Athletic performance results from complex interactions between genetic and environmental factors. This review compiles and synthesizes available literature on polymorphic genes associated with endurance, power, and strength performance, as well as their links to injury susceptibility and chronic metabolic diseases. Endurance performance is modulated by ACE, PPARGC1A, HFE, UCP2, UCP3, CDKN1A, and PPARA, regulating mitochondrial biogenesis, oxygen utilization, and muscle fiber composition. Power performance involves ACTN3, MCT1, IGF1, AMPD1, AGT, and AGTR2, affecting anaerobic metabolism, lactate clearance, and fast-twitch fiber recruitment. Strength performance is influenced by AR, PPARG, ARK2N, MMS22L, LRPPRC, PHACTR1, and MTHFR, related to androgen signaling, muscle hypertrophy, and recovery. Injury-related genes (COL1A1, COL5A1, IL6, VEGFA, NOG) and metabolic risk genes (FTO, PPARG, ADRB3) further highlight the clinical relevance of genomics. Collectively, these insights support the application of genetic information to personalize training, enhance performance, prevent injuries, and guide exercise interventions to mitigate metabolic disease risk.

Background: Athletic performance is a result of interaction between genetic and environmental factors, including training, nutrition, rest, hormonal and psychophysiological status. Numerous genes and polymorphisms can influence key physiological processes, such as muscle contractility, energy metabolism, oxidative capacity, and cardiovascular efficiency. Among these, the ACTN3 gene, and in particular the polymorphism rs1815739 (NM_001104.4:c.1729C>T; p.Arg577Ter), is well known for its association with athletic performance, but emerging evidence suggests broader effects on metabolic and inflammatory phenotypes. Methods: For this study, 109 elite athletes were enrolled and genotyped for ACTN3 c.1729C>T by Sanger sequencing. Moreover, biochemical markers, such as creatine kinase (CK), C-reactive protein (CRP), lipid profile, adiponectin, and neutrophil-to-lymphocyte ratio (NLR), were examined. Finally, to better investigate the association between genotype and athletic performance, the genotypes and the results of standardized physical tests were correlated in a small exploratory subgroup of 28 professional basketball athletes. Normality was assessed using the Shapiro–Wilk test; non-normally distributed variables were summarized as median [IQR] and analyzed with non-parametric methods. Genotype comparisons were adjusted for multiple testing using the Holm procedure, and principal biochemical models were adjusted for age, BMI, sex, and ancestry. Results: The c.1729C>T genotype distribution differed between the two ancestry groups (exact p = 0.049), and all three variants were compatible with Hardy–Weinberg equilibrium within the ancestry groups. No biochemical genotype association remained statistically significant after Holm correction. In the performance subgroup, no genotype–performance association remained significant after Holm correction. Conclusions: In this cohort, the corrected analyses did not provide robust evidence for genotype-dependent biochemical or performance effects. The results are exploratory and require replication in larger cohorts, considering ancestry and gender differences.