Dataset Information

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

ABSTRACT: Background: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. 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 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. Conclusion: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.

SUBMITTER: Zheng Q

PROVIDER: S-EPMC7186505 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Zheng Qiwen Q Ma Yujia Y Chen Si S Che Qianzi Q Chen Dafang D

Frontiers in genetics 20200421

<h4>Background</h4>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 l ...[more]

PMID: 32373157

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Project description:BackgroundHundreds 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.MethodsWe 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).ResultsWe 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 CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1; 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.ConclusionsWe 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.

Project description:Coronary artery disease (CAD) is the leading cause of death worldwide. A recent meta-analysis of genome-wide association studies (GWAS) identified 175 loci associated with CAD. The majority of these loci are in non-coding regions and are predicted to regulate gene expression. We hypothesized that a subset of the CAD GWAS loci was associated with the regulation of transcription in vascular smooth muscle cells (SMCs), which play critical roles in the development of CAD. We measured gene expression in SMCs isolated from the ascending aortas of 151 ethnically diverse heart transplant donors in quiescent or proliferative conditions and calculated the association of their expression and splicing with ~6.3 million imputed single nucleotide polymorphism (SNP) markers across the genome. We identified 4,910 expression and 4,412 splice quantitative trait loci (sQTL) that represent regions of the genome associated with transcript abundance and splicing. 3,660 of the expression quantitative trait loci (eQTL) had not been observed in the publicly available Genotype-Tissue Expression dataset. Further, 29 and 880 of the eQTLs were SMC- and sex-specific, respectively. To identify the effector transcripts regulated by the CAD GWAS loci, we used four distinct colocalization approaches and identified 84 eQTL and 164 sQTLs that colocalized with CAD loci, suggesting a more significant role for the genetic regulation of mRNA splicing as a molecular mechanism for CAD genetic risk. 20% and 35% of the eQTLs were unique to quiescent or proliferative SMCs, respectively. Two of the CAD loci colocolized with SMC eQTL showed sex-specific (AL160313.1 and TERF2IP) and one of the loci colocalized with SMC-specific eQTLs (ALKBH8). 27% and 37% of the sQTLs were unique to quiescent or proliferative SMCs, respectively. The most significantly associated CAD locus, 9p21, was an sQTL for the long non-coding RNA CDKN2B-AS1, also known as ANRIL, in proliferative SMCs. Collectively, these results provide evidence for the molecular mechanisms of genetic susceptibility to CAD in vascular smooth muscle cells.'

Project description:Coronary artery disease (CAD) is a major cause of end-stage cardiac disease. Although profound efforts have been made to illuminate the pathogenesis, the molecular mechanisms of CAD remain to be analyzed. To identify the candidate genes in the advancement of CAD, microarray dataset GSE23766 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and pathway and gene ontology (GO) enrichment analyses were performed. The protein-protein interaction network was constructed and the module analysis was performed using the Biological General Repository for Interaction Datasets (BioGRID) and Cytoscape. Additionally, target genes-miRNA regulatory network and target genes-TF regulatory network were constructed and analyzed. There were 894 DEGs between male human CAD samples and female human CAD samples, including 456 up regulated genes and 438 down regulated genes. Pathway enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the superpathway of steroid hormone biosynthesis, ABC transporters, oxidative ethanol degradation III and Complement and coagulation cascades. Similarly, geneontology enrichment analyses revealed that DEGs (up and down regulated) were mostly enriched in the forebrain neuron differentiation, filopodium membrane, platelet degranulation and blood microparticle. In the PPI network and modules (up and down regulated), MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74 and VHL were hub genes. In the target genes-miRNA regulatory network and target genes-TF regulatory network (up and down regulated), TAOK1, KHSRP, HSD17B11 and PAH were target genes. In conclusion, the pathway and GO ontology enriched by DEGs may reveal the molecular mechanism of CAD. Its hub and target genes, MYC, NPM1, TRPC7, UBC, FN1, HEMK1, IFT74, VHL, TAOK1, KHSRP, HSD17B11 and PAH were expected to be new targets for CAD. Our finding provided clues for exploring molecular mechanism and developing new prognostics, diagnostic and therapeutic strategies for CAD.

Project description:ObjectiveOnly a small fraction of coronary artery disease (CAD) heritability has been explained by common variants identified to date. Interactions between genes of importance to cardiovascular regulation may account for some of the missing heritability of CAD. This study aimed to investigate the role of gene-gene interactions in common variants in candidate cardiovascular genes in CAD.Approach and results2,101 patients with CAD from the British Heart Foundation Family Heart Study and 2,426 CAD-free controls were included in the discovery cohort. All subjects were genotyped with the Illumina HumanCVD BeadChip enriched for genes and pathways relevant to the cardiovascular system and disease. The primary analysis in the discovery cohort examined pairwise interactions among 913 common (minor allele frequency >0.1) independent single nucleotide polymorphisms (SNPs) with at least nominal association with CAD in single locus analysis. A secondary exploratory interaction analysis was performed among all 11,332 independent common SNPs surviving quality control criteria. Replication analyses were conducted in 2,967 patients and 3,075 controls from the Myocardial Infarction Genetics Consortium. None of the interactions amongst 913 SNPs analysed in the primary analysis was statistically significant after correction for multiple testing (required P<1.2x10-7). Similarly, none of the pairwise gene-gene interactions in the secondary analysis reached statistical significance after correction for multiple testing (required P = 7.8x10-10). None of 36 suggestive interactions from the primary analysis or 31 interactions from the secondary analysis was significant in the replication cohort. Our study had 80% power to detect odds ratios > 1.7 for common variants in the primary analysis.ConclusionsModerately large additive interactions between common SNPs in genes relevant to cardiovascular disease do not appear to play a major role in genetic predisposition to CAD. The role of genetic interactions amongst less common SNPs and with medium and small magnitude effects remain to be investigated.

Dataset Information

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

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

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