Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

<p>We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P&thinsp;&lt;&thinsp;5&thinsp;&times;&thinsp;10&minus;9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with &gt;50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.</p>

section_content: individuals per ancestry group, and approximate conditional analyses may therefore be susceptible to unstable effect size estimates at lower frequency variants. 

An alternative approach is to make use of individual-level genotype data from GWAS contributing to the multi-ancestry meta-regression as a reference for LD in approximate conditional analyses. These studies typically include larger numbers of individuals than are available in the 1000 Genomes Project reference panel. However, these GWAS will usually have been imputed, such that many variants present in the reference panel will fail imputation quality control. Furthermore, the approximate conditional analyses implemented in GCTA require that imputed genotypes be converted to hard calls, which can lead to over-confidence in downstream analyses by ignoring imputation uncertainty. A single study may also not be representative of the genetic diversity amongst GWAS from an ancestry group, particularly those with variable levels of admixture. 

To gain insight into the robustness of our approximate conditional analyses to the choice of LD reference panel in admixed ancestry groups, we considered subsets of 1,000 African American and 1,000 Hispanic individuals from the Resource for Genetic Epidemiology on Adult Health and Aging (GERA), a large multi-ancestry population-based cohort, created for investigating the genetic and environmental basis of age-related diseases [database of Genotypes and Phenotypes (dbGaP) phs000674. p1]. GERA participants have previously been genotyped using one of four custom arrays, which have been designed to maximise coverage of common and low-frequency variants in non-Hispanic white, East Asian, African American and Hispanic individuals 5, 6. We undertook quality control of these genotype data, removing individuals from known pedigrees and/or with call rate (<97%), and excluding SNVs with call rate (<95%) and extreme deviation from Hardy-Weinberg equilibrium (autosomes only, exact p<10 -6 ). We constructed a genetic relationship matrix (GRM) from pair-wise identity by descent metrics estimated from LD pruned (r 2 <0. 01 across individuals) autosomal SNVs shared across the four genotyping arrays, and with MAF ≥1%, after exclusion of those in high-LD and complex regions, and those mapping to established T2D loci. We defined related individuals with pair-wise pi-hat >0. 2 and removed those with the lowest call rate from each related set. 

We applied multi-dimensional scaling, implemented in PLINK 7, to the GRM to obtain principal components to represent axes of genetic variation that separate the major ancestry groups. Clusters of African American and Hispanic individuals were identified in principal component space, and subsets of 1,000 randomly selected individuals from each cluster for use as a reference for LD in approximate conditional analyses. 

For each subset of individuals, we constructed a scaffold for imputation after excluding SNVs with MAF<1%. The scaffold was then pre-phased using SHAPEITv2. 5 8, based on estimates of recombination rate from the International HapMap Project 9. The resulting haplotypes were imputed up to the 1000 Genomes Project reference panel (phase 3, October 2014 release) using minimac4 via the Michigan Imputation Server 10. SNVs with r 2 ≥0. 4 were retained for downstream analyses. African and Hispanic LD reference panels were then obtained by converting imputed genotype dosages to hard calls using PLINKv1. 9 11. 

For each locus with more than one distinct association signal in the multi-ancestry meta-regression, we used GCTA in each African and Hispanic ancestry GWAS, removing each SNV, in turn, from the conditional set, and adjusting for the remainder, using the "--cojocond" option. If any SNV from the conditional set failed imputation quality control in the LD reference panel, the locus was excluded from downstream analyses. We aggregated allelic log-ORs from the approximate conditional analyses across African and Hispanic ancestry Supplementary Methods Exemplar power calculations. Assuming homogeneous effects on T2D across ancestry groups, we estimated power to detect association with an SNV under an additive model from the non-centrality parameter of a chi-squared distribution with one degree of freedom, given by Neffψ 2 q(1-q), where q denotes the mean effect allele frequency across populations and ψ denotes the allelic log-OR, and Neff is the total effective sample size across studies 1. At our multi-ancestry genome-wide significance threshold, p<5x10 -9, under an additive genetic model, we had ≥80% power to detect association of SNVs with MAF ≥5% and OR ≥1. 045 or MAF ≥0. 5% and OR ≥1. 145.
section_name: Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

section_content: We used iterative approximate conditioning, implemented in GCTA 2, making use of forward selection and backward elimination, to identify index SNVs at multi-ancestry genome-wide significance (p<5x10 -9 ). We used haplotypes from the 1000 Genomes Project reference panel (phase 3, October 2014 release) 3 that were specific to each ancestry group (Supplementary Table 22 ) as a reference for LD between SNVs across loci in the approximate conditional analysis. 

For each locus, we first used GCTA in each ancestry-specific GWAS, using the studylevel association summary statistics and ancestry-matched LD reference, implementing a forward selection scheme. At each iteration, we adjusted for the "conditional set" of variants at the locus using the "--cojo-cond" option. In the first iteration, the conditional set included only the lead SNV at the locus. Allelic log-ORs from the approximate conditional analyses across GWAS were modelled in the multi-ancestry meta-regression framework, incorporating the three axes of genetic variation as covariates, and weighted by the inverse of the variance of the effect estimates. The meta-regression association p-values were corrected for inflation due to residual structure between GWAS by using the same genomic control adjustment as in the unconditional analysis ( = 1. 052). If no SNVs attained genome-wide significant (p<5x10 -9 ) evidence of residual T2D association in the metaregression, the iterative approximate conditional analysis for the locus was stopped. Otherwise, the SNV with the strongest residual association signal was added to the conditional set. This process continued, at each iteration adding the SNV with the strongest residual T2D association from the meta-regression to the conditional set, until no remaining SNVs attained genome-wide significance. Note, that at each iteration, GWAS with missing association summary statistics for any variant in the conditional set were excluded from the meta-regression. 

For each locus with more than one SNV in the conditional set, we then checked that all variants in the conditional set attained genome-wide significant evidence of association in a joint model after meta-regression. To do this, we used GCTA in each GWAS, but this time using the "--cojo-joint" option, including all SNVs in the conditional set in the joint model. Allelic log-ORs from the approximate conditional analyses across GWAS were modelled in the multi-ancestry meta-regression framework, incorporating the three axes of genetic variation as covariates, and weighted by the inverse of the variance of the effect estimates. The meta-regression association p-values were corrected for inflation due to residual structure between GWAS by using the same genomic control adjustment as in the unconditional analysis ( = 1. 052). If any SNV in the conditional set did not attain genomewide significant evidence of association, the SNV with the least significant p-value was removed (backward elimination). The procedure then iterated between forward selection and backward elimination steps until: (i) no SNVs outside the conditional set attained genome-wide significant evidence of residual association in the meta-regression; and (ii) all SNVs in the conditional set attained genome-wide significant evidence of association in the joint model after meta-regression. 

For each locus including more than one SNV in the conditional set, we next dissected each distinct association signal. We again used GCTA in each GWAS, but this time removing each SNV, in turn, from the conditional set, and adjusting for the remainder, using the "--cojo-cond" option. Allelic log-ORs from the approximate conditional analyses across GWAS were modelled in the multi-ancestry meta-regression framework, incorporating the three axes of genetic variation as covariates, and weighted by the inverse of the variance of the effect estimates. The meta-regression association p-values were corrected for inflation due to residual structure between GWAS by using the same genomic control adjustment as in the unconditional analysis ( = 1. 052). The variant with the strongest residual association was defined as the "index SNV" for the signal. We also aggregated allelic log-ORs from the approximate conditional analyses across GWAS via fixed-effects meta-analysis using METAL 4 based on inverse-variance weighting. Standard errors were corrected for residual inflation due to structure between GWAS using the same genomic control adjustment as in the unconditional analysis ( = 1. 253). 

Ancestry-specific meta-analyses. We aggregated association summary statistics across GWAS from the same ancestry group via fixed-effects meta-analysis using METAL 4 based on inverse-variance weighting of allelic log-OR to obtain effect size estimates. We corrected association p-values and standard errors of allelic effects from each ancestry group for residual inflation due to structure between GWAS by genomic control adjustment: African = 1. 056; East Asian = 1. 111; European = 1. 096; Hispanic = 1. 008; South Asian = 0. 973 (no correction made). We estimated the mean effect allele frequency across GWAS from each ancestry group, weighted by the effective sample size of the study. We also aggregated association summary statistics across GWAS from non-European ancestry groups via fixed-effects meta-analysis using METAL based on inverse-variance weighting of allelic log-OR to obtain effect size estimates. We corrected association p-values and standard errors of allelic effects for residual inflation due to structure between GWAS by genomic control adjustment: = 1. 133. Finally, we aggregated association summary statistics across GWAS from East Asian and European ancestry groups via fixedeffects meta-analysis using METAL based on inverse-variance weighting of allelic log-OR to obtain effect size estimates. We corrected association p-values and standard errors of allelic effects for residual inflation due to structure between GWAS by genomic control adjustment: = 1. 147. 

For each locus with more than one distinct association signal in the multi-ancestry meta-regression, we used GCTA in each GWAS, removing each SNV, in turn, from the conditional set, and adjusting for the remainder, using the "--cojo-cond" option. We aggregated allelic log-ORs from the approximate conditional analyses across GWAS within the same ancestry group (and combined across East Asian and European ancestry groups) via fixed-effects meta-analysis using METAL based on inverse-variance weighting. We corrected association p-values and standard errors of allelic effects from each ancestry group for residual inflation due to structure between GWAS using the same genomic control adjustments as in the unconditional analyses.
section_name: Dissection of distinct multi-ancestry association signals.

section_content: For the multi-ancestry meta-regression, we approximated the Bayes' factor for the th SNV at the th distinct association signal by

where is the deviance across Kij contributing GWAS 5. 

For the European ancestry-specific meta-analysis and combined East Asian and European ancestry meta-analysis, we approximated the Bayes' factor for the th SNV at the th distinct association signal by

where = 2 ⁄, and and are the allelic log-OR and corresponding variance, respectively, across Kij contributing GWAS. 

Assessment of fine-mapping resolution in "down-sampled" multi-ancestry metaregression. We selected GWAS contributing to the multi-ancestry meta-regression to approximate the effective sample size of the European ancestry-specific meta-analysis and maintain the distribution of effective sample size across ancestry groups (Supplementary Table 10 ). The selected GWAS were: African ancestry BIOME (AFR 2), CARDIA, CHS, EMERGE, GENOA (AFR), GERA (AFR), REGARDS, WHI (AFR); East Asian ancestry BBJ (1), CAGE-KING (2), CKB-12, CKB-16, CKB-58, CKB-68, CKB-78, CKB-88, KBA (2), SCES (2), SCHS; European ancestry BIOME (EUR), DECODE, DGI, EPIC-INTERACT (2), FHS, FUSION, GCKD, GENOA (EUR), GERA (EUR), GODARTS, GOMAP, KORA, METSIM, MGI, NUGENE, PIVUS, PROSPER, RS (1), UCPH, ULSAM; Hispanic ancestry BIOME (HIS), HCHS/SOL, MACAD, MC (1), MC (2), MESA (HIS); South Asian EPIDREAM, GRCCDS, INDICO, INTERHEART (2), LOLIPOP (1), LOLIPOP (2), LOLIPOP (4), PROMIS (1). 

We conducted a "down-sampled" multi-ancestry meta-regression, implemented in the MR-MEGA software 5, for the selected studies. For each SNV, we modelled allelic log-ORs across GWAS in a linear regression framework, weighted by the inverse of the variance of the effect estimates, incorporating the same three axes of genetic variation as covariates (Extended Data Figure 2 ). We corrected the meta-regression association p-values for inflation due to residual structure between the selected GWAS using genomic control adjustment (allowing for four degrees of freedom): * = 1. 012. 

For each locus with more than one distinct association signal in the complete multiancestry meta-regression, we used GCTA in each GWAS, removing each SNV, in turn, from the conditional set, and adjusting for the remainder, using the "--cojo-cond" option (as described above). Allelic log-ORs from the approximate conditional analyses across GWAS were modelled in the multi-ancestry meta-regression framework, incorporating the three axes of genetic variation as covariates, and weighted by the inverse of the variance of the effect estimates. The meta-regression association p-values were corrected for inflation due to residual structure between GWAS by using the same genomic control adjustment as in the unconditional analysis ( * = 1. 012). 

