Cano-Gamez Eddie, Trynka Gosia
Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom.
Open Targets, Wellcome Genome Campus, Cambridge, United Kingdom.
Front Genet. 2020 May 13;11:424. doi: 10.3389/fgene.2020.00424. eCollection 2020.
Genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits. These associations could reveal the molecular mechanisms altered in common complex diseases and result in the identification of novel drug targets. However, GWAS have also left a number of outstanding questions. In particular, the majority of disease-associated loci lie in non-coding regions of the genome and, even though they are thought to play a role in gene expression regulation, it is unclear which genes they regulate and in which cell types or physiological contexts this regulation occurs. This has hindered the translation of GWAS findings into clinical interventions. In this review we summarize how these challenges have been addressed over the last decade, with a particular focus on the integration of GWAS results with functional genomics datasets. Firstly, we investigate how the tissues and cell types involved in diseases can be identified using methods that test for enrichment of GWAS variants in genomic annotations. Secondly, we explore how to find the genes regulated by GWAS loci using methods that test for colocalization of GWAS signals with molecular phenotypes such as quantitative trait loci (QTLs). Finally, we highlight potential future research avenues such as integrating GWAS results with single-cell sequencing read-outs, designing functionally informed polygenic risk scores (PRS), and validating disease associated genes using genetic engineering. These tools will be crucial to identify new drug targets for common complex diseases.
全基因组关联研究(GWAS)已成功定位了数千个与复杂性状相关的基因座。这些关联可能揭示常见复杂疾病中改变的分子机制,并有助于识别新的药物靶点。然而,GWAS也留下了一些悬而未决的问题。特别是,大多数与疾病相关的基因座位于基因组的非编码区域,尽管它们被认为在基因表达调控中起作用,但尚不清楚它们调控哪些基因,以及这种调控发生在哪些细胞类型或生理环境中。这阻碍了GWAS研究结果转化为临床干预措施。在本综述中,我们总结了过去十年中如何应对这些挑战,特别关注GWAS结果与功能基因组学数据集的整合。首先,我们研究如何使用检测GWAS变异在基因组注释中富集情况的方法来识别参与疾病的组织和细胞类型。其次,我们探讨如何使用检测GWAS信号与分子表型(如数量性状基因座(QTL))共定位的方法来找到受GWAS基因座调控的基因。最后,我们强调了未来潜在的研究途径,如将GWAS结果与单细胞测序读数整合、设计功能知情的多基因风险评分(PRS)以及使用基因工程验证疾病相关基因。这些工具对于识别常见复杂疾病的新药物靶点至关重要。