic acid molecule, nucleic acid sequence, nucleic acid fragment, oligonucleotide and polynucleotide generally refer to a polynucleotide, which can be of varying length, such as either deoxyribonucleotides or ribonucleotides (RNA) or analogs thereof. The nucleic acid molecule may have a length of at least about 5 bases, 10 bases, 20 bases, 30 bases, 40 bases, 50 bases, 60 bases, 70 bases, 80 bases, 90 bases, 100 bases, 110 bases, 120 bases, 130 bases , 140 bases, 150 bases, 160 bases, 170 bases, 180 bases, 190 bases, 200 bases, 300 bases, 400 bases, 500 bases, 1 thousand base pairs (kb), 2 kb ., 3 kb, 4 kb, 5 kb, 10 kb. or 50 kb. or may have any number of bases between any two of the above values. An oligonucleotide generally consists of a specific sequence of four nucleotide bases: adenine (A), cytosine (C), guanine (G) and thymine (T) (uracil (U) instead of thymine (T) when the polynucleotide is RNA). Thus, the terms nucleic acid molecule, nucleic acid sequence, nucleic acid fragment, oligonucleotide, and polynucleotide are at least in part intended to be an alphabetical representation of a polynucleotide molecule. Alternatively, the terms may be applied to the polynucleotide molecule itself. This alphabetical representation can be entered into databases on a central processing unit and/or used for bioinformatics applications such as functional genomics and homology searching. Oligonucleotides may include one or more non-standard nucleotides, nucleotide analogues and/or modified nucleotides.

In the context of the present invention, the term sample generally refers to a biological sample. Examples of biological samples include nucleic acid molecules, amino acids, polypeptides, proteins, carbohydrates, fats, or viruses. In an example, the biological sample is a nucleic acid sample including one or more nucleic acid molecules. The biological sample may contain or be derived from blood samples, saliva samples, swab samples, cell samples, or tissue samples. The nucleic acid molecules may be cell-free nucleic acid molecules, such as cell-free DNA (cfRNA) or cell-free RNA (cfRNA). Nucleic acid molecules can be obtained from a variety of sources, including

- 23 046341 sources from humans, mammals (eg dogs), non-human mammals, apes, macaques, chimpanzees, reptiles, amphibians or birds. In addition, samples may be extracted from a variety of animal fluids, including, but not limited to, body fluid samples such as blood, serum, plasma, vitreous, sputum, urine, tears, sweat, saliva, semen, mucosal secretions, mucus, spinal fluid fluid, cerebrospinal fluid (CSF), pleural fluid, peritoneal fluid, amniotic fluid, lymph, etc. Biological samples can be collected or obtained from subjects using an ethylenediaminetetraacetic acid (EDTA) collection tube, a cell-free RNA collection tube (eg, Streck), or a cell-free DNA collection tube (eg, Streck). Biological samples can be obtained from whole blood samples by fractionation. Biological samples or derivatives thereof may contain cells. For example, the biological sample may be a blood sample or derivative thereof (eg, blood collected using a collection tube or blood drop) or a cell or tissue sample (eg, a swab).

In the context of the present invention, the term whole blood generally refers to a blood sample that has not been separated into subcomponents (eg, by centrifugation). The whole blood of the blood sample may contain cNA and/or germline DNA. Whole blood DNA (which may contain cf DNA and/or germline DNA) can be extracted from the blood sample. Whole blood DNA sequencing reads (which may contain cf DNA sequencing reads and/or germline DNA sequencing reads) can be extracted from whole blood DNA.

In an aspect, the present invention relates to a computerized method for assessing the degree of relatedness between two individuals in a diploid population, comprising: (a) obtaining haplotype data for a population of test individuals, wherein the haplotype data comprises a plurality of genetic markers common to the population of test individuals, (b) separating haplotype data data into segments based on multiple genetic markers, (c) for each population of test individuals: (i) matching, based on multiple genetic markers, segments of haplotype data that are identical in ancestry, between a first individual and a second individual within the population of test individuals, each of the matched segments has a first size that is at least a predetermined threshold size, and contains at least a predetermined number of genetic markers, (ii) for each of the matched segments between the first individual and the second individual: dividing the matched segment into a plurality of discrete genomic intervals, estimating each of a plurality of discrete genomic intervals based on (i) the degree of homozygosity of a discrete genomic interval in a first individual or a second individual, or (ii) the degree of pairwise matching of a discrete genomic interval between a first individual and a second individual, thereby obtaining a plurality of estimates , adjusting the plurality of scores based on the consistency of the plurality of scores, thereby obtaining a plurality of adjusted scores, and assigning a plurality of weights to the plurality of discrete genomic intervals based on the plurality of adjusted discrete genomic bin scores, and (iii) calculating a weighted sum for the plurality of discrete genomic bins of the matched segment based on a plurality of adjusted scores and a plurality of weights, and (d) estimating the degree of relatedness between the first individual and the second individual based on the weighted sums of the matched segments.

In fig. 1 illustrates an exemplary method 100 for estimating the degree of relatedness between two individuals of a diploid population, according to some embodiments. At step 102, method 100 may include obtaining haplotype data for a population of test individuals. For example, haplotype data may contain multiple genetic markers common to a population of individuals being tested. Next, at step 104, method 100 may include dividing the haplotype data into segments based on a plurality of genetic markers. Then, at step 106, for each population of test individuals, method 100 may include matching, based on the plurality of genetic markers, segments of haplotype data that are identical in ancestry between a first individual and a second individual within the population of test individuals. For example, each of the matched segments may have a first size that is at least a predetermined threshold size, and contain at least a predetermined number of genetic markers. Then, at step 108, for each population of test individuals, method 100 may include, for each of the matched segments between the first individual and the second individual: (a) dividing the matched segment into a plurality of discrete genomic intervals, (b) estimating each of the plurality of discrete genomic intervals, thereby obtaining a plurality of scores, (c) adjusting the plurality of scores, thereby obtaining a plurality of adjusted scores, and (d) assigning a plurality of weights to the plurality of discrete genomic intervals. In some embodiments, each of the multiple discrete genomic intervals is scored based on (i) the degree of homozygosity match of the discrete genomic interval in the first individual or the second

- 24,046,341 individuals, and/or (ii) the degree of pairwise matching of a discrete genomic interval between the first individual and the second individual. In some embodiments, the plurality of estimates are adjusted based on the consistency of the plurality of estimates. In some embodiments, a plurality of weights are assigned to a plurality of discrete genomic intervals based on a plurality of adjusted discrete genomic interval scores.

In some embodiments, a method for estimating the degree of relatedness between two individuals of a diploid population may involve calculating a relatedness ratio (COR) between pairs of individuals (eg, dogs) within the population. Based on the calculated COR values, a list can be created for each individual in the population that contains a list of the individual's closest genetic relatives. For example, such a list of relatives can be sorted in descending COR order. Additionally, social network connections can be derived from COR between two related individuals (e.g., related dog owners).

Other ways to calculate COR values for dogs can use pedigrees, which can consist of a family tree showing the dog's ancestors and other relatives. Such methods may be limited at least in that they cannot be used to estimate COR for dogs without known pedigree information (eg, rescued animals). Additionally, using pedigrees to calculate COR can also be problematic because accuracy may not be consistently high, pedigrees may only provide ancestry information for a few generations, and pedigrees can be used to calculate only expected relatedness (e.g., the amount of DNA that is expected to be is common to two individuals based on their relationship). In general, the actual relatedness of two individuals (eg dogs) may also depend on random segregation and transmission of DNA along the family tree, and accurate calculations of relatedness may require identifying and counting shared DNA regions between animals. Although some methods can be used to phase genotypic data and identify common sites between individuals, it may be statistically difficult to distinguish sites that are truly ancestrally identical from those that are statistical artifacts, to properly weight sites in COR calculations, and to handle noise in data from structural variants and missing or poor-quality markers.

