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

Grealy R, Herruer J, Smith CL, Hiller D, Haseler LJ, Griffiths LR - PLoS ONE (2015)

Bottom Line: 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).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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Medical 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.

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Receiver operating characteristic curves (ROC) determining potential for PT prediction using four models.
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pone.0145171.g005: Receiver operating characteristic curves (ROC) determining potential for PT prediction using four models.

Mentions: Furthermore, ROC AUC analysis determined that TGS alone could not significantly predict whether an athlete would finish in (a) less than the median PT of 681.33 min (AUC = 0.52, p = 0.674); (b) less than the mean PT of 708.39 min (AUC = 0.48, p = 0.626); or (c) the top 10% fastest PT i.e. less than 593.7 min (AUC = 0.61, p = 0.132). However, models with the demographic variables of age, sex, and continent of origin only, demographic variables and TGS, and demographic variables and AMPD1 genotype were all found to significantly predict athlete finishing time for all three outcomes (less than median PT, less than mean PT, or in the top 10%). ROC AUC graphs for all analyses are shown in Fig 5. The model with age, sex, continent and AMPD1 genotype was found to be the most significant for predicting whether athletes would finish in less time than both the mean and median (Median AUC = 0.82, p = 8.92 x 10−13, 95%CI = 0.75 to 0.88; Mean AUC = 0.81, p = 4.72 x 10−12, 95%CI = 0.75 to 0.87), while the model with age, sex, continent and TGS was the most significant model for predicting whether athletes would finish in the top 10% (AUC = 0.91, p = 3.50 x 10−8, 95%CI = 0.86 to 0.96). However, the model with age, sex, continent, and AMPD1 genotype had similar though slightly less significant results (AUC = 0.90, p = 4.93 x 10−8, 95%CI = 0.85 to 0.96). Of all the ROC AUC analyses (Fig 5), the models for predicting top 10% finishers had the highest discrimination of performance in terms of sensitivity and specificity. The point where sensitivity was maximized (sensitivity = 1.000) while minimizing the false positive rate and thus maximizing specificity (specificity = 0.742) corresponded to a model value of 672.28. Using the model equation PT = (4.65 • age) + (79.90 • sex) + (-21.36 • continent) + (-0.42 • TGS) + 552.6, this would indicate that a North American male aged 35 yrs old would need a TGS of 51 or more in order to obtain the identified criteria cutoff of 672.28; however, a trade-off among the variables means that a lower TGS in combination with optimal values for the demographic variables would be equally likely to finish in the top 10%.


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

Grealy R, Herruer J, Smith CL, Hiller D, Haseler LJ, Griffiths LR - PLoS ONE (2015)

Receiver operating characteristic curves (ROC) determining potential for PT prediction using four models.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4696732&req=5

pone.0145171.g005: Receiver operating characteristic curves (ROC) determining potential for PT prediction using four models.
Mentions: Furthermore, ROC AUC analysis determined that TGS alone could not significantly predict whether an athlete would finish in (a) less than the median PT of 681.33 min (AUC = 0.52, p = 0.674); (b) less than the mean PT of 708.39 min (AUC = 0.48, p = 0.626); or (c) the top 10% fastest PT i.e. less than 593.7 min (AUC = 0.61, p = 0.132). However, models with the demographic variables of age, sex, and continent of origin only, demographic variables and TGS, and demographic variables and AMPD1 genotype were all found to significantly predict athlete finishing time for all three outcomes (less than median PT, less than mean PT, or in the top 10%). ROC AUC graphs for all analyses are shown in Fig 5. The model with age, sex, continent and AMPD1 genotype was found to be the most significant for predicting whether athletes would finish in less time than both the mean and median (Median AUC = 0.82, p = 8.92 x 10−13, 95%CI = 0.75 to 0.88; Mean AUC = 0.81, p = 4.72 x 10−12, 95%CI = 0.75 to 0.87), while the model with age, sex, continent and TGS was the most significant model for predicting whether athletes would finish in the top 10% (AUC = 0.91, p = 3.50 x 10−8, 95%CI = 0.86 to 0.96). However, the model with age, sex, continent, and AMPD1 genotype had similar though slightly less significant results (AUC = 0.90, p = 4.93 x 10−8, 95%CI = 0.85 to 0.96). Of all the ROC AUC analyses (Fig 5), the models for predicting top 10% finishers had the highest discrimination of performance in terms of sensitivity and specificity. The point where sensitivity was maximized (sensitivity = 1.000) while minimizing the false positive rate and thus maximizing specificity (specificity = 0.742) corresponded to a model value of 672.28. Using the model equation PT = (4.65 • age) + (79.90 • sex) + (-21.36 • continent) + (-0.42 • TGS) + 552.6, this would indicate that a North American male aged 35 yrs old would need a TGS of 51 or more in order to obtain the identified criteria cutoff of 672.28; however, a trade-off among the variables means that a lower TGS in combination with optimal values for the demographic variables would be equally likely to finish in the top 10%.

Bottom Line: 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).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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Medical 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.

Show MeSH
Related in: MedlinePlus