Evaluation of a 7-Gene Genetic Profile for Athletic Endurance Phenotype in Ironman Championship Triathletes

Rebecca Grealy; Jasper Herruer; Carl L E Smith; Doug Hiller; Luke J Haseler; Lyn R Griffiths

doi:10.1371/journal.pone.0145171

Evaluation of a 7-Gene Genetic Profile for Athletic Endurance Phenotype in Ironman Championship Triathletes

PLoS One. 2015 Dec 30;10(12):e0145171. doi: 10.1371/journal.pone.0145171. eCollection 2015.

Authors

Rebecca Grealy^{1

2}, Jasper Herruer^{1

2}, Carl L E Smith^{1

2}, Doug Hiller³, Luke J Haseler⁴, Lyn R Griffiths²

Affiliations

¹ School of Medical Science, Griffith University, Gold Coast, Australia.
² Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia.
³ North Hawaii Community Hospital, John A Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, United States of America.
⁴ Heart Foundation Research Centre, School of Physiotherapy and Exercise Science, Griffith University, Gold Coast, Australia.

Abstract

Polygenic profiling has been proposed for elite endurance performance, using an additive model determining the proportion of optimal alleles in endurance athletes. To investigate this model's utility for elite triathletes, we genotyped seven polymorphisms previously associated with an endurance polygenic profile (ACE Ins/Del, ACTN3 Arg577Ter, AMPD1 Gln12Ter, CKMM 1170bp/985+185bp, HFE His63Asp, GDF8 Lys153Arg and PPARGC1A Gly482Ser) in a cohort of 196 elite athletes who participated in the 2008 Kona Ironman championship triathlon. Mean performance time (PT) was not significantly different in individual marker analysis. Age, sex, and continent of origin had a significant influence on PT and were adjusted for. Only the AMPD1 endurance-optimal Gln allele was found to be significantly associated with an improvement in PT (model p = 5.79 x 10-17, AMPD1 genotype p = 0.01). Individual genotypes were combined into a total genotype score (TGS); TGS distribution ranged from 28.6 to 92.9, concordant with prior studies in endurance athletes (mean±SD: 60.75±12.95). TGS distribution was shifted toward higher TGS in the top 10% of athletes, though the mean TGS was not significantly different (p = 0.164) and not significantly associated with PT even when adjusted for age, sex, and origin. Receiver operating characteristic curve analysis determined that TGS alone could not significantly predict athlete finishing time with discriminating sensitivity and specificity for three outcomes (less than median PT, less than mean PT, or in the top 10%), though models with the age, sex, continent of origin, and either TGS or AMPD1 genotype could. These results suggest three things: that more sophisticated genetic models may be necessary to accurately predict athlete finishing time in endurance events; that non-genetic factors such as training are hugely influential and should be included in genetic analyses to prevent confounding; and that large collaborations may be necessary to obtain sufficient sample sizes for powerful and complex analyses of endurance performance.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Alleles
Athletes
Athletic Performance / physiology*
Female
Gene Frequency / genetics
Genotype
Humans
Male
Middle Aged
Phenotype
Physical Endurance / genetics*
Polymorphism, Genetic / genetics

Grants and funding

This work was supported by an Australian International Science Linkages grant and by infrastructure purchased with Australian Government EIF Super Science Funds as part of the Therapeutic Innovation Australia - Queensland Node project with equipment and technical support staff from the National Collaborative Research Infrastructure Strategy (NCRIS) government funding.