Additionally, approaches to calculating COR between dogs prove to be more complex compared to humans due to the much higher frequency of inbreeding in dogs than in typical human populations. Recognizing this need, the present invention provides methods and systems for determining COR (eg, between dogs) that take into account inbreeding when determining COR between two individuals. For example, a two-sib pair in an outbred population may have a COR of approximately 0.5 compared to another two-sib pair in an inbred population, which may have a COR of 0.7 or more. Using the methods and systems of the present invention, it is possible to determine the type of relationship between two individuals even in cases where the individuals are part of inbred populations. For example, instead of determining that each pair of two dogs with a COR of 0.5 has a full-sib-like relationship, the methods and systems of the present invention can be used to distinguish the first instance in which a particular pair of dogs with a COR 0.5 are full siblings, compared to the second case in which another pair of dogs with a COR of 0.5 are related as uncle/aunt-nephew/niece in the inbred line.

In addition, the methods and systems of the present invention can be used to add a valuable social component to the results of canine genetic testing. By allowing dog owners to connect directly with each other based on their pet's relatedness, they can receive more information from related dog owners about their own dog's history (for example, this may be important or desirable information for owners of adopted dogs) and the potential health risks associated with complex traits that are not part of the genetic analysis (for example, if the dog's sibling has a history of cancer, the dog may be at higher risk of developing cancer).

The methods and systems of the present invention may use one or more algorithms to determine the degree of relatedness (COR) between two individuals (eg, a diploid population). For example, the diploid population may be a mammalian population (eg, a canine population, a feline population, a sporting animal population, or a rodent population). In some embodiments, the canid population is a population of dogs. In some embodiments, the dog population comprises one or more dog breeds selected from the group consisting of: Affenpinscher, Afghan Hound, Africanis, Aidi, Airedale Terrier, Akbash, Akita Inu, Bully Cutta, Spanish Alano, Alapaha Bulldog, Alaskan Klee Kai , Alaskan Malamute, Alan, Alopekis, Alpine Dachshund, American Shepherd, American Akita, American Bulldog, American Cocker Spaniel, American Eskimo Dog, American Foxhound, American

- 25 046341 hairless terrier, American Mastiff, American Pit Bull Terrier, American Staffordshire Terrier, American Water Spaniel, Anatolian Shepherd, Anglo-French Miniature Hound, Appenzeller Mountain Dog, Dogo Argentino, Ariège Braque, Ariège Hound, Armant, Artois Hound, Australian Bulldog, Australian Cattle Dog, Australian Kelpie, Australian Shepherd, Australian Silky Terrier, Australian Short-Tailed Cattle Dog, Australian Terrier, Austrian Hound, Austrian Pinscher, Azawakh, Baharwal Dog, Barbet, African Non-Barking Dog, Basque Shepherd Dog, Artesian-Normandy Basset, Blue Gascon Basset, Red Breton Basset, Great Vendeen Basset Griffon, Small Vendeen Basset Griffon, Bavarian Mountain Hound, Beagle, Beagle Harrier, Bearded Collie, Beauceron, Bedlington Terrier, Belgian Shepherd, Belgian Shepherd (Groenendael), Belgian Shepherd (Laekenois), Belgian Shepherd (Malinois), Belgian Shepherd (Tervuren), Bergamasco Shepherd, White Swiss Shepherd, Picardy Shepherd, Bernese Hound, Bernese Mountain Dog, Bichon Frize, Biyi, Bisben, Black and Tan Coonhound, Ruffed Norwegian Elk Husky, Bullenbeiser, Norwegian Black Elkhound , Black Russian Terrier, Yellow with Black Mask Southern Hound, Greater Blue Gascony Hound, Small Blue Gascony Hound, Bloodhound, Blue Lacy, Blue Pol Terrier, Blue Spotted Coonhound, Boerboel, Czech Cattle Dog, Bolognese, Border Collie , Border Terrier, Russian Greyhound, Bosnian Wirehaired Hound, Boston Terrier, Bouvier des Ardennes, Bouvier des Flandres, Boxer, Boykin Spaniel, Italian Braque, Auvergne Pointing Dog, Bourbon Braque, Dupuis Braque, French Braque, Saint-Germain Braque, Brazilian Terrier, Briard, Middle Vendeen Griffon, Breton Spaniel, Broholmer, Jurassic Hound, Bukovinian Shepherd Dog, Bull and Terriers, Bull Terrier, Bull Terrier (Miniature), Bullmastiff, Bully Cutta, Cairn Terrier, Canaan Dog, Canadian Eskimo Dog, Canadian Pointer, Cane -Corso, Portuguese Shepherd Dog, Cao de Castro Laboreiro, Azores Cattle Dog, Carolina Dog, Romanian Carpathian Shepherd Dog, Catahoula Leopard Dog, Catalan Shepherd Dog, Caucasian Shepherd Dog, Cavalier King Charles Spaniel, Central Asian Shepherd Dog, Czech Fousek, Czech Terrier, Polish Greyhound, Chesapeake Bay Retriever, Gray and Blue Hound, French Black and Pinto Hound, French White and Black Dog, French Tricolor Hound, Chihuahua, Chilean Fox Terrier, Chinese Chongqing, Chinese Crested Dog, Chinese Imperial Dog, Chinook, Chippiparai, Chow -chow, Uruguayan Cimarron, Black Shepherd Dog, Cirneco del Etna, Clumber Spaniel, Longhaired Collie, Shorthaired Collie, Combay, Cordoba Fighting Dog, Coton de Tulear, Cretan Hound, Croatian Shepherd Dog, Cumberland Shepherd Dog, Curly-Coated Retriever, Czechoslovakian Wolfdog , Dachshund, Dalmatian, Dandie Dinmont Terrier, Danish-Swedish Farm Dog, Dingo, Doberman, Dogue de Bordeaux, Dogue de Cuba, Guatemalan Bull Terrier, Miniature Dogo, Drentic Partridge Dog, Drever, Dunker, Dutch Shepherd, Dutch Smoushond, East European Shepherd, East Siberian Laika, Elo, English Cocker Spaniel, English Coon Hound, English Foxhound, English Mastiff, Pointer, English Setter, English Shepherd, English Springer Spaniel, English Toy Terrier, English Water Spaniel, English White Terrier, Entlebucher Mountain Dog, Blue Picardy Spaniel, Estonian Hound, Estrela Shepherd Dog, Eurasier, Field Spaniel, Brazilian Fila, Fido, Finnish Hound, Finnish Lapphund, Finnish Spitz, Flat-Coated Retriever, Taiwanese Dog, Smooth Fox Terrier, Wire Fox Terrier, Breton Epagnole, French Bulldog, French Spaniel, Spanish Galgo, Langhaar, German Pinscher, German Shepherd, Shorthaired Pointer, German Wachtelhund, German Spitz, Drathaar, Giant Schnauzer, Glen of Imaal Terrier, Golden Retriever, Scottish Setter, Greater Anglo-French Black and Piebald Hound, Greater Anglo-French Red and Piebald hound, Greater Anglo-French Tricolor Hound, Greater Griffon Vendean, Puerto Rican Mastiff, Great Dane, Great Pyrenees Mountain Dog, Greater Swiss Mountain Dog, Greenland Dog, Greyhound, Blue Gascony Griffon, Belgian Griffon, Breton Tawny Griffon, Nivernes Griffon, Gul-dong, Gul- terr, Indian Hare Dog, Hamilton Hound, Hanoverian Hound, Harrier, Havanese Bichon, Hawaiian Poi Dog, Himalayan Sheepdog, Hokkaido, Hortaya Greyhound, Hovawart, Transylvanian Hound, New Zealand Sheepdog, Hugenhund, Podenco Ibicenco, Icelandic Dog, Indian Spitz, Irish Bull Terrier, Irish Red and White Setter, Irish Red Setter, Irish Staffordshire Bull Terrier, Irish Terrier, Irish Water Spaniel, Irish Wolfhound, Istrian Shorthaired Hound, Istrian Wirehaired Hound, Italian Greyhound, Jack Russell Terrier, German Jagdterrier, Swedish Elkhound, Japanese Chin, Japanese Spitz, Japanese Terrier, Jonangi, Kaikadi, Kai, Kangal, Kanny, Karakachan Dog, Karelian Bear Dog, Krash Shepherd Dog, Keeshond, Kerry Beagle, Kerry Blue Terrier, Cavalier King Charles Spaniel, Royal Shepherd, Kintamani, Kishu, Komondor, Kooikerhondje, Kuli, Korean Chindo, Korean Mastiff, Cromforlander, Kunming Shepherd Dog, Kuri, Kuvasz, Kai Leo, Labrador Husky, Labrador Retriever, Lagotto Romagnolo, Lakeland Terrier, Lancashire Heeler, Landseer, Lapp Reindeer Dog, Leonberger, Lhasian APSO, Lithuanian messenger, long-haired Wippet, Brindiz Fighting Dog, Small Lion Dog, Hungarian Agar, Bolshaya Wood Hound, Maltese Bologna, Manchester Terrier, Mrammome-Abrutsky Shepherd Office, Maknab, Ksolitzkuyuventi, Miniature Australian Shepherd. Atyurian Pinscher, Miniature Schnauzer, Mi

