Genetic profiles to identify talents in elite endurance athletes and professional football players

Abstract

The genetic profile that is needed to identify talents has been studied extensively in recent years. The main objective of this investigation was to approach, for the first time, the study of genetic variants in several polygenic profiles and their role in elite endurance and professional football performance by comparing the allelic and genotypic frequencies to the non-athlete population. In this study, genotypic and allelic frequencies were determined in 452 subjects: 292 professional athletes (160 elite endurance athletes and 132 professional football players) and 160 non-athlete subjects. Genotyping of polymorphisms in liver metabolisers (CYP2D6, GSTM1, GSTP and GSTT), iron metabolism and energy efficiency (HFE, AMPD1 and PGC1a), cardiorespiratory fitness (ACE, NOS3, ADRA2A, ADRB2 and BDKRB2) and muscle injuries (ACE, ACTN3, AMPD1, CKM and MLCK) was performed by Polymerase Chain Reaction-Single Nucleotide Primer Extension (PCR-SNPE). The combination of the polymorphisms for the "optimal" polygenic profile was quantified using the genotype score (GS) and total genotype score (TGS). Statistical differences were found in the genetic distributions between professional athletes and the non-athlete population in liver metabolism, iron metabolism and energy efficiency, and muscle injuries (p<0.001). The binary logistic regression model showed a favourable OR (odds ratio) of being a professional athlete against a non-athlete in liver metabolism (OR: 1.96; 95% CI: 1.28-3.01; p = 0.002), iron metabolism and energy efficiency (OR: 2.21; 95% CI: 1.42-3.43; p < 0.001), and muscle injuries (OR: 2.70; 95% CI: 1.75-4.16; p < 0.001) in the polymorphisms studied. Genetic distribution in professional athletes as regards endurance (professional cyclists and elite runners) and professional football players shows genetic selection in these sports disciplines.

Fig 1. TGS distribution of liver metabolism genes in a) professional athletes and non-athlete subjects and b) elite endurance athletes and professional football players with regard to non-athlete subjects.

Fig 2. ROC curve summarising the ability of TGS of liver metabolism genes to distinguish potential professional athletes from non-athletes.

Fig 3. TGS distribution of iron metabolism and energy efficiency in a) professional athletes and non-athlete subjects and b) elite endurance athletes and professional football players with regard to non-athlete subjects.

Fig 4. ROC curve summarising the ability of TGS of iron metabolism and energy efficiency genes to distinguish potential professional athletes from non-athletes.

Fig 5. TGS distribution of cardiorespiratory fitness in a) professional athletes and non-athlete subjects and b) endurance athletes and football players with regard to non-athlete subjects.

Fig 6. ROC curve summarising the ability of TGS in cardiorespiratory fitness genes to distinguish potential professional athletes from non-athletes.

Fig 7. TGS distribution for muscle injuries genes in a) professional athletes and non-athlete subjects and b) endurance athletes and football players with regard to non-athlete subjects.

Fig 8. ROC curve summarising the ability of TGS for muscle injuries genes to distinguish potential professional athletes from non-athletes.