Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes
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Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. / Mahajan, Anubha; Wessel, Jennifer; Willems, Sara M; Zhao, Wei; Robertson, Neil R; Chu, Audrey Y; Gan, Wei; Kitajima, Hidetoshi; Taliun, Daniel; Rayner, N William; Guo, Xiuqing; Lu, Yingchang; Li, Man; Jensen, Richard A; Hu, Yao; Huo, Shaofeng; Lohman, Kurt K; Zhang, Weihua; Cook, James P; Prins, Bram Peter; Flannick, Jason; Grarup, Niels; Trubetskoy, Vassily Vladimirovich; Kravic, Jasmina; Kim, Young Jin; Rybin, Denis V; Yaghootkar, Hanieh; Müller-Nurasyid, Martina; Meidtner, Karina; Li-Gao, Ruifang; Varga, Tibor V; Marten, Jonathan; Li, Jin; Afzal, Shoaib; Bork-Jensen, Jette; Tybjærg-Hansen, Anne; Jørgensen, Marit E; Jørgensen, Torben; Kovacs, Peter; Linneberg, Allan; Liu, Jun; Nielsen, Sune F; Rode, Line; Witte, Daniel R; Hansen, Torben; Karpe, Fredrik; Lind, Lars; Loos, Ruth J F; Nordestgaard, Børge G; Pedersen, Oluf; ExomeBP Consortium ; V Varga, Tibor.
I: Nature Genetics, Bind 50, Nr. 4, 04.2018, s. 559-571.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes
AU - Mahajan, Anubha
AU - Wessel, Jennifer
AU - Willems, Sara M
AU - Zhao, Wei
AU - Robertson, Neil R
AU - Chu, Audrey Y
AU - Gan, Wei
AU - Kitajima, Hidetoshi
AU - Taliun, Daniel
AU - Rayner, N William
AU - Guo, Xiuqing
AU - Lu, Yingchang
AU - Li, Man
AU - Jensen, Richard A
AU - Hu, Yao
AU - Huo, Shaofeng
AU - Lohman, Kurt K
AU - Zhang, Weihua
AU - Cook, James P
AU - Prins, Bram Peter
AU - Flannick, Jason
AU - Grarup, Niels
AU - Trubetskoy, Vassily Vladimirovich
AU - Kravic, Jasmina
AU - Kim, Young Jin
AU - Rybin, Denis V
AU - Yaghootkar, Hanieh
AU - Müller-Nurasyid, Martina
AU - Meidtner, Karina
AU - Li-Gao, Ruifang
AU - Varga, Tibor V
AU - Marten, Jonathan
AU - Li, Jin
AU - Afzal, Shoaib
AU - Bork-Jensen, Jette
AU - Tybjærg-Hansen, Anne
AU - Jørgensen, Marit E
AU - Jørgensen, Torben
AU - Kovacs, Peter
AU - Linneberg, Allan
AU - Liu, Jun
AU - Nielsen, Sune F
AU - Rode, Line
AU - Witte, Daniel R
AU - Hansen, Torben
AU - Karpe, Fredrik
AU - Lind, Lars
AU - Loos, Ruth J F
AU - Nordestgaard, Børge G
AU - Pedersen, Oluf
AU - ExomeBP Consortium
AU - V Varga, Tibor
PY - 2018/4
Y1 - 2018/4
N2 - 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.
AB - 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.
U2 - 10.1038/s41588-018-0084-1
DO - 10.1038/s41588-018-0084-1
M3 - Journal article
C2 - 29632382
VL - 50
SP - 559
EP - 571
JO - Nature Genetics
JF - Nature Genetics
SN - 1061-4036
IS - 4
ER -
ID: 199333913