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• W2103226344 abstract "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." @default.
• W2103226344 created "2016-06-24" @default.
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• W2103226344 date "2008-09-01" @default.
• W2103226344 modified "2023-10-02" @default.
• W2103226344 title "Prioritization of candidate disease genes for metabolic syndrome by computational analysis of its defining phenotypes" @default.
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• W2103226344 doi "https://doi.org/10.1152/physiolgenomics.90247.2008" @default.
• W2103226344 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18612082" @default.
• W2103226344 hasPublicationYear "2008" @default.
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