Quality Control based on Identity-by-descent (IBD) computed by PLINK 1.9 using its method-of-moments.

snp_plinkIBDQC(
  plink.path,
  bedfile.in,
  bedfile.out = NULL,
  pi.hat = 0.08,
  ncores = 1,
  pruning.args = c(100, 0.2),
  do.blind.QC = TRUE,
  extra.options = "",
  verbose = TRUE
)

Arguments

plink.path

Path to the executable of PLINK 1.9.

bedfile.in

Path to the input bedfile.

bedfile.out

Path to the output bedfile. Default is created by appending "_norel" to prefix.in (bedfile.in without extension).

pi.hat

PI_HAT value threshold for individuals (first by pairs) to be excluded. Default is 0.08.

ncores

Number of cores used. Default doesn't use parallelism. You may use nb_cores.

pruning.args

A vector of 2 pruning parameters, respectively the window size (in variant count) and the pairwise $r^2$ threshold (the step size is fixed to 1). Default is c(100, 0.2).

do.blind.QC

Whether to do QC with pi.hat without visual inspection. Default is TRUE. If FALSE, return the data.frame of the corresponding ".genome" file without doing QC. One could use ggplot2::qplot(Z0, Z1, data = mydf, col = RT) for visual inspection.

extra.options

Other options to be passed to PLINK as a string (for the IBD part). More options can be found at http://www.cog-genomics.org/plink/1.9/ibd.

verbose

Whether to show PLINK log? Default is TRUE.

Value

The path of the new bedfile. If no sample is filter, no new bed/bim/fam files are created and then the path of the input bedfile is returned.

References

Chang, Christopher C, Carson C Chow, Laurent CAM Tellier, Shashaank Vattikuti, Shaun M Purcell, and James J Lee. 2015. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4 (1): 7. http://dx.doi.org/10.1186/s13742-015-0047-8.

See also

Examples

if (FALSE) { bedfile <- system.file("extdata", "example.bed", package = "bigsnpr") plink <- download_plink() bedfile <- snp_plinkIBDQC(plink, bedfile, bedfile.out = tempfile(fileext = ".bed"), ncores = 2) df_rel <- snp_plinkIBDQC(plink, bedfile, do.blind.QC = FALSE, ncores = 2) str(df_rel) library(ggplot2) qplot(Z0, Z1, data = df_rel, col = RT) qplot(y = PI_HAT, data = df_rel) + geom_hline(yintercept = 0.2, color = "blue", linetype = 2) snp_plinkRmSamples(plink, bedfile, bedfile.out = tempfile(fileext = ".bed"), df.or.files = subset(df_rel, PI_HAT > 0.2)) }