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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