Estimate L for all regions by running finemapping with uniform prior

Usage

estimate_region_L(
  region_data,
  LD_map,
  weights,
  init_L = 5,
  min_abs_corr = 0.1,
  null_method = c("ctwas", "susie", "none"),
  null_weight = NULL,
  snps_only = FALSE,
  LD_format = c("rds", "rdata", "mtx", "csv", "txt", "custom"),
  LD_loader_fun = NULL,
  snpinfo_loader_fun = NULL,
  ncore = 1,
  verbose = FALSE,
  ...
)

Arguments

region_data: a list object indexing regions, variants and genes.
LD_map: a data frame with filenames of LD matrices for each of the regions.
weights: a list of preprocessed weights.
init_L: upper bound of the number of causal signals
min_abs_corr: Minimum absolute correlation allowed in a credible set.
null_method: Method to compute null model, options: "ctwas", "susie" or "none".
null_weight: Prior probability of no effect (a number between 0 and 1, and cannot be exactly 1). Only used when null_method = "susie".
snps_only: If TRUE, use only SNPs in the region data.
LD_format: file format for LD matrix. If "custom", use a user defined LD_loader_fun() function to load LD matrix.
LD_loader_fun: a user defined function to load LD matrix when LD_format = "custom".
snpinfo_loader_fun: a user defined function to load SNP information file, if SNP information files are not in standard cTWAS reference format.
ncore: The number of cores used to parallelize susie over regions
verbose: If TRUE, print detail messages
...: Additional arguments of susie_rss.

Value

a vector of estimated L for all regions