HHS Public Access Author manuscript Nature. Author manuscript; available in PMC 2022 June 09. Author Manuscript Published in final edited form as: Nature. 2021 December ; 600(7890): 675–679. doi:10.1038/s41586-021-04064-3. The power of genetic diversity in genome-wide association studies of lipids A full list of authors and affiliations appears at the end of the article. Abstract Elevated blood lipid levels are heritable risk factors of cardiovascular disease with varying Author Manuscript prevalence worldwide due to differing dietary patterns and medication use1. Despite advances in prevention and treatment, particularly through the lowering of low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wide association studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4–23 have been conducted in European ancestry populations and may have missed genetic variants contributing to lipid level variation in other ancestry groups due to differences in allele frequencies, effect Correspondence and requests for materials should be addressed to Cristen Willer, cristen@umich.edu, or Themistocles L. Assimes, tassimes@stanford.edu. †These authors jointly supervised this work #Present Address: Genentech, 1 DNA Way, South San Francisco, CA 94080 A full list of consortia members can be found in the supplementary information Author Manuscript Author Contributions S.L.C., K-H.H.W., S.K., G.J.M.Z., S.R. contributed equally to this work (as co-second authors). All authors reviewed the manuscript. Consortium management: G.M.P., P.N., T.L.A., M.B., S.K., C.J.W. Study design, interpretation of results, and drafting of manuscript: S.E.G., S.L.C., K-H.H.W., S.K., G.J.M.Z., S.R., I.S., I.N., E.M., K.L.M., T.M.F., J.N.H., S.K., M.B., P.N., G.M.P., C.D.B., A.P.M., Y.V.S., P.D., T.L.A., C.J.W. Primary meta-analysis and quality control: S.E.G., S.V., T.W.W., A.E.L. Polygenic score analysis and development: S.E.G., S.L.C., K-H.H.W., S.K., M.Y.H., S.H., A.N., A.C., A.R.B., K.E., A.V., B.T., H.C.M., K.A.H., C.N.R., S.H., M.R., R.C.T., D.A.vH., G.T., M.Y., B-J.K. Individual study genetic analysis: S.E.G., S.K., S.V., A.E.L., K.L.M., G.M.P., P.D., C.J.W., Q.H., D.K., X.Z., G.T., A.H., D.F.G., H.H., I.O., M.A., S.S., C.T., M.K., W.Z., B.M.B., H.R., S.E.R., A.S.H., Y.V., Q.F., E.A.R., T.L., J.A.P., S.A.P., J.H., F.G., Y.B., J.E.M., A.C., K.L., I.Y.M., G.H., A.R., J.D.F., W.Z., D.R.W., C.T., H.H., M.G., A.M., M.R.B., W.Z., K.Y., E.M.S., A.P., S.G., X.Y., J.L., J.Z., F.M., H.J., K.Y., C.M., A.P., J.H., G.W., A.R.W., Y.J., Z.G., S.H., R.E.M., J.C., M.A., J.Y., A.M., H.R.W., J.R., J.B., L.L.K., A.G., M.S., R.N., C.S., E.F., A.F.M., P.M., M.W., S.T., N.S., L.T.M., B.H.T., M.M., L.Z., J.H., B.Y., A.P., A.K., C.L., L.F., M.S., T.E.G., J.P.B., E.W.D., J.M.Z., J.S.M., C.F., H.C., J.A.B., M.F.F., M.K.W., M.P., M.M., P.C., N.V., J.W.B., J.E., R.L.K., R.C.S., K.L., N.R.Z., P.L., M.E.K., G.E.D., S.H., D.D.I., H.I., J.Y., J.L., H.L.L., J.M., B.S., M.A., L.J.S., M.C., C.W., M.N., A.W., N.H., X.S., R.X., A.H., J.C.F., V.L., M.A., A.U.J., M.R.I., C.O., H.K., S.R., P.R.T., L.A., R.D., L.A.L., X.C., G.P., L.L., M.P., J.L., X.L., E.T., F.T., C.N.S., A.L., S.B., S.C.W., Y.W., W.B.W., T.N., D.R., Y.S., Y.H., S.C., F.L., J.Y., K.A.K., M.G., M.B., K.M., L.F.B., J.A.S., P.H., A.F., E.H., M.L., C.X., J.Z., M.C., S.V., P.J.v., N.P., B.E.C., J.L., S.v., K.C., S.W., M.E.Z., J.L., H.C., M.N., S.F., L.S., N.W.R., C.A.W., S.L., J.W., C.C., L.L., K.N., G.C., H.V., B.H., O.G., Q.C., M.O.O., J.v., X.L., K.S., N.T., J.S., R.D.J., A.P.R., L.W.M., Z.C., L.L., H.M.H., K.L.Y., T.K., J.T., J.C.B., G.N.N., L.J.L., H.L., M.A.N., O.T.R., S.I., S.H.W., C.P.N., H.C., S.J., T.N., F.A., H.N., P.S.B., I.K., P.K., T.G., T.K., K.B., D.d., G.d., E.K., H.H.A., M.I., X.Z., F.W.A., A.O.K., J.W.B., X.S., L.S.R., O.P., Author Manuscript T.H., P.M., A.W.H., M.K., L.P., C.B., A.T., Y.C., C.E.P., T.A.M., W.L., A.F., C.O., D.M., Y.C., H.L., J.Y., W.K., S.R., J.W., I.M.H., K.J.S., H.V., G.H., M.K.E., A.B.Z., O.P., G.P., I.E.H., S.R., K.P., A.J.O., H.S., G.B., R.S., H.S., Y.E.C., S.B., G.D., T.T., S.L.K., N.K., M.B.S., G.G., B.J., C.A.B., P.K.J., D.A.B., P.L.D., X.L., V.M., M.B., M.J.C., P.B.M., X.G., M.C., J.B.J., N.J.S., D.I.C., J.K., P.P., T.T., C.A.A., L.S.A., S.A.B., H.d., A.R.W., R.K., J.W., W.Z., A.I.d., D.B., A.C., J.G.W., L.L., C.H., A.E.N., Y.M.G., J.F.W., B.P., H.K., J.A., R.J.S., D.C.R., D.K.A., M.W., H.A.K., G.R.C., C.S.Y., J.M.M., T.T., C.A., C.G.V., L.O., M.F., E.T., R.M.v., T.L., N.C., M.Y., J.L., D.F.R., A.M., F.K., K.J., M.I.M., C.N.P., V.V., C.H., E.S., C.M.v., F.L., J.Q., H.H., X.L., W.M., E.J.P., M.C., V.G., J.T., G.L., L.M.t., P.J.E., D.J.R., S.M.D., M.K., M.K., P.v., T.D.S., R.J.L., M.A.P., B.M.P., I.B., P.P.P., K.C., S.R., E.W., H.H., S.F.G., L.A.K., J.d., M.L., F.K., D.G., J.E., H.S., P.W.F., A.L., J.W.J., A.V.K., M.M., M.J., Z.K., F.C., D.O.M., K.W., H.W., D.P.S., N.G., P.S., N.P., J.I.R., T.M.D., F.K., M.J.N., N.J.T., C.C., T.W., C.K., C.S., A.P., C.G., A.T.H., N.L.P., P.K.M., D.I.B., E.J.d., L.A.C., J.B.v., M.G., P.G., W.H., Y.K., Y.T., N.J.W., C.L., E.Z., J.K., M.L., E.I., G.A., J.C.C., J.S.K., P.S.d., A.C.M., K.E.N., M.D., P.K., N.G.M., J.B.W., S.A., D.S., R.G.W., M.V.H., C.B., B.H.S., A.E.J., A.B., J.E.B., P.M.R., D.I.C., C.K., W.W., G.P.J., B.N., M.H., M.D.R., P.J., V.S., K.H., B.Å., M.K., Y.K., Y.O., Y.M., U.T., K.S., Y.H., J.A.L., D.R., P.S.T., K.C., K.C., C.J.O., J.M.G., P.W. Code Availability EasyQC: www.genepi-regensburg.de/easyqc; Raremetal: https://github.com/SailajaVeda/raremetal Graham et al. Page 2 sizes, and linkage-disequilibrium (LD) patterns24. Here we conduct a multi-ancestry genome-wide Author Manuscript genetic discovery meta-analysis of lipid levels in ~1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support expanding recruitment into new ancestries even with relatively smaller sample sizes. We find that increasing diversity rather than studying additional European ancestry individuals results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in N~295,000 from 6 ancestries), with modest gains in the number of discovered loci and ancestry-specific variants. As GWAS expands its emphasis beyond identifying genes and fundamental biology towards using genetic variants for preventive and precision medicine25, we anticipate that increased participant diversity will lead to more accurate and equitable26 application of polygenic scores in clinical practice. The Global Lipids Genetics Consortium aggregated GWAS results from 1,654,960 Author Manuscript individuals from 201 primary studies representing five genetic ancestry groups: Admixed African or African (AdmAFR, N=99.4k, 6.0% of sample), East Asian (EAS, N=146.5k, 8.9%), European (EUR, N=1.32m, 79.8%), Hispanic (HIS, N=48.1k, 2.9%), and South Asian (SAS, N=41.0k, 2.5%) (Table 1, Supplementary Table 1, Supplementary Figure 1). We performed GWAS for five blood lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), total cholesterol (TC), and non-high-density lipoprotein cholesterol (nonHDL-C). Of the 91 million variants imputed from the Haplotype Reference Consortium or 1000 Genomes Phase 3 that successfully passed variant-level QC, 52 million variants were present in at least two cohorts and had sufficient minor allele counts (> 30 in the meta-analysis) to be evaluated as a potential index variant. Author Manuscript Ancestry-specific genetic discovery We first quantified the number of genome-wide significant loci identified in at least one of the five ancestry-specific meta-analyses. We found 773 lipid-associated genomic regions containing 1,765 distinct index variants that reached genome-wide significance (p-value < 5x10−8, ±500 kb, Supplementary Tables 2–3, Supplementary Figures 2–3) for at least one ancestry group and lipid trait. Of these regions, 237 were novel based on the most-significant index variant in each region being >500 kb from variants previously reported as associated with any of the five lipid traits4–23,27. Of these loci, 76% were identified only in the European ancestry-specific analyses (N~1.3m, 80% of sample). Of the non-European ancestries, the African ancestry GWAS (N~99k, primarily African American) identified more ancestry-specific loci (15 unique to AdmAFR) than any other non-European ancestry group (six loci unique to EAS, six to HIS, one to SAS). The Author Manuscript difference is likely attributable to allele frequencies being most different between African and European ancestry populations (Figure 1a–d) and to African populations having greater genetic diversity28. Trans-ancestry genetic discovery We next performed trans-ancestry meta-analyses using the meta-regression approach implemented in MR-MEGA30 to account for heterogeneity in variant effect sizes on lipids Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 3 between ancestry groups. A total of 1,750 index variants at 923 loci (±500 kb regions) Author Manuscript reached genome-wide significance for at least one lipid trait. These included 168 regions not identified by ancestry-specific analysis, 120 (71%) of which were novel (Supplementary Tables 4–5, Supplementary Figure 4, Extended Data Figure 1). Almost all (98%) index variants from the ancestry-specific analysis remained significant (p-value<5x10−8) after meta-analysis across all ancestry groups, although fifteen AdmAFR, nine EAS, three HIS, and one SAS index variants from ancestry-specific analysis did not (trans-ancestry p-value 7.7x10−6 to 5.9x10−8, Supplementary Figure 5, Supplementary Note). In total, we identified 941 lipid-associated loci including 355 novel loci from either single- or trans-ancestry analyses. Next, we compared the number of loci identified per 100,000 participants in each ancestry group and the combined dataset (Figure 1e). African and Hispanic ancestry-specific analyses identified the most loci per genotyped individual, perhaps due to African ancestry and/or Author Manuscript increased genetic diversity. European and trans-ancestry analyses identified slightly fewer loci per 100,000 individuals, likely reflecting a slight reduction in the benefit from new samples added to very large sample sizes (>1m). For the genome-wide significant variants discovered in each ancestry, we estimated the proportion of ancestry-enriched variants by enumerating the number of other ancestries with sufficient power to detect association (range 0 to 4). We estimated the power for discovery of each variant by assuming an equivalent discovery sample size in the other ancestries, fixed effect size, and observed allele frequencies from the other ancestries (Figure 1f). To allow for comparison at similar sample sizes across ancestry groups, we selected European ancestry index variants identified from a meta-analysis of ~100,000 individuals subsampled from the present study. African ancestry index-variants were most ancestry-enriched, with only 61% of index variants demonstrating sufficient power in at least one other ancestry group (equal N, power>80% Author Manuscript to reach alpha=5x10−8), likely due to population-enriched allele frequencies. In comparison, 88% of South Asian index variants had estimated power >80% in at least one other ancestry. Finally, we found that both the number of identified variants and the mean observed chi-squared values from genome-wide lipid association tests were approximately linearly related to meta-analysis sample size across ancestries (Supplementary Table 6, Extended Data Figure 2). However, in the European ancestry group the incremental increase in either the number of loci or chi-squared value was slightly attenuated at the largest sample sizes. Taken together, these results suggest that once sufficiently well-powered GWAS sample sizes are reached within a given ancestry group, assembling large sample sizes of other under-represented groups will modestly enhance variant discovery relative to increasing the sample size of the dominant ancestry. Author Manuscript Comparison of effects across ancestries Differences in association signals across ancestries despite similar sample sizes could be due to variation in allele frequencies and/or effect sizes. This could reflect differing patterns of LD with the underlying causal variant or an interaction with an environmental risk factor whose prevalence varies by ancestry and/or geography. We found that effect size estimates of individual variants were largely similar based on pairwise comparison Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 4 between ancestries (r2=0.93 for variants with p-value<5x10−8) (Extended Data Figure Author Manuscript 3, Supplementary Table 7, Supplementary Figure 6). We additionally tested for genome- level differences in effect size correlation between East Asian, European, and South Asian ancestry groups using Popcorn29, which were not significantly different from 1 (p- value>0.05, Supplementary Figures 7 and 8). We tested for differences in genetic correlation between Admixed African and European ancestries in the UK Biobank and Million Veteran Program (MVP) using bivariate GREML30,31 as the Popcorn method does not account for long-range LD in admixed populations. Genetic correlation between Admixed African and European ancestries for HDL-C (r=0.84) was not significantly different from 1 in the UK Biobank (possibly due to relatively small numbers of African ancestry individuals), while correlations for the other traits ranged from 0.52-0.60 in UK Biobank and 0.47-0.69 in MVP (Supplementary Table 8). These results indicate moderately high correlation in lipid effect sizes across ancestry groups when considering all genome-wide variants. Author Manuscript Of the 2,286 variants that reached genome-wide significance in the trans-ancestry meta- analysis across all five lipid traits, 159 (7%) showed significant heterogeneity of effect size due to ancestry (p-value<2.2x10−5; Bonferroni correction for 2,286 variants, Supplementary Table 5). Of these 159, 31 showed the largest effect in African ancestry analyses, 24 in East Asian, 67 in European, 20 in Hispanic, and 17 in South Asian. Only 49 (2%) of these variants from trans-ancestry meta-analysis showed significant residual heterogeneity not due to ancestry, which may be attributable to differences in ascertainment or analysis strategy between cohorts (Supplementary Table 5), suggesting cohort-related factors are a less important driver of heterogeneity than genetic ancestry. Trans-ancestry analyses aid fine-mapping Author Manuscript We next assessed whether trans-ancestry fine-mapping narrowed the set of likely causal variants at each of the independent trans-ancestry association signals (LD r2<0.7), assuming one shared causal variant per ±500 kb region (Supplementary Table 9). 