GWAF (version 2.2)

geepack.lgst.imputed: function for testing association between a dichotomous trait and an imputed SNP in family data using Generalized Estimation Equation model

Description

Fit logistic regression via Generalized Estimation Equation (GEE) to test association between a dichotomous phenotype and one imputed SNP in a genotype file in family data under additive genetic model. Each family is treated as a cluster, with independence working correlation matrix used in the robust variance estimator. The trait-SNP association test is carried out by the geese function from package geepack. This function is called in geepack.lgst.batch.imputed function to apply association test to all imputed SNPs in a genotype file.

Usage

geepack.lgst.imputed(snp, phen, test.dat, covar = NULL)

Arguments

snp

imputed genotype data of a SNP

phen

a character string for a phenotype name in test.dat

test.dat

the product of merging phenotype, genotype and pedigree data, should be ordered by "famid"

covar

a character vector for covariates in test.dat

Value

Please see output in geepack.lgst.batch.imputed.

Details

Similar to the details for geepack.lgst function but here the SNP data contains imputed genotypes (allele dosages) that are continuous and range from 0 to 2. In addition, the user-specified genetic model argument is not available.

References

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.

See Also

geese function from package geepack

Examples

## Not run: 
# geepack.lgst.imputed(snp=data[,"rs123"],phen="CVD",test.dat=data,covar=c("age",sex"))
# ## End(Not run)