Improved human disease candidate gene prioritization using mouse phenotype
Open accessAbstract
Background: The majority of common diseases are multi-factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors. High-throughput genome-wide studies like linkage analysis and gene expression profiling, tend to be most useful for classification and characterization but do not provide sufficient information to identify or prioritize specific disease causal genes. Results: Extending on an earlier hypothesis that the majority of genes that impact or cause disease share membership in any of several functional relationships we, for the first time, show the utility of mouse phenotype data in human disease gene prioritization. We study the effect of different data integration methods, and based on the validation studies, we show that our approach, ToppGene http://toppgene.cchmc.org , outperforms two of the existing candidate gene prioritization methods, SUSPECTS and ENDEAVOUR. Conclusion: The incorporation of phenotype information for mouse orthologs of human genes greatly improves the human disease candidate gene analysis and prioritization.
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Hep-IDCONNECT transcription factor-encoding genes are linked to abnormal liver phenotypes and metabolic functions, albeit less significantly than Hep-ID transcription factors.
“mining genes associated with phenotypes from the Mammalian Phenotype Ontology resource (Smith et al, 2005; Chen et al, 2007) revealed that, albeit less significant when compared to Hep‐ID TFs, Hep‐IDCONNECT TF‐encoding genes are linked to abnormal liver phenotypes and metabolic functions (Fig 2G).”
From An extended transcription factor regulatory network controls hepatocyte identity (2023) · cites this paper as Chen et al, 2007
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Self-citation
- Authors
- Chen, J., Xu, H., Aronow, B. J., & Jegga, A. G.
- Affiliations
- Cincinnati Children's Hospital Medical Center (US)
- Corresponding author
- Jegga, Anil G
- Venue
- BMC Bioinformatics
- Published
- 2007-10-16
- Volume
- 8
- Issue
- 1
- Pages
- 392-392
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC2194797
- OpenAlex
- W2128245545
- Cited by
- 284
- References
- 42
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 13 · 2023: 21 · 2022: 18 · 2021: 15 · 2020: 13 · 2019: 18 · 2018: 22 · 2017: 7 · 2016: 20 · 2015: 9 · 2014: 19 · 2013: 11 · 2012: 21
- FWCI
- 4.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Cancer Institute (U01 CA84291-07, UO1 CA84291-07, CA84291, U01CA084291, CA84291-07)
- 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 › Genetics › Genomics and Rare Diseases
- Keywords
- candidate gene prioritization, human disease genes, mouse phenotypes, mouse orthologs, data integration, polygenic disease
- MeSH terms
- Algorithms, Animals, Chromosome Mapping, Humans, Phenotype, Species Specificity, Linkage Disequilibrium, Genetic Predisposition to Disease, Mice
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