Kazakh populations occupy an important position within the genetic landscape of Central Asia and Inner Eurasia, yet compact ancestry-informative marker panels evaluated for Kazakh-related ancestry remain limited. We analyzed genome-wide single-nucleotide polymorphism data from 624 Kazakh individuals together with data from external reference populations. After harmonization, the discovery dataset comprised 1607 individuals from 41 population groups and 80,766 shared autosomal variants. Candidate ancestry-informative markers were ranked using allele-frequency differentiation and informativeness for assignment, followed by linkage disequilibrium filtering. The final panel comprised 385 markers. Because marker availability differed across external datasets, transferability assessment in two independent Kazakh cohorts (n = 224 and n = 132) was performed using the 341-marker subset shared across the internal dataset and both external cohorts. Principal component analysis showed that both external Kazakh cohorts overlapped substantially with the internal Kazakh cluster. In nearest-centroid analysis, 94.6% of internal Kazakh individuals and 92.0% and 91.7% of individuals from the two external Kazakh cohorts, respectively, were assigned as Kazakh-related. Among non-Kazakh reference individuals used in the discovery framework, 4.1% were assigned as Kazakh-related; this internal, selection-informed estimate was mainly driven by genetically close Central Asian and Inner Eurasian populations. These findings support the transferability of the shared marker subset across independent Kazakh datasets, while also showing limited discrimination between Kazakhs and several closely related neighboring populations.

Central Asian populations remain underrepresented in pharmacogenomic research, limiting the availability of population-specific data for genotype-informed prescribing and precision medicine. This study analyzed clinically relevant pharmacogenomic variant frequencies in Kazakh, Russian, and Uzbek population groups residing in Kazakhstan using genome-wide genotype data from 1301 individuals: Kazakh (n = 1111), Russian (n = 156), and Uzbek (n = 34). ClinPGx, a PharmGKB-based clinical annotation framework that prioritizes variant–drug associations according to levels of evidence, was used to select variants with evidence levels 1A, 1B, and 2A. In total, 112 directly genotyped variants were retained for population-specific allele and genotype frequency analysis. All 112 variants were queried against the gnomAD v4.1 genome and exome reference datasets. Of these, matching allele-frequency data for the predefined reported allele were available in at least one of the two gnomAD datasets for 103 variants, whereas for 9 variants the VEP-based query did not return a matching gnomAD frequency for that allele. Frequencies were reported for the same predefined reported allele across all groups, and differences between the study groups were assessed using 95% confidence intervals, Fisher’s exact tests, and false discovery rate correction. Genotype counts and the proportions of individuals carrying at least one copy of the reported allele were also summarized for all selected variants. Several pharmacogenomic variants showed population-specific frequency patterns, including NUDT15 rs116855232, SLCO1B1 rs4149056, VKORC1 rs9934438, and UGT1A1 rs10929302. Comparison with gnomAD showed that the observed frequencies were variant-specific and could not be consistently approximated by a single broad genetic ancestry group. Reference-based population structure analysis provided additional ancestry context and supported separate reporting by population group. The study did not evaluate clinical outcomes or make individual prescribing recommendations, and the small Uzbek sample size limits the precision of frequency estimates for this group, particularly for rare variants. Overall, this study provides a clinically prioritized pharmacogenomic frequency resource for underrepresented population groups in Kazakhstan and supports broader Central Asian representation in pharmacogenomic implementation research.

Turkmen populations remain underrepresented in genome-wide reference datasets, limiting comparative analyses of genetic diversity in Central Asia. This study provides an initial regional genome-wide characterization of self-identified Turkmen students who were studying in Kazakhstan at the time of sample collection and who reported the Dashoguz Region, Turkmenistan, as their place of birth in the questionnaire. Approximately 650,000 markers were genotyped in 25 participants, of whom 23 unrelated individuals were retained after genotype quality control and relatedness filtering. Population structure was evaluated with 600 present-day Eurasian reference individuals using principal component analysis, ADMIXTURE v1.3.0, and pairwise Weir–Cockerham F_ST estimates based on 17,471 linkage-disequilibrium-pruned autosomal SNPs. Runs of homozygosity, Y-chromosome haplogroups, and mitochondrial DNA haplogroups were also examined. The analyzed cohort was positioned within the Central Asian genetic landscape and showed the lowest pairwise differentiation from Uzbek, Tajik, and AADR Turkmen reference groups. Runs-of-homozygosity analysis showed mostly low-to-moderate individual burdens, although one individual had elevated long-segment homozygosity; sensitivity analyses indicated that the main autosomal results were not driven by this individual. Paternal and maternal haplogroup assignments revealed diverse uniparental lineages. These data provide a regional genome-wide resource for future comparative studies of Central Asian genetic diversity and have been deposited in the European Variation Archive.