European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK; Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK.
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, CB10 1SD, UK; Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK; Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, 8093, Switzerland.
Curr Opin Chem Biol. 2022 Dec;71:102206. doi: 10.1016/j.cbpa.2022.102206. Epub 2022 Sep 7.
Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.
在过去的几十年中,全基因组关联研究 (GWAS) 导致了与人类特征和疾病相关的遗传变异的急剧增加。这些进展有望产生新的药物靶点,但从 GWAS 中确定因果基因和人类疾病的细胞生物学仍然具有挑战性。在这里,我们回顾了基于蛋白质相互作用网络的方法来分析 GWAS 数据。这些方法可以对 GWAS 相关基因座上的候选药物靶点或疾病基因的相互作用子进行排名,而无需直接的遗传支持。这些方法确定了在多种疾病中普遍受到影响的细胞生物学,为药物再利用提供了机会,并可以与表达数据结合使用,以确定焦点组织和细胞类型。展望未来,我们预计这些方法将从特定于上下文的相互作用网络的特征描述以及罕见和常见遗传信号的联合分析的进展中进一步得到改进。