Approaches for Bayesian analysis of multi-SNP GWAS data include exhaustive search as implemented in software BIMBAM (Servin and Stephens, 2007), MCMC algorithms (Guan and Stephens, 2011), variational approximations (Carbonetto and Stephens, 2012) and stochastic search as implemented in software GUESS (Bottolo and Richardson, 2010, Bottolo et al., 2013) and GUESSFM (Wallace et al., 2015). Bayesian fine-mapping has also been conducted under a simplified assumption of a single causal variant in the region (WTCCC et al., 2012). Common to these approaches is that they require original genotype-phenotype data as input, which is becoming impractical or even impossible as the size of current GWAS meta-analyses rises to several hundreds of thousands of samples (Wood et al., 2014). For this reason, fine-mapping methods have recently been extended to use only GWAS summary data together with a SNP correlation estimate from a reference panel. To our knowledge, the existing fine-mapping implementations using GWAS summary data are PAINTOR (Kichaev et al., 2014, Kichaev and Pasaniuc, 2015), CAVIAR (Hormozdiari et al., 2014) and CAVIARBF (Chen et al., 2015).
