extract_snp_subset | R Documentation |
Use tabix to extract a locus subset from the full summary statistics file.
extract_snp_subset( subset_path, locus = NULL, colmap = echodata::construct_colmap(), fullSS_path, topSNPs, LD_reference, force_new_subset = FALSE, force_new_maf = FALSE, bp_distance = 5e+05, superpopulation = "EUR", compute_n = "ldsc", query_by = "tabix", download_method = "axel", nThread = 1, conda_env = "echoR_mini", verbose = TRUE )
subset_path |
Path where the |
locus |
Locus name to fine-map (e.g. |
colmap |
Column name mappings in 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. |
topSNPs |
A data.frame with the genomic coordinates of the lead SNP
for each locus.
The lead SNP will be used as the center of the window when extracting
subset from the full GWAS/QTL summary statistics file.
Only one SNP per Locus should be included.
At minimum,
|
LD_reference |
LD reference to use:
|
force_new_subset |
By default, if a subset of the full
summary stats file for a given locus is already present,
then echolocatoR will just use the pre-existing file.
Set |
force_new_maf |
Download UKB_MAF file again. |
bp_distance |
Distance around the lead SNP to include. |
superpopulation |
Superpopulation to subset LD panel by
(used only if |
compute_n |
How to compute per-SNP sample size (new column "N").
|
query_by |
Choose which method you want to use to extract locus subsets from the full summary stats file. Methods include:
|
download_method |
|
nThread |
Number of threads to parallelise saving across. |
conda_env |
Conda environment to use. |
verbose |
Print messages. |
Other query functions:
query_handler()
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