- 26 046341 miniature Siberian Husky, Romanian Mioritic Shepherd Dog, Molossian Dog, Montenegrin Mountain Hound, Moscow Watchdog, Moscow Diver,

Mountain Hound, Kentucky Mountain Hound, Mucoochies, Moody, Caravan Hound, Greater Munsterlander, Lesser Munsterlander, Murray River Curly Coated Retriever, Neapolitan Mastiff, Newfoundland, New Guinea Singing Dog, Norfolk Spaniel, Norfolk Terrier, Norbotten Husky, Northern Beagle, Northern Inuit dog, Norwegian Buhund, Norwegian Gray Elkhound, Norwegian Lundehund, Norwich Terrier, Nova Scotia Retriever, Old Danish Pointer, Bobtail, Old English Bulldog, Black and Tan Terrier, Old German Shepherd, Old English Bulldog, Otterhound, Navarre Pachon, Paisley Terrier, Continent al toy -spaniel, Parson Russell Terrier, Paterdale Terrier, Pekingese, Dogo Canario, Ca-de-bo, Peruvian Inca Orchid, Phalene, Pharaoh Hound, Picardy Spaniel, Plott Hound, Podenco Canario, Pointer, Polish Ogar, Polish Hound , Polish Lowland Shepherd, Polish Podgalian Shepherd, Pomeranian Spitz, Pont-Audemer Spaniel, Poodle, Porcelain Hound, Portuguese Podengo, Portuguese Pointer, Portuguese Water Dog, Prague Rat, Poodle Pointer, Pug, Puli, Pumi, Pungsan, Pyrenean Mastiff, Pyrenean Shepherd, Rafeiro do Alentejo, Rajapalayam, Rampur Greyhound, Brazilian Hunting Hound, Andalusian Ratonero, Rat Terrier, Red Coonhound, Rhodesian Ridgeback, Rottweiler, Russian Hunting Spaniel, Russian Toy, Russian-European Laika, Jack Russell Terrier, Wolfdog Saarloos dog, Spanish Hound, Persian Shepherd, Iranian Mazandarani Shepherd, Sakhalin Husky, Saluki, Samoyed, Sapsari, Sharplaninac, Chapendois, Schillerstevare, Schipperke, Old Croatian Greyhound, Giant Schnauzer, Miniature Schnauzer, Medium Schnauzer, Swiss Hound, Small Swiss Hound, Longhair collie , Deerhound, Scottish Terrier, Sealyham Terrier, Italian Shorthair Hound, Siberian Sled Dog, Serbian Hound, Serbian Tricolor Hound, Charley, Sheltie, Shiba Inu, Shih Tzu, Shikoku, Shiloh Shepherd, Lapdog, Siberian Husky, Silky Windhound, Sinhala Hound, Skye Terrier, Slugi, Slovak Chuvach, Slovak Rough-haired Stavach, Slovak Copov, Smaland Hound, Small Greek Dog, Irish Soft Coated Wheaten Terrier, South Russian Shepherd Dog, Southern Hound, Spanish Mastiff, Spanish Water Dog, Spinone, Lucas Terrier, St. Bernard, Small Newfoundland, Stabyhune, Staffordshire Bull Terrier, Stephen's Hound, Austrian Broodstock, Sussex Spaniel, Swedish Lapphund, Swedish Vallhund, Swedish Beagle, Tahltan Bear Dog, Taigan, Tamaskan Dog, Teddy Roosevelt Terrier, Telomian, Tenterfield Terrier, Thai Bangku , Thai Ridgeback, Tibetan Mastiff, Tibetan Spaniel, Tibetan Terrier, Tornjak, Tosa Inu, Toy Bulldog, Toy Fox Terrier, Toy Manchester Terrier, Treeing Cure, Treeing Walker Coonhound, Tyrolean Hound, Utonagan, Hungarian Vizsla, Volpino Italiano, Weimaraner, Cardigan Welsh Corgi, Pembroke Welsh Corgi, Welsh Sheepdog, Welsh Springer Spaniel, Welsh Terrier, West Highland White Terrier, West Siberian Husky, Westphalian Dachshund Hound, Wetterhun, Whippet, White English Bulldog, White Swiss Shepherd , Hungarian Wirehaired Vizsla, Griffon Korthals and Yorkshire Terrier. In some embodiments, the population includes one or more purebred dogs (eg, having one breed type) or one or more mixed breed dogs (eg, having multiple breed types). In some embodiments, the population is a population of mixed breed dogs having DNA from any number (eg, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more than 10) or combinations of purebred dogs.

In some embodiments, % relatedness may be calculated as the proportion of homologous alleles shared by 2 individuals due to identity by descent from a common ancestor, and may be equal to twice the relatedness factor f _xy , assuming the individual is not inbred. The relatedness coefficient can reflect the probability that 2 alleles taken from the same locus in 2 individuals are identical in origin. It may be equal to the coefficient of inbreeding for the potential offspring between two individuals x and y. However, in the case where two henna individuals are inbred, the assumption that r _xy = 2*f _xy is unacceptable.

In some embodiments, a method for determining the degree of relatedness (COR) between two individuals is called WOOPHS (weighted observation of common pairwise haplotype). In some embodiments, WOOPHS is configured to calculate a relatedness ratio (COR) estimate between the test individual and other individuals in the genetic database. In some embodiments, WOOPHS is configured to calculate an estimate of the degree of inbreeding of the estimated offspring of two individuals. The method may include obtaining genotypic data as input. For example, genotypic data can be obtained by analyzing biological samples obtained from a population of test individuals. In some embodiments, the biological samples comprise blood samples, saliva samples, swab samples, cell samples (eg, an oral or buccal swab), or tissue samples. In some embodiments, the assessment involves sequencing biological samples or derivatives thereof to obtain genotypic data. For example, sequencing reads can be obtained from biological samples using any suitable sequencing method. Sequé method

- 27 046341 sequencing may be a first generation sequencing method, such as Maxam-Gilbert or Sanger sequencing, or a high throughput sequencing method (eg, next generation sequencing or NGS). A high-throughput sequencing method can sequence at least about 10,000, 100,000, 1 million, 10 million, 100 million, 1 billion, or more polynucleotide molecules simultaneously (or substantially simultaneously). Sequencing methods may include, but are not limited to: pyrosequencing, sequencing by synthesis, single molecule sequencing, nanopore sequencing, semiconductor sequencing, sequencing by ligation, sequencing by hybridization, digital gene expression (Helicos), massively parallel sequencing, e.g. Helicos, clonal single molecule array ( Solexa/nlumina), sequencing using PacBio, SOLiD, Ion Torrent or Nanopore platforms.

In some embodiments, the sequencing involves whole genome sequencing (WGS). Sequencing can be performed at a depth sufficient to obtain the desired haplotype with the desired characteristics (eg, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), or area under the curve (AUC) plot of sensitivity versus false positive rate conclusions (ROC)). In some embodiments, sequencing is performed at a depth of about 20X, about 30X, about 40X, about 50X, about 60X, about 70X, about 80X, about 90X, about 100X, about 150X, about 200X, about 250X, about 300X, about 350X, about 400X, about 450X, about 500X or about more than 500X. In some embodiments, sequencing is performed in a low pass manner, e.g., at a depth of no more than about 12X, no more than about 11X, no more than about 10X, no more than about 9X, no more than about 8X, no more than about 7X, no more than about 6X, no more than about 5X, no more than about 4X, no more than about 3.5X, no more than about 3X, no more than about 2.5X, no more than about 2X, no more than about 1.5X or no more than about 1X.

