Open AccessCandidate 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.
Publication year - 2014
Publication title -
molecular genetics and genomic medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.765
H-Index - 29
ISSN - 2324-9269
DOI - 10.1002/mgg3.40
Subject(s) - genome wide association study , candidate gene , single nucleotide polymorphism , genetic association , computational biology , snp , disease , locus (genetics) , genetics , coronary artery disease , biology , gene , bioinformatics , medicine , genotype , pathology , psychiatry
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 ( SNP s) ( 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.