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

Research output: Contribution to journalArticle

Original languageEnglish
JournalNature Genetics
Publication statusAccepted/In press - 8 Dec 2017

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Abstract

We aggregated oding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40coding variant association signals(p<2.2x10-7415): of these,16 map outside known risk loci. We make two important observations. First, only five of these signals are driven bylow-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when weused large-scale genome-wide associationdatato fine-map the associatedvariants in their regional context, accounting forthe global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality wasobtained for only 16 signals. At 13 others,the associated codingvariants clearly represent“false leads”with potential to generate erroneous mechanistic inference. Coding variant associations offer adirect route to biological insight for complex diseasesand identification of validated therapeutic targets: however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

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