We observed that option 2 was faster than option 1 and therefore option 2 is used by default in FINEMAP. Ideally, p∗(γ|y,X) were normalized over all ∑k=1K(mk) causal configurations. Unfortunately, this is computationally intractable already for modest values of K > 5. However, as we show in the results section, typically a large majority of the causal configurations have negligible posterior probability and hence a good approximation for the posterior can be achieved by concentrating on only those with non-negligible probability. We explore the space of causal configurations with a Shotgun Stochastic Search (SSS) algorithm (Hans et al., 2007) that rapidly evaluates many configurations and is designed to discover especially those with highest posterior probability.