In some embodiments, the sequencing reads may be aligned to a reference genome. The reference genome may comprise at least a portion of a genome (eg, a dog genome or a human genome). The reference genome may contain a complete genome (eg, a complete dog genome or a complete human genome). The reference genome may comprise a database containing a plurality of genomic regions that correspond to coding and/or non-coding genomic regions of the genome. The database may contain multiple genomic regions that correspond to IBD coding and/or non-coding genomic regions of the genome, such as single nucleotide variants (SNVs), single nucleotide polymorphisms (SNPs), copy number variants (CNVs), insertions or deletions (indels), and fusion genes . Alignment can be performed using the Burrows-Wheeler algorithm or another alignment algorithm.

In some embodiments, sequencing read counts may be obtained for each of a plurality of genomic regions. Quantitative measures of sequencing reads, such as the number of DNA sequencing reads that align to a given genomic region, can be generated. Sequencing reads in which some or all of the sequencing reads match a given genomic region can be counted as a score for that genomic region. In some embodiments, genomic regions may contain genetic markers, such as IBD markers. Patterns of specific and nonspecific regions of the genome can indicate relatedness between individuals.

In some embodiments, measuring multiple DNA sequencing reads involves taking binding measurements of multiple DNA molecules in each of a plurality of genomic regions. In some embodiments, performing binding measurements involves analyzing a plurality of DNA molecules using probes that are selective for at least a portion of a plurality of genomic regions in a plurality of DNA molecules. In some embodiments, the probes are nucleic acid molecules whose sequences are complementary to the nucleic acid sequences of a plurality of genomic regions. In some embodiments, the nucleic acids are primers or enrichment sequences. In some embodiments, the analysis involves the use of array hybridization, polymerase chain reaction (PCR), or nucleic acid sequencing.

In some embodiments, the method further comprises enriching a plurality of DNA molecules in at least a portion of a plurality of genomic regions. In some embodiments, the enrichment involves amplifying multiple DNA molecules. For example, multiple DNA molecules can be amplified by selective amplification (eg, using a set of primers or probes containing nucleic acid molecules whose sequences are complementary to the nucleic acid sequences of multiple genomic regions). Alternatively or in combination, multiple DNA molecules can be amplified by universal amplification (eg, using universal primers). In some embodiments, the enrichment involves selectively separating at least

- 28 046341 at least parts (for example, mononucleotides and/or dinucleotides) of a plurality of DNA molecules.

In some embodiments, the DNA sequencing read count may be normalized or adjusted. For example, DNA sequencing read counts may be normalized and/or adjusted for known sequencing and library production biases and/or known sequencing and library production biases.

In some embodiments, a subset of quantitative measurements or counts may be filtered, for example, based on a quality score of the sequencing reads.

A method for determining the degree of relatedness (COR) between two individuals may involve the use of a phasing algorithm to process the genotypic data to obtain haplotype data. For example, this can be achieved by dividing the observed genotypes for a large number of single nucleotide polymorphisms (SNPs) shared by individuals along a chromosome into series of haplotypes (e.g., alleles that are likely to occur on the same parental chromosome and are inherited together). For example, the haplotype phasing algorithm may provide a reference-based haplotype phasing algorithm (eg, comprising a hidden Markov model search). In some embodiments, the reference-based haplotype phasing algorithm comprises the Eagle1 algorithm, the Eagle2 algorithm, the PHASE algorithm, the fastPHASE algorithm, the BEAGLE algorithm, the Findhap algorithm, the Impute algorithm, the Flmpute algorithm, the Alphalmpute algorithm, the IMPUTE2 algorithm, the MaCH algorithm, the SHAPEIT1 algorithm, the SHAPEIT2 algorithm, SHAPEIT3 algorithm, SHAPEIT4 algorithm or a combination of both. Alternatively, the haplotype phasing algorithm may include a cohort-based haplotype phasing algorithm.

In some embodiments, a method for determining the degree of relatedness (COR) between two individuals involves processing haplotype data to identify haplotype matches that are identical by descent (IBD) between multiple individuals in a population. For example, IBD haplotypes can be identified between all individuals in a population or between a set of test individuals and a set of reference individuals. In some embodiments, haplotype matches are identified when they meet a threshold size (e.g., greater than a threshold size, such as about 100 thousand base pairs (kb), about 200 kb, about 300 kb. bp, about 400 kbp, about 500 kbp, about 600 kbp, about 700 kbp, about 800 kbp, about 900 kb or about 1000 kb) and/or span a minimum threshold number of genetic markers (e.g., about 30, about 40, about 50, about 60, about 70, about 80, about 90 or about 100 different genetic markers). Identification of haplotype matches can be performed using the GERMLINE algorithm (e.g., with a haploid tag), the PLINK algorithm, the PREST algorithm, the Random Projection Algorithm for IBD Detection (RaPID), the Rapid IBD Shared Haplotype Discovery (FISHR) algorithm, the enhanced Identity by Descent (IBD) algorithm ), fastIBD algorithm, KING algorithm, HaploScore algorithm, TRUFFLE algorithm or a combination thereof.

In some embodiments, a method for determining the degree of relatedness (COR) between two individuals involves using a matching post-processing algorithm to account for matching haplotypes between two individual dogs (e.g., dog A and dog B), including two haplotypes per dog (e.g., A.0, A.1, B.0 and B.1) and dividing them into discrete genomic intervals.

In some embodiments, a method for determining the degree of relatedness (COR) between two individuals involves, for each discrete genomic interval, estimating the states of all possible types of haplotype matches in the discrete genomic interval and counting the number of such matches (yielding one or two estimates per genomic interval). For example, matches may include homozygosity matches within one dog (eg, A.0|A.1) or pairwise matches between haplotypes in two dogs (eg, A.0|B.1). In some embodiments, discrete genomic intervals are scored based on (i) the degree of homozygosity match of the discrete genomic interval in the first individual or the second individual and (ii) the degree of pairwise matching of the discrete genomic interval between the first individual and the second individual, thereby obtaining a plurality of match scores by homozygosity and multiple pairwise match scores.

In some embodiments, a method for determining the degree of relatedness of relationship (COR) between two individuals involves adjusting a plurality of pairwise match scores based on the consistency of the pairwise match score with a corresponding homozygosity match score, thereby obtaining a plurality of adjusted pairwise match scores. For example, homozygosity matches can be used to correct or adjust the estimate of discrete genomic intervals with inconsistent or impossible numbers of pairwise matches. Due to the inherent uncertainty of haplotype phase, haplotype matches within a dog may be more likely to represent true IBD than haplotype matches between two dogs. Therefore, WOOPHS may involve the use of homozygosity matches to correct discrete genomic intervals with inconsistent or impossible

- 29 046341 number of pairwise matches. For example, true matches may only occur in a limited number of cases. For example, if one and only one dog among a pair of dogs (A and B) is homozygous (for example, A.0=A,1), then there can only be 2 or 4 pairwise matches (for example, {A.0=A,1= B.0, or A.0=A,1=B,1} or {A.0=A,1=B.0=B,1}). This adjustment can be made to adjust the estimate of all discrete intervals to match the possible states.

