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Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility

Abstract

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.

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Figure 1: Signal plots of the trans-ethnic discovery-stage GWAS meta-analysis for new T2D susceptibility loci.
Figure 2: Signal plots presenting 99% credible sets of SNPs at the JAZF1 and SLC30A8 loci.

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Acknowledgements

Funding for the research undertaken in this study has been received from the following: the Canadian Institutes of Health Research; the European Commission (ENGAGE FP7 HEALTH-F4-2007- 201413); the Medical Research Council UK (G0601261); the Mexico Convocatoria (SSA/IMMS/ISSSTE-CONACYT 2012-2, clave 150352, IMSS R-2011-785-018 and CONACYT Salud-2007-C01-71068); the US National Institutes of Health (DK062370, HG000376, DK085584, DK085545, DK073541 and DK085501); and the Wellcome Trust (WT098017, WT090532, WT090367, WT098381, WT081682 and WT085475). We acknowledge the many colleagues who contributed to collection and phenotypic characterization of the clinical samples and the genotyping and analysis of the GWAS data, full details of which are provided in the contributing consortia papers5,11,13,15. We also thank those individuals who agreed to participate in this study.

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Authors and Affiliations

Authors

Consortia

Contributions

Writing group: A. Mahajan, M.J.G., W. Zhang, J.E.B., K.J.G., M.H., A.D.J., I.P., E.Z., Y.Y.T., M.B., E.J.P., J.C.C., E.S.T., M.I.M. and A.P.M.

Analysis group: A. Mahajan, M.J.G., W. Zhang, J.E.B., K.J.G., T. Ferreira, M.H., A.D.J., M.C.Y.N., I.P., D.S., X.W., E.Z., Y.Y.T., M.B., E.J.P., M.I.M. and A.P.M.

DIAGRAM Consortium samples, genotyping, analysis and management: A. Mahajan, M.H., I.P., D.S., E.Z., G.R.A., P.A., M.A., D.B., B.B., I.B., J.B., R.B., R.N.B., B.O.B., E.B., L.L.B., N.B., H. Campbell, J.C., S.C., G.C., H. Chen, P.S.C., F.S.C., M.C.C., D.J.C., A.T.C., R.M.v.D., J. Danesh, U.d.F., G.D., P.D., A.S.D., C.D., A.S.F.D., P.J.D., M.D., C.v.D., J. Dupuis, S.E., V.E., R.E., J.G.E., T.E., E.E., T. Ferreira, J.C.F., P. Fontanillas, N.G.F., T. Forsen, C.F., R.M.F., T.M.F., P. Froguel, K.G., C. Gieger, B.G., H.G., G.B.G., L.C.G., C.J.G., C. Guiducci, A. Hamsten, A.T.H., C. Hayward, C. Herder, A. Hofman, O.L.H., K. Hovingh, A.B.H., F.B.H., J.H., S.E. Humphries, S.E. Hunt, D.J.H., K. Hveem, T.I., E.I., B.I., A.U.J., A. James, K.-H.J., A. Jonsson, H.M.K., S. Kanoni, W.H.L.K., S. Kathiresan, S.M.K.-K., H.K., K.-T.K., L.K., N. Klopp, A. Kong, E.K.-H., P. Kraft, J. Kravic, A. Kumar, J. Kuusisto, M. Laakso, V. Lagou, T.A.L., C. Langenberg, C. Langford, R.L., K.L., M. Li, L.L., C.M.L., E.L., C.-T.L., S. Lobbens, R.J.F.L., J. Luan, V. Lyssenko, R.M., S. Männistö, J.B.M., O.M., A. Metspalu, J.M., G.M., E.M., S. Moebus, K.L.M., A.D.M., T.W.M., M.M.-N., B.M., P.N., P.M.N., I.N., M.M.N., K.R.O., C.N.A.P., J.S.P., M.P., S. Pechlivanis, N.L.P., L.P., J.R.B.P., A.P., C.G.P.P., S. Potter, J.F.P., L.Q., L.R., W.R., R.R., S. Raychaudhuri, N.W.R., E.R., S. Ripatti, N.R., M.R., E.J.R., I.R., D.R., T.E.S., V. Salomaa, J. Saltevo, J. Saramies, L.J.S., R.A.S., A.V.S., B.S., S. Shah, A.R.S., G. Sigurðsson, E.S., A. Silveira, S. Sivapalaratnam, A. Stančáková, K. Stefansson, G. Steinbach, V. Steinthorsdottir, K. Stirrups, R.J.S., H.M.S., Q.S., A.-C.S., T.M.T., B.T., G.T., U.T., E. Tikkanen, J. Trakalo, E. Tremoli, M.D.T., T.T., J. Tuomilehto, A.G.U., S.V., F.V., B.F.V., N.J.W., R.W., T.W., J.F.W., S.W., W.W., A.R. Wood, L.Y., D.Z., D.A., M.B., M.I.M. and A.P.M.

AGEN-T2D Consortium samples, genotyping, analysis and management: M.J.G., X.W., L.S.A., T.A., Y.B., Q.C., J.C.N.C., L.-C.C., T.-J.C., Y.-C.C., C.-H.C., Y.-T.C., N.H.C., Y.M.C., L.-M.C., Y.G., B.-G.H., K. Hara, A.K.H., C. Hu, F.B.H., H.I., W.J., T.K., N. Kato, H.-L.K., S. Kim, Y.J.K., S.H.K., J.-M.L., N.R.L., Y.L., J.J.L., J. Long, W.L., R.C.W.M., S. Maeda, K.L.M., J.N., E.N., P.-K.N., K.O., T.H.O., K.S.P., X.O.S., X.S., W.Y.S., R.T., W.T.T., F.J.T., C.W., T.Y.W., J.-Y.W., Y.W., K.Y., T.Y., M. Yokota, R.Z., W. Zheng, Y.S.C., J.-Y.L., M. Seielstad, Y.Y.T., E.S.T. and M.I.M.

