We use SSS to efficiently evaluate many causal configurations and discover especially those with highest posterior probability. SSS conducts a pre-defined number of iterations within the space of causal configurations. In each iteration (Fig. 2), the neighborhood of the current causal configuration is defined by configurations that result from deleting, changing or adding a causal SNP from the current configuration. The next iteration starts by sampling a new causal configuration from the neighborhood based on p∗(γ|y,X) normalized within the neighborhood. All evaluated causal configurations and their unnormalized posterior probabilities are saved in a list Γ∗ for downstream analyses. The aim of the algorithm is that Γ∗ contains all relevant causal configurations, that is, those with non-negligible posterior probabilities.
