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Do exercise-associated genes explain phenotypic variance in the three components of fitness? a systematic review & meta-analysis

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  • Henry C Chung
  • Don R Keiller
  • Justin D Roberts
  • Dan A Gordon

Abstract

The aim of this systematic review and meta-analysis was to identify a list of common, candidate genes associated with the three components of fitness, specifically cardiovascular fitness, muscular strength, and anaerobic power, and how these genes are associated with exercise response phenotype variability, in previously untrained participants. A total of 3,969 potentially relevant papers were identified and processed for inclusion. After eligibility and study selection assessment, 24 studies were selected for meta-analysis, comprising a total of 3,012 participants (male n = 1,512; females n = 1,239; not stated n = 261; age 28 ± 9 years). Meta-Essentials spreadsheet 1.4 (Microsoft Excel) was used in creating the forest plots and meta-analysis. IBM SPSS statistics V24 was implemented for the statistical analyses and the alpha was set at p ≤ 0.05. 13 candidate genes and their associated alleles were identified, which were associated with the phenotypes of interest. Analysis of training group data showed significant differential phenotypic responses. Subgroup analysis showed; 44%, 72% and 10% of the response variance in aerobic, strength and power phenotypes, respectively, were explained by genetic influences. This analysis established that genetic variability explained a significant proportion of the adaptation differences across the three components of fitness in the participants post-training. The results also showed the importance of analysing and reporting specific gene alleles. Information obtained from these findings has the potential to inform and influence future exercise-related genes and training studies.

Suggested Citation

  • Henry C Chung & Don R Keiller & Justin D Roberts & Dan A Gordon, 2021. "Do exercise-associated genes explain phenotypic variance in the three components of fitness? a systematic review & meta-analysis," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-18, October.
  • Handle: RePEc:plo:pone00:0249501
    DOI: 10.1371/journal.pone.0249501
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    Cited by:

    1. Kaiyuan Huang & Jingwen Song & Weishuai Kong & Zhongyuan Deng, 2022. "Exe-Muscle: An Exercised Human Skeletal Muscle Gene Expression Database," IJERPH, MDPI, vol. 19(14), pages 1-12, July.

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