1-poissonGWAS.R

library(snpStats)
load("conversionTable.RData")

pathM <- paste("public/Genomics/108Malay_2527458snps", c(".bed", ".bim", ".fam"), sep = "")
SNP_M <- read.plink(pathM[1], pathM[2], pathM[3])

pathI <- paste("public/Genomics/105Indian_2527458snps", c(".bed", ".bim", ".fam"), sep = "")
SNP_I <- read.plink(pathI[1], pathI[2], pathI[3])

pathC <- paste("public/Genomics/110Chinese_2527458snps", c(".bed", ".bim", ".fam"), sep = "")
SNP_C <- read.plink(pathC[1], pathC[2], pathC[3])

# Ensure == number of markers across the three populations
if(ncol(SNP_C$genotypes) != ncol(SNP_I$genotypes)){
        stop("Different number of columns in input files detected. This is not allowed.")
}
if(ncol(SNP_I$genotypes) != ncol(SNP_M$genotypes)){
        stop("Different number of columns in input files detected. This is not allowed.")
}

# Merge the three SNP datasets
SNP <- SNP_M
SNP$genotypes <- rbind(SNP_M$genotypes, SNP_I$genotypes, SNP_C$genotypes)
colnames(SNP$map) <- c("chr", "SNP", "gen.dist", "position", "A1", "A2") # same for all three
SNP$fam<- rbind(SNP_M$fam, SNP_I$fam, SNP_C$fam)

# Rename SNPs present in the conversion table into rs IDs
mappedSNPs <- intersect(SNP$map$SNP, names(conversionTable))
newIDs <- conversionTable[match(SNP$map$SNP[SNP$map$SNP %in% mappedSNPs], names(conversionTable))]
SNP$map$SNP[rownames(SNP$map) %in% mappedSNPs] <- newIDs