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

Ballouz, Sara, Liu, Jason Y, Oti, Martin, Gaeta, Bruno, Fatkin, Diane, Bahlo, Melanie and Wouters, Merridee A 2013, Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease, Molecular genetics and genomic medicine, vol. 2, no. 1, pp. 44-57, doi: 10.1002/mgg3.40.

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Title Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease
Author(s) Ballouz, Sara
Liu, Jason Y
Oti, Martin
Gaeta, Bruno
Fatkin, Diane
Bahlo, Melanie
Wouters, Merridee A
Journal name Molecular genetics and genomic medicine
Volume number 2
Issue number 1
Start page 44
End page 57
Total pages 14
Publisher Wiley
Place of publication London, Eng.
Publication date 2013
ISSN 2324-9269
Keyword(s) candidate gene prediction
cis-ruption
complex diseases
coronary artery disease
genome-wide association study
miRNA
Summary 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.
Language eng
DOI 10.1002/mgg3.40
Field of Research 060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics)
060102 Bioinformatics
Socio Economic Objective 920110 Inherited Diseases (incl. Gene Therapy)
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2013, Wiley
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057303

Document type: Journal Article
Collection: School of Life and Environmental Sciences
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Created: Sat, 26 Oct 2013, 09:27:22 EST by Merridee Wouters

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