Advancing sports and exercise genomics: moving from hypothesis-driven single study approaches to large multi-omics collaborative science

Abstract

The primary use of the traditional candidate gene approach over the past decades in sports genetics has had limited success in identifying genes associated with elite athletic performance. Advances in high-throughput technologies now permit the application of "omics" (e.g. genomics, transcriptomics, metabolomics, proteomics and epigenomics) approaches to examine the global features of a cell, tissue or organism. "Omics" approaches are being applied with some success to a wide range of pertinent biomedical problems such as cancer diagnosis but also in sports science and sports medicine such as for the identification of biomarkers of trainability or blood doping. There is good evidence to suggest that a combined "omics" solution will greatly facilitate discovery of the genetic influences on sporting performance, training response, injury predisposition and other potential determinants of successful human performance. In this regard, large-scale, collaborative efforts involving well-phenotyped cohorts will be essential for major progress to be made. A recent consensus emerged among 15 research groups active in the field of sports genetics to unite their efforts under one new collaborative initiative named the Athlome Project Consortium. The primary aim of the Athlome Project is to combine resources from individual studies and consortia worldwide to collectively study the genotype and phenotype data available on elite athletes, the adaptation to exercise training (in both human and animal models) and the determinants of exercise-related musculoskeletal injuries. This editorial summarizes the challenges and opportunities facing the Athlome Project Consortium and the field of sports and exercise genomics in general.


Autor / Fonte:Masashi Tanaka, Guan Wang, Yannis P Pitsiladis Physiological Genomics 2016 January 26, : physiolgenomics.00009.2016
Link: http://physiolgenomics.physiology.org/content/early/2016/01/19/physiolgenomics.00009.2016