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Diabetologia. 2011 Aug;54(8):2047-55. doi: 10.1007/s00125-011-2188-3. Epub 2011 Jun 7.

Genome-wide association and meta-analysis in populations from Starr County, Texas, and Mexico City identify type 2 diabetes susceptibility loci and enrichment for expression quantitative trait loci in top signals.

Author information

1
Department of Human Genetics, University of Chicago, Chicago, IL, USA.

Abstract

AIMS/HYPOTHESIS:

We conducted genome-wide association studies (GWASs) and expression quantitative trait loci (eQTL) analyses to identify and characterise risk loci for type 2 diabetes in Mexican-Americans from Starr County, TX, USA.

METHOD:

Using 1.8 million directly interrogated and imputed genotypes in 837 unrelated type 2 diabetes cases and 436 normoglycaemic controls, we conducted Armitage trend tests. To improve power in this population with high disease rates, we also performed ordinal regression including an intermediate class with impaired fasting glucose and/or glucose tolerance. These analyses were followed by meta-analysis with a study of 967 type 2 diabetes cases and 343 normoglycaemic controls from Mexico City, Mexico.

RESULT:

The top signals (unadjusted p value <1 × 10(-5)) included 49 single nucleotide polymorphisms (SNPs) in eight gene regions (PER3, PARD3B, EPHA4, TOMM7, PTPRD, HNT [also known as RREB1], LOC729993 and IL34) and six intergenic regions. Among these was a missense polymorphism (rs10462020; Gly639Val) in the clock gene PER3, a system recently implicated in diabetes. We also report a second signal (minimum p value 1.52 × 10(-6)) within PTPRD, independent of the previously implicated SNP, in a population of Han Chinese. Top meta-analysis signals included known regions HNF1A and KCNQ1. Annotation of top association signals in both studies revealed a marked excess of trans-acting eQTL in both adipose and muscle tissues.

CONCLUSIONS/INTERPRETATION:

In the largest study of type 2 diabetes in Mexican populations to date, we identified modest associations of novel and previously reported SNPs. In addition, in our top signals we report significant excess of SNPs that predict transcript levels in muscle and adipose tissues.

PMID:
21647700
PMCID:
PMC3761075
DOI:
10.1007/s00125-011-2188-3
[Indexed for MEDLINE]
Free PMC Article

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