geepack.quant.int.batch: function to test gene-environment or gene-gene interactions 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 interactions 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=",")

Arguments

genfile

a character string naming the genotype file for reading (see format requirement in details)

phenfile

a character string naming the phenotype file for reading (see format requirement in details)

pedfile

a character string naming the pedigree file for reading (see format requirement in details)

outfile

a character string naming the result file for writing

phen

a character string for a phenotype name in phenfile

covars

a character vector for covariates in phenfile

cov.int

a character string naming the covariate for interaction, the covariate has to be included in covars

sub

"N" (default) for no stratified analysis, and "Y" for requesting stratified analyses (only when cov.int is dichotomous)

col.names

a logical value indicating whether the output file should contain column names

sep.ped

the field separator character for pedigree file

sep.phe

the field separator character for phenotype file

sep.gen

the field separator character for genotype file

Value

phen: phenotype name
snp: SNP name
covar_int: the covariate for interaction
n: sample size used in analysis
AF: allele frequency of the coded allele
model: genetic model used in analysis, additive model only
beta_snp: regression coefficient of SNP covariate
se_snp: standard error of beta_beta
pval_snp: p-value of testing beta_beta not equal to zero
beta_snp_cov0: regression coefficient of SNP covariate in stratified analysis using the subset where cov.int level is 0
se_snp_cov0: standard error of beta_snp_cov0
pval_snp_cov0: p-value of testing beta_snp_cov0 not equal to zero
beta_snp_cov1: regression coefficient of SNP covariate in stratified analysis using the subset where cov.int level is 1
se_snp_cov1: standard error of beta_snp_cov1
pval_snp_cov1: p-value of testing beta_snp_cov1 not equal to zero
beta_int: regression coefficient of the interaction term
se_int: standard error of beta_int
pval_int: p-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.

Examples

Run this code

## Not run: 
# geepack.quant.int.batch(phenfile="simphen.csv",genfile="simgen.csv",
# pedfile="simped.csv",phen="SIMQT",outfile="simout.csv",col.names=T,covars="age",
# cov.int="age",sep.ped=",",sep.phe=",",sep.gen=",")
# ## End(Not run)

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