Multi‐Omic Insight Into the Molecular Networks in the Pathogenesis of Coronary Artery Disease

• DOI: 10.1161/jaha.124.037203
• Publication Date: 2025-03-26T10:46:27Z
• Authors: Qinghua Fang; Hongdan Fan; Qiaoqiao Li; Muzi Zhang; Zhengzhong Zhou; Jianlin Du; Jing Huang

Background Genome‐wide association studies have revealed numerous loci associated with coronary artery disease (CAD). However, some potential causal/risk genes remain unidentified, and causal therapies are lacking.

Methods and Results

We integrated multi‐omics data from gene methylation, expression, and protein levels using summary data‐based Mendelian randomization and colocalization analysis. Candidate genes were prioritized based on protein‐level associations, colocalization probability, and links to methylation and expression. Single‐cell RNA sequencing data were used to assess differential expression in the coronary arteries of patients with CAD. TAGLN2 ( Transgelin 2 ), APOB ( Apolipoprotein B ), and GIP ( Glucose‐dependent insulinotropic polypeptide ) were identified as the genes most strongly associated with CAD, with TAGLN2 exhibiting the most significant association. Higher methylation levels of TAGLN2 at specific Cytosine‐phosphate‐Guanine sites were negatively correlated with its gene expression and associated with a lower risk of CAD, whereas higher circulating TAGLN2 protein levels were positively associated with CAD risk (odds ratio,1.66 [95% CI, 1.32–2.08). These results suggest distinct regulatory mechanisms for TAGLN2 . In contrast, APOB and GIP showed positive associations with CAD risk, whereas DHX58 ( DExH‐box helicase 58 ) and SWAP70 ( Switch‐associated protein 70 ) were associated with decreased risk.

Conclusions

Our findings provide multi‐omics evidence suggesting that TAGLN2 , APOB , GIP , DHX58 , and SWAP70 genes are associated with CAD risk. This work provides novel insights into the molecular mechanisms of CAD and highlights the potential of integrating multi‐omics data to uncover potential causal relationships that cannot be fully captured by traditional genome‐wide association studies.