Bayesian refinement of association signals for 14 loci in 3 common diseases

Julian B. Maller, Gilean McVean, Jake Byrnes, Damjan Vukcevic, Kimmo Palin, Zhan Su, Joanna M.M. Howson, Adam Auton, Simon Myers, Andrew Morris, Matti Pirinen, Matthew A. Brown, Paul R. Burton, Mark J. Caulfield, Alastair Compston, Martin Farrall, Alistair S. Hall, Andrew T. Hattersley, Adrian V.S. Hill, Christopher G. MathewMarcus Pembrey, Jack Satsangi, Michael R. Stratton, Jane Worthington, Nick Craddock, Matthew Hurles, Willem Ouwehand, Miles Parkes, Nazneen Rahman, Audrey Duncanson, John A. Todd, Dominic P. Kwiatkowski, Nilesh J. Samani, Stephen C.L. Gough, Mark I. McCarthy, Panagiotis Deloukas, Peter Donnelly

Research output: Contribution to journalArticlepeer-review

323 Scopus citations


To further investigate susceptibility loci identified by genome-wide association studies, we genotyped 5,500 SNPs across 14 associated regions in 8,000 samples from a control group and 3 diseases: type 2 diabetes (T2D), coronary artery disease (CAD) and Graves' disease. We defined, using Bayes theorem, credible sets of SNPs that were 95% likely, based on posterior probability, to contain the causal disease-associated SNPs. In 3 of the 14 regions, TCF7L2 (T2D), CTLA4 (Graves' disease) and CDKN2A-CDKN2B (T2D), much of the posterior probability rested on a single SNP, and, in 4 other regions (CDKN2A-CDKN2B (CAD) and CDKAL1, FTO and HHEX (T2D)), the 95% sets were small, thereby excluding most SNPs as potentially causal. Very few SNPs in our credible sets had annotated functions, illustrating the limitations in understanding the mechanisms underlying susceptibility to common diseases. Our results also show the value of more detailed mapping to target sequences for functional studies.

Original languageEnglish (US)
Pages (from-to)1294-1301
Number of pages8
JournalNature Genetics
Issue number12
StatePublished - Dec 2012
Externally publishedYes

ASJC Scopus subject areas

  • Genetics


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