Based on per-SNP heritability estimates, we propose two different priors for SNP effect sizes to add flexibility against different genetic architecture. For the first prior, we assume that SNP effect size follows a spike-and-slab distribution βi∼p0N(0,σ^i2p0)+(1−p0)δ0 where p0 is the proportion of causal SNPs in the dataset, and δ0 is a Dirac function representing a point mass at zero. The empirical variance of each SNP, i.e. σ^i2, is determined by the annotation categories it falls in. More specifically, we assume σ^i2=c(∑j:i∈Sjτ^j), where c is a constant calculated from the following equation ∑iσ^i2=H^2.
