AUTHOR=Zheng Qiwen , Ma Yujia , Chen Si , Che Qianzi , Chen Dafang 

TITLE=The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease

JOURNAL=Frontiers in Genetics

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.00320

DOI=10.3389/fgene.2020.00320

ISSN=1664-8021

ABSTRACT=Background: Genome-wide association studies (GWAS) 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, due to the complex of gene regulatory and linkage disequilibrium pattern. We aim to prioritize the plausible causal genes for CAD at genome-wide level.
Methods: 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.
Results: Our analysis identified 55 high-confidence causal genes for CAD, among which 15 genes (LPL, COL4A2, PLG, CDKN2B, COL4A1, FES, FLT1, FN1, IL6R, LPA, PCSK9, PSRC1, SMAD3, SWAP70, and VAMP8) 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 region. 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 involving in the pathogenesis of CAD.
Conclusions: Our study delineated the landscape of CAD potential causal genes and highlighted several biological process 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.