R: Microsatellites genotypes of 15 cattle breeds

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.

### 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)

[Package adegenet version 2.1.4 Index]