Description Usage Arguments Details Value Author(s) Examples
Function to calculate populationspecific inbreeding coefficients, and to predict inbreeding outliers that are five standard deviation beyond the mean.
1 2 3  Inbreeding(seqfile, remove.samples = NULL, LDprune = TRUE,
missing.rate = 0.1, ss.cutoff = 300, maf = 0.01, hwe = 1e06,
...)

seqfile 
SeqSQC object, which includes the merged gds file for study cohort and benchmark. 
remove.samples 
a vector of sample names for removal from inbreeding coefficient calculation. Could be problematic samples identified from previous QC steps, or userdefined samples. 
LDprune 
whether to use LDpruned snp set. 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 HardyWeinberg equilibrium p >=

... 
Arguments to be passed to other methods. 
Using LDpruned variants (by default), we calculate the
inbreeding coefficients for each sample in the study cohort and
for benchmark samples of the same population as the study
cohort. Samples with inbreeding coefficients that are five
standard deviations beyond the mean are considered problematic
and are shown as "Yes" in the column of
outlier.5sd
. Benchmark samples in this column are set to
be <e2><80><9c>NA<e2><80><9d>.
a data frame with sample name, inbreeding coefficient, and an indicator of whether the inbreeding coefficient is five standard deviation beyond the mean.
Qian Liu qliu7@buffalo.edu
1 2 3 4 5 6  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 < Inbreeding(seqfile, remove.samples=NULL, LDprune=TRUE, missing.rate=0.1)
res.inb < QCresult(seqfile)$Inbreeding
tail(res.inb)

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