We now provide a brief conceptual description of PolyLoc (a full description is provided in the Supplementary Note). Briefly, PolyLoc proceeds by (1) partitioning SNPs with similar βi2 posterior mean estimates (using PolyFun + SuSiE estimates) into bins; (2) treating βi as a zero-mean random variable and jointly estimating var[βi] in every bin using S-LDSC; and (3) finding the smallest integer k such that ∑i∈{s^1,…,s^k}var[βi]/∑i=1mvar[βi]≥p, where s^j denotes the original ranking of βi2 posterior mean estimates from PolyFun + SuSiE. The use of var[βi]=E[βi2]−E[βi]2 instead of βi2 uses the assumption that βi has zero mean in each bin. The partitioning into bins in step 1 induces a piecewise-linear approximation of the function (k)=∑i∈{s^1,…,s^k}βi2/ ∑i=1mβi2. We use different datasets to estimate βi2 posterior means and to estimate var[βi] to prevent winner’s curse. Our approach is conservative by design due to using an imperfect ranking compared to the true ranking s1, …, sm. The degree of conservativeness is a function of fine-mapping power, and thus depends on factors affecting fine-mapping power such as sample size, levels of LD at causal SNPs, MAFs of causal SNPs, and trait polygenicity.
