Genetic drivers of heterogeneity in type 2 diabetes pathophysiology In the format provided by the authors and unedited Nature | www.nature.com/nature Supplementary information https://doi.org/10.1038/s41586-024-07019-6 Genetic drivers of heterogeneity in type 2 diabetes pathophysiology Ken Suzuki, Konstantinos Hatzikotoulas, Lorraine Southam, Henry J. Taylor, Xianyong Yin, Kim M. Lorenz, Ravi Mandla, et al. SUPPLEMENTARY INFORMATION Supplementary Note Supplementary Text Supplementary Methods Acknowledgements and Funding Ethics Statements VA Million Veteran Program: Core Acknowledgement for Publications Contributors to AMED GRIFIN Diabetes Initiative Japan Contributors to Biobank Japan Project Penn Medicine BioBank Banner Author List and Contribution Statements Regeneron Genetics Center Banner Author List and Contribution Statements Genes & Health Research Team Contributors to eMERGE Consortium Membership of the International Consortium of Blood Pressure Membership of the Meta-Analyses of Glucose and Insulin-Related Traits Consortium Supplementary Figures Supplementary Text Summary of loci identified through recent large-scale multi-ancestry meta-analyses. Two recent partially overlapping multi-ancestry meta-analyses of T2D GWAS together account for 69.3% of the total effective sample size of the multi-ancestry meta-regression undertaken by the T2D Global Genomics Initiative (Supplementary Figure 1). First, the meta-analysis of GWAS from the Million Veteran Program1, which includes 228,499 T2D cases and 1,178,783 controls. Second, the meta-analysis of GWAS from the DIAMANTE Consortium2, which includes 180,834 cases and 1,159,055 controls. We aimed to provide a comprehensive overview of the genetic contribution to T2D by summarising loci reported in these multi- ancestry GWAS meta-analyses at the conventional genome-wide significance threshold (P<5x10-8) and a more stringent multi-ancestry genome-wide significance threshold (P<5x10-9) proposed by the DIAMANTE Consortium. We aggregated loci reported in each of the three meta-analyses, ensuring no overlap between adjacent loci. Taken together, the three studies report 636 non-overlapping loci spanning 835.5Mb, of which 536 (84.3%) meet stringent multi-ancestry genome-wide significance in at least one of the multi-ancestry meta-analyses (Supplementary Table 25). We investigated the likelihood that loci reported at the conventional genome-wide significance threshold by the DIAMANTE Consortium meet the more stringent multi-ancestry threshold in the larger sample size afforded by the T2D Global Genomics Initiative. We focussed on comparing results from these two efforts because both used the same meta- regression approach (MR-MEGA) to aggregate association summary statistics across GWAS. Of 39 loci with association signals meeting 5x10-9≀P<5x10-8 reported by the DIAMANTE Consortium, 36 (92.3%) attained multi-ancestry genome-wide significance in the T2D Global Genomics Initiative (Supplementary Table 25). Of the three loci that did not meet the more stringent threshold, the signal at the RASA1 locus was marginally more strongly associated (lead SNV rs11953892, P=1.6x10-8 versus P=1.9x10-8) in the T2D Global Genomics Initiative meta-analysis than in the DIAMANTE Consortium meta-analysis. However, association signals at the two remaining loci were weaker in the T2D Global Genomics Initiative than in the DIAMANTE Consortium, despite the increase in sample size. At the locus encompassing CCDC39 and FXR1, the association signal was nominally significant in the Million Veteran Program (lead SNV rs4854992, P=0.0081) with the same direction of effect as in the DIAMANTE Consortium meta-analysis. However, at the CFAP6 locus, there was no association in the Million Veteran Program (lead SNV rs7261425, P=0.13). Taken together, these results indicate that index SNVs attaining the conventional threshold of P<5x10-8 are unlikely to be false positive association signals but have modest effects that require larger sample sizes to meet multi-ancestry genome-wide significance. Clusters are differentially associated with insulin-related endophenotypes. We assessed the association of index SNVs with insulin-related endophenotypes that were not used for clustering and derived from: hyperinsulinemic-euglycemic clamp assessments and oral glucose tolerance tests (OGTT) in up to 1,316 Mexican American participants without diabetes from the GUARDIAN Consortium3; and homeostatic model assessment measures of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 36,466 non-diabetic EUR individuals from MAGIC4 (Supplementary Methods). We observed significant heterogeneity in the effects of T2D risk alleles at index SNVs between clusters on HOMA-B (PHET<2.2x10-16), HOMA-IR (PHET=4.1x10-15), insulin secretion (OGTT-derived area under the curve for insulin normalised for glucose from baseline to 30 minutes, PHET=0.0026), and insulin sensitivity (clamp-derived glucose infusion rate, PHET=0.026). T2D risk alleles at index SNVs showed a gradient of effects on these correlated measures across clusters (Extended Data Figure 4, Supplementary Tables 10 and 11), representing a cline from insulin production and processing in the two beta-cell dysfunction clusters (increased insulin sensitivity; decreased insulin secretion, HOMA-B, and HOMA-IR) through to insulin resistance (decreased insulin sensitivity; increased insulin secretion, HOMA-B, and HOMA-IR) that was most extreme in the lipodystrophy cluster. Clusters are differentially associated with insulin resistance-related disorders. To understand the shared biological pathways driving genetic correlations with gestational diabetes mellitus (GDM) and polycystic ovary syndrome (PCOS), we extracted association summary statistics for each T2D index SNV from the largest available published GWAS for both disorders5,6 (Supplementary Methods). We observed significant heterogeneity in the effects of T2D risk alleles at index SNVs between clusters for both disorders (Extended Data Figure 5, Supplementary Table 12): GDM (PHET=7.0x10-16) and PCOS (PHET=0.00022). Index SNVs in the beta-cell +PI cluster demonstrated the strongest associations with GDM. This cluster includes T2D index SNVs that overlap with association signals previously reported for GDM, mapping to/near MTNR1B, CDKAL1, TCF7L2, and CDKN2A-CDKN2B, consistent with hyperglycaemia due to beta-cell dysfunction on a background of pregnancy-induced physiologic insulin resistance7. In contrast, PCOS is most strongly associated with index SNVs in the obesity cluster, consistent with previous Mendelian randomization studies that report a strong causal effect of higher BMI on increased PCOS risk8. Cluster-specific associations of index SNVs with circulating GLP-1 concentrations. The beta- cell -PI cluster was enriched in adult enterochromaffin cells, a type of enteroendocrine cell that plays an essential role in regulating intestinal motility and secretion in the gastrointestinal tract9. Enterochromaffin cells are a major target for GLP-1 and highly express GLP-1 receptor, whose agonists are widely used as medications for T2D10. Between clusters, we compared the associations of index SNVs with 2-hour and fasting circulating GLP-1 concentrations in up to 3,514 EUR individuals from the Malmo Diet and Cancer Study11 and the PPP-Botnia Study12 (Supplementary Methods). Whilst differences in the effects of index SNVs on these measures were not significant between clusters (P>0.05), T2D risk alleles for index SNVs in the beta-cell -PI cluster showed a trend of association with decreased 2-hour GLP-1, whilst those in other clusters showed a trend of association with increased fasting GLP-1 (Supplementary Figure 13). Additional analyses in GLP-1 GWAS with larger sample sizes will be required to validate this finding. T2D association signals are differentially enriched for ancestry-correlated heterogeneity across mechanistic clusters. To understand better the impact of genetic diversity on differences in allelic effects between GWAS at T2D association signals, we assessed the contribution of each of the three axes of genetic variation to heterogeneity (Methods). For 118 (92.9%) of the 127 association signals with significant evidence of ancestry-correlated heterogeneity, allelic effect sizes were most strongly associated with the first two axes of genetic variation (Extended Data Figure 1, Supplementary Table 16). This may simply reflect greater power to detect heterogeneity because these two axes separate GWAS from the three ancestry groups (AFA, EAS, and EUR) that make the largest contributions to the effective sample size of the multi-ancestry meta-analysis. The magnitude and direction of the association of index SNVs with these two axes reflected differences in allelic effect size between AFA/EUR and EAS GWAS on the AFA-EAS axis, and AFA/EAS and EUR GWAS on the AFA-EUR axis (Extended Data Figure 6). For example, the T2D association signal indexed by rs7766070 at the CDKAL1 locus was positively associated with the AFA-EAS axis (P=4.2x10-14), but not the AFA-EUR axis (P=0.74) and is therefore characterised by a larger allelic effect in EAS GWAS than in AFA and EUR GWAS. On the other hand, at the locus encompassing CILP2, CRTC1, and TM6SF2, the T2D association signal indexed by rs8107974 has a larger allelic effect in EUR GWAS than in AFA and EAS GWAS, consistent with a positive association with the AFA-EUR axis (P=3.7x10-10), but not the AFA-EAS axis (P=0.72). The most significant evidence of ancestry-correlated heterogeneity was observed for the T2D association signal at the HNF1A locus indexed by rs1169299 (PHET=4.8x10-35). This index SNV was negatively associated with the AFA-EAS axis (PHET=2.7x10-11), and positively associated with the AFA-EUR axis (PHET=4.6x10-9), corresponding to an AFA allelic effect (OR=1.02) that was intermediate between the EAS and EUR allelic effects (OR=0.95 and OR=1.05, respectively). In contrast, the association signal indexed by rs2237884, at the locus encompassing INS, IGF2, and KCNQ1, was not associated with either the AFA-EAS axis (PHET=0.61) or AFA-EUR axis (PHET=0.56), indicating no difference in allelic effects between AFA, EAS, and EUR GWAS (OR=1.03 for all three ancestry groups). Instead, the heterogeneity for this signal was driven by association with the third axis of genetic variation (PHET=2.8x10-8), which separates HIS and SAS GWAS (OR=1.09 and OR=0.97, respectively). We investigated whether the observed ancestry-correlated differences in allelic effects on T2D between ancestry groups varied across mechanistic clusters. To do this, we compared the magnitude and direction of association of index SNVs in each cluster with the first three axes of genetic variation (Methods). We observed significant differences in mean Z-scores for association between clusters for both the AFA-EAS axis (P=4.1x10-6) and the AFA- EUR axis (P=1.5x10-6), but not for the HIS-SAS axis (P=0.17), reflecting at least in part differences in sample size and therefore statistical power. Index SNVs in the two beta-cell clusters were most positively associated with the AFR-EAS axis, indicating allelic effects on T2D that were greater in EAS than in AFA and EUR GWAS (Extended Data Figure 7, Supplementary Table 17). In contrast, index SNVs in the lipodystrophy and obesity clusters were most positively associated with the AFA-EUR axis, indicating allelic effects on T2D that were greater in EUR GWAS than in EAS/AFA GWAS. Impact of BMI on ancestry-correlated heterogeneity between GWAS. To investigate the impact of ancestry-correlated heterogeneity in allelic effects between GWAS, we extended the MR-MEGA meta-regression model to account for mean BMI in T2D cases and controls, in addition to axes of genetic variation (Methods). After adjustment for study-level mean BMI in T2D cases and in controls, only 24 association signals retained significant evidence of ancestry-correlated heterogeneity (P<3.9x10-5), compared with 127 signals without adjustment (Supplementary Table 18). For example, at the HNF1A locus, the ancestry- correlated heterogeneity at the T2D association indexed by rs1169299 was attenuated after BMI adjustment (P=0.00016 versus P=4.8x10-35 without adjustment), which is consistent with the assignment of this signal to the beta-cell -PI cluster. In contrast, at the association signal indexed by rs2237884, at the locus encompassing INS, IGF2, and KCNQ1, which was assigned to the body fat cluster, ancestry-correlated heterogeneity was not meaningfully impacted by BMI adjustment (P=5.0x10-7 versus P=2.7x10-7 without adjustment). After adjustment for BMI, significant differences in mean Z-scores for association between clusters for the AFA-EUR axis were maintained (P=3.2x10-5 versus P=1.5x10-6 without adjustment), whilst those for the AFA-EAS axis were not (P=0.18 versus P=4.1x10-6 without adjustment). Furthermore, after adjustment for BMI, the two beta-cell clusters were no longer strongly positively associated with the AFA-EAS axis (Extended Data Figure 7, Supplementary Table 19). 1. Vujkovic, M. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet 52, 680-691 (2020). 2. Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 54, 560-572 (2022). 3. Palmer, N. D. et al. Genetic variants associated with quantitative glucose homeostasis traits translate to type 2 diabetes in Mexican Americans: The GUARDIAN (Genetics Underlying Diabetes in Hispanics) Consortium. Diabetes 64, 1853-1866 (2015). 4. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42, 105-116 (2010). 5. Day, F. et al. Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria. PLoS Genet 14, e1007813 (2018). 6. Pervjakova, N. et al. Multi-ancestry genome-wide association study of gestational diabetes mellitus highlights genetic links with type 2 diabetes. Hum Mol Genet 31, 3377-3391 (2022). 7. Plows, J. F., Stanley, J. L., Baker, P. N., Reynolds, C. M. & Vickers, M. H. The Pathophysiology of Gestational Diabetes Mellitus. Int J Mol Sci 19, 3342 (2018). 8. Zhu, T. & Goodarzi, M. O. Causes and Consequences of Polycystic Ovary Syndrome: Insights From Mendelian Randomization. J Clin Endocrinol Metab 107, e899-e911 (2022). 9. Bertrand, P. P. & Bertrand, R. L. Serotonin release and uptake in the gastrointestinal tract. Auton Neurosci 153, 47-57 (2010). 10. Lund, M. L. et al. Enterochromaffin 5-HT cells - A major target for GLP-1 and gut microbial metabolites. Mol Metab 11, 70-83 (2018). 11. Rosvall, M. et al. Risk factors for the progression of carotid intima-media thickness over a 16-year follow-up period: the Malmo Diet and Cancer Study. Atherosclerosis 239, 615-621 (2015). 12. Isomaa, B. et al. A family history of diabetes is associated with reduced physical fitness in the Prevalence, Prediction and Prevention of Diabetes (PPP)-Botnia study. Diabetologia 53, 1709-1713 (2010). Supplementary Methods Cluster-specific associations of index SNVs with insulin-related endophenotypes and insulin resistance-related disorders. We extracted association summary statistics for measures of glucose homeostasis derived from hyperinsulinemic-euglycemic clamp assessments and oral glucose tolerance tests (OGTT) performed by the GUARDIAN Consortium1, which were obtained from GWAS undertaken in up to 1,316 non-diabetic Mexican American participants from the Mexican American Coronary Artery Disease (MACAD) study2 and the Hypertension and Insulin Resistance (HTN-IR) study3. The measures used were: insulin sensitivity (clamp-derived glucose infusion rate in 1,316 participants from MACAD and HTN-IR); insulin clearance (clamp-derived metabolic clearance rate of insulin in 1,261 participants from MACAD and HTN-IR); and insulin secretion (OGTT-derived area under the curve for insulin normalised for glucose from baseline to 30 minutes in 513 participants from MACAD). We also extracted association summary statistics for homeostatic model assessment measures of beta-cell function (HOMA-B) and insulin resistance (HOMA- IR) from published GWAS meta-analyses of up to 36,466 non-diabetic European ancestry individuals from MAGIC4. We also extracted association summary statistics for insulin resistance-related disorders from published GWAS meta-analyses of: (i) 5,485 GDM cases and 347,856 female controls of diverse ancestry from the GenDIP Consortium5; and (ii) 10,074 PCOS cases and 103,164 female controls of European ancestry6. For each endophenotype/disorder, we aligned the effect estimate to the T2D risk allele from the fixed-effects multi-ancestry meta-analysis, denoted đ›œđ‘— for the 𝑗th index SNV. We then calculated the Z-score, given by 𝑍𝑗 = đ›œđ‘— 𝑠𝑗⁄ , where 𝑠𝑗 is the standard error of the effect estimate of the 𝑗th index SNV. We tested for association of each endophenotype with index SNVs across clusters in a linear regression model, given by 𝐾(𝑍𝑗) = ∑ đ›Ÿđ‘˜đ¶đ‘—đ‘˜đ‘˜ , where đ¶đ‘—đ‘˜ is an indicator variable that takes the value “1” if the 𝑗th index SNV was assigned to the 𝑘th cluster and “0” otherwise. We tested for heterogeneity in cluster effects on each endophenotype by comparing the deviance of this model with that of 𝐾(𝑍𝑗) = đ›Ÿ0. Regression models were fitted using the glm function in R. Cluster-specific associations of index SNVs with circulating GLP-1 concentrations. The Malmo Diet and Cancer Study (MDCS) is a prospective population-based cohort study that includes 31088 men and women aged 44 to 74 who completed a baseline examination between 1991 and 1996 and lived in Malmo7. A random subset was invited to a reinvestigation starting in 2007, where GLP-1 was measured8. Individuals with diabetes were excluded from the analysis. An overnight fast was followed by the administration of 75g OGTT for diabetes free individuals. Blood samples were analyzed for GLP-1 concentrations at 0 and 120 minutes. Total plasma GLP-1 concentrations, including intact GLP-1 and the metabolite GLP-1 9-36 amide, were determined radioimmunologically with an in-house anti- serum (no. 89390; sensitivity <1 pmol/l)9,10. The Prevalence, Prediction and Prevention of type 2 diabetes (PPP)-Botnia Study is a population-related study that began in 2004 in Finland. Participants were randomly selected from the National Finnish Population Registry, representing 6%-7% of the 18-75 age population. Of the original 5,208 participants, 3,850 (77%) attended the first follow-up study in 2011-2015, where GLP-1 was measured11. A 75g OGTT was conducted after overnight 10- 12 hours fasting with blood samples drawn at 0, 30, and 120 minutes. GLP-1 was measured at 0 and 120 minutes. GLP-1 was measured using GLP-1 (total) radioimmunoassay (GLP1T- 36HK, EMD Millipore) with high specificity to GLP-1 (GLP-2, glucagon, and exendin <0.2%). The range was 3–333 pmol/l. Serum insulin was measured by an AutoDelfia fluoroimmunometric assay (B080-101, PerkinElmer)11. MDCS was genotyped at the Broad genotyping facility using the Infinium OmniExpressExome v1.0 B Beadchip array (Illumina). PPP-Botnia genotyping was performed on a FinnGen ThermoFisher Axiom custom array12 at the Thermo Fisher genotyping service facility in San Diego. Standard quality control filters were applied to filter SNvs and samples before imputation. SNVs were excluded for monomorphism, low call rate, or Hardy- Weinberg deviation. Samples with duplications or low call rates, unexpected relatives, sex mismatches, heterozygosity outliers, ancestral outliers (non-EUR) were excluded. For MDCS, genotype imputation for autosomal chromosomes was performed using the Haplotype Reference Consortium version 1.0.3 on the Michigan Server. For PPP-Botnia, genotype imputation was carried out using the population-specific SISu v3 reference panel12 with Beagle 4.113. In both studies, GLP-1 hormone levels were log-transformed before analysis. SNPTEST v.2.5.614 was used for genome-wide association analyses, using frequentist score method adjusted for age, sex and first four principal components. The results were filtered based on MAF >0.01, Hardy-Weinberg equilibrium P>5x10-7, and imputation info >0.4. A fixed effect meta-analysis (inverse-variance weighting) was performed using GWAMA15. The final analysis included 3,514 individuals with fasting GLP-1 and 3,511 individuals with 2-hour GLP-1. All of Us Research Program (AoURP) cohort description, sequencing, quality control, and phenotype derivation. We considered participants with whole-genome sequencing (WGS) and electronic health record (EHR) data from the AoURP Controlled Tier Dataset v716,17. Details of the generation and quality control of the genomic data can be found in the AoURP Genomic Quality Report release C2022Q4R9 (https://support.researchallofus.org/hc/en- us/article_attachments/17973653017236). Briefly, we used computed genetic ancestries and removed related individuals in the maximal independent set (kinship score >0.1). To reduce the computational burden of the WGS dataset, we considered only high-quality SNVs (as defined in the AoURP Genomic Quality Report release C2022Q4R9) with MAF >1% or MAC >100 in at least one of the computed genetic ancestries. To correct for population structure, within each computed genetic ancestry, we derived principal components using the smartpca function from EIGENSOFT v7.2.1 with the “fastmode” option enabled18. In the principal component calculations, we excluded SNVs that were not present in the 1000 Genomes Project (phase 3, October 2014 release) reference panel19. We also excluded SNVs with MAF <1%, that deviated from Hardy-Weinberg equilibrium (P<10-6), or were located in the major histocompatibility complex and regions of high LD. Subsequently, we extracted autosomal LD-pruned SNVs (r2<0.05) using PLINK v2.020. Cases of T2D, T2D-related macrovascular outcomes, and microvascular complications were derived from the combination of diagnosis codes (ICD-9-CM and ICD-10-CM), drug exposures, and LOINC codes for laboratory test results, extracted from EHR data. Age of T2D onset was defined by age at the first diagnosis code or age at the first drug exposure code. Derivation of T2D cases and controls. For T2D cases, we used a previously developed method (https://phekb.org/phenotype/type-2-diabetes-mellitus). Briefly, we considered participants as T2D cases if they fit the following criteria: (a) at least one T2D diagnosis code and at least one drug exposure for T2D medications, unless at least one type 1 diabetes (T1D) diagnosis code; (b) at least one T2D diagnosis code, at least two drug exposures for T1D and T2D medications with a T2D drug exposure occurring at least one day before T1D drug exposure, unless at least one T1D diagnosis code; (c) at least two T2D diagnosis codes and at least one drug exposure for T1D medication, unless at least one T1D diagnosis code; or (d) at least one drug exposure for T2D medications and at least one abnormal laboratory test result (random glucose, fasting glucose, or HbA1c), unless at least one T1D diagnosis codes. For controls, we considered those participants that were free of all diabetes diagnosis codes, including T2D, T1D, and other forms of diabetes. Additionally, we excluded participants that matched criteria (d) from the T2D definition. Age of T2D onset was defined by age at the first diagnosis code under criteria (a-c), and by age at the first drug exposure code under criteria (d). For T2D, we used diagnosis codes 250.00, 250.02, 250.20, 250.22, 250.30, 250.32, 250.40, 250.42, 250.50, 250.52, 250.60, 250.62, 250.70, 250.72, 250.80, 250.82, 250.90, 250.92 from ICD-9-CM and E11.00, E11.01, E11.21, E11.29, E11.311, E11.319, E11.36, E11.39, E11.40, E11.51, E11.618, E11.620, E11.621, E11.622, E11.628, E11.630, E11.638, E11.641, E11.649, E11.65, E11.69, E11.8, E11.9 from ICD-10-CM. For T2D drug exposures, we used the following medications: acarbose, acetohexamide, albiglutide, alogliptin, canagliflozin, chlorpropamide, colesevelam, dapagliflozin, dulaglutide, empagliflozin, exenatide, glimepiride, glipizide, glyburide, linagliptin, liraglutide, lixisenatide, metformin, miglitol, nateglinide, pioglitazone, repaglinide, rosiglitazone, saxagliptin, semaglutide, sitagliptin, tolazamide, and troglitazone. Finally, we considered the following abnormal lab results: random glucose (LOINC codes: 2339-0, 2345-7) > 200mg/dl, fasting glucose (LOINC code: 1558-6) ≄ 125mg/dl, and HbA1c (LOINC codes: 4548-4, 17856-6, 4549-2, 17855-8) ≄ 6.5%. For T1D, we used diagnosis codes 250.01, 250.03, 250.11, 250.13, 250.21, 250.23, 250.31, 250.33, 250.41, 250.43, 250.51, 250.53, 250.61, 250.63, 250.71, 250.73, 250.81, 250.83, 250.91, 250.93 from ICD-9-CM and E10.10, E10.11, E10.21, E10.29, E10.311, E10.319, E10.36, E10.39, E10.40, E10.51, E10.618, E10.620, E10.621, E10.622, E10.628, E10.630, E10.638, E10.641, E10.649, E10.65, E10.69, E10.8, E10.9 from ICD-10-CM. For T1D drug exposures, we used the following medications: insulin, insulin NPH, insulin aspart, insulin degludec, insulin detemir, insulin glargine, insulin glulisine, insulin lispro, pramlintide. For other forms of diabetes, we used diagnosis codes 249*, 648.0*, 648.8* in ICD-9-CM and E08*, E09*, E13*, O24* in ICD-10-CM. Derivation of cases and controls for T2D-related clinical outcomes. For each T2D- related clinical outcome, we used previously-defined ICD-9-CM and ICD-10-CM diagnosis codes from EHR data to identify cases and controls21-24. For macrovascular outcomes (CAD, ischemic stroke, and peripheral artery disease), we defined cases and controls as participants with and without, respectively, the relevant diagnosis codes, irrespective of T2D status. For CAD, we used 410*, 411*, 412*, 413* in ICD-9-CM and I20*, I21*, I22*, I23*, I24*, I25* in ICD-10-CM. For ischemic stroke, we used 433*, 434* in ICD-9-CM and I63* in ICD-10-CM. For peripheral artery disease, we used 4400, 4402, 4438, 4439 in ICD-9-CM and I70.0, I70.00, I70.01, I70.2, I70.20, I70.21, I70.8, I70.80, I70.9, I70.90, I73.8, I73.9 in ICD-10- CM. For microvascular complications (ESDN and proliferative diabetic retinopathy), we considered only T2D cases. ESDN cases were defined with relevant diagnosis codes for both diabetic nephropathy and end-stage kidney disease (ESKD), and ESDN controls were defined as being free of any diagnosis code for diabetic nephropathy, defined using the AoURP cohort builder. For ESKD, we used 403.01, 403.11, 403.91, 404.02, 404.03, 404.12, 404.13, 404.92, 404.93, 585.6 in ICD-9-CM and I12.0, I13.11, I13.2, N18.6 in ICD-10-CM. For DN, we used E11.21 in ICD-10-CM. Proliferative diabetic retinopathy cases were defined with relevant diagnosis codes. Proliferative diabetic retinopathy controls were defined as being free of any diagnosis code for diabetic retinopathy. For proliferative diabetic retinopathy, we used 362.02 in ICD-9-CM and E08.35*, E09.35*, E10.35*, E11.35*, E13.35* in ICD-10-CM. For diabetic retinopathy, we used 362.0* in ICD-9-CM and E08.31*, E08.32*, E08.33*, E08.34*, E08.35*, E09.31*, E09.32*, E09.33*, E09.34*, E09.35*, E10.31*, E10.32*, E10.33*, E10.34*, E10.35*, E11.31*, E11.32*, E11.33*, E11.34*, E11.35*, E13.31*, E13.32*, E13.33*, E13.34*, E13.35* in ICD-10-CM. Biobank Japan (BBJ) cohort description, genotyping, quality control, and phenotype derivation. BBJ is a multi-institutional hospital-based registry that comprises DNA and medical records from individuals of Japanese ancestry25,26. The first BBJ cohort comprises approximately 200,000 participants with at least one of 47 common diseases collected between 2003 and 2007. The second BBJ cohort comprises approximately 67,000 participants with at least one of 38 common diseases collected between 2013 and 2017. Physicians of 66 cooperating hospitals determined the eligibility of cases. Only those individuals who were not included in the multi-ancestry meta-analysis were considered for testing of the partitioned GRS. Genomic DNA was prepared following standard protocols from peripheral blood samples and genotyped using the Illumina Asian Screening Array, following the manufacturer’s instructions. We excluded individuals with call rate <98% and outliers from the cluster of East Asian populations based on principal component analysis with reference individuals from Phase II HapMap27. We excluded SNVs with call rate <99%, MAC <5, exact Hardy-Weinberg equilibrium P<10-10, and >5% difference in MAF when compared with Japanese whole-genome sequence data28,29 and the Tohoku Medical Megabank Project30. After quality control, we performed pre-phasing using SHAPEIT431. Phased haplotypes were imputed to the combined reference panel of 1000 Genomes Project Phase 3 and Japanese whole-genome sequencing data from 1,037 individuals28,29 using Minimac432. We subsequently excluded individuals with a mismatch between inferred genetic sex and sex registered in clinical information, who were not in a set of unrelated individuals defined by using PLINK with KING-cutoff <0.09375, or were outliers of heterozygosity rates (more than 5 SD from the mean). To correct for population structure, we derived principal components using PLINKv2.020, calculated from a set of autosomal LD-pruned SNVs (r2<0.1) with MAF ≄0.5% after excluding the major histocompatibility complex region. We selected participants of at least 18 years of age for PS analyses. We defined T2D cases as participants with a diagnosis of T2D, made by physicians at participating hospitals, but not type 1 diabetes, mitochondrial diabetes, maturity-onset diabetes of the young, or any other type of diabetes33. We extracted cases of microvascular complications from medical records in which diagnosis was made by physicians at participating hospitals. We defined controls for microvascular complications as T2D cases without any diagnosis of diabetic nephropathy or diabetic retinopathy. We defined CAD as a composite of stable angina, unstable angina, and myocardial infarction. These conditions, in addition to ischemic stroke and peripheral artery disease, were diagnosed by physicians at collaborating hospitals based on general medical practices following relevant guidelines. Age of T2D onset was defined from a questionnaire of medical history. Genes & Health (G&H) cohort description, genotyping, quality control, and phenotype derivation. G&H is a UK-based cohort of British Pakistani and Bangladeshi individuals recruited and consented for lifelong electronic health record access and genetic analysis34. Medical records are linked to ICD-10-CM, OPCS and SNOMED diagnosis and procedural codes across inpatient and hospital settings as well as clinical laboratory measurements, and a baseline questionnaire containing demographic information. Individuals were genotyped using the Illumina Infinium Global Screening Array. Full details of quality control have been reported previously35. KING was used to calculate kinship metrics36 and individuals with at least second-degree relatedness were subsequently removed. Ancestry outliers based on principal component analysis were also excluded. Individuals were imputed to the TOPMed r2 reference panel37. Cases of T2D, T2D-related macrovascular outcomes, and microvascular complications were derived from the combination of diagnosis codes (ICD-10-CM), drug exposures, and laboratory test results, extracted from EHR data. Age of T2D onset was defined as the date a diagnosis was made (ICD-10-CM), or a medication was prescribed, or an abnormal laboratory test was recorded, whichever occurred first. Derivation of T2D cases and controls. We considered participants as T2D cases if they fit the following criteria: (a) at least one T2D diagnosis code and at least one drug exposure for T2D medications, unless at least one type 1 diabetes (T1D) diagnosis code; (b) at least one T2D diagnosis code, at least two drug exposures for T1D and T2D medications with a T2D drug exposure occurring at least one day before T1D drug exposure, unless at least one T1D diagnosis code; (c) at least two T2D diagnosis codes and at least one drug exposure for T1D medication, unless at least one T1D diagnosis code; or (d) at least one drug exposures for T2D medications and at least one abnormal laboratory test result (random glucose, fasting glucose, or HbA1c), unless at least one T1D diagnosis codes. For controls, we considered those participants that were free of all diabetes diagnosis codes, including T2D, T1D, and other forms of diabetes. Additionally, we excluded participants that matched criteria (d) from the T2D definition. For T2D, we used diagnosis codes E11.00, E11.01, E11.21, E11.29, E11.311, E11.319, E11.36, E11.39, E11.40, E11.51, E11.618, E11.620, E11.621, E11.622, E11.628, E11.630, E11.638, E11.641, E11.649, E11.65, E11.69, E11.8, E11.9 from ICD-10-CM. For T2D drug exposures, we used the following medications: acarbose, acetohexamide, albiglutide, alogliptin, canagliflozin, chlorpropamide, colesevelam, dapagliflozin, dulaglutide, empagliflozin, exenatide, glimepiride, glipizide, glyburide, linagliptin, liraglutide, lixisenatide, metformin, miglitol, nateglinide, pioglitazone, repaglinide, rosiglitazone, saxagliptin, semaglutide, sitagliptin, tolazamide, and troglitazone. Finally, we considered the following abnormal lab results: random glucose > 200mg/dl, fasting glucose ≄ 125mg/dl, and HbA1c ≄ 6.5%. For T1D, we used diagnosis codes E10.10, E10.11, E10.21, E10.29, E10.311, E10.319, E10.36, E10.39, E10.40, E10.51, E10.618, E10.620, E10.621, E10.622, E10.628, E10.630, E10.638, E10.641, E10.649, E10.65, E10.69, E10.8, E10.9 from ICD-10-CM. For T1D drug exposures, we used the following medications: insulin, insulin NPH, insulin aspart, insulin degludec, insulin detemir, insulin glargine, insulin glulisine, insulin lispro, pramlintide. For other forms of diabetes, we used diagnosis codes E08*, E09*, E13*, O24* in ICD-10-CM. Derivation of cases and controls for T2D-related clinical outcomes. For macrovascular outcomes (CAD, ischemic stroke, and peripheral artery disease), we defined cases and controls as participants with and without, respectively, the relevant diagnosis codes, irrespective of T2D status. For CAD, we used I20*, I21*, I22*, I23*, I24*, I25* in ICD-10-CM. For ischemic stroke, we used I63* in ICD-10-CM. For peripheral artery disease, we used I70.0, I70.00, I70.01, I70.2, I70.20, I70.21, I70.8, I70.80, I70.9, I70.90, I73.8, I73.9 in ICD-10- CM. For microvascular complications (ESDN and proliferative diabetic retinopathy), we considered only T2D cases. ESDN cases were defined with relevant diagnosis codes for both diabetic nephropathy and end-stage kidney disease (ESKD), and ESDN controls were defined as being free of any diagnosis code for diabetic nephropathy. For ESKD, we used I12.0, I13.11, I13.2, N18.6 in ICD-10-CM. For DN, we used E11.21 in ICD-10-CM. Proliferative diabetic retinopathy cases were defined with relevant diagnosis codes. Proliferative diabetic retinopathy controls were defined as being free of any diagnosis code for diabetic retinopathy. For proliferative diabetic retinopathy, we used E08.35*, E09.35*, E10.35*, E11.35*, E13.35* in ICD-10-CM. For diabetic retinopathy, we used E08.31*, E08.32*, E08.33*, E08.34*, E08.35*, E09.31*, E09.32*, E09.33*, E09.34*, E09.35*, E10.31*, E10.32*, E10.33*, E10.34*, E10.35*, E11.31*, E11.32*, E11.33*, E11.34*, E11.35*, E13.31*, E13.32*, E13.33*, E13.34*, E13.35* in ICD-10-CM. No cases with proliferative diabetic retinopathy were identified in the G&H cohort. Clinical trials from the Thrombolysis in Myocardial Infarction (TIMI) Study. ENGAGE AF- TIMI 48 was a 3-arm trial comparing two doses of the Factor Xa inhibitor edoxaban to warfarin in patients with atrial fibrillation and CHADS2 risk score of 2 or higher, where co- morbidities included diabetes (38%), stroke (28%), and heart failure (57%). SOLID-TIMI 52 was a trial of the lipoprotein-associated phospholipase A2 inhibitor darapladib versus placebo in patients with recent acute coronary syndrome on optimal background medical therapy, where co-morbidities included hypertension (73%), hyperlipidemia (64%), and diabetes (35%). SAVOR-TIMI 53 was a trial of the DPP4 inhibitor saxagliptin in patients with T2D, where co-morbidities included atherosclerosis (78%) and hypertension (81%). PEGASUS-TIMI 54 was a trial of the antiplatelet drug ticagrelor in patients with prior myocardial infarction, where co-morbidities included smoking (17%), hypertension (78%), diabetes (32%), prior percutaneous coronary intervention (83%), and prior coronary artery bypass graft (5%). FOURIER-TIMI 59 was a trial of the PCSK9 inhibitor evolocumab in patients with myocardial infarction, stroke, or peripheral artery disease, where co-morbidities included hypertension (80%), diabetes (37%), and prior myocardial infarction (81%). DECLARE-TIMI 58 was a trial of the SGLT-2 inhibitor dapaglifozin in patients with T2D, where co-morbidities included established atherosclerotic cardiovascular disease (40%) or multiple risk factors for atherosclerotic cardiovascular disease (60%). Genotyping was performed on the Infinium Global Array chip (FOURIER-TIMI 59), Affymetrix Biobank Array (SOLID-TIMI 52), Infinium Global Screening Array MD (DECLARE- TIMI 58) and Illumina Multi-Ethnic Genotyping Array (ENGAGE AF-TIMI 48, PEGASUS-TIMI 54 and SAVOR-TIMI 53). PLINK v2.020 was used for pre-imputation quality control, which included mapping to hg38 coordinates, removing SNVs and individuals with missingness >0.2 (first round) and >0.02 (second round), removing individuals with sex discrepancies based on X-chromosome F-values (<0.2 for females and >0.8 for males) and heterozygosity more than 3 SD from the mean, and removing SNVs with MAF <1% and extreme deviation from Hardy- Weinberg equilibrium (P<10-6). Imputation was performed on the Michigan Imputation Server using Eagle v2.438 for phasing and Minimac432 on TOPMed Freeze 5 reference panel37 with imputation quality filter r2>0.3. 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Acknowledgements and Funding 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. All Of Us Research Program (AOURP) is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants. 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). This study was funded by the BioBank Japan project, which is supported by the Ministry of Education, Culture, Sports, Sciences and Technology (MEXT) of Japanese government and the Japan Agency for Medical Research and Development (AMED, grant ID JP21km0605001). AMED GRIFIN Diabetes Initiative Japan was supported by Japan Agency for Medical Research and Development (JP20km0405202, JP21tm0424218). Scarda was supported by AMED under Grant Number 223fa627011. 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. Vanderbilt University Medical Center’s BioVU (BIOVU) projects are supported by numerous sources: institutional funding, private agencies, and federal grants. These include NIH funded Shared Instrumentation Grant S10OD017985, S10RR025141, and S10OD025092; CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, and R01HD074711. This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN, supported in part by an S10 instrumentation award (1S10OD023680-01). Bangladesh Population Cohort (BPC) was supported by US National Institute of Environmental Health Sciences Grants P42 ES10349 and P30 ES09089. Cardiometabolic Genome Epidemiology (CAGE-AMAGASKI and CAGE-GWAS) was 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). CAGE-AMAGASKI thanks Drs. Toshio Ogihara, Yukio Yamori, Akihiro Fujioka, Chikanori Makibayashi, Sekiharu Katsuya, Ken Sugimoto, Kei Kamide, and Ryuichi Morishita and the many physicians of the participating hospitals and medical institutions in Amagasaki Medical Association for their assistance in collecting the DNA samples and accompanying clinical information. Cardiometabolic Genome Epidemiology Kita-Nagoya Genomic 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). Coronary Artery Risk 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 (82192901, 82192904, 82192900), 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, GĂ©nome QuĂ©bec, 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- RĂ©gion"), and the charities: “Association Française des DiabĂ©tiques”, “Programme National de Recherche sur le DiabĂšte” and “Association de Langue Française pour l'Etude du DiabĂšte et des Maladies MĂ©taboliques”. 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 FĂ©dĂ©ration 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. Franc were instrumental in recruitment and S. Brunet, F. Bacot, R. Frechette, V. Catudal, M. Deweirder, F. Allegaert, P. Laflamme, P. Lepage, W. Astle, M. Leboeuf, and S. Leroux 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 (“RĂ©seaux en SantĂ© Publique, Interactions entre les dĂ©terminants de la santĂ©â€), by “Association DiabĂšte Risque Vasculaire”, “FĂ©dĂ©ration Française de Cardiologie”, “Fondation de France”, ALFEDIAM, ONIVINS, Ardix Medical, Bayer Diagnostics, Becton Dickinson, Cardionics, Merck SantĂ©, Novo Nordisk, Pierre Fabre, Roche, Topcon. The D.E.S.I.R. Study Group: INSERM U780: B. Balkau, P. DucimetiĂšre, E. EschwĂšge; 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. Caces, M. Cailleau, J. G. Moreau, F. Rakotozafy, J. Tichet, S. Vol. DGDG thank M. Deweider and F. Allegaert for the DNA bank management and are sincerely indebted to all study participants. Diabetes Genetics Initiative (DGI) was supported by the Novartis Institute for BioMedical 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 Academy of Finland, University of Helsinki, Finnish Diabetes Research Foundation, Foundation for Life and Health in Finland, Finnish Medical Society, Helsinki University Central Hospital Research Foundation, PerklĂ©n Foundation, Ollqvist Foundation, NĂ€rpes Health Care Foundation, Municipal Heath Care Center and Hospital in Jakobstad and Health Care Centers in Vasa, NĂ€rpes and Korsholm. The work in Malmö, Sweden, was also funded by a LinnĂ© grant from the Swedish Research Council (349-2006-237). The contribution of the Botnia and Skara research teams is gratefully acknowledged. Electronic Medical Records and Genomics 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). Epidemiologic Study of the 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. Estonian Biobank (ESTBB) was funded by the Estonian Research Council Grant IUT20-60, IUT24-6, PRG687, and the European Union through the European Regional Development Fund Project No. 2014-2020.4.01.15-0012 GENTRANSMED. 