longskat_gene_test: Assoictaion test using LSKAT

In ZWang-Lab/LSKAT: SNP-set (Sequence) Kernel Association Test for longitudinal data.

Description

Assoictaion test for the comined effect of common and rare variants using LSKAT

Usage

longskat_gene_test( r.model, 
    snp.mat, 
    weights.common=c(0.5,0.5), 
    weights.rare=c(1,25),
    rare.cutoff=NULL, 
    test.type="Joint", 
    snp.impute = "mean",
    run.cpp=F, 
    verbose=F)

Arguments

r.model	The list object obtained from the function longskat_est_model, including the estimated parameters and residuals.
snp.mat	Matrix with m row for individuals and n columns for the variaints(SNPs), also with the individuals' ID as the row names.
weights.common	a numeric vector of parameters of beta weights for common variants (default=c(0.5,0.5)).
weights.rare	a numeric vector of parameters of beta weights for rare variants (default=c(1,25)).
rare.cutoff	Numeric, a value of MAF cutoff for the rare SNPs. Only SNPs that have MAFs smaller than this are considered as rare SNP. The default criterion of rare SNP is calculated by the formula 1/√{2*sample}
test.type	String, Three models can be selected, "joint", "Common.Only", "rare.Only".
snp.impute	String, indicating the method of SNP imputation, the default model uses the mean of each variant to replace the missing SNP data.
run.cpp	Logical, indicating whether C/C++ functions are used to compute LSKAT.
verbose	Logical variable, indicating whether some debug information can be outputted.

Value

The list object is returned by this function with the following items:

snp.NMISS	Vector, the missing rate for each SNP.
snp.MAF	Vector, the MAF for each SNP.
snp.total	Numeric, the total number of variants.
snp.rare	Numeric, the number of rare variants.
q.lskat	Numeric, the statistical test of LSKAT
p.lskat	Numeric, the p-value of LSKAT
q.burden	Numeric, the statistical test of L-Burden Test
p.burden	Numeric, the p-value of L-Burden Test

References

Wang Z., Xu K., Zhang X., Wu X., and Wang Z., (2016) Longitudinal SNP-set association analysis of quantitative phenotypes. Genetic Epidemiology.

See Also

longskat_est_model

Examples

## data simulation for the power test
p0 <- longskat_gene_simulate( plink.format=T, file.plink.prefix="tmp-gene-test", 
      power.test=T, n.gene=5 );

## model estimation
r.reml <- longskat_est_model( p0$phe.long, p0$phe.cov, phe.time = NULL, g.maxiter=3,  
      method="REML", verbose=T )

print(r.reml);

## test the genes using the function 'longskat_gene_test'
for(i in 1:length(p0$snp.mat))
{
    r.lskat0 <- longskat_gene_test(r.reml, p0$snp.mat[[i]], snp.impute="mean");
    print(r.lskat0);
}

ZWang-Lab/LSKAT documentation built on May 10, 2019, 1:55 a.m.