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
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
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 email@example.com
## 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")
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