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

2.50
Hdl Handle:
http://hdl.handle.net/11287/620844
Title:
Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes
Authors:
Mahajan, A. [et al]; Hattersley, Andrew T.
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.
Citation:
Mahajan et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nature Genetics 2018 50(4) 559-571
Publisher:
Nature
Journal:
Nature Genetics
Issue Date:
Apr-2018
URI:
http://hdl.handle.net/11287/620844
DOI:
10.1038/s41588-018-0084-1
PubMed ID:
29632382
Additional Links:
http://dx.doi.org/10.1038/s41588-018-0084-1
Type:
Journal Article
Language:
en
Appears in Collections:
Diabetes/Endocrine Services; 2018 RD&E publications

Full metadata record

DC FieldValue Language
dc.contributor.authorMahajan, A. [et al]en
dc.contributor.authorHattersley, Andrew T.en
dc.date.accessioned2018-10-04T09:28:05Z-
dc.date.available2018-10-04T09:28:05Z-
dc.date.issued2018-04-
dc.identifier.citationMahajan et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nature Genetics 2018 50(4) 559-571en
dc.identifier.pmid29632382-
dc.identifier.doi10.1038/s41588-018-0084-1-
dc.identifier.urihttp://hdl.handle.net/11287/620844-
dc.description.abstractWe 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.en
dc.language.isoenen
dc.publisherNatureen
dc.relation.urlhttp://dx.doi.org/10.1038/s41588-018-0084-1en
dc.subjectWessex Classification Subject Headings::Endocrinology::Diabetesen
dc.titleRefining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetesen
dc.typeJournal Articleen
dc.identifier.journalNature Geneticsen
dc.type.versionPublisheden

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