19% of the association signals had only one variant in the 99% credible set and 55% (816/1,486) had ≤10. In contrast, 5% (73/1486) had >100. Of the 407 variants with >90% posterior probability of being the causal variant at a locus in the trans-ancestry meta-analysis, 56 (14%) were missense variants, 7 (2%) were splice-region variants, and 4 (1%) were stop- gain variants (CD36, HBB, ANGPTL8, PDE3B). (Supplementary Tables 10–12). The median number of variants in 99% credible sets from European ancestry analysis was 13; this was reduced to 8 in the trans-ancestry analysis. Of 1,486 association signals, 825 (56%) had reduced credible set size in the trans-ancestry analysis. At these 825 loci, the number of variants in the trans-ancestry credible sets were reduced by 40% relative to the Author Manuscript minimum credible set size in either Admixed African (the most genetically diverse group) or European ancestry analyses (Extended Data Figure 4). We estimate that increasing the sample size of European ancestry samples to that of the trans-ancestry analysis would yield a 20% reduction in credible set size, approximately half of the 40% reduction observed in trans-ancestry analysis. This suggests that sample size differences alone do not explain the reduction, rather differences in LD patterns and effect sizes across ancestries likely contribute to the improved fine-mapping (Supplementary Note). For example, rs900776, Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 5 an intronic variant in the DMTN region with many high LD variants in the European Author Manuscript ancestry group, has a posterior probability of being causal of 0.86 in the African ancestry derived credible sets, >0.99 in the trans-ancestry analysis, but only 0.51 in the European ancestry-specific analysis (Figure 2). Trans-ancestry PRS are most predictive We evaluated the potential of polygenic risk scores (PRS, sometimes also called polygenic scores or PGS) to predict elevated LDL-C, a major causal risk factor of CAD, in diverse ancestry groups. We created three non-overlapping datasets to separately: i) perform ancestry-specific or trans-ancestry GWAS to estimate variant effect sizes, ii) optimize risk score parameters, and iii) evaluate the utility of the resulting scores. For each ancestry- specific or trans-ancestry GWAS we created multiple polygenic score weights -- either genome-wide with PRS-CS32 or using pruning and thresholding to select independent Author Manuscript variants. We tested each score in the optimizing dataset, which was matched for ancestry to the GWAS (AdmAFR, EAS, EUR, SAS, ALL from UK biobank or HIS from Michigan Genomics Initiative (MGI), Extended Data Figures 5 and 6, Supplementary Tables 13–15). The top-performing score from each GWAS was selected: PRS-CS for East Asian ancestry, European ancestry, and European ancestry 2010 scores from a previous GLGC GWAS4, and an optimized pruning and threshold-based score for all others. We then evaluated the polygenic scores in 8 cohorts of individuals (N=295,577, Supplementary Table 16), not included in the discovery GWAS, from 6 ancestral groups: East Asian (146,477), European American (85,571), African American (21,730), African (2,452 East Africa, 4,972 South Africa, 7,309 West Africa), South Asian (15,242), Hispanic American (7,669), and Asian American (4,155). Author Manuscript The polygenic score developed from trans-ancestry meta-analysis consistently showed the best or near-best performance in each group tested, with improved or comparable prediction relative to ancestry-matched scores (adjusted R2 ~ 0.10-0.16, Figure 3, Supplementary Table 17, Extended Data Figure 7). This observation was especially evident for ancestries with smaller GWAS sample sizes, as was the case for HIS and SAS. For African Americans in MGI and MVP, polygenic prediction was similar for individuals with different levels of recent African ancestry admixture (Extended Data Figure 8) and reached the level of prediction observed for European ancestry individuals from the same dataset. The increase in LDL-C per each standard deviation increase in the polygenic score was also similar between ancestry groups in MVP: 13.2±0.22 mg/dL for African American, 8.9±0.47 mg/dL for Asian (EAS/SAS), 10.5±0.10 mg/dL for European, and 10.6±0.32 mg/dL for Hispanic ancestry individuals. We repeated the evaluation of trans-ancestry vs Author Manuscript single-ancestry polygenic scores with a set GWAS with sample size of ~100k individuals and with fixed methodology; results were consistent with those from the full dataset (Figure 3b, Supplementary Figure 9). Thus, polygenic prediction for LDL-C in all ancestries appears to benefit the most from adding samples of diverse ancestries once relatively large numbers of European ancestry individuals have already been included. Additional studies are needed to determine if this applies to other phenotypes with different genetic architectures and heritabilities. Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 6 Discussion Author Manuscript Genome-wide discovery for blood lipid traits based on ~1.65 million individuals from five ancestry groups confirmed that the contributions of common genetic variation to blood lipids are largely similar across diverse populations. First, we found that the number of significant loci relative to sample size was similar within each ancestry group, and approximately linearly related to sample size, with a small increase in ancestry-specific variants observed in African ancestry cohorts relative to the others. Second, we demonstrated that inclusion of additional ancestries through trans-ancestry fine-mapping reduces the set of candidate causal variants in credible sets and does so more rapidly than in single-ancestry analysis. Trans-ancestry GWAS should therefore facilitate identification of effector genes at GWAS loci and allow for accelerated biological insight and identification of potential drug targets. Third, we found that a polygenic score derived from ~88k African ancestry and ~830k European ancestry individuals was correlated with observed lipid levels among individuals Author Manuscript with admixed African ancestry as well as among individuals with European ancestry. We hypothesize that the inclusion of African ancestry individuals in the GWAS yields improvement in polygenic prediction performance through the general fine-mapping of loci and the improved prioritization of trans-ancestry causal variants. Fourth, and perhaps most important, the trans-ancestry score was generally most informative across all major population groups examined. This provides useful information for other genetic discovery efforts and investigations of the utility of the polygenic scores in diverse populations. Generalizability of these findings regarding portability of polygenic scores from the trans- ancestry meta-analysis to other traits may depend on the heritability, degree of polygenicity, level of genetic correlation, allele frequencies of causal variants across ancestry groups, gene-environment interactions, and representation of diverse populations in the GWAS33,34. Author Manuscript While many traits show a high degree of shared genetic correlation across ancestries31,35,36 others have distinct genetic variants with large effects that are more common in specific ancestry groups33 which may limit the utility of trans-ancestry polygenic scores for particular phenotypes in some ancestries. The benefits from genetic discovery efforts as GWAS sample sizes increase will likely not be measured just by the number of loci discovered. Rather, the focus will increasingly turn to improving our understanding of the biology at established loci, identifying potential therapeutic targets, and efficiently identifying individuals at high-risk of adverse health outcomes across population groups without exacerbating existing health disparities. Considering the results presented here, and those of related studies37–39, we believe future genetic studies will benefit substantially from meta-analysis across participants of diverse Author Manuscript ancestries. Further gains in the depth and number of sequenced individuals of diverse ancestries40,41 may additionally improve discovery of novel variants and loci in diverse cohorts, particularly variants absent from arrays and imputation reference panels. Our results suggest that diversifying the populations under study, rather than simply increasing the sample size, is now the single most efficient approach to achieving these goals, at least for blood lipids and likely for tightly related downstream adverse health outcomes such as cardiovascular disease. However, if costs for recruitment of diverse populations are higher than recruitment of individuals from previously studied ancestry groups, and total number Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 7 of genome-wide significant index variants is the goal, then continued low-cost recruitment Author Manuscript of majority ancestry groups is expected to still provide some benefit. Taken together, our results also strongly support ongoing and future large-scale recruitment efforts targeted at the enrollment and DNA collection of non-European ancestry participants. Geneticists and those responsible for cohort development must continue diversifying genetic discovery datasets, while increasing sample size in a cost-effective manner, to ensure genetic studies reduce rather than exacerbate existing health inequities across race, ancestry, geographic region, and nationality. Methods: Cohort level analysis Each cohort contributed GWAS summary statistics for HDL-C, LDL-C, nonHDL-C, TC and TG, imputation quality statistics, and analysis metrics for quality control (QC), following Author Manuscript a detailed analysis plan (Supplementary File 1). Briefly, we requested that each cohort perform imputation to 1000 Genomes Phase 3 (1KGP3), with European ancestry cohorts additionally imputing with the Haplotype Reference Consortium (HRC) panel using the Michigan Imputation Server (https://imputationserver.sph.umich.edu/index.html#!) which uses Minimac software42. Detailed pre-imputation QC guidelines were provided; these included removing samples with call rate < 95%, samples with heterozygosity > median + 3(interquartile range), ancestry outliers from principal component analysis within each ancestry group, and variants deviating from Hardy-Weinberg equilibrium (p-value < 10−6) or with variant call rate < 98%. Analyses were carried out separately by ancestry group and were additionally stratified by cases and controls where appropriate (i.e. for a disease- focused cohort such as CAD). Residuals were generated separately in males and females adjusting for age, age2, principal components of ancestry, and any necessary study-specific Author Manuscript covariates. Triglyceride levels were natural-log transformed before generating residuals. Inverse normalization was then done on the residual values. Individuals on cholesterol lowering medication had their pre-medication levels43 approximated by dividing the LDL-C value by 0.7 and the TC value by 0.8. Association analysis of the residuals for the majority of cohorts was carried out using a linear mixed-model approach in rvtests or with other similar software including BOLT-LMM44, SAIGE45, or deCode association software. Quality Control Each input file was assessed for quality control using the EasyQC software46 (www.genepi- regensburg.de/easyqc). We generated QQ plots by minor allele frequency (MAF) bins, assessed trends in standard errors relative to sample size for each cohort, and checked MAF of submitted variants relative to their expected value based on the imputation reference Author Manuscript panel. In addition, we checked that each cohort reproduced the expected direction of effect at most known loci relative to the cohort sample size. Cohorts identified to have issues with the submitted files were contacted and corrected files were submitted or the cohort was excluded from meta-analysis. Results from either sex-stratified analysis or sex-combined analysis with sex as a covariate were used. During the QC process, within each cohort we removed poorly imputed variants (info score or r2 < 0.3), variants deviating from Hardy- Weinberg Equilibrium (HWE p-value < 10−8, except for MVP which used HWE p-value Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 8 < 10−20), and variants with minor allele count < 3. An imputation info score