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

Anubha Mahajan 1, Jennifer Wessel 2, Sara M Willems 3, Wei Zhao 4, Neil R Robertson 5 6, Audrey Y Chu 7 8, Wei Gan 5, Hidetoshi Kitajima 5, Daniel Taliun 9, N William Rayner 5 6 10, Xiuqing Guo 11, Yingchang Lu 12, Man Li 13 14, Richard A Jensen 15, Yao Hu 16, Shaofeng Huo 16, Kurt K Lohman 17, Weihua Zhang 18 19, James P Cook 20, Bram Peter Prins 10, Jason Flannick 21 22, Niels Grarup 23, Vassily Vladimirovich Trubetskoy 9, Jasmina Kravic 24, Young Jin Kim 25, Denis V Rybin 26, Hanieh Yaghootkar 27, Martina Müller-Nurasyid 28 29 30, Karina Meidtner 31 32, Ruifang Li-Gao 33, Tibor V Varga 34, Jonathan Marten 35, Jin Li 36, Albert Vernon Smith 37 38, Ping An 39, Symen Ligthart 40, Stefan Gustafsson 41, Giovanni Malerba 42, Ayse Demirkan 40 43, Juan Fernandez Tajes 5, Valgerdur Steinthorsdottir 44, Matthias Wuttke 45, Cécile Lecoeur 46, Michael Preuss 12, Lawrence F Bielak 47, Marielisa Graff 48, Heather M Highland 49, Anne E Justice 48, Dajiang J Liu 50, Eirini Marouli 51, Gina Marie Peloso 21 26, Helen R Warren 51 52; ExomeBP Consortium; MAGIC Consortium; GIANT Consortium; Saima Afaq 18, Shoaib Afzal 53 54 55, Emma Ahlqvist 24, Peter Almgren 56, Najaf Amin 40, Lia B Bang 57, Alain G Bertoni 58, Cristina Bombieri 42, Jette Bork-Jensen 23, Ivan Brandslund 59 60, Jennifer A Brody 15, Noël P Burtt 21, Mickaël Canouil 46, Yii-Der Ida Chen 11, Yoon Shin Cho 61, Cramer Christensen 62, Sophie V Eastwood 63, Kai-Uwe Eckardt 64, Krista Fischer 65, Giovanni Gambaro 66, Vilmantas Giedraitis 67, Megan L Grove 68, Hugoline G de Haan 33, Sophie Hackinger 10, Yang Hai 11, Sohee Han 25, Anne Tybjærg-Hansen 54 55 69, Marie-France Hivert 70 71 72, Bo Isomaa 73 74, Susanne Jäger 31 32, Marit E Jørgensen 75 76, Torben Jørgensen 55 77 78, Annemari Käräjämäki 79 80, Bong-Jo Kim 25, Sung Soo Kim 25, Heikki A Koistinen 81 82 83 84, Peter Kovacs 85, Jennifer Kriebel 32 86, Florian Kronenberg 87, Kristi Läll 65 88, Leslie A Lange 89, Jung-Jin Lee 4, Benjamin Lehne 18, Huaixing Li 16, Keng-Hung Lin 90, Allan Linneberg 77 91 92, Ching-Ti Liu 26, Jun Liu 40, Marie Loh 18 93 94, Reedik Mägi 65, Vasiliki Mamakou 95, Roberta McKean-Cowdin 96, Girish Nadkarni 97, Matt Neville 6 98, Sune F Nielsen 53 54 55, Ioanna Ntalla 51, Patricia A Peyser 47, Wolfgang Rathmann 32 99, Kenneth Rice 100, Stephen S Rich 101, Line Rode 53 54, Olov Rolandsson 102, Sebastian Schönherr 87, Elizabeth Selvin 13, Kerrin S Small 103, Alena Stančáková 104, Praveen Surendran 105, Kent D Taylor 11, Tanya M Teslovich 9, Barbara Thorand 32 106, Gudmar Thorleifsson 44, Adrienne Tin 107, Anke Tönjes 108, Anette Varbo 53 54 55 69, Daniel R Witte 109 110, Andrew R Wood 27, Pranav Yajnik 9, Jie Yao 11, Loïc