MiST (version 1.0)

logit.test: Mixed effects Score Test for binary outcomes

Description

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

Usage

logit.test(y, X, G, Z, method = "liu")

Arguments

y

a numeric (0 or 1) vector 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.

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.

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.test(y.bin, X, G, Z) 


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

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