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

Item Type:	Article
Title:	Prioritization of candidate disease genes for metabolic syndrome by computational analysis of its defining phenotypes
Creators Name:	Tiffin, N. and Okpechi, I. and Perez-Iratxeta, C. and Andrade-Navarro, M.A. and Ramesar, R.
Abstract:	There is a rapid increase in world-wide burden of disease attributed to metabolic syndrome, as defined by co-occurrence of an array of phenotypes including abdominal obesity, dysglycemia, hypertrigylceridemia, low levels of high density lipoprotein (HDL) 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 prioritise 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 nineteen 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 prioritisation of disease-causing genes for that trait.
Keywords:	Metabolic Syndrome, Candidate Genes, Computational Analysis, Disease Genes, Complex Disease, Computational Biology, Genetic Predisposition to Disease, Linkage, Metabolic Syndrome X, Phenotype, Software
Source:	Physiological Genomics
ISSN:	1094-8341
Publisher:	American Physiological Society
Volume:	35
Number:	1
Page Range:	55-64
Date:	17 September 2008
Official Publication:	https://doi.org/10.1152/physiolgenomics.90247.2008
PubMed:	View item in PubMed

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