Diabetes Research Today is a free monthly online journal that collates and summarizes the latest research about Diabetes, including details on insulin, type i, type ii, diet, treatment, prevention.

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

Tiffin N, Adie E, Turner F, Brunner HG, van Driel MA, Oti M, Lopez-Bigas N, Ouzounis C, Perez-Iratxeta C, Andrade-Navarro MA, Adeyemo A, Patti ME, Semple CA, Hide W

South African National Bioinformatics Institute, University of the Western Cape, Bellville, 7535, South Africa. nicki@sanbi.ac.za

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.

Published 7 June 2006 in Nucleic Acids Res, 34(10): 3067-81.
Full-text of this article is available online (may require subscription).

© 2004-2008 Diabetes Research Today. All Rights Reserved.