microbov: Microsatellites genotypes of 15 cattle breeds
In adegenet: Exploratory Analysis of Genetic and Genomic Data

microbov

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.

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


## Not run: 
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),Y@loc.n.all)

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 %PCA
par(mfrow=c(2,2))
for(i in sel) {
s.multinom(kpca[[i]]$c1,kX[[i]],n.sample=nsamp[,i],coulrow=col,sub=locNames(Y)[i])
add.scatter.eig(kpca[[i]]$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 %PCA
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=locNames(Y)[i])
add.scatter.eig(var.coord[[i]],2,xax=1,yax=2,posi="top")
}

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

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)
}

## End(Not run)

adegenet documentation built on Feb. 16, 2023, 6 p.m.