Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, De Boelelaan 1105, Amsterdam 1081HV, The Netherlands.
Hum Mol Genet. 2022 Oct 20;31(R1):R73-R83. doi: 10.1093/hmg/ddac198.
Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and their mechanism of action. In this review, we give an overview of current tools and methods for the analysis of non-coding GWAS variants in disease. We provide a workflow that allows for the accumulation of in silico evidence to generate novel hypotheses on mechanisms underlying disease and prioritize targets for follow-up study using non-coding GWAS variants. Lastly, we discuss the need for comprehensive benchmarks and novel tools for the analysis of non-coding variants.
全基因组关联研究(GWAS)发现大多数与疾病相关的变异是非编码的。对非编码调控景观的绘制进行了重大努力,开发了旨在帮助识别因果变异及其作用机制的工具和方法。在这篇综述中,我们概述了用于分析疾病中非编码 GWAS 变异的当前工具和方法。我们提供了一个工作流程,允许积累计算机模拟证据,从而生成有关疾病潜在机制的新假设,并使用非编码 GWAS 变异对后续研究的目标进行优先级排序。最后,我们讨论了分析非编码变异需要全面的基准和新工具。