Minsk, Belarus
Pinsk, Belarus
Pinsk, Belarus
VAK Russia 1.1.10
VAK Russia 1.2.1
VAK Russia 1.2.2
VAK Russia 1.2.3
VAK Russia 1.5.2
VAK Russia 1.5.5
VAK Russia 1.5.8
VAK Russia 3.3.9
VAK Russia 3.1.33
VAK Russia 5.2.3
VAK Russia 5.2.6
VAK Russia 5.3.2
VAK Russia 5.3.4
VAK Russia 5.8.4
VAK Russia 5.8.5
VAK Russia 5.8.6
VAK Russia 5.8.7
VAK Russia 5.6.6
VAK Russia 5.12.1
VAK Russia 5.12.4
UDC 575.162
UDC 347.514.3
UDC 355.233.22
UDC 351.855.3
UDC 00
UDC 31
CSCSTI 77.00
CSCSTI 14.00
CSCSTI 20.00
CSCSTI 83.00
Russian Classification of Professions by Education 02.00.00
Russian Classification of Professions by Education 06.00.00
Russian Classification of Professions by Education 09.00.00
Russian Classification of Professions by Education 32.00.00
Russian Classification of Professions by Education 39.00.00
Russian Classification of Professions by Education 44.00.00
Russian Library and Bibliographic Classification 1
Russian Library and Bibliographic Classification 22
Russian Library and Bibliographic Classification 28
Russian Library and Bibliographic Classification 3
Russian Library and Bibliographic Classification 58
Russian Library and Bibliographic Classification 73
Russian Library and Bibliographic Classification 74
Russian Library and Bibliographic Classification 75
Russian Trade and Bibliographic Classification 5
Russian Trade and Bibliographic Classification 7
BISAK SEL SELF-HELP
BISAK COM014000 Computer Science
BISAK COM072000 Computer Simulation
BISAK COM017000 Cybernetics
BISAK COM018000 Data Processing
BISAK COM062000 Data Modeling & Design
BISAK COM089000 Data Visualization
BISAK COM021000 Databases / General
BISAK COM021030 Databases / Data Mining
BISAK COM023000 Educational Software
BISAK COM025000 Expert Systems
BISAK COM074000 Hardware / Mobile Devices
BISAK COM080000 History
BISAK COM079010 Human-Computer Interaction (HCI)
BISAK COM032000 Information Technology
BISAK COM031000 Information Theory
BISAK COM004000 Intelligence (AI) & Semantics
BISAK COM060000 Internet / General
BISAK COM039000 Management Information Systems
BISAK COM077000 Mathematical & Statistical Software
BISAK COM051000 Programming / General
BISAK COM051300 Programming / Algorithms
BISAK COM012040 Programming / Games
BISAK COM060170 Web / Content Management Systems
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
1. Kovalevich A., Padutov V., Baranov O. Genome-wide sequencing is a new stage of genetic research. Science and innovation, 2015, 5(147), pp. 56-58. (in Russ.) EDN: https://elibrary.ru/tzfqqx
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