threshold of Author Manuscript 0.3 was selected to balance the inclusion of variants across diverse studies while removing poorly imputed variants. Summary statistics were then genomic-control (GC) corrected using the λGC value calculated from the median p-value of variants with MAF > 0.5%. To capture as many variants as possible, summary statistics from cohorts that had submitted both HRC and 1KGP3 imputed files were joined, selecting variants imputed from HRC where both imputed versions of a variant existed. For variants imputed by both panels, we observed that variants imputed from the HRC panel resulted in a higher imputation info score for 94% of variants when compared to the imputation info score from 1KGP3. Meta-analysis Ancestry-specific meta-analysis was performed using RAREMETAL47 (https://github.com/ SailajaVeda/raremetal). Trans-ancestry meta-analysis was performed using MR-MEGA48 Author Manuscript with 5 principal components of ancestry. The choice of 5 principal components was made after comparing the λGC values across minor allele frequency bins from meta-analysis of HDL-C with MR-MEGA using from 2 up to 10 principal components. In addition, fixed- effects meta-analysis was carried out with METAL49 to calculate effect sizes for use in the creation of polygenic scores. Study-level principal components were plotted for each cohort by ancestry group to verify that the reported ancestry for each cohort was as expected. Following meta-analysis, we identified loci based on a genome-wide significance threshold of 5x10−8 after GC correction using the λGC value calculated from the median p-value of variants with MAF > 0.5%. The choice of double-GC correction was made to be most conservative and to minimize potential false-positive findings. Observed λGC values were within the expected range for similarly sized studies and are included in Supplementary Tables 2 and 4. Variants with a cumulative minor allele count ≤ 30 and those found in Author Manuscript a single study were excluded from index variant selection. Index variants were identified following an iterative procedure starting with the most significant variant and grouping the surrounding region into a locus based on the larger of either ± 500 kb or ± 0.25 cM. cM positions were interpolated using the genetic map distributed with Eagle v2.3.2 (genetic_map_hg19_withX.txt)50. Variants were annotated using WGSA51 including the summary of each variant from SnpEff52 and the closest genes for intergenic variants from ANNOVAR53. Annotation of variants as known or novel was done based on manual review of previously published variants and with variants reported in the GWAS catalog27 for any of the studied lipid traits (accessed May 2020, provided as Supplementary Table 18). For comparison between ancestries and lipid traits, index variants were grouped into genomic regions starting with the most significantly associated variant and grouping all surrounding index variants within ± 500 kb into a single region. Author Manuscript Power to detect association within each ancestry was determined using the effect size and sample size of the variant within the original discovery ancestry group and the observed allele frequency from the other ancestry groups with alpha set to 5x10−8. We excluded variants that were only successfully imputed in a single ancestry group to account for imputation panel differences between groups (ie. Haplotype Reference Consortium for European ancestry individuals and 1000 Genomes for other ancestries). Variants that were successfully imputed in 2 or more ancestries were assumed to have zero power in any Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 9 other ancestry where the variant was not successfully imputed. The proportion of variance explained by each variant was estimated as 2β2(1-f)f where β is the effect size from METAL Author Manuscript and f is the effect allele frequency (Supplementary Table 19). The proportion of variance explained within each ancestry was estimated using the trans-ancestry effect size from METAL with the ancestry-specific allele frequency. Coverage of the genome by associated genetic regions was calculated using BEDTools54 for the regions defined by the minimum and maximum position within each locus having p-value < 5x10−8. Conditional analysis Approximate conditional analysis was performed using rareGWAMA55 to identify index variants that were shadows of nearby, more significant associations. LD reference populations were taken from UK Biobank specific to Admixed African, European (subset of 40,000), or South Asian ancestry individuals or from the 1000 Genomes project (1KGP3) Author Manuscript for East Asian or Hispanic ancestry individuals. Conditional analysis was carried out using the individual cohort level summary statistics as was done for meta-analysis with RAREMETAL. rareGWAMA requires imputation quality scores which were set to 1 for all variants that had previously passed quality control (pre-filtered at imputation info/r2 > 0.3). The European ancestry subset of UK Biobank was used as the reference population for the conditional analysis of the trans-ancestry meta-analysis (~80% European ancestry). Stepwise conditional analysis was performed sequentially for the index variants within each chromosome ranked by most to least significant. Index variants were then flagged as not independent from other more significant variants if the absolute value of the ratio of the original effect size to the effect size after conditional analysis was greater than the 95th percentile of all values (Supplementary Figure 10). This threshold was selected to remove variants whose effects were driven by nearby, more strongly associated variants in LD. This Author Manuscript corresponded to a ratio of original to conditional effect size of 1.6 for ancestry-specific conditional analysis and a ratio of 1.7 for the trans-ancestry conditional analysis. The effect sizes from meta-analysis with METAL were used for comparison with the trans-ancestry conditional analysis results. Variants flagged as non-independent were excluded from the summary results in the manuscript and are flagged as non-independent in Supplementary Tables 3 and 5. Genetic correlation Popcorn29 was used to assess the degree of correlation in effect sizes between ancestry groups for each of the lipid traits with 1000 Genomes phase 3 as the reference LD panel. Only variants with MAF > 0.01 in each ancestry individually were included in the comparison. Both the genetic effect and genetic impact models were tested. Bivariate Author Manuscript GREML from GCTA was used to calculate the genetic correlation between unrelated Admixed Africans and a subset of white British individuals in the UK Biobank following the method of Guo et al30,31. HapMap3 variants with MAF > 0.01 in each ancestry were used to construct the genetic relationship matrix (GRM) with the allele frequencies standardized in each population. Individuals with genetic relatedness > 0.05 were removed. A total of up to 5,575 AdmAfr and 38,668 white British individuals from UK Biobank were included in the analysis of each trait after removal of related individuals. The measured lipid traits were corrected for medication use and were inverse-normalized after correction for age, sex, and Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 10 batch. Principal components 1-20 constructed from the GRM were included as covariates in Author Manuscript the calculation of genetic correlation. Analysis within the Million Veteran Program included 24,502 European ancestry and 21,950 African American unrelated individuals. Maximum measured values were used for LDL-C, TC, and triglycerides and minimum values for HDL-C. Lipid traits were inverse-normalized after correction for age and sex with principal components 1-20 included as covariates in the calculation of genetic correlation. Credible sets Credible sets of potentially causal variants were generated for each of the loci identified in the trans-ancestry meta-analysis. We determined 99% credible sets of variants that encompassed the causal variant with 99% posterior probability. Regions for construction of credible sets were defined as the ± 500 kb region around each index variant. Bayes factors56,57 (BF) for each variant in the ancestry-specific meta-analysis were approximated Author Manuscript by: β2 BF ≈ exp 0.5 − log NAS SE2 where β and SE are the effect sizes and standard errors from the RAREMETAL meta- analysis, and NAS is the ancestry-specific sample size. A full derivation is included in the Supplementary Methods. To account for the difference in sample sizes between ancestry groups, we additionally approximated the Bayes factors after adjustment for the total trans- ancestry sample size for each trait (NTE) relative to the ancestry-specific sample size for that trait using the following equation: β2NT E Author Manuscript BF ≈ exp 0.5 − log NT E SE2NAS Credible sets for the trans-ancestry meta-analysis were generated using the Bayes factors as output by MR-MEGA. The credible sets within each region were generated by ranking all variants by Bayes factor and calculating the number of variants required to reach a cumulative probability of 99%. In addition, we calculated credible sets in the same manner using the European ancestry and trans-ancestry meta-analysis results but including only the set of variants present in the AdmAFR meta-analysis. To summarize the size of the credible sets across the 5 lipid traits examined, we identified the set of independent index variants from the trans-ancestry meta-analysis after grouping variants based on LD. For each ± 500kb region centered around the most-significantly associated index variant for Author Manuscript any trait, we determined the pairwise LD between all index variants in this region using LDpair58 with all reference populations (1000 Genomes AFR, AMR, EAS, EUR, and SAS) included. We considered variants to be independent if they were outside of this region, had LD r2 < 0.7, or were not available in the LDpair reference populations. Variants within the credible sets were annotated with SnpEff52 using WGSA51 and with VEP59. The number of variants in LD with an index variant was determined using LDproxy58 (Supplementary Table 20). Protein numbering was taken from dbSNP60. eQTL colocalization was performed Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 11 using coloc61 version 3.2.1 with R version 3.4.3 using the default parameters. Results from GTEx V862 were compared with the GWAS signals in the region defined by the larger of Author Manuscript ±0.25cM or ±500kb surrounding each index variant. The eQTL and GWAS signals (based on p-values from MR-MEGA) were considered to be colocalized if PP3 + PP4 ≥ 0.8 and if PP4/(PP3+PP4) > 0.9, where PP3 is the probability of two independent causal variants while PP4 is the probability of a single, shared causal variant. LDL-C polygenic scores Weights for the LDL-C polygenic scores were derived from beta estimates generated from each of the ancestry-specific meta-analyses and from the trans-ancestry results using METAL. Additional meta-analyses were carried out using the 2010 Global Lipids Genetics Consortium LDL-C meta-analysis results4 in combination with the i) Admixed African or ii) Admixed African, East Asian, Hispanic, and South Asian ancestry results from the present Author Manuscript meta-analysis for comparison. Furthermore, we performed a meta-analysis of European ancestry cohorts randomly selected to reach a total sample size near 100K, 200K, or 400K to understand the role of increasing European ancestry sample size and the influence of imputation panel. In addition, we tested possible methods for improving performance of European ancestry derived scores in African ancestry individuals by separately fitting the European ancestry polygenic scores in the UK Biobank Admixed African ancestry subset to determine the best set of risk score parameters (various pruning and thresholding parameters or PRS-CS, Supplementary Note). We generated polygenic score weights using both: i) significant variants only (at a variety of p-value thresholds) and ii) using genome-wide methods. Meta-analysis results were first filtered to variants present in UK Biobank, MGI, and MVP with imputation info score > 0.3. Pruning and thresholding was performed in PLINK63 with ancestry-matched Author Manuscript subsets of UK Biobank individuals (AdmAFR N=7,324, EUR N=40,000, SAS N=7,193, trans-ancestry: N=10,000 (80% EUR, 15% AdmAFR, 5% SAS)) or 1KGP3 (HIS N=347 , EAS N=504) used for LD reference. We additionally tested 1000 Genomes phase 3 with all populations included as the LD reference panel for the trans-ancestry score (results not shown), which gave very similar results to those of the UK Biobank trans-ancestry reference set originally selected for its larger sample size. P-value thresholds (after GC correction) of 5x10−10, 5x10−9, 5x10−8, 5x10−7, 5x10−6, 5x10−5, 5x10−4, 5x10−3, and 5x10−2 were tested with distance thresholds of 250 and 500 kb and LD r2 thresholds of 0.1 and 0.2. Polygenic score weights were also generated using PRS-CS32 with the LD reference panels for African, East Asian, and European ancestry populations from 1000 Genomes provided by the developers. PRS-CS LD reference panels for the other ancestries were generated using 1000 Genomes following the same protocol as provided by the PRS-CS authors32. Author Manuscript This included removing variants with MAF ≤ 0.01, ambiguous A/T or G/C variants, and restricting to variants included in HapMap3. Pairwise LD matrices within pre-defined LD blocks64 (using European LDetect blocks for Hispanic and trans-ancestry LD calculations and Asian blocks for South Asian) were then calculated using PLINK and converted to HDF5 format. Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 12 For each individual in the testing cohorts, polygenic scores were calculated as the sum of Author Manuscript the dosages multiplied by the given weight at each variant. UK Biobank individuals not present in datasets used to generate the summary statistics (either Admixed African, white British, both Admixed African and white British, East Asian, South Asian, or all individuals excluding South Asian) were used to select the best performing Admixed African, European, Admixed African+European, East Asian, South Asian, and trans-ancestry polygenic scores, respectively. UK Biobank South Asian ancestry individuals were included in the trans- ancestry risk score weights but excluded from the UK Biobank trans-ancestry testing set due to an initial focus on comparing predictions among European and African ancestry individuals. Sample sizes of the ancestry groups in UK Biobank used to test PRS performance included: AdmAFR N=6,863; EAS N=1,441; EUR N=389,158; SAS N=6,814; ALL=461,918. The best performing Hispanic ancestry polygenic score weights were selected based on performance in Hispanic ancestry individuals in the Michigan Genomics Initiative dataset. Model fit was assessed by the adjusted R2 of a linear model Author Manuscript for LDL-C value at initial assessment adjusted for cholesterol medication (divided by 0.7 to estimate pre-medication levels) with sex, batch, age at initial assessment, and PCs1-4 as covariates (Supplementary Tables 21–23). Python and R were used for analysis of PRS models. The best performing polygenic score in each ancestry group was then tested in the validation cohorts: the Michigan Genomics Initiative (EUR N=17,190; AFRAMR N=1,341), East London Genes and Health65 (ELGH; SAS N=15,242), Tohoku Medical Megabank Community Cohort Study (ToMMo; EAS N=28,217), Korean Genome and Epidemiology Study66 (KoGES; EAS N=118,260), Penn Medicine BioBank (PMBB; AFRAMR=2,138), Africa America Diabetes Mellitus (AADM; 3,566 West AFR; 707 East AFR), Africa Wits-INDEPTH partnership for Genomic Studies (AWI-Gen; 1,744 East AFR; 4,972 South Author Manuscript AFR; 3,744 West AFR) and Million Veteran Program participants not included in the discovery meta-analysis (MVP; EUR N=68,381; AFRAMR N=18,251; EAS/SAS N=4,155; HIS N=7,669). Adjusted R2 values were reported for each cohort and ancestry group, with 95% confidence intervals for the adjusted R2 values calculated using bootstrapping. Within each cohort, covariates used were: MGI- sex, batch, PC1-4, and birth year; PMBB- birth year, sex, and PC1-4; ELGH- age, sex, and PC1-10; MVP- sex, PC1-4, birth year, and mean age; ToMMo-sex, age, recruitment method, and PC1-20 (only participants from Miyagi Prefecture were included); KoGES-age, sex, and recruitment area, AADM-age, sex, PC1-3, AWI-Gen East Africa- age, sex, PC1-6, AWI-Gen South Africa- age, sex, PC1-6, and AWI- Gen West Africa- age, sex, and PC1-4. The type of LDL-C value used in the model varied depending on the measurements selected by each cohort. Mean LDL-C values were used for MGI, MVP and PMBB, maximum LDL-C values for ELGH, and baseline measurements Author Manuscript for AADM, AWI-Gen, ToMMo and KoGES. A descriptive summary of each validation cohort is included in Supplementary Table 16. African admixture for MGI was calculated using all African ancestry individuals in 1000 Genomes with ADMIXTURE v1.367. African admixture for MVP was calculated using the YRI and LWK African ancestry individuals in 1000 Genomes. Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 13 Extended Data Author Manuscript Author Manuscript Author Manuscript Author Manuscript Extended Data Figure 1: Effect sizes of identified index variants from trans-ancestry meta- analysis Index variants associated with a) HDL cholesterol, b) LDL cholesterol, c) triglycerides, d) nonHDL cholesterol and e) total cholesterol include both common variants of small to moderate effect and low frequency variants of moderate to large effect. Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 14 Author Manuscript Author Manuscript Author Manuscript Extended Data Figure 2: Comparison of the number of index variants by sample size a) Comparison of the number of index variants reaching genome-wide significance (p < 5x10−8) from meta-analysis of LDL-C in each ancestry group. A meta-analysis of five random subsets of European cohorts selected to reach sample sizes of approximately 100,000, 200,000, 400,000, 600,000, or 800,000 individuals is also shown. b) Comparison of chi-squared values from meta-analysis of LDL-C for each possible combination of ancestry groups (without genomic-control correction) for variants with minor allele frequency (MAF) ≥ 5%. The colored lines indicate a linear regression model of Author Manuscript all meta-analyses for a specific ancestry (eg. all analyses including European individuals). c) Comparison of chi-squared values from meta-analysis of LDL-C for variants with MAF ≤ 5%. d) Comparison of chi-squared valued for variants with MAF ≥ 5% for LDL-C without genomic-control correction in a meta-analysis of all European cohorts as well as five subsets selected to reach sample sizes of approximately 100,000, 200,000, 400,000, 600,000, or 800,000 individuals. Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 15 Author Manuscript Author Manuscript Author Manuscript Extended Data Figure 3: Effect sizes by ancestry for unique index variants from ancestry- specific meta-analysis Comparison of effect sizes and standard errors for variants reaching genome-wide significance (p-value < 5x10−8 as given by RAREMETAL) in both ancestry groups. Variants with discordant directions of effect between ancestries are labeled by chromosome and position (build 37). Association results for all index variants are given in Supplementary Table 3. The red line depicts an equivalent European ancestry and non-European ancestry effect size while the black line depicts a linear regression model. R2=0.93 Author Manuscript Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 16 Author Manuscript Author Manuscript Extended Data Figure 4: Comparison of credible set size The number of variants in the 99% credible sets for each association signal are compared between a) Admixed African ancestry and trans-ancestry analysis and b) European ancestry and trans-ancestry analysis Author Manuscript Author Manuscript Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 17 Author Manuscript Author Manuscript Author Manuscript Extended Data Figure 5: Overview of LDL-C polygenic score generation and validation Author Manuscript Polygenic scores were calculated separately in each ancestry group or in all ancestries combined using either pruning and thresholding or PRS-CS. The polygenic scores were then taken forward for testing in ancestry-matched participants followed by validation in independent data sets. Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 18 Author Manuscript Author Manuscript Author Manuscript Extended Data Figure 6: Optimal polygenic score threshold by ancestry group for either PRS-CS or pruning and thresholding based LDL-C polygenic scores Adjusted R2 estimated upon testing in UK Biobank ancestry-matched participants (not Author Manuscript included in GWAS summary statistics). a. Admixed African, East Asian and South Asian ancestry polygenic scores b. European and trans-ancestry polygenic scores c. European ancestry (GLGC 2010) and trans-ancestry polygenic scores d. All polygenic scores across all thresholds used for score construction Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 19 e. Comparison of adjusted R2 across ancestry groups relative to the maximum Author Manuscript for covariates alone, polygenic scores from PRS-CS or polygenic scores from pruning and thresholding Author Manuscript Author Manuscript Author Manuscript Extended Data Figure 7: Comparison of PRS performance by admixture quartile We divided the testing cohorts into quartiles by proportion of African ancestry and estimated the performance of the PRS separately within each quartile in a) the Michigan Genomics Initiative (N = 1,341) and b) in the Million Veteran Program (N = 18,251). Error bars represent 95% confidence intervals. Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 20 Author Manuscript Author Manuscript Author Manuscript Extended Data Figure 8: Improvement in PRS performance in African Americans when starting with ancestry-mismatched European ancestry scores by updating weights, updating variant lists, or updating both variants and weights to be ancestry-matched. By comparison to the gold-standard performance of the trans-ancestry-derived PRS in African Americans (adjusted R2 = 0.12), a European ancestry derived score capture only 47% of the variance explained by the trans-ancestry PRS. When LD and association information from the target population is used to optimize the list of variants for inclusion in the PRS, but with ancestry-mismatched weights from European ancestry GWAS, the variance explained reaches 71% of the gold standard. If the PRS variant list selected in Author Manuscript European ancestry individuals were genotyped in the target population, and PRS weights were updated using a GWAS from the target population, the variance explained reached 87% of the gold standard. Finally, deriving both the marker list and weights from the target population (single-ancestry GWAS) explained 94% of the variance relative to the gold-standard trans-ancestry PRS. Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 21 Supplementary Material Author Manuscript Refer to Web version on PubMed Central for supplementary material. Authors Sarah E Graham1, Shoa L Clarke2,3, Kuan-Han H Wu4, Stavroula Kanoni5, Greg JM Zajac6, Shweta Ramdas7, Ida Surakka1, Ioanna Ntalla8, Sailaja Vedantam9,10, Thomas W Winkler11, Adam E Locke12, Eirini Marouli5, Mi Yeong Hwang13, Sohee Han13, Akira Narita14, Ananyo Choudhury15, Amy R Bentley16, Kenneth Ekoru16, Anurag Verma17, Bhavi Trivedi18, Hilary C Martin19, Karen A Hunt18, Qin Hui20,21, Derek Klarin22,23,24, Xiang Zhu25,26,27,28, Gudmar Thorleifsson29, Anna Helgadottir29, Daniel F Gudbjartsson29,30, Hilma Holm29, Isleifur Olafsson31, Masato Akiyama32,33, Saori Sakaue34,32,35, Chikashi Terao36, Author Manuscript Masahiro Kanai37,38,39, Wei Zhou40,41,42, Ben M Brumpton43,44,45, Humaira Rasheed43,44, Sanni E Ruotsalainen46, Aki S Havulinna46,47, Yogasudha Veturi48, QiPing Feng49, Elisabeth A Rosenthal50, Todd Lingren51, Jennifer Allen Pacheco52, Sarah A Pendergrass53, Jeffrey Haessler54, Franco Giulianini55, Yuki Bradford48, Jason E Miller48, Archie Campbell56,57, Kuang Lin58, Iona Y Millwood58,59, George Hindy60, Asif Rasheed61, Jessica D Faul62, Wei Zhao63, David R Weir62, Constance Turman64, Hongyan Huang64, Mariaelisa Graff65, Anubha Mahajan66,#, Michael R Brown67, Weihua Zhang68,69,70, Ketian Yu71, Ellen M Schmidt71, Anita Pandit71, Stefan Gustafsson72, Xianyong Yin73, Jian’an Luan74, Jing-Hua Zhao75, Fumihiko Matsuda76, Hye-Mi Jang13, Kyungheon Yoon13, Carolina Medina-Gomez77,78, Achilleas Pitsillides79, Jouke Jan Hottenga80,81, Gonneke Willemsen80,82, Andrew R Wood83, Yingji Ji83, Zishan Gao84,85,86, Simon Haworth87,88, Ruth E Mitchell87,89, Author Manuscript Jin Fang Chai90, Mette Aadahl91, Jie Yao92, Ani Manichaikul93, Helen R Warren94,95, Julia Ramirez94, Jette Bork-Jensen96, Line L Kårhus91, Anuj Goel97,98, Maria Sabater-Lleal99,100, Raymond Noordam101, Carlo Sidore102, Edoardo Fiorillo103, Aaron F McDaid104,105, Pedro Marques-Vidal106, Matthias Wielscher107, Stella Trompet108,109, Naveed Sattar110, Line T Møllehave91, Betina H Thuesen91, Matthias Munz111, Lingyao Zeng112,113, Jianfeng Huang114, Bin Yang114, Alaitz Poveda115, Azra Kurbasic115, Claudia Lamina116, Lukas Forer116, Markus Scholz117,118, Tessel E. Galesloot119, Jonathan P. Bradfield120, E Warwick Daw121, Joseph M Zmuda122, Jonathan S Mitchell123, Christian Fuchsberger123, Henry Christensen124, Jennifer A Brody125, Mary F Feitosa121, Mary K Wojczynski121, Michael Preuss126, Massimo Mangino127,128, Paraskevi Christofidou127, Niek Verweij129, Jan W Benjamins129, Jorgen Engmann130,131, Rachel L Kember132, Author Manuscript Roderick C Slieker133,134, Ken Sin Lo135, Nuno R Zilhao136, Phuong Le137, Marcus E Kleber138,139, Graciela E Delgado138, Shaofeng Huo140, Daisuke D Ikeda141, Hiroyuki Iha141, Jian Yang142,143, Jun Liu144, Hampton L Leonard145,146, Jonathan Marten147, Börge Schmidt148, Marina Arendt148,149, Laura J Smyth150, Marisa Cañadas-Garre150, Chaolong Wang151,152, Masahiro Nakatochi153, Andrew Wong154, Nina Hutri-Kähönen155,156, Xueling Sim90, Rui Xia157, Alicia Huerta- Chagoya158, Juan Carlos Fernandez-Lopez159, Valeriya Lyssenko160,161, Meraj Ahmed162, Anne U Jackson6, Marguerite R Irvin163, Christopher Oldmeadow164, Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 22 Han-Na Kim165,166, Seungho Ryu167,168, Paul RHJ Timmers169,147, Liubov Arbeeva170, Rajkumar Dorajoo152, Leslie A Lange171, Xiaoran Chai172,173, Gauri Author Manuscript Prasad174,175, Laura Lorés-Motta176, Marc Pauper176, Jirong Long177, Xiaohui Li92, Elizabeth Theusch178, Fumihiko Takeuchi179, Cassandra N Spracklen180,181, Anu Loukola46, Sailalitha Bollepalli46, Sophie C Warner182,183, Ya Xing Wang184, Wen B. Wei185, Teresa Nutile186, Daniela Ruggiero186,187, Yun Ju Sung188, Yi- Jen Hung189, Shufeng Chen114, Fangchao