SAT2D Consortium samples, genotyping, analysis and management. W. Zhang, I.P., D.S., G.R.A., T.A., A.H.B., A.B., L.F.B., M. Caulfield, K.-S.C., M. Chidambaram, J. Danesh, D.D., P.D., A.S.D., P.E., T.M.F., P. Froguel, P. Frossard, E.G., N.H., A.K.H., Z.I.H., M.I., T.J., J.B.M.J., N. Kato, P. Katulanda, A.M.K., C.-C.K., S. Kowlessur, M.M.K., X.L., J. Liang, S. Liju, W.-Y.L., J.J.L., D.R.M., V.M., A.C.N., J.M.P., V.R., A.R., S.D.R., M. Samuel, D.K.S., J. Scott, J. Sehmi, N.S., A.S.S., X.S., K.S.S., C.S., R.T., F.T., A.R. Wickremasinghe, T.Y.W., M. Yang, R.Y., F.Z., P.Z.Z., J. Kooner, M. Seielstad, Y.Y.T., J.C.C., E.S.T. and M.I.M.

MAT2D Consortium samples, genotyping, analysis and management: J.E.B., G.I.B., J.E., S. Krithika, J. Kumate, A.V.-S., N.J.C., M. Cruz, C.L.H. and E.J.P.

Project management: D.A., D.W.B., Y.S.C., N.J.C., M. Cruz, C.L.H., J. Kooner, J.-Y.L., M. Seielstad, Y.Y.T., M.B., E.J.P., J.C.C., E.S.T., M.I.M. and A.P.M.

Corresponding authors

Correspondence to Michael Boehnke, Esteban J Parra, John C Chambers, E Shyong Tai, Mark I McCarthy or Andrew P Morris.

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Competing interests

K. Stefansson, V. Steinthorsdottir, G.T. and U.T. are employed by deCODE Genetics/Amgen inc. I.B. and spouse own stock in GlaxoSmithKline and Incyte.

Additional information

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

Integrated supplementary information

Supplementary Figure 1 Manhattan plot of trans-ethnic “discovery” GWAS meta-analysis.

The trans-ethnic meta-analysis comprises 26,488 T2D cases and 83,964 controls from populations of European, East Asian, South Asian, and Mexican and Mexican American ancestry, imputed up to 2.5 million Phase II/III HapMap autosomal SNPs. Previously established T2D susceptibility loci are highlighted in red. Novel loci achieving nominal significance (p<10-5) in the stage 1 meta-analysis, and genome-wide significance (p<5x10-8) after the addition of the “validation“ meta-analysis of 21,491 cases and 55,647 controls of European ancestry, are highlighted in green. Loci achieving nominal significance in the discovery meta-analysis, but not achieving genome-wide significance after the addition of the validation meta-analysis are highlighted in yellow.

Supplementary Figure 2 ENCODE annotation of LPP and FAF1 loci.

Transcription factor binding ChIP sites (TFBS) and Dnase I hypersensitivity sites (DNase HS) are highlighted in black. Chromatin states in 9 ENCODE cell lines (GM12878, HepG2, hESC, HMEC, HSMM, HUVEC, K562, NHEK, and NHLF) are highlighted as follows: strong enhancer (orange), weak enhancer (yellow), active promoter (red), poised promoter (pink), insulator (blue), transcribed (pale green), transcription transition (dark green), repressed (dark grey) and heterochromatin (pale grey).

Supplementary Figure 3 Signal plots constructed on the basis of ancestry-specific meta-analyses at two loci showing greatest improvements in fine-mapping resolution after trans-ethnic meta-analysis: JAZF1 (top) and SLC30A8 (bottom).

The ancestry-specific meta-analyses were imputed at up to 2.5 million Phase II/III HapMap autosomal SNPs. Each point represents a SNP passing quality control in the ancestry-specific meta-analysis, plotted with their p-value (on a -log10 scale) as a function of genomic position (NCBI Build 36). In each plot, the lead SNP from the trans-ethnic meta-analysis across ancestry groups is represented by the purple symbol. The colour coding of all other SNPs indicates LD with the lead SNP (estimated by r2 from the most closely related reference panel from Phase II HapMap, i.e. CEU for the European, South Asian, and Mexican and Mexican American ancestry groups, and CHB+JPT for the East Asian ancestry group): red r2≥0.8; gold 0.6≤r2<0.8; green 0.4≤r2<0.6; cyan 0.2≤r2<0.4; blue r2≤0.2; grey r2 unknown. The shape of the plotting symbol corresponds to the annotation of the SNP: upward triangle for framestop or splice; downward triangle for non-synonymous; square for synonymous or UTR; and circle for intronic or non-coding. Recombination rates are estimated from Phase II HapMap and gene annotations are taken from the University of California Santa Cruz genome browser.

Supplementary Figure 4 Dendogram representing relatedness between ancestry groups included in the trans-ethnic meta-analysis.

The distance between each ethnic group is estimated by the genome-wide autosomal mean effect allele frequency difference from the ancestry-specific meta-analysis. The dendogram represents our prior beliefs about the heterogeneity in allelic effects on T2D susceptibility between ancestry groups in the MANTRA analysis.

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Supplementary Figures 1–4, Supplementary Tables 1–14 and Supplementary Note (PDF 4336 kb)

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DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium., Asian Genetic Epidemiology Network Type 2 Diabetes (AGEN-T2D) Consortium., South Asian Type 2 Diabetes (SAT2D) Consortium. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 46, 234–244 (2014). https://doi.org/10.1038/ng.2897

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