Namba Shinichi, Konuma Takahiro, Wu Kuan-Han, Zhou Wei, Okada Yukinori
Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan.
Central Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki 569-1125, Japan.
Cell Genom. 2022 Oct 12;2(10):100190. doi: 10.1016/j.xgen.2022.100190.
Genomics-driven drug discovery is indispensable for accelerating the development of novel therapeutic targets. However, the drug discovery framework based on evidence from genome-wide association studies (GWASs) has not been established, especially for cross-population GWAS meta-analysis. Here, we introduce a practical guideline for genomics-driven drug discovery for cross-population meta-analysis, as lessons from the Global Biobank Meta-analysis Initiative (GBMI). Our drug discovery framework encompassed three methodologies and was applied to the 13 common diseases targeted by GBMI ( = 1,329,242). Individual methodologies complementarily prioritized drugs and drug targets, which were systematically validated by referring previously known drug-disease relationships. Integration of the three methodologies provided a comprehensive catalog of candidate drugs for repositioning, nominating promising drug candidates targeting the genes involved in the coagulation process for venous thromboembolism and the interleukin-4 and interleukin-13 signaling pathway for gout. Our study highlighted key factors for successful genomics-driven drug discovery using cross-population meta-analyses.
基因组学驱动的药物发现对于加速新型治疗靶点的开发至关重要。然而,基于全基因组关联研究(GWAS)证据的药物发现框架尚未建立,尤其是对于跨人群GWAS荟萃分析。在此,我们借鉴全球生物银行荟萃分析倡议(GBMI)的经验,介绍一种用于跨人群荟萃分析的基因组学驱动药物发现的实用指南。我们的药物发现框架包含三种方法,并应用于GBMI针对的13种常见疾病(n = 1,329,242)。个体方法互补地对药物和药物靶点进行优先级排序,并通过参考先前已知的药物 - 疾病关系进行系统验证。三种方法的整合提供了用于重新定位的候选药物综合目录,提名了针对静脉血栓栓塞凝血过程中涉及的基因以及痛风的白细胞介素 - 4和白细胞介素 - 13信号通路的有前景的候选药物。我们的研究强调了使用跨人群荟萃分析成功进行基因组学驱动药物发现的关键因素。