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

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.

Affiliations

simpleSouth African National Bioinformatics Institute, University of the Western Cape Bellville, 7535, South Africa

¹simpleMedical Genetics Section, Department of Medical Sciences, The University of Edinburgh Edinburgh, UK

²simpleMRC Human Genetics Unit Crewe RoadsimpleWestern General Hospital Edinburgh, EH42XU, UK

³simpleDepartment of Human Genetics, University Medical Centre Nijmegen PO Box 9101, 6500HB Nijmegen, The Netherlands

⁴simpleDepartment of Molecular Biology, Nijmegen Center for Molecular Life Sciences, Radboud University 6500 HB Nijmegen, The Netherlands

⁵simpleCentre for Molecular and Biomolecular Informatics, Radboud University Nijmegen PO Box 9010, 6500GL Nijmegen, The Netherlands

⁶simpleResearch Unit on Biomedical Informatics (GRIB), Universitat Pompeu Fabra Passeig Martim de la Barceloneta 37–49, 08003, Barcelona, Spain

⁷simpleComputational Genomics Group, The European Bioinformatics Institute EMBL Cambridge Outstation, Cambridge CB10 1SD, UK

⁸simpleOntario Genomics Innovation Centre, Ottawa Health Research Institute 501 Smyth, Ottawa, ON, Canada K1H 8L6

⁹simpleDepartment of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa Ottawa, ON, Canada

¹⁰simpleNational Human Genome Center, Howard University, Genetic Epidemiology Unit, College of Medicine 2216 6th Street, NW, Washington, DC 20059, USA

¹¹simpleUniversity of Ibadan, College of Medicine Ibadan, Nigeria

¹²simpleHarvard Medical School, Joslin Diabetes Center 1 Joslin Place, Boston, MA 02215, USA

Author notes

^*To whom correspondence should be addressed. Tel: [phone]; Fax: [phone]; Email: nicki@ 123456sanbi.ac.za

Present address: Christos Ouzounis, Computational Genomics Unit & Institute of Agrobiotechnology, Centre for Research and Technology Hellas, Thessalonica, GR-57001, Greece

Journal

Journal ID (nlm-ta): Nucleic Acids Res

Journal ID (publisher-id): Nucleic Acids Research

Title: Nucleic Acids Research

Publisher: Oxford University Press

ISSN (Print): 0305-1048

ISSN (Electronic): 1362-4962

Publication date Collection 2006

Publication date (Print): 2006

Publication date (Electronic): 6 June 2006

Volume: 34

Issue: 10

Pages: 3067-3081

PMC ID: 1475747

DOI: 10.1093/nar/gkl381

PubMed ID: 16757574

Copyright statement: © 2006 The Author(s)

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.

Categories

Subject: Survey and Summary