weightFuncs: Weighting Functions

In UBod/podkat: Position-Dependent Kernel Association Test

Weighting Functions

Description

Functions for computing SNV weights from minor allele frequences (MAF)

Usage

betaWeights(x, shape1=1, shape2=25)
logisticWeights(x, th=0.07, slope=150)
invSdWeights(x)

Arguments

x	a numeric vector of minor allele frequencies (MAFs); see details below
shape1, shape2	shape parameters of Beta distribution weighting function (see dbeta for details)
th, slope	parameters of the logistic weighting function (see details below)

Details

The function betaWeights is a wrapper around the dbeta function. It uses the same parameters shape1 and shape2, but does not support the non-centrality parameter ncp. The defaults are shape1=1 and shape2=25 as suggested by Wu et al. (2011) and implemented in the SKAT package. If called without argument x, a function with a single argument x is returned that can directly be used as weighting function, e.g. passed as weightFunc argument to the assocTest method.

The function logisticWeights provides a logistic weighting that corresponds to a soft threshold function. The th parameter corresponds to the threshold and the slope parameter corresponds to the steepness of the soft threshold. Like betaWeights, this function can be called without x argument to produce a parameter-free weighting function.

The function invSdWeights computes weights as suggested by Madsen and Browning (2009). For consistency, this function also returns a single-argument function if called without x argument.

For mathematical details, see Subsection 9.3 of the package vignette.

Value

a numeric vector with weights as long as the argument x, a function if x was missing;

Author(s)

Ulrich Bodenhofer

References

https://github.com/UBod/podkat

Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., and Lin, X. (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82-93. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ajhg.2011.05.029")}.

Madsen, B. E. and Browning, S. R. (2009) A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genetics 5, e1000384. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pgen.1000384")}

See Also

GenotypeMatrix, dbeta, assocTest

Examples

## create a toy example
A <- matrix(rbinom(50, 2, prob=0.2), 5, 10)
MAF <- colSums(A) / (2 * nrow(A))

## compute some weight vectors
betaWeights(MAF, 1, 25)
betaWeights(MAF, 1, 30)
logisticWeights(MAF)
invSdWeights(MAF)

## plot weighting functions (note the missing 'x' arguments)
plot(betaWeights(shape2=30), xlim=c(0, 1))
plot(logisticWeights(), xlim=c(0, 1))
plot(invSdWeights, xlim=c(0, 1))

UBod/podkat documentation built on May 5, 2024, 6:37 a.m.