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. Genes & Health (G&H) is/has recently been core-funded by Wellcome (WT102627, WT210561), the Medical Research Council (UK) (M009017, MR/X009777/1, MR/X009920/1), Higher Education Funding Council for England Catalyst, Barts Charity (845/1796), Health Data Research UK (for London substantive site), and research delivery support from the NHS National Institute for Health Research Clinical Research Network (North Thames). Genes & Health is/has recently been funded by Alnylam Pharmaceuticals, Genomics PLC; and a Life Sciences Industry Consortium of Astra Zeneca PLC, Bristol-Myers Squibb Company, GlaxoSmithKline Research and Development Limited, Maze Therapeutics Inc, Merck Sharp & Dohme LLC, Novo Nordisk A/S, Pfizer Inc, Takeda Development Centre Americas Inc. We thank Social Action for Health, Centre of The Cell, members of our Community Advisory Group, and staff who have recruited and collected data from volunteers. We thank the NIHR National Biosample Centre (UK Biocentre), the Social Genetic & Developmental Psychiatry Centre (King's College London), Wellcome Sanger Institute, and Broad Institute for sample processing, genotyping, sequencing and variant annotation. We thank: Barts Health NHS Trust, NHS Clinical Commissioning Groups (City and Hackney, Waltham Forest, Tower Hamlets, Newham, Redbridge, Havering, Barking and Dagenham), East London NHS Foundation Trust, Bradford Teaching Hospitals NHS Foundation Trust, Public Health England (especially David Wyllie), Discovery Data Service/Endeavour Health Charitable Trust (especially David Stables), Voror Health Technologies Ltd (especially Sophie Don), NHS England (for what was NHS Digital) - for GDPR-compliant data sharing backed by individual written informed consent. Most of all we thank all of the volunteers participating in Genes & Health. German Chronic Kidney Disease (GCKD) was funded by the German Ministry of Research and Education (Bundesminsterium fĂŒr Bildung und Forschung, BMBF) and by the Foundation KfH Stiftung PrĂ€ventivmedizin. 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 Julie Cunningham. GENOA thanks Eric Boerwinkle and Megan Grove from the Human Genetics Center and Institute of Molecular Medicine and Division of Epidemiology, University of Texas Health Science Center, Houston, Texas, USA 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. Genetics of Diabetes and Audit 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 is grateful to: Georgia Markou, Laiko General Hospital Diabetes Centre; Maria Emetsidou and Panagiota Fotinopoulou, Hippokratio General Hospital Diabetes Centre; Athina Karabela, Dafni Psychiatric Hospital; Eirini Glezou and Marios Mangioros, Dromokaiteio Psychiatric Hospital; Angela Rentari, Harokopio University of Athens; and Danielle Walker, Wellcome Trust Sanger Institute. 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). 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/cgi- bin/study.cgi?study_id=phs000091.v1.p1 through dbGaP accession number phs000091.v1.p. Mexican American Hypertension and Insulin 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. Korean Association Resource (KARE) was supported by grants from Korea Centers for Disease Control and Prevention (4845–301, 4851–302, 4851–307) 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 Control and Prevention (4845–301, 4851–302, 4851–307) 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 MĂŒnchen – 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-UniversitĂ€t, 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 InvestigaciĂłn 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 FundaciĂłn IMSS. MC thanks Jaime GĂłmez Zamudio and Araceli MĂ©ndez PadrĂłn 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). 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, UL1TR001881, DK063491, and R01HL105756. 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. The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutes can be found at http://www.mesa-nhlbi.org. Metabolic Syndrome in 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. Mass General Brigham Biobank (MGB) acknowledges the Partners HealthCare System for support of the MGB biobank and MGB patients for providing samples, genomic data, and health information data, as well as research support by NIDDK K24 DK110550 (to J.C.F.), K24 DK080140 (to J.B.M.) and NIDDK K23DK114551 (to M.S.U). 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. VA Million Veteran Program (MVP). This research is based on data from the MVP, Office of Research and Development, Veterans Health Administration and was supported by award MVP000. This publication does not represent the views of the Department of Veterans Affairs, the US Food and Drug Administration, or the US Government. This research was also supported by funding from the Department of Veterans Affairs awards I01- BX003362 (P.S.T. and K.-M.C.). K.-M.C. and P.S.T. are supported by the VA Cooperative Studies Program. Research support for this study was generously provided by the Department of Veterans Affairs (VA) Informatics and Computing Infrastructure (VINCI) (VA HSR RES 13-457). 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 GĂ©notypage (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/gap through dbGaP accession number phs000237.v1.p1. Prospective Investigation of the 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. Penn Medicine BioBank (PMBB). We acknowledge the PMBB for providing data and thank the patient-participants of Penn Medicine who consented to participate in this research program. We would also like to thank the Penn Medicine BioBank team and Regeneron Genetics Center for providing genetic variant data for analysis. The PMBB is approved under IRB protocol# 813913 and supported by Perelman School of Medicine at University of Pennsylvania, a gift from the Smilow family, and the National Center for Advancing Translational Sciences of the National Institutes of Health under CTSA award number UL1TR001878. 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). This work was conducted as part of the Slim Initiative for Genomic Medicine, a joint U.S.-Mexico project funded by the Carlos Slim Health Institute. The UNAM/INCMNSZ diabetes study was supported by Consejo Nacional de Ciencia y TecnologĂ­a grants 138826, 128877, CONACyT- SALUD 2009-01-115250, and a grant from DirecciĂłn General de Asuntos del Personal AcadĂ©mico, UNAM, IT 214711. The Diabetes in Mexico Study was supported by Consejo Nacional de Ciencia y TecnologĂ­a grant 86867 and by Instituto Carlos Slim de la Salud, A.C. The Mexico City Diabetes Study was supported by National Institutes of Health (NIH) grant R01HL24799 and by the Consejo Nacional de Ciencia y Tenologia grants: 2092, M9303, F677-M9407, 251M, and 2005-C01- 14502, SALUD 2010-2-151165. The Multiethnic Cohort was supported by NIH grants CA164973, CA054281, and CA063464. Singapore Malay Eye 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). Thrombolysis in Myocardial Infarction Study Group (TIMI) acknowledge Marc S. Sabatine, Robert P. Giugliano, Steve D. Wiviott, Ben M. Scirica, Michelle L. O'Donoghue, and Elliott Antman. 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). UK Biobank (UKBB) analyses were conducted using the UK Biobank resource under applications 236, 9161, and 10035. This research was supported by the British Heart Foundation (grant SP/13/2/30111). Large-scale comprehensive genotyping of UK Biobank for cardiometabolic traits and diseases: UK CardioMetabolic Consortium (UKCMC). Uppsala Longitudinal Study of Adult Men (ULSAM) was supported by Wellcome Trust 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). The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through 75N92021D00001, 75N92021D00002, 75N92021D00003, 75N92021D00004, 75N92021D00005. 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. A list of WHI investigators is available at: https://www-whi-org.s3.us-west-2.amazonaws.com/wp-content/uploads/WHI- Investigator-Short-List.pdf. Wellcome Trust Case Control Consortium (WTCCC) analysis and genotyping was supported by: Wellcome Trust funding 090367, 098381, 090532, 083948, 085475, 101630 and 203141; MRC (G0601261); EU (Framework 7) HEALTH-F4-2007-201413; and NIDDK DK098032 and U01-DK105535. GWAS analyses of GLP-1 concentrations. The PPP-Botnia Study has been financially supported by grants from FolkhĂ€lsan Research Foundation, the Sigrid Juselius Foundation, The Academy of Finland (grants no. 263401, 267882, 312063, 336822, 312072 and 336826), University of Helsinki, Nordic Center of Excellence in Disease Genetics, EU (EXGENESIS, MOSAIC FP7-600914), Ollqvist Foundation, Swedish Cultural Foundation in Finland, Finnish Diabetes Research Foundation, Foundation for Life and Health in Finland, Finnish Medical Society, State Research Funding via the Helsinki University Hospital, PerklĂ©n Foundation, NĂ€rpes Health Care Foundation and Ahokas Foundation. The study has also been supported by the Ministry of Education in Finland, the Municipal Heath Care Center and Hospital in Jakobstad and Health Care Centers in Vasa, NĂ€rpes and Korsholm. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 269045. The role of the founding PI, Professor Leif Groop, and the skilful assistance of the Botnia Study Group is gratefully acknowledged. Personal acknowledgements. K.Suzuki was supported by Japan Agency for Medical Research and Development (JP21km0405213, JP20km0405202, JP21tm0424218). R.Mandla was supported by NHGRI U01HG011723, 1-19-ICTS-068. A.H.-C. was supported by NHGRI U01HG011723, 1-19-ICTS-068. O.B. was supported by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 101017802 (OPTOMICS). L.E.P. was supported by R01GM133169, R01HL142302, R01DK127084. P.S. was supported by NHGRI U01HG011723, 1-19-ICTS-068. F.B. was supported by BHF Centre of Research Excellence, Oxford (RE/13/1/30181). W.Zhang acknowledges support from iHealth-T2D, 643774 and the National Institute for Health Research/Wellcome Trust Imperial Clinical Research Facility. R.A.S. acknowledges support from the Medical Research Council Epidemiology Unit (MC_UU_12015/1). D.T. acknowledges funding from US National Institutes of Health grant DK062370. E.J.P. was supported by the Canadian Institutes of Health Research (CIHR) and the Banting and Best Diabetes Center, University of Toronto. M.W. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – CRC 1453 Project-ID 431984000. C.Sarnowski acknowledges support from NIA R00 AG066849. D.N. acknowledges support from NIEHS grant T32ES013678. S.-H.K. acknowledges funding from Korea Health Technology R&D Project through the Korea Health Industry Development Institute (grant number HI15C3131). A.W. is supported by a PhD studentship funded by the Wellcome Trust. L.S.A. acknowledges support from the National Institute for Health (NIH), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) for R01 HD30880, National Institute on Aging (NIA) for R01 AG065357, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) for R01 DK104371 and R01 HL108427. C.F.B. acknowledges funding from the Dr. Robert C. and Veronica Atkins Foundation. J.Chen acknowledges InĂȘs Barroso for supervision and support (Wellcome WT098051 and WT206194). J.Danesh holds a British Heart Foundation Professorship and a NIHR Senior Investigator Award, and this work was supported by core funding from the: British Heart Foundation (RG/13/13/30194; RG/18/13/33946) and NIHR Cambridge Biomedical Research Centre (BRC-1215-20014).S.K.D. acknowledges support from the NIH/NIDDK grant R01 DK090111. S.D. acknowledges funding from the US National Institutes of Health Fogarty grant D43 TW009077, the National Institute for Health (NIH), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) for R01 HD30880, National Institute on Aging (NIA) for R01 AG065357, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) for R01DK104371 and R01HL108427. D.S.E. acknowledges support from the US National Institutes of Health U24AG051129. P.G.-L. acknowledges support from the National Institute for Health (NIH), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) for R01 HD30880, National Institute on Aging (NIA) for R01 AG065357, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) for R01DK104371 and R01HL108427. A.T.H. acknowledges support from a Wellcome Trust Senior Investigator award (grant number 098395/Z/12/Z). K.LĂ€ll acknowledges funding from the Estonian Research Council grant 1911. 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, Widenlife, and NIH (5P50HD028138-27). A.E.L. acknowledges funding from US National Institutes of Health grant DK062370. J.Luan acknowledges support from the Medical Research Council Epidemiology Unit (MC_UU_12015/1). S.Maeda is supported by the grant for Okinawa innovation/eco-system promotion project from the Okinawa prefecture. 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. Y.O. was supported by JSPS KAKENHI (22H00476), and AMED (JP21gm4010006, JP22km0405211, JP22ek0410075, JP22km0405217, JP22ek0109594, JP223fa627002, JP223fa627010, JP233fa627011), JST Moonshot R&D (JPMJMS2021, JPMJMS2024), Takeda Science Foundation, Bioinformatics Initiative of Osaka University Graduate School of Medicine, Institute for Open and Transdisciplinary Research Initiatives and Center for Infectious Disease Education and Research (CiDER), Osaka University. H.G.P. was supported by R01GM133169, R01HL142302, R01DK127084. N.Sattar is supported by British Heart Foundation Centre of Excellence Grant RE/18/6/34217. N.Shojima was supported by Japan Agency for Medical Research and Development (JP20km0405202, JP21tm0424218). E.W. acknowledges InĂȘs Barroso for supervision and support (Wellcome WT098051 and WT206194), and acknowledges support from the Medical Research Council Epidemiology Unit (MC_UU_12015/1). Y.S.C. acknowledges support from the National Research Foundation of Korea (NRF) Grant funded by the Ministry of Education (NRF-2020R1I1A2075302). E.Ingelsson was supported by NIH/NIDDK 1R01DK106236-01A1. J.-Y.W. was supported by Academia Sinica GMM Study. 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. F.S.C. acknowledges support from United States’ National Institutes of Health (NIH) grant ZIA- HG000024. K.-S.P. acknowledges funding from Korea Health Technology R&D Project through the Korea Health Industry Development Institute (grant numbers HI15C1595, HI14C0060). R.M.-C. acknowledges support from grants NIH U10 EY 11753 and NIH U10 EY 11753. C.-Y.C. acknowledges funding from the National Medical Research Council (NMRC), Singapore (CSA- SI/0012/2017). J.Dupuis is supported by R01 DK078616 and U01 DK078616. A.Köttgen was supported by the by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) KO 3598/5-1 and CRC 1453 Project-ID 431984000. D.W.B. acknowledges support from the US National Institutes of Health U01DK105556 and R01DK66358. K.E.N. acknowledges support by R01HD057194, R01DK122503, R01HG010297, R01HL142302, R01HL143885, R01HG009974, and R01DK101855. D.S. has received funding from NHLBI, NINDS, the British Heart Foundation, Pfizer, Regeneron, Genentech, and Eli Lilly pharmaceuticals. N.J.W. acknowledges support from the Medical Research Council Epidemiology Unit (MC_UU_12015/1). E.A. was funded by grants from the Swedish Research Council (2020-02191) and the Novo Nordisk Foundation (NNF21OC0070457). M.O.G. acknowledges support from the US National Institutes of Health grants P30DK063491 and UL1TR001881, as well as the Eris M. Field Chair in Diabetes Research. K.L.M. acknowledges funding from the US National Institutes of Health R01DK072193, R01DK093757, U01DK105561. C.L. acknowledges support from the Medical Research Council Epidemiology Unit (MC_UU_12015/1). R.J.F.L. acknowledges support from R01DK110113, R01DK107786, R01HL142302, and R56HG010297. J.C.F. is a Massachusetts General Hospital Research Scholar and was supported by NIDDK U01 DK105554 and NIDDK K24 DK110550. J.C.D. acknowledges support from United States’ National Institutes of Health (NIH) grant ZIA- HG200417. T.Y. was supported by Japan Agency for Medical Research and Development (JP20km0405202, JP21tm0424218). T.Kadowaki was supported by Japan Agency for Medical Research and Development (JP20km0405202, JP21tm0424218). J.C.C. acknowledges support from the Singapore Ministry of Health’s National Medical Research Council under its Singapore Translational Research Investigator (STaR) Award (NMRC/STaR/0028/2017), iHealth-T2D 643774, and the National Institute for Health Research/Wellcome Trust Imperial Clinical Research Facility. M.C.Y.N. acknowledges support from the US National Institutes of Health U01DK105556, R01DK66358, and a supplement to R01DK78616-06S1. J.E.B. acknowledges support from R01GM133169, R01HL142302, and R01DK127084. 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). J.B.M. acknowledges funding through NIH grants R01DK078616, U01DK078616 and K24DK080140. C.N.S. was supported by American Heart Association Postdoctoral Fellowship 15POST24470131 and 17POST33650016, and American Diabetes Association 11-22-JDFPM-06. J.M.M. is funded by American Diabetes Association Innovative and Clinical Translational Award 1-19-ICTS-068, and NHGRI U01HG011723. M.B. acknowledges funding from US National Institutes of Health grant DK062370. M.V. acknowledges support from the Corporal Michael J. Crescenz VA Medical Center Research Department. B.F.V. acknowledges support from the NIH/NIDDK (DK126194). A.P.M. acknowledges support from US National Institutes of Health U01DK105535, Versus Arthritis (grant reference 21754), NIHR Manchester Biomedical Research Centre (NIHR203308), and MRC (MR/W029626/1). The views expressed in this article are those of the authors and do not necessarily represent those of: the UK National Health Service, the UK National Institute for Health Research, or the UK Department of Health and Social Care; the US National Heart, Lung, and Blood Institute, the US National Institute of Neurological Disorders and Stroke, the US National Institute on Aging, the US National Institutes of Health, the US Department of Health and Human Services, the US Department of Veterans Affairs, the US Food and Drug Administration, or the US Government. Ethics statements 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. All Of Us Research Program (AOURP). All research was conducted under the guidelines defined by the All of Us Ethical Conduct of Research Policy. 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, the University of Tokyo. Ethical approvals of AMED GRIFIN Diabetes Initiative Japan were gained from the Ethics Committees of Osaka University and the 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. BioMe Biobank (BIOME). 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. Vanderbilt University Medical Center’s BioVU (BIOVU). Analyses of DIAMANTE data at Vanderbilt University Medical Center are approved under IRB #190891 and analysis of BioVU data are approved under IRBs #210163 and #171279. In all three cases, the data analyzed received non-human subject determinations. Bangladesh Population Cohort (BPC). 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. Cardiometabolic Genome Epidemiology (CAGE-AMAGASKI and CAKE-GWAS). Approval was obtained from the Institutional Review Boards at the National Center for Global Health and Medicine. All participants provided written informed consent. Cardiometabolic Genome Epidemiology (CAGE-KING). 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. Coronary Artery Risk Development in Young Adults (CARDIA). 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. Cleveland Family Study (CFS). 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. Cardiovascular Health Study (CHS). 