Shared genetics across 178 phenotypes predicts novel drug therapeutic and side effects

Human genetics holds great potential for drug discovery, but challenges in identifying causal genes limit its clinical translation. Pleiotropy, the phenomenon where genetic variants or genes influence multiple traits, has been previously used to explain clinical associations between diseases and propose drug repurposing opportunities. However, its potential to systematically inform drug discovery remains unknown. To this end, we evaluate whether genetic similarity between 178 phenotypes across 17 body systems can predict novel therapeutic applications and side effects of drugs. We develop five complementary genetic similarity metrics, capturing genome-wide genetic correlation, gene-level associations, tissue-specific gene regulation, and molecular QTL colocalization. By integrating these metrics with data on drug indications and side effects, we find that diseases with greater genetic similarity also share more drugs, regardless of whether they affect the same or different body systems. We also train logistic regression models and show that genetic similarity between known drug effects and other phenotypes is predictive of known drug indications and side effects. Notably, drug-phenotype pairs in advanced trial phases receive higher predicted probabilities, underscoring clinical relevance of our predictions. Furthermore, our indications models predict side effects better than expected by chance, and vice versa, suggesting shared genetic basis for therapeutic and adverse drug effects. Overall, by shifting focus from single-disease genetics to pleiotropic signals, our approach bypasses the need to pinpoint causal genes and enables gene-target agnostic drug repurposing and risk prediction. These findings position pleiotropy as a powerful tool for accelerating drug effect discovery across a broad spectrum of diseases.