Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15×10−94<P<5×10−8, odds ratio (OR) = 1.09 to 1.36). Fine-mapping revealed that 88 of 158 previously identified T2D or glucose homeostasis loci demonstrated nominal to highly significant association (2.2×10−23 < locus-wide P<0.05). These novel and previously identified loci yielded a sibling relative risk of 1.19, explaining 17.5% of the phenotypic variance of T2D on the liability scale in African Americans. Overall, this study identified two novel susceptibility loci for T2D in African Americans. A substantial number of previously reported loci are transferable to African Americans after accounting for linkage disequilibrium, enabling fine mapping of causal variants in trans-ethnic meta-analysis studies.
Despite the higher prevalence of type 2 diabetes (T2D) in African Americans than in Europeans, recent genome-wide association studies (GWAS) were examined primarily in individuals of European ancestry. In this study, we performed meta-analysis of 17 GWAS in 8,284 cases and 15,543 controls to explore the genetic architecture of T2D in African Americans. Following replication in additional 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry, we identified two novel and three previous reported T2D loci reaching genome-wide significance. We also examined 158 loci previously reported to be associated with T2D or regulating glucose homeostasis. While 56% of these loci were shared between African Americans and the other populations, the strongest associations in African Americans are often found in nearby single nucleotide polymorphisms (SNPs) instead of the original SNPs reported in other populations due to differential genetic architecture across populations. Our results highlight the importance of performing genetic studies in non-European populations to fine map the causal genetic variants.
Citation: Ng MCY, Shriner D, Chen BH, Li J, Chen W-M, Guo X, et al. (2014) Meta-Analysis of Genome-Wide Association Studies in African Americans Provides Insights into the Genetic Architecture of Type 2 Diabetes. PLoS Genet 10(8): e1004517. doi:10.1371/journal.pgen.1004517
Editor: Eleftheria Zeggini, Wellcome Trust Sanger Institute, United Kingdom
Received: January 21, 2014; Accepted: June 5, 2014; Published: August 7, 2014
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: Atherosclerosis Risk in Communities Study (ARIC) is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. Funding for Coronary Artery Risk Development in Young Adults (CARDIA) include support to University of Alabama at Birmingham (N01-HC-48047), University of Minnesota (N01-HC-48048), Northwestern University (N01-HC-48049), Kaiser Foundation Research Institute (N01-HC-48050), University of Alabama at Birmingham (N01-HC-95095), Tufts-New England Medical Center (N01-HC-45204), Wake Forest School of Medicine (N01-HC-45205), Harbor-UCLA Research and Education Institute (N01-HC-05187), University of California Irvine (N01-HC-45134, N01-HC-95100). Funding to Candidate-gene Association Resource (CARe) (http://public.nhlbi.nih.gov/GeneticsGenomics/home/care.aspx) include support to Massachusetts Institute of Technology - Broad Institute (N01-HC-65226). Cleveland Family Study (CFS) was supported by a grant to Case Western Reserve University (NIH HL 46380, M01RR00080). Cardiovascular Health Study (CHS) was supported by NHLBI contracts HL085251, HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI grants HL080295, HL087652, HL105756, HL103612, and HL120393 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through AG023629 rom the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org/. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. DNA handling and genotyping was supported in part by National Center of Advancing Translational Technologies CTSI grant UL1TR000124, the National Institute of Diabetes and Digestive and Kidney Diseases grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The Electronic Medical Records and Genomics Network (eMERGE) Network was initiated and funded by NHGRI, with additional funding from NIGMS through the following grants: U01-HG-004610 (Group Health Cooperative); U01-HG-004608 (Marshfield Clinic); U01-HG-04599 (Mayo Clinic); U01HG004609 (Northwestern University); U01-HG-04603 (Vanderbilt University, also serving as the Coordinating Center), and the State of Washington Life Sciences Discovery Fund award to the Northwest Institute of Medical Genetics. The Northwestern University Enterprise Data Warehouse was funded in part by a grant from the National Center for Research Resources, UL1RR025741. The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center's BioVU which is supported by institutional funding and by the Vanderbilt CTSA grant UL1 TR000445 from NCATS/NIH. Family Heart Study (FamHS) was supported by NIH grants R01-HL-087700 and R01-HL-088215 (MAP, Principal Investigator) from NHLBI; and R01-DK-8925601 and R01-DK-075681 (IBB, Principal Investigator) from NIDDK. Family Investigation of Nephropathy in Diabetes (FIND) was supported by FIND grants U01DK57292, U01DK57329, U01DK057300, U01DK057298, U01DK057249, U01DK57295, U01DK070657, U01DK057303, and U01DK070657, U01DK57304. This project has been funded in whole or in part with federal funds from the NIH National Cancer Institute (NCI) under contract N01-CO-12400 and the Intramural Research Program of the NIH-NCI Center for Cancer Research. This work also was supported by the National Center for Research Resources for the General Clinical Research Center grants: Case Western Reserve University, M01-RR-000080; Wake Forest University, M01-RR-07122; Harbor-University of California, Los Angeles Medical Center, M01-RR-00425; College of Medicine, University of California, Irvine, M01-RR-00827-29; University of New Mexico, HSC M01-RR-00997; and Frederic C. Bartter, M01-RR-01346. The CHOICE Study was supported in part by HS08365 from the Agency for Healthcare Research and Quality, Rockville, MD, and HL62985 from the National Heart, Lung, and Blood Institute, Bethesda, MD. Genetic Study of Atherosclerosis Risk (GeneSTAR) was supported by NIH grants through the National Heart, Lung, and Blood Institute (HL58625-01A1, HL59684, HL071025-01A1, U01HL72518, and HL087698) and the National Institute of Nursing Research (NR0224103) and by M01-RR000052 to the Johns Hopkins General Clinical Research Center. Genetic Epidemiology Network of Arteriopathy (GENOA) study is supported by the National Institutes of Health grant numbers HL087660, HL100245 and HL100185 from the National Heart, Lung, and Blood Institute. Healthy Aging in Neighborhoods of Diversity across the Life Span Study (HANDLS) was supported by the Intramural Research Program of the NIH, National Institute on Aging and the National Center on Minority Health and Health Disparities (project # Z01-AG000513 and human subjects protocol # 2009-149). Health, Aging, and Body Composition Study (Health ABC Study) was supported by NIA contracts N01AG62101, N01AG62103, and N01AG62106. The genome-wide association study was funded by NIA grant 1R01AG032098-01A1 to Wake Forest University Health Sciences and genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C. This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging. Howard University Family Study (HUFS) was supported by National Institutes of Health grants S06GM008016-320107 to CNR and S06GM008016-380111 to AA. Participant enrollment was carried out at the Howard University General Clinical Research Center, supported by National Institutes of Health grant 2M01RR010284. Genotyping support was provided by the Coriell Institute for Medical Research. This research was supported by the Intramural Research Program of the Center for Research on Genomics and Global Health (CRGGH). The CRGGH is supported by the National Human Genome Research Institute, the National Institute of Diabetes and Digestive and Kidney Diseases, the Center for Information Technology, and the Office of the Director at the National Institutes of Health (Z01HG200362). The Charles Bronfman