Inbreeding {SeqSQC} | R Documentation

## Sample inbreeding check with SeqSQC object input file.

### Description

Function to calculate population-specific inbreeding coefficients, and to predict inbreeding outliers that are five standard deviation beyond the mean.

### Usage

    Inbreeding(seqfile, remove.samples = NULL, LDprune = TRUE,
      missing.rate = 0.1, ss.cutoff = 300, maf = 0.01, hwe = 1e-06, ...)

### Arguments

`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 user-defined samples.
`LDprune` | whether to use LD-pruned snp set. The default is TRUE.
`missing.rate` | to use the SNPs with "<= `missing.rate`" only; if NaN, no threshold. By default, we use `missing.rate = 0.1` to filter out variants with missing rate greater than 10%.
`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 ">= `maf`" if sample size defined in above argument is greater than `ss.cutoff`; otherwise NaN is used by default for no MAF threshold.
`hwe` | to use the SNPs with Hardy-Weinberg equilibrium p >= `hwe` if sample size defined in above argument is greater than `ss.cutoff`; otherwise no hwe threshold. The default is 1e-6.
`...` | Arguments to be passed to other methods.

### Details

Using LD-pruned 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 “NA”.

### Value

a data frame with sample name, inbreeding coefficient, and an indicator of whether the inbreeding coefficient is five standard deviation beyond the mean.

### Author(s)

Qian Liu qliu7@buffalo.edu

### Examples

    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)

[Package SeqSQC version 1.2.0 Index]