Zheng Qiwen, Ma Yujia, Chen Si, Che Qianzi, Chen Dafang
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
Front Genet. 2020 Apr 21;11:320. doi: 10.3389/fgene.2020.00320. eCollection 2020.
Genome-wide association studies (GWASs) have identified more than 150 genetic loci that demonstrate robust association with coronary artery disease (CAD). In contrast to the success of GWAS, the translation from statistical signals to biological mechanism and exploration of causal genes for drug development remain difficult, owing to the complexity of gene regulatory and linkage disequilibrium patterns. We aim to prioritize the plausible causal genes for CAD at a genome-wide level.
We integrated the latest GWAS summary statistics with other omics data from different layers and utilized eight different computational methods to predict CAD potential causal genes. The prioritized candidate genes were further characterized by pathway enrichment analysis, tissue-specific expression analysis, and pathway crosstalk analysis.
Our analysis identified 55 high-confidence causal genes for CAD, among which 15 genes (, , , , , , , , , , , , , , and ) ranked the highest priority because of consistent evidence from different data-driven approaches. GO analysis showed that these plausible causal genes were enriched in lipid metabolic and extracellular regions. Tissue-specific enrichment analysis revealed that these genes were significantly overexpressed in adipose and liver tissues. Further, KEGG and crosstalk analysis also revealed several key pathways involved in the pathogenesis of CAD.
Our study delineated the landscape of CAD potential causal genes and highlighted several biological processes involved in CAD pathogenesis. Further studies and experimental validations of these genes may shed light on mechanistic insights into CAD development and provide potential drug targets for future therapeutics.
全基因组关联研究（GWAS）已鉴定出150多个与冠状动脉疾病（CAD）有强关联的基因位点。与GWAS的成功形成对比的是，由于基因调控和连锁不平衡模式的复杂性，从统计信号到生物学机制的转化以及用于药物开发的因果基因探索仍然困难重重。我们旨在在全基因组水平上对CAD可能的因果基因进行优先级排序。
我们将最新的GWAS汇总统计数据与来自不同层面的其他组学数据整合，并利用八种不同的计算方法来预测CAD潜在的因果基因。通过通路富集分析、组织特异性表达分析和通路串扰分析对优先级候选基因进行进一步表征。
我们的分析确定了55个CAD的高置信度因果基因，其中15个基因（，，，，，，，，，，，，，，和）因来自不同数据驱动方法的一致证据而排名最高。基因本体（GO）分析表明，这些可能的因果基因在脂质代谢和细胞外区域富集。组织特异性富集分析显示，这些基因在脂肪组织和肝脏组织中显著过表达。此外，京都基因与基因组百科全书（KEGG）和串扰分析还揭示了CAD发病机制中涉及的几个关键通路。
我们的研究描绘了CAD潜在因果基因的全貌，并突出了CAD发病机制中涉及的几个生物学过程。对这些基因的进一步研究和实验验证可能会为CAD发展的机制洞察提供线索，并为未来治疗提供潜在的药物靶点。
