Prioritization of candidate disease genes for metabolic syndrome by computational analysis of its defining phenotypes.
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.
Tiffin
Nicki
N
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. nickitiffin@imaginet.co.za
Okpechi
Ikechi
I
Perez-Iratxeta
Carolina
C
Andrade-Navarro
Miguel A
MA
Ramesar
Rajkumar
R
D019295:Computational Biology / Q000379:methods*; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D006801:Humans; D024821:Metabolic Syndrome / Q000235:genetics*; D010641:Phenotype; D012984:Software