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A Systems Biology Framework Identifies Molecular Underpinnings of Coronary Heart Disease
Open accessAbstract
Objective—: Genetic approaches have identified numerous loci associated with coronary heart disease (CHD). The molecular mechanisms underlying CHD gene–disease associations, however, remain unclear. We hypothesized that genetic variants with both strong and subtle effects drive gene subnetworks that in turn affect CHD. Approach and Results—: We surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 age- and sex-matched controls. Twenty-four coexpression modules were identified, including 1 case-specific and 1 control-specific differential module (DM). The DMs were enriched for genes involved in B-cell activation, immune response, and ion transport. By integrating the DMs with gene expression–associated single-nucleotide polymorphisms and with results of genome-wide association studies of CHD and its risk factors, the control-specific DM was implicated as CHD causal based on its significant enrichment for both CHD and lipid expression–associated single-nucleotide polymorphisms. This causal DM was further integrated with tissue-specific Bayesian networks and protein–protein interaction networks to identify regulatory key driver genes. Multitissue key drivers ( SPIB and TNFRSF13C ) and tissue-specific key drivers (eg, EBF1 ) were identified. Conclusions—: Our network-driven integrative analysis not only identified CHD-related genes, but also defined network structure that sheds light on the molecular interactions of genes associated with CHD risk.
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“One way to model such networks is as graphs consisting of elements that exhibit specific interactions with other elements [60–64].”
From Multi-omics approaches to disease (2017) · cites this paper as [61]
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“Communities of co-expressed proteins can be linked to disease processes, and the most strongly correlated proteins, or “hubs,” within these co-expression modules are enriched in key drivers of disease pathogenesis5–10.”
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“Systems-based approaches such as weighted gene co-expression network analysis (WGCNA) can be used to analyze biochemical and cellular changes in brain, and are useful to help capture the complexity of perturbations in biological networks that are related to disease [2–4].”
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Metadata
- Authors
- Huan, T., Zhang, B., Wang, Z., Joehanes, R., Zhu, J., Johnson, A. D., Ying, S., Munson, P. J., Raghavachari, N., Wang, R., Liu, P., Courchesne, P., Hwang, S.-J., Assimes, T. L., McPherson, R., Samani, N. J., Schunkert, H., Coronary ARteryDIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) Consortium, I. C. F. B. P. G. (., Meng, Q., Suver, C., O’Donnell, C. J., Derry, J., Yang, X., & Levy, D.
- Affiliations
- National Heart, Lung, and Blood Institute (US)
- ORCID
- Venue
- Arteriosclerosis, Thrombosis, and Vascular Biology
- Published
- 2013-06
- Volume
- 33
- Issue
- 6
- Pages
- 1427-1434
- ISSN
- 1079-5642 (ISSN-L)
- 1524-4636
- PMC
- PMC3752786
- OpenAlex
- W2169785917
- Cited by
- 167
- References
- 52
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 10 · 2023: 10 · 2022: 8 · 2021: 16 · 2020: 14 · 2019: 16 · 2018: 11 · 2017: 24 · 2016: 18 · 2015: 15 · 2014: 14 · 2013: 7
- FWCI
- 8.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Arteriosclerosis Thrombosis and Vascular Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Funders
- National Institute for Health and Care Research (NF-SI-0611-10170)
- British Heart Foundation
- National Institutes of Health (n01hc25195-41-0-1)
- National Heart, Lung, and Blood Institute (N01‐HC25195, Z01HL006001, N01-HC-025195, ZIAHL006001)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Immunology and Microbiology › Immunology › Atherosclerosis and Cardiovascular Diseases
- Keywords
- coronary artery disease, gene co-expression network, differential modules, whole blood gene expression, single nucleotide polymorphisms, B cell activation, immune response, ion transport, Spi-B, TNFRSF13C, EBF1, protein-protein interaction network, Bayesian networks
- MeSH terms
- Aged, Bayes Theorem, Coronary Artery Disease, Female, Gene Expression Regulation, Humans, Male, Middle Aged, Reference Values, Genetic Variation, Incidence, Case-Control Studies, Age Distribution, Sex Distribution, Risk Assessment, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Systems Biology, Gene Regulatory Networks, Genome-Wide Association Study
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