Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes.
SourceNucleic Acids Research, 34, 10, (2006), pp. 3067-81
Article / Letter to editor
Display more detailsDisplay less details
Nucleic Acids Research
SubjectIGMD 3: Genomic disorders and inherited multi-system disorders; NCMLS 6: Genetics and epigenetic pathways of disease; UMCN 5.1: Genetic defects of metabolism; UMCN 5.3: Cellular energy metabolism
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.
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.