Within each locus, we approximated the Bayes' factor 6,, in favour of T2D association of the th SNV at the th distinct association signal on the basis of summary statistics from the down-sampled multi-ancestry meta-regression. For loci with a single association signal, the association summary statistics were obtained from unconditional analysis. For loci with multiple distinct association signals, the association summary statistics were obtained from the approximate conditional analyses. The posterior probability for the th SNV at the th distinct signal, was then given by ∝, where

and is the deviance across Kij contributing GWAS 5. We derived a 99% credible set 7 for the th distinct association signal by: (i) ranking all SNVs according to their posterior probability ; and (ii) including ranked SNVs until their cumulative posterior probability attains or exceeds 0. 99.
section_name: Derivation of approximate Bayes' factors in favour of association.

section_content: We tested for enrichment of distinct T2D association signals from the multi-ancestry meta-regression (as measured by the approximate Bayes' factor) that map to genomic annotations using fGWAS 8 with the region-based input format (-fine). We first considered each annotation separately and identified those with significant enrichment (p<0. 00023, Bonferroni correction for 220 annotations), which we refer to as the "enriched set". We then used an iterative approach to identify a joint model of enriched annotations. At each iteration, we added the annotation from the enriched set to the joint model that maximised the improvement in the penalised likelihood. We continued until no additional annotations improved the fit of the joint model at nominal significance (p<0. 05). We next used the crossvalidation likelihood because the significance of parameter estimates from the penalised likelihood cannot be assessed using standard statistical approaches. For the selected joint model, we identified the penalty that maximised the cross-validation likelihood. Finally, we dropped any annotations from the joint model that resulted in a decrease in the crossvalidation likelihood. 

Transferability of multi-ancestry GRS across ancestry groups. We selected two studies per ancestry group as test GWAS, prioritising those with larger effective sample sizes and greater genetic diversity: DDS/DCC, WHI (AFR), KBA, SIMES, EPIC-INTERACT (2), UKBB, HCHS/SOL, MC, PROMIS and RHS. We repeated the multi-ancestry meta-regression, after excluding the ten test GWAS, incorporating the same three axes of genetic variation as covariates to account for ancestry. The association p-values from this "reduced" metaregression were then corrected for inflation due to residual structure between GWAS by means of genomic control adjustment (allowing for four degrees of freedom): = 1. 037. SNVs reported in ≥50% of the total effective sample size of the "reduced" meta-regression (NTE≥179,074) were included in downstream analyses. We identified loci attaining genomewide significant evidence of association (p<5x10 -9 ) in the "reduced" meta-regression, and the lead SNV for each locus was selected as the variant with minimum association p-value. 

For each test GWAS, we next estimated population-specific "predicted" allelic effects for each lead SNV to be used as weights in the GRS. For the th study, we estimated the allelic effect of the th SNV by

where is the position of the th study on the th axis of genetic variation from the "complete" multi-ancestry meta-regression, and 0 and denote the intercept and effect of the th axis of genetic variation for the SNV from the "reduced" multi-ancestry meta-regression. For each test GWAS, we then regressed the observed allelic effect estimates at lead SNVs, weighted by their corresponding variances, on the "predicted" allelic effect estimates, as implemented in grs. summary function 9 of the gtx package in R. We estimated the OR per unit of the weighted GRS and the corresponding percentage of T2D variance explained, measured by pseudo R 2. 

Transferability of ancestry-specific GRS across ancestry groups. We selected two studies per ancestry group as test GWAS. We repeated each of the ancestry-specific fixed-effects meta-analyses after excluding the ten test GWAS. We aggregated association summary statistics across GWAS from the same ancestry group via fixed-effects meta-analysis using METAL 4 based on inverse-variance weighting of allelic log-OR to obtain effect size estimates. We corrected association p-values for residual inflation due to structure between GWAS by genomic control adjustment: African = 1. 049; East Asian = 1. 092; European = 1. 180; Hispanic = 1. 004; South Asian = 0. 974 (no correction made). SNVs reported in ≥50% of the total effective sample size of the "reduced" ancestry-specific meta-analyses were included in downstream analyses: African NAFR≥11,613; East Asian NEAS≥57,129; European NEUR≥85,062; Hispanic NHIS≥9,480; South Asian NSAS≥15,789. We identified loci attaining genome-wide significant evidence of association (p<5x10 -8 ) in each of the "reduced" ancestry-specific meta-analyses, and the lead SNV for each locus was selected as the variant with minimum association p-value. For each test GWAS, we then regressed the observed allelic effect estimates at lead SNVs, weighted by their corresponding variances, on the allelic effect estimates from the each of the ancestry-specific meta-analyses, as implemented in grs. summary function 9 of the gtx package in R, and estimated the OR per unit of the weighted GRS and the corresponding percentage of T2D variance explained, measured by pseudo R 2. 

Predictive power of GRS in FinnGen. Individuals from FinnGen were genotyped with Illumina and Affymetrix arrays. After quality control, individuals were imputed with Beagle 4 10 up to the Finnish population-specific reference panel (SISu version 3), comprising 3,775 whole genome sequences (www. sisuproject. fi). We excluded individuals due to non-Finnish ancestry, relatedness, or missing age and/or sex. We estimated the positions of FinnGen on the three axes of genetic variation from the multi-ancestry meta-regression as the mean of the two Finnish studies, FUSION and METSIM (Supplementary Tables 1 and 2 ), which we denoted 1, 2 and 3. We derived Finnish-specific "predicted" allelic effect estimates for each lead SNV from the multi-ancestry meta-regression to be used as weights in the GRS. For the th SNV, the "predicted" effect was given by

where 0 and denote the intercept and effect of the th axis of genetic variation for the SNV from the multi-ancestry meta-regression. 

For each individual, we calculated the centred GRS from the multi-ancestry metaregression given by

for the th individual. In this expression, denotes the frequency of the effect allele at the th SNV, and is the effect allele dosage for the th individual, which we replaced by 2 if the genotype was missing. We excluded lead SNVs from the GRS that were not reported in FinnGen. T2D status was defined using two variables: T2D-I and T2D-II. T2D-I included individuals with ICD-10 E11 and/or ICD-9 250*A, but excluded individuals with pancreatitis. T2D-II included individuals with ICD-10 E11, T2D complications, or medicine purchases of Anatomical Therapeutic Chemical (ATC) class A10B (blood glucose lowering drugs, excluding insulins). We defined controls as non-diabetic individuals (type 1, type 2, or undefined). We excluded individuals with missing T2D status or BMI from subsequent analyses, resulting in a total of 18,111 affected individuals and 111,119 unaffected individuals. 

In a logistic regression framework, we first fitted a "null" model that included age, sex, genotyping batch, and ten axes of genetic variation to account for population structure. We then fitted models that additionally added BMI only, GRS only, and both BMI and GRS. For each model, we calculated the variance in T2D status explained (pseudo R 2 ) and the AUROC (calculated with a 10-fold cross-validation). We also conducted age-stratified analyses, after excluding age from the "null" model, within five age groups: under 50 years; 50-60 years; 60-70 years; 70-80 years; and over 80 years. We next considered the subset of individuals in the highest and lowest deciles of the GRS, testing for association of T2D status with an indicator variable of high/low decile in a logistic regression framework after adjustment for age, sex, BMI, genotyping batch, and ten axes of genetic variation. Finally, we considered T2D cases only, and tested for association of age of diagnosis of the disease with GRS in a linear regression framework after adjustment for sex, BMI, genotyping batch, and ten axes of genetic variation. 

Selection analyses. We tested for evidence of selection for index SNVs for distinct T2D association signals, which were partitioned into two groups, risk and protective, according to the direction of the allelic effect when aligned to the derived allele. For each population, we excluded index SNVs that were not segregating in that population. We also excluded SNVs with T2D association p-value >0. 5 for the ancestry group to which the population belongs because allelic effect estimates are close to zero and are imprecise. To test for selection, we sampled 20 variants for each index SNV, selected at random from those with the same derived allele frequency for the population in the 1000 Genomes Project reference panel. We conducted a one-sided Wilcoxon rank-sum test of whether the index SNVs have smaller than expected selection p-values, derived by Relate 11, when compared to the rest of the genome. We repeated this test 20 times and reported the mean p-value across replicates. 

We next tested for selection on a range of traits available in the UK Biobank 12 at the subset of index SNVs for which the derived allele increased risk of T2D. We downloaded association summary statistics from http://www. nealelab. is/uk-biobank, which were derived from 361,194 white British individuals using PHESANT 13. We considered traits for which the number of significantly associated T2D index SNVs (p<0. 00015, Bonferroni Inserted DNA sequences and primers used for functional experimentation at the PROX1 locus. 

UK Biobank (UKBB) analyses were conducted using the UK Biobank resource under application 9161.
section_name: Enrichment of distinct T2D association signals in genomic annotations.

section_content: Grants (WT098017, WT064890, WT090532), Uppsala University, Uppsala University Hospital, the Swedish Research Council and the Swedish Heart-Lung Foundation. 

Wake Forest School of Medicine (WFSM) was supported by NIH grants K99 DK081350, R01 DK066358, R01 DK053591, R01 DK087914, U01 DK105556, R01 HL56266, R01 DK070941 and in part by the General Clinical Research Center of the Wake Forest School of Medicine grant M01 RR07122. Genotyping services were provided by the Center for Inherited Disease Research (CIDR), which is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSC268200782096C. 

Women's Health Initiative (WHI) 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. The Molecular Epidemiology of Diabetes in the WHI is supported by R01DK125403 (to S. Liu). 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. FinnGen is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and eleven industry partners (AbbVie Inc, AstraZeneca UK Ltd, Biogen MA Inc, Celgene Corporation, Celgene International II Sàrl, Genentech Inc, Merck Sharp & Dohme Corp, Pfizer Inc., GlaxoSmithKline, Sanofi, Maze Therapeutics Inc., Janssen Biotech Inc). Following biobanks are acknowledged for collecting the FinnGen project samples: Auria Biobank (www. auria. fi/biopankki), THL Biobank (www. thl. fi/biobank), Helsinki Biobank (www. helsinginbiopankki. fi), Biobank Borealis of Northern Finland (www. oulu. fi/university/node/38474), Finnish Clinical Biobank Tampere (www. tays. fi/en-US/Research_and_development/Finnish_Clinical_Biobank_Tampere), Biobank of Eastern Finland (www. ita-suomenbiopankki. fi/en), Central Finland Biobank (www. ksshp. fi/fi-FI/Potilaalle/Biopankki), Finnish Red Cross Blood Service Biobank (www. veripalvelu. fi/verenluovutus/biopankkitoiminta) and Terveystalo Biobank (www. terveystalo. com/fi/Yritystietoa/Terveystalo-Biopankki/Biopankki/). All Finnish Biobanks are members of BBMRI. fi infrastructure (www. bbmri. fi).
section_name: Uppsala Longitudinal Study of Adult Men (ULSAM) was supported by Wellcome Trust

section_name: Wellcome Trust Case Control

section_name: Individual author acknowledgements. L.S.A. acknowledges support from the

