Description Usage Arguments Details Value Note Author(s) References See Also Examples
SKAT is a regression method to test for association between genetic variants (common and rare) in a region. A score-based variance-component test.
1 |
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" ( |
weights |
optional numeric vector with weights for the genetic variants ( |
a |
positive numeric value for the parameter |
b |
positive numeric vallue for the parameter |
perm |
positive integer indicating the number of permutations ( |
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)
An object of class "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 |
This method is computationally expensive
Gaston Sanchez
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ## 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)
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