multifinemap_handler_method: Multi-finemap handler: select fine-mapping method

In RajLabMSSM/echofinemap: echoverse module: Fine-mapping functions

Multi-finemap handler: select fine-mapping method

Description

Multi-finemap handler: select fine-mapping method

Usage

multifinemap_handler_method(
  dat,
  locus_dir,
  fullSS_path = NULL,
  finemap_method,
  finemap_args = NULL,
  dataset_type = "GWAS",
  force_new_finemap = FALSE,
  LD_matrix = NULL,
  n_causal = 5,
  conditioned_snps = NULL,
  compute_n = "ldsc",
  credset_thresh = 0.95,
  case_control = TRUE,
  priors_col = NULL,
  seed = 2022,
  verbose = TRUE,
  nThread = 1,
  conda_env = "echoR_mini"
)

Arguments

dat	Fine-mapping results data.
locus_dir	Locus-specific directory to store results in.
fullSS_path	Path to the full summary statistics file (GWAS or QTL) that you want to fine-map. It is usually best to provide the absolute path rather than the relative path.
finemap_args	A named nested list containing additional arguments for each fine-mapping method. e.g. finemap_args = list(FINEMAP=list(), PAINTOR=list(method=""))
dataset_type	The kind dataset you're fine-mapping (e.g. GWAS, eQTL, tQTL). This will also be used when creating the subdirectory where your results will be stored (e.g. Data/<dataset_type>/Kunkle_2019).
force_new_finemap	By default, if an fine-mapping results file for a given locus is already present, then echolocatoR will just use the preexisting file. Set force_new_finemap=T to override this and re-run fine-mapping.
LD_matrix	Linkage Disequilibrium (LD) matrix to use for fine-mapping.
n_causal	The maximum number of potential causal SNPs per locus. This parameter is used somewhat differently by different fine-mapping tools. See tool-specific functions for details.
conditioned_snps	Which SNPs to conditions on when fine-mapping with (e.g. COJO).
compute_n	How to compute per-SNP sample size (new column "N"). If the column "N" is already present in dat, this column will be used to extract per-SNP sample sizes and the argument compute_n will be ignored. If the column "N" is not present in dat, one of the following options can be supplied to compute_n: 0: N will not be computed. >0: If any number >0 is provided, that value will be set as N for every row. **Note**: Computing N this way is incorrect and should be avoided if at all possible. "sum": N will be computed as: cases (N_CAS) + controls (N_CON), so long as both columns are present. "ldsc": N will be computed as effective sample size: Neff =(N_CAS+N_CON)*(N_CAS/(N_CAS+N_CON)) / mean((N_CAS/(N_CAS+N_CON))(N_CAS+N_CON)==max(N_CAS+N_CON)). "giant": N will be computed as effective sample size: Neff = 2 / (1/N_CAS + 1/N_CON). "metal": N will be computed as effective sample size: Neff = 4 / (1/N_CAS + 1/N_CON).
credset_thresh	The minimum mean Posterior Probability (across all fine-mapping methods used) of SNPs to be included in the "mean.CS" column.
case_control	Whether the summary statistics come from a case-control study (e.g. a GWAS of having Alzheimer's Disease or not) (TRUE) or a quantitative study (e.g. a GWAS of height, or an eQTL) (FALSE).
priors_col	[Optional] Name of the a column in dat to extract SNP-wise prior probabilities from.
seed	Set the random seed for reproducible results.
verbose	Print messages.
nThread	Number of threads to parallelise across (when applicable).
conda_env	Conda environment to use.

See Also

Other finemapping functions: create_method_path(), multifinemap_handler(), multifinemap()

RajLabMSSM/echofinemap documentation built on Jan. 3, 2023, 1:42 a.m.