Institute for Personalized Medicine (IPM) BioBank Program is supported by The Andrea and Charles Bronfman Philanthropies. Insulin Resistance Atherosclerosis Study (IRAS) was supported by the National Heart, Lung, and Blood Institute (HL047887, HL047889, HL047890, HL47902). IRAS Family Study was supported by the National Heart, Lung, and Blood Institute (HL060944, HL060894, HL061210). The Jackson Heart Study (JHS) is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. Multi-Ethnic Study of Atherosclerosis (MESA), MESA Family, and the MESA SHARe project are conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts N01-HC-95159 through N01-HC-95169 and UL1-RR-024156. Funding for MESA Family is provided by grants R01-HL-071051, R01-HL-071205, R01-HL-071250, R01-HL-071251, R01-HL-071252, R01-HL-071258, R01-HL-071259, and UL1-RR-025005. Funding for genotyping was provided by NHLBI Contract N02-HL-6-4278 and N01-HC-65226. MESA Air is funded by the US EPA - Science to Achieve Results (STAR)Program Grant #RD831697. The project described was supported by the National Center for Research Resources, Grant UL1RR033176, and is now at the National Center for Advancing Translational Sciences, Grant UL1TR000124. In Southern Community Cohort Study (SCCS), sample preparation was conducted at the Survey and Biospecimen Shared Resources, which is supported in part by Vanderbilt-Ingram Cancer Center (P30 CA68485). The SCCS dataset used for the present analyses was supported by U.S. NIH grant R01CA92447. In Sea Islands Genetic Network (SIGNET) - Reasons for Geographic And Racial Differences in Stroke (SIGNET-REGARDS), the REGARDS Study is supported by a cooperative agreement U01 NS041588 (PI George Howard) and SIGNET was supported by R01 DK084350 (MMS) from the National Institutes of Health. In Wake Forest School of Medicine (WFSM), genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSC268200782096C. The work at Wake Forest was supported by NIH grants K99 DK081350 (NDP), R01 DK066358 (DWB), R01 DK053591 (DWB), R01 HL56266 (BIF), R01 DK070941 (BIF) and in part by the General Clinical Research Center of the Wake Forest School of Medicine grant M01 RR07122. Women's Health Initiative (WHI) program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts N01-WH-22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, and 44221. Funding for WHI SHARe genotyping was provided by NHLBI Contract N02-HL-64278. BHC was funded by the Burroughs Wellcome Fund Inter-school Training Program in Metabolic Diseases and UCLA Genomic Analysis Training Program (NHGRI T32-HG002536). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official view of the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
The prevalence of type 2 diabetes (T2D) among adults in the USA is currently 11.3%, with substantially higher prevalence in African Americans (18.7%) than in European Americans (10.2%) . To date, genome-wide association studies (GWAS) have identified >70 susceptibility loci for T2D –. While it is known that T2D is heritable in African Americans , it is unclear how much heritability is explained by the known genetic associations discovered primarily from European ancestry populations and whether there are risk loci specific to African Americans. Given that individuals of African ancestry tend to harbor more genetic diversity than individuals of other ancestries , we hypothesized that large-scale association analyses in African Americans could shed light on the genetic architecture of T2D and the risk attributable to cosmopolitan vs. population-specific variants.
We conducted a meta-analysis of 17 African American GWAS on T2D comprising 8,284 cases and 15,543 controls (Tables S1 and S2). Missing genotypes in individual studies were imputed to one of the HapMap reference panels (Phase II release 21–24 CEU+YRI, Phase II release 22 all populations, Phase II+III release 27 CEU+YRI, Phase II+III release 27 CEU+YRI+ASW or Phase II+III release 27 all populations) using MACH, IMPUTE2 or BEAGLE (Table S3). Genomic control corrections  were applied to each study (λ = 1.01–1.08) and after meta-analysis (λ = 1.06) due to modest inflated association results (Table S3) . Association results for ∼2.6M SNPs were subsequently examined.
From stage 1 meta-analysis, 49 SNPs moderately associated with T2D (P<1×10−5) and two candidate SNPs near the p value threshold (rs231356 at KCNQ1, P = 2.84×10−5 and rs2244020 at HLA-B, P = 1.02×10−5) totaling 51 SNPs in 21 loci were followed up for replication. rs231356 is 14 kb downstream of the reported T2D index SNP, rs231362, in Europeans . Moderate associations have also been observed across the HLA region in Europeans . The stage 2 replication included in silico and de novo replication in up to 11,544 African American T2D cases and controls, as well as in silico replication in 47,117 individuals of European ancestry from DIAGRAMv2  (Table S4). Meta-analyses were performed to combine results from African Americans (stage 1+2a, n≤35,371, Table S4) and both African Americans and Europeans (stage 1+2a+2b, n≤82,488, Table S4).
T2D loci reaching genome-wide significance
Five independent loci reached genome-wide significance (P<5×10−8). Stage 1 meta-analysis identified the established TCF7L2 locus. Stage 1+2a meta-analysis identified the established KCNQ1 and HMGA2 loci. Stage 1+2a+2b meta-analysis identified a second signal at KCNQ1 and a novel HLA-B locus. Secondary analysis including body mass index (BMI) adjustment in stage 1+2a meta-analysis identified the second novel locus at INS-IGF2 (Table 1 and Figure 1). None of the most strongly associated SNPs at these loci demonstrated significant heterogeneity of effect sizes among studies within each stage, between African Americans in stages 1 and 2a, or between African Americans in stage 1+2a and Europeans in stage 2b after Bonferroni correction of multiple comparisons (Phet>0.001) (Figure S1).
(A) Manhattan plot. Previously identified loci are denoted in red. Novel loci identified in this study are denoted in blue. (B) Quantile-quantile plot. The observed P values (y axis) were compared with the expected P values under the null distribution (x axis).
At the TCF7L2 locus, the most strongly associated SNP in stage 1+2a African Americans samples was rs7903146 (OR = 1.33, P = 4.78×10−44, Table 1 and Figure 2). rs7903146 is also the index SNP (most significantly associated with T2D in prior studies) in Europeans (OR = 1.40, P = 2.21×10−51) , South Asians (OR = 1.25, P = 3.4×10−19)  and East Asians (OR = 1.48, P = 2.44×10−15) .
Association P values (on a −log10 scale) of genotyped and imputed SNPs from stage 1 meta-analysis are plotted as a function of genomic position (NCBI Build 36). Plots for HLA-B, TCF7L2, KCNQ1, and HMGA2 used the model without BMI adjustment whereas plots for INS-IGF2 used the model with BMI adjustment. In each panel, the most strongly associated SNP from stage 1 and stage 1+2a+2b meta-analysis is denoted by a purple circle and a purple diamond, respectively. The color of all other SNPs indicates LD with the most strongly associated SNP based on the HapMap 2 YRI data. At KCNQ1, two independent signals are shown.
Two association signals were observed at KCNQ1 (Table 1 and Figure 2). The first association signal was represented by rs2283228 located at the 3′ end of KCNQ1 (stage 1+2a OR = 1.20, P = 9.90×10−11; stage 1+2a+2b OR = 1.19, P = 4.87×10−13). Using data from individuals of African ancestry in Southwest USA (ASW) from the 1000 Genomes Project (1KGP) , rs2283228 mapped to the same linkage disequilibrium (LD)-based interval as index SNPs from other populations (rs2283228  and rs2237892 – in Japanese, rs2237892 in Hispanics , rs163182  and rs2237895  in Han Chinese). The second association signal was represented by rs231356 (r2 = 0 with rs2283228 in both ASW and CEU) (stage 1+2a OR = 1.11, P = 1.94×10−5; stage 1+2a+2b OR = 1.09, P = 3.93×10−8), located 144 kb upstream of the first signal. rs231356 is located at the same LD interval as the index SNPs rs231362 in Europeans  and rs231359 in Chinese .
