Predictive accuracy of genetic variants for eye color in a Kazakh population using the IrisPlex system.

OBJECTIVE: This study assesses the accuracy of the IrisPlex system, a genetic eye color prediction tool for forensic analysis, in the Kazakh population. The study compares previously published genotypes of 515 Kazakh individuals from varied geographical and ethnohistorical contexts with phenotypic data on their eye color, introduced for the first time in this research. RESULTS: The IrisPlex panel's effectiveness in predicting eye color in the Kazakh population was validated. It exhibited slightly lower accuracy than in Western European populations but was higher than in Siberian populations. The sensitivity was notably high for brown-eyed individuals (0.99), but further research is needed for blue and intermediate eye colors. This study establishes IrisPlex as a useful predictive tool in the Kazakh population and provides a basis for future investigations into the genetic basis of phenotypic variations in this diverse population.

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.

Reconstructing complex admixture history using a hierarchical model.

Various methods have been proposed to reconstruct admixture histories by analyzing the length of ancestral chromosomal tracts, such as estimating the admixture time and number of admixture events. However, available methods do not explicitly consider the complex admixture structure, which characterizes the joining and mixing patterns of different ancestral populations during the admixture process, and instead assume a simplified one-by-one sequential admixture model. In this study, we proposed a novel approach that considers the non-sequential admixture structure to reconstruct admixture histories. Specifically, we introduced a hierarchical admixture model that incorporated four ancestral populations and developed a new method, called HierarchyMix, which uses the length of ancestral tracts and the number of ancestry switches along genomes to reconstruct the four-way admixture history. By automatically selecting the optimal admixture model using the Bayesian information criterion principles, HierarchyMix effectively estimates the corresponding admixture parameters. Simulation studies confirmed the effectiveness and robustness of HierarchyMix. We also applied HierarchyMix to Uyghurs and Kazakhs, enabling us to reconstruct the admixture histories of Central Asians. Our results highlight the importance of considering complex admixture structures and demonstrate that HierarchyMix is a useful tool for analyzing complex admixture events.