geepack.quant.batch.imputed: function to test associations between a continuous trait and a batch of imputed SNPs in families using Generalized Estimation Equation model

Description

Fit Generalized Estimation Equation (GEE) model to test association between a continuous phenotype and all imputed SNPs 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 proportion of phenotype variation explained by the tested SNP is not provided. This function applies the same trait-SNP association test to all imputed SNPs in the genotype data. The trait-SNP association test is carried out by using the geese function from package geepack.

Usage

geepack.quant.batch.imputed(phenfile, genfile, pedfile, phen, covars = NULL, 
outfile, col.names = T, sep.ped = ",", sep.phe = ",", sep.gen = ",")

Arguments

Value

No value is returned. Instead, results are written to outfile.phenphenotype namesnpSNP nameNthe number of individuals in analysisAFimputed allele frequency of coded allelebetaregression coefficient of SNP covariatesestandard error of betapvalp-value of testing beta not equal to zero

Details

Similar to the details for geepack.quant.batch 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.