Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, NY (K.H., H.C., L.M., L.A., J.L.M.B.).
Sema4, Stamford, CT (K.H.).
Circ Genom Precis Med. 2022 Feb;15(1):e003365. doi: 10.1161/CIRCGEN.121.003365. Epub 2021 Dec 28.
Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets.
We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task).
We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were (associated with the 9p21.3 risk locus) and ; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks.
We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.
通过全基因组关联研究，已经发现了数百个与冠状动脉疾病（CAD）相关的候选基因。然而，系统地理解这些基因的因果机制以及为进一步研究对它们进行优先级排序的方法一直缺失。这对于为 CAD 患者开发新的疾病和基因特异性治疗方法是一个主要的障碍。最近，强大的整合基因组学分析管道已经出现，通过整合组织/细胞特异性基因表达数据与全基因组关联研究数据集，来识别和优先考虑候选因果基因。
我们旨在开发一个全面的 CAD 整合基因组学分析管道，并提供一个因果 CAD 基因的优先级列表。为此，我们利用几种互补的信息学方法，将 CAD 全基因组关联研究（来自英国生物库和 CARDIOGRAMplusC4D）的汇总统计数据与 STARNET 研究（斯德哥尔摩-塔尔图动脉粥样硬化反向网络工程任务）中的 9 种心脏代谢组织/细胞类型的转录组和表达数量性状基因座数据进行整合。
我们确定了 162 个独特的候选因果 CAD 基因，这些基因在 1 到 7 个与疾病相关的组织/细胞类型中发挥作用，包括动脉壁、血液、肝脏、骨骼肌、脂肪、泡沫细胞和巨噬细胞。当它们的因果效应被排序时，候选因果 CAD 基因的前 10 位分别是和（与 9p21.3 风险位点相关），它们都在动脉壁中发挥因果作用。大多数候选因果基因都存在于与 CAD 相关的跨组织基因调控共表达网络中，其中 22/162 是这些网络中的关键驱动因素。
我们确定并优先考虑了候选因果 CAD 基因，还定位了它们的因果作用组织。这些结果应该作为一种资源，并促进有针对性的研究，以确定顶级因果 CAD 基因的功能影响。
