CZ2012793A3 - Prediction method of visible phenotype markers and biogeographical origin, especially for forensic purposes - Google Patents

Abstract

The present invention provides a method of predicting the visible phenotypic traits and biogeographical origin of a person, especially for forensic purposes, by determining the genotype of the following single nucleotide polymorphism (SNP) markers: rs16891982, rs2031526, rs12896399, rs7495174, rs12913832, rs916977, rs1426654, rs1667394, rs28049897. Comparison of genotypic combinations of subsets of this marker group determining the affiliation of a given phenotypic trait to a population sample by comparison of genotype and phenotype results in the following phenotypic characteristics: blue vs. blue eyes. blue on subset of rs16891982, rs2031526, rs12913832, rs916977; eye color brown vs. not brown on a set of markers rs7495174, rs12913832; white vs. biogeographical origin asat on subset of markers rs16891982, rs1426654; hair color bright vs. dark on subset of markers rs12896399, rs12913832, rs8049897, rs1667394. Also included is a probe for performing said method comprising probes complementary to both alleles of said markers.

Description

Technical field

The present invention relates to a method of predicting human phenotypic traits and the biogeographic origin of an individual based on genetic analysis of a set of SNP markers.

BACKGROUND OF THE INVENTION

Forensic genetics is currently one of the most important branches in the forensic sciences. Molecular genetic analysis of DNA polymorphisms has almost all the characteristics of an "ideal identification system": evidence does not change over time, the technique allows to identify a person's unique characteristics, this technique can connect people to the crime scene and is relatively cheap and fast. Although the current analysis of DNA polymorphisms usually produces very relevant evidence, it has low discriminatory power for investigators. This means that if a DNA profile of a suspect who has been arrested for non-DNA reasons is available, or after searching the database, a match analysis of the DNA profile from the crime scene and the suspect's DNA can be very strong evidence. However, unless we have a suspect, DNA analysis from the crime scene using current methods is not able to identify visible phenotypic traits that could reduce the number of possible offenders.

This has prompted studies to explore areas of the human genome whose variability correlates with visible phenotypic traits. Whole genome studies involving different populations have contributed to the dissemination of knowledge in this area. An example of this is the Rotterdam study, containing a total of 7983 persons (Kayser M et al. Three genome-wide association studies and a linkge analysis to identify HERC2 as a human iris color gene, American Journal of Human Genetics 2008; 82 (2): 411-23 Liu F et al., PloS Genetics PLoS Genet 2010 May 6; 6 (5): el000934). Another example is an all-genome study of the Icelandic population that has been validated on the Dutch population (Sulem et al. Genetic determinants of hair, eye and skin pigmentation in Europeans. Nature Genetics 2007; 39: 1443 - 1452; Sulem et al. Two newly identified genetic determinants of pigmentation in Europeans, Nature Genetics 2008; 40: 835-837). These studies have identified several t iai i ř * 45 * 45 _{4 4 4} ^t _{4 4 4} ^t ₄ ^t _{4 4} ^t ₄ >

55 major genes involved in melanogenesis, a process of crucial importance in the development of individual pigmentation.

Melanogenesis occurs in melanocytes, where melanosomes are formed, which are then transported to keratinocytes, which protect the skin against UV radiation. According to the maturation process, melanosomes are divided into two types: pheomelanosomes, in which the maturation process stops at the first stage, and eumelanosomes, which undergo a complete maturation process. Feomelanosomes produce light feomelanin, while eumelanosomes produce darker eumelanin. The melanogenesis process begins with the production of MCR1 activating hormones (melanocortin-receptor 1), which are stimulated by UV. MCR1 subsequently increases the level of cAMP (cyclic adenosine monophosphate), which activates transcription factors that stimulate expression of proteins involved in melanosome maturation (Scherer D et Kumar R, Genetics of Pigmentation in Skin Cancer — a review, Mutation Research 2010; 705 (2): 141 -53). The first enzyme that converts tyrosine to melanin is TYR (tyrosinase), which is present in both types of melanosomes. Other enzymes, TYRP1 (tyrosine-related protein) and DCT (dopachrome tautomerase), are required to produce exclusively eumelanin. Another group of proteins involved in melanogenesis are membrane transporters that provide transport of ions and small molecules, such as tyrosine, and pH regulation. Polymorphisms in these proteins can be used to predict phenotypic traits and associated biogeographical origin.

