Tutorial for enrichment analysis

Kaixuan Luo, Alan Selewa

Source: vignettes/enrichment_tutorial.Rmd

In this tutorial, we perform enrichment analysis (using TORUS) using GWAS summary statistics and functional annotations.

Load the packages.

Please see the data preparation tutorial about preparing GWAS summary statistics. The result from the enrichment analysis will be used in the fine-mapping tutorial.

Enrichment analysis

Next, we perform enrichment analysis using the software TORUS, which requires annotation files in .bed format.

Use the prepare_torus_input_files() function, which takes the GWAS summary statistics, and annotation files (in .bed format).

The annotation files should be in .bed format, and contain the columns “chr”, “start”, “end”. Chromosomes should be just the numbers (no “chr”).

annotation_bed_files <- list.files(path = system.file('extdata', 'test_bed_dir', package='mapgen'),
                                   pattern = '*.bed', full.names = TRUE)

# annotation_bed_files: Path to the annotation (.bed) files.
gwas.sumstats <- readRDS(system.file('extdata', 'test.processed.sumstats.rds', package='mapgen'))
torus.files <- prepare_torus_input_files(gwas.sumstats, annotation_bed_files, torus_input_dir = './torus_input')

Now that the appropriate files have been generated, let’s perform the enrichment analysis using TORUS.

run_torus() with option = "est-prior" returns a list with: enrichment estimates (log odds ratio) and 95% confidence intervals for each annotation category, as well as SNP-level priors (which could be used as priors in fine-mapping).

torus.res <- 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.res$enrich
torus.prior <- torus.res$snp_prior

If you only want to estimate enrichment but do not need SNP-level priors, you can set option = "est" to save time computing the priors.

torus.enrich <- run_torus(torus.files$torus_annot_file, 
                       torus.files$torus_zscore_file,
                       option = 'est',
                       torus_path = 'torus')$enrich

TORUS also gives us the uncertainty of whether each locus contains a causal variant or not. You can set option = "fdr" to get the FDR associated with each locus.

torus.fdr <- run_torus(torus.files$torus_annot_file, 
                       torus.files$torus_zscore_file,
                       option = 'fdr',
                       torus_path = 'torus')$fdr