section_name: Acknowledgements and Funding

section_content: Council Epidemiology Unit ( MC_UU_12015/1 ). A. Leong is supported by grant 2020096 from the Doris Duke Charitable Foundation. N. R. L. acknowledges funding from the US National Institutes of Health TW008288. C. M. L is supported by: the Li Ka Shing Foundation ; WT-SSI/John Fell funds, Oxford ; NIHR Oxford Biomedical Research Centre, Oxford; Widenlife; and NIH ( 5P50HD028138-27 ). A. E. L. acknowledges funding from US National Institutes of Health grant DK062370. R. J. F. L. acknowledges funding from the US National Institutes of Health grants R01DK110113, R01DK107786, R01HL142302 and R56HG010297. J. Luan acknowledges support from the Medical Research Council Epidemiology Unit ( MC_UU_12015/1 ). R. C. W. M acknowledges funding from the Research Grants Council Theme-based Research Scheme ( T12-402/13-N ), the RGC Research Impact Fund ( R4012-18 ), and a Croucher Foundation Senior Medical Research Fellowship. M. I. M. acknowledges funding from: The European Commission (ENGAGE: HEALTH-F4-2007-201413 ); MRC ( G0601261, L020149 ); National Institutes of Health ( RC2-DK088389, DK085545, R01-DK098032, U01-DK105535 ); Wellcome ( 083948, 085475, 090367, 090532, 098381, 101630, 203141, 212259 ). R. M. -C. acknowledges support from grants NIH U10 EY 11753 and NIH U10 EY 11753. J. B. M acknowledges funding through NIH grants R01DK078616, U01DK078616 and K24DK080140. J. M. M. is funded by American Diabetes Association Innovative and Clinical Translational Award 1-19-ICTS-068. K. L. M. acknowledges funding from the US National Institutes of Health R01DK072193, R01DK093757, U01DK105561. A. P. M. acknowledges support from US National Institutes of Health U01DK105535 ; Versus Arthritis (grant reference 21754 ); the Estonian Research Council grant PRG687 ; Wellcome Trust Core Award Grant Number 203141/Z/16/Z with additional support from the NIHR Oxford BRC ; NIHR Manchester Biomedical Research Centre. S. R. M acknowledges funding from the Wellcome Trust [ 200186/Z/15/Z ] and is supported by a Wellcome Trust Investigator Award [ 212284/Z/18/Z ]. G. N. N. is supported by NIH grant R01DK127139. M. A. N. was supported in part by the Intramural Research Program of the NIH, National Institute on Aging (NIA), National Institutes of Health, Department of Health and Human Services ; project number ZO1 AG000535, as well as the National Institute of Neurological Disorders and Stroke (NINDS) ; participation in this project was part of a competitive contract awarded to Data Tecnica International LLC by the National Institutes of Health to support open science research. M. C. Y. N. acknowledges support from the US National Institutes of Health U01DK105556, R01DK66358, and a supplement to R01DK78616-06S1. K. E. N. acknowledges support by R01HD057194, R01DK122503, R01HG010297, R01HL142302, R01HL143885, R01HG009974, and R01DK101855. D. N. acknowledges support from NIEHS grant T32ES013678. Y. O. was supported by JSPS KAKENHI ( 19H01021, 20K21834 ), and AMED ( JP21km0405211, JP21ek0109413, JP21ek0410075, JP21gm4010006, and JP21km0405217 ), JST Moonshot R&D ( JPMJMS2021, JPMJMS2024 ), Takeda Science Foundation. K. -S. P. acknowledges funding from Korea Health Technology R&D Project through the Korea Health Industry Development Institute (grant numbers HI15C1595, HI14C0060 ). E. J. P. was supported by the Canadian Institutes of Health Research (CIHR) and the Banting and Best Diabetes Center, University of Toronto. L. E. P. acknowledges support from the US National Institutes of Health R01DK127084, R01GM133169, R01HL142302, and U01DK105554. D. Saleheen has received funding from NHLBI, NINDS, the British Heart Foundation, Pfizer, Regeneron, Genentech, and Eli Lilly pharmaceuticals. M. Sander acknowledges support from the US National Institutes of Health 5U01DK105554. N. S. is supported by British Heart Foundation Centre of Excellence Grant RE/18/6/34217. R. A. S. acknowledges support from the Medical Research Council Epidemiology Unit ( MC_UU_12015/1 ). L. S. acknowledges support provided by a Sir Henry Wellcome Fellowship [ 220457/Z/20/Z ] and funding from the Wellcome Trust [ 200186/Z/15/Z ]. C. N. S. was supported by American Heart Association Postdoctoral Fellowship 15POST24470131 and 17POST33650016. D. T. acknowledges funding from US National Institutes of Health grant DK062370. M. S. U. acknowledges funding from NIH-NIDDK grant K23DK114551. N. J. W. acknowledges support from the Medical Research Council Epidemiology Unit ( MC_UU_12015/1 ). E. W. acknowledges Inês Barroso for supervision and support ( Wellcome WT098051 and WT206194 ), and acknowledges support from the Medical Research Council Epidemiology Unit ( MC_UU_12015/1 ). J. -Y. W. was supported by Academia Sinica GMM Study. M. W. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) - CRC 1453 Project -ID 431984000. W. Zhang acknowledges support from iHealth-T2D, 643774 and the National Institute for Health Research/Wellcome Trust Imperial Clinical Research Facility. Kenilworth, NJ, United States ; Anders Malarstig, Pfizer, New York, NY, United States ; Catherine Marshall, Pfizer, New York, NY, United States ; Xinli Hu, Pfizer, New York, NY, United States; Kathy Call, Sanofi, Paris, France ; Kathy Klinger, Sanofi, Paris, France ; Matthias Gossel, Sanofi, Paris, France. University of Helsinki and Biobanks: Samuli Ripatti, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland ; Johanna Schleutker, Auria Biobank / Univeristy of Turku / Hospital District of Southwest Finland, Turku, Finland ; Markus Perola, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland ; Mikko Arvas, Finnish Red Cross Blood Service / Finnish Hematology Registry and Clinical Biobank, Helsinki, Finland ; Olli Carpén, Hospital District of Helsinki and Uusimaa, Helsinki, Finland ; Reetta Hinttala, Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland ; Johannes Kettunen, Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland ; Reijo Laaksonen, Finnish Clinical Biobank Tampere / University of Tampere / Pirkanmaa Hospital District, Tampere, Finland ; Arto Mannermaa, Biobank of Eastern Finland / University of Eastern Finland / Northern Savo Hospital District, Kuopio, Finland ; Urho Kujala, Central Finland Biobank / University of Jyväskylä / Central Finland Health Care District, Jyväskylä, Finland. 

Other Experts and Non-Voting Members: Outi Tuovila, Business Finland, Helsinki, Finland; Minna Hendolin, Business Finland, Helsinki, Finland ; Raimo Pakkanen, Business Finland, Helsinki, Finland.
section_name: Acknowledgements and Funding

section_content: Neurology Group: Hilkka Soininen, Northern Savo Hospital District, Kuopio, Finland ; Valtteri Julkunen, Northern Savo Hospital District, Kuopio, Finland ; Anne Remes, Northern Ostrobothnia Hospital District, Oulu, Finland ; Reetta Kälviäinen, Northern Savo Hospital District, Kuopio, Finland ; Mikko Hiltunen, Northern Savo Hospital District, Kuopio, Finland ; Jukka Peltola, Pirkanmaa Hospital District, Tampere, Finland ; Pentti Tienari, Hospital District of Helsinki and Uusimaa, Helsinki, Finland ; Juha Rinne, Hospital District of Southwest Finland, Turku, Finland; Adam Ziemann, Abbvie, Chicago, IL, United States; Jeffrey Waring, Abbvie, Chicago, IL, United States; Sahar Esmaeeli, Abbvie, Chicago, IL, United States; Nizar Smaoui, Abbvie, Chicago, IL, United States; Anne Lehtonen, Abbvie, Chicago, IL, United States; Susan Eaton, Biogen, Cambridge, MA, United States; Heiko Runz, Biogen, Cambridge, MA, United States; Sanni Lahdenperä, Biogen, Cambridge, MA, United States; Janet van Adelsberg, Celgene, Summit, NJ, United States; Shameek Biswas, Celgene, Summit, NJ, United States ; John Michon, Genentech, San Francisco, CA, United States; Geoff Kerchner, Genentech, San Francisco, CA, United States; Julie Hunkapiller, Genentech, San Francisco, CA, United States; Natalie Bowers, Genentech, San Francisco, CA, United States; Edmond Teng, Genentech, San Francisco, CA, United States; John Eicher, Merck, Kenilworth, NJ, United States; Vinay Mehta, Merck, Kenilworth, NJ, United States; Padhraig Gormley, Merck, Kenilworth, NJ, United States; Kari Linden, Pfizer, New York, NY, United States ; Christopher Whelan, Pfizer, New York, NY, United States ; Fanli Xu, GlaxoSmithKline, Brentford, United Kingdom; David Pulford, GlaxoSmithKline, Brentford, United Kingdom. 

Gastroenterology Group: Martti Färkkilä, Hospital District of Helsinki and Uusimaa, Helsinki, Finland; Sampsa Pikkarainen, Hospital District of Helsinki and Uusimaa, Helsinki, Finland ; Airi Jussila, Pirkanmaa Hospital District, Tampere, Finland ; Timo Blomster, Northern Ostrobothnia Hospital District, Oulu, Finland ; Mikko Kiviniemi, Northern Savo Hospital District, Kuopio, Finland ; Markku Voutilainen, Hospital District of Southwest Finland, Turku, Finland; Bob Georgantas, Abbvie, Chicago, IL, United States; Graham Heap, Abbvie, Chicago, IL, United States; Jeffrey Waring, Abbvie, Chicago, IL, United States; Nizar Smaoui, Abbvie, Chicago, IL, United States; Fedik Rahimov, Abbvie, Chicago, IL, United States; Anne Lehtonen, Abbvie, Chicago, IL, United States; Keith Usiskin, Celgene, Summit, NJ, United States ; Joseph Maranville, Celgene, Summit, NJ, United States ; Tim Lu, Genentech, San Francisco, CA, United States; Natalie Bowers, Genentech, San Francisco, CA, United States; Danny Oh, Genentech, San Francisco, CA, United States; John Michon, Genentech, San Francisco, CA, United States; Vinay Mehta, Merck, Kenilworth, NJ, United States; Kirsi Kalpala, Pfizer, New York, NY, United States ; Melissa Miller, Pfizer, New York, NY, United States ; Xinli Hu, Pfizer, New York, NY, United States ; Linda McCarthy, GlaxoSmithKline, Brentford, United Kingdom. 

Rheumatology Group: Kari Eklund, Hospital District of Helsinki and Uusimaa, Helsinki, Finland; Antti Palomäki, Hospital District of Southwest Finland, Turku, Finland ; Pia Isomäki, Pirkanmaa Hospital District, Tampere, Finland ; Laura Pirilä, Hospital District of Southwest Finland, Turku, Finland ; Oili Kaipiainen-Seppänen, Northern Savo Hospital District, Kuopio, Finland ; Johanna Huhtakangas, Northern Ostrobothnia Hospital District, Oulu, Finland ; Bob Georgantas, Abbvie, Chicago, IL, United States; Jeffrey Waring, Abbvie, Chicago, IL, United States; Fedik Rahimov, Abbvie, Chicago, IL, United States; Apinya Lertratanakul, Abbvie, Chicago, IL, United States; Nizar Smaoui, Abbvie, Chicago, IL, United States; Anne Lehtonen, Abbvie, Chicago, IL, United States ; David Close, Astra Zeneca, Cambridge, United Kingdom ; Marla Hochfeld, Celgene, Summit, NJ, United States ; Natalie Bowers, Genentech, San Francisco, CA, United States; John Michon, Genentech, San Francisco, CA, United States; Dorothee Diogo, Merck, Kenilworth, NJ, United States; Vinay Mehta, Merck, Kenilworth, NJ, United States; Kirsi Kalpala, Pfizer, New York, NY, United States ; Nan Bing, Pfizer, New York, NY, United States ; Xinli Hu, Pfizer, New York, NY, United States ; Jorge Esparza Gordillo, GlaxoSmithKline, Brentford, United Kingdom; Nina Mars, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland. Pulmonology Group: Tarja Laitinen, Pirkanmaa Hospital District, Tampere, Finland ; Margit Pelkonen, Northern Savo Hospital District, Kuopio, Finland ; Paula Kauppi, Hospital District of Helsinki and Uusimaa, Helsinki, Finland ; Hannu Kankaanranta, Pirkanmaa Hospital District, Tampere, Finland ; Terttu Harju, Northern Ostrobothnia Hospital District, Oulu, Finland; Nizar Smaoui, Abbvie, Chicago, IL, United States ; David Close, Astra Zeneca, Cambridge, United Kingdom; Steven Greenberg, Celgene, Summit, NJ, United States ; Hubert Chen, Genentech, San Francisco, CA, United States; Natalie Bowers, Genentech, San Francisco, CA, United States; John Michon, Genentech, San Francisco, CA, United States; Vinay Mehta, Merck, Kenilworth, NJ, United States; Jo Betts, GlaxoSmithKline, Brentford, United Kingdom ; Soumitra Ghosh, GlaxoSmithKline, Brentford, United Kingdom. Cardiometabolic Diseases Group : Veikko Salomaa, The National Institute of Health and Welfare, Helsinki, Finland ; Teemu Niiranen, The National Institute of Health and Welfare,
section_name: Clinical Groups

section_name: Cardiometabolic Genome Epidemiology (CAGE-AMAGASKI and CAKE-GWAS) was

section_content: Anti-aging study cohort (AASC) is supported by the Grant-in-Aid for Scientific Research ( 20018020, 19659163, 20390185, 23659382, 24390084, 23659352, 25293141, 26670313, 17H04123 ) from the Ministry of Education, Culture, Sports, Science and Technology of Japan, research grant from the Japan Atherosclerosis Prevention Found, National Cardiovascular Research Grants, and Research Promotion Award from Ehime University. 

