adegenet (version 1.4-2)

microbov: Microsatellites genotypes of 15 cattle breeds

Description

This data set gives the genotypes of 704 cattle individuals for 30 microsatellites recommended by the FAO. The individuals are divided into two countries (Afric, France), two species (Bos taurus, Bos indicus) and 15 breeds. Individuals were chosen in order to avoid pseudoreplication according to their exact genealogy.

Usage

data(microbov)

encoding

UTF-8

source

Data prepared by Katayoun Moazami-Goudarzi and Denis Lalo"e (INRA, Jouy-en-Josas, France)

References

Lalo"e D., Jombart T., Dufour A.-B. and Moazami-Goudarzi K. (2007) Consensus genetic structuring and typological value of markers using Multiple Co-Inertia Analysis. Genetics Selection Evolution. 39: 545--567.

Examples

data(microbov)
microbov
summary(microbov)

# make Y, a genpop object
Y <- genind2genpop(microbov)

# make allelic frequency table
temp <- makefreq(Y,missing="mean")
X <- temp$tab
nsamp <- temp$nobs

# perform 1 PCA per marker 

kX <- ktab.data.frame(data.frame(X),[email protected])

kpca <- list()
for(i in 1:30) {kpca[[i]] <- dudi.pca(kX[[i]],scannf=FALSE,nf=2,center=TRUE,scale=FALSE)}


sel <- sample(1:30,4)
col = rep('red',15)
col[c(2,10)] = 'darkred'
col[c(4,12,14)] = 'deepskyblue4'
col[c(8,15)] = 'darkblue'

# displaypar(mfrow=c(2,2))
for(i in sel) {
s.multinom(kpca[[i]]$c1,kX[[i]],n.sample=nsamp[,i],coulrow=col,[email protected][i])
add.scatter.eig(kpca[[i]]$eig,3,xax=1,yax=2,posi="top")
}

# perform a Multiple Coinertia Analysis
kXcent <- kX
for(i in 1:30) kXcent[[i]] <- as.data.frame(scalewt(kX[[i]],center=TRUE,scale=FALSE))
mcoa1 <- mcoa(kXcent,scannf=FALSE,nf=3, option="uniform")

# coordinatedmcoa.axes <- split(mcoa1$axis,[email protected])
mcoa.coord <- split(mcoa1$Tli,mcoa1$TL[,1])
var.coord <- lapply(mcoa.coord,function(e) apply(e,2,var))

par(mfrow=c(2,2))
for(i in sel) {
s.multinom(mcoa.axes[[i]][,1:2],kX[[i]],n.sample=nsamp[,i],coulrow=col,[email protected][i])
add.scatter.eig(var.coord[[i]],2,xax=1,yax=2,posi="top")
}

# reference typology
par(mfrow=c(1,1))
s.label(mcoa1$SynVar,[email protected],sub="Reference typology",csub=1.5)
add.scatter.eig(mcoa1$pseudoeig,nf=3,xax=1,yax=2,posi="top")

# typologial values
tv <- mcoa1$cov2
tv <- apply(tv,2,function(c) c/sum(c))*100
rownames(tv) <- [email protected]
tv <- tv[order([email protected]),]

par(mfrow=c(3,1),mar=c(5,3,3,4),las=3)
for(i in 1:3){
barplot(round(tv[,i],3),ylim=c(0,12),yaxt="n",main=paste("Typological value -
structure",i))
axis(side=2,at=seq(0,12,by=2),labels=paste(seq(0,12,by=2),"%"),cex=3)
abline(h=seq(0,12,by=2),col="grey",lty=2)
}