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

Anubha Mahajan; 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 Prins; Jason 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; Shoaib Afzal; Jette Bork-Jensen; Anne Tybjærg-Hansen; Marit E Jørgensen; Torben Jørgensen; Peter Kovacs; Allan Linneberg; Jun Liu; Sune F Nielsen; Line Rode; Daniel R Witte; Torben Hansen; Fredrik Karpe; Lars Lind; Ruth J F Loos; Børge G Nordestgaard; Oluf Pedersen; ExomeBP Consortium

doi:10.1038/s41588-018-0084-1

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

Anubha Mahajan, 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, Shoaib Afzal, Jette Bork-Jensen, Anne Tybjærg-Hansen, Marit E Jørgensen, Torben Jørgensen, Peter Kovacs, Allan Linneberg, Jun Liu, Sune F Nielsen, Line Rode, Daniel R Witte, Torben Hansen, Fredrik Karpe, Lars Lind, Ruth J F Loos, Børge G Nordestgaard, Oluf Pedersen, ExomeBP Consortium

130 Citations (Scopus)

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 language	English
Journal	Nature Genetics
Volume	50
Issue number	4
Pages (from-to)	559-571
Number of pages	13
ISSN	1061-4036
DOIs	https://doi.org/10.1038/s41588-018-0084-1
Publication status	Published - 1 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

Mahajan, A, Wessel, J, Willems, SM, Zhao, W, Robertson, NR, Chu, AY, Gan, W, Kitajima, H, Taliun, D, Rayner, NW, Guo, X, Lu, Y, Li, M, Jensen, RA, Hu, Y, Huo, S, Lohman, KK, Zhang, W, Cook, JP, Prins, BP, Flannick, J, Grarup, N, Trubetskoy, VV, Kravic, J, Kim, YJ, Rybin, DV, Yaghootkar, H, Müller-Nurasyid, M, Meidtner, K, Li-Gao, R, Varga, TV, Marten, J, Li, J, Afzal, S, Bork-Jensen, J, Tybjærg-Hansen, A, Jørgensen, ME, Jørgensen, T, Kovacs, P, Linneberg, A, Liu, J, Nielsen, SF, Rode, L, Witte, DR, Hansen, T, Karpe, F, Lind, L, Loos, RJF, Nordestgaard, BG , Pedersen, O & ExomeBP Consortium 2018, 'Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes', Nature Genetics, vol. 50, no. 4, pp. 559-571. https://doi.org/10.1038/s41588-018-0084-1

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title = "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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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

PY - 2018/4/1

Y1 - 2018/4/1

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

SN - 1061-4036

VL - 50

SP - 559

EP - 571

JO - Nature: New biology

JF - Nature: New biology

IS - 4

ER -

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

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