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

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 HERС2 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.

Genotype data for 60 SNP genetic markers associated with eye, hair, skin color, ABO blood group, sex, core Y-chromosome haplogroups in Kazakh population.

OBJECTIVES: The collection of genotype data was conducted as an essential part of a pivotal research project with the goal of examining the genetic variability of skin, hair, and iris color among the Kazakh population. The data has practical application in the field of forensic DNA phenotyping (FDA). Due to the limited size of forensic databases from Central Asia (Kazakhstan), it is practically impossible to obtain an individual identification result based on forensic profiling of short tandem repeats (STRs). However, the pervasive use of the FDA necessitates validation of the currently employed set of genetic markers in a variety of global populations. No such data existed for the Kazakhs. The Phenotype Expert kit (DNA Research Center, LLC, Russia) was used for the first time in this study to collect data. DATA DESCRIPTION: The present study provides genotype data for a total of 60 SNP genetic markers, which were analyzed in a sample of 515 ethnic Kazakhs. The dataset comprises a total of 41 single nucleotide polymorphisms (SNPs) obtained from the HIrisPlex-S panel. Additionally, there are 4 SNPs specifically related to the AB0 gene, 1 marker associated with the AMELX/Y genes, and 14 SNPs corresponding to the primary haplogroups of the Y chromosome. The aforementioned data could prove valuable to researchers with an interest in investigating genetic variability and making predictions about phenotype based on eye color, hair color, skin color, AB0 blood group, gender, and biogeographic origin within the male lineage.

Origins of East Caucasus Gene Pool: Contributions of Autochthonous Bronze Age Populations and Migrations from West Asia Estimated from Y-Chromosome Data.

The gene pool of the East Caucasus, encompassing modern-day Azerbaijan and Dagestan populations, was studied alongside adjacent populations using 83 Y-chromosome SNP markers. The analysis of genetic distances among 18 populations (N = 2216) representing Nakh-Dagestani, Altaic, and Indo-European language families revealed the presence of three components (Steppe, Iranian, and Dagestani) that emerged in different historical periods. The Steppe component occurs only in Karanogais, indicating a recent medieval migration of Turkic-speaking nomads from the Eurasian steppe. The Iranian component is observed in Azerbaijanis, Dagestani Tabasarans, and all Iranian-speaking peoples of the Caucasus. The Dagestani component predominates in Dagestani-speaking populations, except for Tabasarans, and in Turkic-speaking Kumyks. Each component is associated with distinct Y-chromosome haplogroup complexes: the Steppe includes C-M217, N-LLY22g, R1b-M73, and R1a-M198; the Iranian includes J2-M172(×M67, M12) and R1b-M269; the Dagestani includes J1-Y3495 lineages. We propose J1-Y3495 haplogroup's most common lineage originated in an autochthonous ancestral population in central Dagestan and splits up ~6 kya into J1-ZS3114 (Dargins, Laks, Lezgi-speaking populations) and J1-CTS1460 (Avar-Andi-Tsez linguistic group). Based on the archeological finds and DNA data, the analysis of J1-Y3495 phylogeography suggests the growth of the population in the territory of modern-day Dagestan that started in the Bronze Age, its further dispersal, and the microevolution of the diverged population.

Chromosome-Length Assembly of the Baikal Seal (Pusa sibirica) Genome Reveals a Historically Large Population Prior to Isolation in Lake Baikal.

Pusa sibirica, the Baikal seal, is the only extant, exclusively freshwater, pinniped species. The pending issue is, how and when they reached their current habitat-the rift lake Baikal, more than three thousand kilometers away from the Arctic Ocean. To explore the demographic history and genetic diversity of this species, we generated a de novo chromosome-length assembly, and compared it with three closely related marine pinniped species. Multiple whole genome alignment of the four species compared with their karyotypes showed high conservation of chromosomal features, except for three large inversions on chromosome VI. We found the mean heterozygosity of the studied Baikal seal individuals was relatively low (0.61 SNPs/kbp), but comparable to other analyzed pinniped samples. Demographic reconstruction of seals revealed differing trajectories, yet remarkable variations in Ne occurred during approximately the same time periods. The Baikal seal showed a significantly more severe decline relative to other species. This could be due to the difference in environmental conditions encountered by the earlier populations of Baikal seals, as ice sheets changed during glacial-interglacial cycles. We connect this period to the time of migration to Lake Baikal, which occurred ~3-0.3 Mya, after which the population stabilized, indicating balanced habitat conditions.

Determining the Area of Ancestral Origin for Individuals From North Eurasia Based on 5,229 SNP Markers.

Currently available genetic tools effectively distinguish between different continental origins. However, North Eurasia, which constitutes one-third of the world's largest continent, remains severely underrepresented. The dataset used in this study represents 266 populations from 12 North Eurasian countries, including most of the ethnic diversity across Russia's vast territory. A total of 1,883 samples were genotyped using the Illumina Infinium Omni5Exome-4 v1.3 BeadChip. Three principal components were computed for the entire dataset using three iterations for outlier removal. It allowed the merging of 266 populations into larger groups while maintaining intragroup homogeneity, so 29 ethnic geographic groups were formed that were genetically distinguishable enough to trace individual ancestry. Several feature selection methods, including the random forest algorithm, were tested to estimate the number of genetic markers needed to differentiate between the groups; 5,229 ancestry-informative SNPs were selected. We tested various classifiers supporting multiple classes and output values for each class that could be interpreted as probabilities. The logistic regression was chosen as the best mathematical model for predicting ancestral populations. The machine learning algorithm for inferring an ancestral ethnic geographic group was implemented in the original software "Homeland" fitted with the interface module, the prediction module, and the cartographic module. Examples of geographic maps showing the likelihood of geographic ancestry for individuals from different regions of North Eurasia are provided. Validating methods show that the highest number of ethnic geographic group predictions with almost absolute accuracy and sensitivity was observed for South and Central Siberia, Far East, and Kamchatka. The total accuracy of prediction of one of 29 ethnic geographic groups reached 71%. The proposed method can be employed to predict ancestries from the populations of Russia and its neighbor states. It can be used for the needs of forensic science and genetic genealogy.