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

Authors:
Publisher website
Google Scholar
Abstract
Peter Donnelly and colleagues report fine mapping of 14 susceptibility loci in 8,000 cases and controls for type 2 diabetes, coronary artery disease and Graves' disease. They apply a new Bayesian method for analysis of fine-mapping data sets, using this to define sets of SNPs likely to contain causal disease-associated variants. 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.

This publication has 9 references indexed in Scilit: