Advances in exercise, fitness, and performance genomics in 2015

dc.contributor.authorSarzynski, Mark A.
dc.contributor.authorLoos, Ruth
dc.contributor.authorLucía Mulas, Alejandro
dc.contributor.authorPerusse, Louis
dc.contributor.authorRoth, Stephen M.
dc.contributor.authorWolfarth, Bernd
dc.contributor.authorRankinen, Tuomo
dc.contributor.authorBouchard, Claude
dc.date.accessioned2017-01-19T12:14:55Z
dc.date.available2017-01-19T12:14:55Z
dc.date.issued2016
dc.description.abstractThis review of the exercise genomics literature encompasses the highest-quality articles published in 2015 across seven broad topics: physical activity behavior, muscular strength and power, cardiorespiratory fitness and endurance performance, body weight and adiposity, insulin and glucose metabolism, lipid and lipoprotein metabolism, and hemodynamic traits. One study used a quantitative trait locus for wheel running in mice to identify single nucleotide polymorphisms (SNPs) in humans associated with physical activity levels. Two studies examined the association of candidate gene ACTN3 R577X genotype on muscular performance. Several studies examined gene-physical activity interactions on cardiometabolic traits. One study showed that physical inactivity exacerbated the body mass index (BMI)-increasing effect of an FTO SNP but only in individuals of European ancestry, whereas another showed that high-density lipoprotein cholesterol (HDL-C) SNPs from genome-wide association studies exerted a smaller effect in active individuals. Increased levels of moderate-to-vigorous-intensity physical activity were associated with higher Matsuda insulin sensitivity index in PPARG Ala12 carriers but not Pro12 homozygotes. One study combined genome-wide and transcriptome-wide profiling to identify genes and SNPs associated with the response of triglycerides (TG) to exercise training. The genome-wide association study results showed that four SNPs accounted for all of the heritability of △TG, whereas the baseline expression of 11 genes predicted 27% of △TG. A composite SNP score based on the top eight SNPs derived from the genomic and transcriptomic analyses was the strongest predictor of ΔTG, explaining 14% of the variance. The review concludes with a discussion of a conceptual framework defining some of the critical conditions for exercise genomics studies and highlights the importance of the recently launched National Institutes of Health Common Fund program titled "Molecular Transducers of Physical Activity in Humans."spa
dc.description.filiationUEMspa
dc.description.impact4.141 JCR (2016) Q1, 6/81 Sport Sciencesspa
dc.description.impact2.052 SJR (2016) Q1, 13/280 Orthopedics and Sports Medicine, 5/195 Physical Therapy, Sports Therapy and Rehabilitation, 12/127 Sports Sciencespa
dc.description.impactNo data IDR 2016spa
dc.description.sponsorshipSin financiaciónspa
dc.identifier.citationSarzynski, M. A., Loos, R. J., Lucía, A., Pérusse, L., Roth, S. M., Wolfarth, B., ... & Bouchard, C. (2016). Advances in Exercise, Fitness, and Performance Genomics in 2015. Medicine and science in sports and exercise, 48(10), 1906-1916. DOI: 10.1249/MSS.0000000000000982spa
dc.identifier.doi10.1249/MSS.0000000000000982
dc.identifier.issn01959131
dc.identifier.issn15300315
dc.identifier.urihttp://hdl.handle.net/11268/6182
dc.language.isoengspa
dc.peerreviewedSispa
dc.rights.accessRightsopen accessspa
dc.subject.uemGenómicaspa
dc.subject.uemEjercicio físicospa
dc.subject.unescoGenomaspa
dc.subject.unescoDeportespa
dc.titleAdvances in exercise, fitness, and performance genomics in 2015spa
dc.typejournal articlespa
dspace.entity.typePublication
relation.isAuthorOfPublicationd3691359-d7bd-4a12-b84e-338e28c81f9f
relation.isAuthorOfPublication.latestForDiscoveryd3691359-d7bd-4a12-b84e-338e28c81f9f

Files