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. China Kadoorie Biobank (CKB). All participants provided written informed consent. Ethical approval was obtained from Oxford Tropical Research Ethics Committee (OxTREC) and from the Ethical Review Committees of the Chinese Centre for Disease Control and Prevention and the Chinese Academy of Medical Sciences/Peking Union Medical College. Cebu Longitudinal Health and Nutrition Survey (CLHNS). Written informed consent was obtained from all participants. Study protocols were approved by the University of North Carolina Institutional review Board for the Protection of Human Subjects. Diabetic Cohort and Singapore Prospective Study Program (DC/SP2). Study protocols were approved by the Singapore General Hospital Ethics Committee, and National University of Singapore Institutional Review Board. All participants provided written informed consent. Durban Diabetes Study and Durban Diabetes Case Control (DDS/DCC). Approvals were granted by the Biomedical Research Ethics Committee at the University of KwaZulu-Natal and the UK National Research Ethics Service. All participants provided written informed consent. deCODE genetics (DECODE). 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). Diabetes Gene Discovery Group (DGDG). All participants signed informed consent, and the protocol was approved by the French ethics committee. Diabetes Genetics Initiative (DGI). The study was approved by the Ethics Committees of the Helsinki University Hospital, Helsinki, Finland, and Lund University, Sweden. Estonian Genome Center of the University of Tartu (EGCUT). All analyses were approved by the Ethics Review Committee of the University of Tartu. All participants provided written informed consent. Electronic Medical Records and Genomics Network (EMERGE). Approval was obtained from the Institutional Review Boards at Boston Children’s Hospital, Children’s Hospital of Philadelphia, Cincinnati Children’s Hospital Medical Center, Essentia Institute of Rural Health, Geisinger Clinic, Group Health Cooperative, Marshfield Clinic Research Foundation, Mayo Clinic, Icahn School of Medicine at Mount Sinai, Northwestern University, Pennsylvania State University, Vanderbilt University Medical Center, and University of Washington. All participants provided written informed consent. European Prospective Investigation into Cancer and Nutrition (EPIC-INTERACT). The EPIC- InterAct study was approved by the local ethics committee in the participating countries and the Internal Review Board of the International Agency for Research on Cancer. All participants gave written informed consent. The study was coordinated by the Medical Research Council Epidemiology Unit at the University of Cambridge. Epidemiologic Study of the Screenees for Diabetes Reduction Assessment with Ramipril and Rosiglitazone Medication (EPIDREAM). All study participants consented to analysis of blood samples. Approval was granted by the Hamilton Integrated Research Ethics Board, at McMaster University, Hamilton, Canada. Family Heart Study (FAMHS). Approval was obtained from the Institutional Review Board at Washington University, St. Louis. Written informed consent, including consent to participate in genetic studies, was obtained from all participants. Framingham Heart Study (FHS). Approval was obtained from the Institutional review Board of Boston University Medical Campus. All study participants provided written informed consent. Finland-United States Investigation of NIDDM Genetics (FUSION). Approval was obtained from the coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa. All participants provided written informed consent. Genes & Health (G&H). Genes & Health has NHS Health Research Authority favourable ethical opinion from NRES Committee London – South East 14/LO/1240. German Chronic Kidney Disease (GCKD). 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. Genetic Study of Atherosclerosis Risk (GENESTAR). Approval was obtained from the Johns Hopkins Medicine Institutional Review Board. All participants gave written informed consent. Genetic Epidemiology Network of Arteriosclerosis (GENOA). Approval was granted by 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. Genetics of Diabetes and Audit Research in Tayside Scotland (GODARTS). Approval was obtained from the Tayside Medical Ethics Committee. Informed consent was obtained for all participants. Genetics of Latinos Diabetic Retinopathy (GOLDR). 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. Genomic Research Cohort for CCMB Diabetes Study (GRCCDS). Ethics committees of CSIR- Centre for Cellular and Molecular Biology and KEM Hospital and Research Centre approved the project. Health, Aging and Body Composition Study (HABC). 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. Healthy Aging in Neighborhoods of Diversity Across the Life Span Study (HANDLS). Approval was granted by the National Institutes of Health Institutional Review Board (study number 09AGN248). All participants provided written informed consent. Hispanic Community Health Study/Study of Latinos (HCHS/SOL). 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. Hong Kong Diabetes Registry (HKDR). Approval was obtained from the Chinese University of Hong Kong Clinical Research Ethics Committee. Health Professionals’ Follow-Up Study (HPFS). Approval was obtained from the Human Research Committee at the Brigham and Women’s Hospital. All participants provided written informed consent. Mexican American Hypertension and Insulin Resistance (HTNIR). Approval was granted by 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. Howard University Family Study (HUFS). 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. Indian Diabetes Consortium (INDICO). Approval was obtained by the Human Ethics 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. INTERHEART (INTERHEART). All study participants consented to analysis of blood samples. Approval was granted by the Hamilton Integrated Research Ethics Board, at McMaster University, Hamilton, Canada. Jackson Heart Study (JHS). Approval was obtained from Institutional Review Boards at Jackson State University, Tougaloo College and the University of Mississippi Medical Center. All participants provided written informed consent. Korean Association Resource (KARE). Approval was granted by the Institutional review Board at the Korean National Institute of Health. All participants provided written informed consent. Korean Biobank Array from the Korean Genome and Epidemiology (KoGES) Consortium (KBA). Approval was granted by the Institutional Review Board of the Korean National Institute of Health. All participants provided written informed consent. Collaborative Health Research in the Region of Augsburg (KORA). Approval was granted by the Ethics Committee of the Medical Association of Bavaria (number 06068). All participants provided informed consent. Los Angeles Latino Eye Study (LALES). Approval was obtained from the Los Angeles County/University of Southern California Institutional Review Board, and Western Institutional Review Board at Southern California Eye Institute. All participants provided written informed consent. London Life Sciences Prospective Population (LOLIPOP). Approval was obtained from the London-Fulham Research Ethics Committee (ref 07/H0712/150). All participants gave an written informed consent. Mexican American Study of Coronary Artery Disease (MACAD). Approval was granted by 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. Mexico City (MC). 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). Malmo Diet and Cancer Study (MDCS). The study protocol for MDC was sanctioned by the Ethics Review Committee of Lund University (approval numbers 532/2006, 51-90). All participants provided their written consent. Multi-Ethnic Study of Atherosclerosis (MESA). Approval was obtained from Institutional 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. Metabolic Syndrome in Men (METSIM). Approval was granted by the Ethics Committee of the University of Kuopio and the Kuopio University Hospital. All participants gave written informed consent. Mass General Brigham Biobank (MGB). 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. Michigan Genomics Initiative (MGI). Approval was granted by the IRBMED Institutional Review Board of the University of Michigan. All participants gave written informed consent. VA Million Veteran Program (MVP). All participating studies were conducted in compliance with the Declaration of Helsinki and comply with all relevant ethical and local regulatory requirements. Specifically, the contributing genetic association studies were approved by the Department of Veteran’s Affairs central IRB. 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. Netherlands Epidemiology of Obesity (NEO). Approval was obtained from the Medical Ethics Committee of Leiden University Medical Center. All participants gave written informed consent. Nurses Health Study (NHS). Approval was obtained from the Human Research Committee at the Brigham and Women’s Hospital. All participants provided written informed consent. NIDDM-Atherosclerosis Study Hispanic Cohorts (NIDDM). Approval was granted by Human Subjects Protection Institutional Review Boards at the University of California at Los Angeles, University of Southern California, City of Hope, Lundquist/LABioMed/Harbor-UCLA and Cedars-Sinai Medical Center. Northewestern University Genetics (NUGENE). Approval was obtained from Institutional Review Boards at Northwestern University and Vanderbilt University. Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS). Approval was granted by the Ethics Committee of Uppsala University. All participants provided written informed consent. Penn Medicine BioBank (PMBB). All participating studies were conducted in compliance with the Declaration of Helsinki and comply with all relevant ethical and local regulatory requirements. Specifically, the contributing genetic association studies were approved by the IRB of Perelman School of Medicine at the University of Pennsylvania (IRB protocol #813913). Prevalence, Prediction and Prevention of type 2 diabetes (PPP)-Botnia Study. The study protocol was sanctioned by the Ethics Committee of Helsinki University (approval number 608/2003). All participants provided their written consent. Pakistan Risk of Myocardial Infarction Study (PROMIS). The study was approved by the Institutional Review Board of the Center for Non-Communicable Diseases Pakistan and by regional Ethical Review Committees in the different centres across Pakistan involved in the study. Institutional Review Boards at the National Institute of Cardiovascular Disorders, Karachi, Punjab Institute of Cardiology, Lahore, and Tabba Heart Institute, Karachi approved the study. All participants provided written informed consent. Prospective Study of Pravastatin in the Elderly at Risk (PROSPER). Approval was obtained from the Institutional Ethics Review Boards of Cork University (Ireland), Glasgow University (UK) and Leiden University Medical Center (The Netherlands). All participants gave written informed consent. Sea Islands Genetic Network Reasons for Geographic and Racial Differences in Stroke (REGARDS). 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. Rotterdam Study (RS). Approval was granted by the Institutional review Board at Erasmus University Medical Center. All participants provided written informed consent. Shanghai Breast Cancer Study and Shanghai Women’s Health Study (SBCS/SWHS). 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. Singapore Chinese Eye Study (SCES). 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. Singapore Chinese Health Study (SCHS). 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. Singapore Malay Eye Study (SIMES). 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. Singapore Indian Eye Study (SINDI). 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. Samsung Medical Center (SMC). Approval was obtained from the Institutional Review Board of the Samsung Medical Center (No. 2004-12-005). All participants provided written informed consent. Seoul National University Hospital (SNUH). The Institutional Review Board of the Biomedical Research Institute at Seoul National University Hospital approved the study protocol (1205–130–411). Written informed consent was obtained from each participant. Taiwan Metabochip Consortium Zhonghua (TAICHI-G). Approval was granted by Institutional Review Boards at Stanford University School of Medicine, Hudson-Alpha Biotechnology Institute, Lundquist/LABioMed/Harbor-UCLA, Cedars-Sinai Medical Center, Taichung Veterans General Hospital, Taipei Veterans General Hospital, National Health Research Institute, Tri-Service General Hospital, and National Taiwan University Hospital. Thrombolysis in Myocardial Infarction Study Group (TIMI). All individuals from the clinical trials signed informed consent. The institutional review board or ethics committee of each participating site approved each of the clinical trial protocols. Taiwan Type 2 Diabetes (TWT2D). Approval was obtained from Institutional Review Boards at China Medical University Hospital, Chia-Yi Christian Hospital, and National Taiwan University Hospital. Danish T2D Case-Control Study (UCPH). The studies included in the Danish T2D Case- Control Study (UCPH) were conducted in accordance with the Declaration of Helsinki II and were approved by the local Ethical Committees of Copenhagen County, the Capital Region of Denmark, or the Region of Southern Denmark. UK Biobank (UKBB). Approval was obtained from the North West Centre for Research Ethics Committee (11/NW/0382). Uppsala Longitudinal Study of Adult Men (ULSAM). Approval was granted by the Ethics Committee of Uppsala University. All participants provided written informed consent. Wake Forest School of Medicine (WFSM). Approval was granted by the Institutional Review Board at Wake Forest School of Medicine. All participants provided written informed consent. Women’s Health Initiative (WHI). Approval was granted by the Institutional review Board at the Fred Hutchinson Cancer Research Centre in accordance with the US Department of Health and Human Services regulations at 45 CFR 46 (approval number IR# 3467-EXT). All participants provided written informed consent. Additional written consent to review medical records was obtained. The Fred Hutchinson Cancer Research Centre has an approved FWA on file with the Office for Human Research Protections under assurance number 0001920. Wellcome Trust Case Control Consortium (WTCCC). Approval for the study was obtained from Peterborough & Fenland Local Research Ethics Committee, National Research Ethics Service, Leeds (East) Research Ethics Committee, South West Multicentre Research Ethics Committee, Tayside Committee on Medical Research Ethics and Oxford Tropical Research Ethics Committee. VA Million Veteran Program: Core Acknowledgement for Publications MVP Program Office ‱ Sumitra Muralidhar, Ph.D., Program Director US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420 ‱ Jennifer Moser, Ph.D., Associate Director, Scientific Programs US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420 ‱ Jennifer E. Deen, B.S., Associate Director, Cohort & Public Relations US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420 MVP Executive Committee ‱ Co-Chair: Philip S. Tsao, Ph.D. VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304 ‱ Co-Chair: Sumitra Muralidhar, Ph.D. US Department of Veterans Affairs, 810 Vermont Avenue NW, Washington, DC 20420 ‱ J. Michael Gaziano, M.D., M.P.H. VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 ‱ Elizabeth Hauser, Ph.D. Durham VA Medical Center, 508 Fulton Street, Durham, NC 27705 ‱ Amy Kilbourne, Ph.D., M.P.H. VA HSR&D, 2215 Fuller Road, Ann Arbor, MI 48105 ‱ Shiuh-Wen Luoh, M.D., Ph.D. VA Portland Health Care System, 3710 SW US Veterans Hospital Rd, Portland, OR 97239 ‱ Michael Matheny, M.D., M.S., M.P.H. VA Tennessee Valley Healthcare System, 1310 24th Ave. South, Nashville, TN 37212 ‱ Dave Oslin, M.D. Philadelphia VA Medical Center, 3900 Woodland Avenue, Philadelphia, PA 19104 MVP Co-Principal Investigators ‱ J. Michael Gaziano, M.D., M.P.H. VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 ‱ Philip S. Tsao, Ph.D. VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304 MVP Core Operations ‱ Lori Churby, B.S., Director, MVP Regulatory Affairs VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304 ‱ Stacey B. Whitbourne, Ph.D., Director, MVP Cohort Management VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 ‱ Jessica V. Brewer, M.P.H., Director, MVP Recruitment & Enrollment VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 ‱ Shahpoor (Alex) Shayan, M.S., Director, MVP Recruitment and Enrollment Informatics VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 ‱ Luis E. Selva, Ph.D., Executive Director, MVP Biorepositories VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 ‱ Saiju Pyarajan Ph.D., Director, Data and Computational Sciences VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 ‱ Kelly Cho, M.P.H, Ph.D., Director, MVP Phenomics Data Core VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 ‱ Scott L. DuVall, Ph.D., Director, VA Informatics and Computing Infrastructure (VINCI) VA Salt Lake City Health Care System, 500 Foothill Drive, Salt Lake City, UT 84148 ‱ Mary T. Brophy M.D., M.P.H., Director, VA Central Biorepository VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 ‱ MVP Coordinating Centers o MVP Coordinating Center, Boston - J. Michael Gaziano, M.D., M.P.H. VA Boston Healthcare System, 150 S. Huntington Avenue, Boston, MA 02130 o MVP Coordinating Center, Palo Alto – Philip S. Tsao, Ph.D. VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304 o MVP Information Center, Canandaigua – Brady Stephens, M.S. Canandaigua VA Medical Center, 400 Fort Hill Avenue, Canandaigua, NY 14424 o Cooperative Studies Program Clinical Research Pharmacy Coordinating Center, Albuquerque – Todd Connor, Pharm.D.; Dean P. Argyres, B.S., M.S. New Mexico VA Health Care System, 1501 San Pedro Drive SE, Albuquerque, NM 87108 MVP Publications and Presentations Committee ‱ Co-Chair: Themistocles L. Assimes, M.D., Ph. D VA Palo Alto Health Care System, 3801 Miranda Avenue, Palo Alto, CA 94304 ‱ Co-Chair: Adriana Hung, M.D.; M.P.H VA Tennessee Valley Healthcare System, 1310 24th Ave. South, Nashville, TN 37212 ‱ Co-Chair: Henry Kranzler, M.D. Philadelphia VA Medical Center, 3900 Woodland Avenue, Philadelphia, PA 19104 MVP Local Site Investigators ‱ Samuel Aguayo, M.D., Phoenix VA Health Care System 650 E. Indian School Road, Phoenix, AZ 85012 ‱ Sunil Ahuja, M.D., South Texas Veterans Health Care System 7400 Merton Minter Boulevard, San Antonio, TX 78229 ‱ Kathrina Alexander, M.D., Veterans Health Care System of the Ozarks 1100 North College Avenue, Fayetteville, AR 72703 ‱ Xiao M. Androulakis, M.D., Columbia VA Health Care System 6439 Garners Ferry Road, Columbia, SC 29209 ‱ Prakash Balasubramanian, M.D., William S. Middleton Memorial Veterans Hospital 2500 Overlook Terrace, Madison, WI 53705 ‱ Zuhair Ballas, M.D., Iowa City VA Health Care System 601 Highway 6 West, Iowa City, IA 52246-2208 ‱ Jean Beckham, Ph.D., Durham VA Medical Center 508 Fulton Street, Durham, NC 27705 ‱ Sujata Bhushan, M.D., VA North Texas Health Care System 4500 S. Lancaster Road, Dallas, TX 75216 ‱ Edward Boyko, M.D., VA Puget Sound Health Care System 1660 S. Columbian Way, Seattle, WA 98108-1597 ‱ David Cohen, M.D., Portland VA Medical Center 3710 SW U.S. Veterans Hospital Road, Portland, OR 97239 ‱ Louis Dellitalia, M.D., Birmingham VA Medical Center 700 S. 19th Street, Birmingham AL 35233 ‱ L. Christine Faulk, M.D., Robert J. Dole VA Medical Center 5500 East Kellogg Drive, Wichita, KS 67218-1607 ‱ Joseph Fayad, M.D., VA Southern Nevada Healthcare System 6900 North Pecos Road, North Las Vegas, NV 89086 ‱ Daryl Fujii, Ph.D., VA Pacific Islands Health Care System 459 Patterson Rd, Honolulu, HI 96819 ‱ Saib Gappy, M.D., John D. Dingell VA Medical Center 4646 John R Street, Detroit, MI 48201 ‱ Frank Gesek, Ph.D., White River Junction VA Medical Center 163 Veterans Drive, White River Junction, VT 05009 ‱ Jennifer Greco, M.D., Sioux Falls VA Health Care System 2501 W 22nd Street, Sioux Falls, SD 57105 ‱ Michael Godschalk, M.D., Richmond VA Medical Center 1201 Broad Rock Blvd., Richmond, VA 23249 ‱ Todd W. Gress, M.D., Ph.D., Hershel “Woody” Williams VA Medical Center 1540 Spring Valley Drive, Huntington, WV 25704 ‱ Samir Gupta, M.D., M.S.C.S., VA San Diego Healthcare System 3350 La Jolla Village Drive, San Diego, CA 92161 ‱ Salvador Gutierrez, M.D., Edward Hines, Jr. VA Medical Center 5000 South 5th Avenue, Hines, IL 60141 ‱ John Harley, M.D., Ph.D., Cincinnati VA Medical Center 3200 Vine Street, Cincinnati, OH 45220 ‱ Kimberly Hammer, Ph.D., Fargo VA Health Care System 2101 N. Elm, Fargo, ND 58102 ‱ Mark Hamner, M.D., Ralph H. Johnson VA Medical Center 109 Bee Street, Mental Health Research, Charleston, SC 29401 ‱ Adriana Hung, M.D., M.P.H., VA Tennessee Valley Healthcare System 1310 24th Avenue, South Nashville, TN 37212 ‱ Robin Hurley, M.D., W.G. (Bill) Hefner VA Medical Center 1601 Brenner Ave, Salisbury, NC 28144 ‱ Pran Iruvanti, D.O., Ph.D., Hampton VA Medical Center 100 Emancipation Drive, Hampton, VA 23667 ‱ Frank Jacono, M.D., VA Northeast Ohio Healthcare System 10701 East Boulevard, Cleveland, OH 44106 ‱ Darshana Jhala, M.D., Philadelphia VA Medical Center 3900 Woodland Avenue, Philadelphia, PA 19104 ‱ Scott Kinlay, M.B.B.S., Ph.D., VA Boston Healthcare System 150 S. Huntington Avenue, Boston, MA 02130 ‱ Jon Klein, M.D., Ph.D., Louisville VA Medical Center 800 Zorn Avenue, Louisville, KY 40206 ‱ Michael Landry, Ph.D., Southeast Louisiana Veterans Health Care System 2400 Canal Street, New Orleans, LA 70119 ‱ Peter Liang, M.D., M.P.H., VA New York Harbor Healthcare System 423 East 23rd Street, New York, NY 10010 ‱ Suthat Liangpunsakul, M.D., M.P.H., Richard Roudebush VA Medical Center 1481 West 10th Street, Indianapolis, IN 46202 ‱ Jack Lichy, M.D., Ph.D., Washington DC VA Medical Center 50 Irving St, Washington, D. C. 20422 ‱ C. Scott Mahan, M.D., Charles George VA Medical Center 1100 Tunnel Road, Asheville, NC 28805 ‱ Ronnie Marrache, M.D., VA Maine Healthcare System 1 VA Center, Augusta, ME 04330 ‱ Stephen Mastorides, M.D., James A. Haley Veterans’ Hospital 13000 Bruce B. Downs Blvd, Tampa, FL 33612 ‱ Elisabeth Mates M.D., Ph.D., VA Sierra Nevada Health Care System 975 Kirman Avenue, Reno, NV 89502 ‱ Kristin Mattocks, Ph.D., M.P.H., Central Western Massachusetts Healthcare System 421 North Main Street, Leeds, MA 01053 ‱ Paul Meyer, M.D., Ph.D., Southern Arizona VA Health Care System 3601 S 6th Avenue, Tucson, AZ 85723 ‱ Jonathan Moorman, M.D., Ph.D., James H. Quillen VA Medical Center Corner of Lamont & Veterans Way, Mountain Home, TN 37684 ‱ Timothy Morgan, M.D., VA Long Beach Healthcare System 5901 East 7th Street Long Beach, CA 90822 ‱ Maureen Murdoch, M.D., M.P.H., Minneapolis VA Health Care System One Veterans Drive, Minneapolis, MN 55417 ‱ James Norton, Ph.D., VA Health Care Upstate New York 113 Holland Avenue, Albany, NY 12208 ‱ Olaoluwa Okusaga, M.D., Michael E. DeBakey VA Medical Center 2002 Holcombe Blvd, Houston, TX 77030 ‱ Kris Ann Oursler, M.D., Salem VA Medical Center 1970 Roanoke Blvd, Salem, VA 24153 ‱ Ana Palacio, M.D., M.P.H., Miami VA Health Care System 1201 NW 16th Street, 11 GRC, Miami FL 33125 ‱ Samuel Poon, M.D., Manchester VA Medical Center 718 Smyth Road, Manchester, NH 03104 ‱ Emily Potter, Pharm.D., VA Eastern Kansas Health Care System 4101 S 4th Street Trafficway, Leavenworth, KS 66048 ‱ Michael Rauchman, M.D., St. Louis VA Health Care System 915 North Grand Blvd, St. Louis, MO 63106 ‱ Richard Servatius, Ph.D., Syracuse VA Medical Center 800 Irving Avenue, Syracuse, NY 13210 ‱ Satish Sharma, M.D., Providence VA Medical Center 830 Chalkstone Avenue, Providence, RI 02908 ‱ River Smith, Ph.D., Eastern Oklahoma VA Health Care System 1011 Honor Heights Drive, Muskogee, OK 74401 ‱ Peruvemba Sriram, M.D., N. FL/S. GA Veterans Health System 1601 SW Archer Road, Gainesville, FL 32608 ‱ Patrick Strollo, Jr., M.D., VA Pittsburgh Health Care System University Drive, Pittsburgh, PA 15240 ‱ Neeraj Tandon, M.D., Overton Brooks VA Medical Center 510 East Stoner Ave, Shreveport, LA 71101 ‱ Philip Tsao, Ph.D., VA Palo Alto Health Care System 3801 Miranda Avenue, Palo Alto, CA 94304-1290 ‱ Gerardo Villareal, M.D., New Mexico VA Health Care System 1501 San Pedro Drive, S.E. Albuquerque, NM 87108 ‱ Agnes Wallbom, M.D., M.S., VA Greater Los Angeles Health Care System 11301 Wilshire Blvd, Los Angeles, CA 90073 ‱ Jessica Walsh, M.D., VA Salt Lake City Health Care System 500 Foothill Drive, Salt Lake City, UT 84148 ‱ John Wells, Ph.D., Edith Nourse Rogers Memorial Veterans Hospital 200 Springs Road, Bedford, MA 01730 ‱ Jeffrey Whittle, M.D., M.P.H., Clement J. Zablocki VA Medical Center 5000 West National Avenue, Milwaukee, WI 53295 ‱ Mary Whooley, M.D., San Francisco VA Health Care System 4150 Clement Street, San Francisco, CA 94121 ‱ Allison E. Williams, N.D., Ph.D., R.N, Bay Pines VA Healthcare System 10,000 Bay Pines Blvd Bay Pines, FL 33744 ‱ Peter Wilson, M.D., Atlanta VA Medical Center 1670 Clairmont Road, Decatur, GA 30033 ‱ Junzhe Xu, M.D., VA Western New York Healthcare System 3495 Bailey Avenue, Buffalo, NY 14215-1199 ‱ Shing Shing Yeh, Ph.D., M.D., Northport VA Medical Center 79 Middleville Road, Northport, NY 11768 Contributors to AMED GRIFIN Diabetes Initiative Japan Ken Suzuki. Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. Department of Statistical Genetics, Osaka University, Graduate School of Medicine, Suita, Japan. Kyoto Sonehara. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan. Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan. Shinichi Namba. Department of Statistical Genetics, Osaka University, Graduate School of Medicine, Suita, Japan. Kenichi Yamamoto. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan. Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan. Nobuhiro Shojima. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. Momoko Horikoshi. Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan. Shiro Maeda. Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan. Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Okinawa, Japan Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan. Koichi Matsuda. Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan Yukinori Okada. Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan. Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan. Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan. Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan. Toshimasa Yamauchi. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. Takashi Kadowaki. Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. Toranomon Hospital, Tokyo, Japan. Contributors to Biobank Japan Project Koichi Matsuda. Laboratory of Genome Technology, Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan. Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan. Yuji Yamanashi. Division of Genetics, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan. Yoichi Furukawa. Division of Clinical Genome Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan. Takayuki Morisaki. Division of Molecular Pathology, IMSUT Hospital Department of Internal Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan. Yoshinori Murakami. Department of Cancer Biology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan. Yoichiro Kamatani. Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan. Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan. Kaori Muto. Department of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan. Akiko Nagai. Department of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan. Wataru Obara. Department of Urology, Iwate Medical University, Iwate, Japan. Ken Yamaji. Department of Internal Medicine and Rheumatology, Juntendo University Graduate School of Medicine, Tokyo, Japan. Kazuhisa Takahashi. Department of Respiratory Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. Satoshi Asai. Division of Pharmacology, Department of Biomedical Science, Nihon University School of Medicine, Tokyo, Japan. Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University. School of Medicine, Tokyo, Japan. Yasuo Takahashi. Division of Genomic Epidemiology and Clinical Trials, Clinical Trials Research Center, Nihon University School of Medicine, Tokyo, Japan. Takao Suzuki. Tokushukai Group, Tokyo, Japan. Nobuaki Sinozaki. Tokushukai Group, Tokyo, Japan. Hiroki Yamaguchi. Department of Hematology, Nippon Medical School, Tokyo, Japan. Shiro Minami. Department of Bioregulation, Nippon Medical School, Kawasaki, Japan. Shigeo Murayama. Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan. Kozo Yoshimori. Fukujuji Hospital, Japan Anti-Tuberculosis Association, Tokyo, Japan. Satoshi Nagayama. The Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan. Daisuke Obata. Center for Clinical Research and Advanced Medicine, Shiga University of Medical Science, Shiga, Japan. Masahiko Higashiyama. Department of General Thoracic Surgery, Osaka International Cancer Institute, Osaka, Japan. Akihide Masumoto. Iizuka Hospital, Fukuoka, Japan. Yukihiro Koretsune. National Hospital Organization Osaka National Hospital, Osaka, Japan. Penn Medicine BioBank Banner Author List and Contribution Statements PMBB Leadership Team Daniel J. Rader, M.D., Marylyn D. Ritchie, Ph.D., Michael D. Feldman M.D. Contribution: All authors contributed to securing funding, study design and oversight. All authors reviewed the final version of the manuscript. Patient Recruitment and Regulatory Oversight JoEllen Weaver, Nawar Naseer, Ph.D., M.P.H., Afiya Poindexter, Ashlei Brock, Khadijah Hu- Sain, Yi-An Ko Contributions: JW manages patient recruitment and regulatory oversight of study. N.N. manages participant engagement, assists with regulatory oversight, and researcher access. A.P., A.B., K.H., Y.K. perform recruitment and enrollment of study participants. Lab Operations JoEllen Weaver, Meghan Livingstone, Fred Vadivieso, Ashley Kloter, Stephanie DerOhannessian, Teo Tran, Linda Morrel, Ned Haubein, Joseph Dunn Contribution: J.W., M.L., F.V., S.D. conduct oversight of lab operations. M.L., F.V., A.K., S.D., T.T., L.M. perform sample processing. N.H., J.D. are responsible for sample tracking and the laboratory information management system. Clinical Informatics Anurag Verma, Ph.D., Colleen Morse, M.S., Marjorie Risman, M.S., Renae Judy, B.S. Contribution: All authors contributed to the development and validation of clinical phenotypes used to identify study subjects and (when applicable) controls. Genome Informatics Anurag Verma Ph.D., Shefali S. Verma, Ph.D., Yuki Bradford, M.S., Scott Dudek, M.S., Theodore Drivas, M.D., PH.D. Contribution: A.V., S.S.V. are responsible for the analysis, design, and infrastructure needed to quality control genotype and exome data. Y.B. performs the analysis. T.D. and A.V. provides variant and gene annotations and their functional interpretation of variants. Regeneron Genetics Center Banner Author List and Contribution Statements RGC Management and Leadership Team Goncalo Abecasis, D.Phil., Aris Baras, M.D., Michael Cantor, M.D., Giovanni Coppola, M.D., Andrew Deubler, Aris Economides, Ph.D., Luca A. Lotta, M.D., Ph.D., John D. Overton, Ph.D., Jeffrey G. Reid, Ph.D., Katherine Siminovitch, M.D., Alan Shuldiner, M.D. Sequencing and Lab Operations Christina Beechert, Caitlin Forsythe, M.S., Erin D. Fuller, Zhenhua Gu, M.S., Michael Lattari, Alexander Lopez, M.S., John D. Overton, Ph.D., Maria Sotiropoulos Padilla, M.S., Manasi Pradhan, M.S., Kia Manoochehri, B.S., Thomas D. Schleicher, M.S., Louis Widom, Sarah E. Wolf, M.S., Ricardo H. Ulloa, B.S. Clinical Informatics Amelia Averitt, Ph.D., Nilanjana Banerjee, Ph.D., Michael Cantor, M.D., Dadong Li, Ph.D., Sameer Malhotra, M.D., Deepika Sharma, M.H.I., Jeffrey Staples, Ph.D. Genome Informatics Xiaodong Bai, Ph.D., Suganthi Balasubramanian, Ph.D., Suying Bao, Ph.D., Boris Boutkov, Ph.D., Siying Chen, Ph.D., Gisu Eom, B.S., Lukas Habegger, Ph.D., Alicia Hawes, B.S., Shareef Khalid, Olga Krasheninina, M.S., Rouel Lanche, B.S., Adam J. Mansfield, B.A., Evan K. Maxwell, Ph.D., George Mitra, B.A., Mona Nafde, M.S., Sean O’Keeffe, Ph.D., Max Orelus, B.B.A., Razvan Panea, Ph.D., Tommy Polanco, B.A., Ayesha Rasool, M.S., Jeffrey G. Reid, Ph.D., William Salerno, Ph.D., Jeffrey C. Staples, Ph.D., Kathie Sun, Ph.D. Analytical Genomics and Data Science Goncalo Abecasis, D.Phil., Joshua Backman, Ph.D., Amy Damask, Ph.D., Lee Dobbyn, Ph.D., Manuel Allen Revez Ferreira, Ph.D., Arkopravo Ghosh, M.S., Christopher Gillies, Ph.D., Lauren Gurski, B.S., Eric Jorgenson, Ph.D., Hyun Min Kang, Ph.D., Michael Kessler, Ph.D., Jack Kosmicki, Ph.D., Alexander Li, Ph.D., Nan Lin, Ph.D., Daren Liu, M.S., Adam Locke, Ph.D., Jonathan Marchini, Ph.D., Anthony Marcketta, M.S., Joelle Mbatchou, Ph.D., Arden Moscati, Ph.D., Charles Paulding, Ph.D., Carlo Sidore, Ph.D., Eli Stahl, Ph.D., Kyoko Watanabe, Ph.D., Bin Ye, Ph.D., Blair Zhang, Ph.D., Andrey Ziyatdinov, Ph.D. Therapeutic Area Genetics Ariane Ayer, B.S., Aysegul Guvenek, Ph.D., George Hindy, Ph.D., Giovanni Coppola, M.D., Jan Freudenberg, M.D., Jonas Bovijn M.D., Katherine Siminovitch, M.D., Kavita Praveen, Ph.D., Luca A. Lotta, M.D., Manav Kapoor, Ph.D., Mary Haas, Ph.D., Moeen Riaz, Ph.D., Niek Verweij, Ph.D., Olukayode Sosina, Ph.D., Parsa Akbari, Ph.D., Priyanka Nakka, Ph.D., Sahar Gelfman, Ph.D., Sujit Gokhale, B.E., Tanima De, Ph.D., Veera Rajagopal, Ph.D., Alan Shuldiner, M.D., Bin Ye, Ph.D., Gannie Tzoneva, Ph.D., Juan Rodriguez-Flores, Ph.D. Research Program Management and Strategic Initiatives Esteban Chen, M.S., Marcus B. Jones, Ph.D., Michelle G. LeBlanc, Ph.D., Jason Mighty, Ph.D., Lyndon J. Mitnaul, Ph.D., Nirupama Nishtala, Ph.D., Nadia Rana, Ph.D., Jaimee Hernandez Genes & Health Research Team Shaheen Akhtar, Mohammad Anwar, Elena Arciero, Omar Asgar, Samina Ashraf, Saeed Bidi, Gerome Breen, James Broster, Raymond Chung, David Collier, Charles J Curtis, Shabana Chaudhary, Megan Clinch, Grainne Colligan, Panos Deloukas, Ceri Durham, Faiza Durrani, Fabiola Eto, Sarah Finer, Joseph Gafton, Ana Angel Garcia, Chris Griffiths, Joanne Harvey, Teng Heng, Sam Hodgson, Qin Qin Huang, Matt Hurles, Karen A Hunt, Shapna Hussain, Kamrul Islam, Vivek Iyer, Ben Jacobs, Ahsan Khan, Cath Lavery, Sang Hyuck Lee, Robin Lerner, Daniel MacArthur, Daniel Malawsky, Hilary Martin, Dan Mason, Rohini Mathur, Mohammed Bodrul Mazid, John McDermott, Caroline Morton, Bill Newman, Elizabeth Owor, Asma Qureshi, Samiha Rahman, Shwetha Ramachandrappa, Mehru Reza, Jessry Russell, Nishat Safa, Miriam Samuel, Michael Simpson, John Solly, Marie Spreckley. Daniel Stow, Michael Taylor, Richard C Trembath, Karen Tricker, Nasir Uddin, David A van Heel, Klaudia Walter, Caroline Winckley, Suzanne Wood, John Wright, Julia Zollner. Contributors to eMERGE Consortium Debra Abrams3, Samuel E Adunyah4, Ladia Albertson-Junkans5, Berta Almoguera6, Darren C Ames7, Paul Appelbaum8, Samuel Aronson9, Sharon Aufox10, Lawrence J Babb11, Adithya Balasubramanian1,12, Hana Bangash13, Melissa Basford14, Lisa Bastarache15, Samantha Baxter11, Meckenzie Behr3, Barbara Benoit16, Elizabeth Bhoj3, Suzette J Bielinski17, Sarah T Bland15, Carrie Blout18, Kenneth Borthwick19, Erwin P Bottinger20, Mark Bowser21, Harrison Brand22, Murray Brilliant23, Wendy Brodeur24, Pedro Caraballo25, David Carrell5, Andrew Carroll26, Lisa Castillo27, Victor Castro28, Gauthami Chandanavelli1, Theodore Chiang29, Rex L Chisholm30, Kurt D Christensen31, Wendy Chung32, Christopher G Chute33, Brittany City14, Beth L Cobb34, John J Connolly3, Paul Crane35, Katherine Crew36, David R Crosslin37, Jyoti Dayal38, Mariza De Andrade17, Jessica De la Cruz1,12, Josh C Denny39, Shawn Denson1,2, Tim DeSmet11, Ozan Dikilitas13, Michael J Dinsmore11, Sheila Dodge11, Phil Dunlea11, Todd L Edwards40, Christine M Eng12, David Fasel41, Alex Fedotov42, Qiping Feng43, Mark Fleharty11, Andrea Foster1,2, Robert Freimuth44, Christopher Friedrich11, Stephanie M Fullerton45, Birgit Funke46, Stacey Gabriel24, Vivian Gainer47, Ali Gharavi48, Richard A Gibbs1,12, Andrew M Glazer49, Joseph T Glessner50, Jessica Goehringer51, Adam S Gordon52,53, Chet Graham54, Robert C Green55, Justin H Gundelach13, Heather S Hain56, Hakon Hakonarson57, Maegan V Harden24, John Harley58, Margaret Harr59, Andrea Hartzler60, M Geoffrey Hayes61, Scott Hebbring62, Nora Henrikson63, Andrew Hershey64, Christin Hoell30, Ingrid Holm65, Kayla M Howell14, George Hripcsak41,66, Jianhong Hu1, Elizabeth Duffy Hynes21, Gail P Jarvik52,67, Joy C Jayaseelan1, Yunyun Jiang1,12, Yoonjung Yoonie Joo68, Sheethal Jose38, Navya Shilpa Josyula69, Anne E Justice70, Sara E Kalla1, Divya Kalra1, Elizabeth W Karlson71, Brendan J Keating72, Melissa A Kelly73, Eimear E Kenny74, Dustin Key5, Krzysztof Kiryluk75, Terrie Kitchner23, Barbara Klanderman76, Eric Klee77, David C Kochan78, Viktoriya Korchina1, Leah Kottyan79, Christie Kovar1, Emily Kudalkar54, Alanna Kulchak Rahm80, Iftikhar J Kullo81, Philip Lammers82, Eric B Larson83, Matthew S Lebo84, Magalie Leduc85, Ming Ta Lee86, Niall J Lennon24, Kathleen A Leppig87, Nancy D Leslie88, Rongling Li89, Wayne H Liang90, Chiao-Feng Lin91, Jodell E Linder14, Noralane M Lindor92, Todd Lingren93, James G Linneman23, Cong Liu94, Wen Liu1, Xiuping Liu1, John Lynch95, Hayley Lyon96, Alyssa Macbeth97, Harshad Mahadeshwar1, Lisa Mahanta98, Bradley Malin99, Teri Manolio38, Maddalena Marasa100, Keith Marsolo101, Michelle L McGowan102, Elizabeth McNally53, Jim Meldrim24, Frank Mentch3, Hila Milo Rasouly103, Jonathan Mosley104, Shubhabrata Mukherjee35, Thomas E Mullen24, Jesse Muniz1, David R Murdock1,12, Shawn Murphy105, Mullai Murugan106, Donna Muzny107, Melanie F Myers108, Bahram Namjou34,109, Yizhao Ni110, Robert C Onofrio24, Aniwaa Owusu Obeng111,112, Thomas N Person113, Josh F Peterson114, Lynn Petukhova115, Cassandra J Pisieczko116, Siddharth Pratap117, Cynthia A Prows118, Megan J Puckelwartz119, Ritika Raj1, James D Ralston120, Arvind Ramaprasan5, Andrea Ramirez121, Luke Rasmussen122, Laura Rasmussen- Torvik123, Soumya Raychaudhuri124, Heidi L Rehm125, Marylyn D Ritchie126, Catherine Rives127, Beenish Riza128, Dan M Roden129, Elisabeth A Rosenthal130, Avni Santani131, Dan Schaid17, Steven Scherer1,12, Stuart Scott132, Aaron Scrol133, Soumitra Sengupta134, Ning Shang41, Himanshu Sharma135, Richard R Sharp136, Rajbir Singh137, Patrick M A Sleiman138, Kara Slowik139, Joshua C Smith140, Maureen E Smith141, Duane T Smoot142, Jordan W Smoller143, Sunghwan Sohn144, Ian B Stanaway37, Justin Starren145, Mary Stroud15, Jessica Su146, Casey Overby Taylor147, Kasia Tolwinski148, Sara L Van Driest149,150, Sean M Vargas151, Matthew Varugheese152, David Veenstra153, Eric Venner1,12, Miguel Verbitsky154, Gina Vicente155, Michael Wagner156, Kimberly Walker157, Theresa Walunas158, Liwen Wang159, Qiaoyan Wang160, Wei-Qi Wei15, Scott T Weiss161, Quinn S Wells162, Chunhua Weng163, Peter S White164, Georgia L Wiesner165, Ken L Wiley Jr38, Janet L Williams166, Marc S Williams167, Michael W Wilson24, Leora Witkowski168, Laura Allison Woods14, Betty Woolf24, Tsung-Jung Wu1, Julia Wynn169, Yaping Yang170, Victoria Yi1, Ge Zhang171,172, Lan Zhang1, Hana Zouk173. 1Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA. 2Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. 3Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA. 4Department of Biochemistry and Cancer Biology, Meharry Medical College, Nashville, TN, USA. 5Kaiser Permanente of WA Health Research Institute, Seattle, WA, USA. 6Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA. 7DNAnexus Inc, Mountain View, CA, USA. 8Department of Psychiatry, Columbia University, New York State Psychiatric Institute, NYSPI, New York, NY, USA. 9Partners HealthCare, Cambridge, MA, USA. 10Center for Genetic Medicine, Northwestern University, Chicago, IL, USA. 11Broad Institute, Massachusetts, MA, USA. 12Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA. 13Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA. 14Vanderbilt Institute for Clinical & Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA. 15Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. 16Research Information Science and Computing, Partners Healthcare, Somerville, MA, USA. 17Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. 18Brigham and Women’s Hospital, Boston, MA, USA. 19Geisinger, Hood Center for Health Research, Danville, PA, USA. 20Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 21Partners HealthCare Personalized Medicine, Cambridge, MA, USA. 22Massachusetts General Hospital, Boston, MA, USA. 23Marshfield Clinic Research Institute, Marshfield, WI, USA. 24Broad Institute of MIT and Harvard, Massachusetts, MA, USA. 25Mayo Clinic, Rochester, MN, USA. 26Google Inc, Mountain View, CA, USA. 27Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Department of Cardiology, The Louis A Simpson and Kimberly K Querrey Biomedical Research Center Room 5-408, Chicago, IL, USA. 28Research Information Science and Computing (RISC), Partners Healthcare, Somerville, MA, USA. 29Baylor College of Medicine, One Baylor Plaza, Houston, USA. 30Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. 31Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA. 32Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA. 33Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA. 34Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, OH, USA. 35Department of Medicine, University of Washington, Seattle, WA, USA. 36Columbia University Irving Medical Center, New York, NY, USA. 37Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA. 38National Human Genome Research Institute, Maryland, MD, USA. 39Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, TN, USA. 40Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA. 41Department of Biomedical Informatics, Columbia University, New York, NY, USA. 42Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, USA. 43Department of Medicine, Division of Clinical Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA. 44Department of Health Sciences Research, Mayo Clinic, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA. 45Department of Bioethics & Humanties, University of Washington, Seattle, WA, USA. 46Harvard Medical School, Boston, MA, USA. 47Partners HealthCare, Somerville, MA, USA. 48Department of Medicine, Division of Nephrology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA. 49Vanderbilt University Medical Center, Department of Medicine, Nashville, TN, USA. 50Center for Applied Genomics Children’s Hospital of Philadelphia, Division of Human Genetics Children’s Hospital of Philadelphia, Department of Pediatrics Perelman School of Medicine University of Pennsylvania, Philadelphia, PA, USA. 51Geisinger Medical Center, Danville, PA, USA. 52Department of Medicine (Medical Genetics), University of Washington School of Medicine, Seattle, WA, USA. 53Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 54Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, MA, USA. 55Brigham and Women’s Hospital, Broad Institute, Harvard Medical School, EC Alumnae Building, Boston, MA, USA. 56Center for Applied Genomics Children’s Hospital of Philadelphia, Division of Human Genetics Children’s Hospital of Philadelphia, Philadelphia, PA, USA. 57Center for Applied Genomics Children’s Hospital of Philadelphia, Divisions of Human Genetics and Pulmonary Medicine Children’s Hospital of Philadelphia, Department of Pediatrics Perelman School of Medicine University of Pennsylvania, Philadelphia, PA, USA. 58Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, US Department of Veterans Affairs Medical Center, Cincinnati, Cincinnati, OH, USA. 59Center for Applied Genomics Children’s Hospital of Philadelphia, Philadelphia, PA, USA. 60Department of Biomedical Informatics and Medical Education, University of Washignton School of Medicine, KP Washington Health Research Institute, Seattle, WA, USA. 61Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Department of Anthropology, Northwestern University, Chicago, IL, USA. 62Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, USA. 63KP Washington Health Research Institute, Univ of Washington School of Public Health, Dept of Health Services, Seattle, WA, USA. 64Cincinnati Children’s Hospital Medical Center (CCHMC), University of Cincinnati College of Medicine, Cincinnati, OH, USA. 65Division of Genetics and Genomics, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA. 66Medical Informatics Services, NewYork- Presbyterian Hospital, New York, NY, USA. 67Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, USA. 68Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 69Biomedical and Translational Informatics, Geisinger, Fremont, CA, USA. 70Biomedical and Translational Informatics, Geisinger, Danville, PA, USA. 71Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA. 72Children’s Hospital of Philadelphia, Department of Surgery, University of Pennsylvania, Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA. 73Geisinger, Danville, PA, USA. 74Center for Genomic Health, Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, Departments of Genetics and Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 75Columbia University, New York, NY, USA. 76Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Brigham and Women’s Hospital, Cambridge, MA, USA. 77Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. 78Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA. 79Department of Pediatrics, University of Cincinnati college of Medicine, University of Cincinnati, Center of Autoimmune Genomics and Etiology, Division of Allergy & Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA. 80Geisinger Genomic Medicine Institute, Danville, PA, USA. 81Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA. 82Meharry Medical College, Baptist Cancer Center, Memphis, TN, USA. 83Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA. 84Partners Healthcare Personalized Medicine, Brigham and Women’s Hospital, Harvard Medical School, Cambridge, MA, USA. 85Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA. 86Geisinger, Danville, PA, USA. 87Genetic Services Kaiser Permanente of Washington, Seattle, WA, USA. 88Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA. 89National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. 90University of Alabama at Birmingham, Birmingham, AL, USA. 91Partners Healthcare Personalized Medicine, Harvard Medical School, Mountain View, CA, USA. 92Mayo Clinic, Scottsdale, AZ, USA. 93Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA. 94Department of Biomedical Informatics, Columbia University Medical Center, Columbia University, New York, NY, USA. 95Department of Communication, University of Cincinnati, Cincinnati, OH, USA. 96Broad Institute of MIT & Harvard, Cambridge, MA, USA. 97Broad Institute of MIT & Harvard, Cambridge, MA, USA. 98Partners Healthcare Personalized Medicine, Cambridge, MA, USA. 99Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. 100Department of Medicine, Division of Nephrology, Columbia University, New York, NY, USA. 101Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA. 102Ethics Center, Cincinnati Children’s Hospital Medical Center, Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA. 103Department of Medicine, Division of Nephrology, Columbia University, New York, NY, USA. 104Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. 105Massachusetts General Hospital, Partners Healthcare, Harvard Medical School, Somerville, MA, USA. 106Human Genome Sequencing Center at the Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA. 107Human Genome Sequencing Center at the Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA. 108Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, College of Medicine, University of Cincinnati, Cincinnati, OH, USA. 109Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA. 110Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA. 111The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 112Pharmacy Department, Mount Sinai Hospital, New York, NY, USA. 113Geisinger, Danville, PA, USA. 114Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. 115Department of Dermatology, Columbia University, New York, NY, USA. 116Geisinger, Danville, PA, USA. 117School of Graduate Studies and Research, Meharry Medical College, Nashville, TN, USA. 118Division of Human Genetics, Division of Patient Services, Cincinnati Children’s Hospital, Cincinnati, OH, USA. 119Department of Pharmacology, Northwestern University Feinberg School of Medicine, Center for Genetic Medicine, Northwestern University, Chicago, IL, USA. 120Kaiser Permanente Washington Health Research Institute, University of Washington Department of Biomedical Informatics and Medical Education, Seattle, WA, USA. 121Vanderbilt University Medical Center Department of Medicine, Nashville, TN, USA. 122Department of Preventive Medicine Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 123Department of Preventive Medicine Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 124Medical and Population Genetics, Broad Institute or MIT and Harvard, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. 125Center for Genomic Medicine, Massachusetts General Hospital, Department of Pathology, Massachusetts General Hospital/Harvard Medical School, Broad Institue Clinical Research Sequencing Platform (CRSP), Simches Research Building, Boston, MA, USA. 126University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA. 127Human Genome Sequencing Center Baylor College of Medicine, Houston, TX, USA. 128University of Texas at Arlington, Human Genome Sequencing Center Baylor College of Medicine, Houston, TX, USA. 129Vanderbilt University Medical Center, Nashville, TN, USA. 130Division of Medical Genetics, School of Medicine, University of Washington, Seattle, WA, USA. 131Center for Applied Genomics, Children’s Hospital of Philadelphia, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA. 132Icahn School of Medicine at Mount Sinai, New York, NY, USA. 133KP Washington Health Research Institute, Seattle, WA, USA. 134Columbia University, New York, NY, USA. 135Partners Healthcare, Cambridge, MA, USA. 136Biomedical Ethics Program, Mayo Clinic, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. 137Clinical and Translational Research Center, Meharry Medical College, Nashville, TN, USA. 138Center for Applied Genomics, Children’s Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 139Broad Institute of MIT & Harvard, Cambridge, MA, USA. 140Department of Biomedical Informatics, Vanderbilt University Medical Center, Center for Patient and Professional Advocacy, Vanderbilt University, Nashville, TN, USA. 141Northwestern University, Chicago, IL, USA. 142Department of Internal Medicine, Meharry Medical College, Nashville, TN, USA. 143Department of Psychiatry and Center for Genomic Medicine, Massachusetts General Hospital, Stanley Center for Psychiatric Research, Simches Research Building, Boston, MA, USA. 144Mayo Clinic, Rochester, MN, USA. 145Feinberg School of Medicine, Northwestern University, Chicago, IL, USA. 146Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, USA. 147Johns Hopkins University, Geisinger, Baltimore, MD, USA. 148Biomedical Ethics Unit, Social Studies of Medicine, Faculty of Medicine, McGill University, Montreal, QC, Canada. 149Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA. 150Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. 151The University of Texas at San Antonio, San Antonio, TX, USA. 152Massachusetts General Hospital, Partners HealthCare, Cambridge, MA, USA. 153Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, Department of Pharmacy, University of Washington, Seattle, WA, USA. 154Division of Nephrology Department of Medicine, Columbia University, New York, NY, USA. 155Broad Institute, Cambridge, MA, USA. 156Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA. 157Human Genome Sequencing Center, Baylor College of Medicine, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA. 158Northwestern University, Chicago, IL, USA. 159Human Genome Sequencing Center at the Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA. 160Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA. 161Channing Division of Network Medicine, Brigham and Women’s Hospital, Department of Medicine, Harvard Medical School, Boston, MA, USA. 162Department of Medicine, Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. 163Columbia University, New York, NY, USA. 164Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. 165Department of Medicine, Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA. 166Geisinger, Danville, PA, USA. 167Geisinger, Danville, PA, USA. 168Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Department of Pathology, Massachusetts General Hospital/Harvard Medical School, Cambridge, MA, USA. 169Departments of Pediatrics, Columbia University, New York, NY, USA. 170Department of Molecular and Genetics, Baylor College of Medicine, Houston, TX, USA. 171Division of Human Genetics, Center for Prevention of Preterm Birth, Perinatal Institute and March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati Children’s Hospital Medical Center, Cincinnati, Cincinnati, OH, USA. 172Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. 173Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Department of Pathology, Massachusetts General Hospital/Harvard Medical School, Cambridge, MA, USA. Membership of the International Consortium of Blood Pressure Authors and contributors associated with the 2018 Nature Genetics publication: “Genetic analysis of over one million people identifies 535 new loci associated with blood pressure traits”. Evangelos Evangelou1,2, Helen R Warren3,4, He Gao1,5, Georgios Ntritsos2, Niki Dimou2, Tonu Esko16,17, Reedik MĂ€gi16, Lili Milani16, Peter Almgren18, Thibaud Boutin19, StĂ©phanie Debette20,21, Jun Ding22, Franco Giulianini23, Elizabeth G Holliday24, Anne U Jackson25, Ruifang Li-Gao26, Wei-Yu Lin27, Jian'an Luan28, Massimo Mangino29,30, Christopher Oldmeadow24, Bram Peter Prins31, Yong Qian22, Muralidharan Sargurupremraj21, Nabi Shah32,33, Praveen Surendran27, SĂ©bastien ThĂ©riault34,35, Niek Verweij17,36,37, Sara M Willems28, Jing-Hua Zhao28, Philippe Amouyel38, John Connell39, RenĂ©e de Mutsert26, Alex SF Doney32, Martin Farrall40,41, Cristina Menni29, Andrew D Morris42, Raymond Noordam43, Guillaume ParĂ©34, Neil R Poulter44, Denis C Shields45, Alice Stanton46, Simon Thom47, Gonçalo Abecasis48, Najaf Amin49, Dan E Arking50, Kristin L Ayers51,52, Caterina M Barbieri53, Chiara Batini54, Joshua C Bis55, Tineka Blake54, Murielle Bochud56, Michael Boehnke25, Eric Boerwinkle57, Dorret I Boomsma58, Erwin P Bottinger59, Peter S Braund60,61, Marco Brumat62, Archie Campbell63,64, Harry Campbell65, Aravinda Chakravarti50, John C Chambers1,5,66-68, Ganesh Chauhan69, Marina Ciullo70,71, Massimiliano Cocca72, Francis Collins73, Heather J Cordell51, Gail Davies74,75, Martin H de Borst76, Eco J de Geus58, Ian J Deary74,75, Joris Deelen77, Fabiola Del Greco M78, Cumhur Yusuf Demirkale79, Marcus Dörr80,81, Georg B Ehret50,82, Roberto Elosua83,84, Stefan Enroth85, A Mesut Erzurumluoglu54, Teresa Ferreira86,87, Mattias FrĂ„nberg88-90, Oscar H Franco91, Ilaria Gandin62, Paolo Gasparini62,72, Vilmantas Giedraitis92, Christian Gieger93-95, Giorgia Girotto62,72, Anuj Goel40,41, Alan J Gow74,96, Vilmundur Gudnason97,98, Xiuqing Guo99, Ulf Gyllensten85, Anders Hamsten88,89, Tamara B Harris100, Sarah E Harris63,74, Catharina A Hartman101, Aki S Havulinna102,103, Andrew A Hicks78, Edith Hofer104,105, Albert Hofman91,106, Jouke-Jan Hottenga58, Jennifer E Huffman19,107,108, Shih-Jen Hwang107,108, Erik Ingelsson109,110, Alan James111,112, Rick Jansen113, Marjo-Riitta Jarvelin1,5,114- 116, Roby Joehanes107,117, Åsa Johansson85, Andrew D Johnson107,118, Peter K Joshi65, Pekka Jousilahti102, J Wouter Jukema119, Antti Jula102, Mika KĂ€hönen120,121, Sekar Kathiresan17,36,122, Bernard D Keavney123,124, Kay-Tee Khaw125, Paul Knekt102, Joanne Knight126, Ivana Kolcic127, Jaspal S Kooner5,67,68,128, Seppo Koskinen102, Kati Kristiansson102, Zoltan Kutalik56,129, Maris Laan130, Marty Larson107, Lenore J Launer100, Benjamin Lehne1, Terho LehtimĂ€ki131,132, David CM Liewald74,75, Li Lin82, Lars Lind133, Cecilia M Lindgren40,87,134, YongMei Liu135, Ruth JF Loos28,59,136, Lorna M Lopez74,137,138, Yingchang Lu59, Leo-Pekka LyytikĂ€inen131,132, Anubha Mahajan40, Chrysovalanto Mamasoula139, Jaume Marrugat83, Jonathan Marten19, Yuri Milaneschi140, Anna Morgan62, Andrew P Morris40,141, Alanna C Morrison142, Peter J Munson79, Mike A Nalls143,144, Priyanka Nandakumar50, Christopher P Nelson60,61, Teemu Niiranen102,145, Ilja M Nolte146, Teresa Nutile70, Albertine J Oldehinkel147, Ben A Oostra49, Paul F O'Reilly148, Elin Org16, Sandosh Padmanabhan64,149, Walter Palmas150, Aarno Palotie103,151,152, Alison Pattie75, Brenda WJH Penninx140, Markus Perola102,103,153, Annette Peters94,95,154, Ozren Polasek127,155, Peter P Pramstaller78,156,157, Quang Tri Nguyen79, Olli T Raitakari158,159, Rainer Rettig161, Kenneth Rice162, Paul M Ridker23,163, Janina S Ried94, HarriĂ«tte Riese147, Samuli Ripatti103,164, Antonietta Robino72, Lynda M Rose23, Jerome I Rotter99, Igor Rudan165, Daniela Ruggiero70,71, Yasaman Saba166, Cinzia F Sala53, Veikko Salomaa102, Nilesh J Samani60,61, Antti-Pekka Sarin103, Reinhold Schmidt104, Helena Schmidt166, Nick Shrine54, David Siscovick167, Albert V Smith97,98, Harold Snieder146, Siim SĂ”ber130, Rossella Sorice70, John M Starr74,168, David J Stott169, David P Strachan170, Rona J Strawbridge88,89, Johan Sundström133, Morris A Swertz171, Kent D Taylor99, Alexander Teumer81,172, Martin D Tobin54, Maciej Tomaszewski123,124, Daniela Toniolo53, Michela Traglia53, Stella Trompet119,173, Jaakko Tuomilehto174-177, Christophe Tzourio21, AndrĂ© G Uitterlinden91,178, Ahmad Vaez146,179, Peter J van der Most146, Cornelia M van Duijn49, Germaine C Verwoert91, Veronique Vitart19, Uwe Völker81,180, Peter Vollenweider181, Dragana Vuckovic62,182, Hugh Watkins40,41, Sarah H Wild183, Gonneke Willemsen58, James F Wilson19,65, Alan F Wright19, Jie Yao99, Tatijana Zemunik184, Weihua Zhang1,67, John R Attia24, Adam S Butterworth27,185, Daniel I Chasman23,163, David Conen186,187, Francesco Cucca188,189, John Danesh27,185, Caroline Hayward19, Joanna MM Howson27, Markku Laakso190, Edward G Lakatta191, Claudia Langenberg28, Olle Melander18, Dennis O Mook-Kanamori26,192, Colin NA Palmer32, Lorenz Risch193-195, Robert A Scott28, Rodney J Scott24, Peter Sever128, Tim D Spector29, Pim van der Harst196, Nicholas J Wareham28, Eleftheria Zeggini31, Daniel Levy107,118, Patricia B Munroe3,4, Christopher Newton-Cheh134,197,198, Morris J Brown3,4, Andres Metspalu16, Bruce M. Psaty201,202, Louise V Wain54, Paul Elliott1,5,203-205, Mark J Caulfield3,4 1Department of Epidemiology and Biostatistics, Imperial College London, London, UK. 2Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece. 3William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK. 4National Institute for Health Research, Barts Cardiovascular Biomedical Research Center, Queen Mary University of London, London, UK. 5MRC-PHE Centre for Environment and Health, Imperial College London, London, UK. 7Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA. 8Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center; Tennessee Valley Health Systems VA, Nashville, TN, USA. 9Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA. 10Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA. 11Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA. 12Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, Department of Medicine, Harvard Medical School, Boston, MA, USA. 13Atlanta VAMC and Emory Clinical Cardiovascular Research Institute, Atlanta, GA, USA. 14VA Palo Alto Health Care System; Division of Cardiovascular Medicine, Stanford University School of Medicine, CA, USA. 15Nephrology Section, Memphis VA Medical Center and University of Tennessee Health Science Center, Memphis, TN, USA. 16Estonian Genome Center, University of Tartu, Tartu, Estonia. 17Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA. 18Department Clinical Sciences, Malmö, Lund University, Malmö, Sweden. 19MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, Scotland, UK. 20Department of Neurology, Bordeaux University Hospital, Bordeaux, France. 21Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, CHU Bordeaux, Bordeaux, France. 22Laboratory of Genetics and Genomics, NIA/NIH, Baltimore, MD, USA. 23Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA. 24Hunter Medical Reseach Institute and Faculty of Health, University of Newcastle, New Lambton Heights, New South Wales, Australia. 25Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA. 26Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands. 27MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. 28MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK. 29Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK. 30NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK. 31Wellcome Trust Sanger Institute, Hinxton, UK. 32Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee, UK. 33Department of Pharmacy, COMSATS Institute of Information Technology, Abbottabad, Pakistan. 34Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada. 35Institut universitaire de cardiologie et de pneumologie de QuĂ©bec-UniversitĂ© Laval, Quebec City, Canada. 36Cardiovascular Research Center and Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, ΜΑ, USA. 37University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands. 38University of Lille, Inserm, Centre Hosp. Univ Lille, Institut Pasteur de Lille, UMR1167 - RID-AGE - Risk factors and molecular determinants of aging-related diseases, Epidemiology and Public Health Department, Lille, France. 39University of Dundee, Ninewells Hospital & Medical School, Dundee, UK. 40Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. 41Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK. 42Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, UK. 43Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, The Netherlands. 44Imperial Clinical Trials Unit, Stadium House, 68 Wood Lane, London, UK. 45School of Medicine, University College Dublin, Ireland. 46Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland. 47International Centre for Circulatory Health, Imperial College London, London, UK. 48Center for Statistical Genetics, Dept. of Biostatistics, SPH II, Washington Heights, Ann Arbor, MI, USA. 49Genetic Epidemiology Unit, Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands. 50Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 51Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK. 52Sema4, a Mount Sinai venture, Stamford, CT, USA. 53Division of Genetics and Cell Biology, San Raffaele Scientific Institute, Milano, Italy. 54Department of Health Sciences, University of Leicester, Leicester, UK. 55Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA. 56Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne, Switzerland. 57Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston and Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX, USA. 58Department of Biological Psychology, Vrije Universiteit Amsterdam, EMGO+ institute, VU University medical center, Amsterdam, the Netherlands. 59The Charles Bronfman Institute for Personalized Medicine, Icachn School of Medicine at Mount Sinai, NY, USA. 60Department of Cardiovascular Sciences, University of Leicester, Leicester, UK. 61NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester, UK. 62Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy. 63Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK. 64Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK. 65Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, UK. 66Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore. 67Department of Cardiology, Ealing Hospital, Middlesex, UK. 68Imperial College Healthcare NHS Trust, London, UK. 69Centre for Brain Research, Indian Institute of Science, Bangalore, India. 70Institute of Genetics and Biophysics "A. Buzzati-Traverso", CNR, Napoli, Italy. 71IRCCS Neuromed, Pozzilli, Isernia, Italy. 72Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste, Italy. 73Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, NIH, Bethesda, MD, USA. 74Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, UK. 75Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, UK. 76Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 77Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands. 78Institute for Biomedicine, Eurac Research, Bolzano, Italy - Affiliated Institute of the University of LĂŒbeck, LĂŒbeck, Germany. 79Mathematical and Statistical Computing Laboratory, Office of Intramural Research, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA. 80Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany. 81DZHK (German Centre for Cardiovascular Research), partner site Greifswald, Greifswald, Germany. 82Cardiology, Department of Medicine, Geneva University Hospital, Geneva, Switzerland. 83CIBERCV & Cardiovascular Epidemiology and Genetics, IMIM. Dr Aiguader 88, Barcelona, Spain. 84Faculty of Medicine, Universitat de Vic-Central de Catalunya, Vic, Spain. 85Department of Immunology, Genetics and Pathology, Uppsala Universitet, Science for Life Laboratory, Uppsala, Sweden. 86Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, UK. 87Big Data Institute, Li Ka Shing Center for Health for Health Information and Discovery, Oxford University, Old Road, Oxford, UK. 88Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden. 89Centre for Molecular Medicine, L8:03, Karolinska Universitetsjukhuset, Solna, Sweden. 90Department of Numerical Analysis and Computer Science, Stockholm University, Stockholm, Sweden. 91Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands. 92Department of Public Health and Caring Sciences, Geriatrics, Uppsala, Sweden. 93Research Unit of Molecular Epidemiology, Helmholtz Zentrum MĂŒnchen, German Research Center for Environmental Health, Neuherberg, Germany. 94Institute of Epidemiology, Helmholtz Zentrum MĂŒnchen, German Research Center for Environmental Health, Neuherberg, Germany. 95German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany. 96Department of Psychology, School of Social Sciences, Heriot-Watt University, Edinburgh, UK. 97Faculty of Medicine, University of Iceland, Reykjavik, Iceland. 98Icelandic Heart Association, Kopavogur, Iceland. 99The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA. 100Intramural Research Program, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD, USA. 101Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 102Department of Public Health Solutions, National Institute for Health and Welfare (THL), Helsinki, Finland. 103Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland. 104Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria. 105Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria. 106Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 107National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA. 108The Population Science Branch, Division of Intramural Research, National Heart Lung and Blood Institute national Institute of Health, Bethesda, MD, USA. 109Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden. 110Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. 111Department of Pulmonary Physiology and Sleep, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, Australia. 112School of Medicine and Pharmacology, University of Western Australia, Australia. 113Department of Psychiatry, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands. 114Biocenter Oulu, University of Oulu, Oulu, Finland. 115Center For Life-course Health Research, University of Oulu, Oulu, Finland. 116Unit of Primary Care, Oulu University Hospital, Oulu, Finland. 117Hebrew SeniorLife, Harvard Medical School, Boston, MA, USA. 118Population Sciences Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA. 119Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands. 120Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland. 121Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland. 122Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA. 123Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. 124Division of Medicine, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK. 125Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Cambridge, UK. 126Data Science Institute and Lancaster Medical School, Lancaster, UK. 127Department of Public Health, Faculty of Medicine, University of Split, Croatia. 128National Heart and Lung Institute, Imperial College London, London, UK. 129Swiss Institute of Bioinformatics, Lausanne, Switzerland. 130Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia. 131Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland. 132Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland. 133Department of Medical Sciences, Cardiovascular Epidemiology, Uppsala University, Uppsala, Sweden. 134Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA. 135Division of Public Health Sciences, Wake Forest School of Medicine, Winston- Salem, NC, USA. 136Mindich Child health Development Institute, The Icahn School of Medicine at Mount Sinai, New York, NY, USA. 137Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin, Ireland. 138University College Dublin, UCD Conway Institute, Centre for Proteome Research, UCD, Belfield, Dublin, Ireland. 139Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK. 140Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, VU University Medical Center/GGZ inGeest, Amsterdam, The Netherlands. 141Department of Biostatistics, University of Liverpool, Block F, Waterhouse Building, Liverpool, UK. 142Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA. 143Data Tecnica International, Glen Echo, MD, USA. 144Laboratory of Neurogenetics, National Institute on Aging, Bethesda, USA. 145Department of Medicine, Turku University Hospital and University of Turku, Finland. 146Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 147Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 148SGDP Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. 149British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK. 150Department of Medicine, Columbia University Medical Center, New York, NY, USA. 151Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry Massachusetts General Hospital, Boston, MA, USA. 152The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA. 153University of Tartu, Tartu, Estonia. 154German Center for Cardiovascular Disease Research (DZHK), partner site Munich, Neuherberg, Germany. 155Psychiatric hospital “Sveti Ivan”, Zagreb, Croatia. 156Department of Neurology, General Central Hospital, Bolzano, Italy. 157Department of Neurology, University of LĂŒbeck, LĂŒbeck, Germany. 158Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland. 159Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland. 161Institute of Physiology, University Medicine Greifswald, Karlsburg, Germany. 162Department of Biostatistics University of Washington, Seattle, WA, USA. 163Harvard Medical School, Boston, MA, USA. 164Public health, Faculty of Medicine, University of Helsinki, Finland. 165Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Scotland, UK. 166Gottfried Schatz Research Center for Cell Signaling, Metabolism & Aging, Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria. 167The New York Academy of Medicine, New York, NY, USA. 168Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK. 169Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, University of Glasgow, UK. 170Population Health Research Institute, St George's, University of London, London, UK. 171Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 172Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany. 173Department of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands. 174Dasman Diabetes Institute, Dasman, Kuwait. 175Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland. 176Department of Public Health, University of Helsinki, Helsinki, Finland. 177Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia. 178Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands. 179Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran. 180Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany. 181Department of Internal Medicine, University Hospital, CHUV, Lausanne, Switzerland. 182Experimental Genetics Division, Sidra Medical and Research Center, Doha, Qatar. 183Centre for Population Health Sciences, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Scotland, UK. 184Department of Biology, Faculty of Medicine, University of Split, Croatia. 185The National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, UK. 186Division of Cardiology, University Hospital, Basel, Switzerland. 187Division of Cardiology, Department of Medicine, McMaster University, Hamilton, Canada. 188Institute of Genetic and Biomedical Research, National Research Council (CNR), Monserrato, Cagliari, Italy. 189Department of Biomedical Sciences, University of Sassari, Sassari, Italy. 190Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland. 191Laboratory of Cardiovascular Science, NIA/NIH, Baltimore, MD, USA. 192Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands. 193Labormedizinisches Zentrum Dr. Risch, Schaan, Liechtenstein. 194Private University of the Principality of Liechtenstin, Triesen, Liechtenstein. 195University Insitute of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland. 196Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands. 197Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. 198Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA. 201Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA. 202Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA. 203National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare NHS Trust and Imperial College London, London, UK. 204UK Dementia Research Institute (UK DRI) at Imperial College London, London, UK. 205Health Data Research- UK London substantive site, London, UK. Membership of the Meta-Analyses of Glucose and Insulin-Related Traits Consortium Authors and contributors associated with the 2023 American Journal of Human Genetics publication: “Loci for insulin processing and secretion provide insight into type 2 diabetes risk”. K Alaine Broadway1, Xianyong Yin2,3, Alice Williamson4,5, Victoria A Parsons1, Emma P Wilson1, Anne H Moxley1, Swarooparani Vadlamudi1, Arushi Varshney6, Anne U Jackson3, Vasudha Ahuja7, Stefan R Bornstein8,9,10, Laura J Corbin11,12, Graciela E Delgado13, Om P Dwivedi14,15, Lilian Fernandes Silva16, Timothy M Frayling17, Harald Grallert18,19,10, Stefan Gustafsson20, Liisa Hakaste7, Ulf Hammar21, Christian Herder10,22,23, Sandra Herrmann24,9, Kurt HĂžjlund25, David A Hughes11,12, Marcus E Kleber13,26, Cecilia M Lindgren27,28,29,30, Ching- Ti Liu31, Jian'an Luan4, Anni Malmberg32, Angela P Moissl33,34,13, Andrew P Morris35, Nikolaos Perakakis8,9,10, Annette Peters19,10, John R Petrie36, Michael Roden22,23,10, Peter EH Schwarz24,9,10, Sapna Sharma10,18,19,37, Angela Silveira38,39, Rona J Strawbridge40,38, Tiinamaija Tuomi7,15,41, Andrew R Wood42, Peitao Wu31, Björn Zethelius43, Damiano Baldassarre44,45, Johan G Eriksson46,47,48, Tove Fall21, Jose C Florez49,50,51, Andreas Fritsche52,53,10, Bruna Gigante38, Anders Hamsten38, Eero Kajantie54,55,56,57, Markku Laakso16, Jari Lahti32, Deborah A Lawlor11,12, Lars Lind20, Winfried MĂ€rz58,13, James B Meigs59,51,60, Johan Sundström20, Nicholas J Timpson11,12, Robert Wagner52,53,10, Mark Walker61, Nicholas J Wareham4,62, Hugh Watkins63, InĂȘs Barroso64, Stephen O’Rahilly65, Niels Grarup66, Stephen CJ Parker6,67,2, Michael Boehnke2,3, Claudia Langenberg4,68,69, Eleanor Wheeler4, Karen L Mohlke1. 1Department of Genetics, University of North Carolina, Chapel Hill, NC, USA. 2Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA. 3Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA. 4MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK. 5University of Cambridge Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK. 6Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. 7Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. 8Department of Internal Medicine, Metabolic and Vascular Medicine, Medical Faculty Carl Gustav Carus, Dresden, Germany. 9Helmholtz Zentrum MĂŒnchen, Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany. 10German Center for Diabetes Research, Neuherberg, Germany. 11Medical Research Council Integrative Epidemiology Unit (MRC IEU) at the University of Bristol, Bristol, UK. 12Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK. 13Medical Faculty Mannheim, Heidelberg University, Mannheim, BW, Germany. 14University of Helsinki, Helsinki, Finland. 15FolkhĂ€lsan Research Center, Helsinki, Finland. 16Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland. 17College of Medicine and Health, Exeter University, Exeter, UK. 18Research Unit of Molecular Epidemiology, Helmholtz Zentrum MĂŒnchen-German Research Center for Environmental Health, Neuherberg, Germany. 19Institute of Epidemiology, Helmholtz Zentrum MĂŒnchen-German Research Center for Environmental Health, Neuherberg, Germany. 20Department of Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Sweden. 21Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden. 22Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University DĂŒsseldorf, DĂŒsseldorf, Germany. 23Department of Endocrinology and Diabetology, Medical Faculty and University Hospital DĂŒsseldorf, Heinrich Heine University DĂŒsseldorf, DĂŒsseldorf, Germany. 24Department of Internal Medicine, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, Germany. 25Steno Diabetes Center Odense, Odense, Denmark. 26SYNLAB MVZ Humangenetik Mannheim, Mannheim, BW, Germany. 27Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK. 28Nuffield Department of Population Health, University of Oxford, Oxford, UK. 29Wellcome Trust Centre Human Genetics, University of Oxford, Oxford, UK. 30Broad Institute, Cambridge, MA, USA. 31Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. 32Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland. 33Institute of Nutritional Sciences, Friedrich-Schiller-University, Jena, Germany. 34Competence Cluster for Nutrition and Cardiovascular Health (nutriCARD), Halle-Jena-Leipzig, Germany. 35Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK. 36School of Health and Wellbeing, University of Glasgow, Glasgow, UK. 37Chair of Food Chemistry and Molecular Sensory Science, Technische UniversitĂ€t MĂŒnchen, Freising, Germany. 38Department of Medicine Solna, Division of Cardiovascular Medicine, Karolinska Institutet, Stockholm, Sweden. 39Oxford Biomedical Research Centre, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK. 40Institute of Health and Wellbeing, Mental Health and Wellbeing, University of Glasgow, Glasgow, UK. 41Abdominal Center, Endocrinology, Helsinki University Hospital, Helsinki, Finland. 42Genetics of Complex Traits, College of Medicine and Health, University of Exeter, Exeter, UK. 43Department of Geriatrics, Uppsala University, Uppsala, Sweden. 44Department of Medical Biotechnology and Translational Medicine, UniversitĂ  degli Studi di Milano, Milan, Italy. 45Cardiovascular Prevention Area, Centro Cardiologico Monzino I.R.C.C.S., Milan, Italy. 46Department of General Practice and Primary Health Care, Faculty of Medicine, University of Helsinki, Helsinki, Finland. 47FolkhĂ€lsan Research Centre, Helsinki, Finland. 48Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore. 49Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. 50Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA. 51Department of Medicine, Harvard Medical School, Boston, MA, USA. 52Department of Internal Medicine, Diabetology, TĂŒbingen, Germany. 53Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich, University of TĂŒbingen, TĂŒbingen, Germany, TĂŒbingen, Germany. 54Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland. 55PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland. 56Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway. 57Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland. 58Synlab Academy, SYNLAB Holding Deutschland GmbH, Mannheim, BW, Germany. 59Department of Medicine, Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA. 60Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA. 61Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK. 62Health Data Research UK, Gibbs Building, London, UK. 63Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK. 64Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK. 65MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK. 66Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 67Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA. 68Computational Medicine, Berlin Institute of Health at Charité– UniversitĂ€tsmedizin Berlin, Berlin, Germany. 