An innovative way to highlight the power of each polymorphism on elite athletes phenotype expression

Abstract

The purpose of this study was to determine the probability of soccer players having the best genetic background that could increase performance, evaluating the polymorphism that are considered Performance Enhancing Polymorphism (PEPs) distributed on five genes: PPARα, PPARGC1A, NRF2, ACE e CKMM. Particularly, we investigated how each polymorphism works directly or through another polymorphism to distinguish elite athletes from non-athletic population. Sixty professional soccer players (age 22.5 ± 2.2) and sixty healthy volunteers (age 21.2± 2.3) were enrolled. Samples of venous blood was used to prepare genomic DNA. The polymorphic sites were scanned using PCR-RFLP protocols with different enzyme. We used a multivariate logistic regression analysis to demonstrate an association between the five PEPs and elite phenotype. We found statistical significance in NRF2 (AG/GG genotype) polymorphism/soccer players association (p < 0.05) as well as a stronger association in ACE polymorphism (p =0.02). Particularly, we noticed that the ACE ID genotype and even more the II genotype are associated with soccer player phenotype. Although the other PEPs had no statistical significance, we proved that some of these may work indirectly, amplifying the effect of another polymorphism; for example, seems that PPARα could acts on NRF2 (GG) enhancing the effect of the latter, notwithstanding it had not shown a statistical significance. In conclusion, to establish if a polymorphism can influence the performance, it is necessary to understand how they act and interact, directly and indirectly, on each other.

Keywords: Polymerase chain reaction-restriction fragment length polymorphism; performance; performance-enhancing polymorphisms.

Fig 1.

Graphical model to evaluate the relative importance of each predictor to the regressors with regression type graphical models

Fig 2.

Receiver operating characteristic curve (ROC) summarizing the ability of elite genotype score to classify potential elite athletes from non-athletes (controls). AUC indicates the area under the curve (95% confidence intervals).