Abstract
Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
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Data availability
Ancestry-specific and overall meta-analysis summary level results are available through the MAGIC website (https://www.magicinvestigators.org/). Summary statistics are also available through the GWAS catalog (https://www.ebi.ac.uk/gwas/) with the following accession codes: GCST90002225, GCST90002226, GCST90002227, GCST90002228, GCST90002229, GCST90002230, GCST90002231, GCST90002232, GCST90002233, GCST90002234, GCST90002235, GCST90002236, GCST90002237, GCST90002238, GCST90002239, GCST90002240, GCST90002241, GCST90002242, GCST90002243, GCST90002244, GCST90002245, GCST90002246, GCST90002247 and GCST90002248.
Code availability
Source code implementing the methods described in the paper are publicly available at https://doi.org/10.5281/zenodo.4607311.
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Acknowledgements
We thank all investigators, staff members and study participants for their contribution to all participating studies. The funders had no role in study design, data collection, analysis, decision to publish or preparation of the manuscript. The authors received no specific funding for this work. A full list of funding as well as individual and study acknowledgments appears in the Supplementary Note.
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Project coordination: I.B. Writing group: J.C., C.N.S., G. Marenne, A.V., L.J.C., S.C.J.P., K.L.M., C. Langenberg, E.W., A.P.M. and I.B. Central analysis group: J.C., C.N.S., G. Marenne, A.V., L.J.C, J’an Luan, S.W., Y. Wu, X.Z., M.H., T.S.B., R.M., J.W., A.P., R.L.-G., K.H.K.C., J.Y., M.D.A., A.Y.C., A. Claringbould, J. Heikkinen, J. Hong, J.-J.H., S. Huo, M.A.K., T.L., W.M., H.M.-M., A. Ndungu, S.C.N., K.N., C.K.R., D. Ray, R. Rohde, D. Rybin, C. Schurmann, X.S., L.S., I.D.S., C.A.W., Y. Wang, P.W., W. Zhang, J.I.R., A.L.G., M.I.M., J.D., J.B.M., R.A.S., I.P., A. Leong, C.-T.L., S.C.J.P., K.L.M., C. Langenberg, E.W., A.P.M. and I.B. Cohort analysts: T.S.A., E.V.R.A., L.F.B., J.A.B., N.P.B., C.P.C., B.E.C., J.C., X.C., L.-C.C., C.-H.C., B.H.C., K.C., Y.-F.C., H.G.d.H., G.E.D., A. Demirkan, Q.D., J.E., S.A.F., J.G., F.G., J.G., S. Gustafsson, Y. Hai, F.P.H., J.-J.H., Y. Heianza, T. Huang, A.H.-C., M.H., R.A.J., T. Kawaguchi, K.A.K., Y.K., M.E.K., I.K.K., S. Lai, L.A.L., C.D.L., M. Lauzon, M. Li, S. Ligthart, J. Liu, M. Loh, J. Long, V.L., M.M., C.M., M.E.M., A. Nag, M. Nakatochi, D.N., R.N., G.P., M.P., L.R., L.J.R.-T., S.S.R., N.R.R., R. Rueedi, K. Ryan, S.S., R.S., K.E.S., B.S., J. He, K. Setoh, A.V.S., L.S., T. Sparsø, R.J.S., F.T., J. Tan, S.T., E.v.d.A., P.J.v.d.M., N.V., M.V., H. Wang, C.W., N.W., H.R.W., W.W., T. Wilsgaard, A.W., A. R. Wood, T.X., M.Z., J.-H.Z. and W. Zhao. Cohort genotyping and phenotyping: N.A., Z.A., A.A., S.J.L.B., D.B., M. Beekman, R.N.B., A.B., M. Blüher, L.L.B., S.R.B., D.W.B., Q.C., A. Campbell, H.C., Y.-F.C., E.J.C.d.G., A. Dehghan, S.D., G.E., A.F., M.F., C.F., Y.G., A.P.G., A.G., S. Han, C.A.H., C.-H.H., A.A.H., C. Herder, Y.C.C., W.A.H., S.I., M.I., M.A.I., W.C.J., M.E.J., P.K.J., R.R.K., F.R.K., T. Katsuya, C.K., W.K., I.K., T. Kuulasmaa, J.K., K. Läll, K. Lam, D.A.L., N.R.L., R.N.L., Honglan Li, S.-Y.L., J. Lindström, A. Linneberg, J. Liu, C. Lorenzo, T.M., F.M., G. Mingrone, S.M., S.M., T.N., G.N.N., J.L.N., M. Nelis, M.J.N., J.M.N., Y.O., A.P., P.A.P., O. Polasek, Q.Q., D. Raven, D.F.R., A.R., F.R., K. Roll, I.R., C. Sabanayagam, K. Sandow, N. Sattar, A. Schürmann, J. Shi, H.M.S., K.D.T., T.M.T., B.T., P.R.H.J.T., E.T., M.Y.T., A.U., R.M.v.D., D.v.H., A.v.H.V., J.V.v.V.-O., J.V., H.V., T. Wang, T.-Y.W., K.W.v.D. and T.Z. Cohort oversight and/or principal investigator: G.R.A., L.S.A., C.A.A.-S., M.E.A.-R., P.A., L.A.-S., D.M.B., L.J.B., S.B., H.B., C.B., M. Boehnke, E.B., B.O.B., K.B., D.I.B., E.P.B., T.A.B., M.C., M.J.C., J.C.C., D.I.C., Y.-D.I.C., C.-Y.C., F.S.C., A. Correa, F.C., H.G.d.H., G.D., S.E., M.K.E., E.F., L.F., J.C.F., P.W.F., T.M.F., P.F., B.G., M.O.G., P.G.-L., H.G., N.G., S. Grimsgaard, L.G., V.G., X.G., A.H., T. Hansen, C. Hayward, S.R.H., B.L.H., W.H., E.I., P.S.J., M.-R.J., J.B.J., J.W.J., P. Kaleebu, R.K., S.L.R.K., N.K., S.M.K.