logit.weight.test: Weighted Mixed effects Score Test for binary outcomes

In MiST: Mixed effects Score Test for continuous outcomes

Description

Test for association between a set of SNPS/genes and binary outcomes by including variant characteristic information and using weighted score statistics.

Usage

logit.weight.test(y, X, G, Z, maf, weight.beta = c(1, 25), method = "liu")

Arguments

y	a numeric vector (0 or 1) of the binary outcome variables. Missing values are not allowed.
X	a numeric matrix of covariates with rows for individuals and columns for covariates.
G	a numeric genotype matrix with rows for individuals and columns for SNPs. Each SNP should be coded as 0, 1, and 2 for AA, Aa, aa, where A is a major allele and a is a minor allele. Missing genotypes are not allowed.
Z	a numeric matrix of second level covariates for variant characteristics. Each row corresponds to a variant and each column corresponds to a variant characteristic. If there is no second level covariates, a vector of 1 should be used.
maf	a numeric vector of MAF (minor allele frequency) for each SNP.
weight.beta	a numeric vector of parameters of beta function which is the weight for score statistics. The default value is "c(1,25)".
method	a method to compute the p-value and the default value is "liu". Method "davies" represents an exact method that computes the p-value by inverting the characteristic function of the mixture chisq. Method "liu" represents an approximation method that matches the first 3 moments.

Value

S.tau	score statistic for the variant hetergenous effect.
S.pi	score statistic for the variant mean effect.
p.value.S.tau	p-value for testing the variant hetergenous effect.
p.value.S.pi	p-value for testing the variant mean effect.
p.value.overall	overall p-value for testing the association between the set of SNPS/genes and outcomes. It combines p.value.S.pi and p.value.S.tau by using Fisher's procedure.

Author(s)

Jianping Sun, Yingye Zheng, and Li Hsu.

References

Sun, J., Zheng, Y., and Hsu, L. (2013) A Unified Mixed-Effects Model for Rare-Variant Association in Sequencing Studies. Genet Epidemiol. 2013 Mar 9. doi: 10.1002/gepi.21717

H. Liu, Y. Tang, H.H. Zhang (2009) A new chi-square approximation to the distribution of non-negative definite quadratic forms in non-central normal variables, Computational Statistics and Data Analysis, 53, 853-856.

Examples

data(MiST.data)
attach(MiST.data)

######################################################################
# Test the association between a set of SNPs and binary outcomes    
#   - without information about SNP characteristics. Z is a vector of 1's.      

out <- logit.weight.test(y.bin, X, G, Z, maf) 


######################################################################
#	Test the association between a set of SNPs and bianry outcomes    
#	- including SNP characteristics        

out <- logit.weight.test(y.bin, X, G, Z.func, maf) 

MiST documentation built on May 1, 2019, 8:45 p.m.