ld_clump {ieugwasr}

Perform LD clumping on SNP data

Description

Uses PLINK clumping method, where SNPs in LD within a particular window will be pruned. The SNP with the lowest p-value is retained.

Usage

ld_clump(
  dat = NULL,
  clump_kb = 10000,
  clump_r2 = 0.001,
  clump_p = 0.99,
  pop = "EUR",
  opengwas_jwt = get_opengwas_jwt(),
  bfile = NULL,
  plink_bin = NULL
)

Arguments

dat

Dataframe. Must have a variant name column (rsid) and pval column called pval. If id is present then clumping will be done per unique id.

clump_kb

Clumping kb window. Default is very strict, 10000

clump_r2

Clumping r2 threshold. Default is very strict, 0.001

clump_p

Clumping sig level for index variants. Default = 1 (i.e. no threshold)

pop

Super-population to use as reference panel. Default = "EUR". Options are "EUR", "SAS", "EAS", "AFR", "AMR". 'legacy' also available - which is a previously used verison of the EUR panel with a slightly different set of markers

opengwas_jwt

Used to authenticate protected endpoints. Login to https://api.opengwas.io to obtain a jwt. Provide the jwt string here, or store in .Renviron under the keyname OPENGWAS_JWT.#'

bfile

If this is provided then will use the API. Default = NULL

plink_bin

If NULL and bfile is not NULL then will detect packaged plink binary for specific OS. Otherwise specify path to plink binary. Default = NULL

Details

This function interacts with the OpenGWAS API, which houses LD reference panels for the 5 super-populations in the 1000 genomes reference panel. It includes only bi-allelic SNPs with MAF > 0.01, so it's quite possible that a variant you want to include in the clumping process will be absent. If it is absent, it will be automatically excluded from the results.

You can check if your variants are present in the LD reference panel using ld_reflookup().

This function does put load on the OpenGWAS servers, which makes life more difficult for other users. We have implemented a method and made available the LD reference panels to perform clumping locally, see ld_clump() and related vignettes for details.

Value

Data frame