-K., B.-J.K., M. Kivimaki, H.A.K., J.S.K., A.K., P. Kovacs, D.K., M. Kumari, Z.K., M. Laakso, T.A.L., L.J.L., K. Leander, Huaixing Li, X.L., L.L., C. Lindgren, S. Liu, R.J.F.L., P.K.E.M., A. Mahajan, A. Metspalu, D.O.M.-K., T.A.M., P.B.M., I.N., J.R.O., A.J.O., K.K.O., S.P., C.N.A.P., N.D.P., O. Pedersen, C.E.P., D.J.P., P.P.P., M.A.P., B.M.P., L.Q., L.J.R., R. Rauramaa, S.R., P.M.R., F.R.R., T.E.S., M. Sandhu, J. Saramies, N. Schneiderman, P. Schwarz, L.J.S., E.S., P. Sever, X-o.S., P.E.S., K.S.S., B.H.S., H.S., T. Sofer, T.I.A.S., T.D.S., A. Stanton, C.J.S., M. Stumvoll, Y.T., E.T., N.J.T., A.T., J. Tuomilehto, T.T., M.U., P.v.d.H., C.v.D., P.V., T.G.M.V., L.E.W., M.W., Y.X.W., N.J.W., R.M.W., H. Watkins, W.B.W., A. R. Wickremasinghe, G.W., J.F.W., T.-Y.W., J.-Y.W., A.H.X., L.R.Y., L.Y., M.Y., E.Z., W. Zheng, A.B.Z., J.I.R., A.L.G., M.I.M., J.D., J.B.M., R.A.S., I.P., A.L., C.-T.L., S.C.J.P., K.L.M., C. Langenberg, E.W., A.P.M. and I.B. All authors read, edited and approved the final version of the manuscript.
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A.A. is the recipient of honoraria as a speaker for a wide range of Danish and international concerns and receives royalties from textbooks, and from popular diet and cookery books. A.A. is also co-inventor of a number of patents, including methods of inducing weight loss, treating obesity and preventing weight gain (licensee Gelesis) and biomarkers for predicting the degree of weight loss (licensee Nestec), owned by the University of Copenhagen, in accordance with Danish law. I.B. and spouse own stock in GlaxoSmithKline and Incyte Corporation. B.H.C. is now an employee of Life Epigenetics; all work was completed before employment by Life Epigenetics. A.Y.C. is now an employee of Merck & Co.; all work was completed before employment by Merck & Co. J.C.F. has received consulting honoraria from Janssen. J.G. is now an employee of F. Hoffmann-La Roche, and owns stock in Roche and GlaxoSmithKline. A.L.G. has received honoraria from Merck and Novo Nordisk. As of June 2019, A.L.G. discloses that her spouse is an employee of Genentech and hold stock options in Roche. E.I. is now an employee of GlaxoSmithKline; all work was completed before his employment by GlaxoSmithKline. W.M. has received grants and/or personal fees from the following companies/corporations: Siemens Healthineers, Aegerion Pharmaceuticals, AMGEN, AstraZeneca, Sanofi, Alexion Pharmaceuticals, BASF, Abbott Diagnostics Numares, Berlin-Chemie, Akzea Therapeutics, Bayer Vital, Bestbion dx, Boehringer Ingelheim Pharma, Immundiagnostik, Merck Chemicals, MSD Sharp and Dohme, Novartis Pharma, Olink Proteomics and Synlab Holding Deutschland. M.I.M. has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, and has received honoraria from Merck, Pfizer, NovoNordisk and Eli Lilly. He holds stock options in Zoe Global and has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. He is now an employee of Genentech and a holder of Roche stock. J.B.M. has consulted for Quest Diagnostics, who is a manufacturer of an HbA1c assay. M.E.M. has received grant funding from Regeneron Pharmaceuticals. M.E.M. is also an inventor on a patent that was published by the US Patent and Trademark Office on 6 December 2018 under Publication Number US 2018-0346888, and international patent application that was published on 13 December 2018 under Publication Number WO-2018/226560; all work was completed before these competing interests arose, and are unrelated to this work. D.O.M.-K. is a part-time clinical research consultant for Metabolon. J.L.N. is a member of the Scientific Advisory Board for Veralox Therapeutics. C.N.A.P. has received research support from GlaxoSmithKline and AstraZeneca unrelated to this project. B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. N. Sattar has consulted for AstraZeneca, Boehringer Ingelheim, Eli Lilly, Novo Nordisk, Napp and Sanofi, and received grant support from Boehringer Ingelheim. R.A.S. is an employee and shareholder of GlaxoSmithKline. T.D.S. is the founder of Zoe Global. J. Tuomilehto receives research support from Bayer, is a consultant for Eli Lily and holds stock in Orion Pharma and Aktivolabs.