At the HMGA2 locus, the most strongly associated SNP was rs343092 (stage 1+2a OR = 1.16, P = 8.79×10−9; stage 1+2a+2b OR = 1.14, P = 2.75×10−12; Table 1 and Figure 2). rs343092 is located 76 kb downstream and at the same LD interval as of the index SNP rs1531343 reported in Europeans .
Two novel T2D loci were identified. The effect sizes of rs2244020 located near HLA-B were similar in African Americans and Europeans (OR = 1.11 vs. 1.07, Phet = 0.26; stage 1+2a+2b P = 6.57×10−9) (Table 1 and Figure 2). HLA-B encodes the class I major histocompatibility complex involved in antigen presentation in immune responses.
The most strongly associated SNP near INS-IGF2 was rs3842770 in African Americans (OR = 1.14, P = 2.78×10−8, stage 1+2a BMI adjusted, Table 1 and Figure 2) but the risk A allele was absent in the CEU population. Insulin plays a key role in glucose homeostasis. Mutations at INS lead to neonatal diabetes, type 1 diabetes, and hyperinsulinemia . Insulin-like growth factor 2 (IGF2) is involved in growth and development. IGF2 overexpression in transgenic mice leads to islet hyperplasia  and IGF2 deficiency in the Goto–Kakizaki rat leads to beta cell mass anomaly .
Associations at previously reported T2D and glucose homeostasis loci
We investigated index SNPs from 158 independent loci associated with T2D and/or glucose homeostasis from prior genome-wide and candidate gene studies in individuals of European, East Asian, South Asian, or African American ancestry (Table S5). Among the 104 T2D-associated index SNPs, 19 were associated with T2D in stage 1 African American samples (P<0.05). Most of the 17 T2D-associated SNPs that showed consistent direction of effects had similar effect sizes between this study and prior reports, despite that rs10440833 at CDKAL1 had substantially stronger effect size in Europeans (OR = 1.25) than in African Americans (OR = 1.06, Phet = 5.86×10−6). Additionally, 3 out of 54 trait-increasing alleles from glucose homeostasis-associated index SNPs were associated with increased T2D risk in African Americans (P<0.05).
We also performed a locus-wide analysis to test for associations of all SNPs within the LD region at r2≥0.3 with the previously reported index SNPs and results were corrected for the effective number of SNPs . Since the causal variant(s) at each locus may be different or reside on different haplotypes across populations with different LD structures, this approach allows the identification of the most strongly associated SNPs in African Americans that may or may not be in LD with the index SNPs reported in other populations. A total of 55 T2D- and 29 glucose-associated loci were associated with T2D in African Americans (Plocus<0.05, corrected for LD in ASW for SNPs within a locus; Table S6). We compared the genetic architecture between the previously reported index SNPs and our fine-mapped SNPs for these 84 loci. The respective average risk allele frequencies were 0.51 and 0.46, and the distributions or pairwise differences of risk allele frequencies were not significantly different (P = 0.255, Wilcoxon rank sum test; and P = 0.295, Wilcoxon signed-rank test, respectively, Figure S2). In contrast, the average odds ratios for the risk alleles were higher for the fine-mapped SNPs as compared to the index SNPs (1.14 vs. 1.05). The distributions and pairwise differences of risk allele odds ratios were significantly different (P = 1.18×10−19 and 5.55×10−14, respectively, Figure S2). Thus, the locus-wide analysis identified variants with larger effect sizes and similar allele frequencies.
We leveraged differences in LD between African Americans and Europeans to fine-map and re-annotate several established loci. The association signal spanning ∼100 kb at INTS8 in African Americans overlapped the ∼200 kb TP53INP1 T2D locus in Europeans . The most strongly associated SNP in MEDIA tended to have larger effect size in African Americans than in Europeans (rs17359493, OR = 1.13 vs. 1.06, P = 1.39×10−7 vs. 3.20×10−2, respectively, Phet = 0.06) (Table S4). However, rs17359493 at intron 10 of INTS8 was only in weak LD with the reported index SNP rs896854 in Europeans (r2 = 0.21 in CEU, 0.10 in ASW). Neither the reported index SNP rs896854 nor its proxies from the CEU data demonstrated significant association to T2D in African Americans (Table S6 and Figure S3a,b), suggesting that rs17359493 may be an independent novel signal. INTS8 encodes a subunit of the integrator complex which is involved in the cleavage of small nuclear RNAs. At KCNQ1, the most strongly associated SNP rs231356 was in weak LD with the index SNP rs231362 reported in Europeans  (r2 = 0.24 in CEU and 0.17 in ASW). Given rs231362 was modestly associated with T2D in African American (P = 0.04) and was in weak LD (r2 = 0.21 to 0.46 in CEU) with other associated SNPs in this region (Table S6 and Figure S3c,d), the results suggest a refinement of the localization of causal variant(s) to variants in strong LD with rs231356. At HMGA2, the most strongly associated SNP rs343092 was in moderate LD with the index SNP rs1531343 (r2 = 0.60 in CEU and 0.32 in ASW). Despite rs1531343 and its proxies in high LD were not associated with T2D in African Americans (P>0.05), several SNPs in moderate LD, including rs343092, showed nominal to strong associations (Table S6 and Figure S3e,f). Trans-ethic fine mapping will be particularly useful to dissect the causal variant(s) at this locus.
Effect of obesity on T2D susceptibility loci
We investigated the influence of obesity by comparing the stage 1 meta-analysis results with or without adjustment for BMI at the 51 most significantly associated SNPs from the GWAS for follow up (Tables S4 and S7) and 158 established T2D or glucose homeostasis index SNPs (Table S5). Association results were highly similar with and without BMI adjustment (correlation coefficients were 0.99 for both effect sizes and −logP values). Of particular note, FTO is suggested to influence T2D primarily through modulation of adiposity in Europeans , , but evidence is contradictory across multiple ethnic groups –. The index SNP rs11642841 was not significantly associated with T2D in African Americans without and with BMI adjustment (P = 0.06 and 0.23, respectively) (Table S5). The frequency of the risk A allele was 0.13 in this study. It had 100% power to detect association at the reported OR of 1.13 at type 1 error rate of 0.05, suggesting that FTO is unlikely a key T2D susceptibility gene in African Americans.
Gene expression and bioinformatics analyses
Among the six genome-wide significant loci (Table 1), we found no coding variants in the most significantly associated SNPs or their proxies. These SNPs demonstrated only weak associations with expression quantitative trait loci (eQTLs) (P>0.001, Table S8). Examination of the ENCODE data  revealed that several SNPs at TCF7L2, KCNQ1, and HMGA2 were located at protein binding sites or were predicted to alter motif affinity for transcription factors implicated in energy homeostasis (Table S9). The most strongly associated SNP rs7903146 in TCF7L2 is predicted to alter the binding affinity for a POU3F2 regulatory motif . POU3F2 is a neural transcription factor that enhances the activation of genes regulated by corticotropin-releasing hormone which stimulates adrenocorticotropic hormone (ACTH). ACTH is synthesized from pre-pro-opiomelanocortin (pre-POMC) which regulates energy homeostasis. For the 3′ signal at KCNQ1, several tag SNPs are predicted to alter the binding affinity for regulatory motifs, including SREBP, CTCF and HNF4A. SREBP is a transcription factor involved in sterol biosynthesis. CTCF regulates the expression of IGF2 . HNF4A is a master regulator of hepatocyte and islet transcription. The tag SNP rs2257883 at HMGA2 is predicted to alter the binding affinity of MEF2, which regulates GLUT4 transcription in insulin responsive tissues .
