Background:In this study, we aimed to investigate functional mechanisms underlying coronary artery disease (CAD) loci and find molecular biomarkers for CAD.Methods: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.Results: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 C5orf38, SF3A3, DHX36, and MRPL33. 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 ABO 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 BSN gene exert its effect on CAD through central nervous system-lifestyle risk factors. Finally, genes with a transcriptional regulatory role (SF3A3, ILF3, and N4BP2L2) exert their effect on CAD through height.Conclusions: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.

中文翻译：

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多组学筛查确定与冠状动脉疾病风险有因果关系的分子生物标志物。

背景：在本研究中，我们旨在研究冠状动脉疾病 (CAD) 位点的功能机制并寻找 CAD 的分子生物标志物。已知与 CAD 相关的基因组区域内的 CAD 风险。结果：通过我们以 CAD 为中心的多组学数据分析方法，我们确定了 33 个与 CAD 风险有因果关系的分子生物标志物（探针）。其中大部分 (N=19) 是甲基化探针；此外，甲基化通常是表达/蛋白质探针的因果效应背后的原因。我们确定了许多对 CAD 有因果影响的新位点，包括C5orf38、SF3A3、DHX36和MRPL33。此外，通过在我们的分析中整合 CAD 的风险因素，我们能够研究我们的几个探针发挥作用的临床途径。我们发现血液中的 SELE 蛋白水平受到ABO基因内甲基化位点的跨调节影响，并且 SELE 通过免疫、血糖和脂质代谢对 CAD 发挥作用，使其成为治疗干预的候选者. 我们发现BSN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，具有转录调节作用的基因（SF3A3、ILF3和N4BP2L2) 通过高度对 CAD 产生影响。结论：我们证明多组学数据分析是揭示 CAD 基因座的功能机制和识别新分子生物标志物的有力方法。我们的结果表明表观遗传修饰在 CAD 的发病机制中很重要，识别和靶向这些位点对于解决环境和遗传因素的不利影响具有潜在的治疗意义。