Inferring genetic variant networks by leveraging pleiotropy shows trait relationships drive massive pleiotropy in GWAS

Abstract

Genetic variants have been associated with multiple traits through genome-wide association studies (GWASs), but pinpointing causal variants and their mechanisms remains challenging. Molecular phenotypes, such as eQTLs, are routinely used to interpret GWAS results. However, much concern has recently been raised about their weak overlap. Taking the opposite approach with PRISM (Pleiotropic Relationships to Infer the SNP Model), we leverage pleiotropy to pinpoint direct effects and build variant-trait networks. PRISM clusters variant-trait effects into confounder-mediated, trait-mediated, and direct effects, and builds individual variant networks by cross-referencing results from all traits. In simulations, PRISM demonstrated high precision in identifying direct effects and reconstructing variant-trait networks. Applying PRISM to a set of 70 complex traits and diseases representative of the phenome from the UK Biobank, we found that direct effects accounted for only [~]11% of significant effects, yet were highly enriched in heritability. Multiple lines of evidence showed that PRISM networks are consistent with established biological mechanisms.