Yengo 46, Robin Young 105 111, Philippe Amouyel 112, Heiner Boeing 113, Eric Boerwinkle 68 114, Erwin P Bottinger 12, Rajiv Chowdhury 115, Francis S Collins 116, George Dedoussis 117, Abbas Dehghan 40 118, Panos Deloukas 51 119, Marco M Ferrario 120, Jean Ferrières 121 122, Jose C Florez 70 123 124 125, Philippe Frossard 126, Vilmundur Gudnason 37 38, Tamara B Harris 127, Susan R Heckbert 15, Joanna M M Howson 115, Martin Ingelsson 67, Sekar Kathiresan 21 123 125 128, Frank Kee 129, Johanna Kuusisto 104, Claudia Langenberg 3, Lenore J Launer 127, Cecilia M Lindgren 5 21 130, Satu Männistö 131, Thomas Meitinger 132 133, Olle Melander 56, Karen L Mohlke 134, Marie Moitry 135 136, Andrew D Morris 137 138, Alison D Murray 139, Renée de Mutsert 33, Marju Orho-Melander 140, Katharine R Owen 6 98, Markus Perola 131 141, Annette Peters 30 32 106, Michael A Province 39, Asif Rasheed 126, Paul M Ridker 8 125, Fernando Rivadineira 40 142, Frits R Rosendaal 33, Anders H Rosengren 24, Veikko Salomaa 131, Wayne H-H Sheu 143 144 145, Rob Sladek 146 147 148, Blair H Smith 149, Konstantin Strauch 28 150, André G Uitterlinden 41 142, Rohit Varma 151, Cristen J Willer 152 153 154, Matthias Blüher 85 108, Adam S Butterworth 105 155, John Campbell Chambers 18 19 156, Daniel I Chasman 8 125, John Danesh 105 155 157 158, Cornelia van Duijn 40, Josée Dupuis 7 26, Oscar H Franco 40, Paul W Franks 34 102 159, Philippe Froguel 46 160, Harald Grallert 32 86 161 162, Leif Groop 24 141, Bok-Ghee Han 25, Torben Hansen 23 163, Andrew T Hattersley 164, Caroline Hayward 35, Erik Ingelsson 36 41, Sharon L R Kardia 47, Fredrik Karpe 6 98, Jaspal Singh Kooner 19 156 165, Anna Köttgen 45, Kari Kuulasmaa 132, Markku Laakso 104, Xu Lin 16, Lars Lind 166, Yongmei Liu 58, Ruth J F Loos 12 167, Jonathan Marchini 5 168, Andres Metspalu 65, Dennis Mook-Kanamori 33 169, Børge G Nordestgaard 53 54 55, Colin N A Palmer 170, James S Pankow 171, Oluf Pedersen 23, Bruce M Psaty 15 172, Rainer Rauramaa 173, Naveed Sattar 174, Matthias B Schulze 31 32, Nicole Soranzo 10 155 175, Timothy D Spector 103, Kari Stefansson 38 44, Michael Stumvoll 176, Unnur Thorsteinsdottir 38 44, Tiinamaija Tuomi 74 82 141 177, Jaakko Tuomilehto 81 178 179 180, Nicholas J Wareham 3, James G Wilson 181, Eleftheria Zeggini 10, Robert A Scott 3, Inês Barroso 10 182, Timothy M Frayling 27, Mark O Goodarzi 183, James B Meigs 184, Michael Boehnke 9, Danish Saleheen 4 126, Andrew P Morris 5 20 65, Jerome I Rotter 185 186, Mark I McCarthy 187 188 189

Affiliations

01 April 2018

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doi: 10.1038/s41588-018-0084-1

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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