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.
# S4 method for GWASdata
calc_kernel(
object,
pathway,
knots = NULL,
type = c("lin", "sia", "net"),
calculation = c("cpu", "gpu"),
...
)# S4 method for GWASdata
lin_kernel(object, pathway, knots = NULL, calculation = c("cpu", "gpu"), ...)
# S4 method for GWASdata
sia_kernel(object, pathway, knots = NULL, calculation = c("cpu", "gpu"), ...)
# S4 method for GWASdata
net_kernel(object, pathway, knots = NULL, calculation = c("cpu", "gpu"), ...)
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.
GWASdata object containing the genotypes of the
individuals for which a kernel will be calculated.
object of the class pathway specifying the SNP set
for which a kernel will be calculated.
GWASdata object, if specified a kernel
will be computed.
character indicating the kernel type:
Use 'lin' to specify the linear kernel, 'sia' for the
size-adjusted or 'net' for the network-based kernel.
character specifying if the kernel matrix
is computed on CPU or GPU.
further arguments to be passed to kernel computations.
lin_kernel(GWASdata):
sia_kernel(GWASdata):
net_kernel(GWASdata):
Stefanie Friedrichs, Juliane Manitz
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.
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.
kernel-class,pathway
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)
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