One of the pioneers in the application of this knowledge to forensic genetics was the scientific group of T. Frudakis, who was involved in the research and subsequent application of markers determining biogeographical origin (Frudakis T., Pharmacogenomics, 2008, DNAPrint Genomics, Inc .: Better drugs for segmented markets, Feb 9 (2): 247-51. Studies have been conducted to determine the phenotypic traits and biogeographic origins of skeletal remains (Bouakaze et al. Pigment phenotype and biogeographical ancestry from ancient skeletal remains: inferences from multiplexed autosomal SNP analysis, International Journal of Legal Medicine 2009; 123: 315-325) . Irisplex (Walsh et al. IrisPlex: A sensitive DNA tool for accurate prediction of blue and brown eye color in the absence of ancestry information, Forensic Science International Genetics 2010; : 170-180. This method is based on the analysis of 6 SNP markers from 6 different genes (HERC2, OCA2, SLC24A4, SLC45A2, TYR, IRF4).

However, a test kit has not yet been created to identify all important phenomenon features with high accuracy.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide a method for predicting visible phenomenon types and a biogeographic origin of a person, particularly for forensic purposes, by determining the genotype of single-nucleotide polymorphism (SNP) markers by determining the genotype of the following single nucleotide polymorphism: , rs12896399, rs7495174, rsl2913832, rs916977, rs1426654, rsl667394, rs28049897, and comparing to genotypic combinations of subsets of this group of markers identifying a group of individuals having a given phenotypic trait and determining the phenotypic trait to a population sample

eye color blue vs. blue on subset of markers rs16891982, rs2031526, rsl2913832, rs916977;

eye color brown vs. brown on subset of markers rs7495174, rs 12913832; biogeographical origin of white vs. white Asian on subset of markers rs16891982, rs1426654; Hair color vs. light dark on subset of markers rs 12896399, rs 12913832, rs8049897, rsl667394.

By examining the pooled multilocus genoyp, the appropriate phenotype can be predicted with the accuracy given in Table 1 below:

Table 1: Properties of selected combinations of SNP markers

Set Description - determination of characteristics SNP in set Accuracy of prediction SI Eye color: Blue vs Blue rsl6891982, rs2031526, rsl2913832, rs916977 > 80% S2 Eye color: Brown vs Brown rs7495174, rsl2913832 > 80% S3 Biogeographical origin: White vs Asian rsl6891982, rsl426654 > 90% S4 Hair color: light vs dark rs12896399, rs 12913832, rs8049897, rs 1667394 > 70%

Preferably, the genotypes are determined by RealTime PCR.

i, »· 'i * 4 9 i 9 tt» · · · » ^{9 9} « 4 ♦ »♦ r · -S * i *» · · i «· · · ·« «· ···« «· ·· * «. 4 Genotypic combinations identifying belonging to a group of individuals exhibiting a given phenomenon are preferably obtained on a population sample by obtaining data on phenotypic features (eye color, hair color, biogeographical origin) for individual individuals from that population sample, followed by sampling DNA samples (e.g. buccal swab), and these samples are grouped according to phenotypic traits. Individual samples are then subjected to DNA isolation and genotyping, i.e., determining the genotype for each SNP marker, and generating tables of probabilities or odds shares for a given population by comparing the frequencies of the individual phenotypes for each multilocus genotype.

Comparison with genotypic combinations identifying belonging to a group of persons exhibiting a given phenotypic trait and thus determining the genotype of an unknown person from a sample of its DNA (e.g. left at the crime scene) is preferably performed by isolating DNA from the sample; SNP marker sets and corresponding multilocus genotypes are found in the probability or chance tables, and the probability or chance ratio is found to express how many times it is likely that the unknown person with the detected multilocus genotype belongs to a group exhibiting a given phenotypic trait character.

