Integrative pathway analysis of a genome-wide association study of V̇<scp>o</scp><sub>2max</sub>response to exercise training — Sujoy Ghosh (2013) | RDL Network
Integrative pathway analysis of a genome-wide association study of V̇<scp>o</scp><sub>2max</sub>response to exercise training
Journal of Applied Physiology 115(9): 1343-1359
Article 2013 English
Authors
SG
Sujoy Ghosh
JV
Juan C. Vivar
MS
Mark A. Sarzynski
Abstract
1 min read
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.
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