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

Anubha Mahajan (Lead / Corresponding author), Jennifer Wessel, Sara M. Willems, Wei Zhao, Neil R. Robertson, Audrey Y. Chu, Wei Gan, Hidetoshi Kitajima, Daniel Taliun, N. William Rayner, Xiuqing Guo, Yingchang Lu, Man Li, Richard A. Jensen, Yao Hu, Shaofeng Huo, Kurt K. Lohman, Weihua Zhang, James P. Cook, Bram Peter PrinsJason Flannick, Niels Grarup, Vassily Vladimirovich Trubetskoy, Jasmina Kravic, Young Jin Kim, Denis V. Rybin, Hanieh Yaghootkar, Martina Müller-Nurasyid, Karina Meidtner, Ruifang Li-Gao, Tibor V. Varga, Jonathan Marten, Jin Li, Albert Vernon Smith, Ping An, Symen Ligthart, Stefan Gustafsson, Giovanni Malerba, Ayse Demirkan, Juan Fernandez Tajes, Valgerdur Steinthorsdottir, Matthias Wuttke, Cécile Lecoeur, Michael Preuss, Lawrence F. Bielak, Marielisa Graff, Heather M. Highland, Andrew D. Morris, Blair H. Smith, Colin N. A. Palmer, ExomeBP Consortium

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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.

Original languageEnglish
Pages (from-to)559-571
Number of pages13
JournalNature Genetics
Volume50
Issue number4
DOIs
Publication statusPublished - 9 Apr 2018

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    Mahajan, A., Wessel, J., Willems, S. M., Zhao, W., Robertson, N. R., Chu, A. Y., Gan, W., Kitajima, H., Taliun, D., Rayner, N. W., Guo, X., Lu, Y., Li, M., Jensen, R. A., Hu, Y., Huo, S., Lohman, K. K., Zhang, W., Cook, J. P., ... ExomeBP Consortium (2018). Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nature Genetics, 50(4), 559-571. https://doi.org/10.1038/s41588-018-0084-1