Minsk, Belarus
Pinsk, Belarus
Pinsk, Belarus
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Based on the assumption that some gene variants in combination may show cumulative or epistatic effects, it is relevant to explore the combination of genotypes of independently acting and interacting polymorphic genes. The article describes the application of nonparametric method of multifactor dimensionality reduction (MDR) on a cohort of highly qualified athletes in order to determine the most preferable combination of genotypes of genes of neurotransmitter systems that determine the functioning of the higher nervous system. Based on the obtained data on the patterns of intergenic interactions, we performed cluster analysis showing the type of interaction between polymorphic loci of neurotransmitter system genes. In addition, we obtained models of the most favorable combination of genotypes for cyclic and acyclic sports. The MDR method in our researches was also applied to identify the unfavorable combination of genotypes in the development of such pathologies as nephropathy and disorder of bone tissue remodeling in young athletes.
genetics, genetic research, data analysis, bioinformatics, statistics, intergenic interactions
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2. Motsinger A. Multifactor dimensionality reduction: an analysis strategy for modelling and detecting gene-gene interactions in human genetics and pharmacogenomics studies. Human Genomics, 2007, 2(5), pp. 318-328. DOI: https://doi.org/10.1186/1479-7364-2-5-318
3. Ponomarenko I.V. Using the method of Multifactor Dimensionality Reduction (MDR) and its modifications for the analysis of gene-gene and gene-environment interactions in genetic and epidemiological studies (review). Scientific results of biomedical research, 2019, 5(1), pp. 4-21. (in Russ.) EDN: https://elibrary.ru/umbgkq
4. Multifactor Dimensionality Reduction [Electronic resource] 24.12.2014. URL: https://sourceforge.net/projects/mdr/ (Access date: 05.11.2024)
5. Shapialevich N.V., Marinich V.V., Melnov S.B. Genetic markers of neurotransmitters and psychophysiological features of qualified athletes of various sports. Applied Sports Science, 2024, 1 (19), pp. 47-54. (in Russ.) URL: https://rep.polessu.by/handle/123456789/31678
6. Zhur N.V., Lebed T.L., Kruchinski N.G. Application of analysis of genetic studies for the diagnosis and prevention of nephrological complications in patients with type 2 diabetes mellitus. Health for all: a scientific and practical journal, 2023, (1), pp. 28-36. (in Russ.) URL: https://rep.polessu.by/handle/123456789/29037
7. Zhur N.V., Evdolyuk S.V., Lebed T.L., Marinich T.V., Kruchinski N.G. Molecular genetic methods in diagnosing and predicting individual predisposition to bone mineral density disorders. Bulletin of the Polessky State University. Natural Sciences Series, 2024, (1), pp. 66-72. (in Russ.) URL: https://rep.polessu.by/handle/123456789/31760
8. Ivaniukovich U., Nikalayenka K., Melnov S., Zhur N., Lebed T. Influence of classification of outcomes on the result of simulation and forecasting by the MDR method. Sakharov Readings 2023: Environmental Problems of the XXI century : proceedings of the 23rd International Scientific Conference, Minsk, Republic of Belarus, May 18-19, 2023. A.D. Sakharov International State Ecological Institute of the Belarusian State University, Minsk : ISEI named after A.D. Sakharov BSU, 2023, (2), pp. 301-305. (in Russ.) URL: https://rep.polessu.by/handle/123456789/28924