We have performed the largest genetic association analysis to date for T2D in African Americans. Our data support the hypothesis that risk for T2D is partly attributable to a large number of common variants with small effects . We identified HLA-B and INS-IGF2 as novel T2D loci, the latter specific to African Americans. We found evidence supporting association for 88 previously identified T2D and glucose homeostasis loci. Taken together, these 90 loci yielded a sibling relative risk of 1.19. The phenotypic variance measured on the liability scale is substantially larger in African Americans than in European Americans (17.5% vs. 5.7%)  due to larger effect sizes upon fine-mapping as well as higher disease prevalence in African Americans.
The two novel T2D loci, HLA-B and INS-IGF2, have been implicated in type 1 diabetes (T1D) risk in Europeans –. One limitation of our study is the lack of autoantibody measurement. However, our results are unlikely to be confounded by the presence of misclassified patients. Among diabetic youth aged <20 years, T2D characterized by insulin resistance without autoimmunity is more prevalent in African Americans (40.1%) than in European Americans (6.2%), while African Americans less often present with autoimmunity and insulin deficiency resembling T1D compared to European Americans (32.5% vs. 62.9%, respectively) . Autoimmunity is also uncommon in African American diabetic adults . Furthermore, associations for T1D are stronger at HLA class II (HLA-DRB1, -DQA1, and -DQB1) than HLA class I regions in Europeans –, – (http://www.t1dbase.org). In African Americans, T1D individuals showed both shared and unique risk and protective HLA class II haplotypes as compared to European T1D individuals –. More importantly, these individuals also showed substantially stronger associations at HLA class II (P<1×10−25) than class I regions (P<1×10−5) , which is in contradiction with our finding of stronger associations at HLA class I than class II regions in T2D individuals (HLA-B, Figure S4). The observed HLA-B association may be due to LD with nearby causal gene(s) since there is long range LD in this region. Recently, rs3130501 near POU5F1 and TCF19 was reported for association with T2D in a trans-ancestry meta-analysis . rs3130501 was located 211 kb upstream of rs2244020 and mapped to the same LD interval. However, the two SNPs were not correlated in both CEU (D′ = 0.57, r2 = 0.05) and ASW (D′ = 0.68, r2 = 0.16) from 1KGP nor strongly associated with T2D in the stage 1 meta-analysis (P = 0.04). Other potential non-HLA candidate genes may include TNFA which regulates immune and inflammatory response. It has been hypothesized that activated innate and adaptive immune cells stimulate release of cytokines such as TNFα and IL-1β, which promote both systemic insulin resistance and β-cell damage . On the other hand, evidence has implicated T1D loci HLA-DQ/DR, GLIS3 and INS in the susceptibility of latent autoimmune diabetes in adults (LADA) and/or T2D , , –, while T2D loci such as PPARG and TCF7L2 was associated with T1D  and LADA , , respectively. More comprehensive studies are needed to understand the shared and distinct genetic risks in different forms of diabetes which will facilitate diagnosis and personalized treatment.
Our results have several implications regarding the genetic architecture of T2D. First, fine-mapping suggests that currently known loci explain more of the risk than previously estimated. Second, the loci conferring the largest risk for T2D appear to act through regulatory rather than protein-coding changes. Third, many, but not all, of the previously identified T2D loci are shared across ancestries. The differential LD structure of African-ancestry populations at shared loci provides an opportunity for fine mapping in trans-ethnic meta-analysis. Fourth, the ∼2.6M MEDIA SNPs achieved only 43.3% coverage of the 1KGP ASW common SNPs, suggesting that risk loci that are specific to African-ancestry individuals are difficult to discover with the genotyping arrays being used. Large-scale sequencing studies, such as those focusing on whole genomes, exomes, and targeted resequencing for associated non-coding regions, will be necessary to further delineate the causal variants for T2D risk in African Americans.
Materials and Methods
Samples and clinical characterization
Stage 1 discovery samples included 17 T2D GWAS studies (ARIC, CARDIA, CFS, CHS, FamHS, GeneSTAR, GENOA, HANDLS, Health ABC, HUFS, JHS, MESA, MESA Family, SIGNET-REGARDS, WFSM, FIND, and WHI) with up to 23,827 African American subjects (8,284 cases and 15,543 controls). Stage 2 replication samples included up to 11,544 African American subjects (6,061 cases and 5,483 controls), using in silico replication of GWAS data from eMERGE and IPM Biobank and de novo genotyping in IRAS, IRASFS, SCCS, and WFSM. In general, T2D cases were defined as having at least one of the following: fasting plasma glucose ≥126 mg/dl, 2 hour glucose during oral glucose tolerance test (OGTT) ≥200 mg/dl, random glucose ≥200 mg/dl, oral hypoglycemic agent or insulin treatment, or physician-diagnosed diabetes. All cases were diagnosed at ≥25 years (or age at study ≥25 years if age at diagnosis was not available). For cohort studies, individuals who met the criteria at any of the visits were defined as cases. Controls with normal glucose tolerance (NGT) were defined by satisfying all the following criteria: fasting plasma glucose <100 mg/dl, 2 hour OGTT<140 mg/dl (if available), no treatment of diabetes, and age ≥25 years. For cohort studies, individuals who met the criteria at all visits were defined as controls. All study participants provided written informed consent, except for eMERGE that use an opt out program, and approval was obtained from the institutional review board (IRB) from the respective local institutions. Detailed descriptions of the participating studies are provided in Text S1.
Genotyping, imputation and quality control
For stage 1 and 2 GWAS studies, genotyping was performed with Affymetrix or Illumina genome-wide SNP arrays. Imputation of missing genotypes was performed using MACH , IMPUTE2  or BEAGLE  using HapMap reference haplotypes. For each study, samples reflecting duplicates, low call rate, gender mismatch, or population outliers were excluded. In general, SNPs were excluded by the following criteria: call rate <0.95, minor allele frequency (MAF)<0.01, minor allele count <10, Hardy-Weinberg P-value <1×10−4, or imputation quality score <0.5 (Table S3). For de novo replication studies, genotyping was performed using the Sequenom MassArray platform (Sequenom; San Diego, CA). Sample and SNP quality controls were performed as with GWAS data.
Single SNP association was performed for each study by regressing T2D case/control status on genotypes. To account for uncertainty of genotype calls during imputation, genotype probabilities or dosage were used for association tests in imputed SNPs. The association tests assumed an additive genetic model and adjusted for age, sex, study centers, and principal components. Principal components were included to control for confounding effects of admixture proportion and population structure. Secondary analysis with additional adjustment for BMI was performed for SNPs with P<1×10−5 in stage 1 meta-analysis and index SNPs previously reported to be associated with T2D or glucose homeostasis traits. BMI adjustment allows increasing power to detect T2D loci independent of BMI effect and diminish associations at T2D loci with effects modulated through BMI. Logistic regression was used for samples of unrelated individuals. Generalized estimating equations  or SOLAR  were used for samples of related individuals. Association results with extreme values (absolute beta coefficient or standard error >10), primarily due to low cell counts resulting from small sample sizes and/or low minor allele frequencies, were excluded (Table S3).