Liu114, Jingyun Yang190,191, Katherine A Kentistou169, Mathias Gorski11,192, Marco Brumat193, Karina Meidtner194,195, Lawrence F Bielak196, Jennifer A Smith196,62, Prashantha Hebbar197, Aliki-Eleni Farmaki198,199, Edith Hofer200,201, Maoxuan Lin202, Chao Xue1, Jifeng Zhang1, Maria Pina Concas203, Simona Vaccargiu204, Peter J van der Most205, Niina Pitkänen206,207, Brian E Cade208,209, Jiwon Lee208, Sander W. van der Laan210, Kumaraswamy Naidu Chitrala211, Stefan Weiss212, Martina E Zimmermann11, Jong Young Lee213, Hyeok Sun Choi214, Maria Nethander215,216, Sandra Author Manuscript Freitag-Wolf217, Lorraine Southam218,219, Nigel W Rayner220,221,222,218, Carol A Wang223, Shih-Yi Lin224,225,226, Jun-Sing Wang227,228, Christian Couture229, Leo-Pekka Lyytikäinen230,231, Kjell Nikus232,233, Gabriel Cuellar-Partida234, Henrik Vestergaard235, Bertha Hildalgo236, Olga Giannakopoulou5, Qiuyin Cai177, Morgan O Obura237, Jessica van Setten238, Xiaoyin Li239, Karen Schwander240, Natalie Terzikhan241, Jae Hun Shin214, Rebecca D Jackson242, Alexander P Reiner243, Lisa Warsinger Martin244, Zhengming Chen245,246, Liming Li247, Heather M Highland65, Kristin L Young65, Takahisa Kawaguchi76, Joachim Thiery248,118, Joshua C Bis125, Girish N. Nadkarni126, Lenore J Launer249, Huaixing Li140, Mike A Nalls145,146, Olli T Raitakari250,251,252, Sahoko Ichihara253, Sarah H Wild254, Christopher P Nelson182,183, Harry Campbell169, Susanne Jäger194,195, Toru Nabika255, Fahd Al-Mulla256, Harri Niinikoski257,258, Peter S Braund182,183, Author Manuscript Ivana Kolcic259, Peter Kovacs260, Tota Giardoglou261, Tomohiro Katsuya262,263, Konain Fatima Bhatti5, Dominique de Kleijn264, Gert J. de Borst264, Eung Kweon Kim265, Hieab H.H. Adams241,266, M. Arfan Ikram241, Xiaofeng Zhu239, Folkert W Asselbergs238, Adriaan O Kraaijeveld238, Joline WJ Beulens133,267, Xiao-Ou Shu177, Loukianos S Rallidis268, Oluf Pedersen96, Torben Hansen96, Paul Mitchell269, Alex W Hewitt270,271, Mika Kähönen272,273, Louis Pérusse229,274, Claude Bouchard275, Anke Tönjes276, Yii-Der Ida Chen92, Craig E Pennell223, Trevor A Mori277, Wolfgang Lieb278, Andre Franke279, Claes Ohlsson280,281, Dan Mellström280,282, Yoon Shin Cho214, Hyejin Lee283, Jian-Min Yuan284,285, Woon-Puay Koh286,287, Sang Youl Rhee288, Jeong-Taek Woo288, Iris M Heid11, Klaus J Stark11, Henry Völzke289, Georg Homuth212, Michele K Evans290, Alan B Zonderman290, Ozren Polasek259, Gerard Pasterkamp210, Imo E Hoefer210, Author Manuscript Susan Redline208,209, Katja Pahkala206,207,291, Albertine J Oldehinkel292, Harold Snieder205, Ginevra Biino293, Reinhold Schmidt200, Helena Schmidt294, Y Eugene Chen1, Stefania Bandinelli295, George Dedoussis198, Thangavel Alphonse Thanaraj256, Sharon LR Kardia196, Norihiro Kato179, Matthias B Schulze194,195,296, Giorgia Girotto193,297, Bettina Jung298, Carsten A Böger298,299,300, Peter K Joshi169, David A Bennett190,191, Philip L De Jager301,302, Xiangfeng Lu114, Vasiliki Mamakou303,304, Morris Brown305,95, Mark J Caulfield94,95, Patricia Nature. 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Page 25 13Division of Genome Science, Department of Precision Medicine, National Institute Author Manuscript of Health, Chungcheongbuk-do, South Korea 14Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan 15Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa 16Centerfor Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, 12 South Drive, Room 4047, Bethesda, MD, 20892, USA 17Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA Author Manuscript 18Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK 19Wellcome Sanger Institute, Hinxton, UK 20Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, Georgia, USA 21Atlanta VA Health Care System, Decatur, Georgia, USA 22Malcolm Randall VA Medical Center, Gainesville, FL, USA 23Divisionof Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville, FL, USA 24Programin Medical and Population Genetics, Broad Institute of MIT and Harvard, Author Manuscript Cambridge, Massachusetts, USA 25Department of Statistics, The Pennsylvania State University, University Park, PA, USA 26Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA 27VA Palo Alto Health Care System, Palo Alto, CA, USA 28Department of Statistics, Stanford University, Stanford, CA, USA 29deCODE genetics/Amgen, Inc. Sturlugata 8, Reykjavik, 102, Iceland 30School of Engineering and Natural Sciences, University of Iceland, Author Manuscript Sæmundargötu 2, Reykjavik, 102, Iceland 31Department of Clinical Biochemistry, Landspitali - National University Hospital of Iceland, Hringbraut, Reykjavik, 101, Iceland 32Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Japan Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 26 33Department of Ophthalmology, Graduate School of Medical Sciences, Kyushu Author Manuscript University, Fukuoka, Japan 34Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan 35Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan 36Laboratoryfor Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan 37Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan 38Programin Medical and Population Genetics, Broad Institute of MIT and Harvard, Author Manuscript Cambridge, MA, USA 39Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA 40Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA 41Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA 42Programin Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA 43K.G.Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Author Manuscript Norway 44MRC Integrative Epidemiology Unit, University of Bristol, UK 45Department of Thoracic Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway 46Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Tukholmankatu 8, 00014 Helsinki, Finland 47Finnish institute for Health and Welfare, Helsinki, Finland 48Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA 49Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Author Manuscript Medical Center, Nashville, TN 50Department of Medicine (Medical Genetics), University of Washington, USA 51Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, USA 52Center for Genetic Medicine, Northwestern University, USA Nature. 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Page 27 53Genentech, 1 DNA Way, South San Francisco, 94084, USA Author Manuscript 54Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle WA 9810, USA 55Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA 02215, USA 56Centre for Genomic and Experimental Medicine , Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, United Kingdom 57Usher Institute for Population Health Sciences and Informatics, The University of Edinburgh, Nine, Edinburgh Bioquarter, 9 Little France Road, Edinburgh, EH16 4UX, UK Author Manuscript 58Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom 59Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, United Kingdom 60Department of Population Medicine, Qatar University College of Medicine, QU Health, Doha, Qatar 61Center for Non-Communicable Diseases, Karachi, Sindh, Pakistan 62SurveyResearch Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA 63Department of Epidemiology, School of Public Health, University of Michigan, Ann Author Manuscript Arbor, MI, 48109, USA 64Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA 65Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA 66Wellcome Centre for Human Genetics, University of Oxford, UK 67Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, 77030, USA 68Department of Epidemiology and Biostatistics, Imperial College London, London Author Manuscript W2 1PG, UK 69Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex UB1 3HW, UK 70Imperial College Healthcare NHS Trust, London W12 0HS, UK 71Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, USA Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 28 72Department of Medical Sciences, Molecular Epidemiology and Science for Life Author Manuscript Laboratory, Uppsala University, Uppsala, Sweden 73Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA 74MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK 75Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge Strangeways Research Laboratory, Cambridge, CB1 8RN, UK 76Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan Author Manuscript 77Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands 78Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, the Netherlands 79Departmentof Biostatistics, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA 02118, USA 80Department of Biological Psychology, Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Netherlands 81Amsterdam Public Health, VU medical center Amsterdam, Netherlands 82Amsterdam Public Health research institute, VU medical center Amsterdam, Author Manuscript Netherlands 83Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, EX2 5DW, UK 84Departmentof Clinical Acupuncture and Moxibustion, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210029, China 85ResearchUnit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany 86Instituteof Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany 87MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield Road, Author Manuscript Bristol, BS8 2BN, United Kingdom 88Bristol Dental School, University of Bristol, Lower Maudlin Street, Bristol BS1 2LY, United Kingdom 89Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield Grove, Bristol, BS8 2BN, United Kingdom Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 29 90Saw Swee Hock School of Public Health, National University of Singapore and Author Manuscript National University Health System, 117549, Singapore 91Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark 92The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovations (Formerly LABioMed) at Harbor-UCLA Medical Center, Torrance, CA 90502, USA 93Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903 USA 94WilliamHarvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London, EC1M 6BQ, UK Author Manuscript 95NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK 96NovoNordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark 97Division of Cardiovascular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford. United Kingdom. OX3 9DU 98WellcomeCentre for Human Genetics, University of Oxford, Oxford. United Kingdom. OX3 7BN 99Group of Genomics of Complex Diseases. Research Institute of Hospital de la Author Manuscript Santa Creu i Sant Pau (IIB Sant Pau), Barcelona, Spain 100Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden 101Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands 102Institute for Genetic and Biomedical Research, Italian National Council of Research (IRGB CNR), Cagliari Italy 103Institute for Genetic and Biomedical Research, Italian National Council of Research (IRGB CNR), Lanusei, Italy 104UniversityCenter for Primary Care and Public Health, University of Lausanne, Author Manuscript Rte de la Corniche 10, Lausanne, 1010, Switzerland 105Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland 106Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, Lausanne, 1011, Switzerland 107Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 30 108Dept of Cardiology, Leiden University Medical Center, Leiden, the Netherlands Author Manuscript 109Dept of Internal Medicine, Section of Gerontology and Geriatrics, Leiden university Medical Center, Leiden, the Netherlands 110BHF Glasgow Cardiovascular Research Centre, Faculty of Medicine, Glasgow, United Kingdom 111Institute for Cardiogenetics, University of Lübeck, DZHK (German Research Centre for Cardiovascular Research), partner site Hamburg/Lübeck/Kiel, University Heart Center Lübeck, Lübeck and Charité – University Medicine Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute for Dental and Craniofacial Sciences, Department of Periodontology and Synoptic Dentistry, Berlin, Germany 112DeutschesHerzzentrum München, Klinik für Herz- und Kreislauferkrankungen, Author Manuscript Technische Universität München,Munich, Germany 113Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK) e.V., partner site Munich Heart Alliance,Munich, Germany 114Key Laboratory of Cardiovascular Epidemiology & Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China 115Lund University Diabetes Centre, Malmö, Sweden 116Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria and German Chronic Kidney Author Manuscript Disease study, Austria 117Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany 118LIFEResearch Centre for Civilization Diseases, University of Leipzig, Philipp- Rosenthal-Straße 27, 04103 Leipzig, Germany 119Radboud university medical center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands 120Quantinuum Research LLC, Wayne, PA, 19087 USA 121Divisionof Statistical Genomics, Department of Genetics; Washington University School of Medicine; St. Louis, MO, USA Author Manuscript 122Department of Epidemiology; University of Pittsburgh; Pittsburgh, PA, USA 123Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, Via Galvani 31, 39100, Bolzano, Italy 124Department of Clinical Biochemistry, Lillebaelt Hospital, Vejle, Denmark 125CardiovascularHealth Research Unit, Department of Medicine, University of Washington, Seattle, 98101, USA Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 31 126The Charles Bronfman Institute for Personalized Medicine, Icahn School of Author Manuscript Medicine at Mount Sinai, New York, NY, 10029, USA 127Departmentof Twin Research and Genetic Epidemiology, King’s College London, London SE1 7EH, UK 128NIHRBiomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London