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Oxford University Press, Nucleic Acids Research, 10(34), p. 3067-3081, 2006

DOI: 10.1093/nar/gkl381

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Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Journal article published in 2006 by Frances Turner, Marc A. van Driel, Euan Adie, Han G. Brunner, M. A. van Driel, Martin O. Oti, Nuria Lopez-Bigas, Christos A. Ouzounis, Carolina Perez-Iratxeta, Miguel A. Andrade-Navarro, Adebowale Adeyemo, Mary Elizabeth Patti ORCID, Colin A. M. Semple, Nicki Tiffin ORCID, Winston Hide
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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. UR - http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16757574