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    Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease.

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    Author
    Ballouz, S; Liu, JY; Oti, M; Gaeta, B; Fatkin, D; Bahlo, M; Wouters, MA
    Date
    2014-01
    Source Title
    Molecular Genetics and Genomic Medicine
    Publisher
    Wiley
    University of Melbourne Author/s
    Bahlo, Melanie
    Affiliation
    School of Mathematics and Statistics
    Metadata
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    Document Type
    Journal Article
    Citations
    Ballouz, S., Liu, J. Y., Oti, M., Gaeta, B., Fatkin, D., Bahlo, M. & Wouters, M. A. (2014). Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease.. Mol Genet Genomic Med, 2 (1), pp.44-57. https://doi.org/10.1002/mgg3.40.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/258145
    DOI
    10.1002/mgg3.40
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907915
    Abstract
    Current single-locus-based analyses and candidate disease gene prediction methodologies used in genome-wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple-locus-based approach, drawing on protein pathway- or domain-based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC-implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.

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