Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes - PubMed
- ️Sun Jan 01 2006
. 2006 Jun 6;34(10):3067-81.
doi: 10.1093/nar/gkl381. Print 2006.
Euan Adie, Frances Turner, Han G Brunner, Marc A van Driel, Martin Oti, Nuria Lopez-Bigas, Christos Ouzounis, Carolina Perez-Iratxeta, Miguel A Andrade-Navarro, Adebowale Adeyemo, Mary Elizabeth Patti, Colin A M Semple, Winston Hide
Affiliations
- PMID: 16757574
- PMCID: PMC1475747
- DOI: 10.1093/nar/gkl381
Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes
Nicki Tiffin et al. Nucleic Acids Res. 2006.
Abstract
Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.
Figures
Involvement of candidate T2D genes in cellular metabolism pathways, selected candidates are shown in red.
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