ArticlePloS one2015
Evaluation of a 7-Gene Genetic Profile for Athletic Endurance Phenotype in Ironman Championship Triathletes.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 5 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Association Between the c.34C > T (rs17602729) Polymorphism of the AMPD1 Gene and the Status of Endurance and Power Athletes: A Systematic Review and Meta-Analysis.Sports medicine (Auckland, N.Z.) · 2025Pooled it
- Association between theFrontiers in physiology · 2025Pooled it
- Pooled it
- The association of HFE gene H63D polymorphism with endurance athlete status and aerobic capacity: novel findings and a meta-analysis.European journal of applied physiology · 2020Pooled it
- Association of PPARGC1A Gly428Ser (rs8192678) polymorphism with potential for athletic ability and sports performance: A meta-analysis.PloS one · 2019Pooled it
- Muscle Work and Its Relationship withGenes · 2021Trial
- Reduction of Skeletal Muscle Power in Adolescent Males Carrying H63D Mutation in theBioMed research international · 2017Trial
- Observational
- Decoding endurance performance: integrative genomic and epigenetic insights into the molecular basis of athletic adaptation.Frontiers in systems biology · 2026Review
- Review
- Genetic Determinants of Endurance: A Narrative Review on Elite Athlete Status and Performance.International journal of molecular sciences · 2024Review
- Genome-wide assessment reveals a significant association between ACSS3 and physical activity.Genes, brain, and behavior · 2023Article
- Genetics and sports performance: the present and future in the identification of talent for sports based on DNA testing.European journal of applied physiology · 2022Review
- Genetic profiles to identify talents in elite endurance athletes and professional football players.PloS one · 2022Article
- Article
- Genetic Profile in Genes Associated with Cardiorespiratory Fitness in Elite Spanish Male Endurance Athletes.Genes · 2021Article
- Total Genotype Score Modelling of Polygenic Endurance-Power Profiles in Lithuanian Elite Athletes.Genes · 2021Article
- Association of Genetic Variances inSports (Basel, Switzerland) · 2021Article
- Prediction and Identification of Power Performance Using Polygenic Models of Three Single-Nucleotide Polymorphisms in Chinese Elite Athletes.Frontiers in genetics · 2021Article
- Genetic test for the personalization of sport training.Acta bio-medica : Atenei Parmensis · 2020Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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