The present invention also provides a kit for performing a method of predicting visible phenotypic traits and biogeographic origin, comprising probes complementary to both alleles of markers exhibiting single nucleotide polymorphisms, rs16891982, rs12896399, rs7495174, rs11616667, rs1293866, rs1293866, rs1293866, rs1293866, rs1293866, rs1293866, rs1293866, Preferably, the probes are fluorescently labeled.

Preferably, already amplified DNAs of all three possible genotypes for each of the SNP markers included in the panel are further included in the kit of the present invention for the purpose of checking for correct time genotyping by the Real Time PCR method.

The present invention provides a set of SNP markers that allows the phenotypic features of an unknown person to be determined quickly and with great reliability. The high accuracy of prediction is achieved because it is based on combinations of SNP markers. Although SNP markers used are known in the literature, their use for predicting combinations in the kit has not been suggested.

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These combinations were based on a population study conducted on 131 samples. The obtained data were subsequently analyzed using the MDR (Multifactorial Dimension Reduction) method. This method consists of selecting combinations of SNP markers with the highest prediction rate and the lowest prediction error. Dimension reduction occurs because markers are considered together as a multilocus genotype. Following this analysis, the best models (sets of SNP markers) were selected for each phenotypic trait of interest (see Table 1). Based on the study described above, the 9 most informative SNPs listed in Table 2 were selected. All SNP markers examined are located on autosomes. The accuracy of phenotypic feature prediction shown in Table 1 was determined using the multidimensional reduction method.

Table 2: Overview of SNP markers selected as part of the kit based on a custom population study and historical literature characterizing these SNPs:

rs number Gene Chromo- zom Publications DNA fragment including polymorphic site rsl6891982 SLC45A1 (MATP) 5p Han J et al., PLoS Genetics, 2008, A genome-wide association study identifying novel alleles associated with hair color and skin pigmentation, May 16; 4 (5) AGTTGATGCA [C / G1AAGCCC CAA rs2031526 DCT (TYRP1) 13q Myles S et al., Human Genetics, 2007, Identifying genes underlying skin pigmentation differences among human populations, 120 (5): 613-21 GGGTAGGAA [A / G] CAAAAGC AAA rsl2896399 SLC24A4 14q Liu F et al., PloS genetics, 2010, Digital quantification of human eye color highlights genetic association of three new loci, May 6; 6 (5) TATTTTGGG | G / T | TCTCTTTG TCAC rs7495174 0CA2 15q Kayser et al., American Joumal of Human Genetics, 2008, Three genome-wide association studies and a linege identity analysis of HERC2 as a human iris color gene, 82 (2): 411-23 TGTGCACACT [A / G] ACCTTTA GGG rs! 2913833 HERC2 15q Sturm RA et al., American Journal of Human Genetics, 2008, A single SNP in the evolutionary conserved region within intron 86 of the HERC2 gene determines human blue-brown eye color, 82 (2): 424-31 TTG AGC ATTAA TGTC AND AGTT

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rs916977 HERC2 15q Han J et al., PLoS Genetics, 2008, A genome-wide association study identifying novel alleles associated with hair color and skin pigmentation, May 16; 4 (5) TTTGAGTAGA [C / T] AGAAGG CTGG rs 1426654 SLC24A5 ( NCKX5) 15q Nan H et al., Intemational Joumal of Cancer, 2009, Genetic variants in pigmentation genes, pigmentary phenotypes, and risk of skin cancer in Caucasians, Aug 15; 125 (4) TGTTGCAGGC [A / G] CAACTT TCAT rs 1667394 OCA2 15q Kayser et al., American Joumal of Human Genetics, 2008, Three genome-wide association studies and a linege identity analysis of HERC2 as a human iris color gene, 82 (2): 411-23 TTTGTTTGGT [A / G] TATGCAC GTT rs28049897 MCR1 16q Nan H et al., The Journal of Investigative Dermatology 2009, Genome-wide association study of tanning phenotype in the European ancestry population, Sep; 129 (9): 2250-7 CTGAGGTAGAA [G / G] GGCAC GAG

The invention can be applied to human DNA isolated by any of the commercially available isolation methods, to DNA samples obtained by manual isolation as well as to DNA obtained in a robotic isolation process in high-throughput laboratories.

