R: Extended HGDP-CEPH dataset

eHGDP {adegenet} | R Documentation

## Extended HGDP-CEPH dataset

### Description

This dataset consists of 1350 individuals from native Human populations distributed worldwide typed at 678 microsatellite loci. The original HGDP-CEPH panel [1-3] has been extended by several native American populations [4]. This dataset was used to illustrate the Discriminant Analysis of Principal Components (DAPC, [5]).

### Format

`eHGDP` is a genind object with a data frame named `popInfo` as supplementary component (`eHGDP@other$popInfo`), which contains the following variables:

Population:

  a character vector indicating populations.

Region:

  a character vector indicating the geographic region of each population.

Label:

  a character vector indicating the correspondence with population labels used in the genind object (i.e., as output by `pop(eHGDP)`).

Latitude,Longitude:

  geographic coordinates of the populations, indicated as north and east degrees.

### Source

Original panel by Human Genome Diversity Project (HGDP) and Centre d'Etude du Polymorphisme Humain (CEPH). See reference [4] for Native American populations.

This copy of the dataset was prepared by Francois Balloux.

### References

[1] Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, et al. (2002) Genetic structure of human populations. Science 298: 2381-2385.

[2] Ramachandran S, Deshpande O, Roseman CC, Rosenberg NA, Feldman MW, et al. (2005) Support from the relationship of genetic and geographic distance in human populations for a serial founder effect originating in Africa. Proc Natl Acad Sci U S A 102: 15942-15947.

[3] Cann HM, de Toma C, Cazes L, Legrand MF, Morel V, et al. (2002) A human genome diversity cell line panel. Science 296: 261-262.

[4] Wang S, Lewis CM, Jakobsson M, Ramachandran S, Ray N, et al. (2007) Genetic Variation and Population Structure in Native Americans. PLoS Genetics 3: e185.

[5] Jombart, T., Devillard, S. and Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. Submitted to BMC genetics.

### Examples

    ## Not run:
    ## LOAD DATA
    data(eHGDP)
    eHGDP

    ## PERFORM DAPC - USE POPULATIONS AS CLUSTERS
    ## to reproduce exactly analyses from the paper, use "n.pca=1000"
    dapc1 <- dapc(eHGDP, all.contrib=TRUE, scale=FALSE,
    n.pca=200, n.da=80) # takes 2 minutes
    dapc1

    ## (see ?dapc for details about the output)

    ## SCREEPLOT OF EIGENVALUES
    barplot(dapc1$eig, main="eHGDP - DAPC eigenvalues",
    col=c("red","green","blue", rep("grey", 1000)))

    ## SCATTERPLOTS
    ## (!) Note: colors may be inverted with respect to [5]
    ## as signs of principal components are arbitrary
    ## and change from one computer to another
    ##
    ## axes 1-2
    s.label(dapc1$grp.coord[,1:2], clab=0, sub="Axes 1-2")
    par(xpd=T)
    colorplot(dapc1$grp.coord[,1:2], dapc1$grp.coord, cex=3, add=TRUE)
    add.scatter.eig(dapc1$eig,10,1,2, posi="bottomright", ratio=.3, csub=1.25)

    ## axes 2-3
    s.label(dapc1$grp.coord[,2:3], clab=0, sub="Axes 2-3")
    par(xpd=T)
    colorplot(dapc1$grp.coord[,2:3], dapc1$grp.coord, cex=3, add=TRUE)
    add.scatter.eig(dapc1$eig,10,1,2, posi="bottomright", ratio=.3, csub=1.25)

    ## MAP DAPC1 RESULTS
    if(require(maps)){

    xy <- cbind(eHGDP$other$popInfo$Longitude, eHGDP$other$popInfo$Latitude)

    par(mar=rep(.1,4))
    map(fill=TRUE, col="lightgrey")
    colorplot(xy, -dapc1$grp.coord, cex=3, add=TRUE, trans=FALSE)
    }

    ## LOOK FOR OTHER CLUSTERS
    ## to reproduce results of the reference paper, use :
    ## grp <- find.clusters(eHGDP, max.n=50, n.pca=200, scale=FALSE)
    ## and then
    ## plot(grp$Kstat, type="b", col="blue")

    grp <- find.clusters(eHGDP, max.n=30, n.pca=200,
    scale=FALSE, n.clust=4) # takes about 2 minutes
    names(grp)

    ## (see ?find.clusters for details about the output)

    ## PERFORM DAPC - USE POPULATIONS AS CLUSTERS
    ## to reproduce exactly analyses from the paper, use "n.pca=1000"
    dapc2 <- dapc(eHGDP, pop=grp$grp, all.contrib=TRUE,
    scale=FALSE, n.pca=200, n.da=80) # takes around a 1 minute
    dapc2

    ## PRODUCE SCATTERPLOT
    scatter(dapc2) # axes 1-2
    scatter(dapc2,2,3) # axes 2-3

    ## MAP DAPC2 RESULTS
    if(require(maps)){
    xy <- cbind(eHGDP$other$popInfo$Longitude,
    eHGDP$other$popInfo$Latitude)

    myCoords <- apply(dapc2$ind.coord, 2, tapply, pop(eHGDP), mean)

    par(mar=rep(.1,4))
    map(fill=TRUE, col="lightgrey")
    colorplot(xy, myCoords, cex=3, add=TRUE, trans=FALSE)
    }

    ## End(Not run)

[Package adegenet version 2.1.4 Index]