ArticleScientific reports2018
Network analysis of coronary artery disease risk genes elucidates disease mechanisms and druggable targets.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prioritization of causal genes for coronary artery disease based on cumulative evidence from experimental and in silico studies.Scientific reports · 2020Pooled it
- Insights from GWAS, TWAS, and DEG analysis: CDKN2B-AS1, MAP3K4, and P2RX2-encoded P2X2 receptor identified as potential therapeutic targets for ischemic heart disease in Russian adults and long-living individuals.Frontiers in cardiovascular medicine · 2026Article
- Variant-to-Biomarker Pathways in Peripheral Artery Disease: Multiomics Integration and Clinical Translation.Human mutation · 2026Review
- Mitochondrial related variants associated with cardiovascular traits.Frontiers in physiology · 2024Article
- From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators.Current atherosclerosis reports · 2023Review
- The applications of deep learning algorithms on in silico druggable proteins identification.Journal of advanced research · 2022Article
- A role for artificial intelligence in molecular imaging of infection and inflammation.European journal of hybrid imaging · 2022Review
- Examining the Association between Mitochondrial Genome Variation and Coronary Artery Disease.Genes · 2022Article
- Assessment of Immunological Effects of Low-Level Er: YAG Laser Radiation.Journal of lasers in medical sciences · 2022Article
- Olfactory neuronal cells as a promising tool to realize the "druggable genome" approach for drug discovery in neuropsychiatric disorders.Frontiers in neuroscience · 2022Article
- Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease.Frontiers in microbiology · 2022Review
- Vascular Tissue Specific miRNA Profiles Reveal Novel Correlations with Risk Factors in Coronary Artery Disease.Biomolecules · 2021Article
- Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence.International journal of molecular sciences · 2021Review
- Multi-omics approaches for revealing the complexity of cardiovascular disease.Briefings in bioinformatics · 2021Review
- Network medicine in Cardiovascular Research.Cardiovascular research · 2021Review
- Systems biology in cardiovascular disease: a multiomics approach.Nature reviews. Cardiology · 2021Review
- Inflammation-Related Risk Loci in Genome-Wide Association Studies of Coronary Artery Disease.Cells · 2021Review
- Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis.Cellular immunology · 2020Review
- Should We Use Genetic Scores in the Determination of Treatment Strategies to Control Dyslipidemias?Current cardiology reports · 2020Review
- Probing the epigenetic signatures in subjects with coronary artery disease.Molecular biology reports · 2020Article
Corrections and comments
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Authors and funding
27 authors.
Funding
Abstract
Genome-wide association studies (GWAS) have identified over two hundred chromosomal loci that modulate risk of coronary artery disease (CAD). The genes affected by variants at these loci are largely unknown and an untapped resource to improve our understanding of CAD pathophysiology and identify potential therapeutic targets. Here, we prioritized 68 genes as the most likely causal genes at genome-wide significant loci identified by GWAS of CAD and examined their regulatory roles in 286 metabolic and vascular tissue gene-protein sub-networks ("modules"). The modules and genes within were scored for CAD druggability potential. The scoring enriched for targets of cardiometabolic drugs currently in clinical use and in-depth analysis of the top-scoring modules validated established and revealed novel target tissues, biological processes, and druggable targets. This study provides an unprecedented resource of tissue-defined gene-protein interactions directly affected by genetic variance in CAD risk loci.
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Registered trials
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