In stage 1, association results were combined by a fixed effect model with inverse variance weighted method using the METAL software . Genomic control correction  was applied to each study before meta-analysis, and to the overall results after meta-analysis. Results from SNPs genotyped in <10,000 samples and those with allele frequency difference >0.3 among studies were excluded. A total of 2,579,389 SNPs were analyzed in the meta-analysis (Table S3). In stage 2a, association results from African American replication studies were also combined using a fixed effect inverse variance weighted method. To assess the overall effects in African Americans (stage 1+2a) and both African Americans and Europeans (stage 1+2a+2b), association results from studies in the respective stages were combined using a fixed effect inverse variance weighted method. Genome-wide significance is declared at P<5×10−8 from the meta-analysis result of all stages, which has better power than the replication-based strategy .
Among the 51 SNPs carried forward for replication, heterogeneity of effect sizes across studies within each stage was assessed using Cochran's Q statistic implemented in METAL. Meta-analysis results from stages 1 and 2a, stage 1+2a and 2b were used to assess heterogeneity of effect sizes between discovery and replication stages in African Americans, and between African Americans and Europeans, respectively. For SNPs with significant heterogeneous effect size after multiple comparison corrections (Phet<0.001), meta-analysis results including studies of all stages assessed by the random effect model implemented in GWAMA  were reported. Heterogeneous associations may partly due to differences in ascertainment scheme across studies. For index SNPs reported in prior studies, assessment of heterogeneity using Cochran's Q statistic between prior studies and this study were also reported.
Index SNPs associated with T2D or glucose homeostasis traits from prior GWAS and candidate gene studies were examined for association with T2D in African Americans (Table S5). For the index SNP association tests, a per-SNP P value <0.05 was defined as significant. In the locus-wide analysis, the boundaries of a locus were defined by the most distant markers (within ±500 kb) using the 1KGP CEU data with r2≥0.3 with the index SNP. All MEDIA SNPs within these bounds were examined for association analysis. All pairwise LD values within each locus were estimated using the 1KGP CEU and ASW data. To estimate the effective number of SNPs at a locus, we retrieved genotypes from the 1KGP ASW data for markers present in MEDIA, estimated the sample covariance matrix from those genotypes, and spectrally decomposed the covariance matrix . The effective number of SNPs was estimated using the relationship , in which λk is the kth eigenvalue of the K×K covariance matrix for the K SNPs in the locus . The per-locus significance level was defined as 0.05/effective number of SNPs (Table S6). By accounting for all SNPs within the bounds of LD, the per-locus significance level is corrected to account for markers in LD with the index SNP as well as markers not in LD with the index SNP, thereby potentially allowing for discovery of new associations at markers not tagged by the index SNP.
Liability-scale variance explained
For each independent locus, we estimated the sibling relative risk using the most strongly associated SNP within that locus. Let pi and ψi be the risk allele frequency and the corresponding odds ratio at the ith SNP, respectively. Assuming the additive genetic model and independence between SNPs, the contribution to the sibling relative risk λs for a set of N SNPs is given by . Let K be the disease prevalence. The liability-scale variance explained by the set of N SNPs is given by , in which , , and , with representing the standard normal quantile function and z representing the standard normal density at T .
The coverage of MEDIA SNPs to the human genome was estimated using HaploView  via pairwise tagging at the r2 = 0.8 threshold. We used all SNPs with minor allele frequencies ≥1% in both MEDIA and the 1KGP ASW sequence data. Coverage was estimated using non-overlapping bins of 1,000 SNPs.
Study power was calculated using the genetic power calculator . For SNPs with MAF≥0.3, our study had >80% power to detect odds ratios for T2D at OR≥1.06 and ≥1.13 at P<0.05 and P<5×10−8, respectively, in stage 1 samples under an additive model. The observed odds ratios among our stage 1 most significantly associated SNPs with P<1×10−5 ranged from 1.11 to 1.56 (Table S4). Given our African American sample size in stage 1+2a, our study had >80% power to detect OR≥1.1 at P<5×10−8 at MAF≥0.3, thus provided good power to detect genome-wide significance among the most significantly associated SNPs using all African American samples. For T2D SNPs reported from the literature, power was also calculated from the reported effect size using the risk allele frequency from this study for stage 1 samples at P<0.05 and P<5×10−8, respectively (Table S5).
Gene expression analysis
The MuTHER resource (www.muther.ac.uk) includes lymphoblastoid cell lines (LCLs), skin, and adipose tissue derived simultaneously from a subset of well-phenotyped healthy female twins from the TwinsUK adult registry . Whole-genome expression profiling of the samples, each with either two or three technical replicates, was performed using the Illumina Human HT-12 V3 BeadChips (Illumina Inc.) according to the protocol supplied by the manufacturer. Log2-transformed expression signals were normalized separately per tissue as follows: quantile normalization was performed across technical replicates of each individual followed by quantile normalization across all individuals. Genotyping was performed with a combination of Illumina arrays (HumanHap300, HumanHap610Q, 1M-Duo, and 1.2MDuo 1M). Untyped HapMap2 SNPs were imputed using the IMPUTE2 software package. In total, 776 adipose and 777 LCL samples had both expression profiles and imputed genotypes. Association between all SNPs (MAF>5%, IMPUTE info >0.8) within a gene or within 1 Mb of the gene transcription start or end site and normalized expression values were performed with the GenABEL/ProbABEL packages – using the polygenic linear model incorporating a kinship matrix in GenABEL followed by the ProbABEL mmscore score test with imputed genotypes. Age and experimental batch were included as cofactors.
Genotype and gene expression in LCL in HapMap samples were also available . Association of genotypes and gene expression of transcripts within 1 MB of tested SNPs were analyzed separately for CEU and YRI populations. The variance components model implemented in SOLAR was used for association analysis which accounts for correlation among related individuals .
In this study, we examined the association of the most significantly associated SNPs from the six genome-wide significant loci and their proxies (r2≥0.8 in ASW) within 1 Mb of the associated SNPs with cis-expression quantitative trait loci (eQTLs) in peripheral blood leukocytes (LCL) and adipose tissue (Table S8).
ENCODE data analysis
We examined putative function of non-coding genome-wide significant SNPs and their proxies within 1 Mb (r2≥0.8 in 1KGP ASW) using HaploReg  and RegulomeDB . These databases interrogated multiple chromatin features from the Encyclopedia of DNA Elements (ENCODE) project . High priority was given to variants annotated as protein-binding via ChIP-seq, and motif-changing via position weight matrices, with the respective transcription factors implicated in diabetes pathogenesis and related biological processes.
Forest plots of the most strongly associated SNPs at five previously and newly identified T2D loci in African Americans. Odds ratio and 95% CIs are presented for individual studies (black circle and line) and meta-analysis results (red diamond and line). At KCNQ1, two independent associated SNPs are shown.
