According to the Bayesian paradigm, we want to base our inference on the posterior of causal configurations p(γ|y,X). The unnormalized posterior can be evaluated by combining the prior with the marginal likelihood (option 1) as p1∗(γ|y,X)=(mk)−1pk×p(y|γ,X), where k is the number of causal SNPs in configuration γ. In addition, we can compute unnormalized posterior by using the Bayes factor (option 2) p2∗(γ|y,X)=(mk)−1pk×BF(γ:NULL).
