Nothing
#dichotomous traits only
#phenfile: phenotype file name in quotation marks,must provide
#genfile" genotype file name in quotation marks, must provide
#outfile: output file name in quotation marks,must provide
#library: path of the library with GEE packge
#pedfile: famid id fa mo sex
#famid is cluster id
geepack.lgst.int.batch.imputed=function(genfile,phenfile,pedfile,outfile,phen,covars,cov.int,sub="N",col.names=T,sep.ped=",",sep.phe=",",sep.gen=","){
print(paste("phenotype data = ", phenfile))
print(paste("genotype data = ", genfile))
print(paste("pedigree data = ", pedfile))
print(paste("Result of GEE analyses =",outfile))
if (missing(covars) | missing(cov.int)| length(cov.int)!=1 | sum(cov.int %in% covars)!=1) stop('no covariates or no covariate for interaction or other covariate issue')
read.in.data <- function(phenfile,genfile,pedfile,sep.ped=sep.ped,sep.phe=sep.phe,sep.gen=sep.gen) {
print("Reading in Data")
ped.dat <- read.table(genfile,header=TRUE,na.strings="",sep=sep.gen)
snp.names <- names(ped.dat)[-1]
pedigree <- read.table(pedfile,header=TRUE,sep=sep.ped)
gntp.all <- merge(pedigree,ped.dat,by="id")
#read in phenotype data
phen.dat=read.table(phenfile,header=TRUE,sep=sep.phe)
phen.name=colnames(phen.dat)[-1]
n.snp=length(names(gntp.all))
if(length(grep("^sex$",colnames(phen.dat)))==0) {
phensnp.dat<-merge(gntp.all,phen.dat,by=c("id"))
} else {
## sex is one of the columns in the phenotype file
phensnp.dat<-merge(gntp.all,phen.dat,by=c("id","sex"))
}
print("Done reading in data")
return(list(data=phensnp.dat,snps=snp.names,phen.name=phen.name))
}
phensnp.dat <- read.in.data(phenfile,genfile,pedfile,sep.ped=sep.ped,sep.phe=sep.phe,sep.gen=sep.gen)
snplist<-phensnp.dat$snps
if (sum(phensnp.dat$phen.name %in% covars)==length(covars)) phenlist<-phensnp.dat$phen.name[!phensnp.dat$phen.name %in% covars] else
stop('some covariates are not available')
test.dat<-phensnp.dat$data
if (length(table(test.dat[,cov.int]))!=2 & !is.na(sub) & sub=="Y") stop('No subset analysis for non-binary interaction covariate!') ####061209
test.dat<-test.dat[order(test.dat$famid),]
if (sum(is.na(covars))==0 & sum(snplist %in% covars)>=1) {
names(test.dat)[which(snplist %in% covars)+6] <- snplist[snplist %in% covars]
covars[covars %in% snplist] <- paste(covars[covars %in% snplist],".y",sep="")
}
cov.int.snp <- NA
if (sum(is.na(cov.int))==0 & sum(snplist %in% cov.int)==1) {
cov.int.snp <- snplist[snplist %in% cov.int]
cov.int <- paste(cov.int,".y",sep="")
}
covars.dat <- na.omit(test.dat[,covars])
single.cov <- F
if (length(covars)==1) single.cov <- var(covars.dat)==0 else {
single.cov <- any(apply(covars.dat,2,var)==0)
if (single.cov) stop(paste("Single category in covariates!"))
for (i in covars){
cov1 <- covars.dat[,i]
if (!is.numeric(cov1)) cov1 <-as.numeric(as.factor(cov1))
for (j in covars[covars!=i]){
cov2 <- covars.dat[,j]
if (!is.numeric(cov2)) cov2 <-as.numeric(as.factor(cov2))
if (abs(cor(cov1,cov2))>0.99999999) stop(paste("Highly correlated covariates ",i," and ",j,"!!",sep=""))
}
}
}
#library(geepack)
final1<-c()
print(paste("Covariates, Running:",phen))
if (length(snplist)<2) {
temp.out <- c(phen,snplist,geepack.lgst.int.imputed(snp=test.dat[,snplist],phen=phen,test.dat=test.dat,covar=covars,cov.int=cov.int,sub=sub))
final1 <- rbind(final1,temp.out)
} else {
for (i in 1:length(snplist)) {
temp.out <-c(phen,snplist[i],geepack.lgst.int.imputed(snp=test.dat[,snplist[i]],phen=phen,test.dat=test.dat,covar=covars,cov.int=cov.int,sub=sub))
final1 <- rbind(final1,temp.out)
}
}
if (ncol(final1)==16) {
colnames(final1)<-c("phen","snp","covar_int","n","AF","nd","AFd","cov_beta_snp_beta_int","model","beta_snp","se_snp","pval_snp","beta_int","se_int","pval_int","remark")
final1 <- final1[,c(1:14,16,15)] } else {
colnames(final1)<-c("phen","snp","covar_int","n","AF","nd","AFd","model","beta_snp","se_snp","pval_snp","beta_snp_cov0",
"se_snp_cov0","pval_snp_cov0","beta_snp_cov1","se_snp_cov1","pval_snp_cov1","beta_int","se_int","pval_int","remark")
final1 <- final1[,c(1:19,21,20)]
}
if (sum(is.na(cov.int.snp))==0 & length(cov.int.snp)==1) {
final1[,"covar_int"] <- cov.int.snp
}
write.table(as.matrix(final1),outfile,col.names=T, row.names=F,quote=F,sep=",",na="",append=T)
}
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