SE1 9RT, UK 129Universityof Groningen, University Medical Center Groningen, Department of Cardiology, 9700RB Groningen, The Netherlands 130Institute of Cardiovascular Sciences, University College London, Gower Street, WC1E 6BT London, UK 131Department of Epidemiology and Public Health, University College London, 1-19 Author Manuscript Torrington Place, WC1E 6BT London, United Kingdom 132Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA 133Amsterdam UMC, Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam, 1081HV, the Netherlands 134Leiden University Medical Center, Department of Cell and Chemical Biology, Leiden, 2333ZA, The Netherlands 135Montreal Heart Institute, 5000 Belanger Street, Montreal, Quebec, H1T 1C8, Canada 136Icelandic Heart Association, 201 Kopavogur, Iceland Author Manuscript 137Departmentof Anthropology, University of Toronto at Mississauga, Mississauga, ON L5L 1C6, Canada 138VthDepartment of Medicine, Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany 139SYNLAB MVZ Humangenetik Mannheim GmbH, 68163 Mannheim, Germany 140CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China 141Biomedical Technology Research Center, Tokushima Research Institute, Otsuka Pharmaceutical Co., Ltd., Tokushima, Japan Author Manuscript 142Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland 4072, Australia 143Institute for Advanced Research, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China 144Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 32 145Laboratory of Neurogenetics, National Institute on Aging, NIH, Bethesda MD, Author Manuscript USA 146Data Tecnica International, Glen Echo MD, USA 147MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, Scotland 148Institute for Medical Informatics, Biometrie and Epidemiology, University of Duisburg-Essen, Essen, Germany 149Departmentof Computer Science, University of Applied Sciences and Arts Dortmund, Emil-Figge-Str. 42, 44227 Dortmund, Germany 150Centre for Public Health, Queen’s University of Belfast, Northern Ireland 151Department of Epidemiology and Biostatistics, School of Public Health, Tongji Author Manuscript Medical College, Huazhong University of Science and Technology, Wuhan, China 152Genome Institute of Singapore, Agency for Science, Technology and Research, 138672, Singapore 153Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, 461-8673, Japan 154MRCUnit for Lifelong Health and Ageing at UCL, 1-19 Torrington Place, London, WC1E 7HB, United Kingdom 155Department of Pediatrics, Tampere University Hospital, Tampere 33521, Finland 156Department of Pediatrics, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland Author Manuscript 157Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston TX 77030, USA 158Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, UNAM, Ciudad de Mexico, Mexico 159Departamentode Genómica Computacional, Instituto Nacional de Medicina Genómica, Ciudad de Mexico, Mexico 160Center for diabetes research, University of Bergen, Bergen, Norway 161Lund University Diabetes Center, Lunds University, Malmö, Sweden 162Genomic Research on Complex diseases (GRC Group), CSIR-Centre for Cellular Author Manuscript and Molecular Biology, Hyderabad, Telangana, India 163University of Alabama at Birmingham, Epidemiology, School of Public Health, Birmingham, Alabama, USA 164Hunter Medical Research Institute, Newcastle, Australia 165Medical Research Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, 03181, Korea Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 33 166Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan Author Manuscript University, Seoul, 06355, Korea 167Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, 04514, Korea 168Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, 03181, Korea 169Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland 170Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, North Carolina, USA 171Divisionof Biomedical Informatics and Personalized Medicine, Department of Author Manuscript Medicine, Anschutz Medical Campus, University of Colorado, Denver, Aurora, CO 80045, USA 172Ocular Epidemiology, Singapore Eye Research Institute, Singapore National Eye Centre, 168751, Singapore 173Department of Ophthalmology, National University of Singapore and National University Health System, 119228, Singapore 174Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi - 110020, India 175Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi 110020, India Author Manuscript 176Departmentsof Ophthalmology and Human Genetics, Radboud University Nijmegen Medical Center, Philips van Leydenlaan 15, Nijmegen, 6525 EX, the Netherlands 177Vanderbilt Epidemiology Center, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA 178Department of Pediatrics, University of California San Francisco, Oakland, CA 94609 USA 179National Center for Global Health and Medicine, Tokyo, 1628655, Japan 180Department of Genetics, University of North Carolina, Chapel Hill, NC 27599 USA Author Manuscript 181Departmentof Biostatistics and Epidemiology, University of Massachusetts- Amherst, Amherst, MA 01003 USA 182Department of Cardiovascular Sciences, University of Leicester, Leicester, UK 183NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 34 184BeijingInstitute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Author Manuscript Visual Sciences, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, 17 Hougou Lane, Chong Wen Men, Beijing, 100005 , China 185Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, 1 Dong Jiao Min Xiang, Dong Cheng District, Beijing, 100730, China 186Institute of Genetics and Biophysics “Adriano Buzzati-Traverso” - CNR, Naples, Italy 187IRCCS Neuromed, Pozzilli, Isernia, Italy 188Department of Psychiatry, Washington University, St. Louis, MO 63110, USA 189Division of Endocrinology and Metabolism, Tri-Service General Hospital Songshan Branch, Taipei, Taiwan Author Manuscript 190Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA 191Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA 192Department of Nephrology, University Hospital Regensburg, Regensburg, Germany 193Department of Medicine, Surgery and Health Sciences, University of Trieste, Strada di Fiume 447, 34149, Trieste, Italy 194Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany Author Manuscript 195German Center for Diabetes Research (DZD), München-Neuherberg, Germany 196Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA 197Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait City, Kuwait 198Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University of Athens, Athens, Greece 199Departmentof Population Science and Experimental Medicine, University College London, London, UK 200Clinical Division of Neurogeriatrics, Department of Neurology, Medical University Author Manuscript of Graz, Graz, Austria 201Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria 202Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, NC 28223 USA 203Institute for Maternal and Child Health, IRCCS Burlo Garofolo, Trieste, Italy Nature. 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Page 35 204Institute of Genetic and Biomedical Research, National Research Council of Italy, Author Manuscript UOS of Sassari, Sassari, Italy 205University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, 9700 RB, the Netherlands 206Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland 207Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland 208SleepMedicine and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA 209Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts Author Manuscript 02115, USA 210Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical genetics, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands 211Laboratory of Epidemiology and Population Science National Institute on Aging Intramural Research Program, NIH 251 Bayview Blvd, NIH Biomedical Research Center, NIA, Baltimore, MD 21224, USA 212Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics, University of Greifswald and University Medicine Greifswald, Greifswald, Germany 213Oneomics.co. ltd. 2F, Soonchunhyang Mirai Medical Center 173, Buheuyng-ro, Author Manuscript Bucheon-si Gyeonggi-do, 14585, Korea 214Department of Biomedical Science, Hallym University, Chuncheon, Gangwon-do 24252, Korea 215Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden 216Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden 217Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany 218Institute of Translational Genomics, Helmholtz Zentrum München – German Author Manuscript Research Center for Environmental Health, Neuherberg, Germany 219Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK 220Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK 221Oxford Centre for Diabetes Endocrinology and Metabolism, Oxford, UK 222Wellcome Sanger Institute, Hinxton, Cambridge, HH CB10 1 UK Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 36 223School of Medicine and Public Health, College of Health, Medicine and Author Manuscript Wellbeing, University of Newcastle, Newcastle, New South Wales, 2308, Australia 224Center for Geriatrics and Gerontology, Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan 225School of Medicine, National Yang-Ming University, Taipei, Taiwan 226School of Medicine, National Defense Medical Center, Taipei, Taiwan 227Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan 228Department of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan Author Manuscript 229Dept of Kinesiology, Université Laval, Québec, Canada 230Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland 231Department of Clinical Chemistry, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland 232Department of Cardiology, Heart Center, Tampere University Hospital, Tampere 33521, Finland 233Department of Cardiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland Author Manuscript 234University of Queensland Diamantina Institute, Translational Research Institute, Kent St, Woolloongabba, Brisbane, QLD, 4102, Australia 235Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, Department of Medicine, Bornholms Hospital, Rønne, Denmark 236Schoolof Public Health, University of Alabama at Birmingham, Birmingham, Alabama, USA 237Amsterdam UMC, Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam, 1081HV, the Netherlands 238Cardiology, Division Heart & Lungs, University Medical Center Utrecht, Utrecht Author Manuscript University, Utrecht, the Netherlands 239Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA 240Divisionof Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 37 241Department of Epidemiology - Erasmus MC - University Medical Center Author Manuscript Rotterdam, Rotterdam, the Netherlands 242Ohio State University, Division of Endocrinology, Columbus OH 43210, USA 243University of Washington, Department of Epidemiology, Seattle WA 98195, USA 244GeorgeWashington University, School of Medicine and Health Sciences, Washington DC 20037, USA 245ClinicalTrial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK 246Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK 247Department of Epidemiology, School of Public Health, Peking University Health Author Manuscript Science Center, Beijing, China 248Institute for Laboratory Medicine, University Hospital Leipzig, Paul-List-Strasse 13/15, 04103 Leipzig, Germany 249Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIH, Baltimore,MD, 20892-9205, USA 250Centre for Population Health Research, University of Turku and Turku University Hospital, Finland 251Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Finland 252Department of Clinical Physiology and Nuclear Medicine, Turku University Author Manuscript Hospital, Turku, Finland 253Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, 329-0498, Japan 254Centre for Population Health Sciences, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland 255Departmentof Functional Pathology, Shimane University School of Medicine, Izumo, 6938501, Japan 256Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait 257Department of Pediatrics and Adolescent Medicine, Turku University Hospital and University of Turku, Turku, Finland Author Manuscript 258Department of Physiology, University of Turku, Turku, Finland 259Faculty of Medicine, University of Split, Šoltanska 2, HR-21000, Split, Croatia 260MedicalDepartment III – Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Liebigstr. 21, 04103 Leipzig, Germany Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 38 261Department of Nutrition-Dietetics, Harokopio University, Eleftheriou Venizelou, Author Manuscript Athens, 17676, Greece 262Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Suita, 5650871, Japan 263Department of Geriatric and General Medicine, Osaka University Graduate School of Medicine, Suita, 5650871, Japan 264Department of Vascular Surgery, Division of Surgical Specialties, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands 265Corneal Dystrophy Research Institute, Department of Ophthalmology, Yonsei University College of Medicine, Seoul 03722, Korea 266Deptof Radiology and Nuclear Medicine, Erasmus MC - University Medical Author Manuscript Center Rotterdam, Rotterdam, the Netherlands 267JuliusCentre for Health Sciences and Primary Care, University Medical Centre Utrecht, 3584CG, the Netherlands 268Second Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Attikon University Hospital, Athens, Greece 269Center for Vision Research, Department of Ophthalmology and The Westmead Institute, University of Sydney, Hawkesbury Rd, Sydney, New South Wales, 2145, Australia 270Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Liverpool St, Hobart, Tasmania, 7000, Australia Author Manuscript 271Centrefor Eye Research Australia, University of Melbourne, Melbourne, Victoria, 3002, Australia 272Department of Clinical Physiology, Tampere University Hospital, Tampere 33521, Finland 273Department of Clinical Physiology, Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere 33014, Finland 274Institute of Nutrition and Functional Foods (INAF), Université Laval, Québec, Canada 275Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA Author Manuscript 276MedicalDepartment III – Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Liebigstr. 