kangar00: calc_kernel – R documentation

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calc_kernel

Calculate the kernel-matrix for a pathway

Description

Uses individuals' genotypes to create a kernel object including the calculated kernel matrix for a specific pathway. Each numeric value within this matrix is calculated from two individuals' genotypevectors of the SNPs within the pathway by a kernel function. It can be interpreted as the genetic similiarity of the individuals. Association between the pathway and a binary phenotype (case-control status) can be evaluated in the logistic kernel machine test, based on the kernel object. Three kernel functions are available.

Usage

## S4 method for signature 'GWASdata'
calc_kernel(object, pathway, knots = NULL,
  type = c("lin", "sia", "net"), calculation = c("cpu", "gpu"), ...)

## S4 method for signature 'GWASdata'
lin_kernel(object, pathway, knots = NULL,
  calculation = c("cpu", "gpu"), ...)

## S4 method for signature 'GWASdata'
sia_kernel(object, pathway, knots = NULL,
  calculation = c("cpu", "gpu"), ...)

## S4 method for signature 'GWASdata'
net_kernel(object, pathway, knots = NULL,
  calculation = c("cpu", "gpu"), ...)

Arguments

`object`	`GWASdata` object containing the genotypes of the individuals for which a `kernel` will be calculated.
`pathway`	object of the class `pathway` specifying the SNP set for which a `kernel` will be calculated.
`knots`	`GWASdata` object, if specified a `kernel` will be computed.
`type`	`character` indicating the `kernel` type: Use `'lin'` to specify the linear kernel, `'sia'` for the size-adjusted or `'net'` for the network-based kernel.
`calculation`	`character` specifying if the kernel `matrix` is computed on CPU or GPU.
`...`	further arguments to be passed to `kernel` computations.

Details

Different types of kernels can be constructed:

type='lin' creates the linear kernel assuming additive SNP effects to be evaluated in the logistic kernel machine test.
type='sia' calculates the size-adjusted kernel which takes into consideration the numbers of SNPs and genes in a pathway to correct for size bias.
type='net' calculates the network-based kernel. Here not only information on gene membership and gene/pathway size in number of SNPs is incorporated, but also the interaction structure of genes in the pathway.

For more details, check the references.

Value

Returns an object of class kernel, including the similarity matrix of the pathway for the considered individuals.
If knots are specified low-rank kernel of class a lowrank_kernel will be returned, which is not necessarily quadratic and symmetric.

Methods (by class)

GWASdata:
GWASdata:
GWASdata:

Author(s)

Stefanie Friedrichs, Juliane Manitz

References

Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, Lin X Powerful SNP-Set Analysis for Case-Control Genome-Wide Association Studies. Am J Hum Genet 2010, 86:929-42
Freytag S, Bickeboeller H, Amos CI, Kneib T, Schlather M: A Novel Kernel for Correcting Size Bias in the Logistic Kernel Machine Test with an Application to Rheumatoid Arthritis. Hum Hered. 2012, 74(2):97-108.
Freytag S, Manitz J, Schlather M, Kneib T, Amos CI, Risch A, Chang-Claude J, Heinrich J, Bickeboeller H: A network-based kernel machine test for the identification of risk pathways in genome-wide association studies. Hum Hered. 2013, 76(2):64-75.

Examples

data(gwas)
data(hsa04020)
lin_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='lin', calculation='cpu')
summary(lin_kernel)
net_kernel <- calc_kernel(gwas, hsa04020, knots=NULL, type='net', calculation='cpu')
summary(net_kernel)

kangar00

Kernel Approaches for Nonlinear Genetic Association Regression

v1.4

GPL-2

Authors

Juliane Manitz [aut], Stefanie Friedrichs [aut], Patricia Burger [aut], Benjamin Hofner [aut], Ngoc Thuy Ha [aut], Saskia Freytag [ctb], Heike Bickeboeller [ctb]

Initial release

2020-02-12