GWAF (version 2.0)

geepack.quant.int.batch: function to test gene-environment or gene-gene interaction for a continuous trait and a batch of genotyped SNPs in families using Generalized Estimation Equation model

Description

Fit Generalized Estimation Equation (GEE) model to test gene-environment or gene-gene interaction for a continuous phenotype and all genotyped SNPs in a genotype file in family data 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 pedigree is treated as a cluster, with independence working correlation matrix used in the robust variance estimator. This function applies the same interaction test to all genotyped SNPs in the genotype data. In each test for interaction, the geese function from geepack package is used.

Usage

geepack.quant.int.batch(phenfile,genfile,pedfile,phen,covars,cov.int,sub="N",outfile,
col.names=T,sep.ped=",",sep.phe=",",sep.gen=",")

Value

No value is returned. Instead, results are written to outfile. If stratified analyses are requested, the result file will include the following columns. Otherwise, cov_beta_snp_beta_int will be included instead of the results from stratified analyses, that is, beta_snp_cov0, se_snp_cov0, pval_snp_cov0, beta_snp_cov1, se_snp_cov1, and pval_snp_cov1.phenphenotype namesnpSNP namecovar_intthe covariate for interactionnsample size used in analysisAFallele frequency of the coded allelemodelgenetic model used in analysis, additive model onlybeta_snpregression coefficient of SNP covariatese_snpstandard error of beta_betapval_snpp-value of testing beta_beta not equal to zerobeta_snp_cov0regression coefficient of SNP covariate in stratified analysis using the subset where cov.int level is 0se_snp_cov0standard error of beta_snp_cov0pval_snp_cov0p-value of testing beta_snp_cov0 not equal to zerobeta_snp_cov1regression coefficient of SNP covariate in stratified analysis using the subset where cov.int level is 1se_snp_cov1standard error of beta_snp_cov1pval_snp_cov1p-value of testing beta_snp_cov1 not equal to zerobeta_intregression coefficient of the interaction termse_intstandard error of beta_intpval_intp-value of testing beta_int not equal to zero

Details

For a continuous trait, the geepack.quant.int.batch function first reads in and merges phenotype-covariates, genotype and pedigree files, then tests gene-environment or gene-gene interaction and the association of phen against all genotyped SNPs in genfile. Only one interaction term is allowed, so is the covariate for interaction (cov.int). 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. genfile contains unique individual id and genotype data, with the column names being "id" and SNP names. For each SNP, the genotype data should be coded as 0, 1, 2 indicating the numbers of the coded alleles. The SNP name in genotype file should not have any dash, '-' and other special characters(dots and underscores are OK). phenfile contains unique individual id, phenotype and covariate data, with the column names being "id" and phenotype and covaraite names. pedfile contains pedigree informaion, with the column names being "famid","id","fa","mo","sex". In all files, missing value should be an empty space, except missing parental id in pedfile. SNPs with low genotype counts (especially minor allele homozygote) may be omitted. The geepack.quant.int.batch function fits GEE model using geese function from geepack package.

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.