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.
Highlights
The ability of sport scientists to predict which athletes amongst an elite group will become world-class is limited because the interactions between biological factors, training, recovery and competitive performance are not fully understood [1]
This study investigated whether the seven polymorphisms strongly associated with an endurance polygenic profile as described in Ruiz et al 2009 [5]—angiotensin converting enzyme (ACE) Ins/Del, ACTN3 Arg577Ter, adenosine monophosphate deaminase 1 (AMPD1) Gln12Ter, creatine kinase-MM (CKMM) 1170 bp/985+185bp, high iron Fe (HFE) His63Asp, growth differentiation factor 8 (GDF8) Lys153Arg and PPARGC1A Gly482Ser—were individually associated with performance time or whether the combined influence of these polymorphisms as a total genotype score (TGS) could distinguish ‘faster’ from ‘slower’ performance time of the Ironman athletes
Genotyping success rate ranged from 99–100% for all markers except HFE (97.4% of samples successfully genotyped)
Summary
The ability of sport scientists to predict which athletes amongst an elite group will become world-class is limited because the interactions between biological factors, training, recovery and competitive performance are not fully understood [1]. This is further supported both by mouse studies, which show that PPARGC1A overexpression increases type I fiber ratio while knockout models show a decrease in type I and shift to type IIx and IIb fibers, and a recent study examining human muscle biopsies, which showed a lower level of post-training type I fibers in S carriers compared to G carriers, though mitochondrial density and activity, and intracellular lipid content was not different between different genotype groups [36] These data point to a clear advantage of G allele carriers in endurance performance and as such is an important component of any polygenic athletic profile
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