podkat-package: PODKAT Package

podkat-packageR Documentation

PODKAT Package


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.


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.


Ulrich Bodenhofer bodenhofer@bioinf.jku.at




## load genome description

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

UBod/podkat documentation built on Nov. 17, 2022, 6:45 p.m.