Ancient genomic time-transect across the Central Asian Steppe sheds new light onto the origins and demise of the Scythians.

Ethnogenesis of Kazakhs took place in Central Asia, a region of high genetic and cultural diversity. Even though archaeological and historical studies have shed some light on the formation of modern Kazakhs, the process of establishment of hierarchical socioeconomic structure in the Steppe remains contentious. In this study, we analyzed haplotype variation at 15 Y-chromosomal short-tandem-repeats obtained from 1171 individuals from 24 tribes representing the three socio-territorial subdivisions (Senior, Middle and Junior zhuz) in Kazakhstan to comprehensively characterize the patrilineal genetic architecture of the Kazakh Steppe. In total, 577 distinct haplotypes were identified belonging to one of 20 haplogroups; 16 predominant haplogroups were confirmed by SNP-genotyping. The haplogroup distribution was skewed towards C2-M217, present in all tribes at a global frequency of 51.9%. Despite signatures of spatial differences in haplotype frequencies, a Mantel test failed to detect a statistically significant correlation between genetic and geographic distance between individuals. An analysis of molecular variance found that ∼8.9% of the genetic variance among individuals was attributable to differences among zhuzes and ∼20% to differences among tribes within zhuzes. The STRUCTURE analysis of the 1164 individuals indicated the presence of 20 ancestral groups and a complex three-subclade organization of the C2-M217 haplogroup in Kazakhs, a result supported by the multidimensional scaling analysis. Additionally, while the majority of the haplotypes and tribes overlapped, a distinct cluster of the O2 haplogroup, mostly of the Naiman tribe, was observed. Thus, firstly, our analysis indicated that the majority of Kazakh tribes share deep heterogeneous patrilineal ancestries, while a smaller fraction of them are descendants of a founder paternal ancestor. Secondly, we observed a high frequency of the C2-M217 haplogroups along the southern border of Kazakhstan, broadly corresponding to both the path of the Mongolian invasion and the ancient Silk Road. Interestingly, we detected three subclades of the C2-M217 haplogroup that broadly exhibits zhuz-specific clustering. Further study of Kazakh haplotypes variation within a Central Asian context is required to untwist this complex process of ethnogenesis.

The long-term storage of unutilized pesticides raised new problems of long-term environmental contamination. The study presents the results of surveying 151 individuals in 7 villages living close to pesticide-contaminated localities. All individuals have been surveyed concerning their consumption habits and lifestyle characteristics. An assessment of the general exposure risks of the local population was carried out using the analysis of pollutants in food products and the average levels of their consumption in the region. The cohort risk evaluation revealed that the greatest risk was associated with the regular consumption of cucumbers, pears, bell peppers, meat, and milk. The new model to estimate individual risks of long-term pesticide pollution was proposed as a calculation of the combined action of 9 risk factors, including individual genotypes, age, lifestyle, and personal pesticide consumption rates. The analysis of the predictive ability of this model showed that the final score for individual health risks corresponded to the development of chronic diseases. A high level of chromosomal aberrations was evidenced for individual genetic risk manifestations. The combined influence of all risk factors revealed contributions of 24.7% for health status and 14.2% for genetic status, while other impacts go to all unaccounted factors.

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.

Based on the analysis of global experience and our own preliminary studies aimed at identifying associations between athletic performance potential and individual genotypes across 97 SNP polymorphisms (75 candidate genes), interpretations of possible genotype variants for an athlete’s genetic passport were developed. A software program and several auxiliary scripts were created using the RStudio development environment and the Python programming language. The developed tools efficiently create and update databases containing the required genetic information, reducing the time needed to develop a genetic passport while minimizing the risk of errors. These tools significantly improved the efficiency of storing, sorting, and processing primary data obtained from genome-wide SNP microarray genotyping. The reduction of human involvement in the repetitive process of collecting and comparing tables minimized the risk of errors and facilitated the analysis of genotyping data. Automation of primary and secondary processing of genome-wide SNP microarray genotyping data, genotype interpretation, database updating, and generation of completed genetic passport reduced data processing time from several months to 20-30 minutes.

Modern personalized medicine needs the integration of genetic, environmental, and spatial data for the comprehensive assessment of individual disease risk. However, combining disparate data sources into a single, interpretable result remains a complex bioinformatics challenge. Existing approaches to generating individual reports often rely on disparate analytical steps and partial manual processing, limiting their scalability, reproducibility, and standardization. Python pipeline was developed to integrate genetic marker filtering and annotation, individual polygenic risk score (PRS) calculation with percentile ranking, spatial air pollution estimates (LUR), and report generation via Jinja2 and WeasyPrint with separation of logic and visual presentation. The system generates an individual asthma research report with two blocks: genetic predisposition to bronchial asthma and genetic susceptibility to four pollutants corresponding to PM₂.₅, CO, NO₂ and SO₂. Interpretation is based on OR, PRS with positioning in the sampling distribution and an environmental exposure map linked to the participant’s location, ensuring reproducibility and reducing time costs. The proposed tool allows for standardized feedback from study participants, reduces the likelihood of errors in manual report preparation, and ensures transparent interpretation of genetic and environmental data, which is critical for bioinformatics and epidemiological work.