Score Test
Description
Score tests for genetic association incorporating geneenvironment interaction. The function implements two types of scoretests: (1) MScore: A test based on maximum of a class of exposureweighted score test (Han et al, Biomentrics, 2015) (2) JScore: Joint scoretest for genetic association and interaction based on standard logistic model (Song et al., In Prep).
Usage
1 2 
Arguments
data 
Data frame containing all the data. No default. 
response.var 
Name of the binary response variable coded as 0 (controls) and 1 (cases). No default. 
snp.var 
Name of the genotype variable. No default. 
exposure.var 
Character vector of variable names or a formula for the exposure variables. No default. 
main.vars 
Character vector of variable names or a formula for all covariates of interest
which need to be included in the model as main effects.
This argument can be NULL for 
strata.var 
Name of the stratification variable for a retrospective likelihood.
The option allows the genotype frequency to vary by the discrete level of the stratification
variable. Ethnic or geographic origin of subjects, for example, could be used to define
strata. Unlike 
op 
A list of options (see details). The default is NULL. 
Details
The MScore option performs a score test for detecting an association between a SNP and disease risk, encompassing a broad range of risk models, including logistic, probit, and additive models for specifying joint effects of genetic and environmental exposures. The test statistics are obtained my maximizing over a class of score tests, each of which involves modified standard tests of genetic association through a weight function. This weight function reflects the potential heterogeneity of the genetic effects by levels of environmental exposures under a particular model. The MScore test could be performed using either a retrospective or prospective likelihood depending on whether an assumption of geneenvironment independence is imposed or not, respectively. The JScore function performs joint scoretest for genetic association and geneenvironment interaction under a standard logistic regression model. The JScore test could be performed under a prospective likelihood that allows association between gene and environment to remain unrestricted, a retrospective likelihood that assumes geneenvironment independence and an empiricalBayes framework that allows data adaptive shrinkage between retrospective and prospective scoretests. The JScore function is explicitly developed for proper analysis of imputed SNPs under all of the different options. The MScore function should produce valid tests for imputed SNPs under prospective likelihood. But further studies are needed for this method for analysis of imputed SNPs with the retrospective likelihood. The JScore function returns onestep MLE for parameters which can be used to perform metaanalysis across studies using standard techniques.
Options list:
Below are the names for the options list op
.

method
1 or 2 for the test. 1 = MScore, 2 = JScore. The default is 2.
Options for method 1:

thetas
Numeric vector of values in which the test statistic will be calculated over to find the maximum. Theta values correspond to different risk models, which can take any value of real numbers. For example, theta = 1 corresponds to an additive model, 0 to a multiplicative model, and a probit model is between 1 < theta < 0. Supramultiplicative model corresponds to theta > 1. The default is seq(3, 3, 0.1) 
indep
TRUE or FALSE for the geneenvironment independence assumption. The default is FALSE. 
doGLM
TRUE or FALSE for calculating the Wald pvalue for the SNP main effect. The default is FALSE.
Options for method 2:

sandwich
TRUE or FALSE to return tests and pvalues based on a sandwich covariance matrix. The default is FALSE.
Value
For op$method
= 1 (MScore), the returned object is a list with the following components:

maxTheta
Value ofthetas
where the maximum score test occurs. 
maxScore
Maximum value of the score test. 
pval
Pvalue of the score test. 
pval.logit
Pvalue of the standard association test based on logistic regression. 
model.info
List of information from the model.
For op$method
= 2 (JScore), the returned object is
a list containing test statistics, pvalues and onestep MLEs for the parameters and
variancecovariance matrices for the UML, CML and EB methods. Any name in the list
containing the string "test", "pval", "parm" or "var" is a test statistic, pvalue,
parameter estimate or variancecovariance matrix respectively.
References
Han, S.S., Rosenberg, P., Ghosh, A., Landi M.T., Caporaso N. and Chatterjee, N. An exposure weighted score test for genetic association integrating environmental riskfactors. Biometrics 2015 (Article first published online: 1 JUL 2015  DOI: 10.1111/biom.12328)
Song M., Wheeler B., Chatterjee, N. Using imputed genotype data in joint score tests for genetic association and geneenvironment interactions in casecontrol studies (In preparation).
See Also
snp.logistic
Examples
1 2 3 4 5 6 7 8 9 10 11  # Use the ovarian cancer data
data(Xdata, package="CGEN")
table(Xdata[, "gynSurgery.history"])
# Recode the exposure variable so that it is 01
temp < Xdata[, "gynSurgery.history"] == 2
Xdata[temp, "gynSurgery.history"] < 1
out < snp.score(Xdata, "case.control", "BRCA.status", "gynSurgery.history",
main.vars=c("n.children","oral.years"), op=list(method=2))