(A) Distributions of risk allele frequencies for the previously reported index SNPs (in black) vs. the MEDIA most strongly associated SNPs (in red) in African Americans from stage 1 meta-analysis. (B) Distributions of odds ratios for risk alleles of the index SNPs (in black) vs. the most strongly associated MEDIA SNPs (in red) in African Americans from stage 1 meta-analysis.
Regional plots of stage 1 meta-analysis association results in African Americans for the most strongly associated SNPs from this study and the index SNPs from previous studies. (A–B) INTS8-TP53INP1 region; (C–D) KCNQ1 region; (E–F) HMGA2 region. (A, C, E) The most strongly associated SNP in MEDIA is denoted by a purple circle and a red arrow with LD colored based on the HapMap 2 YRI data. (B, D, F) The index SNP is denoted by a purple circle and a blue arrow with LD colored based on the HapMap 2 CEU data.
Regional plots of HLA-B and HLA-DQ/DR regions for (A, C) stage 1 meta-analysis association results in African Americans and HapMap 2 YRI LD data, and (B, D) stage 3 DIAGRAMv2 results in Europeans using HapMap 2 CEU LD data. (A, B) The most strongly associated SNP rs2244020 at HLA-B region from this study is denoted by a purple circle and a red arrow. (C, D) The index SNP rs9272346 from Burton PR et al (2007)  is denoted by a purple circle and a blue arrow.
Design of studies in stage 1 GWAS and stage 2a replication in African Americans.
Clinical characteristics of study samples in stage 1 GWAS and stage 2a replication studies in African Americans.
Genotyping methods, quality controls, imputation and statistical analysis in stage 1 GWAS and stage 2a replication studies in African Americans.
SNPs with P value≤1×10−5 from stage 1 GWAS meta-analysis (BMI unadjusted) selected for stage 2 in silico and de novo replication in African Americans and in silico replication in individuals of European ancestry from DIAGRAMv2.
Stage 1 GWAS meta-analysis results for index SNPs at established T2D or glucose homeostasis loci in African Americans.
Locus-wide association at established T2D or glucose homeostasis loci in stage 1 GWAS meta-analysis in African Americans.
BMI-adjusted association for SNPs from stage 1 GWAS meta-analysis selected for replication.
Expression Quantitative Trait Loci (eQTL) analysis for the genome-wide significant SNPs for T2D. Results are shown for suggestive evidence of cis-association (P<0.05) between the genome-wide significant SNPs and their proxies with the genes within 1 Mb of the associated SNPs.
Putative regulatory SNPs predicted from the ENCODE project for the genome-wide significant SNPs and their proxies at TCF7L2, INS-IGF2, KCNQ1 and HMGA2.
Description of GWAS and replication studies.
We thank all the study participants for their valuable contributions to the parent studies (ARIC, CARDIA, CFS, CHS, eMERGE, FamHS, FIND, GeneSTAR, GENOA, HANDLS, Health ABC, HUFS, IPM Biobank, IRAS, IRASFS, JHS, MESA, MESA Family, SCCS, SIGNET-REGARDS, WFSM and WHI) of the MEDIA consortium. We thank the contributions of investigators and staff of the parent studies for data collection, genotyping, data analysis and sharing of association results to the MEDIA consortium at the discovery and replication stages. We also thank the DIAGRAM Consortium for sharing the European T2D GWAS meta-analysis results for replication. We thank the MuTHER Consortium to share the expression quantitative trait loci data. The Candidate-gene Association Resource (CARe) acknowledge the support of the National Heart, Lung, and Blood Institute and the contributions of the involved research institutions, study investigators, field staff, and study participants of ARIC, CARDIA, CFS, JHS, and MESA in creating the Candidate-gene Association Resource for biomedical research (http://public.nhlbi.nih.gov/GeneticsGenomics/home/care.aspx). The parent studies have contributed parent study data, ancillary study data, and DNA samples through the Massachusetts Institute of Technology - Broad Institute to create this genotype/phenotype database for wide dissemination to the biomedical research community. Women's Health Initiative (WHI) investigators have reviewed and approved this manuscript. WHI investigators are listed at http://www.whiscience.org/publications/WHI_investigators_shortlist_2010-2015.pdf. The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap through dbGaP accession phs000200.v1.p1.
Members of the FIND Consortium are:
Genetic Analysis and Data Coordinating Center, Case Western Reserve University, Cleveland, OH: RC Elston, S Iyengar, K Goddard, J Olson, S Ialacci, S Edwards, C Fondran, A Horvath, G Jun, K Kramp, M Slaughter, E Zaletel.
Case Western Reserve University, Cleveland, OH: JR Sedor, J Schelling, A Sehgal, A Pickens, L Humbert, L Getz-Fradley.
Harbor—University of California Los Angeles Medical Center: S Adler, HE Collins-Schramm§, E Ipp, H Li§, M. Pahl†, MF Seldin§, J LaPage, B Walker, C Garcia, J Gonzalez, L Ingram-Drake.
§University of California, Davis, CA.
†University of California, Irvine, CA.
Johns Hopkins University, Baltimore, MD: M. Klag, J Coresh, L Kao, L Mead, R Parekh, N Fink, P Bayton, Y Long, L Wei, T Whitehead.
National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ: WC Knowler, RL Hanson, RG Nelson, L Jones, R Juan, R Lovelace, C Luethe, LM Phillips, J Sewemaenewa, I Sili, B Waseta.
University of California, Los Angeles, CA: MF Saad, X Guo, J Rotter, K Taylor, M Budgett.
University of New Mexico, Albuquerque, NM: P Zager, V Shah, M Scavini, A Bobelu.
University of Texas Health Science Center at San Antonio, San Antonio, TX: H Abboud, N Arar, R Duggirala, BS Kasinath, R Plaetke, M Stem, C Goyes, V Sartorio.
Wake-Forest University, Winston-Salem, NC: BI Freedman, DW Bowden, SC Satko, SS Rich, S Warren, S Viverette, G Brooks, R Young.
Laboratory of Genomic Diversity, National Cancer Institute, Frederick, MD: C Winkler, MW Smith, M Thompson, R Hanson.
National Institute of Diabetes and Digestive and Kidney Diseases program office: JP Briggs, PL Kimmel, R Rasooly.
External Advisory Committee: D Warnock (chair), R Chakraborty, GM Dunston, RP Lifton, SJ O'Brien (ad hoc), R Spielman.
Members of the eMERGE Consortium are:
Marshfield Clinic, Marshfield, WI: Catherine A. McCarty, Justin Starren, Peggy Peissig, Richard Berg, Luke Rasmussen, James Linneman, Aaron Miller, Vidhu Choudary, Lin Chen, Carol Waudby, Terrie Kitchner, Jonathan Reeser, Norman Fost, Marylyn Ritchie, Russell A. Wilke.
Northwestern University, Chicago, IL: Rex L. Chisholm, Pedro C. Avila, Philip Greenland, M. Geoffrey Hayes, Abel N. Kho, Warren A. Kibbe, Amy A. Lemke, William L. Lowe, Maureen E. Smith, Wendy A. Wolf, Jennifer A. Pacheco, William K. Thompson, Joel Humowiecki, May Law, Laura Rasmussen-Torvik.
Mayo Clinic, Rochester, MN: Christopher Chute, Iftikar Kullo, Barbara Koenig, Mariza de Andrade, Suzette Bielinski, Jyotishman Pathak, Guergana Savova, Joel Wu, Joan Henriksen, Keyue Ding, Lacey Hart, Jeremy Palbicki.
