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| Rmd | d62fc99 | kevinlkx | 2022-05-20 | a tutorial for AF finemapping |
Here we show an example of performing enrichment analysis on AFib
GWAS data using mapgen with TORUS.
library(mapgen)
library(tidyverse)data.dir <- '/project2/xinhe/shared_data/mapgen/example_data'We use a reference genotype panel from European population (1KG).
bigSNP <- bigsnpr::snp_attach(rdsfile = '/project2/xinhe/1kg/bigsnpr/EUR_variable_1kg.rds')Load GWAS summary statistics of AFib
gwas.sumstats <- readRDS(paste0(data.dir, '/GWAS/ebi-a-GCST006414_aFib.df.rds'))
gwas.sumstats <- gwas.sumstats %>% dplyr::rename(ss_index = og_index)
head(gwas.sumstats)Prepare annotations for TORUS
annotation_bed_files <- list.files(paste0(data.dir, '/finemapping/annotations_for_finemapping_hg19'),
pattern = '*.bed', full.names = T)
torus.files <- prepare_torus_input_files(gwas.sumstats,
annotation_bed_files,
torus_input_dir = paste0(data.dir, '/finemapping/torus_input'))Run TORUS to estimate enrichment (joint annotations) and compute SNP-level prior
run_torus() with option = “est-prior” returns a list
with: enrichment estimates (log odds ratio) and 95% confidence intervals
of each annotation, and SNP-level priors using the enrichment
estimates.
torus.result <- run_torus(torus.files$torus_annot_file,
torus.files$torus_zscore_file,
option = "est-prior",
torus_path = "torus") # set the path to 'torus' executable.
torus.enrich <- torus.result$enrich
torus.prior <- torus.result$snp_prior
saveRDS(torus.result, paste0(data.dir, '/finemapping/Torus_Enrichment_Results_Joint.rds'))torus.result <- readRDS(paste0(data.dir, '/finemapping/Torus_Enrichment_Results_Joint.rds'))
torus.enrich <- torus.result$enrich
torus.prior <- torus.result$snp_priorSelect GWAS significant loci with -log10(pval) < 5e-8
sig.loci <- gwas.sumstats %>% dplyr::filter(pval > -log10(5e-8)) %>% dplyr::pull(locus) %>% unique()
gwas.sumstats.sigloci <- gwas.sumstats[gwas.sumstats$locus %in% sig.loci, ]Add Torus priors to GWAS summary statistics
sumstats.sigloci <- prepare_susie_data_with_torus_result(sumstats = gwas.sumstats.sigloci,
torus_prior = torus.prior)
cat("Finemap",length(unique(sumstats.sigloci$locus)), "loci.\n")
saveRDS(sumstats.sigloci, paste0(data.dir, '/finemapping/sumstats.sigloci.rds'))Run finemapping using SuSiE
sumstats.sigloci <- readRDS(paste0(data.dir, '/finemapping/sumstats.sigloci.rds'))
cat("Finemapping",length(unique(sumstats.sigloci$locus)), "loci...\n")
# susie_finemap_L1 is a list of SuSiE results, one for each chunk/LD block.
susie.res <- run_finemapping(sumstats.sigloci, bigSNP, priortype = 'torus', L = 1)
# add susie PIP information to GWAS summary stats
finemap.sumstats <- merge_susie_sumstats(susie.res, sumstats.sigloci)
saveRDS(finemap.sumstats, paste0(data.dir, '/finemapping/AF_finemapping_result_torusprior_122loci.rds'))
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=C
[4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=C
[7] LC_PAPER=C LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1
[5] readr_2.1.4 tidyr_1.3.0 tibble_3.1.8 ggplot2_3.4.1
[9] tidyverse_1.3.2 mapgen_0.5.6 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] bitops_1.0-7 fs_1.6.1 lubridate_1.9.2
[4] httr_1.4.4 rprojroot_2.0.3 GenomeInfoDb_1.34.9
[7] tools_4.2.0 backports_1.4.1 bslib_0.4.2
[10] utf8_1.2.3 R6_2.5.1 DBI_1.1.3
[13] BiocGenerics_0.44.0 colorspace_2.1-0 withr_2.5.0
[16] tidyselect_1.2.0 processx_3.8.0 compiler_4.2.0
[19] git2r_0.30.1 cli_3.6.0 rvest_1.0.3
[22] xml2_1.3.3 sass_0.4.5 scales_1.2.1
[25] callr_3.7.3 digest_0.6.31 rmarkdown_2.20
[28] XVector_0.38.0 pkgconfig_2.0.3 htmltools_0.5.4
[31] dbplyr_2.3.0 fastmap_1.1.0 rlang_1.0.6
[34] readxl_1.4.2 rstudioapi_0.14 jquerylib_0.1.4
[37] generics_0.1.3 jsonlite_1.8.4 googlesheets4_1.0.1
[40] RCurl_1.98-1.10 magrittr_2.0.3 GenomeInfoDbData_1.2.9
[43] Rcpp_1.0.10 munsell_0.5.0 S4Vectors_0.36.1
[46] fansi_1.0.4 lifecycle_1.0.3 stringi_1.7.12
[49] whisker_0.4 yaml_2.3.7 zlibbioc_1.44.0
[52] grid_4.2.0 promises_1.2.0.1 crayon_1.5.2
[55] haven_2.5.1 hms_1.1.2 knitr_1.42
[58] ps_1.7.2 pillar_1.8.1 GenomicRanges_1.48.0
[61] stats4_4.2.0 reprex_2.0.2 glue_1.6.2
[64] evaluate_0.20 getPass_0.2-2 modelr_0.1.10
[67] vctrs_0.5.2 tzdb_0.3.0 httpuv_1.6.5
[70] cellranger_1.1.0 gtable_0.3.1 assertthat_0.2.1
[73] cachem_1.0.6 xfun_0.37 broom_1.0.3
[76] later_1.3.0 googledrive_2.0.0 gargle_1.3.0
[79] IRanges_2.32.0 timechange_0.2.0 ellipsis_0.3.2