Background
Here we will prep the genotypic data used for all analyses. The 44K SNP data Zhao et al (2011) was obtained from ricediversity.org.
Impute missing data
This code loads to the 44k SNP data, and imputes the missing markers. The missing markers were replaced with the mode of SNP values.
library(BGLR)
FAM <- read.table("DataandCode/RiceDiversity_44K_Genotypes_PLINK/sativas413.fam")[1:2]
MAP <- read.table("DataandCode/RiceDiversity_44K_Genotypes_PLINK/sativas413.map")
PED <- read_ped("DataandCode/RiceDiversity_44K_Genotypes_PLINK/sativas413.ped")
m <- PED$p
n <- PED$n
PED <- PED$x
##SNPs in PED are coded as 0, 1, 2, 3. 2 is missing data. 1 are heterozygous, 0 and 3 are homozygous for 1/1 and 2/2 for major allele and minor allele respectively
PED[PED == 2] <- NA
PED[PED == 0] <- 0
PED[PED == 1] <- 1
PED[PED == 3] <- 2
W <- t(matrix(PED, nrow=m, ncol=n, byrow = T))
colnames(W) <- MAP$V2
rownames(W) <- paste0("NSFTV_", FAM$V2)
#Imputation
getmode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
for (j in 1:ncol(W)) {
W[, j] = ifelse(is.na(W[, j]), getmode(W[, j]), W[, j])
}Remove markers with low MAF
Markers with a MAF less than 0.05 were removed. This removed 3,211 markers, leaving a total of 33,690 markers for computing the genomic relationship matrix (GRM).
#load phenotypic data for 378 accessions
PSA.df <- read.csv("DataandCode/Phenotypes/PSA.cleaned.mn.csv")
W <- W[row.names(W) %in% PSA.df$NSFTV.ID ,]
W <- W[order(row.names(W)) ,]
freq <- colMeans(W) / 2
maf <- ifelse(freq > 0.5, 1-freq, freq)
maf.index <- which(maf < 0.05)
length(maf.index)
W <- W[, -maf.index]
MAP <- MAP[-maf.index ,]
write.table(MAP, "DataandCode/CompCluster/MAP_maf0.05.txt", row.names = F, col.names = F, quote = F, sep = "\t")Compute GRM using VanRaden’s method
A genomic relationship matrix (\(\mathbf{G}\)) was calculated using VanRaden (2008). \[\mathbf{G} = \frac{\mathbf{Z_{sc} Z_{sc}}' }{m}\] Here, \(\mathbf{Z_{c}}\) is a centered \(n \times m\) matrix, where \(m\) is 33,564 SNPs and \(n\) is the 357 genotyped rice lines.
Zsc <- scale(x=W,center=T,scale=T)
G <- tcrossprod(Zsc)/ncol(W)
G <- G + diag(nrow(W))*0.001
G <- G[match(unique(PSA.df$NSFTV.ID), row.names(G) ) ,]
G <- G[, match(unique(PSA.df$NSFTV.ID), colnames(G) )]
write.table(t(Zsc), "DataandCode/CompCluster/Zsc.txt", row.names = F, col.names = T, quote = F, sep = "\t")
write.table(G, "DataandCode/CompCluster/G.txt", row.names = F, col.names = T, quote = F, sep = "\t")
G.final <- as.data.frame(which(row(G) >= col(G),arr.ind=TRUE))
G.final$G <- G[lower.tri(G, diag=T)]
G.final <- G.final[,c(2,1,3)]
G.final <- G.final[order(G.final[,2], G.final[,1]),]
G.final <- G.final[,c(2,1,3)]
colnames(G.final)[1:2]=c("Row", "Column")
attr(G.final, "rowNames") <- row.names(G)
write.table(G.final, "DataandCode/CompCluster/RR/G2.grm", col.names=F, row.names=F, quote=F, sep="\t")
write.table(G.final, "DataandCode/CompCluster/TP/G2.grm", col.names=F, row.names=F, quote=F, sep="\t")