Let a binary indicator vector γ determine which SNPs have non-zero causal effects (γℓ=1 if the ℓth SNP is causal and 0, otherwise; see top panel in Fig. 1). For the causal effects, we use the prior p(λ|γ)=N(λ|0,sλ2σ2Δγ), where sλ2 is the user given prior variance for the causal effects in units of σ2, with σ2=1 for quantitative traits and σ2=1/{φ(1−φ)} for binary traits, and Δγ a diagonal matrix with γ on the diagonal. In our examples for quantitative traits, we have set sλ2=0.052. This means that with 95% probability a causal SNP explains less than 1% of the trait variation. When available z-scores originate from logistic regression, a value of sλ2=(005/σ)2=0.052φ(1−φ) means that with 95% probability the effect of a causal SNP on the odds-ratio scale is less than 1.15 for common variants (MAF = 0.5) and less than 2.0 for low-frequency variants (MAF = 0.01), where MAF is the minor allele frequency. Robustness to the values of sλ has been studied previously by Chen et al. (2015).
