Ruddy Canadian Cardiovascular Genetics Centre (M.N., R.M.), University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
Atherogenomics Laboratory (S.S., R.M.), University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
Circ Genom Precis Med. 2020 Dec;13(6):e002876. doi: 10.1161/CIRCGEN.119.002876. Epub 2020 Sep 24.
In this study, we aimed to investigate functional mechanisms underlying coronary artery disease (CAD) loci and find molecular biomarkers for CAD.
We devised a multiomics data analysis approach based on Mendelian randomization and utilized it to search for molecular biomarkers causally associated with the risk of CAD within genomic regions known to be associated with CAD.
Through our CAD-centered multiomics data analysis approach, we identified 33 molecular biomarkers (probes) that were causally associated with the risk of CAD. The majority of these (N=19) were methylation probes; moreover, methylation was often behind the causal effect of expression/protein probes. We identified a number of novel loci that have a causal impact on CAD including , , , and . Furthermore, by integrating the risk factors of CAD in our analysis, we were able to investigate the clinical pathways whereby several of our probes exert their effect. We found that the SELE protein level in the blood is under the trans-regulatory impact of methylation sites within the gene and that SELE exerts its effect on CAD through immune, glycemic, and lipid metabolism, making it a candidate of interest for therapeutic interventions. We found the methylation site, cg05126514 within the gene exert its effect on CAD through central nervous system-lifestyle risk factors. Finally, genes with a transcriptional regulatory role (, , and ) exert their effect on CAD through height.
We demonstrate that multiomics data analysis is a powerful approach to unravel the functional mechanisms underlying CAD loci and to identify novel molecular biomarkers. Our results indicate epigenetic modifications are important in the pathogenesis of CAD and identifying and targeting these sites is of potential therapeutic interest to address the detrimental effects of both environmental and genetic factors.
在这项研究中，我们旨在探讨冠心病（CAD）相关基因座的功能机制，并寻找 CAD 的分子生物标志物。
我们设计了一种基于孟德尔随机化的多组学数据分析方法，并利用该方法在与 CAD 相关的基因组区域内，寻找与 CAD 风险相关的因果分子生物标志物。
通过以 CAD 为中心的多组学数据分析方法，我们确定了 33 个与 CAD 风险相关的因果分子生物标志物（探针）。其中大多数（N=19）是甲基化探针；此外，甲基化通常是表达/蛋白探针因果效应的背后原因。我们确定了一些新的与 CAD 有因果关系的基因座，包括、、、和。此外，通过在我们的分析中整合 CAD 的风险因素，我们能够研究几个探针发挥作用的临床途径。我们发现，血液中 SELE 蛋白水平受到基因内甲基化位点的转录后调控，SELE 通过免疫、血糖和脂质代谢对 CAD 产生影响，使其成为治疗干预的候选靶点。我们发现基因内的甲基化位点 cg05126514 通过中枢神经系统-生活方式风险因素对 CAD 产生影响。最后，具有转录调控作用的基因（、和）通过身高对 CAD 产生影响。
我们证明多组学数据分析是一种强大的方法，可以揭示 CAD 相关基因座的功能机制，并识别新的分子生物标志物。我们的研究结果表明，表观遗传修饰在 CAD 的发病机制中起着重要作用，鉴定和靶向这些位点可能具有治疗意义，有助于解决环境和遗传因素的有害影响。