Atherosclerosis Risk in Communities (ARIC) study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services (contract numbers HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700004I and HHSN268201700005I ), R01HL087641, R01HL059367 and R01HL086694 ; National Human Genome Research Institute contract U01HG004402 ; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. 

Biobank Japan (BBJ) acknowledges all the staff, as well as the doctors and staff of the contributing hospitals for their outstanding work on collecting samples and clinical information, and thanks all the patients participating in this project. This research was supported by the Tailor-Made Medical Treatment Program (the BioBank Japan Project ) of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) and the Japan Agency for Medical Research and Development (AMED). This research was also supported by the Advanced Genome Research and Bioinformatics Study to Facilitate Medical Innovation (GRIFIN) in the Platform Program for Promotion of Genome Medicine ( P3GM ) of AMED ( JP18km0405202 ). 

Beijing Eye Study (BES) was supported by National Natural Science Foundation of China (grant 81570835 ). 

BioMe Biobank (BIOME) is supported by The Andrea and Charles Bronfman Philanthropies and in part by funding of the NIH ( U01HG007417 ; R56HG010297 ; X01HL134588 ). BIOME thanks all participants in the Mount Sinai Biobank, and also thanks all the recruiters who have assisted and continue to assist in data collection and management. BIOME is grateful for the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. Bangladesh Population Cohort (BPC) was supported by US National Institute of Environmental Health Sciences Grants P42 ES10349 and P30 ES09089.

section_content: supported by grants for the Core Research for Evolutional Science and Technology (CREST) from the Japan Science Technology Agency; KAKENHI ( Grant-in-Aid for Scientific Research) from the Ministry of Education, Culture, Sports, Science and Technology of Japan ; and the Grant and research budget of National Center for Global Health and Medicine (NCGM). 

Cardiometabolic Genome Epidemiology (CAGE-KING) was supported in part by Grants-in-Aid from MEXT (nos. 24390169, 16H05250, 15K19242, 16H06277 ) as well as by a grant from the Funding Program for Next-Generation World-Leading Researchers (NEXT Program, no. LS056 ).
section_name: Cardiometabolic Genome Epidemiology (CAGE-AMAGASKI and CAKE-GWAS) was

section_content: Development in Young Adults (CARDIA) was conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with the University of Alabama at Birmingham ( HHSN268201800005I & HHSN268201800007I ), Northwestern University ( HHSN268201800003I ), University of Minnesota ( HHSN268201800006I ), and Kaiser Foundation Research Institute ( HHSN268201800004I ). CARDIA was also partially supported by the Intramural Research Program of the National Institute on Aging (NIA) and an intra-agency agreement between NIA and NHLBI ( AG0005 ). Genotyping was funded as part of the NHLBI Candidate-gene Association Resource ( N01-HC-65226 ) and the NHGRI Gene Environment Association Studies (GENEVA) ( U01-HG004729, U01-HG04424, and U01-HG004446 ). Cleveland Family Study (CFS) is supported by grants to Case Western Reserve University ( NIH HL 46380, M01RR00080 ) and Brigham and Women's Hospital ( K01-HL135405-01, R01-HL113338-04, R35-HL135818-01, 5-R01-HL046380-15 and 5-KL2-RR024990-05 ). China Health and Nutrition Survey (CHNS) was supported by: the National Institute for Nutrition and Health, the Chinese Center for Disease Control and Prevention ; the National Institutes of Health ( R01AG065357, R01HD30880, R01HL108427 and R01DK104371 ); the Fogarty International Center of the National Institutes of Health ( TW009077 ); the China-Japan Friendship Hospital, the Beijing Municipal Center for Disease Prevention and Control, the China National Health Commission (formerly the Chinese Ministry of Health ); the Chinese National Human Genome Center at Shanghai ; and the Carolina Population Center ( P2CHD050924 ), The University of North Carolina at Chapel Hill. Cardiovascular Health Study (CHS) was supported by NHLBI contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006 ; and NHLBI grants U01HL080295, R01HL085251, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and R01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from 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 UL1-TR-001881, 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. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. 

China Kadoorie Biobank (CKB) chiefly acknowledges the participants, project staff, and the China National Centre for Disease Control and Prevention (CDC) and its regional offices. China's National Health Insurance provides electronic linkage to all hospital treatment. Funding sources: Baseline survey and first re-survey - Kadoorie Charitable Foundation, Hong Kong ; long-term follow-up -UK Wellcome Trust ( 212946/Z/18/Z, 202922/Z/16/Z, 104085/Z/14/Z, 088158/Z/09/Z ), National Natural Science Foundation of China ( 91843302 ), and National Key Research and Development Program of China ( 2016YFC 0900500, 0900501, 0900504, 1303904 ); DNA extraction and genotyping -GlaxoSmithKline, and the UK Medical Research Council ( MC-PC-13049, MC-PC-14135 ); core funding for the project to the Clinical Trial Service Unit and Epidemiological Studies Unit at Oxford University -British Heart Foundation ( CH/1996001/9454 ), UK MRC ( MC-UU-00017/1, MC-UU-12026/2, MC_U137686851 ), and Cancer Research UK ( C16077/A29186, C500/A16896 ). Cebu Longitudinal Health and Nutrition Survey (CLHNS) was supported by: US National Institutes of Health grants DK078150, TW005596 and HL085144 ; pilot funds from RR020649, ES010126, and DK056350 ; and the Office of Population Studies Foundation. Diabetic Cohort and Singapore Prospective Study Program ( DC/SP2 ) was supported by the individual research grant and clinician scientist award schemes from the National Medical Research Council (NMRC) and the Biomedical Research Council (BMRC) of Singapore, Ministry of Health, Singapore, National University of Singapore and National University Health System, Singapore. Durban Diabetes Study and Durban Diabetes Case Control (DDS/DCC) was supported by: the Wellcome Trust (grant number 098051 ); the African Partnership for Chronic Disease Research (Medical Research Council UK partnership grant number MR/K013491/1 ); the National Institute for Health Research Cambridge Biomedical Research Centre (UK) ; Novo-Nordisk (South Africa ); Sanofi-Aventis (South Africa); MSD Pharmaceuticals (Pty) Ltd (Southern Africa) ; Servier Laboratories (South Africa ); South African Sugar Association; and the Victor Daitz Foundation. deCODE genetics (DECODE) thank the participants in the deCODE study, the staff at deCODE genetics core facilities and the staff at the Research Service Center for their contribution to this work. 

Diabetes Gene Discovery Group (DGDG) was supported by Genome Canada, Génome Québec, the Canada Foundation for Innovation, the French Government ("Agence Nationale de la Recherche"), the French Region of "Nord Pas De Calais" ("Contrat de Projets État-Région"), and the charities: " Association Française des Diabétiques", "Programme National de Recherche sur le Diabète" and "Association de Langue Française pour l'Etude du Diabète et des Maladies Métaboliques ". This study was also supported in part by a grant from the European Union ( Integrated Project EuroDia LSHM- CT-2006-518153in the Framework Programme 6 [FP6 ] of the European Community). This work was supported by grants from the French National Research Agency ( ANR-10-LABX-46 [ European Genomics Institute for Diabetes] and ANR-10-EQPX-07-01 [LIGAN-PM]). Case and control recruitment was supported by the Fédération Française des Diabetiques, INSERM, CNAMTS, Centre Hospitalier Universitaire Poitiers, La Fondation de France, and the Endocrinology-Diabetology department of the Corbeil-Essonnes Hospital. C. Petit, J. -P. Riveline, and S. provided technical assistance. K. Shazand and N. Foisset provided organizational guidance. The D. E. S. I. R. study, which mostly contributed controls, was supported by CNAMTS, Lilly, Novartis Pharma and Sanofi-Aventis, by INSERM (" Réseaux en Santé Publique, Interactions entre les déterminants de la santé "), by " Association Diabète Risque Vasculaire", "Fédération Française de Cardiologie", "Fondation de France ", ALFEDIAM, ONIVINS, Ardix Medical, Bayer Diagnostics, Becton Dickinson, Cardionics, Merck Santé, Novo Nordisk, Pierre Fabre, Roche, Topcon. The D. E. S. I. R. Study Group: INSERM U780: B. Balkau, P. Ducimetière, E. Eschwège ; INSERM U367 : F. Alhenc-Gelas ; CHU D'Angers: Y. Gallois, A. Girault ; Bichat Hospital : F. Fumeron, M. Marre ; Medical Examination Services: Alençon, Angers, Caen, Chateauroux, Cholet, Le Mans, and Tours; Research Institute for General Medicine: J. Cogneau ; General practitioners of the region; Cross-Regional Institute for Health: C. Born, E. Allegaert for the DNA bank management and are sincerely indebted to all study participants.
section_name: Coronary Artery Risk

section_content: Research with additional support from The Richard and Susan Smith Family Foundation and American Diabetes Association Pinnacle Program Project Award. The Botnia Study (study subject cohort) was financially supported by the Folkhalsan Research Foundation, the Sigrid Juselius Foundation, Nordic Center of Excellence in Disease Genetics, EU (EXGENESIS), The Care Centers in Vasa, Närpes and Korsholm. The work in Malmö, Sweden, was also funded by a Linné grant from the Swedish Research Council ( 349-2006-237 ). The contribution of the Botnia and Skara research teams is gratefully acknowledged.
section_name: Diabetes Genetics Initiative (DGI) was supported by the Novartis Institute for BioMedical

section_content: Research Council Grant IUT20-60, IUT24-6, PRG687, and the European Union through the European Regional Development Fund Project No. 2014-2020. 4. 01. 15-0012GENTRANSMED.
section_name: Estonian Genome Center of the University of Tartu (EGCUT) was funded by the Estonian

section_content: Network (EMERGE) was initiated and funded by NHGRI through the following grants: U01HG006828 ( Cincinnati Children's Hospital Medical Center/Boston Children's Hospital ); U01HG006830 ( Children's Hospital of Philadelphia ); U01HG006389 ( Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University ); U01HG006382 (Geisinger Clinic); U01HG006375 ( Group Health Cooperative/University of Washington ); U01HG006379 ( Mayo Clinic ); U01HG006380 ( Icahn School of Medicine at Mount Sinai ); U01HG006388 ( Northwestern University ); U01HG006378 ( Vanderbilt University Medical Center ); and U01HG006385 ( Vanderbilt University Medical Center serving as the Coordinating Center). The Northwestern University Enterprise Data Warehouse was funded in part by a grant from the National Center for Research Resources, UL1RR025741. Part of 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. The eMERGE imputed merged Phase I and Phase II dataset was generated by genotyping centers CIDR ( U01HG004438 ) and the Broad Institute ( U01HG004424 ). European Prospective Investigation into Cancer and Nutrition (EPIC-INTERACT) project ( LSHM-CT-2006- 037197 ) is a European- Community funded project under Framework Programme 6. EPIC-INTERACT thank all EPIC participants and staff for their contribution to the study. EPIC-INTERACT thank Nicola Kerrison ( MRC Epidemiology Unit, Cambridge ) for managing the data for the InterAct Project and staff from the Laboratory Team, Field Epidemiology Team, and Data Functional Group of the MRC Epidemiology Unit in Cambridge, UK, for carrying out sample preparation, DNA provision and quality control, genotyping, and data-handling work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. GWAS summary statistics from the EPIC-InterAct study are available to download from the Dryad Digital Repository (https://doi. org/10. 5061/dryad. qnk98sfcg).
section_name: Electronic Medical Records and Genomics

section_content: Screenees for Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication (EPIDREAM) was funded by a grant from the Canadian Institutes of Health Research University Industry competition with partner funding from the GlaxoSmithKline and Sanofi Aventis Global, Sanofi Aventis Canada, Genome Quebec Innovation Centre, Heart and Stroke Foundation of Canada. Family Heart Study (FAMHS) was supported by NIH grants R01-HL-087700 and R01-HL-088215 from NHLBI, and R01-DK-089256 and R01-DK-075681 from NIDDK. Framingham Heart Study (FHS) was conducted and supported by the National Heart, Lung and Blood Institute (NHLBI) in collaboration with Boston University (contracts 75N92019D00031, HHSN268201500001I and N01-HC-25195 ), and its contract with Affymetrix, Inc for genotyping services (contract number N02-HL-6-4278 ). The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. FHS was also supported by: NHLBI R01 HL105756, National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) R01 DK078616, U01 DK078616, NIDDK K24 DK080140 and American Diabetes Association Mentor-Based Postdoctoral Fellowship Award # 7-09-MN-32 (to J. B. M. ); and NIDDK K24 DK110550 (to J. C. F. ). Finland-United States Investigation of NIDDM Genetics (FUSION) was supported by DK093757, DK072193, DK062370, and ZIA-HG000024. German Chronic Kidney Disease (GCKD) was funded by the German Ministry of Research and Education (Bundesminsterium für Bildung und Forschung, BMBF) and by the Foundation KfH Stiftung Präventivmedizin. Unregistered grants to support the study were provided by Bayer, Fresenius Medical Care and Amgen. Genotyping was supported by Bayer AG. Genetic Study of Atherosclerosis Risk (GENESTAR) was supported by NIH grants through the National Heart, Lung, and Blood Institute ( HL49762, HL58625, HL59684, HL071025, 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 Arteriosclerosis (GENOA) was supported by the National Institutes of Health grant numbers HL054457, HL054464, HL054481, HL087660 and HL119443 from the National Heart, Lung, and Blood Institute. Genotyping was performed at the Mayo Clinic by Stephen Turner, Mariza de Andrade, and for their help with genotyping. GENOA also thanks the families that participated in the study. 

