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    An integrative modular approach to systematically predict gene-phenotype associations.

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    Author
    Mehan, MR; Nunez-Iglesias, J; Dai, C; Waterman, MS; Zhou, XJ
    Date
    2010-01-18
    Source Title
    BMC Bioinformatics
    Publisher
    Springer Science and Business Media LLC
    University of Melbourne Author/s
    Nunez-Iglesias, Juan
    Affiliation
    Medicine Dentistry & Health Sciences
    Metadata
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    Document Type
    Journal Article
    Citations
    Mehan, M. R., Nunez-Iglesias, J., Dai, C., Waterman, M. S. & Zhou, X. J. (2010). An integrative modular approach to systematically predict gene-phenotype associations.. BMC Bioinformatics, 11 Suppl 1 (SUPPLL.1), pp.S62-. https://doi.org/10.1186/1471-2105-11-S1-S62.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256044
    DOI
    10.1186/1471-2105-11-S1-S62
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3009536
    Abstract
    BACKGROUND: Complex human diseases are often caused by multiple mutations, each of which contributes only a minor effect to the disease phenotype. To study the basis for these complex phenotypes, we developed a network-based approach to identify coexpression modules specifically activated in particular phenotypes. We integrated these modules, protein-protein interaction data, Gene Ontology annotations, and our database of gene-phenotype associations derived from literature to predict novel human gene-phenotype associations. Our systematic predictions provide us with the opportunity to perform a global analysis of human gene pleiotropy and its underlying regulatory mechanisms. RESULTS: We applied this method to 338 microarray datasets, covering 178 phenotype classes, and identified 193,145 phenotype-specific coexpression modules. We trained random forest classifiers for each phenotype and predicted a total of 6,558 gene-phenotype associations. We showed that 40.9% genes are pleiotropic, highlighting that pleiotropy is more prevalent than previously expected. We collected 77 ChIP-chip datasets studying 69 transcription factors binding over 16,000 targets under various phenotypic conditions. Utilizing this unique data source, we confirmed that dynamic transcriptional regulation is an important force driving the formation of phenotype specific gene modules. CONCLUSION: We created a genome-wide gene to phenotype mapping that has many potential implications, including providing potential new drug targets and uncovering the basis for human disease phenotypes. Our analysis of these phenotype-specific coexpression modules reveals a high prevalence of gene pleiotropy, and suggests that phenotype-specific transcription factor binding may contribute to phenotypic diversity. All resources from our study are made freely available on our online Phenotype Prediction Database.

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