Co-expression-wide association studies link genetically regulated interactions with complex traits

Abstract

Transcriptome-wide association studies (TWAS) have proven successful in prioritizing genes and proteins whose genetically regulated expression modulates disease risk, but they ignore potential co-expression and interaction effects. Here we introduce the co-expression-wide association study (COWAS) method to identify pairs of co-expressed genes or proteins that are associated with complex traits. COWAS first trains models to predict co-expression conditional on genetic variation, and then tests for association between imputed co-expression and the trait while also accounting for direct effects from each exposure. We applied our method to plasma proteomic concentrations from the UK Biobank, identifying dozens of interacting protein pairs associated with cholesterol levels, Alzheimer's disease, and Parkinson's disease. Notably, our results demonstrate that co-expression between proteins may affect complex traits even if neither protein is detected to influence the trait when considered on its own.