Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2010-10-15
pubmed:abstractText
Genome-wide association (GWA) studies have identified around 20 common genetic variants influencing the risk of type 2 diabetes (T2D). Likewise, a number of variants have been associated with diabetes-related quantitative glycaemic traits, but to date the overlap between these genes and variants has been low. The majority of genetic studies have focused on fasting plasma glucose levels; however, this measure is highly variable. We have conducted a GWA meta-analysis of glycated haemoglobin (HbA?(C) ) levels within three healthy nondiabetic populations. This phenotype provides an estimate of mean glucose levels over 2-3 months and is a more stable predictor of future diabetes risk. Participants were from three isolated populations: the Orkney Isles in the north of Scotland, the Dalmatian islands of Vis, and Kor?ula in Croatia (total of 1782 nondiabetic subjects). Association was tested in each population and results combined by meta-analysis. The strongest association was with the TCF7L2 gene (rs7903146, P= 1.48 × 10??). This is also the strongest common genetic risk factor for T2D but it has not been identified in previous genome-wide studies of glycated haemoglobin.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1469-1809
pubmed:author
pubmed:copyrightInfo
© 2010 The Authors Annals of Human Genetics © 2010 Blackwell Publishing Ltd/University College London.
pubmed:issnType
Electronic
pubmed:volume
74
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
471-8
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
The TCF7L2 diabetes risk variant is associated with HbA?(C) levels: a genome-wide association meta-analysis.
pubmed:affiliation
Centre for Population Health Sciences, University of Edinburgh, Scotland.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Meta-Analysis