Bao Chaohui, Tan Tingting, Wang Shan, Gao Chenxu, Lu Chang, Yang Siyue, Diao Yizhu, Jiang Lulu, Jing Duohui, Chen Liye, Lv Haitao, Fang Hai
Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
MRC London Institute of Medical Sciences, Imperial College London, W12 0HS London, UK.
Cell Rep Methods. 2024 Apr 22;4(4):100757. doi: 10.1016/j.crmeth.2024.100757. Epub 2024 Apr 16.
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.
跨疾病全基因组关联研究（GWAS）揭示了多效性位点，这些位点大多位于非编码基因组内，每个位点在多种疾病中发挥多效性作用。然而，“W-H-W”挑战（即多效性在何种疾病中、如何以及能否为临床治疗提供信息）需要有效的综合方法和工具。我们在此介绍一种专门为从跨疾病GWAS汇总数据中确定治疗靶点优先级和进行评估而设计的多效性驱动方法，并通过应用于两个疾病系统（神经精神疾病和炎症性疾病）证明了其有效性。我们展示了该方法在恢复临床概念验证治疗靶点方面的改进性能。重要的是，它确定了多效性可为临床治疗提供信息的特定疾病。此外，我们展示了该方法在完成高级任务（包括通路串扰识别和基于下游串扰的分析）方面的通用性。总之，我们的综合解决方案有助于弥合多效性研究与治疗发现之间的差距。