In some embodiments, a method for determining degree of relatedness (COR) between two individuals involves assigning a plurality of weights to a plurality of discrete genomic intervals based on a plurality of identity states for two alleles in two diploid individuals, and calculating a weighted sum of the genomic interval scores to produce a COR and/or COK . For example, weights can be determined for each discrete genomic interval for its partial contribution to similarity and/or relatedness (eg, relative to the coefficient of relatedness (COR) and/or coefficient of relatedness (COK)). This can be carried out in accordance with the approach described, for example, in Hedrick and Lacy, J. Hered, Measuring relatedness between inbred individuals, 2015 Jan-Feb, 106(1): 20-5, which is incorporated herein by reference at in its entirety. For example, a plurality of identity states may include identity states selected from a table, and a plurality of weights may be assigned based on a plurality of relatedness contributions rxy, as listed in the table. In some embodiments, the degree of relationship comprises a relationship coefficient. For example, a weighted sum can be calculated over many discrete genomic intervals of a matched segment, such that 41 + 47 + (0.75x43) + (0.5x48) ^r xv ⁼ --------------- ------------weighted sum expressed by: ^{L L}

In some embodiments, the degree of relatedness comprises a relatedness coefficient. For example, a weighted sum can be calculated over multiple discrete genomic intervals of a matched segment, such that the weighted sum is expressed by:

, _ 41 + (0.5 x (43 +47)) + (0.25 x 48)

Kxy - I identity states for 2 alleles in 2 diploid individuals, where states identical by origin are connected by the line identttch+gpoti Probability Contribution to Contribution to

D ₂ 0 0

Dz 1/23/4

D ₄ 00

D ₅ 1/23/4

Δ, 1/21

D ₈ 1/4 1/2

D, 0 0 alleles in individual x represent a and b, a 2 alleles in individual y represent co

- 30 046341 fight with and d. Horizontal lines indicate an individual's homozygosity for ancestry identity. For given identity states, the values of Δ ₁ give the probability of identity by descent, and the right 2 columns indicate the contribution to the inbreeding coefficient f _xy of the offspring of individuals x and y and the relatedness % between individuals x and y.

To summarize the above, a method for determining the degree of relatedness of relationship (COR) between two individuals may involve identifying IBD regions of phased data, estimating discrete genomic intervals based on the numbers of observed pairwise haplotype matches, adjusting the number of pairwise matches based on homozygosity states at the discrete genomic intervals, assigning weights to discrete areas and calculating the weighted sum to obtain estimates of the relatedness coefficient and/or relatedness coefficient.

Computing systems.

The present invention relates to computer systems that are programmed to perform the methods of the present invention. In fig. 2 illustrates a computing system 201 that is programmed or otherwise configured to, for example, obtain haplotype data for a population of test individuals (e.g., where the haplotype data contains a plurality of genetic markers common to the population of test individuals), partition the haplotype data into segments based on the set genetic markers, matching, based on a plurality of genetic markers, segments of haplotype data that are identical in ancestry, between a first individual and a second individual within a population of test individuals (e.g., where each of the matched segments has a first size that is at least a predetermined threshold size , and contains at least a predetermined number of genetic markers), dividing the matched segments into a plurality of discrete genomic intervals, estimating the discrete genomic intervals based on (I) the degree of homozygosity of the discrete genomic interval in the first individual or the second individual, or (II) the degree of pairwise matching a discrete genomic interval between a first individual and a second individual, thereby obtaining a plurality of scores, adjusting the plurality of scores based on the consistency of the plurality of scores, thereby obtaining a plurality of adjusted scores, assigning a plurality of weights to the plurality of discrete genomic spans (e.g., based on the plurality of adjusted discrete scores genomic intervals), calculating a weighted sum for a plurality of discrete genomic intervals of a matched segment (eg, based on a plurality of adjusted scores and a plurality of weights), and estimating the degree of relatedness between a first individual and a second individual (eg, based on a weighted sum of the matched segments). The computing system 201 may handle various aspects of the analysis, calculation and acquisition according to the present invention, such as, for example, obtaining haplotype data for a population of test individuals (for example, when the haplotype data contains multiple genetic markers common to the population of test individuals), dividing the haplotype data into segments based on multiple genetic markers, matching, based on multiple genetic markers, segments of haplotype data that are identical in ancestry between a first individual and a second individual within a population of test individuals (e.g., where each of the matched segments has a first size that is at least a predetermined threshold size, and contains at least a predetermined number of genetic markers), dividing the matched segments into a plurality of discrete genomic intervals, estimating the discrete genomic intervals based on (I) the degree of homozygosity match of the discrete genomic interval in the first individual or the second individual, or ( ii) the degree of pairwise matching of a discrete genomic interval between a first individual and a second individual, thereby obtaining a plurality of scores, adjusting the plurality of scores based on the consistency of the plurality of scores, thereby obtaining a plurality of adjusted scores, assigning a plurality of weights to the plurality of discrete genomic intervals (e.g., based on a set of adjusted discrete genomic interval scores), calculating a weighted sum for the plurality of discrete genomic intervals of a matched segment (e.g., based on a plurality of adjusted scores and a plurality of weights), and estimating the degree of relatedness between a first individual and a second individual (e.g., based on a weighted sum of matched segments). The computing system 201 may be a user's electronic device or a computing system remotely located to the electronic device. The electronic device may be a mobile electronic device.

The computing system 201 includes a central processing unit (CPU, also processor and computer processor herein) 205, which may be a single-core or multi-core processor, or a plurality of processors for parallel processing. The computing system 201 also includes a memory or memory cell 210 (e.g., random access memory, read only memory, flash memory), an electronic storage unit 215 (e.g., a hard disk), a communications interface

- 31 046341 face 220 (eg, network adapter) for communicating with one or more other systems and peripheral devices 225, such as cache memory, other memory, data storage, and/or electronic display adapters. The memory 210, storage unit 215, interface 220, and peripherals 225 are connected to the CPU 205 via a communication bus (solid lines) such as a motherboard. The storage unit 215 may be a data storage unit (or a data repository) for storing data. The computing system 201 may be operatively connected to a computer network 230 via a communications interface 220. The network 230 may be the Internet, an intranet and/or an external network, or an intranet and/or an external network that are connected to the Internet. Network 230 is in some cases a telecommunications network and/or a data network. Network 230 may include one or more computer servers that may provide distributed computing capabilities, such as cloud computing. For example, one or more computer servers may provide cloud computing capability on network 230 (cloud) to perform various aspects of the analysis, calculation, and acquisition according to the present invention, such as, for example, obtaining haplotype data for a population of test individuals (e.g., where the haplotype data comprises multiple genetic markers common to a population of test individuals), dividing haplotype data into segments based on multiple genetic markers, matching, based on multiple genetic markers, segments of haplotype data that are identical in ancestry between a first individual and a second individual within a population of test individuals (e.g. wherein each of the matched segments has a first size that is at least a predetermined threshold size, and contains at least a predetermined number of genetic markers), dividing the matched segments into a plurality of discrete genomic intervals, estimating the discrete genomic intervals based on (i) the degree of homozygosity matching of a discrete genomic interval in a first individual or a second individual, or (ii) the degree of pairwise matching of a discrete genomic interval between a first individual and a second individual, thereby obtaining a plurality of scores, adjusting the plurality of scores based on the agreement of the plurality of scores, thereby yielding a plurality adjusted scores, assigning a plurality of weights to a plurality of discrete genomic intervals (e.g., based on a plurality of adjusted discrete genomic interval scores), calculating a weighted sum for a plurality of discrete genomic intervals of a matched segment (e.g., based on a plurality of adjusted scores and a plurality of weights), and estimating the degree of relatedness relationship between the first individual and the second individual (eg, based on weighted sums of matched segments). Such cloud computing may be provided by cloud computing platforms such as, for example, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform and IBM cloud. Network 230, in some cases aided by computing system 201, may implement a peer-to-peer network that may allow devices connected to computing system 201 to behave as a client or server.

CPU 205 may execute a sequence of computer-readable instructions that may be embodied in a program or software. The instructions may be stored in a memory location, such as memory 210. The instructions may be directed to the CPU 205, which may subsequently program or otherwise configure the CPU 205 to perform the methods of the present invention. Examples of operations performed by the CPU 205 may include fetch, decode, execute, and write back.

CPU 205 may be part of a circuit such as an integrated circuit. The circuit may include one or more other components of the system 201. In some cases, the circuit is an application specific integrated circuit (ASIC).

The storage unit 215 may store files such as drivers, libraries, and saved programs. The storage unit 215 may store user data, such as user preferences and user programs. The computing system 201 may in some cases include one or more additional data storage units that are external to the computing system 201, such as located on a remote server that is connected to the computing system 201 via an intranet or the Internet.

Computing system 201 may communicate with one or more remote computing systems via network 230. For example, computing system 201 may communicate with a remote computing system of a user (e.g., pet owner, kennel owner, veterinarian, breeder, animal shelter employee, doctor, nurse , caregiver, patient or subject). Examples of remote computing systems include personal computers (e.g., laptop), tablet computers (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, smartphones (e.g., Apple® iPhone, Android devices, Blackberry®), or personal digital devices. auxiliary devices. The user can access the computing system 201 through the network 230.

