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 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.
Tiffin
Nicki
N
South African National Bioinformatics Institute, University of the Western Cape, Bellville, 7535, South Africa. nicki@sanbi.ac.za
Adie
Euan
E
Turner
Frances
F
Brunner
Han G
HG
van Driel
Marc A
MA
Oti
Martin
M
Lopez-Bigas
Nuria
N
Ouzounis
Christos
C
Perez-Iratxeta
Carolina
C
Andrade-Navarro
Miguel A
MA
Adeyemo
Adebowale
A
Patti
Mary Elizabeth
ME
Semple
Colin A M
CA
Hide
Winston
W
D019295:Computational Biology / Q000379:methods*; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics*; D005796:Genes; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D006801:Humans; D020407:Internet; D009765:Obesity / Q000235:genetics*; D012984:Software