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

Abstract

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.

Figure 1

Association plots showing the signal strength in each region as the posterior probability of each SNP passing quality control. The estimated recombination rate is shown in blue (right y axis). Genomic position is shown on the x axis with Human Genome Build 36 coordinates. SNPs are colored according to membership in credible sets: yellow, 95% credible set; purple, 99% credible set; gray outline, neither set. Genes in the region are shown at the bottom in green. For each plot, the region name, phenotype and sizes of the 95% and 99% credible sets are indicated. All SNPs in the 95% credible set are a subset of the 99% credible set. SNPs with large posterior probabilities represent those most likely to be causal among the SNPs typed.