IBDCheck | R Documentation |
Function to calculate the IBD coefficients for all sample pairs and to predict related sample pairs in study cohort.
IBDCheck(
seqfile,
remove.samples = NULL,
LDprune = TRUE,
kin.filter = TRUE,
missing.rate = 0.1,
ss.cutoff = 300,
maf = 0.01,
hwe = 1e-06,
...
)
seqfile |
SeqSQC object, which includes the merged gds file for study cohort and benchmark. |
remove.samples |
a vector of sample names for removal from IBD calculation. Could be problematic samples identified from previous QC steps, or user-defined samples. |
LDprune |
whether to use LD-pruned snp set. The default is TRUE. |
kin.filter |
whether to use "kinship coefficient >= 0.08" as the additional criteria for related samples. The default is TRUE. |
missing.rate |
to use the SNPs with "<= |
ss.cutoff |
the minimum sample size (300 by default) to apply the MAF filter. This sample size is the sum of study samples and the benchmark samples of the same population as the study cohort. |
maf |
to use the SNPs with ">= |
hwe |
to use the SNPs with Hardy-Weinberg equilibrium p >=
|
... |
Arguments to be passed to other methods. |
Using LD-pruned variants (by default), we calculate the
IBD coefficients for all sample pairs, and then predict related
sample pairs in study cohort using the support vector machine
(SVM) method with linear kernel and the known relatedness
embedded in benchmark data as training set.
Sample pairs
with discordant self-reported and predicted relationship are
considered as problematic. All predicted related pairs are also
required to have coefficient of kinship >= 0.08 by default. The
sample with higher missing rate in each related pair is
selected for removal from further analysis by function of
IBDRemove
.
a data frame with sample names, the descent coefficients of k0, k1 and kinship, self-reported relationship and predicted relationship for each pair of samples.
Qian Liu qliu7@buffalo.edu
load(system.file("extdata", "example.seqfile.Rdata", package="SeqSQC"))
gfile <- system.file("extdata", "example.gds", package="SeqSQC")
seqfile <- SeqSQC(gdsfile = gfile, QCresult = QCresult(seqfile))
seqfile <- IBDCheck(seqfile, remove.samples=NULL, LDprune=TRUE, missing.rate=0.1)
res.ibd <- QCresult(seqfile)$IBD
tail(res.ibd)
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