Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 | geepack.lgst.imputed(snp, phen, test.dat, covar = NULL)
|
snp |
imputed 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 |
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.
Please see output in geepack.lgst.batch.imputed
.
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 | ## Not run:
geepack.lgst.imputed(snp=data[,"rs123"],phen="CVD",test.dat=data,covar=c("age",sex"))
## End(Not run)
|
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