The preferred procedure is based on the RealTime PCR method, but other molecular genetic methods capable of detecting selected SNP variants of the human genome may also be used. Such methods are well known to those skilled in the art.

In principle, the character of the polymorphisms to be examined makes it possible to apply one of the whole-genome amplification methods to the samples before the examination of the multilocus genotype of the selected SNP markers, if the low concentration of the DNA of interest in the initial sample so requires.

In a preferred embodiment of the invention, the isolated DNA is subjected to RealTime PCR using fluorescently labeled probes. Each of the probes is complementary to a different allele and is labeled with a different fluorescent label. The genotype of each SNP marker is detected individually using a pair of appropriate probes to determine the correct genotype of the DNA sample under investigation in all 9 SNP markers in 9 separate Real Time PCR reactions.

The proportion of chances can be used to determine the likely phenotype of the agent of the DNA under investigation. It is a feature of the invention that genotypes can be determined, for example, by the Real-Time method.

PCR and that genotypes in selected markers are assessed together and interpreted using the odds ratio.

The obtained results allow to predict phenotypic characteristics and biogeographic origin by probabilities or by using the odds ratio (O - odds). O (X = 1) in this case can be written as follows: O (X = 1) = P (X = 1 | Gx) / P (X = 0 | Gx), where Gx - genotype of person X, symbol "|" denotes "Assuming", X = 1 - person belongs to group 1, X = 0 - person belongs to group 0. The odds ratio expresses how many times it is more likely that a person X with a genotype found belongs to group 1 rather than group 0.

The proportions of chances for an accurate interpretation of a particular outcome within the study population are ascertained, as mentioned above, by a population study.

DETAILED DESCRIPTION OF THE INVENTION

Example 1

Population studies - examination of SNP markers using RealTime PCR method

The population sample examined contained 131 unrelated persons, of whom 31 were Asians (Kazakhs) and 100 were Caucasian. DNA samples from the buccal mucosa were collected after completing the self-assessment questionnaire and signing informed consent. The questionnaire contained questions about the eyes' color and hair and the origin of both the interviewee and his close relatives (parents and grandparents). Four analyzes were performed, the samples analyzed were divided into two groups for each analysis (see Table 3).

Table 3. Grouping of investigated samples

Blue vs blue eyes Brown vs brown eyes Whites vs Asians Bright vs Dark Hair Blue 47, marking 1 Brown 66, marking 1 White 100, label 1 Light 46, label 1 Blue 84, marking 0 Brown 65, marking 0 Asians 31, label 0 Dark 85, marking 0

DNA from buccal swabs was isolated using the QIAmp Blood Mini Kit (QIAGEN,

Germany) on the QIAcube. The final eluate volume was 150 μΐ. Average concentration

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The DNA in the eluate was 10 ng / μΐ. Concentration was measured using a NanoDrop instrument (Thermo Fisher Scientific, USA).

Genotyping analysis was performed using the Allelic Discrimination module on a 7900 HT Fast Real-Time PCR System (Applied Biosystems, USA) using fluorescently labeled probes. Each of the probes was complementary to another allele and was labeled with a different color. Each SNP marker was detected individually using a pair of appropriate probes and the evaluation was based on the determination of the fluorescent signal intensity of a particular color. The DNA input to the reaction was at least 10 ng.

