Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes

Abstract

Genome-wide association (GWA) studies have identified multiple new genomic loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D)1-11. Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to discover loci at which common alleles have modest effects, we performed meta-analysis of three T2D GWA scans encompassing 10,128 individuals of European-descent and ~2.2 million SNPs (directly genotyped and imputed). Replication testing was performed in an independent sample with an effective sample size of up to 53,975. At least six new loci with robust evidence for association were detected, including the JAZF1 (p=5.0×10 −14 ), CDC123/CAMK1D (p=1.2×10 −10 ), TSPAN8/ LGR5 (p=1.1×10 −9 ), THADA (p=1.1×10 −9 ), ADAMTS9 (p=1.2×10 −8 ), and NOTCH2 (p=4.1×10 −8 ) gene regions. The large number of loci with relatively small effects indicates the value of large discovery and follow-up samples in identifying additional clues about the inherited basis of T2D.Genome-wide association studies are unbiased by previous hypotheses concerning candidate genes and pathways, but challenged by the modest effect sizes of individual common susceptibility variants and the need for stringent statistical thresholds. For example, the largest allelic odds ratio of any established common variant for T2D is ~1.35 (TCF7L2), with the nine other validated associations to common variants (excluding FTO, which has its primary effect through obesity) having allelic odds ratios between 1.1 and 1. 21-6,11,12. To augment power to detect additional loci of similar and/or smaller effect, we increased sample size by combining three previously published GWA studies (Diabetes Genetics Initiative [DGI], Finland-United States Investigation of NIDDM Genetics [FUSION], and Wellcome Trust Case Control Consortium [WTCCC])1-4, and extended SNP coverage by imputing untyped SNPs based on patterns of haplotype variation from the HapMap dataset13 (Table 1).We started with a set of genotyped autosomal SNPs that passed quality control (QC) filters in each study: in WTCCC, 393,143 SNPs from the Affymetrix 500k chip (MAF>0.01; 1,924 cases and 2,938 population-based controls from the Wellcome Trust Case Control Consortium3,4); in DGI, 378,860 Using these directly measured and imputed genotypes, we tested for association of each SNP with T2D in each study separately, corrected each study for residual population stratification, cryptic relatedness or technical artifacts using genomic control, and then combined these results in a genome-wide meta-analysis across a total of 10,128 samples (4,549 cases, 5,579 controls) (Methods; Supplementary Methods). We calculated that this sample size provides reasonable power to detect additional variants with properties similar to those previously identified by less formal data combination efforts1,2,4 (Supplementary Table 2). Unless otherwise indicated, results presented are derived from...

Association between type 2 diabetes genetic susceptibility loci and visceral and subcutaneous fat area as determined by computed tomography

“…Risk alleles refer to the type 2 diabetesassociated alleles, according to previous reports. [26][27][28][29][30][31][32][33] Multiple linear regression analyses were performed to test the independent effects of the risk alleles on BMI, VFA and SFA by taking into account the effects of other variables (i.e., age and gender) that were assumed to be independent of the effect of each SNP. The values of BMI, VFA and SFA were logarithmically transformed before multiple linear regression analysis.…”

“…We selected 23 SNPs identified as susceptibility loci for type 2 diabetes by GWAS [26][27][28][29][30][31][32][33] and constructed Invader probes (Third Wave Technologies, Madison, WI, USA) for the following SNPs: notch 2 (NOTCH2) rs10923931, thyroid adenoma-associated (THADA) rs7578597, peroxisome proliferator-activated receptor g (PPARG) rs1801282, ADAM metallopeptidase with thrombospondin type 1 motif, 9 (ADAMTS9) rs4607103, insulinlike growth factor 2 mRNA-binding protein 2 (IGF2BP2) rs1470579, vascular endothelial growth factor A (VEGFA) rs9472138, JAZF zinc finger 1 (JAZF1) rs864745, cyclin-dependent kinase inhibitor 2A and cyclin-dependent kinase inhibitor 2B (CDKN2A/CDKN2B) rs564398 and rs10811661, hematopoietically expressed homeobox (HHEX) rs1111875 and rs5015480, transcription factor 7-like 2 (TCF7L2) rs7901695, potassium voltage-gated channel, KQT-like subfamily, member 1 (KCNQ1) rs2237892, potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11) rs5215 and rs5219, exostosin 2 (EXT2) rs1113132, rs11037909, and rs3740878, melatonin receptor 1B (MTNR1B) rs10830963, dermcidin (DCD) rs1153188, tetraspanin 8/leucine-rich repeat containing G protein-coupled receptor 5 (TSPAN8/LGR5) rs7961581, and FTO rs8050136 and rs9939609. The SNPs were genotyped using Invader assays as previously described.…”

Joint association analysis of a binary and a quantitative trait in family samples

“…24 Among the remaining top 20 SNVs, chromosome 16 SNVs (rs8059849, rs9931529, rs13332434, rs9783765) are near FTO, a gene known for its association with both BMI and T2D. [24][25][26][27][28][29] The SNV rs10894188 (chromosome 11) is near MTNR1B, a gene known to be associated with both T2D and obesity-related traits; 26 rs12097783 (chromosome 1) is near previously identified BMI gene SEC16B; [29][30][31][32] rs11145958 (chromosome 9) is near GPSM1, a T2D-associated gene; 33 5 SNVs on chromosome 1 are near NOTCH2 25 and ADAM30, 25 two genes known for SNVs associated with T2D; rs17863929 (chromosome 4) is approximately 3 Mb away from IL2, 34 a gene known for SNVs in the intron region associated with type-1 diabetes.…”

The pursuit of genome-wide association studies: where are we now?

“…68 Approximately 2.2 million SNPs were analyzed in about 10 000 European individuals in stage 1 of the study; these SNPs were either genotyped directly or inferred in silico using imputation methods. Following stage 1 analysis, replication testing of promising SNPs was carried out in an even larger sample set, with the sample size of the whole study approaching 100 000.…”