Dataset Information

Optimizing the genetic prediction of the eye and hair color for North Eurasian populations.

ABSTRACT:

Background

Predicting the eye and hair color from genotype became an established and widely used tool in forensic genetics, as well as in studies of ancient human populations. However, the accuracy of this tool has been verified on the West and Central Europeans only, while populations from border regions between Europe and Asia (like Caucasus and Ural) also carry the light pigmentation phenotypes.

Results

We phenotyped 286 samples collected across North Eurasia, genotyped them by the standard HIrisPlex-S markers and found that predictive power in Caucasus/Ural/West Siberian populations is reasonable but lower than that in West Europeans. As these populations have genetic ancestries different from that of West Europeans, we hypothesized they may carry a somewhat different allele spectrum. Thus, for all samples we performed the exome sequencing additionally enriched with the 53 genes and intergenic regions known to be associated with the eye/hair color. Our association analysis replicated the importance of the key previously known SNPs but also identified five new markers whose eye color prediction power for the studied populations is compatible with the two major previously well-known SNPs. Four out of these five SNPs lie within the HER?2 gene and the fifth in the intergenic region. These SNPs are found at high frequencies in most studied populations. The released dataset of exomes from Russian populations can be further used for population genetic and medical genetic studies.

Conclusions

This study demonstrated that precision of the established systems for eye/hair color prediction from a genotype is slightly lower for the populations from the border regions between Europe and Asia that for the West Europeans. However, this precision can be improved if some newly revealed predictive SNPs are added into the panel. We discuss that the replication of these pigmentation-associated SNPs on the independent North Eurasian sample is needed in the future studies.

SUBMITTER: Balanovska E

PROVIDER: S-EPMC7488246 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Optimizing the genetic prediction of the eye and hair color for North Eurasian populations.

Balanovska Elena E Lukianova Elena E Kagazezheva Janet J Maurer Andrey A Leybova Natalia N Agdzhoyan Anastasiya A Gorin Igor I Petrushenko Valeria V Zhabagin Maxat M Pylev Vladimir V Kostryukova Elena E Balanovsky Oleg O

BMC genomics 20200910 Suppl 7

<h4>Background</h4>Predicting the eye and hair color from genotype became an established and widely used tool in forensic genetics, as well as in studies of ancient human populations. However, the accuracy of this tool has been verified on the West and Central Europeans only, while populations from border regions between Europe and Asia (like Caucasus and Ural) also carry the light pigmentation phenotypes.<h4>Results</h4>We phenotyped 286 samples collected across North Eurasia, genotyped them by ...[more]

PMID: 32912208

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Project description:Eye and hair colour is highly variable in the European population, and is largely genetically determined. Both linkage and association studies have previously been used to identify candidate genes underlying this variation. Many of the genes found were previously known as underlying mutant mouse phenotypes or human genetic disease, but others, previously unsuspected as pigmentation genes, have also been discovered.We assayed the hair of a population of individuals of Scottish origin using tristimulus colorimetry, in order to produce a quantitative measure of hair colour. Cluster analysis of this data defined two groups, with overlapping borders, which corresponded to visually assessed dark versus red/light hair colour. The Danish population was assigned into categorical hair colour groups. Both cohorts were also assessed for eye colour. DNA from the Scottish group was genotyped at SNPs in 33 candidate genes, using 384 SNPs identified by HapMap as representatives of each gene. Associations found between SNPs and colorimetric hair data and eye colour categories were replicated in a cohort of the Danish population. The Danish population was also genotyped with SNPs in 4 previously described pigmentation genes. We found replicable associations of hair colour with the KITLG and OCA2 genes. MC1R variation correlated, as expected, with the red dimension of colorimetric hair colour in Scots. The Danish analysis excluded those with red hair, and no associations were found with MC1R in this group, emphasising that MC1R regulates the colour rather than the intensity of pigmentation. A previously unreported association with the HPS3 gene was seen in the Scottish population. However, although this replicated in the smaller cohort of the Danish population, no association was seen when the whole study population was analysed.We have found novel associations with SNPs in known pigmentation genes and colorimetrically assessed hair colour in a Scottish and a Danish population.

Project description:Background:DNA prediction of eye color represent one application of the externally visible characteristics (EVC), which attained growing interest in the field of DNA forensic phenotyping. This is mainly due to its ability to narrow the pool of suspects without the need to compare any retrieved DNA material from the crime scene to a reference DNA. Several methods and multiplex genetic panel were proposed with variable prediction accuracy between different populations. However, such panel was not previously tested in the Saudi population, nor any populations of the Middle East and North Africa origin. Method:A panel of eleven single nucleotide polymorphisms (SNPs) was tested for their association with three eye colors (brown, hazel, and intermediate) in 80 volunteer Saudi individuals. SNPs and haplotype association test with eye colors were performed to identify the top significant SNPs with the three eye colors. Also, multinomial logistic regression was used to construct the prediction model using a training set of 60 subjects, and a validation set of 20 subjects. The goodness of fit parameter of the model to correctly predicts each eye color as compared to the other was performed. Results:Eye color was significantly associated with rs12913832, rs7170852, and rs916977 that are located within HERC2. SNP rs12913832 was the top significant SNP (p-value?=?1.78E-15) that accounted for the association in this region, as the other SNPs were not significant after adjusting for rs12913832. A prediction model containing five SNPs showed high prediction accuracy with Area Under the receiver operating characteristic Curves (AUC) equals to 0.95 and 0.83 for brown and intermediate eye colors, respectively. However, the model's performance was very low for predicting the hazel eye color with AUC equals 0.75. Discussion:Despite the small sample size of our study, we reported very significant SNP associations with eye color. Our model to predict eye colors based on DNA material showed high accuracy for brown and intermediate eye colors. The eye color prediction-model underperformed for the hazel eye colors, suggesting that larger sample size, as well as more comprehensive set of SNPs, could improve the model-prediction accuracy.

Dataset Information

Optimizing the genetic prediction of the eye and hair color for North Eurasian populations.

Background

Results

Conclusions

Publications

Optimizing the genetic prediction of the eye and hair color for North Eurasian populations.

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