Tables 4 to 7 show the predictive capabilities of pooled multilocus genotypes for individual phenotypic traits obtained as a result of this population study:

Table 4: Chance odds

Eyes color: blue (1) vs blue (0) Genotype (rs 16891982, rs2031526, rs 12913 832, rs916977) P (X = 1 | Gx) P (X = 0 | Gx) O (X = 1) GG, AG, AA, CT 0,0933 1.0000 0,0933 GG, AG, AA, No 0.7500 0.2500 3,0000 GG, AG, AA, TT 1.0000 0.0533 18.7449 GG, AG, AG, CT 0,0933 1.0000 0,0933 GG, AG, AG, CC 0,0933 1.0000 0,0933 GG, AG, AG, TT 1.0000 0.0533 18.7449 GG, AG, GG, CC 1.0000 0.0533 18.7449 GG, GG, AA, CT 0,0933 1.0000 0,0933 GG, GG, AA, CC 0.2500 0.7500 0.3333 GG, GG, AA, TT 0,0933 1.0000 0,0933 GG, GG, AG, CT 0,0933 1.0000 0,0933 GG, GG, AG, CC 0,0933 1.0000 0,0933 GG, GG, AG, TT 0,0933 1.0000 0,0933 GG, GG, GG, CC 0.8462 0.1538 5,5000 GG, GG, TT, TT 0,0933 1.0000 0,0933 GG, AA, AA, CC 0,0933 1.0000 0,0933 GG, AA, AG, CT 0,0933 1.0000 0,0933

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GG, AA, GG, CC 1.0000 0.0533 18.7449 CG, AG, AA, CT 0,0933 1.0000 0,0933 cg, ag, aa, mt 0,0933 1.0000 0,0933 cg, ag, ag, ct 0,0933 1.0000 0,0933 cg, ag, ag, cc 1.0000 0.0533 18.7449 cg, ag, ag, mt 0,0933 1.0000 0,0933 CG, AG, GG, CC 0.5000 0.5000 1.0000 CG, AG, GG, TT 0,0933 1.0000 0,0933 CG, GG, AA, CT 0,0933 1.0000 0,0933 CG, GG, AA, CC 0,0933 1.0000 0,0933 CG, GG, AA, TT 0,0933 1.0000 0,0933 cg, gg, g, ct 0.5000 0.5000 1.0000 CG, GG, GG, CC 0,6000 0.4000 1,5000 cg, aa, aa, cc 0,0933 1.0000 0,0933 cg, aa, ag, ct 0.5000 0.5000 1.0000 CC, AG, AA, CT 0,0933 1.0000 0,0933 CC, AG, AA, CC 0,0933 1.0000 0,0933 cc, ag, aa, mt 0,0933 1.0000 0,0933 CC, AG, AG, CT 0,0933 1.0000 0,0933 CC, AG, GG, TT 0,0933 1.0000 0,0933 cc, aa, aa, cc 0,0933 1.0000 0,0933 cc, aa, ag, ct 0,0933 1.0000 0,0933 cc, aa, ag, tt 0,0933 1.0000 0,0933

Table 5: Chance shares

Eye color: brown (1) vs brown (0) Genotype (in order of markers rs7495174, rsl2913832) P (X = 1 | Gx) P (X = 0 | Gx) O (X = 1) AG, AA 0.6667 0.3333 2,0000 AG 0.8125 0.1875 4,3333 GG, AA 0,1111 0.8889 0.1250 ΑΑ, ΑΑ 1.0000 0,0684 14.6205 AA, AG 0.7879 0.2121 3,7143 AA, GG 0,0833 0.9167 0,0909

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Table 6: Chance odds

Biogeographical origin: White (1) vs Asian (0) Genotype (in order of markers rsl6891982, rsl426654) P (X = 1 | Gx) P (X = 0 | Gx) O (X = 1) GG, AA 1.0000 0.1380 7.2439 GG AG 0.5000 0.5000 1.0000 GG, GG 0,0450 1.0000 0,0450 cg, aa 0.8421 0.1579 5.3333 CG, AG 0,0450 1.0000 0,0450 CG, GG 0,0450 1.0000 0,0450 cc, aa 0,0450 1.0000 0,0450 cc, ag 0,0450 1.0000 0,0450 CC, GG 0,0450 1.0000 0,0450

