Published online 6 June 2006
© 2006 The Author(s)
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Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes
South African National Bioinformatics Institute, University of the Western Cape Bellville, 7535, South Africa 1 Medical Genetics Section, Department of Medical Sciences, The University of Edinburgh Edinburgh, UK 2 MRC Human Genetics Unit Crewe Road Western General Hospital Edinburgh, EH42XU, UK 3 Department of Human Genetics, University Medical Centre Nijmegen PO Box 9101, 6500HB Nijmegen, The Netherlands 4 Department of Molecular Biology, Nijmegen Center for Molecular Life Sciences, Radboud University 6500 HB Nijmegen, The Netherlands 5 Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen PO Box 9010, 6500GL Nijmegen, The Netherlands 6 Research Unit on Biomedical Informatics (GRIB), Universitat Pompeu Fabra Passeig Martim de la Barceloneta 37–49, 08003, Barcelona, Spain 7 Computational Genomics Group, The European Bioinformatics Institute EMBL Cambridge Outstation, Cambridge CB10 1SD, UK 8 Ontario Genomics Innovation Centre, Ottawa Health Research Institute 501 Smyth, Ottawa, ON, Canada K1H 8L6 9 Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa Ottawa, ON, Canada 10 National Human Genome Center, Howard University, Genetic Epidemiology Unit, College of Medicine 2216 6th Street, NW, Washington, DC 20059, USA 11 University of Ibadan, College of Medicine Ibadan, Nigeria 12 Harvard Medical School, Joslin Diabetes Center 1 Joslin Place, Boston, MA 02215, USA
*To whom correspondence should be addressed. Tel: +27 21 9592611; Fax: 27 21 9592512; Email: nicki{at}sanbi.ac.za
Received January 3, 2006. Revised March 25, 2006. Accepted May 2, 2006.
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.
Present address: Christos Ouzounis, Computational Genomics Unit & Institute of Agrobiotechnology, Centre for Research and Technology Hellas, Thessalonica, GR-57001, Greece
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