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

ExomeBP Consortium, MAGIC Consortium, GIANT Consortium, Daniel Rinse Witte (Medlem af forfattergruppering), Torben Hansen (Medlem af forfattergruppering)

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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Resumé

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.

OriginalsprogEngelsk
TidsskriftNature Genetics
Vol/bind50
Udgave nummer4
Sider (fra-til)559-571
ISSN1061-4036
DOI
StatusUdgivet - 1. apr. 2018

Fingeraftryk

Type 2 Diabetes Mellitus
Causality
Odds Ratio

Citer dette

ExomeBP Consortium ; MAGIC Consortium ; GIANT Consortium ; Witte, Daniel Rinse ; Hansen, Torben. / Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. I: Nature Genetics. 2018 ; Bind 50, Nr. 4. s. 559-571.
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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.",
author = "Anubha Mahajan and {ExomeBP Consortium} and {MAGIC Consortium} and {GIANT Consortium} and Ivan Brandslund and Cramer Christensen and J{\o}rgensen, {Marit E} and Witte, {Daniel Rinse} and Torben Hansen",
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Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. / ExomeBP Consortium; MAGIC Consortium; GIANT Consortium; Witte, Daniel Rinse (Medlem af forfattergruppering); Hansen, Torben (Medlem af forfattergruppering).

I: Nature Genetics, Bind 50, Nr. 4, 01.04.2018, s. 559-571.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

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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.

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