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Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease
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 T...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wiley Periodicals
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907915/ https://www.ncbi.nlm.nih.gov/pubmed/24498628 http://dx.doi.org/10.1002/mgg3.40 |
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author | Ballouz, Sara Liu, Jason Y Oti, Martin Gaeta, Bruno Fatkin, Diane Bahlo, Melanie Wouters, Merridee A |
author_facet | Ballouz, Sara Liu, Jason Y Oti, Martin Gaeta, Bruno Fatkin, Diane Bahlo, Melanie Wouters, Merridee A |
author_sort | Ballouz, Sara |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3907915 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Wiley Periodicals |
record_format | MEDLINE/PubMed |
spelling | pubmed-39079152014-02-04 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 Wouters, Merridee A Mol Genet Genomic Med Original Articles 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. Wiley Periodicals 2014-01 2013-11-13 /pmc/articles/PMC3907915/ /pubmed/24498628 http://dx.doi.org/10.1002/mgg3.40 Text en © 2013 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc. http://creativecommons.org/licenses/by/3.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Ballouz, Sara Liu, Jason Y Oti, Martin Gaeta, Bruno Fatkin, Diane Bahlo, Melanie Wouters, Merridee A Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease |
title | Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease |
title_full | Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease |
title_fullStr | Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease |
title_full_unstemmed | Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease |
title_short | Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease |
title_sort | candidate disease gene prediction using gentrepid: application to a genome-wide association study on coronary artery disease |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907915/ https://www.ncbi.nlm.nih.gov/pubmed/24498628 http://dx.doi.org/10.1002/mgg3.40 |
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