Description Usage Arguments Details Value Author(s) References See Also Examples
Fit logistic regression via Generalized Estimation Equation (GEE) to test gene-environment or gene-gene interaction between a dichotomous phenotype
and one imputed SNP in a genotype file under additive genetic model. The interaction term is the product of SNP genotype and a covariate for interaction (cov.int
).
The covariate for interaction (cov.int
) can be SNP genotype (gene-gene interaction) or an environmental factor (gene-environment interaction). Only one
interaction term is allowed. When cov.int
is dichotomous, stratified analyses can be requested by specifying sub
="Y". The covariance between the main
effect (SNP) and the interaction effect is provided in the output when stratified analysis is not requested. Each family is treated as
a cluster, with independence working correlation matrix used in the robust variance estimator.
This function is called in geepack.lgst.int.batch.imputed
function to apply interaction test to all imputed SNPs in a
genotype file. The interaction test is carried out by the geese
function from package geepack
.
1 | geepack.lgst.int.imputed(snp,phen,test.dat,covar,cov.int,sub="N")
|
snp |
genotype data of a SNP |
phen |
a character string for a phenotype name in |
test.dat |
the product of merging phenotype, genotype and pedigree data, should be ordered by "famid" |
covar |
a character vector for covariates in |
cov.int |
a character string naming the covariate for interaction, the covariate has to be included in |
sub |
"N" (default) for no stratified analysis, and "Y" for requesting stratified analyses (only when |
Similar to the details for geepack.lgst.int.batch
function but here the SNP data contains imputed genotypes (allele dosages)
that are continuous and range from 0 to 2.
Please see value in geepack.lgst.int.batch.imputed
function.
Qiong Yang <qyang@bu.edu> and Ming-Huei Chen <mhchen@bu.edu>
Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika, 73 13–22.
Zeger, S.L. and Liang, K.Y. (1986) Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42 121–130.
Yan, J and Fine, J. (2004) Estimating equations for association structures. Stat Med, 23 859–874.
geese
function from package geepack
1 2 3 4 5 | ## Not run:
geepack.lgst.int.imputed(snp=data[,"rs123"],phen="CVD",test.dat=data,covar=c("age",sex"),
cov.int="sex",sub="Y")
## End(Not run)
|
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