Advances in Exercise, Fitness, and Performance Genomics in 2011

ROTH, STEPHEN M.1; RANKINEN, TUOMO2; HAGBERG, JAMES M.1; LOOS, RUTH J. F.3,4; PÉRUSSE, LOUIS5; SARZYNSKI, MARK A.2; WOLFARTH, BERND6; BOUCHARD, CLAUDE2

Medicine & Science in Sports & Exercise:
doi: 10.1249/MSS.0b013e31824f28b6
Basic Sciences
Abstract

ABSTRACT: This review of the exercise genomics literature emphasizes the highest quality articles published in 2011. Given this emphasis on the best publications, only a small number of published articles are reviewed. One study found that physical activity levels were significantly lower in patients with mitochondrial DNA mutations compared with controls. A two-stage fine-mapping follow-up of a previous linkage peak found strong associations between sequence variation in the activin A receptor, type-1B (ACVRIB) gene and knee extensor strength, with rs2854464 emerging as the most promising candidate polymorphism. The association of higher muscular strength with the rs2854464 A allele was confirmed in two separate cohorts. A study using a combination of transcriptomic and genomic data identified a comprehensive map of the transcriptomic features important for aerobic exercise training–induced improvements in maximal oxygen consumption, but no genetic variants derived from candidate transcripts were associated with trainability. A large-scale de novo meta-analysis confirmed that the effect of sequence variation in the fat mass and obesity-associated (FTO) gene on the risk of obesity differs between sedentary and physically active adults. Evidence for gene–physical activity interactions on type 2 diabetes risk was found in two separate studies. A large study of women found that physical activity modified the effect of polymorphisms in the lipoprotein lipase (LPL), hepatic lipase (LIPC), and cholesteryl ester transfer protein (CETP) genes, identified in previous genome-wide association study reports, on HDL cholesterol. We conclude that a strong exercise genomics corpus of evidence would not only translate into powerful genomic predictors but also have a major effect on exercise biology and exercise behavior research.

Author Information

1Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD; 2Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA; 3Medical Research Council Epidemiological Unit, Institute of Metabolic Science, Cambridge, UNITED KINGDOM; 4Charles R. Bronfman Institute for Personalized Medicine, Mount Sinai School of Medicine, New York, NY; 5Department of Kinesiology, Laval University, Sainte-Foy, Québec, CANADA; and 6Department of Prevention, Rehabilitation and Sports Medicine, Technical University Munich, Munich, GERMANY

Address for correspondence: Claude Bouchard, Ph.D., Human Genomics Laboratory, Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA 70808-4124; E-mail: claude.bouchard@pbrc.edu.

Submitted for publication January 2012.

Accepted for publication February 2012.

©2012The American College of Sports Medicine