OPTIMIZATION OF GENOME-WIDE SNP MICROARRAY DATA PROCESSING FOR GENERATION OF AN ATHLETE GENETIC PROFILE
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Authors
T.N. Kereyev
Institute of Genetics and Physiology, CS MSHE RK, Almaty, Kazakhstan
A.V. Mustafayeva
Institute of Genetics and Physiology, CS MSHE RK, Almaty, Kazakhstan
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Zh.A. Zhaniyazov
Institute of Genetics and Physiology, CS MSHE RK, Almaty, Kazakhstan
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Satbayev Kazakh National Research Technical University, Almaty, Kazakhstan
N.K. Altynova
Institute of Genetics and Physiology, CS MSHE RK, Almaty, Kazakhstan
G.S. Zhunussova
Institute of Genetics and Physiology, CS MSHE RK, Almaty, Kazakhstan
L.B. Djansugurova
Institute of Genetics and Physiology, CS MSHE RK, Almaty, Kazakhstan
Al-Farabi Kazakh National University, Almaty, Kazakhstan
N.M. Kassymbek
Al-Farabi Kazakh National University, Almaty, Kazakhstan
S.T. Mukhambetzhanov
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Abstract
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.
Keywords
sports genetics, genetic passport, genome-wide SNP microarray genotyping, single nucleotide polymorphism (SNP), software code
Article Details
References
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