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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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Frequency distribution of total genotype score (TGS) in overall Ironman cohort.
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pone.0145171.g002: Frequency distribution of total genotype score (TGS) in overall Ironman cohort.

Mentions: Though these markers were not shown to be associated with being in the top 10% or significantly influence mean performance time individually, the combined effect of multiple optimal alleles was determined by calculating the TGS as per Ruiz et al. (2009), which is a percentage of optimal alleles obtained across all seven markers. In the total cohort of Ironman athletes, the mean ± SD of the TGS was 60.75 ± 12.95 (Fig 2). The TGS ranged from a minimum score of 28.6 to 92.9, with only two athletes having both the lowest and highest scores, and the distribution was both symmetrical (skewness statistic ± SE: -0.003 ± 0.18) and mesokurtic (kurtosis statistic ± SE: -0.230 ± 0.35). In the top and bottom 10% performers (Fig 3), the mean ± SD of the TGS was 65.1 ± 13.09 and 58.9 ± 11.81, respectively (n = 17 for top 10%; n = 16 for bottom 10%). The TGS distribution was also symmetrical and mesokurtic in both the top 10% (skewness statistic ± SE: -0.610 ± 0.55; kurtosis statistic ± SE: -0.734 ±1.06) and bottom 10% (skewness statistic ± SE: -0.354 ± 0.56; kurtosis statistic ± SE: -0.354 ± 1.09). The distribution in the top 10% was shifted to the right (towards higher TGS) compared to the bottom 10%. This difference was more clearly observed when TGS distribution was grouped into 10-unit intervals (Fig 4). Though mean TGS was smaller by ~6.2 units in the bottom performers compared with the top performers (or approximately one optimal allele fewer on average), this was not shown to be significant by t-test analysis (t = 1.425, df = 31, p = 0.164).


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)

Frequency distribution of total genotype score (TGS) in overall Ironman cohort.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0145171.g002: Frequency distribution of total genotype score (TGS) in overall Ironman cohort.
Mentions: Though these markers were not shown to be associated with being in the top 10% or significantly influence mean performance time individually, the combined effect of multiple optimal alleles was determined by calculating the TGS as per Ruiz et al. (2009), which is a percentage of optimal alleles obtained across all seven markers. In the total cohort of Ironman athletes, the mean ± SD of the TGS was 60.75 ± 12.95 (Fig 2). The TGS ranged from a minimum score of 28.6 to 92.9, with only two athletes having both the lowest and highest scores, and the distribution was both symmetrical (skewness statistic ± SE: -0.003 ± 0.18) and mesokurtic (kurtosis statistic ± SE: -0.230 ± 0.35). In the top and bottom 10% performers (Fig 3), the mean ± SD of the TGS was 65.1 ± 13.09 and 58.9 ± 11.81, respectively (n = 17 for top 10%; n = 16 for bottom 10%). The TGS distribution was also symmetrical and mesokurtic in both the top 10% (skewness statistic ± SE: -0.610 ± 0.55; kurtosis statistic ± SE: -0.734 ±1.06) and bottom 10% (skewness statistic ± SE: -0.354 ± 0.56; kurtosis statistic ± SE: -0.354 ± 1.09). The distribution in the top 10% was shifted to the right (towards higher TGS) compared to the bottom 10%. This difference was more clearly observed when TGS distribution was grouped into 10-unit intervals (Fig 4). Though mean TGS was smaller by ~6.2 units in the bottom performers compared with the top performers (or approximately one optimal allele fewer on average), this was not shown to be significant by t-test analysis (t = 1.425, df = 31, p = 0.164).

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