Table 7: Chance shares

Hair color: light (1) vs dark (0) Genotype (rsl2896399, rsl2913832, rs8049897, rsl667394 P (X = 1 | Gx) P (X = 0 | Gx) O (X = 1) GT, AA, GG, AG 0,0953 1.0000 0,0953 GT, AA, GG, AA 0.7143 0.2857 2,5000 GT, AA, GG, GG 0,0953 1.0000 0,0953 GT, AA, AG, AG 1.0000 0,0527 18.9620 GT, AA, AG, AA 0,0953 1.0000 0,0953 GT, AA, AG, GG 0,0953 1.0000 0,0953 GT, AA, AA, AA 0,0953 1.0000 0,0953 GT, AA, AA, GG 1.0000 0,0527 18.9620 GT, AG, GG, AG 0.1429 0.8571 0.1667 GT, AG, GG, GG 0,0909 0.9091 0.1000 GT, AG, AG, AG 0,0953 1.0000 0,0953 GT, AG, AG, GG 0.5000 0.5000 1.0000 GT, AG, AA, AG 0,0953 1.0000 0,0953 GT, AG, AA, AA 0,0953 1.0000 0,0953 GT, AG, AA, GG 1.0000 0,0527 18.9620 GT, GG, GG, AA 0,6000 0.4000 1,5000

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GT, GG, GG 0.8889 0,1111 8.0000 gt, gg, ag, anda 0,0953 1.0000 0,0953 GT, GG, AG, GG 0,0953 1.0000 0,0953 gg, aa, gg, ag 0,0953 1.0000 0,0953 gg, aa, gg, aa 0,0953 1.0000 0,0953 GG, AA, GG, GG 0,0953 1.0000 0,0953 GG, AA, AG, AA 0,0953 1.0000 0,0953 gg, aa, ag, gg 0,0953 1.0000 0,0953 gg, ag, gg, ag 0,1111 0.8889 0.1250 gg, ag, gg, and 0,0953 1.0000 0,0953 gg, g, gg, gg 0.2000 0.8000 0.2500 gg, ag, ag, ag 0,0953 1.0000 0,0953 gg, ag, ag, gg 0,0953 1.0000 0,0953 GG, AG, AA, AA 0,0953 1.0000 0,0953 GG, GG, AA, AA 0.4615 0.5385 0.8571 GG, GG, GG, GG 0.6667 0.3333 2,0000 gg, gg, ag, anda 0,0953 1.0000 0,0953 GG, GG, AG, GG 0,0953 1.0000 0,0953 gg, gg, aa, aa 1.0000 0,0527 18.9620 TT, AA, GG, GG 1.0000 0,0527 18.9620 TT, AG, GG, AG 0,0953 1.0000 0,0953 TT, AG, GG, AA 0.5000 0.5000 1.0000 TT, AG, GG, GG 1.0000 0,0527 18.9620 tt, gg, gg, anda 1.0000 0,0527 18.9620 TT, GG, GG, GG 0.5000 0.5000 1.0000 TT, GG, AG, AA 1.0000 0,0527 18.9620 TT, GG, AG, GG 1.0000 0,0527 18.9620 TT, GG, AA, GG 1.0000 0,0527 18.9620

Example 2

Determination of phenotype and biogeographic origin using example 5 DNA samples

In five volunteers, buccal mucosa samples were taken from which nuclear DNA was isolated using the Blood Mini Kit (QIAGEN, Germany). The final eluate volume was 150 μΐ. Subsequently, amplification was carried out using primers and probes (TaqMan, Applied III * 4 * 4). «····« and ·· í «·? 4« ♦ «« v

Biosystems, USA) for selected 9 SNP markers. This analysis was performed on a 7900 HT Fast Real-Time PCR System (Applied Biosystems, USA), Allelic Discrimination module. After identifying the genotypes of the subjects analyzed, the plausibility ratio was calculated for all four combinations of SNP markers.

The odds ratio determines whether it is more likely that if the subject has a genotype, it belongs to group 1 or belongs to group 0. For example, for biogeographic origin, group 0 (denominator) is of Asian origin, group 1 (numerator) includes Caucasian of origin (see Table 5). The numerator then has the frequency of persons with the same genotype as the subject under group 1, the denominator is the people with the same genotype as the subject under group 0. O (X = l) = P (X == l | Gx) / P (X = 0 | Gx), where Gx - genotype of person X, symbol "|" stands for "provided", X = 1 - person belongs to group 1, X = 0 - person belongs to group 0.

