podkat-package {podkat} | R Documentation

## PODKAT Package

### Description

This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results.

### Details

The central method of this package is `assocTest`. It provides several different kernel-based association tests, in particular, the position-dependent kernel association test (PODKAT), but also some variants of the SNP-set kernel association test (SKAT). The test can be run for genotype data given in (sparse) matrix format as well as directly on genotype data stored in a variant call format (VCF) file. In any case, the user has to create a null model by the `nullModel` function beforehand. Upon completion of an association test, the package also provides methods for filtering, sorting, multiple testing correction, and visualization of results.

### Author(s)

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

### References

http://www.bioinf.jku.at/software/podkat

### Examples

    ## load genome description
    data(hgA)

    ## partition genome into overlapping windows
    windows <- partitionRegions(hgA)

    ## load genotype data from VCF file
    vcfFile <- system.file("examples/example1.vcf.gz", package="podkat")
    Z <- readGenotypeMatrix(vcfFile)

    ## read phenotype data from CSV file (continuous trait + covariates)
    phenoFile <- system.file("examples/example1lin.csv", package="podkat")
    pheno <-read.table(phenoFile, header=TRUE, sep=",")

    ## train null model with all covariates in data frame 'pheno'
    nm.lin <- nullModel(y ~ ., pheno)

    ## perform association test
    res <- assocTest(Z, nm.lin, windows)

    ## display results
    print(res)
    print(p.adjust(res))
    plot(p.adjust(res), which="p.value.adj")

[Package podkat version 1.12.0 Index]