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

ExomeBP Consortium, MAGIC Consortium, GIANT Consortium

Research output: Contribution to journalArticle

42 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 languageEnglish (US)
Pages (from-to)559-571
Number of pages13
JournalNature Genetics
Volume50
Issue number4
DOIs
StatePublished - Apr 1 2018

Fingerprint

Type 2 Diabetes Mellitus
Causality
Odds Ratio
Genome
Therapeutics

Cite this

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes article. / ExomeBP Consortium; MAGIC Consortium; GIANT Consortium.

In: Nature Genetics, Vol. 50, No. 4, 01.04.2018, p. 559-571.

Research output: Contribution to journalArticle

ExomeBP Consortium ; MAGIC Consortium ; GIANT Consortium. / Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes article. In: Nature Genetics. 2018 ; Vol. 50, No. 4. pp. 559-571.
@article{f9c12948ae2e46eb92c8dd9247c74204,
title = "Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes article",
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.",
author = "{ExomeBP Consortium} and {MAGIC Consortium} and {GIANT Consortium} and Anubha Mahajan and Jennifer Wessel and Willems, {Sara M.} and Wei Zhao and Robertson, {Neil R.} and Chu, {Audrey Y.} and Wei Gan and Hidetoshi Kitajima and Daniel Taliun and Rayner, {N. William} and Xiuqing Guo and Yingchang Lu and Man Li and Jensen, {Richard A.} and Yao Hu and Shaofeng Huo and Lohman, {Kurt K.} and Weihua Zhang and Cook, {James P.} and Prins, {Bram Peter} and Jason Flannick and Niels Grarup and Trubetskoy, {Vassily Vladimirovich} and Jasmina Kravic and Kim, {Young Jin} and Rybin, {Denis V.} and Hanieh Yaghootkar and Martina M{\"u}ller-Nurasyid and Karina Meidtner and Ruifang Li-Gao and Varga, {Tibor V.} and Jonathan Marten and Jin Li and Smith, {Albert Vernon} and Ping An and Symen Ligthart and Stefan Gustafsson and Giovanni Malerba and Ayse Demirkan and Tajes, {Juan Fernandez} and Valgerdur Steinthorsdottir and Matthias Wuttke and C{\'e}cile Lecoeur and Michael Preuss and Bielak, {Lawrence F.} and Marielisa Graff and Highland, {Heather M.} and Justice, {Anne E.} and Liu, {Dajiang J.} and Jim Pankow",
year = "2018",
month = "4",
day = "1",
doi = "10.1038/s41588-018-0084-1",
language = "English (US)",
volume = "50",
pages = "559--571",
journal = "Nature Genetics",
issn = "1061-4036",
publisher = "Nature Publishing Group",
number = "4",

}

TY - JOUR

T1 - Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes article

AU - ExomeBP Consortium

AU - MAGIC Consortium

AU - GIANT Consortium

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 - Smith, Albert Vernon

AU - An, Ping

AU - Ligthart, Symen

AU - Gustafsson, Stefan

AU - Malerba, Giovanni

AU - Demirkan, Ayse

AU - Tajes, Juan Fernandez

AU - Steinthorsdottir, Valgerdur

AU - Wuttke, Matthias

AU - Lecoeur, Cécile

AU - Preuss, Michael

AU - Bielak, Lawrence F.

AU - Graff, Marielisa

AU - Highland, Heather M.

AU - Justice, Anne E.

AU - Liu, Dajiang J.

AU - Pankow, Jim

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.

UR - http://www.scopus.com/inward/record.url?scp=85042368356&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042368356&partnerID=8YFLogxK

U2 - 10.1038/s41588-018-0084-1

DO - 10.1038/s41588-018-0084-1

M3 - Article

VL - 50

SP - 559

EP - 571

JO - Nature Genetics

JF - Nature Genetics

SN - 1061-4036

IS - 4

ER -