The study aimed to identify genetic factors associated with endurance in professional wrestlers from the Republic of Kazakhstan using a genome-wide association study (GWAS). The dataset included 58 elite combat-sport athletes and 60 non-athletic controls. Genotyping was conducted on the iScan platform (Illumina) with the Infinium Global Screening Array-24. Quality control procedures in PLINK v1.9 and subsequent analyses in R (qqman) retained 296,772 SNPs after filtering for call rate, Hardy–Weinberg equilibrium, minor allele frequency, and exclusion of sex-chromosomal and mitochondrial variants. Principal component analysis showed no substantial population stratification, and the genomic inflation factor (λ = 1.02) indicated minimal confounding. At the suggestive significance level (p < 1×10⁻⁵), several loci showed differences between athletes and controls, including rs12916133, rs7765401, GSA-rs1682809, and rs4777639. These variants are located near PPEF2, SV2B, CDC37L1, and the non-coding region LOC105370982, which are genes and regulatory elements involved in calcium-dependent signaling, synaptic regulation, proteostasis, and muscular adaptation. Although none reached genome-wide significance (p < 5×10⁻⁸), the detected trends point to a contribution of regulatory and neurometabolic pathways to endurance-related traits in wrestlers. The study is limited by the moderate sample size and the need for replication in independent Kazakhstani and international cohorts. Nevertheless, the findings form a basis for personalized training strategies and future integrative multi-omics research in sports genomics.

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.