Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci

DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Kyle J Gaulton, Teresa Ferreira, Yeji Lee, Anne Raimondo, Reedik Mägi, Michael E Reschen, Anubha Mahajan, Adam Locke, N William Rayner, Neil Robertson, Robert A Scott, Inga Prokopenko, Laura J Scott, Todd Green, Thomas Sparso, Dorothee Thuillier, Loic Yengo, Harald Grallert, Simone WahlMattias Frånberg, Rona J Strawbridge, Hans Kestler, Himanshu Chheda, Lewin Eisele, Stefan Gustafsson, Valgerdur Steinthorsdottir, Gudmar Thorleifsson, Lu Qi, Lennart C Karssen, Elisabeth M van Leeuwen, Sara M Willems, Man Li, Han Chen, Christian Fuchsberger, Phoenix Kwan, Clement Ma, Michael Linderman, Yingchang Lu, Soren K Thomsen, Jana K Rundle, Nicola L Beer, Martijn van de Bunt, Anil Chalisey, Hyun Min Kang, Benjamin F Voight, Gonçalo R Abecasis, Peter Almgren, Damiano Baldassarre, Beverley Balkau, Andrew D Morris

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.

Original languageEnglish
Pages (from-to)1415-25
Number of pages11
JournalNature Genetics
Issue number12
Publication statusPublished - 9 Nov 2015


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