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Identification of Type 2 Diabetes Genes in Mexican Americans Through Genome-Wide Association Studies

¹ Department of Medicine, University of Chicago, Chicago, Illinois
² Department of Human Genetics, University of Chicago, Chicago, Illinois
³ Human Genetics Center, University of Texas Health Sciences Center, Houston, Texas

Address correspondence and reprint requests to Nancy J. Cox, PhD, Department of Medicine, University of Chicago, 5841 S. Maryland Ave., MC6091, Chicago, IL 60637. E-mail: ncox{at}bsd.uchicago.edu; or Craig L. Hanis, PhD, Human Genetics Center, University of Texas Health Science Center at Houston, P.O. Box 20186, Houston, TX 77225. E-mail: craig.l.hanis{at}uth.tmc.edu

Abbreviations: BRLMM, Bayesian robust-fitting linear model with Mahalanobis distance classifier; DGI, Diabetes Genetics Initiative; DM, dynamic modeling; FDR, false discovery rate; FHS, Framingham Heart Study; GEL, genotype calling algorithm using empirical likelihood; GWAS, genome-wide association study; HWE, Hardy-Weinberg equilibrium; LD, linkage disequilibrium; MAF, minor allele frequency; POA, proportion of ancestry; SNP, single nucleotide polymorphism

OBJECTIVE—The objective of this study was to identify DNA polymorphisms associated with type 2 diabetes in a Mexican-American population.

RESEARCH DESIGN AND METHODS—We genotyped 116,204 single nucleotide polymorphisms (SNPs) in 281 Mexican Americans with type 2 diabetes and 280 random Mexican Americans from Starr County, Texas, using the Affymetrix GeneChip Human Mapping 100K set. Allelic association exact tests were calculated. Our most significant SNPs were compared with results from other type 2 diabetes genome-wide association studies (GWASs). Proportions of African, European, and Asian ancestry were estimated from the HapMap samples using structure for each individual to rule out spurious association due to population substructure.

RESULTS—We observed more significant allelic associations than expected genome wide, as empirically assessed by permutation (14 below a P of 1 x 10^–4 [8.7 expected]). No significant differences were observed between the proportion of ancestry estimates in the case and random control sets, suggesting that the association results were not likely confounded by substructure. A query of our top 1% of SNPs (P < 0.01) revealed SNPs in or near four genes that showed evidence for association (P < 0.05) in multiple other GWAS interrogated: rs979752 and rs10500641 near UBQLNL and OR52H1 on chromosome 11, rs2773080 and rs3922812 in or near RALGPS2 on chromosome 1, and rs1509957 near EGR2 on chromosome 10.

CONCLUSIONS—We identified several SNPs with suggestive evidence for replicated association with type 2 diabetes that merit further investigation.

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