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Extended data
Extended Data Fig. 1 Flow diagram of this study.
The figure shows the data, key methods and main analyses included in this effort.
Extended Data Fig. 2 Locus diagram.
Trans-ancestry locus A contains a trans-ancestry lead variant for one glycemic trait represented by the blue diamond, and another single-ancestry index variant for another glycemic trait represented by the orange triangle. Single-ancestry locus B contains a single-ancestry lead variant represented by the purple square. The orange, blue and purple bars represent a +/− 500Kb window around the orange, blue, and purple variants, respectively. The black bars indicate the full locus window where trans-ancestry locus A contains trans-ancestry lead and single-ancestry index variants for two traits and single-ancestry locus B has a single-ancestry lead variant for a single trait.
Extended Data Fig. 3 Venn diagram.
Overlap of TA loci between traits.
Extended Data Fig. 4 Allele frequency versus effect size.
Allele frequency versus effect size for all signals detected through the trans-ancestry meta-analyses, for each of the four traits. Frequency and effect size are from the European meta-analyses. The power curves were computed based on the European sample size for each trait, and the mean (m) and standard deviation (sd) computed on the FENLAND study: FG, m = 4.83 mmol/l, sd=0.68; FI, m = 3.69 mmol/l, sd=0.60; 2hGlu, m = 5.30 mmol/l, sd=1.74; HbA1c, m = 5.55%, sd=0.48.
Extended Data Fig. 5 EAF correlation and heterogeneity test.
Pearson correlation of EAF on the lower tri-angle and p-value of one-side heterogeneity test without multiple testing corrections on the upper tri-angle of the trans-ancestry lead variants associated with each trait between ancestries. Correlations > 0.7 are in bold.
Extended Data Fig. 6 Forest plot of T2D GRS from HbA1c variants.
The p-value on the right side is from the two-side test without multiple testing corrections. Vertical points of each diamond represent the point estimate of the odds ratio. The horizontal points of each diamond represent the 95% confidence interval of the odds ratio. Figure shows the association results between HbA1c-associated variants built into a GRS for T2D by taking each HbA1c-associated variant and using a weight that corresponds to its T2D effect size (logOR) based on analysis by the DIAGRAM consortium. The overall GRS is subsequently partitioned according to the HbA1c signal classification. The overall and partitioned GRS were tested for association with T2D based on data from UK biobank.
Extended Data Fig. 7 Enrichment of glycemic trait associated GWAS variants to overlap genomic annotations using GREGOR.
Figure shows enrichment for 59 total static and stretch enhancer annotations considered. One-side test significance (red) is determined after Bonferroni correction to account for 59 total annotations tested for each trait; nominal significance (P < 0.05) is indicated in yellow.
Extended Data Fig. 8 Enrichment of glycemic trait associated GWAS variants to overlap genomic annotations using fGWAS.
Figure shows log2(Fold Enrichment) of GWAS variants to overlap 59 static and stretch enhancer annotations calculated. Significant enrichment (red) is considered if the 95% confidence intervals (shown by the error bars) do not overlap 0.
Extended Data Fig. 9 Enrichment of glycemic trait associated GWAS variants to overlap genomic annotations using GARFIELD.
Figure shows the β or effect size (log odds ratio) for GWAS variants to overlap 59 static and stretch enhancer annotations. GWAS variants were included at two significance thresholds, 1e-05 (A) and 1e-08 (B). One-side test significance (red) is determined after Bonferroni correction to account for effective annotations tested for each trait reported by GARFIELD (see Supplementary Note); nominal significance (P < 0.05) is indicated in yellow. The 95% confidence intervals are shown by the error bars.
Supplementary information
Supplementary Information
Supplementary Note
Supplementary Table 1
Supplementary Tables 1–21
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Chen, J., Spracklen, C.N., Marenne, G. et al. The trans-ancestral genomic architecture of glycemic traits. Nat Genet 53, 840–860 (2021). https://doi.org/10.1038/s41588-021-00852-9
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DOI: https://doi.org/10.1038/s41588-021-00852-9
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