The output from FINEMAP is a list of possible causal configurations together with their posterior probabilities and Bayes factors similar to CAVIARBF. These probabilities contain all the information from the model needed for downstream analyses. Examples of useful derived quantities are the single-SNP inclusion probabilities, single-SNP Bayes factors, credible sets of causal variants (WTCCC et al., 2012) and a regional Bayes factor to assess the evidence against the null model where none of the SNPs are causal (Chen et al., 2015). We believe that FINEMAP, or related future applications of shotgun stochastic search to GWAS summary data, enables unprecedented opportunities to reveal valuable information that could otherwise remain undetected due to computational limitations of the existing fine-mapping methods.
