Аңдатпа
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.