What this means
Researchers found gene patterns that differed between athletes in broad sport groups and the general public. Some patterns did not hold up in a separate group, and the work does not predict who will succeed in sport.
Plain-English summary written by an AI model from the abstract. The original is below.
- Who it was tested in
- 3,676 professional athletes and 21,516 general population participants, plus 2,914 adults aged 90 or older
- Kind of research
- A study
The science
- Original title
- GWAS-based polygenic profiling across broad sport categories: shared and category-specific genetic associations with athlete status and polygenic differences from population controls.
- Authors
- Terekhov MV, Abushinova GA, Mamchur AA et al.
- Journal
- Frontiers in sports and active living
- Published
- Oct 15, 2026
- Study type
- Study
- PMID
- 42877100 ↗
Abstract
Distinct physical demands across sports categories suggest a potential genetic component underlying an athlete's suitability for specific disciplines. To examine this hypothesis, the study analyzed a cohort of 3,676 professional athletes from a range of sports categories, including combat sports (n = 875), multi-sports (n = 102), speed- and power-based sports (n = 146), coordination-based sports (n = 630), game-oriented sports (n = 1,143), and cyclic sports (n = 780). Six genome-wide association studies were conducted to identify genetic variants associated with each sports category, with a general population sample of 21,516 participants as the control group. Comparison of the findings revealed both common and distinct genetic markers across the different sports categories. The findings of each study were used to build polygenic score models to identify the genetic differences between each athlete group and the control. In the internal held-out test set, the models demonstrated varying discriminative abilities, with ROC AUC values of 0.67 for coordination-based sports, 0.75 for combat sports, 0.77 for speed- and power-based sports, 0.82 for multi-sports, 0.68 for game-oriented sports, and 0.67 for cyclic sports. Subsequent validation on a fully independent external sample confirmed the discriminative capacity of the scores for combat sports (0.69), cyclic sports (0.66), coordination-based sports (0.64), and game-oriented sports (0.62), whereas the models for multi-sports (0.53) and speed- and power-based sports (0.38) did not validate externally and were excluded from further analyses. In this study, we present a genetic profiling framework based on exploratory polygenic score modeling that serves as a tool to capture genetic associations with broad sport categories rather than to predict individual athletic success. Additionally, long-term effects of the detected genetic variants were analyzed in a cohort of 2,914 older adults (age ≥ 90 years), in whom polygenic scores for coordination-based, cyclic, and game-oriented sports were higher than in the general population sample. Due to the study's cross-sectional design, these findings are reported as associations with a long-living phenotype, rather than a prediction of lifespan.
Terekhov MV, Abushinova GA, Mamchur AA et al. GWAS-based polygenic profiling across broad sport categories: shared and category-specific genetic associations with athlete status and polygenic differences from population controls. Frontiers in sports and active living. 2026. doi:10.3389/fspor.2026.1813231. PMID: 42877100. https://pubmed.ncbi.nlm.nih.gov/42877100/