GWAF (version 2.2)

geepack.lgst.int.batch.imputed: function to test gene-environment or gene-gene interactions between a dichotomous trait and a batch of imputed SNPs in families using Generalized Estimation Equation model

Description

Fit logistic regression via Generalized Estimation Equation (GEE) to test gene-environment or gene-gene interactions between a dichotomous phenotype and all imputed 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 family 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 SNPs in the imputed genotype data. The interaction test is carried out by geepack.lgst.int.imputed function from GWAF where the the geese function from package geepack is used.

Usage

geepack.lgst.int.batch.imputed(genfile,phenfile,pedfile,outfile,phen,
covars,cov.int,sub="N",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

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.

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

nd

the number of individuals in affected sample

AFd

allele frequency of the coded allele in affected sample

model

genetic model used in analysis, additive model only

beta_snp

regression coefficient of SNP covariate

se_snp

standard error of beta_snp

pval_snp

p-value of testing beta_snp 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

remark

warning or additional information for the analysis, 'not converged' indicates the GEE analysis did not converge; 'logistic reg' indicates GEE model is replaced by logistic regression; 'exp count<5' 5="" indicates="" any="" expected="" count="" is="" less="" than="" in="" phenotype-genotype="" table;="" 'not="" converged="" and="" exp="" count<5',="" 'logistic="" reg="" &="" count<5'="" are="" noted="" similarly;="" 'collinearity'="" collinearity="" exists="" between="" snp="" some="" covariates<="" dd="">

Details

Similar to the details for geepack.lgst.int.batch but here the SNP data contains imputed genotypes (allele dosages) that are continuous and range from 0 to 2.

Examples

## Not run: 
# geepack.lgst.int.batch.imputed(phenfile="simphen.csv",genfile="simgen.csv",
# pedfile="simped.csv",phen="CVD",outfile="simout.csv",covars=c("sex","age"),cov.int="sex",
# sub="Y",sep.ped=",",sep.phe=",",sep.gen=",")
# ## End(Not run)