The methods described herein can be implemented by

- 32 046341 by a computer (eg, a computer processor) of a program stored in electronic storage of the computing system 201, such as memory 210 or electronic storage unit 215. The computer-executable program or machine-readable code may be provided in the form of software. During use, the program may be executed by the processor 205. In some cases, the program may be retrieved from the storage unit 215 and stored in the memory 210 for rapid access by the processor 205. In some cases, the electronic storage unit 215 may be omitted and the computer-executed program is stored in the memory 210 .

The program may be precompiled and configured to run on a computer with a processor capable of executing the program, or may be compiled at runtime. The program may be provided in a programming language, which may be selected to execute the program in a precompiled or compiled form.

Aspects of the systems and methods disclosed herein, such as the computing system 201, may be implemented in programming. Various aspects of technology can be considered as products or articles of manufacture, typically in the form of a computer (or processor) executable program and/or associated data that is carried or embodied in a type of computer-readable medium. A computer-executable program may be stored in an electronic storage unit such as memory (eg, read-only memory, random access memory, flash memory) or a hard disk. A storage medium may include any or all of the physical memory of computers, processors, and the like. or associated modules, such as various semiconductor memories, tape drives, disk drives, and the like, which can provide long-term storage at any time for software programming. All or portions of the Software may be transmitted from time to time over the Internet or various other telecommunications networks. Such communications, for example, may allow software to be loaded from one computer or processor to another, for example, from a management server or host computer to an application server computer platform. Thus, another type of media that can carry software elements includes optical, electrical, and electromagnetic waves, such as those used through physical interfaces between local devices, across wired and optical fixed networks, and over various radio links. Physical elements that transmit such waves, such as wired or wireless communication channels, optical communication channels, etc., can also be considered as software-carrying media. In the context of the present invention, terms such as computer or computer readable medium, when not limited to a non-transitory storage medium, refer to any medium that is involved in providing instructions to a processor for execution.

Therefore, a computer-readable medium, such as a computer-executable program, can take many forms, including, but not limited to, a tangible storage medium, a carrier wave medium, or a physical transmission medium. Non-volatile storage media includes, for example, optical or magnetic disks, such as any of the storage devices in any computer(s), etc., for example, which can be used to implement databases, etc., shown in the drawings. Non-volatile storage media includes dynamic memory, such as the main memory of such a computer platform. Material transmission media include coaxial cables, copper wire, and optical fiber, including the wires that make up the bus in a computing system. The carrier wave transmission medium may take the form of electrical or electromagnetic signals, acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data transmission. Thus, common forms of machine-readable media include, for example: floppy disk, floppy disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, DVD or DVD-ROM, any other optical medium, paper punched card tape, any other physical grid-of-hole storage medium, RAM, ROM, PROM and EPROM, FLASH-EPROM, any other memory chip or cartridge carrying a carrier wave carrying data or instructions, cables or lines carrying such a carrier wave or any other media from which the computer can read program code and/or data. Many of these forms of computer-readable media can be used to carry one or more sequences of one or more instructions to a processor for execution.

The computing system 201 may include or be in communication with an electronic display 235 that includes a user interface (UI) 240 for providing, for example, haplotype data, genetic markers, matching segments of haplotype data that are identical in ancestry between a first individual and a second individual, estimates of discrete genomic intervals, adjusted estimates of discrete genomic intervals, calculated weighted sums of multiple discrete genomic intervals, and an estimated degree of relatedness between the first individual and the second individual. Examples of UI include, but are not limited to, a graphical user interface (GUI) and a web user interface.

The methods and systems of the present invention can be accomplished by one

- 33 046341 or several algorithms. The algorithm may be implemented by software when executed by the central processing unit 205. The algorithm may, for example, obtain haplotype data for a population of test individuals (e.g., where the haplotype data contains a plurality of genetic markers common to the population of test individuals), divide the haplotype data into segments based on a plurality of genetic markers, match, based on the plurality of genetic markers, segments of haplotype data that are identical in ancestry between a first individual and a second individual within a population of test individuals (e.g., where each of the matched segments has a first size that is at least a predetermined threshold size, and contains at least a predetermined number of genetic markers), divide the matched segments into a plurality of discrete genomic intervals, evaluate the discrete genomic intervals based on (i) the degree of homozygosity match of the discrete genomic interval in the first individual or the second individual, or (ii) the degree pairwise match of a discrete genomic interval between a first individual and a second individual, thereby obtaining a plurality of scores, adjusting the plurality of scores based on the consistency of the plurality of scores, thereby obtaining a plurality of adjusted scores, estimating a plurality of weights of the plurality of discrete genomic spans (e.g., based on the plurality of adjusted scores discrete genomic intervals), calculate a weighted sum for a plurality of discrete genomic intervals of a matched segment (eg, based on a plurality of adjusted scores and a plurality of weights), and estimate the degrees of relatedness between a first individual and a second individual (eg, based on a weighted sum of the matched segments). The computing system 201 may handle various aspects of the analysis, calculation and production of the present invention, such as, for example, obtaining haplotype data for a population of test individuals (e.g., where the haplotype data contains a plurality of genetic markers common to the population of test individuals), dividing the haplotype data into segments based on a plurality of genetic markers, matching, based on a plurality of genetic markers, segments of haplotype data that are identical in ancestry between a first individual and a second individual within a population of test individuals (e.g., where each of the matched segments has a first size that is at least tentatively a certain threshold size, and contains at least a predetermined number of genetic markers), dividing the matched segments into a plurality of discrete genomic intervals, estimating the discrete genomic intervals based on (i) the degree of homozygosity match of the discrete genomic interval in the first individual or the second individual, or (ii) ) the degree of pairwise matching of a discrete genomic interval between a first individual and a second individual, thereby obtaining a plurality of scores, adjusting the plurality of scores based on the consistency of the plurality of scores, thereby obtaining a plurality of adjusted scores, assigning a plurality of weights to the plurality of discrete genomic intervals (e.g., based on a set of adjusted discrete genomic interval scores), calculating a weighted sum for a plurality of discrete genomic intervals of a matched segment (e.g., based on a plurality of adjusted scores and a plurality of weights), and estimating the degree of relatedness between a first individual and a second individual (e.g., based on weighted sums of matched segments ).

Examples

Example 1: WOOPHS algorithm for estimating the degree of relatedness or relatedness.

Using the methods and systems of the present invention, a weighted pairwise haplotype observation (WOOPHS) algorithm was performed to estimate the degree of relatedness or relatedness between individuals (eg, dogs). First, the genotypic data of the population of individuals was processed using GERMLINE to produce an array of pairwise matches and homozygosity matches of individuals. For a given pair of individuals, the WOOPHS algorithm is then performed as follows. Match intervals (matches_array) are sorted by chromosome, start position, and end position. A zero-filled array (ibd_scores) is then created and initialized to store pairwise and homozygous counts in discrete genomic bins. An empty list (prior_intervals) is then created and initialized to hold all discrete genomic intervals until they are discarded. Then, for each chromosome: (a) a sorted array of all positions on the chromosome, indicated by discrete interval breaks (interval_breaks), is generated and stored in the database and (b) for each row (match_row) in the match_array on the chromosome: the current match row for any discrete intervals already encountered in previous rows are evaluated, any previous intervals that were completely exceeded are removed from memory, new discrete intervals in match_row are created and stored, and new intervals are stored in prior_intervals.

Scoring (based on match types and weights) and adjusting/adjusting counts is done as follows. The initial assessment of matched discrete genomic intervals is empirical. Number of pairwise matches between individuals (0, 1, 2, 3 or 4) or matches

- 34 046341 each individual's homozygosity (0 or 1) is recorded gradually as the WOOPHS algorithm passes through discrete genomic intervals on the chromosome. However, in reality there are a limited number of valid combinations of homozygous and pairwise matches. Because GERMLINE does not necessarily report all matches, depending on the specific labels used, the WOOOPHS algorithm adjusts the observed counts to match the appropriate count configurations (as shown in the summary of these possible count configurations in the table reproduced from Hedrick and Lacy). Of the nine states in this table, only five contribute to relatedness and relatedness, and two (states 3 and 5) can be further compressed because they are identical in terms of weight assignment. The remaining states can be ignored (e.g., assigned zero weight) since they do not contribute to the relatedness or relatedness calculation.

