Trait-Level Rather Than Individual-Level Resolution in Fitness Genomics: A Population-Calibrated Variant Prioritization Framework in a South Asian Cohort

Translating candidate gene panels into objective physiological trait interpretations remains a major hurdle in exercise genomics. Conventional total genotype scoring suffers from severe panel-composition bias, fails to evaluate unannotated or novel variants, and frequently fosters inflated claims regarding individual athletic prediction. Here, we present a population-calibrated, multi-tiered computational framework that evaluates genomic variation across six evidence streams integrating deep learning protein and genomic language models (ESM-2, Evo 2), missense and splice predictors (AlphaMisse

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