fmerg: Merge Two GWAS Result Data Sheets

Description

fmerg can be used to merge two GWAS result data sheets with the same key ID(SNP ID) into one data sheet.

Usage

fmerg(fl1, fl2,ID1, ID2, A, B, method)

Arguments

fl1

R object: data file 1

fl2

R object: data file 2

ID1

key id (SNP ID such as rsid) in file 1

ID2

key id (SNP ID such as rsid) in file 2

postfix for file 1: A=".W1". W1 may be any identifier in file 1. Default is A="".

postfix for file 2: B=".W2". W2 may be any identifier in file2. Default is B="".

method

method for merging. See details.

Value

Return a joined data sheet.

Details

fl1 and fl2 are two GWAS result data files from different studies or with different risk variables. They contain SNPID, hg18, hg19(positions), beta values, allele, frequency, and so on. The method has four options: method="No","NO" or "no" means that all data with unmatch SNPs are not saved in the merged file; method="All","ALL" or "all" lets fmerg save all the data with unmatched SNPs from two files but they are not paired one-by-one. This is different from R merge function. method="file1" will save the data with unmatched SNPs only from file 1 in the merged file and method="file2" allows function fmerg to save the data with unmatched SNPs from file2 in the merged file.

Examples

Run this code

data1<-matrix(NA,20,4)
data2<-matrix(NA,30,7)
SNPID1<-paste("rs",seq(1:20),sep="")
SNPID2<-paste("rs",seq(1:30),sep="")
data1[,1:4]<-c(round(runif(20),4),round(runif(20),4),round(runif(20),4),round(runif(20),4))
data2[,1:4]<-c(round(runif(30),4),round(runif(30),4),round(runif(30),4),round(runif(30),4))
data2[,5:7]<-c(round(seq(30)*runif(30),4),round(seq(30)*runif(30),4),seq(30))
data1<-cbind(SNPID1,as.data.frame(data1))
data2<-cbind(SNPID2,as.data.frame(data2))
dim(data1)
dim(data2)
colnames(data1)<-c("SNP","var1","var2","var3","var4")
colnames(data2)<-c("SNP","var1","var2","var3","var4","V1","V2","V3")
data12<-fmerg(fl1=data1,fl2=data2,ID1="SNP",ID2="SNP",A=".dat1",B=".dat2",method="No")
#data12[1:3,]
#  SNP.dat1 var1.dat1 var2.dat1 var3.dat1 var4.dat1 SNP.dat2 var1.dat2 var2.dat2
#1      rs1    0.9152    0.9853    0.9879    0.9677      rs1    0.5041    0.5734
#2     rs10    0.3357     0.116    0.3408    0.1867     rs10    0.9147    0.9294
#3     rs11    0.8004    0.8856    0.2236    0.4642     rs11    0.9262    0.5831
#  var3.dat2 var4.dat2     V1     V2 V3
#1    0.4933    0.6766 0.1864 0.6836  1
#2    0.4104    0.1327 3.2192 1.4166 10
#3    0.8541    0.6228 1.1803 1.9044 11