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

@article{Tiffin, 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.}, address = {South African National Bioinformatics Institute, University of the Western Cape, Bellville, 7535, South Africa. nicki@sanbi.ac.za}, author = {Tiffin, N. and Adie, E. and Turner, F. and Brunner, H. G. and van Driel, M. A. and Oti, M. and Lopez-Bigas, N. and Ouzounis, C. and Perez-Iratxeta, C. and Andrade-Navarro, M. A. and Adeyemo, A. and Patti, M. E. and Semple, C. A. and Hide, W.}, citeulike-article-id = {933600}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/16757574}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=16757574}, issn = {1362-4962}, journal = {Nucleic Acids Res}, keywords = {human}, number = {10}, pages = {3067--3081}, posted-at = {2008-03-01 04:53:08}, priority = {2}, title = {Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16757574}, volume = {34}, year = {2006} }

TY - JOUR ID - Tiffin L3 - citeulike-article-id:933600 N2 - 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. IS - 10 JF - Nucleic Acids Res SN - 1362-4962 AD - South African National Bioinformatics Institute, University of the Western Cape, Bellville, 7535, South Africa. nicki@sanbi.ac.za EP - 3081 TI - Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes. VL - 34 SP - 3067 KW - human AU - Tiffin, N AU - Adie, E AU - Turner, F AU - Brunner, HG AU - van Driel, MA AU - Oti, M AU - Lopez-Bigas, N AU - Ouzounis, C AU - Perez-Iratxeta, C AU - Andrade-Navarro, MA AU - Adeyemo, A AU - Patti, ME AU - Semple, CA AU - Hide, W PY - 2006/// UR - http://view.ncbi.nlm.nih.gov/pubmed/16757574 ER -