As a key step to evaluate the contribution of variants within a genome-wide significant locus(33), we used our PCBS framework to apply two complementary fine mapping techniques to identify putatively causal genetic variants. The first technique was a Bayesian approach described previously(50) that estimates the posterior probability of association based upon the statistical strength of the association for variants in each locus. We also applied a version of fgwas(51) modified to work within PCBS, which assumes that variants in different functional categories have potentially different prior probability of association. For loci with a single association signal based, effect sizes and variance from single-SNP analyses were used. If a locus contained multiple signals, we used effect sizes and variance from conditional analysis adjusting for all other index variants in this region.
