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This Article Full Text Full Text (PDF) Supplemental Tables All Versions of this Article: 35/1/55    most recent 90247.2008v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Tiffin, N. Articles by Ramesar, R. PubMed PubMed Citation Articles by Tiffin, N. Articles by Ramesar, R.

Prioritization of candidate disease genes for metabolic syndrome by computational analysis of its defining phenotypes

¹ Division of Human Genetics, MRC Human Genetics Research Unit, Institute for Infectious Diseases and Molecular Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
² Nephrology Division and Department of Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
³ Ontario Genomics Innovation Centre, Ottawa Health Research Institute, University of Ottawa, Ottawa, Ontario, Canada
⁴ Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada

There is a rapid increase in the world-wide burden of disease attributed to metabolic syndrome, as defined by co-occurrence of an array of phenotypes including abdominal obesity, dysglycemia, hypertriglyceridemia, low levels of high density lipoprotein cholesterol, and hypertension. Familial studies clearly indicate a genetic component to the disease and many linkage studies have identified a large number of linked loci. No disease-causing genes, however, have been conclusively identified, most likely because this is a multigenic disease for which effects of many causative genes may be small and combined with environmental effects. To assist empirical identification of metabolic syndrome associated genes, we present here a novel computational approach to prioritize candidate genes. We have used linkage studies and the clinical and population-specific presentation of the disease to select a final candidate gene list of 19 most likely disease-causing genes. These are predominantly involved in chylomicron processing, transmembrane receptor activity, and signal transduction pathways. We propose here that information about the clinical presentation of a complex trait can be used to effectively inform computational prioritization of disease-causing genes for that trait.

candidate genes; complex disease