Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis
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BACKGROUND: Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates. METHODOLOGY/PRINCIPAL FINDINGS: We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases. CONCLUSION: Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes.
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GBA approaches are widely used to identify new potential disease genes if a substantial proportion of the genes within a module are associated with a particular disease.
“GBA approaches are also widely used to identify new potential disease genes if a substantial proportion of the genes within a module are associated with a particular disease [26, 121–126] (Figure 1).”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [26]
The ‘Identification of Candidate Genes in Disease Loci’ section describes using coexpression clusters containing at least two genes known to cause similar phenotypes to identify candidate disease genes. It does not require a substantial proportion of a module’s genes to be associated with a particular disease; it also restricts candidates to genes in the mapped disease locus. No page number is available in our copy. (LLM, conf=0.85)
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- Authors
- Ala, U., Piro, R. M., Grassi, E., Damasco, C., Silengo, L., Oti, M., Provero, P., & Di Cunto, F.
- Affiliations
- University of Turin (IT)
- Radboud University Medical Center (NL)
- Corresponding author
- Provero, Paolo
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-03-28
- Volume
- 4
- Issue
- 3
- Pages
- e1000043
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC2268251
- OpenAlex
- W2059682917
- Cited by
- 134
- References
- 51
- Citations by year
- 2024: 6 · 2023: 3 · 2022: 2 · 2021: 10 · 2020: 5 · 2019: 7 · 2018: 2 · 2017: 5 · 2016: 3 · 2015: 9 · 2014: 6 · 2013: 16 · 2012: 12
- FWCI
- 6.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- Regione Piemonte
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- disease gene prediction, gene co-expression, positional candidate genes, phenotype similarity, gene expression profiles, microarray data, candidate gene prioritization
- MeSH terms
- Algorithms, Animals, Chromosome Mapping, Diagnosis, Computer-Assisted, Humans, Biomarkers, Conserved Sequence, Genetic Predisposition to Disease, Proteome, Gene Expression Profiling, Genetic Diseases, Inborn, Mice
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