Group Health/University of Washington/Fred Hutchinson Cancer Research Center, Seattle, WA: Eric B. Larson, Katherine Newton, Evette Ludman, Leslie Spangler, Gene Hart, David Carrell, Gail Jarvik, Paul Crane, Wylie Burke, Stephanie Malia Fullerton, Susan Brown Trinidad, Chris Carlson, Andrew McDavid.
Vanderbilt University, Nashville, TN: Dan M. Roden, Ellen Clayton, Jonathan L. Haines, Daniel R. Masys, Larry R. Churchill, Daniel Cornfield, Dana Crawford, Dawood Darbar, Joshua C. Denny, Bradley A Malin, Marylyn D. Ritchie, Jonathan S. Schildcrout, Hua Xu, Andrea Havens Ramirez, Melissa Basford, Jill Pulley.
Members of the DIAGRAM Consortium are:
Benjamin F. Voight1–3,100, Laura J. Scott4,100, Valgerdur Steinthorsdottir5,100, Andrew P. Morris6,100, Christian Dina7,8,100, Ryan P. Welch9, Eleftheria Zeggini6,10, Cornelia Huth11,12, Yurii S. Aulchenko13, Gudmar Thorleifsson5, Laura J. Mcculloch14, Teresa Ferreira6, Harald Grallert11,12, Najaf Amin13, Guanming Wu15, Cristen J. Willer4, Soumya Raychaudhuri1,2,16, Steve A. Mccarroll1,17, Claudia Langenberg18, Oliver M. Hofmann19, Josée Dupuis20,21, Lu Qi22–24, Ayellet V. Segrè1,2,17, Mandy van Hoek25, Pau Navarro26, Kristin Ardlie1, Beverley Balkau27,28, Rafn Benediktsson29,30, Amanda J. Bennett14, Roza Blagieva31, Eric Boerwinkle32, Lori L. Bonnycastle33, Kristina Bengtsson Boström34, Bert Bravenboer35, Suzannah Bumpstead10, Noisël P. Burtt1, Guillaume Charpentier36, Peter S. Chines33, Marilyn Cornelis24, David J. Couper37, Gabe Crawford1, Alex S. F. Doney38,39, Katherine S. Elliott6, Amanda L. Elliott1,17,40, Michael R. Erdos33, Caroline S. Fox21,41, Christopher S. Franklin42, Martha Ganser4, Christian Gieger11, Niels Grarup43, Todd Green1,2, Simon Griffin18, Christopher J. Groves14, Candace Guiducci1, Samy Hadjadj44, Neelam Hassanali14, Christian Herder45, Bo Isomaa46,47, Anne U. Jackson4, Paul R. V. Johnson48, Torben Jørgensen49,50, Wen H. L. Kao51,52, Norman Klopp11, Augustine Kong5, Peter Kraft22,23, Johanna Kuusisto53, Torsten Lauritzen54, Man Li51, Aloysius Lieverse55, Cecilia M. Lindgren6, Valeriya Lyssenko56, Michel Marre57,58, Thomas Meitinger59,60, Kristian Midthjell61, Mario A. Morken33, Narisu Narisu33, Peter Nilsson56, Katharine R. Owen14, Felicity Payne10, John R. B. Perry62,63, Ann-Kristin Petersen11, Carl Platou61, Christine Proença7, Inga Prokopenko6,14, Wolfgang Rathmann64, N. William Rayner6,14, Neil R. Robertson6,14, Ghislain Rocheleau65–67, Michael Roden45,68, Michael J. Sampson69, Richa Saxena1,2,40, Beverley M. Shields62,63, Peter Shrader3,70, Gunnar Sigurdsson29,30, Thomas Sparsø43, Klaus Strassburger64, Heather M. Stringham4, Qi Sun22,23, Amy J. Swift33, Barbara Thorand11, Jean Tichet71, Tiinamaija Tuomi46,72, Rob M. van Dam24, Timon W. van Haeften73, Thijs van Herpt25,55, Jana V. van Vliet-Ostaptchouk74, G. Bragi Walters5, Michael N Weedon62,63, Cisca Wijmenga75, Jacqueline Witteman13, the MAGIC investigators99, the GIANT consortium99, Richard N. Bergman76, Stephane Cauchi7, Francis S. Collins77, Anna L. Gloyn14, Ulf Gyllensten78, Torben Hansen43,79, Winston A. Hide19, Graham A. Hitman80, Albert Hofman13, David J. Hunter22,23, Kristian Hveem61,81, Markku laakso53, Karen L. Mohlke82, Andrew D. Morris38,39, Colin N. A. Palmer38,39, Peter P. Pramstaller83, Igor Rudan42,84,85, Eric Sijbrands25, Lincoln D. Stein15, Jaakko Tuomilehto86–88, Andre Uitterlinden25, Mark Walker89, Nicholas J. Wareham18, Richard M. Watanabe76,90, Gonçalo R. Abecasis4, Bernhard O. Boehm31, Harry Campbell42, Mark J. Daly1,2, Andrew T. Hattersley62,63, Frank B. Hu22–24, James B. Meigs3,70, James S. Pankow91, Oluf Pedersen43,92,93, H-Erich Wichmann11,12,94, Inês Barroso10, Jose C. Florez1–3,95, Timothy M. Frayling62,63, Leif Groop56,72, Rob Sladek65–67, Unnur Thorsteinsdottir5,96, James F Wilson42, Thomas Illig11, Philippe Froguel17,97, Cornelia M. van duijn13, Kari Stefansson5,96, David Altshuler 1–3,17,40,95, Michael Boehnke4 & Mark I. Mccarthy6,14,98
1 Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA.
2 Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA.
3 Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.
4 Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
5 deCODE Genetics, Reykjavik, Iceland.
6 Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
7 CNRS-UMR-8090, Institute of Biology and Lille 2 University, Pasteur Institute, Lille, France.
8 INSERM UMR915 CNRS ERL3147, Nantes, France.
9 Bioinformatics Program, University of Michigan, Ann Arbor, Michigan, USA.
10 Wellcome Trust Sanger Institute, Hinxton, UK.
11 Institute of Epidemiology, Helmholtz Zentrum Muenchen, Neuherberg, Germany.
12 Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany.
13 Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
14 Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
15 Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
16 Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
17 Department of Molecular Biology, Harvard Medical School, Boston, Massachusetts, USA.
18 Medical Research Council (MRC) Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, UK.
19 Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA.
20 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
21 National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA.
22 Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA.
23 Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.
24 Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
25 Department of Internal Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands.
26 MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, UK.
27 INSERM, CESP Centre for Research in Epidemiology and Population Health, U1018, Epidemiology of Diabetes, Obesity and Chronic Kidney Disease over the Lifecourse, Villejuif, France.
28 University Paris-Sud 11, UMRS 1018, Villejuif, France.
29 Landspitali University Hospital, Reykjavik, Iceland.
30 Icelandic Heart Association, Kopavogur, Iceland.
31 Division of Endocrinology, Diabetes and Metabolism, Ulm University, Ulm, Germany.
32 The Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas, USA.
33 National Human Genome Research Institute, National Institute of Health, Bethesda, Maryland, USA.
34 Research and Development Centre, Skaraborg Primary Care, Skövde, Sweden.
35 Department of Internal Medicine, Catharina Hospital, Eindhoven, The Netherlands.
36 Endocrinology-Diabetology Unit, Corbeil-Essonnes Hospital, Corbeil-Essonnes, France.
37 Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
38 Diabetes Research Centre, Biomedical Research Institute, University of Dundee, Ninewells Hospital, Dundee, UK.
39 Pharmacogenomics Centre, Biomedical Research Institute, University of Dundee, Ninewells Hospital, Dundee, UK.
40 Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA.
