ArticleMolecular genetics & genomic medicine2014
Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease.
Article in Molecular genetics & genomic medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease.Frontiers in genetics · 2020Article
- Turning genome-wide association study findings into opportunities for drug repositioning.Computational and structural biotechnology journal · 2020Review
- Genome-wide and Phenome-wide Approaches to Understand Variable Drug Actions in Electronic Health Records.Clinical and translational science · 2018Review
- Drug repositioning: computational approaches and research examples classified according to the evidence level.Discoveries (Craiova, Romania) · 2017Review
- Decoding the complex genetic causes of heart diseases using systems biology.Biophysical reviews · 2015Article
- Novel therapeutics for coronary artery disease from genome-wide association study data.BMC medical genomics · 2015Article
- Identification of novel therapeutics for complex diseases from genome-wide association data.BMC medical genomics · 2014Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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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Registered trials
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