Clumping

Setup

Clumping GWAS results aims to retain only independent variants, accounting for linkage disequilibrium (highly correlated signals). The clump() function uses PLINK2 in the background and requires a genome reference dataset with linkage disequilibrium information. This reference must contain the same variant IDs as your GWAS results. It is recommended to first annotate your GWAS with rsIDs - this can be achieved with the chrpos_to_rsid() function if your data is not already annotated. The data is also expected in genepi.utils standard form, so run standardise_gwas() if needed. The standardisation and rsID annotation can also be performed in one step with the standardise_gwas() function, by passing an appropriate populate_rsid argument.

A common reference dataset is the 1000 genomes phase 3 which can be downloaded from the PLINK (https://www.cog-genomics.org/plink/2.0/resources#phase3_1kg). Important: if you use a different dataset ensure that the IDs match those in your dataset).

To ready the PLINK2 pfiles for use with clumping you can run the following, as there appear to be some duplicate rsIDs in this dataset that PLINK2 does not like.

plink2 \
  --pfile all_phase3 \
  --rm-dup force-first \
  --make-pgen \
  --out all_phase3_nodup

The path to your processed reference files (path/to/plink_ref/all_phase3_nodup) can be directly passed the the clump() function as the plink_ref argument, or, you can configure the path in the package by using set_1000G_reference() - this only needs to be done once per install. The currently set reference path can be queried with which_1000G_reference().

You will need PLINK2 installed on your computer. The clump() function takes a path to the plink executable as the plink2 argument. Alternatively, you can configure the path in the package by using set_plink2() - this only needs to be done once per install. The currently set PLINK2 path can be queried with which_plink2(). If the plink2 argument is set to NULL the function will attempt to call plink directly, equivalent of running plink2 --options directly in your command line.

library(genepi.utils)

# the gwas data
gwas <- GWAS(dat=system.file("extdata", "example2_gwas_sumstats.tsv", package="genepi.utils"), map="ns_map", fill_rsid="b37_dbsnp156")
gwas <- as.data.table(gwas)


# check path to reference
which_1000G_reference()

# check path to plink2 executable
which_plink2()

Clumping

Running the clumping procedure will annotate your GWAS with two columns:

index: a logical column indicating whether the variant is an index/lead variant. If you only want clumped/index SNPs you should filter on this e.g. gwas[index==TRUE, ]
clump: an integer column indicating the clump that the variant belongs to.

# input gwas; n.b. trailing `[]` just for printing in rmarkdown
gwas[]

# run clumping
clumped_gwas <- clump(gwas, 
                      p1 = 1.0,
                      p2 = 1.0, 
                      r2 = 0.1, 
                      kb = 250, 
                      plink2    = which_plink2(), 
                      plink_ref = which_1000G_reference(build="GRCh37"), 
                      parallel_cores = 4)

# view result
clumped_gwas[]

- Setup
- Clumping