R: SKAT: Sequence Kernel Association Test

SKAT {AssotesteR} | R Documentation

## 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)

### 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)

### Value

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

### Note

This method is computationally expensive

### Author(s)

Gaston Sanchez

### 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)

[Package AssotesteR version 0.1-10 Index]