The results of the odds ratio calculation are presented in Table 8. If O (X = 1) equals one or a number close to one, then the result can be considered indecisive because the numerator's probability equals the denominator probability, or the genotype found has the same frequency as If O (X = 1)> 1, then a person with such a genotype is more likely to belong to group 1. If O (X = 1) <1, then a person with such a genotype is more likely to belong to group 0. In all cases, the data presented in Tables 4 to 7 were used to calculate the odds ratio.

Table 8: Examples of phenotypic character prediction using selected combinations of SNP markers

Number of person Eyes color: blue (1) vs blue (0) P (X = 1 | Gx) P (X = 0 | Gx) O (X = 1) Predicted group Real group 1 0,0934 1.0000 0,0934 0 0 2 0,0934 1.0000 0,0934 0 0 3 0.8462 0.1538 5.5020 1 1 4 0,0934 1.0000 0,0934 0 0 5 0.8462 0.1538 5.5020 1 1 Number Eye color: brown (1) vs brown (0) persons P (X = 1 | Gx) P (X = 0 | Gx) O (X = 1) Predicted group Real group

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1 0.8125 0.1875 4,3333 1 1 2 0.7879 0.2121 3,7143 1 1 3 0,0833 0.9167 0,0909 0 0 4 0.7879 0.2121 3,7143 1 1 5 0,0833 0.9167 0,0909 0 0 Number of person Biogeographical origin: Bě och (1) vs Asian (1) P (X = 1 | Gx) P (X = 0 | Gx) O (X = 1) Predicted group Real group 1 1.0000 0.1378 7,2569 1 1 2 1.0000 0.1378 7,2569 1 1 3 1.0000 0.1378 7,2569 1 1 4 0,0450 1.0000 0,0450 0 0 5 1.0000 0.1378 7,2569 1 1 Number persons Hair color: light (1) vs dark (0) P (X = 1 | Gx) P (X = 0 | Gx) O (X = 1) Predicted group Real group 1 0,1111 0.8889 0.1250 0 0 2 0,0909 0.9091 0.1000 0 0 3 0.6667 0.3333 2,0003 1 0 4 0.1429 0.8571 0.1667 0 0 5 0.5000 0.5000 1.0000 - 1 * O (X = 1) = P (X = 1 | Gx) / P (X = 0 ax)

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Claims (6)

PATENT CLAIMS A method for predicting the visible phenotypic features and biogeographic origin of a person, particularly for forensic purposes, by determining the genotype of markers exhibiting a single nucleotide polymorphism, characterized in that the genotype of the following markers exhibiting a single nucleotide polymorphism is determined: rs16891982, rs2031526, , rs1667394, rs28049897, and by comparing genotypic combinations of subsets of this group of markers identifying belonging to a group of individuals exhibiting a given phenotypic trait, determined on a population sample by comparing genotype and phenotype, the phenotypic characteristics are determined: eye color blue vs. blue on subset of markers rs 16891982, rs2031526, rs 12913832, rs916977; eye color brown vs. brown on subset of markers rs7495174, rs 12913832; biogeographical origin of white vs. white Asian on subset of markers rs 16891982, rs 1426654; Hair color vs. light dark on subset of markers rs12896399, rs 12913832, rs8049897, rs 1667394. Method according to claim 1, characterized in that the genotypes are determined by the RealTime PCR method. Method according to claim 1, characterized in that the determination of phenotypic characteristics is carried out by the odds ratio method. • Ά A kit for carrying out the method according to claim 1 or 2, comprising probes complementary to both alleles of the markers exhibiting single nucleotide polymorphisms rs16891982, rs12896399, rs7495174, rsl2913832, rs916977, rs16766494, rs16766494. Kit according to claim 4, characterized in that the probes are fluorescently labeled. The kit of claim 4 or 5, further comprising amplified DNA of all three possible genotypes for each of the SNP markers included in the panel.