SNPRelate (version 1.6.4)

snpgdsAdmixProp: Estimate ancestral proportions from the eigen-analysis

Description

Estimate ancestral (admixture) proportions based on the eigen-analysis.

Usage

snpgdsAdmixProp(eigobj, groups, bound=FALSE)

Arguments

eigobj

an object of snpgdsEigMixClass from snpgdsEIGMIX, or an object of snpgdsPCAClass from snpgdsPCA

groups

a list of sample IDs, such like groups = list( CEU = c("NA0101", "NA1022", ...), YRI = c("NAxxxx", ...), Asia = c("NA1234", ...))

bound

if TRUE, the estimates are bounded so that no component < 0 or > 1, and the sum of proportions is one

Value

Return a snpgdsEigMixClass object, and it is a list:

sample.id

the sample ids used in the analysis

snp.id

the SNP ids used in the analysis

eigenval

eigenvalues

eigenvect

eigenvactors, "# of samples" x "eigen.cnt"

ibdmat

the IBD matrix

Details

The minor allele frequency and missing rate for each SNP passed in snp.id are calculated over all the samples in sample.id.

References

Zheng X, Weir BS. Eigenanalysis on SNP Data with an Interpretation of Identity by Descent. Theoretical Population Biology. 2015 Oct 23. pii: S0040-5809(15)00089-1. doi: 10.1016/j.tpb.2015.09.004. [Epub ahead of print]

See Also

snpgdsEIGMIX, snpgdsPCA

Examples

# open an example dataset (HapMap)
genofile <- snpgdsOpen(snpgdsExampleFileName())

# get population information
#   or pop_code <- scan("pop.txt", what=character())
#   if it is stored in a text file "pop.txt"
pop_code <- read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))

# get sample id
samp.id <- read.gdsn(index.gdsn(genofile, "sample.id"))

# run eigen-analysis
RV <- snpgdsEIGMIX(genofile)

# eigenvalues
RV$eigenval

# make a data.frame
tab <- data.frame(sample.id = samp.id, pop = factor(pop_code),
    EV1 = RV$eigenvect[,1],    # the first eigenvector
    EV2 = RV$eigenvect[,2],    # the second eigenvector
    stringsAsFactors = FALSE)
head(tab)

# draw
plot(tab$EV2, tab$EV1, col=as.integer(tab$pop),
    xlab="eigenvector 2", ylab="eigenvector 1")
legend("topleft", legend=levels(tab$pop), pch="o", col=1:4)


# define groups
groups <- list(CEU = samp.id[pop_code == "CEU"],
    YRI = samp.id[pop_code == "YRI"],
    CHB = samp.id[is.element(pop_code, c("HCB", "JPT"))])

prop <- snpgdsAdmixProp(RV, groups=groups)

# draw
plot(prop[, "YRI"], prop[, "CEU"], col=as.integer(tab$pop),
    xlab = "Admixture Proportion from YRI",
    ylab = "Admixture Proportion from CEU")
abline(v=0, col="gray25", lty=2)
abline(h=0, col="gray25", lty=2)
abline(a=1, b=-1, col="gray25", lty=2)
legend("topright", legend=levels(tab$pop), pch="o", col=1:4)



# run eigen-analysis
RV <- snpgdsEIGMIX(genofile, sample.id=samp.id[pop_code=="JPT"])
z <- RV$ibdmat

mean(c(z))
mean(diag(z))


# close the genotype file
snpgdsClose(genofile)