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Calculate genetic risk scores from PGEN files on RAP

Source: R/grs.R
grs_score.Rd

Uploads local SNP weight files to the RAP project root, then submits one Swiss Army Knife job per GRS. Each job runs plink2 --score across all 22 chromosomes and saves a single CSV to dest on completion. Jobs run in parallel; use job_ls to monitor progress.

Usage

grs_score(
  file,
  pgen_dir = NULL,
  dest = NULL,
  maf = 0.01,
  instance = "standard",
  priority = "low"
)

Arguments

file

Named character vector of local weight file paths. Names become the GRS identifiers (output column = GRS_<name>). Example: c(grs_a = "weights_a.txt").

pgen_dir

Character scalar. Path to PGEN files on RAP (e.g. "/mnt/project/pgen"). Must be specified explicitly.

dest

Character scalar. RAP destination path for output CSV files (e.g. "/grs/"). Must be specified explicitly.

maf

Numeric scalar. MAF filter threshold used when locating PGEN files. Must match the value used in grs_bgen2pgen. Default: 0.01.

instance

Character scalar. Instance type preset: "standard" or "large". Default: "standard".

priority

Character scalar. Job priority: "low" or "high". Default: "low".

Value

A named character vector of job IDs (one per GRS), returned invisibly. Failed submissions are NA. Use job_ls to monitor progress.

Details

Weight files should have three columns (any delimiter, header required):

Column 1

Variant ID (e.g. rs IDs).

Column 2

Effect allele (A1).

Column 3

Effect weight (beta / log-OR).

This matches the output format of grs_check.

Output per job: dest/<score_name>_scores.csv with columns IID and the GRS score (named GRS_<name>).

Instance types:

"standard"

mem2_ssd1_v2_x4: 4 cores, 12 GB RAM.

"large"

mem2_ssd2_v2_x8: 8 cores, 28 GB RAM.

Examples

if (FALSE) { # \dontrun{
ids <- grs_score(
  file = c(
    grs_a = "weights/grs_a_weights.txt",
    grs_b = "weights/grs_b_weights.txt"
  ),
  pgen_dir = "/mnt/project/pgen",
  dest     = "/grs/",
  priority = "high"
)

job_ls()
} # }