- 作者: Camilla J. Williams, Zhixiu Li, Nicholas Harvey, Rodney A. Lea, Brendon J. Gurd, Jacob T. Bonafiglia, Ioannis Papadimitriou, Macsue Jacques, Ilaria Croci, Dorthe Stensvold, Ulrik Wisloff, Jenna L. Taylor, Trishan Gajanand, Emily R. Cox, Joyce S. Ramos, Robert G. Fassett, Jonathan P. Little, Monique E. Francois, Christopher M. Hearon Jr, Satyam Sarma, Sylvan L. J. E. Janssen, Emeline M. Van Craenenbroeck, Paul Beckers, Véronique A. Cornelissen, Erin J. Howden, Shelley E. Keating, Xu Yan, David J. Bishop, Anja Bye, Larisa M. Haupt, Lyn R. Griffiths, Kevin J. Ashton, Matthew A. Brown, Luciana Torquati, Nir Eynon & Jeff S. Coombes
- 作者服務機構: 1.Centre for Research on Exercise, Physical Activity and Health, School of Human Movement and Nutrition Sciences, University of Queensland, St. Lucia, Brisbane, QLD, Australia 2.Translational Genomics Group, Institute of Health and Biomedical Innovation, Woolloongabba, Brisbane, QLD, Australia 3.Faculty of Health Sciences and Medicine, Bond University, Robina, QLD, Australia 4.Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Kelvin Grove, Brisbane, QLD, Australia 5.School of Kinesiology and Health Studies, Queen’s University, Kingston, ON, Canada 6.Institute for Health and Sport (iHeS), Victoria University, Melbourne, VIC, Australia 7.Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway 8.Department of Sport, Movement and Health, University of Basel, Basel, Switzerland 9.Caring Futures Institute, SHAPE Research Centre, Exercise Science and Clinical Exercise Physiology, College of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia 10.School of Health and Exercise Sciences, University of British Columbia, Kelowna, BC, Canada 11.Internal Medicine, Institute for Exercise and Environmental Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA 12.Department of Physiology, Radboud University Medical Center, Nijmegen, Netherlands 13.Department of Cardiology, Antwerp University Hospital, Antwerp, Belgium 14.Department of Rehabilitation Sciences – Research Group for Rehabilitation in Internal Disorders, Catholic University of Leuven, Leuven, Belgium 15.Baker Heart and Diabetes Institute, Melbourne, VIC, Australia 16.Australia Institute for Musculoskeletal Sciences (AIMSS), Melbourne, VIC, Australia 17.School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia 18.Department of Cardiology, St. Olavs Hospital, Trondheim, Norway 19.Guy’s and St Thomas’ NHS Foundation Trust and King’s College London, London, UK 20.Department of Sport and Health Sciences, University of Exeter, Exeter, UK
- 中文摘要:
- 英文摘要:
Background
Low cardiorespiratory fitness (V̇O2peak) is highly associated with chronic disease and mortality from all causes. Whilst exercise training is recommended in health guidelines to improve V̇O2peak, there is considerable inter-individual variability in the V̇O2peak response to the same dose of exercise. Understanding how genetic factors contribute to V̇O2peak training response may improve personalisation of exercise programs. The aim of this study was to identify genetic variants that are associated with the magnitude of V̇O2peak response following exercise training.
Methods
Participant change in objectively measured V̇O2peak from 18 different interventions was obtained from a multi-centre study (Predict-HIIT). A genome-wide association study was completed (n = 507), and a polygenic predictor score (PPS) was developed using alleles from single nucleotide polymorphisms (SNPs) significantly associated (P < 1 × 10–5) with the magnitude of V̇O2peak response. Findings were tested in an independent validation study (n = 39) and compared to previous research.
Results
No variants at the genome-wide significance level were found after adjusting for key covariates (baseline V̇O2peak, individual study, principal components which were significantly associated with the trait). A Quantile–Quantile plot indicates there was minor inflation in the study. Twelve novel loci showed a trend of association with V̇O2peak response that reached suggestive significance (P < 1 × 10–5). The strongest association was found near the membrane associated guanylate kinase, WW and PDZ domain containing 2 (MAGI2) gene (rs6959961, P = 2.61 × 10–7). A PPS created from the 12 lead SNPs was unable to predict V̇O2peak response in a tenfold cross validation, or in an independent (n = 39) validation study (P > 0.1). Significant correlations were found for beta coefficients of variants in the Predict-HIIT (P < 1 × 10–4) and the validation study (P < × 10–6), indicating that general effects of the loci exist, and that with a higher statistical power, more significant genetic associations may become apparent.
Conclusions
Ongoing research and validation of current and previous findings is needed to determine if genetics does play a large role in V̇O2peak response variance, and whether genomic predictors for V̇O2peak response trainability can inform evidence-based clinical practice. - 中文關鍵字:
- 英文關鍵字: Genetics, V̇O2peak training response, Individual variability, GWAS, Polygenic predictor score