Genetic associations with technical capabilities in English academy football players: a preliminary study
Bibliographic record
Abstract
BACKGROUND: Technical capabilities have significant discriminative and prognostic power in youth football. Although, many factors influence technical performance, no research has explored the genetic contribution. As such, the purpose of this study was to examine the association of several single nucleotide polymorphisms (SNPs) with technical assessments in youth football players. METHODS: Fifty-three male under-13 to under-18 outfield football players from two Category 3 English academies were genotyped for eight SNPs. Objective and subjective technical performance scores in dribbling, passing, and shooting were collated. Simple linear regression was used to analyse individual SNP associations each variable, whereas both unweighted and weighted total genotype scores (TGSs; TWGSs) were computed to measure the combined influence of all SNPs. RESULTS: In isolation, the ADBR2 (rs1042714) C allele, BDNF (rs6265) C/C genotype, DBH (rs1611115) C/C genotype, and DRD1 (rs4532) C allele were associated with superior (8-10%) objective dribbling and/or shooting performance. The TGSs and/or TWGSs were significantly correlated with each technical assessment (except subjective passing), explaining up to 36% and 40% of the variance in the objective and subjective assessments, respectively. CONCLUSIONS: The results of this study suggest inter-individual genetic variation may influence the technical capabilities of youth football players and proposes several candidate SNPs that warrant further investigation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".