Integrative pathway analysis of a genome-wide association study of V̇o_2maxresponse to exercise training

Abstract

We previously reported the findings from a genome-wide association study of the response of maximal oxygen uptake (Vo2max) to an exercise program. Here we follow up on these results to generate hypotheses on genes, pathways, and systems involved in the ability to respond to exercise training. A systems biology approach can help us better establish a comprehensive physiological description of what underlies Vo2maxtrainability. The primary material for this exploration was the individual single-nucleotide polymorphism (SNP), SNP-gene mapping, and statistical significance levels. We aimed to generate novel hypotheses through analyses that go beyond statistical association of single-locus markers. This was accomplished through three complementary approaches: 1) building de novo evidence of gene candidacy through informatics-driven literature mining; 2) aggregating evidence from statistical associations to link variant enrichment in biological pathways to Vo2max trainability; and 3) predicting possible consequences of variants residing in the pathways of interest. We started with candidate gene prioritization followed by pathway analysis focused on overrepresentation analysis and gene set enrichment analysis. Subsequently, leads were followed using in silico analysis of predicted SNP functions. Pathways related to cellular energetics (pantothenate and CoA biosynthesis; PPAR signaling) and immune functions (complement and coagulation cascades) had the highest levels of SNP burden. In particular, long-chain fatty acid transport and fatty acid oxidation genes and sequence variants were found to influence differences in Vo2max trainability. Together, these methods allow for the hypothesis-driven ranking and prioritization of genes and pathways for future experimental testing and validation.

Genomic and transcriptomic predictors of triglyceride response to regular exercise

“…We employed two different approaches to calculate gene-level statistics for GSEA: (1) the second most significantly associated SNP was used to summarise all SNPs for each gene22 and (2) Stouffer's method23 that combines p values from multiple individual statistical tests and calculates the gene set statistic using the inverse normal cumulative distribution function.…”

Letter to the editor: A genetic-based algorithm for personalized resistance training

“…However, Jones et al [27] were the first to present the application of genetics to different customized training interventions, using genetic profiles in which a score was given for each allele based on the cumulative literature reports about polymorphisms (Figure 1). Although several genetic polymorphisms have been associated with particular physiological phenotypes, changes in metabolic pathways molecularly measured or even considered in silico using bioinformatic tools [28], evaluation of a multigenic prior genetic profile had never been used as a variable of physical training itself [29]. The aim of the authors was to compare the chronic effects of strength training using high or low intensity aerobic performance and power programmes, for athletes with power/endurance genotypes.…”

Sports genetics moving forward: lessons learned from medical research

“…Studying these extremes will help identify genes and pathways that contribute to both disease and extreme health states (13,15,22,28). The most probable relevance will be by rare variants with large effects (e.g., 20), since the effect of common variants with small effects might be overridden by environmental effect.…”