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

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 li...

Full description

Saved in:
Bibliographic Details
Published in:Nucleic acids research 2006-01, Vol.34 (10), p.3067-3081
Main Authors: Tiffin, Nicki, Adie, Euan, Turner, Frances, Brunner, Han G., van Driel, Marc A., Oti, Martin, Lopez-Bigas, Nuria, Ouzounis, Christos, Perez-Iratxeta, Carolina, Andrade-Navarro, Miguel A., Adeyemo, Adebowale, Patti, Mary Elizabeth, Semple, Colin A. M., Hide, Winston
Format: Article
Language:English
Subjects:
Computational Biology - methods
Diabetes Mellitus, Type 2 - genetics
Genes
Genetic Linkage
Genetic Predisposition to Disease
Humans
Internet
Obesity - genetics
Software
Survey and Summary
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gkl381