podkat (version 1.4.2)

weights: Extract Contribution Weights of Variants


Method for extracting the contributions that each variant makes to the test statistic of an association test


## S3 method for class 'AssocTestResult':
weights(object, Z, model)
## S3 method for class 'AssocTestResultRanges':
weights(object, Z, model, limit=20, sex=NULL)


an object of class AssocTestResult or AssocTestResultRanges
an object of class GenotypeMatrix, an object of class TabixFile, or a character string with the file name of a VCF file
an object of class NullModel
maximum number of regions to be processed; set to Inf or non-numeric value like NA or NULL to disable limitation. Do this with caution, in particular, when reading from a VCF file, as reading of excessively large regions from VCF files may take very long or even kill the R session because of excessive memory comsumption!
if NULL, all samples are treated the same without any modifications; if sex is a factor with levels F (female) and M (male) that is as long as the number of samples in model, this argument is interpreted as the sex of the samples. In this case, the genotypes corresponding to male samples are doubled before further processing. This is designed for mixed-sex analyses of the X chromosome outside of the pseudoautosomal regions.


  • an object of class GRanges or GRangesList (see details above)


Upon successful completion of an association test, the weights method allows for finding out the individual contributions each of the variants made to the test statistic. This computation is only possible for kernels linear.podkat and linear.SKAT (see computeKernel).

If called for an AssocTestResult object as first argument object, a GenotypeMatrix object Z, and a NullModel object model, weights returns a GRanges object that contains all variants of variantInfo(Z) along with two numerical metadata columns named weight.raw and weight.contribution. The column weight.raw corresponds to raw contributions. These are signed, i.e. a positive value indicates a positive association, while a negative value indicates a negative association. The larger the absolute value, the larger the contribution. The column weight.contribution corresponds to relative contributions. These values are non-negative and they sum up to 1. For mathematical details, see Subsection 9.4 of the package vignette.

If weights is called for an AssocTestResultRanges object object, a second argument Z that is an object of class GenotypeMatrix, an object of class TabixFile, or a character string with the name of a VCF file, and a NullModel object model, the contribution weights described above are computed for each region in object. In this case, the method returns a GRangesList with as many components as object has regions, where each list component is a GRanges object containing the contribution weights as described above.

It is essential for weights to work correctly that object is actually the result of an association test between Z and model. If called for objects that actually do not belong to each other, the results are void. The method is implemented such that all possible checks are made that possibly detect inconsistencies between the input objects. However, the final responsibility is left to user to make sure that all data are consistent. Special caution is necessary if weights is run for an AssocTestResultRanges object that has been obtained by merging multiple AssocTestResultRanges using the c method. The c method performs several checks to ensure consistency of association test parameters among the merged results, but the sex parameter is an exception: if it appears to be inconsistent among the results to merge, it is omitted from the merged object (see also AssocTestResultRanges).

The weights method needs to re-evaluate some computations of the association test. In case it is called for Z being a TabixFile object or file name of a VCF file, weights even needs to re-read the genotype data from the file. Therefore, the method has a safety limit not to process too many regions (see limit argument described above).



See Also

assocTest, AssocTestResult, AssocTestResultRanges, nullModel, NullModel, computeKernel, GenotypeMatrix, p.adjust, filterResult


## 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'
model <- nullModel(y ~ ., pheno)

## perform association test
res <- assocTest(Z, model, windows)

## perform multiple testing correction and filter for
## significant regions
res <- filterResult(p.adjust(res), filterBy="p.value.adj")

## compute contributions
contrib <- weights(res, Z, model)

## extract most indicative variants

## plot contributions
plot(contrib[[1]], "weight.raw")
plot(contrib[[1]], "weight.contribution", type="b", alongGenome=TRUE)