Iteratively run coloc on merged GWAS-QTL datatables

Source: R/coloc.R

Runs colocalization tests (coloc::coloc.abf) on merged GWAS-QTL datatables generated by catalogueR::eQTL_Catalogue.query.

run_coloc(
  gwas.qtl_paths,
  save_path = "./coloc_results.tsv.gz",
  nThread = 4,
  top_snp_only = T,
  split_by_group = F,
  method = "abf",
  PP_threshold = 0.8
)

Value

If top_snp_only=T, returns SNP-level stats for only the SNP with the highest colocalization probability (SNP.PP.H4) If top_snp_only=T, returns SNP-level stats for every SNP. In either case, summary-level coloc stats are added in the columns PP.H0, PP.H1, PP.H2, PP.H3, PP.H4.

Details

Iterately runs coloc across each:

  • QTL dataset

  • GWAS locus

  • QTL gene

See also

Other coloc: COLOC.report_summary(), coloc_QTLs_full, coloc_QTLs, gather_colocalized_data(), get_colocs(), plot_coloc_summary(), retrieve_sumstats_info()

Examples

# With built-in data
gwas.qtl_paths <- example_eQTL_Catalogue_query_paths() 
coloc_QTLs <- run_coloc(gwas.qtl_paths=gwas.qtl_paths, nThread=4, top_snp_only=T, save_path="~/Desktop/coloc_results.tsv.gz")

#> + QTL Group = Alasoo_2018.macrophage_IFNg
#> + QTL Group = Alasoo_2018.macrophage_IFNg+Salmonella
#> ++ GWAS = BST1 x 9 eGenes
#> + QTL Group = Alasoo_2018.macrophage_naive
#> + QTL Group = Alasoo_2018.macrophage_Salmonella
#> ++ GWAS = BST1 x 13 eGenes


if (FALSE) { 
# With full  Nalls et al data (not included)
gwas.qtl_paths <- list.files("/pd-omics/brian/eQTL_catalogue/Nalls23andMe_2019", recursive=T, full.names = T)
coloc_QTLs.Nalls2019 <- run_coloc(gwas.qtl_paths=gwas.qtl_paths[1:100], nThread=4, top_snp_only=T, save_path="~/Desktop/Nall2019.coloc_results.tsv.gz")
}