18, 04103 Leipzig, Germany 277Medical School, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Western Australia, 6000, Australia 278Institute of Epidemiology, Kiel University, Kiel, Germany 279Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 39 280Centre for Bone and Arthritis Research, Department of Internal Medicine Author Manuscript and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden 281Sahlgrenska University Hospital, Department of Drug Treatment, Gothenburg, Sweden 282Geriatric Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden 283Department of Internal Medicine, EwhaWomans University School of Medicine, Seoul, Korea 284Divisionof Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA 15232, USA Author Manuscript 285Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15232, USA 286HealthyLongevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117545, Singapore 287SingaporeInstitute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore 117609, Singapore 288Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul 02447, Korea 289Institute for Community Medicine, University Medicine Greifswald, Germany 290Laboratory of Epidemiology and Population Science National Institute on Aging Author Manuscript Intramural Research Program, NIH 251 Bayview Blvd, NIH Biomedical Research Center, Baltimore, MD 21224, USA 291Paavo Nurmi Centre, Sports and Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland 292University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Groningen, 9700 RB, the Netherlands 293Institute of Molecular Genetics, National Research Council of Italy, Pavia, Italy 294Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz, Austria Author Manuscript 295Local Health Unit Toscana Centro, Firenze, Italy 296Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany 297Institute for Maternal and Child Health IRCCS “Burlo Garofolo”, Via dell’Istria 65/1, 34137, Trieste, Italy 298Dept of Nephrology, University Hospital Regensburg, Regensburg, Germany Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 40 299Dept of Nephrology, Diabetology, Rheumatology; Traunstein Hospital, Traunstein, Author Manuscript Germany 300KfH Kidney Center Traunstein, Traunstein, Germany 301Centerfor Translational and Systems Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, NY, USA 302Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA 303Medical School, National and Kapodistrian University Athens, 75 M. Assias Street, 115 27 Athens, Greece 304Dromokaiteio Psychiatric Hospital, 124 61 Athens, Greece 305ClinicalPharmacology, William Harvey Research Institute, Queen Mary Author Manuscript University of London, London, EC1M 6BQ,UK 306Department of Ophthalmology, Medical Faculty Mannheim, Heidelberg University, Kutzerufer 1, Mannheim, 68167, Germany 307BeijingInstitute of Ophthalmology, Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, 17 Hougou Lane, Chong Wen Men, Beijing, 100005, China 308Institute of Molecular and Clinical Ophthalmology Basel, Switzerland 309Harvard Medical School, Boston MA 02115, USA 310Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA Author Manuscript 311Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico 14080, Mexico 312Instituto de Investigaciones Biomédicas, UNAM, Ciudad de México, CDMX, Mexico 313Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico 14080, Mexico 314Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, 27599 USA 315Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina, 27516 USA Author Manuscript 316USC–Office of Population Studies Foundation, University of San Carlos, Cebu City, 6000, Philippines 317Department of Anthropology, Sociology, and History, University of San Carlos, Cebu City, 6000 Philippines 318Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, 11010, Sri Lanka Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 41 319Department of Public Health, Faculty of Medicine, University of Kelaniya, Author Manuscript Ragama, 11010, Sri Lanka 320Departments of Pediatrics and Medicine, University of California, San Francisco, San Francisco, CA 321Institute of Biomedical Sciences, Academia Sinica, Taiwan 322Academy of Scientific and Innovative Research, CSIR-Institute of Genomics and Integrative Biology Campus, New Delhi 110020, India 323Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi - 110067, India 324Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA Author Manuscript 325Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, 39216, USA 326Department of Medical Sciences, Uppsala University, Sweden 327Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore; and Khoo Teck Puat - National University Children’s Medical Institute, National University Health System, Singapore 328Department of Medicine, University of North Carolina, Chapel Hill, NC, USA 329Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA 330Injury Prevention Research Center, University of North Carolina, Chapel Hill, Author Manuscript North Carolina, USA 331Divisionof Physical Therapy, University of North Carolina, Chapel Hill, North Carolina, USA 332Departmentof Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands 333Amsterdam Public Health research institute, VU medical center Amsterdam, Amsterdam, the Netherlands 334Department of Biochemistry, College of Medicine, Ewha Womans University, Seoul 07804, Korea 335Faculty of Health and Medicine, University of Newcastle, Newcastle, Australia Author Manuscript 336Washington University School of Medicine, Division of Biostatistics, St Louis, MO, USA 337University of Kentucky, College of Public Health, Lexington, KY, USA 338Instituteof Cellular Medicine (Diabetes), The Medical School, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 42 339Department of Population Health, Finnish Institute for Health and Welfare, P.O. Author Manuscript Box 30, FI-00271 Helsinki, Finland 340University of Helsinki and Department of Medicine, Helsinki University Hospital, P.O.Box 340, Haartmaninkatu 4, Helsinki, FI-00029, Finland 341Minerva Foundation Institute for Medical Research, Biomedicum 2U, Tukholmankatu 8, Helsinki, FI-00290, Finland 342Academy of Scientific and Innovative Research (AcSIR), New Delhi, India 343Diabetology Research Centre, KEM Hospital and Research Centre, Pune, Maharashtra, India 344Programsin Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA Author Manuscript 345Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA 346Harvard Medical School, Boston, Massachusetts, USA 347Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Bimédicas UNAM/ Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico 348Dirección de Nutrición and Unidad de Estudios de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico 349Instituto Nacional de Salud Publica y Centro de Estudios en Diabetes, Author Manuscript Cuernavaca, Mexico 350Instituto Nacional de Medicina Genómica, 14610 Ciudad de México, CDMX, Mexico 351Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston TX 77030, USA 352Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, 119228, Singapore 353Kurume University School of Medicine, Kurume, 830-0011, Japan 354Genetics, Merck Sharp & Dohme Corp., Kenilworth, NJ, 07033, USA 355Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Author Manuscript UK 356Population Health and Genomics, University of Dundee, Ninwells Hospital and Medical School, Dundee, DD1 9SY, UK 357Intramural Research Program, National Institute on Aging, 3001 S. Hanover St., Baltimore, MD 21225, USA Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 43 358The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical Author Manuscript University, Wenzhou, Zhejiang 325027, China 359Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China 360SynlabAcademy, SYNLAB Holding Deutschland GmbH, Mannheim and Augsburg, Germany 361ClinicalInstitute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Austria 362Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico Author Manuscript 363Faculty of Medicine, University of Iceland, 101 Reykjavik, Iceland 364Departmentof Medicine, Faculty of Medecine, Université de Montréal, 2900 Edouard Montpetit Blvd, Montreal, Quebec, H3T 1J4, Canada 365Departmentof Medicine, Faculty of Medicine, Université de Montréal, 2900 Edouard Montpetit Blvd, Montreal, Quebec, H3T 1J4, Canada 366LeidenUniversity Medical Center, Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden, 2333ZA, The Netherlands 367Amsterdam UMC, Department of General Practice and Elderly Care, Amsterdam Public Health Research Institute, Amsterdam, 1081HV, The Netherlands 368Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104, Author Manuscript USA 369Department of Surgery, University of Pennsylvania, Philadelphia, PA, 19104, USA 370Corporal Michael Crescenz VA Medical Center, Philadelphia, Pennsylvania, PA, 19104, USA 371Institute of Social and Economic Research, University of Essex, Wivenhoe Park, CO4 3SQ, United Kingdom 372TheMindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA 373CardiovascularHealth Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, 98101, WA, USA Author Manuscript 374Kaiser Permanent Washington Health Research Institute, Seattle, 98101, WA, USA 375Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark 376Danish Aging Research Center, University of Southern Denmark; Odense C, Denmark Nature. 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Page 44 377Public Health, Faculty of Medicine, University of Helsinki, Finland Author Manuscript 378Broad Institute of MIT and Harvard, Cambridge, MA, USA 379Centerfor Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104 USA 380Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104 USA 381Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104 USA 382Department of Genetics, University of Pennsylvania, Philadelphia, PA, 19104 USA 383Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Author Manuscript Medical University of Innsbruck, Innsbruck, Austria and German Chronic Kidney Disease study 384School of Medicine, Southern University of Science and Technology, Shenzhen, China 385Institute for Cardiogenetics, University of Lübeck, DZHK (German Research Centre for Cardiovascular Research), partner site Hamburg/Lübeck/Kiel, and University Heart Center Lübeck, Lübeck, Germany 386DeutschesHerzzentrum München, Klinik für Herz- und Kreislauferkrankungen, Technische Universität München,Munich, Germany 387Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK) e.V., partner site Author Manuscript Munich Heart Alliance,Munich, Germany 388Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark 389Netherlands Heart Institute, Utrecht, the Netherlands 390Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA 391Program of Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA 392Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA Author Manuscript 393Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA 394NorthernFinland Birth Cohorts, Infrastructure for population studies, Faculty of Medicine, University of Oulu, Oulu, Finland 395Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland 396Biocenter of Oulu, University of Oulu, Oulu, Finland Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 45 397University Center for Primary Care and Public Health, Rte de Berne 113, Author Manuscript Lausanne, 1010, Switzerland 398Institute for Genetic and Biomedical Research, Italian National Council of Research (IRGB CNR), Cagliari, Italy 399University of Sassari, Sassari, Italy 400Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands 401Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands 402Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands Author Manuscript 403EinthovenLaboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands 404Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands 405Divisionof Cardiovascular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, UK 406Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK 407Population Health Research Institute, St George’s, University of London, London SW17 0RE, UK Author Manuscript 408National Heart and Lung Institute, Imperial College London, London, W2 1PG, UK 409School of Public Health, Imperial College London, London, W2 1PG, UK 410OCDEM, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK 411NIHR Oxford Biomedical Research Centre, Churchill Hospital, Oxford, UK 412Ophthalmology& Visual Sciences Academic Clinical Program (Eye ACP), Duke- NUS Medical School, 169857, Singapore 413DZHK (German Centre for Cardiovascular Research), Munich Heart Alliance partner site, Munich, Germany Author Manuscript 414German Center for Diabetes Research (DZD), Neuherberg, Germany 415University of Exeter Medical School, University of Exeter, Exeter, EX2 5DW, UK 416Departmentof Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden 417Netherlands Twin Register, Department of Biological Psychology, Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 