This is an analogue to finemap.abf, adapted to find multiple signals where they exist, via conditioning or masking - ie a stepwise procedure
finemap.signals(
D,
LD = D$LD,
method = c("single", "mask", "cond"),
r2thr = 0.01,
sigsnps = NULL,
pthr = 1e-06,
maxhits = 3
)list of summary stats for a single disease, see check_dataset
matrix of signed r values (not rsq!) giving correlation between SNPs
if method="cond", then use conditioning to coloc multiple signals. The default is mask - this is less powerful, but safer because it does not assume that the LD matrix is properly allelically aligned to estimated effect
if mask==TRUE, all snps will be masked with r2 > r2thr with any sigsnps. Otherwise ignored
SNPs already deemed significant, to condition on or mask, expressed as a numeric vector, whose names are the snp names
when p > pthr, stop successive searching
maximum depth of conditioning. procedure will stop if p > pthr OR abs(z)<zthr OR maxhits hits have been found.
use masking if TRUE, otherwise conditioning. defaults to TRUE
list of successively significant fine mapped SNPs, named by the SNPs