Understanding multicellular function and disease with human tissue-specific networks
Open accessAbstract
Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases. We introduce NetWAS, which combines genes with nominally significant genome-wide association study (GWAS) P values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than a hundred human tissues and cell types.
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If multiple GWAS hits exist in the same module, their cumulative presence can significantly contribute to disease development.
“If multiple Genome-Wide Association Study (GWAS) hits exist in the same module, their cumulative presence can significantly contribute to disease development [120, 163, 164].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [120]
The Results section, “Tissue networks are tools for data-driven analysis of GWAS” (page unavailable), says NetWAS uses top GWAS hits to identify network connectivity patterns associated with a phenotype. It does not establish that multiple hits in the same module cumulatively contribute to disease development; it uses their network relationships to prioritize candidate disease genes. (LLM, conf=0.76)
Experimental — accuracy not yet measuredLAMP1, GRSF1, and one other protein are in a brain tissue-specific genetic interaction neighborhood that is highly enriched for the lysosome KEGG pathway.
“Of note, these latter 3 proteins are in a brain tissue-specific genetic interaction neighborhood (Greene et al., 2015) that is highly significantly (FDR-corrected p<1×10−7) enriched for the lysosome KEGG pathway.”
From A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer’s Disease (2017) · cites this paper as Greene et al., 2015
The key advance of the NetWAS 2.0 method is that it leverages GWAS p-values as opposed to treating all non-significant genes as having equal probability of being negative examples as in the original NetWAS method.
“Intuitively, the key advance of the NetWAS 2.0 method is that it leverages the GWAS p-values as opposed to treating all non-significant genes as having equal probability of being negative examples as in the original NetWAS method (Greene et al., 2015).”
From Selective Neuronal Vulnerability in Alzheimer’s Disease: A Network-Based Analysis (2020) · cites this paper as Greene et al., 2015
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References 93 · Cited by 832
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- Authors
- Greene, C. S., Krishnan, A., Wong, A. K., Ricciotti, E., Zelaya, R. A., Himmelstein, D. S., Zhang, R., Hartmann, B. M., Zaslavsky, E., Sealfon, S. C., Chasman, D. I., FitzGerald, G. A., Dolinski, K., Grosser, T., & Troyanskaya, O. G.
- Affiliations
- Dartmouth College (US)
- Princeton University (US)
- University of Pennsylvania (US)
- University of California, San Francisco (US)
- Icahn School of Medicine at Mount Sinai (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Troyanskaya, Olga G
- ORCID
- Venue
- Nature Genetics
- Published
- 2015-04-27
- Volume
- 47
- Issue
- 6
- Pages
- 569-576
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC4828725
- OpenAlex
- W1981409633
- Cited by
- 832
- References
- 93
- Citations by year
- 2026: 37 · 2025: 50 · 2024: 76 · 2023: 71 · 2022: 89 · 2021: 110 · 2020: 86 · 2019: 78 · 2018: 73 · 2017: 71 · 2016: 72 · 2015: 17 · 2014: 1
- FWCI
- 34.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion)
- Funders
- National Institutes of Health (R24OD011194)
- U.S. Public Health Service (HHSN272201000054C)
- National Heart, Lung, and Blood Institute (U54HL117798)
- National Human Genome Research Institute (R01HG005998, T32HG003284)
- National Cancer Institute (T32CA009528)
- National Institute of General Medical Sciences (R01GM071966, P20GM103534, P50GM071508)
- National Institute of Allergy and Infectious Diseases (HHSN272201000054C, U19AI117873)
- National Institute of Environmental Health Sciences (P30ES013508)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- gene-disease association, Bayesian network inference, genome-wide association study, cell lineage, disease relationships, network visualization
- MeSH terms
- Alzheimer Disease, Bayes Theorem, Cells, Cultured, Gene Expression Regulation, Humans, Hypertension, Models, Biological, Organ Specificity, Parkinson Disease, Myocytes, Smooth Muscle, Gene Regulatory Networks, Genome-Wide Association Study, Protein Interaction Maps, Gene Ontology
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