phenomewide

Phenomewide refers to approaches that study associations across a broad array of phenotypes rather than focusing on a single trait. In genetics and biomedicine, phenome-wide association studies, or PheWAS, test whether a genetic variant, biomarker, or exposure is linked to many different phenotypes across the phenome. This is the reverse of traditional genome-wide association studies (GWAS), which examine many genetic variants for association with a single phenotype.

Phenomewide analyses commonly rely on large, richly phenotyped datasets, such as electronic health records, biobanks, or cohort studies. Phenotypes may be defined from diagnostic codes (for example, ICD codes), laboratory measurements, imaging-derived traits, quantitative biomarkers, or self-reported information. Studies can be cross-sectional or longitudinal and may involve a single variant tested against many phenotypes or polygenic scores tested across phenotypes.

Statistical methods in phenomewide studies typically involve regressing each phenotype on the genetic variant or score while adjusting for covariates like age, sex, ancestry, and technical factors. Because many phenotypes are tested simultaneously, stringent corrections for multiple testing are applied to control false positives, and replication in independent cohorts is often pursued. PheWAS can reveal pleiotropy, where one genetic variant influences multiple traits, and can generate hypotheses for biological mechanisms, drug repurposing opportunities, or broader genotype–phenotype mappings.

Limitations include potential phenotype misclassification, variation in phenotype definitions across datasets, selection bias, and limited power for rare conditions. The field emphasizes careful phenotype harmonization, rigorous validation, and transparent reporting to ensure robust, interpretable findings.