Merge fine-mapping results from all loci

Source: R/merge_finemapping_results.R

Gather fine-mapping results from echolocatoR across all loci and merge into a single data.frame.

merge_finemapping_results(
  dataset = file.path(tempdir(), "Data/GWAS"),
  minimum_support = 1,
  include_leadSNPs = TRUE,
  LD_reference = NULL,
  save_path = tempfile(fileext = "merged_results.csv.gz"),
  from_storage = TRUE,
  credset_thresh = 0.95,
  consensus_thresh = 2,
  exclude_methods = NULL,
  top_CS_only = FALSE,
  verbose = TRUE,
  nThread = 1
)

Arguments

dataset

Path to the folder you want to recursively search for results files within (e.g. "Data/GWAS/Nalls23andMe_2019"). Set this to a path that includes multiple subfolders if you want to gather results from multiple studies at once (e.g. "Data/GWAS").

minimum_support

Filter SNPs by the minimum number of fine-mapping tools that contained the SNP in their Credible Set.

include_leadSNPs

Include lead GWAS/QTL SNPs per locus (regardless of other filtering criterion).

LD_reference

LD reference to use:

  • "1KGphase1" : 1000 Genomes Project Phase 1 (genome build: hg19).

  • "1KGphase3" : 1000 Genomes Project Phase 3 (genome build: hg19).

  • "UKB" : Pre-computed LD from a British European-decent subset of UK Biobank. Genome build : hg19

  • "<vcf_path>" : User-supplied path to a custom VCF file to compute LD matrix from.
    Accepted formats: .vcf / .vcf.gz / .vcf.bgz
    Genome build : defined by user with target_genome.

  • "<matrix_path>" : User-supplied path to a pre-computed LD matrix Accepted formats: .rds / .rda / .csv / .tsv / .txt
    Genome build : defined by user with target_genome.

save_path

Path to save merged table to.

from_storage

Search for stored results files.

credset_thresh

The minimum mean Posterior Probability (across all fine-mapping methods used) of SNPs to be included in the "mean.CS" column.

consensus_thresh

The minimum number of fine-mapping tools in which a SNP is in the Credible Set in order to be included in the "Consensus_SNP" column.

exclude_methods

Exclude certain fine-mapping methods when estimating mean.CS and Consensus_SNP.

top_CS_only

Only include the top 1 CS per fine-mapping method.

verbose

Print messages.

nThread

Number of threads to parallelise across.

Examples

dataset <- get_Nalls2019_loci(return_dir = TRUE)
merged_DT <- merge_finemapping_results(dataset = dataset)
#> + Gathering all fine-mapping results from storage...
#> + 3 multi-finemap files found.
#> + Removing duplicate Multi-finemap files per locus.
#> + Importing results... RtmpLQCV4M
#> + Importing results... RtmpLQCV4M
#> + Importing results... RtmpLQCV4M
#> Identifying Consensus SNPs...
#> + support_thresh = 2
#> + Calculating mean Posterior Probability (mean.PP)...
#> + 4 fine-mapping methods used.
#> + 20 Credible Set SNPs identified.
#> + 9 Consensus SNPs identified.
#> + Saving merged results ==> /tmp/RtmpLQCV4M/filebea03a55cf2amerged_results.csv.gz