69Precision Healthcare University Research Institute, Queen Mary University of London, London, UK. Authors and contributors associated with the unpublished manuscript: “Genome-wide association study of postprandial glucose metabolism identifies candidate insulin-stimulated glucose uptake genes”. Alice Williamson1,2, Dougall M Norris2, Xianyong Yin3,4, K. Alaine Broadway5, Anne H Moxley5, Swarooparani Vadlamudi5, Emma P Wilson5, Anne U Jackson4, Vasudha Ahuja6, Mette K Andersen7, Zorayr Arzumanyan8, Lori L Bonnycastle9, Stefan R Bornstein10,11,12, Maxi P. Bretschneider10,11,12, Thomas A Buchanan13, Yi-Cheng Chang14, Lee-Ming Chuang15, Ren-Hua Chung16, Tine D Clausen17,18, Peter Damm19,20,21, Graciela E Delgado22, Vanessa D de Mello23, JosĂ©e Dupuis24,25, Om P Dwivedi6, Michael R Erdos9, Lilian Fernandes Silva26, Tim M Frayling27, Christian Gieger28,29, Mark O Goodarzi30, Xiuqing Guo8, Stefan Gustafsson31, Liisa Hakaste6, Ulf Hammar32, Gad Hatem33, Sandra Herrmann34,35, Kurt HĂžjlund36, Katrin Horn37,38, Willa A Hsueh39, Yi-Jen Hung40, Chii-Min Hwu41, Anna Jonsson7, Line L KĂ„rhus42, Marcus E Kleber43,44, Peter Kovacs45, Timo A Lakka46,47,48, Marie Lauzon49, I-Te Lee50, Cecilia Lindgren51,52,53,54, Jaana Lindström55, Allan Linneberg42,56, Ching-Ti Liu57, Jian'an Luan1, Dina Mansour Aly58, Elisabeth Mathiesen19,20,59, Angela P Moissl60,61,43, Andrew P Morris62, Narisu Narisu9, Nikolaos Perakakis10,63,12, Annette Peters28,29, Rashmi B Prasad33,64, Roman N Rodionov65,66, Kathryn Roll8,67, Carsten F Rundsten7, ChloĂ© Sarnowski68, Kai Savonen48, Markus Scholz37,38, Sapna Sharma69,70, Sara E Stinson7, Sufyan Suleman7, Jingyi Tan8, Kent D Taylor8, Matti Uusitupa71, Dorte Vistisen72,73, Daniel R Witte74,75, Romy Walther76, Anny H Xiang77, Björn Zethelius78, The Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC)79, Emma Ahlqvist58, Richard N Bergman80, Yii-Der Ida Chen49, Francis S Collins9, Tove Fall32, Jose C Florez81,82,83, Andreas Fritsche84, Harald Grallert85,28,29, Leif Groop86,87, Torben Hansen7, Heikki A Koistinen88,89,90, Pirjo Komulainen48, Markku Laakso91, Lars Lind92, Markus Loeffler37,38, Winfried MĂ€rz93,22, James B Meigs94,95,96, Leslie J Raffel97, Rainer Rauramaa48, Jerome I Rotter98, Peter E. H. Schwarz34,99,12, Michael Stumvoll45, Johan Sundström31, Anke Tönjes45, Tiinamaija Tuomi100,101, Jaakko Tuomilehto102,103, Robert Wagner104, InĂȘs Barroso105, Mark Walker106, Niels Grarup107, Michael Boehnke3,4, Nicholas J Wareham1, Karen L Mohlke*,#,5, Eleanor Wheeler*,#,1, Stephen O’Rahilly*,#,2, Daniel J Fazakerley*,#,2, Claudia Langenberg*,#,1,108,109 1MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK, 2Metabolic Research Laboratories, Wellcome Trust-MRC Institute of Metabolic Science, Department of Clincial Biochemistry, University of Cambridge, Cambridge, CB2 0SL, UK, 3Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA, 4Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA, 5Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA, 6Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland, 7Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark, 8Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA, 9Center for Precision Health research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA, 10Department of Internal Medicine III, Metabolic and Vascular Medicine, Medical Faculty Carl Gustav Carus, Dresden, o1307, Germany, 11Helmholtz Zentrum MĂŒnchen, Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine, TU Dresden, Dresden, o1308, Germany, 12German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany, 13Medicine, Endocrine, Keck School of Medicine USC, Los Angeles, CA, 90033, USA, 14Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, 5F, No.2, Xuzhou Rd., Zhongzheng Dist., Taipei City, 100025, Taiwan, 15Internal Medicine, National Taiwan University Hospital; No. 7, Chung-Shan South Road, Taipei City, 100225, Taiwan, 16Institute of Population Health Sciences, National Health Research Institutes; 35 Keyan Road, Zhunan, Miaoli County, 35053, Taiwan, 17Department of Gynecology and Obstetrics, Nordsjaellands Hospital, Hilleroed, 3400, Denmark, 18Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark, 19Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark, 20Center for Pregnant Women with Diabetes, Rigshospitalet, Copenhagen, 2100, Denmark, 21Department of Obstetrics, Rigshospitalet, Copenhagen, 2100, Denmark, 22Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg Univeristy, Mannheim, BW, 68167, Germany, 23Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland, 24Department of Biostatistics, Boston University School of Public Health, Boston, MA, 2118, USA, 25Department of Epidemiology, Biostatistics and Occupational Health, McGill University, MontrĂ©al, Canada, 26Institute of Clinical Medicine, University of Eastern Finland, Kuopio, 70210, Finland, 27College of Medicine and Health, Exeter University, Exeter, UK, 28Institute of Epidemiology II, Helmholtz Zentrum MĂŒnchen-German Research Center for Environmental Health, Neuherberg, Germany, 29German Center for Diabetes Research, Neuherberg, Germany, 30Medicine, Endocrinology, Diabetes & Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA, 31Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, Uppsala, 75185, Sweden, 32Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Uppsala, 75185, Sweden, 33Genomics, Diabetes and Endocrinology, Department of Clinical Sciences, Lund University, Malmö, Sweden, 34Department of Internal Medicine III, Prevention and Care of Diabetes, Medical Faculty Carl Gustav Carus, Dresden, o1307, Germany, 35Helmholtz Zentrum MĂŒnchen, Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine, TU Dresden, Dresden, o1310, Germany, 36Steno Diabetes Center Odense, Odense, Denmark, 37Medical Faculty, Institute for Medical Informatics, Statistics and Epidemiology, Leipzig, 4107, Germany, 38LIFE Research Center for Civilization Diseases, Medical Faculty, Leipzig, 4103, Germany, 39Internal Medicine, Endocrinology, Diabetes & Metabolism, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA, 40Institute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan; Postbox 90048~700, Sanhsia Dist, New Taipei City, 237010, Taiwan, 41Medicine, Section of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan, No. 201, Section 2, Shipai Road, Beitou District, Taipei City, 112201, Taiwan, 42Center for Clinical Research and Prevention, Copenhagen University Hospital – Bispebjerg and Frederiksberg, Copenhagen, 2000, Denmark, 43Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg Univeristy, Mannheim, 68167, Germany, 44SYNLAB MVZ Humangenetik Mannheim, Mannheim, 68163, Germany, 45Medical Department III-Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany, 46Institute of Biomedicine, 47Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland, 48Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland, 49Pediatrics, Genomic Outcomes, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA, 50Internal Medicine, Endocrinology and Metabolism, Taichung Veterans General Hospital; No. 1650, Sec. 4, Taiwan Boulevard, Xitun District, Taichung City, 40705, Taiwan, 51Big Data Institute, Li Ka Shing Centre for Health Information and Discovery,, University of Oxford, Oxford, UK, 52NDPH, NDPH, University of Oxford, Oxford, UK, 53Wellcome Trust Centre Human Genetics, University of Oxford, Oxford, UK, 54Broad Institute of Harvard and MIT, Havard and MIT, Boston, MA, USA, 55Finnish Institute for Health and Welfare, Helsinki, Finland, 56Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark, 57Biostatistics, Boston University School of Public Health, Boston, MA, 2118, USA, 58Clinical Sciences, Genomics, Diabetes and Endocrinology, Lund University, Malmö, 20502, Sweden, 59Department of Endocrinology, Rigshospitalet, Copenhagen, 2100, Denmark, 60Institute of Nutritional Sciences, Friedrich-Schiller-University, Jena, 7743, Germany, 61Competence Cluster for Nutrition and Cardiovascular Health (nutriCARD) Halle-Jena, Jena, 7743, Germany, 62Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK, 63Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine, TU Dresden, Dresden, o1309, Germany, 64Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland, 65Department of Internal Medicine III, University Center for Vascular Medicine, Medical Faculty Carl Gustav Carus, Dresden, o1308, Germany, 66College of Medicine and Public Health, Flinders University and Flinders Medical Centre,, Adelaide, Australia, 67Helmholtz Zentrum MĂŒnchen, Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine, TU Dresden, Dresden, o1311, Germany, 68Epidemiology, Human Genetics & Environmental Sciences, The University of Texas Health Science Center, Houston, TX, 77030, USA, 69Research Unit of Molecular Epidemiology, Helmholtz Zentrum Muenchen, Helmholtz Zentrum MĂŒnchen-German Research Center for Environmental Health, Neuherberg, Germany, 70Chair of Food Chemistry and Molecular and Sensory Science, Technical University of Munich, Freising, Germany, 71Department of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland, 72Clinical Research, Steno Diabetes Center Copenhagen, Herlev, 2730, Denmark, 73Department of Public Health, University of Copenhagen, Copenhagen, 1353, Denmark, 74Steno Diabetes Center Aarhus, Aarhus, 8200, Denmark, 75Department of Public Health, Aarhus University, Aarhus, 8200, Denmark, 76Department of Internal Medicine III, Pathobiochemistry, Medical Faculty Carl Gustav Carus, Dresden, o1308, Germany, 77Pediatrics, Genetic and Genomic medicine, University of California Irvine, Irvine, CA, 92697, USA, 78Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Uppsala, 75237, Sweden, 79(No affiliation data provided), 80Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA, 81Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 2114, USA, 82Programs in Metabolism and Medical & Population Genetics, The Broad Institute, Boston, MA, 2142, USA, 83Harvard Medical School, Boston, MA, 2115, USA, 84Internal Medicine, Diabetology, TĂŒbingen, 72076, Germany, 85Research Unit of Molecular Epidemiology, Helmholtz Zentrum MĂŒnchen-German Research Center for Environmental Health, Neuherberg, Germany, 86Diabetes Centre, Lund University, Lund, Sweden, 87Finnish Institute of Molecular Medicine, Helsinki University, Helsinki, Finland, 88Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, FI-00271, Finland, 89Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, FI-00029, Finland, 90Minerva Foundation Institute for Medical Research, Helsinki, FI-00290, Finland, 91Institute of Clinical Medicine, University of Eastern Finland, 70210, Finland, 92Medical Sciences, Clinical Epidemiology, Uppsala University, Uppsala, 75185, Sweden, 93Synlab Academy, SYNLAB Holding Deutschland GmbH, Mannheim, BW, 68167, Germany, 94Medicine, Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, 2114, USA, 95Medicine, Harvard Medical School, Boston, MA, 2115, USA, 96Broad Institute, Cambridge, MA, 2146, USA, 97Department of Pediatrics, Genetic and Genomic Medicine, University of California, Irvine, CA, USA, 98The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA, 99Helmholtz Zentrum MĂŒnchen, Paul Langerhans Institute Dresden (PLID), University Hospital and Faculty of Medicine, TU Dresden, Dresden, o1307, Germany, 100FolkhĂ€lsan Research Center, Helsinki, Finland, 101Finnish Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland, 102Public Health, University of Helsinki, Helsinki, Finland, 103National Institute for Health and Welfare, Helsinki, Finland, 104Intenal Medicine, Diabetology, TĂŒbingen, 72076, Germany, 105Exeter Centre of Excellence for Diabetes Research (EXCEED), Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK, 106Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK, 107Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark, 108Computational Medicine, Berlin Institute of Health at Charité–UniversitĂ€tsmedizin, Berlin, Germany, 109Precision Healthcare University Research Institute, Queen Mary University of London, London, UK. MVP Vujkovic et al. 2020 134,599 cases 318,290 controls DIAMANTE Consortium Mahajan et al. 2022 86,934 cases 298,562 controls 93,900 cases 860,493 controls T2DGGI 113,019 cases 629,804 controls Supplementary Figure 1. Overlap of samples contributing to recent multi-ancestry T2D GWAS meta-analyses. The Type 2 Diabetes Global Genomics Initiative (T2DGGI) includes 428,452 cases and 2,107,149 controls, of which 315,433 cases and 1,477,345 controls have contributed to previous multi- ancestry investigations of the genetic contribution to T2D from the Million Veterans Program (MVP) and the DIAMANTE Consortium. Supplementary Figure 2. Manhattan plot of genome-wide T2D association from multi-ancestry meta-regression (MR-MEGA) of up to 428,452 T2D cases and 2,107,149 controls across multiple ancestry groups. Each point represents a SNV passing quality control in the multi-ancestry meta- regression, plotted with their association p-value (on a -log10 scale, truncated at 300) as a function of genomic position (NCBI build 37). Genome-wide significance (P<5x10-8) is highlighted by the dashed horizontal red line. >350 Supplementary Figure 3. Distribution of risk allele frequency and odds-ratio at index SNVs for distinct T2D association signals. Each point corresponds to an index SNV, plotted according to the mean risk allele frequency across GWAS (on the x-axis) and the odds-ratio from fixed-effects meta-analysis (on the y-axis). Index SNVs highlighted in blue map to previously reported loci for T2D susceptibility. Index SNVs highlighted in red do not map to previously reported loci for T2D susceptibility. Supplementary Figure 4. Distribution of clusters of SNVs on the first three principal components derived from 37 cardiometabolic traits. The principal components analysis was conducted on the final imputed dataset obtained from K-means clustering with ClustImpute. Each point corresponds to the mean values of the first three principal components for SNVs assigned to the cluster. The bars correspond to +/- standard deviation. The percentage of variance explained by each principal component (PC) was: 16.7% by PC 1, 12.6% by PC 2, and 10.5% by PC 3. Beta cell +PI Beta cell -PI Residual glycaemic Body fat Metabolic syndrome Obesity Lipodystrophy Liver/lipid metabolism Supplementary Figure 5. Distribution of clusters of SNVs on the first two principal components derived from 37 cardiometabolic traits. The principal components analysis was conducted on the final imputed dataset obtained from K-means clustering with ClustImpute. The “X” corresponds to the cluster centroid. The percentage of variance explained by each principal component (PC) was: 16.7% by PC 1 and 12.6% by PC 2. T2D cases Controls Supplementary Figure 6. Distribution of study-level mean BMI in T2D cases and controls across ancestry groups. Each box and whisker plot presents the median (back horizontal line), upper and lower quartiles (extremes of coloured boxes), minimum and maximum (excluding outliers, extremes of black vertical line), and outliers (more than 1.5x inter-quartile range, black dots). AFA: African American ancestry group (n=25 GWAS). EAS: East Asian ancestry group (n=40 GWAS). EUR: European ancestry group (n=36 GWAS). HIS: Hispanic ancestry group (n=17 GWAS). SAF: South African ancestry group (n=1 GWAS). SAS: South Asian ancestry group (n=17 GWAS). Supplementary Figure 7. Association of overall T2D PS and cluster-specific components of partitioned PS with CAD across multiple ancestry groups. In each forest plot, the log-odds ratio (log-OR) of the standardised PS for each ancestry is presented, together with the 95% confidence interval (horizontal bar) and weight (inverse variance, size of grey box). The grey diamonds correspond to the fixed- and random-effects estimates of the log-OR of the PS across ancestry groups (upper/lower points of diamond) and corresponding 95% confidence interval (left/right points of diamond). The cluster-specific components of the partitioned PS are adjusted for the overall T2D PS. Analyses were conducted in all individuals with adjustment for T2D status. AFA: African American ancestry group (3,537 cases and 40,932 controls). EAS: East Asian ancestry group (4,078 cases and 58,904 controls). EUR: European ancestry group (13,602 cases and 96,793 controls). HIS: Hispanic ancestry group (2,171 cases and 31,612 controls). SAS: South Asian ancestry group (2,398 cases and 25,525 controls). Beta cell +PI Beta cell -PI Residual glycaemic Body fat Metabolic syndrome Obesity Lipodystrophy Liver/lipid metabolism Overall Supplementary Figure 8. Association of overall T2D PS and cluster-specific components of partitioned PS with peripheral artery disease across multiple ancestry groups. In each forest plot, the log-odds ratio (log-OR) of the standardised PS for each ancestry is presented, together with the 95% confidence interval (horizontal bar) and weight (inverse variance, size of grey box). The grey diamonds correspond to the fixed- and random-effects estimates of the log-OR of the PS across ancestry groups (upper/lower points of diamond) and corresponding 95% confidence interval (left/right points of diamond). The cluster-specific components of the partitioned PS are adjusted for the overall T2D PS. Analyses were conducted in all individuals with adjustment for T2D status. AFA: African American ancestry group (1,241 cases and 43,228 controls). EAS: East Asian ancestry group (615 cases and 62,367 controls). EUR: European ancestry group (4,847 cases and 105,548 controls). HIS: Hispanic ancestry group (723 cases and 33,060 controls). SAS: South Asian ancestry group (199 cases and 27,724 controls). Overall Beta cell +PI Beta cell -PI Residual glycaemic Body fat Metabolic syndrome Obesity Lipodystrophy Liver/lipid metabolism Supplementary Figure 9. Association of overall T2D PS and cluster-specific components of partitioned PS with ischemic stroke across multiple ancestry groups. In each forest plot, the log-odds ratio (log-OR) of the standardised PS for each ancestry is presented, together with the 95% confidence interval (horizontal bar) and weight (inverse variance, size of grey box). The grey diamonds correspond to the fixed- and random-effects estimates of the log-OR of the PS across ancestry groups (upper/lower points of diamond) and corresponding 95% confidence interval (left/right points of diamond). The cluster-specific components of the partitioned PS are adjusted for the overall T2D PS. Analyses were conducted in all individuals with adjustment for T2D status. AFA: African American ancestry group (1,241 cases and 43,228 controls). EAS: East Asian ancestry group (2,396 cases and 60,586 controls). EUR: European ancestry group (3,782 cases and 106,613 controls). HIS: Hispanic ancestry group (722 cases and 33,061 controls). SAS: South Asian ancestry group (230 cases and 27,693 controls). Overall Beta cell +PI Beta cell -PI Residual glycaemic Body fat Metabolic syndrome Obesity Lipodystrophy Liver/lipid metabolism Supplementary Figure 10. Association of overall T2D PS and cluster-specific components of partitioned PS with ESDN across multiple ancestry groups. In each forest plot, the log-odds ratio (log-OR) of the standardised PS for each ancestry is presented, together with the 95% confidence interval (horizontal bar) and weight (inverse variance, size of grey box). The grey diamonds correspond to the fixed- and random-effects estimates of the log-OR of the PS across ancestry groups (upper/lower points of diamond) and corresponding 95% confidence interval (left/right points of diamond). The cluster-specific components of the partitioned PS are adjusted for the overall T2D PS. Analyses were conducted in individuals with T2D only. AFA: African American ancestry group (105 cases and 5,330 controls). EAS: East Asian ancestry group (133 cases and 3,155 controls). EUR: European ancestry group (116 cases and 9,538 controls). HIS: Hispanic ancestry group (141 cases and 3,695 controls). SAS: South Asian ancestry group (56 cases and 8,019 controls). Overall Beta cell +PI Beta cell -PI Residual glycaemic Body fat Metabolic syndrome Obesity Lipodystrophy Liver/lipid metabolism Supplementary Figure 11. Association of overall T2D PS and cluster-specific components of partitioned PS with end stage diabetic retinopathy across multiple ancestry groups. In each forest plot, the log-odds ratio (log-OR) of the standardised PS for each ancestry is presented, together with the 95% confidence interval (horizontal bar) and weight (inverse variance, size of grey box). The grey diamonds correspond to the fixed- and random-effects estimates of the log-OR of the PS across ancestry groups (upper/lower points of diamond) and corresponding 95% confidence interval (left/right points of diamond). The cluster-specific components of the partitioned PS are adjusted for the overall T2D PS. Analyses were conducted in individuals with T2D only. AFA: African American ancestry group (132 cases and 5,072 controls). EAS: East Asian ancestry group (196 cases and 3,461 controls). EUR: European ancestry group (100 cases and 9,417 controls). HIS: Hispanic ancestry group (146 cases and 3,441 controls). Overall Beta cell +PI Beta cell -PI Residual glycaemic Body fat Metabolic syndrome Obesity Lipodystrophy Liver/lipid metabolism Beta cell +PI Beta cell -PI Residual glycaemic Body fat Metabolic syndrome Obesity Lipodystrophy Liver/lipid metabolism Overall Supplementary Figure 12. Association of overall T2D PS and cluster-specific components of partitioned PS with age of onset of T2D across multiple ancestry groups. In each forest plot, the effect (years) of the standardised PS for each ancestry is presented, together with the 95% confidence interval (horizontal bar) and weight (inverse variance, size of grey box). The grey diamonds correspond to the fixed- and random-effects estimates of the effect (years) of the PS across ancestry groups (upper/lower points of diamond) and corresponding 95% confidence interval (left/right points of diamond). The cluster-specific components of the partitioned PS are adjusted for the overall T2D PS. Analyses were conducted in individuals with T2D only. AFA: African American ancestry group (5,435 individuals). EAS: East Asian ancestry group (3,288 individuals). EUR: European ancestry group (9,654 individuals). HIS: Hispanic ancestry group (3,836 individuals). SAS: South Asian ancestry group (8,075 individuals). M e a n Z s c o re Supplementary Figure 13. Cluster-specific associations of T2D risk alleles at index SNVs with circulating GLP-1 concentrations. Association was assessed in 3,514 individuals of European ancestry from the Malmo Diet and Cancer Study and the PPP-Botnia Study. The height of each bar corresponds to the mean Z-score, and the grey line shows the 95% confidence interval. The liver/lipid metabolism cluster has been removed for ease of presentation. *P<0.05, nominal association. * P=0.99 P=0.020 P=0.17 P=0.96