41 Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
42 Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK.
43 Hagedorn Research Institute, Gentofte, Denmark.
44 Centre Hospitalier Universitaire de Poitiers, Endocrinologie Diabetologie, CIC INSERM 0801, INSERM U927, Université de Poitiers, UFR, Médecine Pharmacie, Poitiers Cedex, France.
45 Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
46 Folkhälsan Research Center, Helsinki, Finland.
47 Malmska Municipal Health Center and Hospital, Jakobstad, Finland.
48 Diabetes Research and Wellness Foundation Human Islet Isolation Facility and Oxford Islet Transplant Programme, University of Oxford, Oxford, UK.
49 Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark.
50 Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark.
51 Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA.
52 Department of Medicine and Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA.
53 Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio, Finland.
54 Department of General Medical Practice, University of Aarhus, Aarhus, Denmark.
55 Department of Internal Medicine, Maxima Medical Center, Eindhoven, The Netherlands.
56 Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden.
57 Department of Endocrinology, Diabetology and Nutrition, Bichat-Claude Bernard University Hospital, Assistance Publique des Hôpitaux de Paris, Paris, France.
58 INSERM U695, Université Paris 7, Paris, France.
59 Institute of Human Genetics, Helmholtz Zentrum Muenchen, Neuherberg, Germany.
60 Institute of Human Genetics, Klinikum rechts der Isar, Technische Universität München, München, Germany.
61 Nord-Trøndelag Health Study (HUNT) Research Center, Department of Community Medicine and General Practice, Norwegian University of Science and Technology, Trondheim, Norway.
62 Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, University of Exeter, Exeter, UK.
63 Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula Medical School, University of Exeter, Exeter, UK.
64 Institute of Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
65 Department of Human Genetics, McGill University, Montreal, Canada.
66 Department of Medicine, Faculty of Medicine, McGill University, Montreal, Canada.
67 McGill University and Genome Quebec Innovation Centre, Montreal, Canada.
68 Department of Metabolic Diseases, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
69 Department of Endocrinology and Diabetes, Norfolk and Norwich University Hospital National Health Service Trust, Norwich, UK.
70 General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, USA.
71 Institut interrégional pour la Santé (IRSA), La Riche, France.
72 Department of Medicine, Helsinki University Hospital, University of Helsinki, Helsinki, Finland.
73 Department of Internal Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
74 Molecular Genetics, Medical Biology Section, Department of Pathology and Medical Biology, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands.
75 Department of Genetics, University Medical Center Groningen and University of Groningen, Groningen, The Netherlands.
76 Department of Physiology and Biophysics, University of Southern California School of Medicine, Los Angeles, California, USA.
77 National Institute of Health, Bethesda, Maryland, USA.
78 Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
79 University of Southern Denmark, Odense, Denmark.
80 Centre for Diabetes, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
81 Department of Medicine, The Hospital of Levanger, Levanger, Norway.
82 Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA.
83 Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy.
84 Croatian Centre for Global Health, Faculty of Medicine, University of Split, Split, Croatia.
85 Institute for Clinical Medical Research, University Hospital ‘Sestre Milosrdnice’, Zagreb, Croatia.
86 Department of Public Health, University of Helsinki, Helsinki, Finland.
87 South Ostrobothnia Central Hospital, Seinäjoki, Finland.
88 Red RECAVA Grupo RD06/0014/0015, Hospital Universitario La Paz, Madrid, Spain.
89 Diabetes Research Group, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK.
90 Department of Preventative Medicine, Keck Medical School, University of Southern California, Los Angeles, California, USA.
91 Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USA.
92 Department of Biomedical Science, Panum, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark.
93 Faculty of Health Science, University of Aarhus, Aarhus, Denmark.
94 Klinikum Grosshadern, Munich, Germany.
95 Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.
96 Faculty of Medicine, University of Iceland, Reykjavík, Iceland.
97 Genomic Medicine, Imperial College London, Hammersmith Hospital, London, UK.
98 Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK.
99 A full list of members is provided in the supplementary Note of the original publication.
100 These authors contributed equally
Members of the MuTHER Consortium are:
Kourosh R. Ahmadi1, Chrysanthi Ainali2, Amy Barrett3, Veronique Bataille1, Jordana T. Bell1,4, Alfonso Buil5, Panos Deloukas6, Emmanouil T. Dermitzakis5, Antigone S. Dimas4,5, Richard Durbin6, Daniel Glass1, Elin Grundberg1,6, Neelam Hassanali3, Åsa K. Hedman4, Catherine Ingle6, David Knowles7, Maria Krestyaninova8, Cecilia M. Lindgren4, Christopher E. Lowe9,10, Mark I. McCarthy3,4,11, Eshwar Meduri 1,6, Paola di Meglio12, Josine L. Min4, Stephen B. Montgomery5, Frank O. Nestle12, Alexandra C. Nica5, James Nisbet6, Stephen O'Rahilly9,10, Leopold Parts6, Simon Potter6, Magdalena Sekowska6, So-Youn Shin6, Kerrin S. Small1,6, Nicole Soranzo1,6, Tim D. Spector1, Gabriela Surdulescu1, Mary E. Travers3, Loukia Tsaprouni6, Sophia Tsoka2, Alicja Wilk6, Tsun-Po Yang6, Krina T. Zondervan4
1. Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
2. Department of Informatics, School of Natural and Mathematical Sciences, King's College London, Strand, London, UK
3. Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Churchill Hospital, Oxford, UK
4. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
5. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
6. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
7. University of Cambridge, Cambridge, UK
8. European Bioinformatics Institute, Hinxton, UK
9. University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke's Hospital Cambridge, UK
10. Cambridge NIHR Biomedical Research Centre, Addenbrooke's Hospital, Cambridge, UK
11. Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
12. St. John's Institute of Dermatology, King's College London, London, UK
Conceived and designed the experiments: MCYN DS XG JLiu LRY MAN LJRT YVS SRP LW BMS CDL KLK BIF LJR HAT DC JSP EB AA GC ABS ABZ EKK DSS YLi LFB MAP EPB OG QC WZhe WJB WLL DCC SSR MGH XOS RJFL IBB PAP SRC BMP MF MKE DMB WHLK JGW JIR MMS SL CNR DWB. Performed the experiments: MAN NDP AA AD ABS YLi EPB JIR. Analyzed the data: MCYN DS BHC JLi WMC XG JLiu LRY MAN MEC RAJ DSE YVS PA SRP YLu JLo LLA LY BMS NDP PM CDL RAM DV ABS BM KR WCH WZha AK OG JAP EG MGH RJFL. Contributed reagents/materials/analysis tools: SJB LW BIF JCM UN LJR MOG YDIC HAT AC MS DC AA AD ABZ RPI JRS LFB EPB QC WZhe WLL DCC XOS PAP BMP SKI MKE JGW JIR MMS SL CNR DWB. Wrote the paper: MCYN DS.
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