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Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

Abstract

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10−7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent ‘false leads’ with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.

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Fig. 1: Posterior probabilities for coding variants across loci with annotation-informed priors.
Fig. 2: Plot of measures of variant-specific and gene-specific features of distinct coding signals to access the functional impact of coding alleles.

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Acknowledgements

A full list of acknowledgments appears in the Supplementary Note. Part of this work was conducted using the UK Biobank Resource under application number 9161.

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Project coordination: A. Mahajan, A.P.M., J.I.R., M.I.M. Core analyses and writing: A. Mahajan, J.W., S.M.W., W. Zhao, N.R.R., A.Y.C., W.G., H.K., R.A.S., I. Barroso, T.M.F., M.O.G., J.B.M., M. Boehnke, D.S., A.P.M., J.I.R., M.I.M. Statistical analysis in individual studies: A. Mahajan, J.W., S.M.W., W. Zhao, N.R.R., A.Y.C., W.G., H.K., D.T., N.W.R., X.G., Y. Lu, M. Li, R.A.J., Y. Hu, S. Huo, K.K.L., W. Zhang, J.P.C., B.P.P., J. Flannick, N.G., V.V.T., J. Kravic, Y.J.K., D.V.R., H.Y., M.M.-N., K.M., R.L.-G., T.V.V., J. Marten, J. Li, A.V.S., P. An, S.L., S.G., G.M., A. Demirkan, J.F.T., V. Steinthorsdottir, M.W., C. Lecoeur, M. Preuss, L.F.B., P. Almgren, J.B.-J., J.A.B., M.C., K.-U.E., K.F.,H.G.d.H., Y. Hai, S. Han, S.J., F. Kronenberg, K.L., L.A.L., J.-J.L., H.L., C.-T.L., J. Liu, R.M., K.R., S.S., P.S., T.M.T., G.T., A. Tin, A.R.W., P.Y., J.Y., L.Y., R.Y., J.C.C., D.I.C., C.v.D., J. Dupuis, P.W.F., A. Köttgen, D.M.-K., N. Soranzo, R.A.S., A.P.M. Genotyping: A. Mahajan, N.R.R., A.Y.C., Y. Lu, Y. Hu, S. Huo, B.P.P., N.G., R.L.-G., P. An, G.M., E.A., N.A., C.B., N.P.B., Y.-D.I.C., Y.S.C., M.L.G., H.G.d.H., S. Hackinger, S.J., B.-J.K., P.K., J. Kriebel, F. Kronenberg, H.L., S.S.R., K.D.T., E.B., E.P.B., P.D., J.C.F., S.R.H., C. Langenberg, M.A.P., F.R., A.G.U., J.C.C., D.I.C., P.W.F., B.-G.H., C.H., E.I., S.L.R.K., J.S.K., Y. Liu, R.J.F.L., N. Soranzo, N.J.W., R.A.S., T.M.F., A.P.M., J.I.R., M.I.M. Cross-trait lookups in unpublished data: S.M.W., A.Y.C., Y. Lu, M. Li, M.G., H.M.H., A.E.J., D.J.L., E.M., G.M.P., H.R.W., S.K., C.J.W. Phenotyping: Y. Lu, Y. Hu, S. Huo, P. An, S.L., A. Demirkan, S. Afaq, S. Afzal, L.B.B., A.G.B., I. Brandslund, C.C., S.V.E., G.G., V. Giedraitis, A.T.-H., M.-F.H., B.I., M.E.J., T.J., A. Käräjämäki, S.S.K., H.A.K., P.K., F. Kronenberg, B.L., H.L., K.-H.L., A.L., J. Liu, M. Loh, V.M., R.M.-C., G.N., M.N., S.F.N., I.N., P.A.P., W.R., L.R., O.R., S.S., E.S., K.S.S., A.S., B.T., A. Tönjes, A.V., D.R.W., H.B., E.P.B., A. Dehghan, J.C.F., S.R.H., C. Langenberg, A.D. Morris, R.d.M., M.A.P., A.R., P.M.R., F.R.R., V. Salomaa, W.H.-H.S., R.V., J.C.C., J. Dupuis, O.H.F., H.G., B.-G.H., T.H., A.T.H., C.H., S.L.R.K., J.S.K., A. Köttgen, L.L., Y. Liu, R.J.F.L., C.N.A.P., J.S.P., O.P., B.M.P., M.B.S., N.J.W., T.M.F., M.O.G. Individual study design and principal investigators: N.G., P. An, B.-J.K., P. Amouyel, H.B., E.B., E.P.B., R.C., F.S.C., G.D., A. Dehghan, P.D., M.M.F., J. Ferrières, J.C.F., P. Frossard, V. Gudnason, T.B.H., S.R.H., J.M.M.H., M.I., F. Kee, J. Kuusisto, C. Langenberg, L.J.L., C.M.L., S.M., T.M., O.M., K.L.M., M.M., A.D. Morris, A.D. Murray, R.d.M., M.O.-M., K.R.O., M. Perola, A.P., M.A.P., P.M.R., F.R., F.R.R., A.H.R., V. Salomaa, W.H.-H.S., R.S., B.H.S., K. Strauch, A.G.U., R.V., M. Blüher, A.S.B., J.C.C., D.I.C., J. Danesh, C.v.D., O.H.F., P.W.F., P. Froguel, H.G., L.G., T.H., A.T.H., C.H., E.I., S.L.R.K., F. Karpe, J.S.K., A. Köttgen, K.K., M. Laakso, X.L., L.L., Y. Liu, R.J.F.L., J. Marchini, A. Metspalu, D.M.-K., B.G.N., C.N.A.P., J.S.P., O.P., B.M.P., R.R., N. Sattar, M.B.S., N. Soranzo, T.D.S., K. Stefansson, M.S., U.T., T.T., J.T., N.J.W., J.G.W., E.Z., I. Barroso, T.M.F., J.B.M., M. Boehnke, D.S., A.P.M., J.I.R., M.I.M.

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Correspondence to Anubha Mahajan, Jerome I. Rotter or Mark I. McCarthy.

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Competing interests

J.C.F. has received consulting honoraria from Merck and from Boehringer-Ingelheim. D.I.C. received funding for exome chip genotyping in the WGHS from Amgen. O.H.F. works in ErasmusAGE, a center for aging research across the life course funded by Nestlé Nutrition (Nestec, Ltd.), Metagenics, Inc., and AXA. Nestlé Nutrition (Nestec, Ltd.), Metagenics, Inc., and AXA had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. E.I. is an advisor and consultant for Precision Wellness, Inc., and an advisor for Cellink for work unrelated to the present project. B.M.P. serves on the DSMB for a clinical trial funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. I. Barroso and spouse own stock in GlaxoSmithKline and Incyte Corporation. T.F. has consulted for Boeringer–Ingelheim and Sanofi on the genetics of diabetes. D.S. has received support from Pfizer, Regeneron, Genentech, and Eli Lilly. M.I.M. has served on advisory panels for Novo Nordisk and Pfizer and received honoraria from Novo Nordisk, Pfizer, Sanofi-Aventis, and Eli Lilly.

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Mahajan, A., Wessel, J., Willems, S.M. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet 50, 559–571 (2018). https://doi.org/10.1038/s41588-018-0084-1

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