Resource for Genetic Epidemiology on Adult Heath and Aging (GERA) was supported by a grant ( RC2 AG033067 ; PIs Schaefer and Risch) awarded to the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and the UCSF Institute for Human Genetics. The RPGEH was supported by grants from the Robert Wood Johnson Foundation, the Wayne and Gladys Valley Foundation, the Ellison Medical Foundation, Kaiser Permanente Northern California, and the Kaiser Permanente National and Northern California Community Benefit Programs.
section_name: Epidemiologic Study of the

section_content: Research in Tayside Scotland (GODARTS) was funded by The Wellcome Trust Study Cohort Functional Genomics Grant ( 2004-2008, 072960/Z/03/Z ) and The Wellcome Trust Scottish Health Informatics Programme (SHIP, 2009-2012, 086113/Z/08/Z ). Genetics of Latinos Diabetic Retinopathy (GOLDR) was supported by grants EY14684 and UL1TR000124. Genetic Overlap Between Metabolic and Psychiatric Traits and Teens of Attica: Genes and Environment (GOMAP-TEENAGE) was funded by the Wellcome Trust ( 098051 ) and was also co-financed by the European Union (European Social Fund -ESF ) and Greek national funds through the Operational Program "Education and Lifelong Learning " of the National Strategic Reference Framework (NSRF) -Research Funding Program: Heracleitus II. GOMAP-TEENAGE thanks all study participants and their families, as well as all volunteers for their contribution in this study. GOMAP-TEENAGE GOMAP-TEENAGE thanks the Sample Management and Genotyping Facilities staff at the Wellcome Trust Sanger Institute for sample preparation, quality control and genotyping. Genomic Research Cohort for CCMB Diabetes Study (GRCCDS) comprises of various cohorts that are supported by: Council of Scientific Industrial Research (CSIR) ; Ministry of Science and Technology, Govt. of India, India ; and Wellcome Trust, London, UK. GRCCDS is grateful to the patients and subjects who voluntarily participated in the study, and thankfully acknowledge other researchers who have supported the study. Health, Aging and Body Composition Study (HABC) 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. 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 (project Z01-AG000513 and human subjects protocol 09 AGN248 ). Data analyses for HANDLS utilized the high-performance computational resources of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD (http://hpc. nih. gov). Hispanic Community Health Study/Study of Latinos (HCHS/SOL ) is a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (NHLBI) to the University of North Carolina ( HHSN268201300001I / N01-HC-65233 ), University of Miami ( HHSN268201300004I / N01-HC-65234 ), Albert Einstein College of Medicine ( HHSN268201300002I / N01-HC-65235 ), University of Illinois at Chicago ( HHSN268201300003I / N01-HC-65236 Northwestern Univ), and San Diego State University ( HHSN268201300005I / N01-HC-65237 ). The following Institutes/Centers/Offices have contributed to the HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, NIH Institution-Office of Dietary Supplements. The Genetic Analysis Center at the University of Washington was supported by NHLBI and NIDCR contracts ( HHSN268201300005C AM03 and MOD03 ). The authors thank the staff and participants of HCHS/SOL for their important contributions. Hong Kong Diabetes Registry (HKDR) acknowledge support from the Theme-based Research Scheme from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project no: T12-402/13-N ), the Research Grants Council Research Impact Fund ( R4012-18 ), the Hong Kong Foundation for Research and Development in Diabetes, the Vice-Chancellor One-off Discretionary Fund, the Focused Innovations Scheme, the Postdoctoral Fellowship Scheme of the Chinese University of Hong Kong, and the Croucher Foundation Senior Medical Research Fellowship. Health Professionals ' Follow-Up Study (HPFS) and Nurses Health Study (NHS ) acknowledge assistance with data cleaning that was provided by the National Center for Biotechnology Information. Support for collection of datasets and samples was provided by the Collaborative Study on the Genetics of Alcoholism (COGA ; U10 AA008401 ), the Collaborative Genetic Study of Nicotine Dependence ( COGEND ; P01 CA089392 ), and the Family Study of Cocaine Dependence (FSCD ; R01 DA013423

). Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the NIH GEI ( U01HG004438 ), the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse, and the NIH contract "High throughput genotyping for studying the genetic contributions to human disease" ( HHSN268200782096C ). The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www. ncbi. nlm. nih. gov/projects/gap/cgibin/study. cgi?study_id=phs000091. v1. p1 through dbGaP accession number phs000091. v1. p.
section_name: Genetics of Diabetes and Audit

section_content: Resistance (HTNIR) was supported by grant HL0597974. 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). Indian Diabetes Consortium (INDICO) was majorly supported by Council of Scientific and Industrial Research (CSIR), Government of India through CARDIOMED project Grant Number: BSC0122 provided to CSIR-Institute of Genomics and Integrative Biology. INDICO was also partially funded by Department of Science and Technology-PURSE-II ( DST/SR/PURSE II/11 ) given to Jawaharlal Nehru University. INDICO are very much thankful to all the volunteers who have participated in the study. INTERHEART (INTERHEART) was funded by: the Canadian Institutes of Health Research, the Heart and Stroke Foundation of Ontario, and the International Clinical Epidemiology Network (INCLEN) ; unrestricted grants from several pharmaceutical companies (with major contributions from AstraZeneca, Novartis, Hoechst Marion Roussel [now Aventis], Knoll Pharmaceuticals [now Abbott], Bristol-Myers Squibb, King Pharma, and Sanofi-Synthelabo) ; and various national bodies in different countries (see Online Appendix at http://image. thelancet. com/extras/04art8001webappendix2. pdf). Funding sources had no involvement in the study design; in the collection, analysis, and interpretation of data; or the writing of the manuscript. Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University ( HHSN268201800013I ), Tougaloo College ( HHSN268201800014I ), the Mississippi State Department of Health ( HHSN268201800015I ) and the University of Mississippi Medical Center ( HHSN268201800010I, HHSN268201800011I and HHSN268201800012I ) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Minority Health and Health Disparities (NIMHD). The authors also wish to thank the staff and participants of the JHS.
section_name: Mexican American Hypertension and Insulin

section_content: Disease and intramural grants from the Korea National Institute of Health ( 2016-NI73001-00, 2019-NG-053-00 ). KARE was performed with bioresources from National Biobank of Korea, the Centers for Disease Control and Prevention, Republic of Korea. 

Korean Biobank Array from the Korean Genome and Epidemiology (KoGES) Consortium (KBA) was supported by grants from Korea Centers for Disease and intramural grants from the Korea National Institute of Health ( 2016-NI73001-00, 2019-NG-053-00 ). KBA was performed with bioresources from National Biobank of Korea, the Centers for Disease Control and Prevention, Republic of Korea. Genotype data were provided by the Collaborative Genome Program for Fostering New Post-Genome Industry ( 3000-3031b ). Collaborative Health Research in the Region of Augsburg (KORA) research platform was initiated and financed by the Helmholtz Zentrum München -German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences ( MC Health ), Ludwig-Maximilians-Universität, as part of LMUinnovativ and by the German Center for Diabetes Research (DZD). Los Angeles Latino Eye Study (LALES) acknowledges funding from NEI grant U10EY011753. London Life Sciences Prospective Population (LOLIPOP) is supported by the National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre Imperial College Healthcare NHS Trust, the British Heart Foundation ( SP/04/002 ), the Medical Research Council ( G0601966, G0700931 ), the Wellcome Trust ( 084723/Z/08/Z, 090532 & 098381 ) the NIHR ( RP-PG-0407-10371 ), the NIHR Official Development Assistance (ODA, award 16/136/68 ), the European Union FP7 (EpiMigrant, 279143) and H2020 programs (iHealth-T2D, 643774). LOLIPOP acknowledges support of the MRC-PHE Centre for Environment and Health, and the NIHR Health Protection Research Unit on Health Impact of Environmental Hazards. The work was carried out in part at the NIHR/Wellcome Trust Imperial Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the Imperial College Healthcare NHS Trust, the NHS, the NIHR or the Department of Health. LOLIPOP thanks the participants and research staff who made the study possible. Mexican American Study of Coronary Artery Disease (MACAD) was supported by grant HL088457. 

Mexico City (MC) was supported, in Mexico, by the Fondo Sectorial de Investigación en Salud y Seguridad Social (SSA/IMSS/ISSSTECONACYT, project 150352 ), Temas Prioritarios de Salud Instituto Mexicano del Seguro Social ( 2014-FIS/IMSS/PROT/PRIO/14/34 ), and the Fundación IMSS. MC thanks Jaime Gómez Zamudio and Araceli Méndez Padrón for technical support. In Canada, computations were performed on the GPC supercomputer at the SciNet HPC Consortium. SciNet is funded by: the Canada Foundation for Innovation under the auspices of Compute Canada ; the Government of Ontario; Ontario Research Fund -Research Excellence ; and the University of Toronto. 

Multi-Ethnic Study of Atherosclerosis (MESA ) and the MESA SHARe projects 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 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420. Also supported by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, 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. Funding for SHARe genotyping was provided by NHLBI Contract N02-HL-64278. Genotyping was performed at Affymetrix (Santa Clara, California, USA) and the Broad Institute of Harvard and MIT (Boston, Massachusetts, USA ) using the Affymetrix Genome-Wide Human SNP Array 6. 0.
section_name: Korean Association Resource (KARE) was supported by grants from Korea Centers for

section_content: Men (METSIM) was supported by the Academy of Finland (contract 124243 ), the Finnish Heart Foundation, the Finnish Diabetes Foundation, Tekes (contract 1510/31/06 ), and the Commission of the European Community ( HEALTH-F2-2007 201681 ), and the US National Institutes of Health grants DK093757, DK072193, DK062370, and ZIA-HG000024. Michigan Genomics Initiative (MGI) was supported by NIH research grants HL117626 and HG007022. MGI was supported by internal research funds from the University of Michigan School of Public Health, the University of Michigan Medical School, and the University of Michigan President 's Office. MGI are especially grateful to the generosity of all research participants. Nagahama Study (NAGAHAMA) was supported by a university grant, The Center of Innovation Program, The Global University Project, and a Grant-in-Aid for Scientific Research ( 25293141, 26670313, 26293198, 17H04182, 17H04126, 17H04123, 18K18450 ) from the Ministry of Education, Culture, Sports, Science and Technology of Japan, the Practical Research Project for Rare/Intractable Diseases ( ek0109070, ek0109070, ek0109196, ek0109348 ), the Comprehensive Research on Aging and Health Science Research Grants for Dementia R&D ( dk0207006, dk0207027 ), the Program for an Integrated Database of Clinical and Genomic Information (kk0205008), the Practical Research Project for Life-style-related Diseases including Cardiovascular Diseases and Diabetes Mellitus ( ek0210066, ek0210096, ek0210116 ), and the Research Program for Health Behavior Modification by Utilizing IoT ( le0110005 ) from Japan Agency for Medical Research and Development (AMED) ; Takeda Medical Research Foundation, and Mitsubishi Foundation, Daiwa Securities Health Foundation, and Sumitomo Foundation. Netherlands Epidemiology of Obesity (NEO) thanks all individuals who participated in the study, all participating general practitioners for inviting eligible participants and all research nurses for collection of the data. NEO thank the study group, Pat van Beelen, Petra Noordijk and Ingeborg de Jonge for the coordination, lab and data management of the study. Genotyping was supported by the Centre National de Génotypage (Paris, France), headed by Jean-Francois Deleuze. NEO is supported by the participating Departments, the Division and the Board of Directors of the Leiden University Medical Center, and by the Leiden University, Research Profile Area Vascular and Regenerative Medicine. NIDDM-Atherosclerosis Study Hispanic Cohorts (NIDDM) was supported by grant HL055798. 

