microbov {adegenet}R Documentation

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)

Format

microbov is a genind object with 3 supplementary components:

coun
a factor giving the country of each individual (AF: Afric; FR: France).
breed
a factor giving the breed of each individual.
spe
is a factor giving the species of each individual (BT: Bos taurus; BI: Bos indicus).

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 

if(require(ade4)){
kX <- ktab.data.frame(data.frame(X),Y@loc.nall)

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'

# display 
par(mfrow=c(2,2))
for(i in sel) {
s.multinom(kpca[[i]]$c1,kX[[i]],n.sample=nsamp[,i],coulrow=col,sub=Y@loc.names[i])
add.scatter.eig(kpca[[i]]$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")

# coordinated 
mcoa.axes <- split(mcoa1$axis,Y@loc.fac)
mcoa.coord <- split(mcoa1$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,sub=Y@loc.names[i])
add.scatter.eig(var.coord[[i]],2,xax=1,yax=2,posi="top")
}

# reference typology
par(mfrow=c(1,1))
s.label(mcoa1$SynVar,lab=microbov@pop.names,sub="Reference typology",csub=1.5)
add.scatter.eig(mcoa1$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) <- Y@loc.names
tv <- tv[order(Y@loc.names),]

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)
}


[Package adegenet version 1.2-2 Index]