Thus, WOOPHS works with the following four hit count state configurations:

(1) State 1 = {Pairwise = 4, homozygosity = 2} (2) State 3 = {Pairwise = 2, homozygosity =1} (3) State 7 = {Pairwise = 2, homozygosity = 0} (4 ) State 8 = {Pairwise = 1, by homozygosity = 0}

The WOOPHS algorithm then adjusts the pairwise match counts in configurations with an invalid match count state as follows:

Observation {Pairwise = 3, homozygosity =1} corrected to {Pairwise = 2, homozygosity = 1} (State 3).

Observation {Pairwise = 3, homozygosity = 0} corrected to {Pairwise = 1, homozygosity = 0 (State 8).

Observation {Pairwise = 4, homozygosity = 0 or 1} corrected to {Pairwise = 2 (State 8 if homozygosity = 0, State 7 if homozygosity = 1).

Observation {Pairwise = 2, by homozygosity = 2} corrected to {Pairwise = 4} (State

1).

Observation {Pairwise = 1, homozygosity = 1 or 2} corrected to {Pairwise = 2 (State 3 if homozygosity = 1, ignored if homozygosity = 2).

Observation {Pairwise = 3, by homozygosity = 2} corrected to {Pairwise = 4 (State

1).

Once matching discrete genomic intervals are counted and adjusted, the coefficient of relatedness ratio (COR) and/or coefficient of relatedness (COK) is calculated as follows. After adjusting the state of the pairwise match count, all sites are assigned weights for their contribution to relatedness and relatedness, as shown in the table. Finally, relatedness and relatedness are calculated as follows. Let Δί represent the total sum of genome lengths assigned to one of the four hit count states i. Then the relatedness rxy and relatedness kxy can be calculated using the following expressions:

+ 47 + (0.75x43) + (0.5x48) ^Gxy ~ L_41 + (0.5 x (43 + 47)) + (0.25x48) ^kxy ~ L where L represents the total length of the genome in question.

In some embodiments, the collected genotypic data and/or haplotype data of a population of individuals may be stored in a database for later retrieval. A sample can then be taken from the test individual, then genotypic data and haplotype data for the test individual can be obtained. The WOOPHS algorithm may then be performed to determine the degree of relatedness or relatedness between the test individual and each of one or more individuals in the population. Finally, the database may be searched to retrieve a subset of a population of individuals having a degree of relatedness or relatedness that exceeds a predetermined threshold value. This subset of related individuals can be sorted or ranked based on the degree of relatedness or relatedness to the individual being tested.

Preferred embodiments of the present invention have been shown and described herein, but those skilled in the art will appreciate that such embodiments are provided by way of example only. It is not intended that the present invention

-

Claims (33)