Northewestern University Genetics (NUGENE) was funded by the Northwestern University's Center for Genetic Medicine, Northwestern University, and Northwestern Memorial Hospital. Samples and data used in this study were provided by the NUgene Project (www. nugene. org). Assistance with phenotype harmonization was provided by the eMERGE Coordinating Center (Grant number U01HG04603 ). This study was funded through the NIH, NHGRI eMERGE Network ( U01HG004609 ). Funding support for genotyping, which was performed at The Broad Institute, was provided by the NIH ( U01HG004424 ). Assistance with phenotype harmonization and genotype data cleaning was provided by the eMERGE Administrative Coordinating Center ( U01HG004603 ) and the National Center for Biotechnology Information (NCBI). The datasets used for the analyses described in this manuscript were obtained from dbGaP at http://www. ncbi. nlm. nih. gov/gapthrough dbGaP accession number phs000237. v1. p1.
section_name: Metabolic Syndrome in

section_content: Vasculature in Uppsala Seniors (PIVUS) was supported by Wellcome Trust Grants ( WT098017, WT064890, WT090532 ), Uppsala University, Uppsala University Hospital, the Swedish Research Council and the Swedish Heart-Lung Foundation. Pakistan Risk of Myocardial Infarction Study (PROMIS) was funded by the Wellcome Trust, UK, and Pfizer (genotyping) and was supported through funds available to investigators at the Center for Non-Communicable Diseases, Pakistan, and the University of Cambridge, UK (fieldwork). Biomarker assays in PROMIS have been funded through grants awarded by the National Institutes of Health ( RC2HL101834 and RC1TW008485 ) and the Fogarty International ( RC1TW008485 ). Prospective Study of Pravastatin in the Elderly at Risk (PROSPER) was supported by an investigator-initiated grant obtained from Bristol-Myers Squibb. Prof. J. W. J. is an Established Clinical Investigator of the Netherlands Heart Foundation (grant 2001 D 032 ). Support for genotyping was provided by the seventh framework program of the European commission (grant 223004 ) and by the Netherlands Genomics Initiative (Netherlands Consortium for Healthy Aging grant 050-060-810 ). Sea Islands Genetic Network Reasons for Geographic and Racial Differences in Stroke (REGARDS) is supported by cooperative agreement U01 NS041588 co-funded by the National Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA), National Institutes of Health, Department of Health and Human Service. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS or the NIA. Additional funding was from R01 DK084350 from the National Institutes of Health. Ragama Health Study (RHS) was supported by a grant from the National Center for Global Health and Medicine (NCGM). Rotterdam Study (RS) are grateful to the participants and staff involved in the study, and the participating general practitioners and pharmacists. RS is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. Shanghai Breast Cancer Study and Shanghai Women's Health Study (SBCS/SWHS) was supported in part by US National Institutes of Health grants R01CA64277 and R01CA124558, as well as Ingram Professorship and Research Reward funds from the Vanderbilt University School of Medicine. We want to thank participants and research staff of the study, Regina Courtney for plasma and DNA sample preparation, and Hui Cai, Ben Zhang and Jing He for data processing and analyses. Singapore Chinese Eye Study (SCES) is supported by the National Medical Research Council (NMRC), Singapore (grants 0796/2003, 1176/2008, 1149/2008, STaR/0003/2008, 1249/2010, CG/SERI/2010, CIRG/1371/2013, and CIRG/1417/2015 ), and Biomedical Research Council (BMRC), Singapore ( 08/1/35/19/550 and 09/1/35/19/616 ). Starr County Health (SCH) was supported by grants from the National Institutes of Health ( DK073541, DK085501, HL102830 and DK116378 ) and funds from the State of Texas. SCH thank the field staff in Starr County for their careful collection of these data and are especially grateful to the participants who so graciously cooperated and gave of their time. Starr County Health Singapore Chinese Health Study (SCHS) was supported by the US National Institutes of Health grants R01DK08072, R01CA144034 and UM1CA182876. Slim Initiative for Genomic Medicine in the Americas (SIGMA) was partially supported by a joint US-Mexico project funded by the Carlos Slim Health Institute. The UNAM/INCMNSZ Diabetes Study was supported by Consejo Nacional de Ciencia y Tecnología grants 138826, 128877, SALUD 2009-01-115250, and a grant from Dirección General de Asuntos del Personal Académico, UNAM, IT 214711. The Mexico City Diabetes Study was supported by the National Heart, Lung and Blood Institute ( ROHL24799 ), the Consejo Nacional de Ciencia y Tecnología (grants 2099, M9303, F671-M9407, 251M, 2005-CO1-14502 and SALUD 2010and SALUD -2-15-1165 )). SIGMA was also supported by funds from the Fundación Carlos Slim (to J. C. F. ).
section_name: Prospective Investigation of the

section_content: Study (SIMES) is supported by the National Medical Research Council (NMRC), Singapore (grants 0796/2003, 1176/2008, 1149/2008, STaR/0003/2008, 1249/2010, CG/SERI/2010, CIRG/1371/2013, and CIRG/1417/2015 ), and Biomedical Research Council (BMRC), Singapore ( 08/1/35/19/550 and 09/1/35/19/616 ). Singapore Indian Eye Study (SINDI) is supported by the National Medical Research Council (NMRC), Singapore (grants 0796/2003, 1176/2008, 1149/2008, STaR/0003/2008, 1249/2010, CG/SERI/2010, CIRG/1371/2013, and CIRG/1417/2015 ), and Biomedical Research Council (BMRC), Singapore ( 08/1/35/19/550 and 09/1/35/19/616 ). Samsung Medical Center (SMC) was supported by a grant from Samsung Biomedical Research Institute. Genotyping of the patients and control subjects from SMC was conducted by Duk-Hwan Kim in the Dept. of Molecular Cell Biology, Sungkyunkwan University School of Medicine, and was supported by a grant from Samsung Biomedical Research Institute. 

Seoul National University Hospital (SNUH) was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare (grant numbers HI15C1595, HI14C0060, HI15C3131 ). Taiwan Metabochip Consortium Zhonghua (TAICHI-G) was supported by grants from: the National Health Research Institutes, Taiwan ( PH-099-PP-03, PH-100-PP-03, and PH-101-PP-03 ); the National Science Council, Taiwan ( NSC 101-2314-B-075A-006-MY3, MOST 104-2314-B-075A-006-MY3, MOST 104-2314-B-075A-007, and MOST 105-2314-B-075A-003 ); and the Taichung Veterans General Hospital, Taiwan ( TCVGH-1020101C, TCVGH-1020102D, TCVGH-1023102B, TCVGH-1023107D, TCVGH-1030101C, TCVGH-1030105D, TCVGH-1033503C, TCVGH-1033102B, TCVGH-1033108D, TCVGH-1040101C, TCVGH-1040102D, TCVGH-1043504C, and TCVGH-1043104B ). TAICHI-G was also supported in part by the National Center for Advancing Translational Sciences ( CTSI grant UL1TR001881 ). Taiwan Type 2 Diabetes (TWT2D) was supported by the GMM Study, Academia Sinica, Taiwan. Danish T2D Case-Control Study ( UCPH ) was undertaken by the Novo Nordisk Foundation Center for Basic Metabolic Research, which is an independent Research Center, based at the University of Copenhagen, Denmark and partially funded by an unconditional donation from the Novo Nordisk Foundation (www. cbmr. ku. dk,Grant number NNF18CC0034900 ). Included study samples were supported by the Danish Research Fund and the National Danish Research Fund (The Vejle Diabetes Biobank), the Velux Foundation, The Danish Medical Research Council and Danish Agency for Science, Technology and Innovation ( Health 2006 ); the Danish Research Council, the Danish Centre for Health Technology Assessment and Novo Nordisk Inc. ( Inter99 ), the Timber Merchant Vilhelm Bang's Foundation and the Danish Heart Foundation ( Health 2008 ), TrygFonden, the Lundbeck Foundation and the Novo Nordisk Foundation ( NNF15OC0015896, DanFunD).
section_name: Singapore Malay Eye

section_name: Scientific Committee

section_content: Pharmaceutical companies: Howard Jacob, Abbvie, Chicago, IL, United States ; Athena Matakidou, Astra Zeneca, Cambridge, United Kingdom; Heiko Runz, Biogen, Cambridge, MA, United States; Sally John, Biogen, Cambridge, MA, United States; Robert Plenge, Celgene, Summit, NJ, United States ; Mark McCarthy, Genentech, San Francisco, CA, United States; Julie Hunkapiller, Genentech, San Francisco, CA, United States; Meg Ehm, GlaxoSmithKline, Brentford, United Kingdom ; Dawn Waterworth, GlaxoSmithKline, Brentford, United Kingdom ; Caroline Fox, Merck, Kenilworth, NJ, United States; Anders Malarstig, Pfizer, New York, NY, United States ; Kathy Klinger, Sanofi, Paris, France ; Kathy Call, Sanofi, Paris, France. 

University of Helsinki and Biobanks: Tomi Mäkelä, HiLIFE, University of Helsinki, Helsinki, Finland; Jaakko Kaprio, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Petri Virolainen, Auria Biobank / University of Turku / Hospital District of Southwest Finland, Turku, Finland; Kari Pulkki, Auria Biobank / University of Turku / Hospital District of Southwest Finland, Turku, Finland ; Terhi Kilpi, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland ; Markus Perola, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland ; Jukka Partanen, Finnish Red Cross Blood Service / Finnish Hematology Registry and Clinical Biobank, Helsinki, Finland ; Anne Pitkäranta, Hospital District of Helsinki and Uusimaa, Helsinki, Finland ; Riitta Kaarteenaho, Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland ; Seppo Vainio, Northern Finland Biobank Borealis / University of Oulu / Northern Ostrobothnia Hospital District, Oulu, Finland ; Kimmo Savinainen, Finnish Clinical Biobank Tampere / University of Tampere / Pirkanmaa Hospital District, Tampere, Finland; Veli-Matti Kosma, Biobank of Eastern Finland / University of Eastern Finland / Northern Savo Hospital District, Kuopio, Finland ; Urho Kujala, Central Finland Biobank / University of Jyväskylä / Central Finland Health Care District, Jyväskylä, Finland. 

