Referenced in: none

Automatically associated channels: Kir1.1 , Kir6.2

Title: Candidate gene association study in type 2 diabetes indicates a role for genes involved in beta-cell function as well as insulin action.

Authors: Inês Barroso, Jian'an Luan, Rita P S Middelberg, Anne-Helen Harding, Paul W Franks, Rupert W Jakes, D Clayton, Alan J Schafer, Stephen O'Rahilly, Nicholas J Wareham

Journal, date & volume: PLoS Biol., 2003 Oct , 1, E20

PubMed link: http://www.ncbi.nlm.nih.gov/pubmed/14551916

Abstract
Type 2 diabetes is an increasingly common, serious metabolic disorder with a substantial inherited component. It is characterised by defects in both insulin secretion and action. Progress in identification of specific genetic variants predisposing to the disease has been limited. To complement ongoing positional cloning efforts, we have undertaken a large-scale candidate gene association study. We examined 152 SNPs in 71 candidate genes for association with diabetes status and related phenotypes in 2,134 Caucasians in a case-control study and an independent quantitative trait (QT) cohort in the United Kingdom. Polymorphisms in five of 15 genes (33%) encoding molecules known to primarily influence pancreatic beta-cell function-ABCC8 (sulphonylurea receptor), KCNJ11 (KIR6.2), SLC2A2 (GLUT2), HNF4A (HNF4alpha), and INS (insulin)-significantly altered disease risk, and in three genes, the risk allele, haplotype, or both had a biologically consistent effect on a relevant physiological trait in the QT study. We examined 35 genes predicted to have their major influence on insulin action, and three (9%)-INSR, PIK3R1, and SOS1-showed significant associations with diabetes. These results confirm the genetic complexity of Type 2 diabetes and provide evidence that common variants in genes influencing pancreatic beta-cell function may make a significant contribution to the inherited component of this disease. This study additionally demonstrates that the systematic examination of panels of biological candidate genes in large, well-characterised populations can be an effective complement to positional cloning approaches. The absence of large single-gene effects and the detection of multiple small effects accentuate the need for the study of larger populations in order to reliably identify the size of effect we now expect for complex diseases.