limited to the specific examples presented in the description. Although the invention has been described with reference to the foregoing specification, the descriptions and illustrations of embodiments herein are not intended to be construed in a limiting sense. Numerous variations, changes and substitutions will be apparent to those skilled in the art without departing from the present invention. Moreover, it should be understood that all aspects of the present invention are not limited to the specific images, configurations or relative proportions set forth herein, which depend on a variety of conditions and variables. It should be understood that various alternatives to the embodiments described herein may be used in the practice of the present invention. It is therefore intended that the present invention also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the present invention, and that methods and structures within the scope of the claims and their equivalents are covered by the present invention. CLAIM 1. A computerized method for assessing the degree of relatedness between two individuals of a diploid population, providing: (a) obtaining haplotype data for a population of test individuals, the haplotype data comprising a plurality of genetic markers common to the population of test individuals, (b) dividing the haplotype data into segments based on the plurality of genetic markers, (c) for each population of test individuals: (i) matching, based on a plurality of genetic markers, segments of haplotype data that are identical by descent (IBD) between a first individual and a second individual within a population of test individuals, each of the matched segments having a first size that is at least a predetermined threshold size, and contains at least a predetermined number of genetic markers, (ii) for each of the matched segments between the first individual and the second individual: dividing the matched segment into a plurality of discrete genomic intervals, estimating each of the plurality of discrete genomic intervals based on (i) the degree homozygosity match of a discrete genomic interval in a first individual or a second individual, wherein the degree of homozygosity match is the number of homozygosity matching haplotypes in a discrete genomic interval within one individual, and (ii) the degree of pairwise matching of a discrete genomic interval between the first individual and the second individual, the degree of pairwise matching being the number of matching haplotypes in a discrete genomic interval between two individuals, thereby obtaining a plurality of scores, adjusting each of the plurality of scores based on the agreement between the degree of homozygosity match and the degree of pairwise matching of the plurality of scores, thereby obtaining a plurality of adjusted scores, and assigning a plurality of weights to the plurality of discrete genomic intervals based at least in part on the plurality of adjusted scores, and (iii) calculating a weighted sum for the plurality of discrete genomic intervals of the matched segment based on the plurality of adjusted scores and the plurality of weights, and (d ) an estimate of the degree of relatedness between the first individual and the second individual based on the weighted sums of the matching segments. 2. The method of claim 1, wherein the diploid population is a mammalian population. 3. The method of claim 1, wherein the haplotype data is obtained, at least in part, by processing the genotypic data of a population of test individuals using a haplotype phasing algorithm. 4. The method of claim 3, wherein the haplotype phasing algorithm comprises a reference-based haplotype phasing algorithm comprising a Hidden Markov Model (HMM) search. 5. The method according to claim 3, where the haplotype phasing algorithm contains a haplotype phasing algorithm based on the standard, and the haplotype phasing algorithm based on the standard contains the Eaglel algorithm, the Eagle2 algorithm, the PHASE algorithm, the fastPHASE algorithm, the BEAGLE algorithm, the Findhap algorithm, the Impute algorithm, Flmpute algorithm, Alphalmpute algorithm, IMPUTE2 algorithm, MaCH algorithm, SHAPEIT1 algorithm, SHAPEIT2 algorithm, SHAPEIT3 algorithm, SHAPEIT4 algorithm or a combination thereof. 6. The method of claim 3, wherein the genotypic data is obtained, at least in part, by evaluating biological samples taken from a population of test individuals, or derivatives thereof. 7. The method of claim 6, wherein the assessment further comprises sequencing the biological samples or derivatives thereof to obtain a plurality of sequencing reads. - 36 046341 8. The method of claim 7, wherein the evaluation further comprises aligning the plurality of sequencing reads to a reference genome. 9. The method of claim 6, wherein the evaluation further comprises using matrix hybridization. 10. The method of claim 1, wherein the plurality of genetic markers comprises at least about 500 different genetic markers. 11. The method according to claim 1, where the comparison of segments of haplotype data that are identical in origin involves the use of the GERMLINE algorithm, the PLINK algorithm, the PREST algorithm, the random projection algorithm for IBD detection (RaPID), the rapid detection of IBD common haplotypes (FISHR) algorithm, advanced Identity by Origin (IBD) algorithm, fastIBD algorithm, KING algorithm, HaploScore algorithm, TRUFFLE algorithm, or a combination thereof. 12. The method of claim 1, wherein the predetermined threshold size is at least about 100 thousand nucleotide pairs (kbp). 13. The method of claim 1, wherein the predetermined number of genetic markers is at least about 30 different genetic markers. 14. The method of claim 1, further comprising dividing the matched segments such that the discrete genomic intervals of the plurality of discrete genomic intervals are of equal size. 15. The method of claim 1, further comprising dividing the matched segments such that the discrete genomic intervals of the plurality of discrete genomic intervals have a variable size. 16. The method of claim 15, wherein the variable size of a given discrete genomic interval of the plurality of discrete genomic intervals is determined, at least in part, based on the starting position and ending position of IBD matches proximal to the given discrete genomic interval, the density of genomic markers in the given discrete genomic interval , the maximum number of markers for a given discrete genomic interval, the maximum length of a given discrete genomic interval, or a combination thereof. 17. The method of claim 1, further comprising estimating each of the plurality of discrete genomic intervals based on (i) the degree of homozygosity match of the discrete genomic interval in the first individual or the second individual and (ii) the degree of pairwise matching of the discrete genomic interval between the first individual and the second individual, thereby obtaining a plurality of homozygosity match scores and a plurality of pairwise match scores. 18. The method of claim 1, further comprising adjusting the plurality of pairwise match scores based on the consistency of the pairwise match score with a corresponding homozygosity match score, thereby obtaining a plurality of adjusted pairwise match scores. 19. The method of claim 1, further comprising assigning a plurality of weights to a plurality of discrete genomic intervals based at least in part on a plurality of identity states for two alleles in two diploid individuals, wherein zero weights are assigned to discrete genomic intervals with identity states indicating absence pairwise match between two diploid individuals, and whereby non-zero weights are assigned only to discrete genomic intervals with identity states indicating a non-zero pairwise match between two diploid individuals. 20. The method of claim 16, wherein the plurality of identity states comprise identity states selected from the following: - 37 046341 ( [E STO JTHTTG π π£ identity Probability Contribution to T _x y Contribution to g _x ? a - b II Δ, 11 c - d a - b Δ _ί О0 с — d а — b I / Δ _Ί 1/23/4 s d a - b δ ₄ o and s dab /1 Ar, 1/23/4 s--d a b Δ* OO o - d a b I I Δ. 1/21 s d a b I Δ* 1/41/2 c d a b Δ, OO c d with the first individual x having alleles a and b, the second individual y having alleles c and d, the horizontal lines of the nine identity states indicate the homozygosity of the individual for identity by origin , and a plurality of weights are assigned further based on the plurality of contributions to the relationship rxy. 21. The method according to claim 20, where the degree of relatedness contains a coefficient of relatedness. 22. The method of claim 21, further comprising calculating a weighted sum for a plurality of discrete genomic intervals of the matched segment, wherein the weighted sum is expressed by: Δ1 + Δ7 + (0.75x213) + (0.5x48) ^Г *У ⁼ L' where Δ1, Δ3, Δ7 and Δ8 represent the total sum of the genome lengths assigned to one of the four hit count states State 1, State 3, State 7, State 8, respectively, while State 1 = {Pairwise = 4, by homozygosity = 2} State 3 = {Pairwise = 2, by homozygosity = 1} State 7 = {Pairwise = 2, by homozygosity = 0} State 8 = {Pairwise = 1, homozygosity = 0}, and L is the total length of the genome in question. 23. The method according to claim 20, where the degree of relationship contains a consanguinity coefficient. 24. The method of claim 23, further comprising calculating a weighted sum for a plurality of discrete genomic intervals of the matched segment, wherein the weighted sum is expressed by: - 38 046341 41 + (0.5 x (43 + 47)) + (0.25x48) ^xy ⁼ 1 / where Δ1, Δ3, Δ7 and Δ8 represent the total sum of genome lengths assigned to one of the four hit count states State 1, State 3, State 7, State 8, respectively, while Condition 1 = {Pairwise = 4, by homozygosity = 2} Condition 3 = {Pairwise = 2, by homozygosity = 1} Condition 7 = {Pairwise = 2, by homozygosity = 0} State 8 = {Pairwise = 1, Homozygosity = 0}, and L is the total length of the genome in question. 25. The method of claim 1, wherein assessing the degree of relatedness between the first individual and the second individual involves determining the degree of inbreeding of the first individual or the second individual. 26. The method of claim 25, further comprising determining the familial relationship between the first individual and the second individual based at least in part on the degree of inbreeding of the first individual and the second individual. 27. The method of claim 26, wherein the family relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle-nephew relationship, a cousin-level relationship, or a grandparent-grandchild relationship. 28. The method of claim 1, further comprising obtaining a social relationship between a first entity associated with the first individual and a second entity associated with the second individual based at least in part on an estimated degree of relatedness between the first individual and the second individual. 29. The method of claim 28, wherein the social connection is obtained when the estimated degree of relatedness between the first individual and the second individual exceeds a predetermined threshold value, where the predetermined threshold value is at least about 0.001. 30. The method of claim 28, wherein the first subject owns the first individual as a pet, and where the second subject owns the second individual as a pet. 31. The method of claim 1, further comprising identifying a familial relationship between the first individual and the second individual based at least in part on the degree of relationship, wherein the familial relationship is a parent-child relationship, a brother-sister relationship, an aunt/uncle relationship -nephew/niece, cousin/cousin-level relationship, or grandparent-grandson/granddaughter relationship. 32. A non-transitory computer-readable medium containing a computer-executable program that, when executed by one or more computer processors, implements a method for estimating the degree of relatedness between two individuals of a diploid population, the method comprising: (a) obtaining haplotype data for a population of test individuals, the haplotype data comprising a plurality of genetic markers common to the population of test individuals, (b) dividing the haplotype data into segments based on the plurality of genetic markers, (c) for each population of test individuals: (i) matching, based on a plurality of genetic markers, segments of haplotype data that are identical by descent (IBD) between a first individual and a second individual within a population of test individuals, each of the matched segments having a first size that is at least a predetermined threshold size, and contains at least a predetermined number of genetic markers, (ii) for each of the matched segments between the first individual and the second individual: dividing the matched segment into a plurality of discrete genomic intervals, estimating each of the plurality of discrete genomic intervals based on (i) the degree homozygosity match of a discrete genomic interval in a first individual or a second individual, wherein the degree of homozygosity match is the number of homozygosity matching haplotypes in a discrete genomic interval within one individual, and (ii) the degree of pairwise matching of a discrete genomic interval between the first individual and the second individual, the degree of pairwise matching being the number of matching haplotypes in a discrete genomic interval between two individuals, thereby obtaining a plurality of scores, adjusting each of the plurality of scores based on the agreement between the degree of homozygosity match and - 39 046341 the degree of pairwise matching of the plurality of scores, thereby obtaining a plurality of adjusted scores, and assigning a plurality of weights to the plurality of discrete genomic intervals based at least in part on the plurality of adjusted scores, and (iii) calculating a weighted sum for the plurality of discrete genomic intervals of the matched segment based on a plurality of adjusted scores and a plurality of weights, and (d) an estimate of the degree of relatedness between the first individual and the second individual based on the weighted sums of the matched segments. 33. A computing system for assessing the degree of relatedness between two individuals of a diploid population, containing: a database that is configured to store haplotype data for a population of test individuals, wherein the haplotype data comprises a plurality of genetic markers common to the population of test individuals, and one or more computer processors operably coupled to the database, wherein the one or more computer processors individually or together programmed to: (a) dividing the haplotype data into segments based on multiple genetic markers, (b) for each population of individuals tested: (i) matching, based on a plurality of genetic markers, segments of haplotype data that are identical by descent (IBD) between a first individual and a second individual within a population of test individuals, each of the matched segments having a first size that is at least a predetermined threshold size, and contains at least a predetermined number of genetic markers, (ii) for each of the matched segments between the first individual and the second individual: dividing the matched segment into a plurality of discrete genomic intervals, estimating each of the plurality of discrete genomic intervals based on (i) the degree homozygosity match of a discrete genomic interval in a first individual or a second individual, wherein the degree of homozygosity match is the number of homozygosity matching haplotypes in a discrete genomic interval within one individual, and (ii) the degree of pairwise matching of a discrete genomic interval between the first individual and the second individual, the degree of pairwise matching being the number of matching haplotypes in a discrete genomic interval between two individuals, thereby obtaining a plurality of scores, and adjusting each of the plurality of scores based on the consistency between the degree of homozygosity match and the degree of pairwise matching of the plurality of scores, thereby obtaining whereby a plurality of adjusted scores, and assigning a plurality of weights to the plurality of discrete genomic intervals based at least in part on the plurality of adjusted scores, and (iii) calculating a weighted sum for the plurality of discrete genomic intervals of the matched segment based on the plurality of adjusted scores and the plurality of weights, and ( c) estimating the degree of relatedness between the first individual and the second individual based on the weighted sums of the matching segments. -