Other Experts and Non-Voting Members: Outi Tuovila, Business Finland, Helsinki, Finland; Minna Hendolin, Business Finland, Helsinki, Finland ; Raimo Pakkanen, Business Finland, Helsinki, Finland.

section_content: Pharmaceutical companies: Jeff Waring, Abbvie, Chicago, IL, United States; Bridget Riley-Gillis, Abbvie, Chicago, IL, United States ; Athena Matakidou, Astra Zeneca, Cambridge, United Kingdom; Heiko Runz, Biogen, Cambridge, MA, United States; Jimmy Liu, Biogen, Cambridge, MA, United States; Shameek Biswas, Celgene, Summit, NJ, United States ; Julie Hunkapiller, Genentech, San Francisco, CA, United States; Dawn Waterworth, GlaxoSmithKline, Brentford, United Kingdom; Meg Ehm, GlaxoSmithKline, Brentford, United Kingdom ; Dorothee Diogo, Merck, Kenilworth, NJ, United States; Caroline Fox, Merck, Oncology Group: Heikki Joensuu, Hospital District of Helsinki and Uusimaa, Helsinki, Finland; Tuomo Meretoja, Hospital District of Helsinki and Uusimaa, Helsinki, Finland; Olli Carpén, Hospital District of Helsinki and Uusimaa, Helsinki, Finland; Lauri Aaltonen, Hospital District of Helsinki and Uusimaa, Helsinki, Finland ; Annika Auranen, Pirkanmaa Hospital District, Tampere, Finland ; Peeter Karihtala, Northern Ostrobothnia Hospital District, Oulu, Finland ; Saila Kauppila, Northern Ostrobothnia Hospital District, Oulu, Finland ; Päivi Auvinen, Northern Savo Hospital District, Kuopio, Finland; Klaus Elenius, Hospital District of Southwest Finland, Turku, Finland; Relja Popovic, Abbvie, Chicago, IL, United States; Jeffrey Waring, Abbvie, Chicago, IL, United States; Bridget Riley-Gillis, Abbvie, Chicago, IL, United States; Anne Lehtonen, Abbvie, Chicago, IL, United States ; Athena Matakidou, Astra Zeneca, Cambridge, United Kingdom ; Jennifer Schutzman, Genentech, San Francisco, CA, United States; Julie Hunkapiller, Genentech, San Francisco, CA, United States; Natalie Bowers, Genentech, San Francisco, CA, United States; John Michon, Genentech, San Francisco, CA, United States; Vinay Mehta, Merck, Kenilworth, NJ, United States; Andrey Loboda, Merck, Kenilworth, NJ, United States ; Aparna Chhibber, Merck, Kenilworth, NJ, United States; Heli Lehtonen, Pfizer, New York, NY, United States ; Stefan McDonough, Pfizer, New York, NY, United States ; Marika Crohns, Sanofi, Paris, France ; Diptee Kulkarni, GlaxoSmithKline, Brentford, United Kingdom. Opthalmology Group : Kai Kaarniranta, Northern Savo Hospital District, Kuopio, Finland; Joni Turunen, Hospital District of Helsinki and Uusimaa, Helsinki, Finland ; Terhi Ollila, Hospital District of Helsinki and Uusimaa, Helsinki, Finland; Sanna Seitsonen, Hospital District of Helsinki and Uusimaa, Helsinki, Finland ; Hannu Uusitalo, Pirkanmaa Hospital District, Tampere, Finland ; Vesa Aaltonen, Hospital District of Southwest Finland, Turku, Finland ; Hannele Uusitalo-Järvinen, Pirkanmaa Hospital District, Tampere, Finland ; Marja Luodonpää, Northern Ostrobothnia Hospital District, Oulu, Finland ; Nina Hautala, Northern Ostrobothnia Hospital District, Oulu, Finland; Heiko Runz,Biogen, Cambridge, MA, United States ; Erich Strauss, Genentech, San Francisco, CA, United States; Natalie Bowers, Genentech, San Francisco, CA, United States; Hao Chen, Genentech, San Francisco, CA, United States; John Michon, Genentech, San Francisco, CA, United States; Anna Podgornaia, Merck, Kenilworth,

FinnGen Teams Administration: Anu Jalanko, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Risto Kajanne, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Ulrike Lyhs, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland. Analysis: Mitja Kurki, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland / Broad Institute, Cambridge, MA, United States; Juha Karjalainen, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland / Broad Institute, Cambridge, MA, United States; Pietro della Briotta Parola, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Sina Rüeger, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Arto Lehistö, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland ; Wei Zhou, Broad Institute, Cambridge, MA, United States ; Masahiro Kanai, Broad Institute, Cambridge, MA, United States Clinical Endpoint Development: Hannele Laivuori, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Aki Havulinna, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Susanna Lemmelä, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Tuomo Kiiskinen, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland. Communication: Mari Kaunisto, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland. Data Management and IT Infrastructure: Jarmo Harju, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Elina Kilpeläinen, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Timo P. Sipilä, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Georg Brein, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Oluwaseun A. Dada, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Ghazal Awaisa, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Anastasia Shcherban, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Tuomas Sipilä, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland. Genotyping: Kati Donner, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland. Sample Collection Coordination: Anu Loukola, Helsinki Biobank / Helsinki University and Hospital District of Helsinki and Uusimaa, Helsinki, Finland. Sample Logistics: Päivi Laiho, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland; Tuuli Sistonen, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland ; Essi Kaiharju, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland ; Markku Laukkanen, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland ; Elina Järvensivu, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland ; Sini Lähteenmäki, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland ; Lotta Männikkö, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland; Regis Wong, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland. Registry Data Operations: Hannele Mattsson, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland ; Kati Kristiansson, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland; Susanna Lemmelä, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland ; Tero Hiekkalinna, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland ; Manuel González Jiménez, THL Biobank / The National Institute of Health and Welfare, Helsinki, Finland. Sequencing Informatics: Priit Palta, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Kalle Pärn, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland; Javier Nunez-Fontarnau, Institute for Molecular Medicine Finland, HiLIFE, University of Helsinki, Helsinki, Finland. 

Trajectory Team: Tarja Laitinen, Pirkanmaa Hospital District, Tampere, Finland. Harri Siirtola, University of Tampere, Tampere, Finland. Javier Gracia Tabuenca, University of Tampere, Tampere, Finland.
section_name: Scientific Committee

section_content: Debra Abrams 3, Samuel E Adunyah 4, Ladia Albertson-Junkans 5, Berta Almoguera 6, Darren C Ames 7, Paul Appelbaum 8, Samuel Aronson 9, Sharon Aufox 10, Lawrence J Babb 11, Adithya Balasubramanian 1,12, Hana Bangash 13, Melissa Basford 14, Lisa Bastarache 15, Samantha Baxter 11, Meckenzie Behr 3, Barbara Benoit 16, Elizabeth Bhoj 3, Suzette J Bielinski 17, Sarah T Bland 15, Carrie Blout 18, Kenneth Borthwick 19, Erwin P Bottinger 20, Mark Bowser 21, Harrison Brand 22, Murray Brilliant 23, Wendy Brodeur 24, Pedro Caraballo 25, David Carrell 5, Andrew Carroll 26, Lisa Castillo 27, Victor Castro 28, Gauthami Chandanavelli 1, Theodore Chiang 29, Rex L Chisholm 30, Kurt D Christensen 31, Wendy Chung 32, Christopher G Chute 33, Brittany City 14, Beth L Cobb 34, John J Connolly 3, Paul Crane 35, Katherine Crew 36, David R Crosslin 37, Jyoti Dayal 38, Mariza De Andrade 17, Jessica De la Cruz 1, 12, Josh C Denny 39, Shawn Denson 1,2, Tim DeSmet 11, Ozan Dikilitas 13, Michael J Dinsmore 11, Sheila Dodge 11, Phil Dunlea 11, Todd L Edwards 40, Christine M Eng 12, David Fasel 41, Alex Fedotov 42, Qiping Feng 43, Mark Fleharty 11, Andrea Foster 1,2, Robert Freimuth 44, Christopher Friedrich 11, Stephanie M Fullerton 45, Birgit Funke 46, Stacey Gabriel 24, Vivian Gainer 47, Ali Gharavi 48, Richard A Gibbs 1,12, Andrew M Glazer 49, Joseph T Glessner 50, Jessica Goehringer 51, Adam S Gordon 52, 53, Chet Graham 54, Robert C Green 55, Justin H Gundelach 13, Heather S Hain 56, Hakon Hakonarson 57, Maegan V Harden 24, John Harley 58, Margaret Harr 59, Andrea Hartzler 60, M Geoffrey Hayes 61, Scott Hebbring 62, Nora Henrikson 63, Andrew Hershey 64, Christin Hoell 30, Ingrid Holm 65, Kayla M Howell 14, George Hripcsak 41, 66, Jianhong Hu 1, Elizabeth Duffy Hynes 21, Gail P Jarvik 52, 67, Joy C Jayaseelan 1, Yunyun Jiang 1,12, Yoonjung Yoonie Joo 68, Sheethal Jose 38, Navya Shilpa Josyula 69, Anne E Justice 70, Sara E Kalla 1, Divya Kalra 1, Elizabeth W Karlson 71, Brendan J Keating 72, Melissa A Kelly 73, Eimear E Kenny 74, Dustin Key 5, Krzysztof Kiryluk 75, Terrie Kitchner 23, Barbara Klanderman 76, Eric Klee 77, David C Kochan 78, Viktoriya Korchina 1, Leah Kottyan 79, Christie Kovar 1, Emily Kudalkar 54, Alanna Kulchak Rahm 80, Iftikhar J Kullo 81, Philip Lammers 82, Eric B Larson 83, Matthew S Lebo 84, Magalie Leduc 85, Ming Ta Lee 86, Niall J Lennon 24, Kathleen A Leppig 87, Nancy D Leslie 88, Rongling Li 89, Wayne H Liang 90, Chiao-Feng Lin 91, Jodell E Linder 14, Noralane M Lindor 92, Todd Lingren 93, James G Linneman 23, Cong Liu 94, Wen Liu 1, Xiuping Liu 1, John Lynch 95, Hayley Lyon 96, Alyssa Macbeth 97, Harshad Mahadeshwar 1, Lisa Mahanta 98, Bradley Malin 99, Teri Manolio 38, Maddalena Marasa 100, Keith Marsolo 101, Michelle L McGowan 102, Elizabeth McNally 53, Jim Meldrim 24, Frank Mentch 3, Hila Milo Rasouly 103, Jonathan Mosley 104, Shubhabrata Mukherjee 35, Thomas E Mullen 24, Jesse Muniz 1, David R Murdock 1,12, Shawn Murphy 105, Mullai Murugan 106, Donna Muzny 107, Melanie F Myers 108, Bahram Namjou 34, 109, Yizhao Ni 110, Robert C Onofrio 24, Aniwaa Owusu Obeng 111, 112, Thomas N Person 113, Josh F Peterson 114, Lynn Petukhova 115, Cassandra J Pisieczko 116, Siddharth Pratap 117, Cynthia A Prows 118, Megan J Puckelwartz 119, Ritika Raj 1, James D Ralston 120, Arvind Ramaprasan 5, Andrea Ramirez 121, Luke Rasmussen 122, Laura Rasmussen-Torvik 123, Soumya Raychaudhuri 124, Heidi L Rehm 125, Marylyn D Ritchie 126, Catherine Rives 127, Beenish Riza 128, Dan M Roden 129, Elisabeth A Rosenthal 130, Avni Santani 131, Dan Schaid 17, Steven Scherer 1,12, Stuart Scott 132, Aaron Scrol 133, Soumitra Sengupta 134, Ning Shang 41, Himanshu Sharma 135, Richard R Sharp 136, Rajbir Singh 137, Patrick M A Sleiman 138, Kara Slowik 139, Joshua C Smith 140, Maureen E Smith 141, Duane T Smoot 142, Jordan W Smoller 143, Sunghwan Sohn 144, Ian B Stanaway 37, Justin Starren 145, Mary Stroud 15, Jessica Su 146, Casey Overby Taylor 147, Kasia Tolwinski 148, Sara L Van Driest 149, 150, Sean M Vargas 151, Matthew Varugheese 152, David Veenstra 153, Eric Venner 1,12, Miguel Verbitsky 154, Gina Vicente 155, Michael Wagner 156, Kimberly Walker 157, Theresa Walunas 158, Liwen
section_name: Contributors to eMERGE Consortium

section_name: Ethics statements

section_content: Anti-aging study cohort (AASC). The ethics committees of Ehime University Graduate School of Medicine approved all study procedures. Written informed consent was obtained from all participants. 

Atherosclerosis Risk in Communities (ARIC). Institutional Review Board approvals were obtained at all study sites: National Heart, Lung, and Blood Institute, University of North Carolina at Chapel Hill, Wake Forest Baptist Medical Center, University of Mississippi Medical Center, University of Minnesota and Johns Hopkins University. All participants provided written informed consent. 

Biobank Japan (BBJ). All participants provided written informed consent as approved by the ethical committees of the RIKEN Yokohama Institute and the Institute of Medical Science, University of Tokyo. 

