New York Genome Center, New York, NY, USA; email:
Department of Systems Biology, Columbia University, New York, NY, USA.
Annu Rev Biomed Data Sci. 2022 Aug 10;5:119-139. doi: 10.1146/annurev-biodatasci-122120-010010. Epub 2022 Apr 28.
Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression.
人群中的数千种常见遗传变异已通过全基因组关联研究（GWAS）与疾病风险和表型变异相关联。然而，大多数 GWAS 变异都发生在基因组的非编码区域，这使得我们难以理解它们的调控功能，并且很少有明确阐明 GWAS 变异效应的分子机制。在这里，我们着手回顾遗传变异效应，重点关注表达数量性状基因座（eQTL），包括它们在解释 GWAS 变异机制中的效用。我们讨论了 eQTL 分析的相互关联的挑战和机遇，涵盖了确定因果变异、阐明作用的分子机制以及理解上下文可变性。解决这些问题可以更好地对疾病相关基因座进行功能表征，并深入了解非编码遗传调控代码及其对基因表达的控制的基本生物学问题。
