Division of Cardiology, University of Ottawa Heart Institute, 40 Ruskin St-H4203, Ottawa, Canada.
Can J Cardiol. 2013 Jan;29(1):23-9. doi: 10.1016/j.cjca.2012.08.017. Epub 2012 Nov 28.
Genome-wide association studies (GWASs) for coronary artery disease (CAD) have identified more than 30 variants robustly associated with CAD risk. The majority are not associated with conventional risk factors but highlight novel pathways, including cellular proliferation. Although some risk variants are nonsynonymous coding variants resulting in an amino acid change in the encoded protein, the majority are in noncoding regions of the genome and may encompass multiple signals of variable effect. The use of genetic data for development of new therapies requires the identification of causative genetic variants and elucidation of the molecular mechanisms by which they predispose to CAD. The computational and laboratory approaches for the interpretation of GWAS data are discussed with a particular focus on noncoding variants, including the study of regulatory elements, the evaluation of nonsynonymous coding variants, and expression quantitative trait locus analysis for the integration of GWAS data with genome-wide messenger RNA expression data.
全基因组关联研究（GWAS）已确定了 30 多种与冠心病（CAD）风险显著相关的变异。大多数变异与传统的危险因素无关，但突出了新的途径，包括细胞增殖。尽管一些风险变异是导致编码蛋白氨基酸改变的非同义编码变异，但大多数变异位于基因组的非编码区域，可能包含多个可变效应的信号。为开发新疗法而使用遗传数据需要确定致病遗传变异，并阐明它们导致 CAD 的分子机制。本文讨论了用于解释 GWAS 数据的计算和实验室方法，特别关注非编码变异，包括调控元件的研究、非编码变异的评估以及表达数量性状基因座分析，用于将 GWAS 数据与全基因组信使 RNA 表达数据进行整合。