Beijing Eye Study (BES). Approval was obtained from the Medical Ethics Committee of the Beijing Tongren Hospital. All participants gave written informed consent.
section_name: Ethics statements

section_content: Approval was obtained from the Institutional Review Board at the Icahn School of Medicine at Mount Sinai. All participants provided written informed consent for genomic data sharing.
section_name: BioMe Biobank (BIOME).

section_content: The conduct of the BPC was reviewed and approved by Ethical Committees of the Bangladesh Medical Research Council and Institutional Review Boards of the University of Chicago.
section_name: Bangladesh Population Cohort (BPC).

section_content: Approval was obtained from the Institutional Review Boards at the National Center for Global Health and Medicine. All participants provided written informed consent.
section_name: Cardiometabolic Genome Epidemiology (CAGE-AMAGASKI and CAKE-GWAS).

section_content: Approval was obtained from the ethics committees of Aichi Gakuin University, Jichi Medical University, Nagoya University and Kyushu University. All participants provided written informed consent.
section_name: Cardiometabolic Genome Epidemiology (CAGE-KING).

section_content: Participating centers (Northwestern University, University of Alabama Birmingham, University of Minnesota, and Kaiser Foundation Research Institute) provided ethics approval for the CARDIA study, and all participants provided written informed consent to participate.
section_name: Coronary Artery Risk Development in Young Adults (CARDIA).

section_content: Approval was obtained from the Institutional Review Board of Mass General Brigham (formerly Partners HealthCare). Written informed consent was obtained from all participants. 

China Health and Nutrition Survey (CHNS). Approval was obtained from the Institutional review Boards at the University of North Carolina at Chapel Hill, the Chinese National Human Genome Center at Shanghai, and the Institute of Nutrition and Food Safety at the China Centers for Disease Control. All participants provided written informed consent.
section_name: Cleveland Family Study (CFS).

section_content: Approval was obtained from the Institutional Review Boards at Wake Forest University, University of California, Davis, Johns Hopkins, University of Pittsburgh, and the University of Washington, Seattle. All participants provided written informed consent.
section_name: Cardiovascular Health Study (CHS).

section_name: China Kadoorie Biobank (CKB

section_content: The study was approved by the Icelandic National Bioethics Committee (approval no. VSN-16-112) after evaluation by the Icelandic Data Protection Authority. We obtained written informed consent for all participants in this study who donated samples. All data processing complies with the Icelandic Data Protection Authority (no. PV_2017060950ÞS).
section_name: deCODE genetics (DECODE).

section_content: All participants signed informed consent, and the protocol was approved by the French ethics committee.
section_name: Diabetes Gene Discovery Group (DGDG).

section_content: The study was approved by the Ethics Committees of the Helsinki University Hospital, Helsinki, Finland, and Lund University, Sweden.
section_name: Diabetes Genetics Initiative (DGI).

section_content: All analyses were approved by the Ethics Review Committee of the University of Tartu. All participants provided written informed consent. Framingham Heart Study (FHS). Approval was obtained from the Institutional review Board of Boston University Medical Campus. All study participants provided written informed consent.
section_name: Estonian Genome Center of the University of Tartu (EGCUT).

section_name: Electronic Medical Records and Genomics Network (EMERGE

section_content: Approval was obtained from the coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa. All participants provided written informed consent.
section_name: Finland-United States Investigation of NIDDM Genetics (FUSION).

section_content: All participants provided written informed consent. The study was registered in the national registry for clinical studies (DRKS 00003971) and was approved by local ethics committees.
section_name: German Chronic Kidney Disease (GCKD).

section_content: Approval was obtained from the Johns Hopkins Medicine Institutional Review Board. All participants gave written informed consent.
section_name: Genetic Study of Atherosclerosis Risk (GENESTAR).

section_content: Institutional Review Boards of the University of Michigan, University of Mississippi Medical Center and Mayo Clinic. Written informed consent was obtained from all participants. 

Resource for Genetic Epidemiology on Adult Heath and Aging (GERA). The Institutional Review Boards for Human Subjects Research of both Kaiser Permanente Medical Care Plan (Northern California Region) and the University of California at San Francisco approved the project.
section_name: Genetic Epidemiology Network of Arteriosclerosis (GENOA). Approval was granted by

section_content: Research in Tayside Scotland (GODARTS). Approval was obtained from the Tayside Medical Ethics Committee. Informed consent was obtained for all participants.
section_name: Genetics of Diabetes and Audit

section_content: Approval was granted by the Institutional Review Board of the Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center. 

Genetic Overlap Between Metabolic and Psychiatric Traits and Teens of Attica: Genes and Environment (GOMAP-TEENAGE). Ethical permission for TEENAGE was obtained from the Bioethics Committee of Harokopio University, Athens. Ethical permission for GOMAP was obtained from the Dromokaiteio Scientific Committee, Dromokaiteio Management Committee, Dafni Scientific Committee, Eginitio Scientific Committee and Harokopio Ethics Committee. All participants of GOMAP-TEENAGE gave written informed consent.
section_name: Genetics of Latinos Diabetic Retinopathy (GOLDR).

section_content: Centre for Cellular and Molecular Biology and KEM Hospital and Research Centre approved the project.
section_name: Genomic Research Cohort for CCMB Diabetes Study (GRCCDS). Ethics committees of CSIR-

section_content: The Institutional Review Boards at the University of Memphis and the University of Pittsburgh granted approval to conduct the Health ABC Study, and all participants provided written informed consent.
section_name: Health, Aging and Body Composition Study (HABC).

section_content: Approval was granted by the National Institutes of Health Institutional Review Board (study number 09AGN248). All participants provided written informed consent.
section_name: Healthy Aging in Neighborhoods of Diversity Across the Life Span Study (HANDLS).

section_content: Approval was obtained from Institutional Review Boards at the University of North Carolina at Chapel Hill, Albert Einstein College of Medicine, University of Illinois at Chicago, University of Miami, and San Diego State University. All participants provided written informed consent.
section_name: Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

section_content: Approval was obtained from the Chinese University of Hong Kong Clinical Research Ethics Committee.
section_name: Hong Kong Diabetes Registry (HKDR).

section_content: Research Committee at the Brigham and Women's Hospital. All participants provided written informed consent.
section_name: Health Professionals' Follow-Up Study (HPFS). Approval was obtained from the Human

section_content: Human Subjects Protection Institutional Review Boards at the University of California at Los Angeles, University of Southern California, Lundquist/LABioMed/Harbor-UCLA and Cedars-Sinai Medical Center.
section_name: Mexican American Hypertension and Insulin Resistance (HTNIR). Approval was granted by

section_content: All human participants from the HUFS included in the analyses of this manuscript provided written informed consent prior to enrollment. The HUFS study was approved by the Institutional Review Board at Howard University.
section_name: Howard University Family Study (HUFS).

section_content: Committees of All India Institute of Medical Sciences, New Delhi and CSIR-Institute of Genomics and Integrative Biology, New Delhi, India, and was conducted in accordance with the principles of Helsinki Declarations. Informed written consent was obtained from all of participants.
section_name: Indian Diabetes Consortium (INDICO). Approval was obtained by the Human Ethics

section_content: All study participants consented to analysis of blood samples. Approval was granted by the Hamilton Integrated Research Ethics Board, at McMaster University, Hamilton, Canada.
section_name: INTERHEART (INTERHEART).

section_name: Jackson Heart Study (JHS

section_content: Approval was obtained from Institutional Review Boards at the Ethics and Scientific Commission members and the AUTHORIZATION is issued with registration number R-2011-785-018 and the Conacyt SALUD-2010-02-150352. In Canada, approval was obtained from the Research Ethics Board from the University of Toronto (Protocol 15770).
section_name: Mexico City (MC).

section_content: Review Boards at the University of Washington, Wake Forest School of Medicine, Northwestern University, University of Minnesota, Columbia University, Johns Hopkins University, Cedars-Sinai Medical Center, and the University of California at Los Angeles.
section_name: Multi-Ethnic Study of Atherosclerosis (MESA). Approval was obtained from Institutional

section_content: Approval was granted by the Ethics Committee of the University of Kuopio and the Kuopio University Hospital. All participants gave written informed consent.
section_name: Metabolic Syndrome in Men (METSIM).

section_content: The MGB Biobank protocol and informed consent documents are reviewed annually by the Partners-MGB Institutional Review Board (#2009P002312). All patients who participate in the MGB Biobank are consented for their samples to be linked to their identified clinical information. They have also consented for their information to be used for a broad range of research and for their deidentified information to be shared outside of MGB.
section_name: Mass General Brigham Biobank (MGB).

section_content: Approval was granted by the IRBMED Institutional Review Board of the University of Michigan. All participants gave written informed consent. 

Nagahama Study (NAGAHAMA). Approval was granted by the ethics committees of Kyoto University Graduate School of Medicine. Written informed consent was obtained from all participants.
section_name: Michigan Genomics Initiative (MGI).

section_content: Approval was obtained from the Medical Ethics Committee of Leiden University Medical Center. All participants gave written informed consent.
section_name: Netherlands Epidemiology of Obesity (NEO).

section_content: Approval was obtained from the Human Research Committee at the Brigham and Women's Hospital. All participants provided written informed consent.
section_name: Nurses Health Study (NHS).

section_name: NIDDM-Atherosclerosis Study Hispanic Cohorts (NIDDM). Approval was granted by

section_content: The REGARDS study protocol was approved by the institutional review boards of each participating institution, and written informed consents were obtained from all participants. 

Ragama Health Study (RHS). Approval was obtained from Institutional Review Boards at the National Center for Global Health and the University of Kelaniya (P38/09/2006). All participants provided written informed consent.
section_name: Sea Islands Genetic Network Reasons for Geographic and Racial Differences in Stroke (REGARDS).

section_content: Approval was granted by the Institutional review Board at Erasmus University Medical Center. All participants provided written informed consent.
section_name: Rotterdam Study (RS).

section_content: Approval was obtained from Institutional review Boards at Vanderbilt University Medical Center and Shanghai Cancer Institute. A written informed consent form was obtained from all study participants.
section_name: Shanghai Breast Cancer Study and Shanghai Women's Health Study (SBCS/SWHS).

section_content: The study adhered to the Declaration of Helsinki. Ethical approval was obtained from the SingHealth Institutional Review Board and National University of Singapore Institutional Review Board. Written informed consent was obtained from all participants. 

Starr County Health (SCH). All protocols were reviewed and approved by the Institutional Committee for the Protection of Human Subjects (HSC-SPH-02-042). All participants provided written informed consent permitting the collection and sharing of data.
section_name: Singapore Chinese Eye Study (SCES).

section_content: Approval was obtained from the Institutional Review Board at the National University of Singapore. All participants provided written informed consent. 

Slim Initiative for Genomic Medicine in the Americas (SIGMA). Approval was obtained from the Institutional Review Board of the Instituto Nacional de Ciencas Medicas y Nutricion Salvador Zubiran. All participants provided written informed consent.
section_name: Singapore Chinese Health Study (SCHS).

section_content: The study adhered to the Declaration of Helsinki. Ethical approval was obtained from the SingHealth Institutional Review Board and National University of Singapore Institutional Review Board. Written informed consent was obtained from all participants.
section_name: Singapore Malay Eye Study (SIMES).

section_content: The study adhered to the Declaration of Helsinki. Ethical approval was obtained from the SingHealth Institutional Review Board and National University of Singapore Institutional Review Board. Written informed consent was obtained from all participants.
section_name: Singapore Indian Eye Study (SINDI).

section_content: Approval was obtained from the Institutional Review Board of the Samsung Medical Center (No. 2004-12-005). All participants provided written informed consent. FinnGen. Patients and control subjects in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, separate research cohorts, collected prior the start of FinnGen (August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Fimea, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Fimea. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS),697,132bp to 3,358,546bp (build 37). Supplementary Figure 3. Signal plots for distinct T2D association signals at the TCF7L2 locus from multi-ancestry meta-regression (MR-MEGA) of up to 180,834 cases and 1,159,055 controls. Association summary statistics for each signal are obtained from approximate conditioning after adjusting for all other index SNVs at the locus. Each point represents a SNV passing quality control in the multi-ancestry meta-regression, plotted with their conditional p-value (on a -log 10 scale) as a function of genomic position (NCBI build 37). In each plot, the index variant is represented by the purple diamond. Gene annotations are taken from the University of California Santa Cruz genome browser. Recombination rates are estimated from the Phase II HapMap. Supplementary Note Table 5. Comparison of Hispanic ancestry-specific association summary statistics obtained from approximate conditional analysis undertaken in loci with multiple distinct signals using two LD reference panels: 347 individuals of American ancestry from the 1000 Genomes Project; and 1,000 Hispanic individuals from GERA.
section_name: Samsung Medical Center (SMC).

section_name: Seoul National University Hospital (SNUH). The Institutional Review