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,
return.pp = FALSE
)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.
if FALSE (default), just return the hits. Otherwise return vectors of PP
use masking if TRUE, otherwise conditioning. defaults to TRUE
list of successively significant fine mapped SNPs, named by the SNPs