Linked Life Data

18612082

Source:http://linkedlifedata.com/resource/pubmed/id/18612082

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2008-9-18
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1531-2267
pubmed:author
pubmed:issnType
Electronic
pubmed:day
17
pubmed:volume
35
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
55-64
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Prioritization of candidate disease genes for metabolic syndrome by computational analysis of its defining phenotypes.
pubmed:affiliation
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
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't