46 418Amsterdam Public Health, VU medical center Amsterdam, Amsterdam, the Author Manuscript Netherlands 419AmsterdamReproduction & Development research institute, VU medical center Amsterdam, Amsterdam, the Netherlands 420Department of Biological Psychology, Behavioral and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands 421Framingham Heart Study, National Heart, Lung, and Blood Institute, US National Institutes of Health, Bethesda, MD, USA 422Departmentof Genetics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran 423Departmentof Genetics, Shanghai-MOST Key Laboratory of Health and Disease Author Manuscript Genomics, Chinese National Human Genome Center at Shanghai, Shanghai, 201203 China 424TUM School of Medicine, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany 425Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland 426Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA 427Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA 428Stanford Diabetes Research Center, Stanford University, Stanford, CA 94305, Author Manuscript USA 429Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden 430Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor 431Regeneron Pharmaceuticals, Tarrytown, NY, USA 432LeeKong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore 433Imperial College Healthcare NHS Trust, Imperial College London, London W12 0HS, UK Author Manuscript 434MRC-PHE Centre for Environment and Health, Imperial College London, London W2 1PG, UK 435National Heart and Lung Institute, Imperial College London, London W12 0NN, UK 436Institutefor Minority Health Research, University of Illinois College of Medicine, Chicago, Illinois, USA Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 47 437Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Author Manuscript Huntington Avenue, Boston, MA, 02115, USA 438QIMR Berghofer Medical Research Institute, 300 Herston Road, Brisbane, Queensland 4006, Australia 439Center for Non-Communicable Diseases, Karachi, Sindh, Pakistan & Faisalabad Institute of Cardiology, Faislabad, Pakistan 440Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA 441Department of Cardiology, Columbia University Irving Medical Center, New York, NY, USA 442Big Data Institute, University of Oxford, Oxford OX3 7LF, UK Author Manuscript 443NationalInstitute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals, Oxford, UK 444Aberdeen Centre for Health Data Science, 1:042 Polwarth Building, School of Medicine, Medical Science and Nutrition, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK 445Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, United Kingdom 446Biomedical and Translational Informatics, Geisinger Health, Danville, PA 17822, USA 447Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA Author Manuscript 02215 448Harvard Medical School, Boston, MA 02115, USA 449Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA 450Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, WA, USA 451Centerfor Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, OH, USA 452Divisionof Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60618, Author Manuscript USA 453Department of Anthropology, Northwestern University, Evanston, IL 60208, USA 454Center for Genetic Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60618, USA 455HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, 7600 Norway Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 48 456Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Author Manuscript Levanger, 7600 Norway 457Department of Endocrinology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway 458RIKEN Center for Integrative Medical Sciences, Yokohama, Japan 459Laboratoryfor Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan 460Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan 461Laboratory of Statistical Immunology, WPI Immunology Frontier Research Center, Author Manuscript Osaka University, Osaka, Japan 462Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan 463Divisionof Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan 464Faculty of Medicine, University of Iceland, Sæmundargötu 2, Reykjavik, 102, Iceland 465VA Boston Healthcare System, Boston, MA, USA 466VAInformatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA Author Manuscript 467University of Massachusetts, Boston, MA, USA 468Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA 469Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, USA 470Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA 471Department of Medicine, Brigham Women’s Hospital, Boston, MA, USA 472Atlanta VA Medical Center, Atlanta, GA, USA 473Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA Author Manuscript 474Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa 475School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 49 476Divisionof Human Genetics, National Health Laboratory Service and School Author Manuscript of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa 477School of Basic and Medical Biosciences, Faculty of Life Sciences and Medicine, King’s College London, London, UK 478Departments of Pediatrics and Genetics, Harvard Medical School, Boston, MA, USA 479Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA 480CardiologyDivision, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Author Manuscript 481Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA 482Programin Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA 483Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA 484Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA 485Centrefor Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester, UK Author Manuscript 486Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia 487Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA 488Department of Human Genetics, University of Michigan, Ann Arbor, MI 48019, USA Acknowledgments Funding for the Global Lipids Genetics Consortium was provided by the NIH (R01-HL127564). This research has been conducted using the UK Biobank Resource under application number 24460. Computing support and file management for central meta-analysis by Sean Caron is gratefully acknowledged. This research is based on data Author Manuscript from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by awards #2I01BX003362-03A1 and 1I01BX004821-01A1#. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. Study-specific acknowledgements are provided in the supplemental material. Competing interests G.J.M.Z. is an employee of Incyte Corporation. G.C-P. is currently an employee of 23andMe Inc. M.J.C. is the Chief Scientist for Genomics England, a UK Government company. B.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. G.T., A.H., D.F.G., H.H., U.T., and K.S. Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 50 are employees of deCODE/Amgen Inc. V.S. has received honoraria for consultations from Novo Nordisk and Sanofi and has an ongoing research collaboration with Bayer Ltd. M.M. has served on advisory panels for Pfizer, Author Manuscript NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. M.M. and A.M. are employees of Genentech and a holders of Roche stock. M.S. receives funding from Pfizer Inc. for a project unrelated to this work. M.E.K. is employed by SYNLAB MVZ Mannheim GmbH. W.M. has received grants from Siemens Healthineers, grants and personal fees from Aegerion Pharmaceuticals, grants and personal fees from AMGEN, grants from Astrazeneca, grants and personal fees from Sanofi, grants and personal fees from Alexion Pharmaceuticals, grants and personal fees from BASF, grants and personal fees from Abbott Diagnostics, grants and personal fees from Numares AG, grants and personal fees from Berlin-Chemie, grants and personal fees from Akzea Therapeutics, grants from Bayer Vital GmbH , grants from bestbion dx GmbH, grants from Boehringer Ingelheim Pharma GmbH Co KG, grants from Immundiagnostik GmbH, grants from Merck Chemicals GmbH, grants from MSD Sharp and Dohme GmbH, grants from Novartis Pharma GmbH, grants from Olink Proteomics, other from Synlab Holding Deutschland GmbH, all outside the submitted work. A.V.K. has served as a consultant to Sanofi, Medicines Company, Maze Pharmaceuticals, Navitor Pharmaceuticals, Verve Therapeutics, Amgen, and Color Genomics; received speaking fees from Illumina, the Novartis Institute for Biomedical Research; received sponsored research agreements from the Novartis Institute for Biomedical Research and IBM Research, and reports a patent related to a genetic risk predictor (20190017119). S.K. is an employee of Verve Therapeutics, and holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis, and San Therapeutics. He is a member of the scientific advisory boards for Regeneron Genetics Center and Author Manuscript Corvidia Therapeutics; he has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest, and Medscape; he reports patents related to a method of identifying and treating a person having a predisposition to or afflicted with cardiometabolic disease (20180010185) and a genetics risk predictor (20190017119). D.K. accepts consulting fees from Regeneron Pharmaceuticals. D.O.M-K. is a part- time clinical research consultant for Metabolon, Inc. D.S. has received support from the British Heart Foundation, Pfizer, Regeneron, Genentech, and Eli Lilly pharmaceuticals. The spouse of C.J.W. is employed by Regeneron. 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Boxplots depict the median value as the center, first and third quartiles as box boundaries and Author Manuscript whiskers extending 1.5 times the inter-quartile range, with points beyond this region shown individually. Sample sizes for each ancestry are provided in Table 1. The mean effect size of Admixed African ancestry identified index variants is larger than from European ancestry analysis, reflecting the difference in power to detect an association within each group as a result of the >10-fold difference in sample size. e) Number of loci identified within each ancestry group, normalized to a constant sample size of 100,000 individuals and averaged across lipid traits. At currently available sample sizes, trans-ancestry and European ancestry analyses identify a lower proportion of loci relative to the number of individuals than analyses of other ancestry groups. However, the larger sample size of European or trans-ancestry analyses leads to a greater relative proportion of novel loci and a higher proportion of loci significant only in European ancestry analyses. f) Proportion of index variants identified from each ancestry-specific meta-analysis that would be well-powered Author Manuscript to detect an association of the same effect size but with ancestry-specific frequencies in the other ancestry groups. Dark blue regions indicate variants likely to be detected at an equivalent sample size only in the original ancestry group (i.e. ancestry-specific). Additional comparisons of allele frequencies and effect sizes across ancestries are provided in Supplementary Figure 3. Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 56 Author Manuscript Figure 2: Inclusion of multiple ancestries drives improved fine-mapping Author Manuscript a) Association of the DMTN intron variant rs900776 with LDL-C or b) DMTN expression. The region spanned by the 99% credible sets are shown in the center box. The LDL-C association signal significantly colocalizes with the GTEx eQTL signal of DMTN in liver. c) The LD patterns for variants in the European ancestry 99% credible set differ greatly between African and European ancestry individuals in 1000 Genomes. The lead variant has a posterior probability of 0.86 in Admixed African, 0.51 in European, and >0.99 in the trans-ancestry analysis. Author Manuscript Author Manuscript Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 57 Author Manuscript Author Manuscript Figure 3: Trans-ancestry LDL-C PRS show similar performance across ancestry groups a) Polygenic scores generated from trans-ancestry meta-analysis show equivalent or better Author Manuscript performance across most ancestry groups relative to ancestry-specific PRS within each cohort, whereas European ancestry-specific scores show less transferability. Adjusted R2 is calculated with the risk score as a predictor of LDL-C in a linear model with covariates. AFR: African, AFRAMR: African American, ASN: Asian American b) Trans-ancestry scores derived from equal proportions of each ancestry group predict LDL-C better for African Americans in MGI than predominantly European ancestry scores at constant sample size. Error bars depict 95% confidence intervals. Sample sizes for each cohort are provided in Supplementary Table 16. Author Manuscript Nature. Author manuscript; available in PMC 2022 June 09. Graham et al. Page 58 Table 1: Meta-analysis sample size by ancestry group Author Manuscript Ancestry Group Sample Size Number of Cohorts Mean Sample Size per Cohort (range) Number of Variants European 1,320,016 146 10,928 (173-389,344) 47 M East Asian 146,492 40 7,448 (150-131,050) 17 M Admixed African/African 99,432 19 5,330 (473-62,022) 33 M Hispanic 48,057 10 6,032 (1,496-22,302) 27 M South Asian 40,963 7 6,413 (1,796-16,110) 17 M Total 1,654,960 201 52 M The present meta-analysis represents a 6-fold overall increase in sample size relative to the most recent 2018 Million Veteran Program blood lipid meta-analysis13, with a 2-fold increase in sample size of Admixed African and Hispanic individuals. Author Manuscript Author Manuscript Author Manuscript Nature. Author manuscript; available in PMC 2022 June 09.