The majority of risk loci identified by genome-wide association studies (GWAS) are in non-coding regions, hampering their functional interpretation. Instead, transcriptome-wide association studies (TWAS) identify gene-trait associations, which can be used to prioritize candidate genes in disease-relevant tissue(s). Here, we aimed to systematically identify susceptibility genes for coronary artery disease (CAD) by TWAS. We trained prediction models of nine CAD-relevant tissues using EpiXcan based on two genetics-of-gene-expression panels, the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) and the Genotype-Tissue Expression (GTEx). Based on these prediction models, we imputed gene expression of respective tissues from individual-level genotype data on 37,997 CAD cases and 42,854 controls for the subsequent gene-trait association analysis. Transcriptome-wide significant association (i.e. P < 3.85e−6) was observed for 114 genes. Of these, 96 resided within previously identified GWAS risk loci and 18 were novel. Stepwise analyses were performed to study their plausibility, biological function, and pathogenicity in CAD, including analyses for colocalization, damaging mutations, pathway enrichment, phenome-wide associations with human data and expression-traits correlations using mouse data. Finally, CRISPR/Cas9-based gene knockdown of two newly identified TWAS genes, RGS19 and KPTN, in a human hepatocyte cell line resulted in reduced secretion of APOB100 and lipids in the cell culture medium. Our CAD TWAS work (i) prioritized candidate causal genes at known GWAS loci, (ii) identified 18 novel genes to be associated with CAD, and iii) suggested potential tissues and pathways of action for these TWAS CAD genes.

全基因组关联研究 (GWAS) 确定的大多数风险位点位于非编码区域，阻碍了它们的功能解释。相反，全转录组关联研究 (TWAS) 可识别基因-性状关联，可用于确定疾病相关组织中候选基因的优先级。在这里，我们旨在通过 TWAS 系统地识别冠状动脉疾病 (CAD) 的易感基因。我们使用 EpiXcan 训练了九个 CAD 相关组织的预测模型，该模型基于两个基因表达遗传学面板，即斯德哥尔摩-塔尔图动脉粥样硬化逆向网络工程任务 (STARNET) 和基因型组织表达 (GTEx)。基于这些预测模型，我们从 37,997 例 CAD 病例和 42 例 CAD 病例的个体水平基因型数据中估算了各个组织的基因表达，854控制用于随后的基因-性状关联分析。转录组范围内的显着关联（即对 114 个基因观察到P  < 3.85e-6)。其中，96 个位于先前确定的 GWAS 风险位点内，18 个是新的。进行了逐步分析以研究它们在 CAD 中的合理性、生物学功能和致病性，包括分析共定位、破坏性突变、通路富集、与人类数据的现象范围关联以及使用小鼠数据的表达特征相关性。最后，基于 CRISPR/Cas9 的两个新发现的 TWAS 基因RGS19和KPTN的基因敲除, 在人肝细胞系中导致细胞培养基中 APOB100 和脂质的分泌减少。我们的 CAD TWAS 工作 (i) 在已知的 GWAS 位点优先考虑候选致病基因，(ii) 确定了 18 个与 CAD 相关的新基因，以及 iii) 提出了这些 TWAS CAD 基因的潜在组织和作用途径。