Bayesian inference of genetic pleiotropy identifies drug targets and repurposable medicines for human complex diseases

Complex diseases share heritable components which can be leveraged to identify drug targets with low side effect or high repurposing potential, but current methods cannot efficiently make these inferences at scale using public data. We introduce a Bayesian model to estimate the polygenic structure of a trait using GWAS summary data (BPACT). Across 32 complex traits, we estimated that 69.5 to 97.5% of disease-associated druggable genes are shared between multiple traits. We observed that targeting KIT for ALS prevention may increase triglyceride levels, but that targeting TBK1 and SCN11B may be safer because of they were not pleiotropic. We additionally found 21 candidate repurposable drug targets for Alzheimers disease (AD) (e.g., PLEKHA1, PPIB) and 5 for ALS (e.g., GAK, DGKQ) based on the directionality of their pleiotropy. Our results demonstrate that modeling shared genetic architecture across traits can uncover safer therapeutic targets and highlight opportunities for drug repurposing in complex diseases. MotivationWe intend to identify genes which are associated with multiple complex traits such that their side effect or therapeutic repurposing potential is large. We present a Bayesian method leveraging gene-based association test statistics that can be used to jointly characterize shared polygenicity between complex trait pairs and make these inferences.