AssotesteR (version 0.1-10)

SKAT: SKAT: Sequence Kernel Association Test

Description

SKAT is a regression method to test for association between genetic variants (common and rare) in a region. A score-based variance-component test.

Usage

SKAT(y, X, kernel = "linear", weights = NULL, a = 1, b = 25, perm = NULL)

Arguments

y

numeric vector with phenotype status: 0=controls, 1=cases. No missing data allowed

X

numeric matrix or data frame with genotype data coded as 0, 1, 2.

kernel

character string indicating the type of kernel to be used. Possible options are "linear", "wlinear", "quadratic", "IBS", "wIBS", "twowayx" (kernel="linear" by default)

weights

optional numeric vector with weights for the genetic variants (NULL by default)

a

positive numeric value for the parameter a in the Beta distribution (a=1 by default)

b

positive numeric vallue for the parameter b in the Beta distribution (b=25 by default)

perm

positive integer indicating the number of permutations (NULL by default)

Value

"assoctest", basically a list with the following elements:

skat.stat

skat statistic

asymp.pval

asymptotic p-value of the applied statistic (distributed as chi-square with df=1)

perm.pval

permuted p-value

args

descriptive information with number of controls, cases, variants, permutations, and selected kernel

name

name of the statistic

Details

The argument kernel is used to specify the kernel function. "linear" indicates the linear kernel, "wlinear" indicates a weighted linear kernel, "quadratic" indicates the quadratic polynomial kernel, "IBS" indicates Identity-By-Share, "wIBS" indicates weighted IBS, and "twowayx" indicates a two-way interaction kernel.

For the weighted kernels ("wlinear" and "wIBS"), there are two options to get the weights. The default option (weights=NULL) involves the calculation of the weights by taking into account the minor allele frequency of the variants. In this case, the weights are calculated from a Beta distribution with parameters a and b. The second option is to specify the weights by providing a vector of weights for the variants; in this case the length of the vector must equal the number of columns in X. For more information see reference Wu et al (2011)

References

Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, Lin X (2010) Powerful SNP-Set Analysis for Case-Control Genome-wide Association Studies. The American Journal of Human Genetics, 86: 929-942

Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X (2011) Rare-Variant Association Testing for Sequencing Data with the Sequence Kernel Association Test. The American Journal of Human Genetics, 89: 82-93

See Also

WSS

Examples

  ## Not run: 
#    
#   # load data genodata
#   data(genodata)
# 
#   # phenotype (first column of genodata)
#   pheno = genodata[,1]
# 
#   # genotype (rest of columns of genodata)
#   geno = genodata[,-1]
# 
#   # apply SKAT with linear kernel 
#   myskat.linear = SKAT(pheno, geno, kernel="linear")
#   myskat.linear
# 
#   # apply SKAT with weighted linear kernel
#   # weights estimated from distribution beta(MAF, a=1, b=25)
#   myskat.wlinear = SKAT(pheno, geno, kernel="wlinear", a=1, b=25)
#   myskat.wlinear
# 
#   # apply SKAT with quadratic kernel
#   myskat.quad = SKAT(pheno, geno, kernel="quadratic")
#   myskat.quad
# 
#   # apply SKAT with IBS kernel
#   myskat.ibs = SKAT(pheno, geno, kernel="IBS")
#   myskat.ibs
#   ## End(Not run)