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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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journal contribution
posted on 2013-01-01, 00:00 authored by S Ballouz, J Liu, M Oti, B Gaeta, D Fatkin, M Bahlo, Merridee Wouters
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.

History

Journal

Molecular genetics and genomic medicine

Volume

2

Issue

1

Pagination

44 - 57

Publisher

Wiley

Location

London, Eng.

ISSN

2324-9269

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2013, Wiley