PheWAS

PheWAS, or Phenome-Wide Association Study, is a method used in genetic epidemiology to investigate associations between genetic variants and a broad range of phenotypes simultaneously. The concept was introduced to complement Genome-Wide Association Studies (GWAS), which examine many genetic loci for a single phenotype, by instead scanning many phenotypes for potentially meaningful associations with a single genetic marker or set of markers.

The approach typically relies on large, routinely collected health data, such as electronic health record (EHR) systems, insurance claims, or biobank repositories. Phenotypes are often coded using standard vocabularies like ICD-9, ICD-10, or SNOMED, which are then collapsed into clinical groupings (phecodes) for statistical analysis. Statistical models, usually regression-based, assess the association of each variant with each phenotype, correcting for multiple testing using methods such as Bonferroni or false discovery rate procedures.

Applications of PheWAS include mapping the phenotypic effects of risk alleles, uncovering pleiotropic relationships where one gene influences multiple traits, and generating hypotheses for mechanistic studies. It has also been used to identify potential drug repurposing opportunities by linking genetic predispositions to pharmacologic responses.

Challenges include phenotype misclassification, population stratification, and the computational burden of testing millions of variant–phenotype pairs. Several software packages, such as the PheWAS R package, have been developed to streamline analyses. PheWAS continues to evolve with the integration of novel data types, more sophisticated phenotype definitions, and advanced statistical methods to improve reproducibility and biological insight.