Functional Genomics, GSK, Stevenage, UK.
Human Genetics, GSK, Stevenage, UK.
Sci Rep. 2020 Dec 1;10(1):20970. doi: 10.1038/s41598-020-77847-9.
Genetic evidence of disease association has often been used as a basis for selecting of drug targets for complex common diseases. Likewise, the propagation of genetic evidence through gene or protein interaction networks has been shown to accurately infer novel disease associations at genes for which no direct genetic evidence can be observed. However, an empirical test of the utility of combining these approaches for drug discovery has been lacking. In this study, we examine genetic associations arising from an analysis of 648 UK Biobank GWAS and evaluate whether targets identified as proxies of direct genetic hits are enriched for successful drug targets, as measured by historical clinical trial data. We find that protein networks formed from specific functional linkages such as protein complexes and ligand-receptor pairs are suitable for even naïve guilt-by-association network propagation approaches. In addition, more sophisticated approaches applied to global protein-protein interaction networks and pathway databases, also successfully retrieve targets enriched for clinically successful drug targets. We conclude that network propagation of genetic evidence can be used for drug target identification.
遗传相关性证据通常被用作选择复杂常见疾病药物靶点的基础。同样,通过基因或蛋白质相互作用网络传播遗传证据,也可以准确推断出那些没有直接遗传证据可观察到的基因的新的疾病相关性。然而,对于将这些方法结合起来用于药物发现的实用性,还缺乏实证检验。在这项研究中,我们检查了来自 648 项英国生物库 GWAS 分析的遗传相关性,并评估了作为直接遗传命中指标的鉴定靶点是否在通过历史临床试验数据来衡量时,富集了成功的药物靶点。我们发现,由特定功能联系(如蛋白质复合物和配体-受体对)形成的蛋白质网络,甚至适合于朴素的关联网络传播方法。此外,应用于全局蛋白质-蛋白质相互作用网络和途径数据库的更复杂的方法,也成功地检索到了富含临床成功药物靶点的目标。我们的结论是,遗传证据的网络传播可用于药物靶点的识别。