R: SNP-set (Sequence) Kernel Association Test for X chromosome...

SKAT_ChrX {SKAT} | R Documentation

## SNP-set (Sequence) Kernel Association Test for X chromosome variables

### Description

Test for association between a set of SNPS/genes in the X chromosome and continuous or dichotomous outcomes using the kernel machine.

### Usage

    SKAT_ChrX(Z, obj, is_X.inact =TRUE
    , kernel = "linear.weighted", method="davies", weights.beta=c(1,25)
    , weights = NULL, impute.method = "fixed", r.corr=0, is_check_genotype=TRUE
    , is_dosage = FALSE, missing_cutoff=0.15, max_maf=1, estimate_MAF=1, SetID=NULL)

### Arguments

`Z` | a numeric genotype matrix with each row as a different individual and each column as a separate gene/snp. Each genotype should be coded as 0, 1, 2, and 9 (or NA) for AA, Aa, aa, and missing, where A is a major allele and a is a minor allele. Missing genotypes will be imputed by the simple Hardy-Weinberg equilibrium (HWE) based imputation.
`obj` | output object of the SKAT_Null_Model_ChrX function.
`is_X.inact` | an indicator variable for the X-inactivation coding (default=TRUE). Male genotypes are coded as g=(0,2) when it is TRUE, and g=(0,1) when it is false.
`kernel` | a type of kernel (default= "linear.weighted").
`method` | a method to compute the p-value (default= "davies"). See SKAT page for details.
`weights.beta` | a numeric vector of parameters of beta weights. See SKAT page for details.
`weights` | a numeric vector of weights for the weighted kernels. See SKAT page for details.
`impute.method` | a method to impute missing genotypes (default= "fixed"). "fixed" imputes missing genotypes by assigning the mean genotype value (2p), and "bestguess" uses best guess genotype values.
`r.corr` | the ρ parameter for the compound symmetric kernel. See SKAT page for details.
`is_check_genotype` | a logical value indicating whether to check the validity of the genotype matrix Z (default= TRUE). See SKAT page for details.
`is_dosage` | a logical value indicating whether the matrix Z is a dosage matrix. If it is TRUE, SKAT will ignore “is_check_genotype”.
`missing_cutoff` | a cutoff of the missing rates of SNPs (default=0.15). Any SNPs with missing rates higher than the cutoff will be excluded from the analysis.
`max_maf` | a cutoff of the maximum minor allele frequencies (MAF) (default=1, no cutoff). Any SNPs with MAF > cutoff will be excluded from the analysis.
`estimate_MAF` | a numeric value indicating how to estimate MAFs for the weight calculation and the missing genotype imputation. See SKAT page for details.
`SetID` | Internal use only.

### Details

For details of parameters, please see SKAT page.

### Value

`p.value` | p-value of SKAT.
`p.value.resampling` | p-values from resampled outcome. You can get it when you use obj from SKAT_Null_Model function with resampling. See the SKAT_Null_Model.
`p.value.noadj` | p-value of SKAT without the small sample adjustment. It only appears when small sample adjustment is applied.
`p.value.noadj.resampling` | p-values from resampled outcome without the small sample adjustment. It only appears when small sample adjustment is applied.
`pval.zero.msg` | (only when p.value=0) text message that shows how small the p.value is. ex. "Pvalue < 1.000000e-60" when p.value is smaller than 10^{-60}
`Q` | the test statistic of SKAT. It has NA when method="optimal.adj" or "optimal".
`param` | estimated parameters of each method.
`param$Is_Converged` | (only with method="davies") an indicator of the convergence. When 0 (not converged), "liu" method is used to compute p-value.
`param$n.marker` | a number of SNPs in the genotype matrix
`param$n.marker.test` | a number of SNPs used for the test. It can be different from param$n.marker when some markers are monomorphic or have higher missing rates than the missing_cutoff.

### Author(s)

Clement Ma and Seunggeun Lee

### Examples

    data(SKAT.example.ChrX)
    attach(SKAT.example.ChrX)

    #############################################################
    #	Compute the P-value of SKAT

    # binary trait
    obj.x<-SKAT_Null_Model_ChrX(y ~ x1 +x2 + Gender, SexVar="Gender", out_type="D")

    # SKAT
    SKAT_ChrX(Z, obj.x, kernel = "linear.weighted")

    # Burden
    SKAT_ChrX(Z, obj.x, kernel = "linear.weighted", r.corr=1)

    # SKAT-O
    SKAT_ChrX(Z, obj.x, kernel = "linear.weighted", method="SKATO")

[Package SKAT version 1.3.2.1 Index]