A cross-disease, pleiotropy-driven approach for therapeutic target prioritization and evaluation
Chaohui Bao, Tingting Tan, Shan Wang, Chenxu Gao, Chang Lu, Siyue Yang, Yizhu Diao, Lulu Jiang, Duohui Jing, Liye Chen, Haitao Lv, Hai Fang
Abstract
Cross-disease genome-wide association studies (GWASs) unveil pleiotropic loci, mostly situated within the non-coding genome, each of which exerts pleiotropic effects across multiple diseases. However, the challenge "W-H-W" (namely, whether, how, and in which specific diseases pleiotropy can inform clinical therapeutics) calls for effective and integrative approaches and tools. We here introduce a pleiotropy-driven approach specifically designed for therapeutic target prioritization and evaluation from cross-disease GWAS summary data, with its validity demonstrated through applications to two systems of disorders (neuropsychiatric and inflammatory). We illustrate its improved performance in recovering clinical proof-of-concept therapeutic targets. Importantly, it identifies specific diseases where pleiotropy informs clinical therapeutics. Furthermore, we illustrate its versatility in accomplishing advanced tasks, including pathway crosstalk identification and downstream crosstalk-based analyses. To conclude, our integrated solution helps bridge the gap between pleiotropy studies and therapeutics discovery.