snpgdsPairIBDMLELogLik: Log likelihood for MLE method in the Identity-By-Descent...

In SNPRelate: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data

Description

Calculate the log likelihood values from maximum likelihood estimation.

Usage

snpgdsPairIBDMLELogLik(geno1, geno2, allele.freq, k0=NaN, k1=NaN,
    relatedness=c("", "self", "fullsib", "offspring", "halfsib",
    "cousin", "unrelated"), verbose=TRUE)

Arguments

geno1	the SNP genotypes for the first individual, 0 – BB, 1 – AB, 2 – AA, other values – missing
geno2	the SNP genotypes for the second individual, 0 – BB, 1 – AB, 2 – AA, other values – missing
allele.freq	the allele frequencies
k0	specified IBD coefficient
k1	specified IBD coefficient
relatedness	specify a relatedness, otherwise use the values of k0 and k1
verbose	if TRUE, show information

Details

If (relatedness == "") and (k0 == NaN or k1 == NaN), then return the log likelihood values for each (k0, k1) stored in ibdobj.

If (relatedness == "") and (k0 != NaN) and (k1 != NaN), then return the log likelihood values for a specific IBD coefficient (k0, k1).

If relatedness is: "self", then k0 = 0, k1 = 0; "fullsib", then k0 = 0.25, k1 = 0.5; "offspring", then k0 = 0, k1 = 1; "halfsib", then k0 = 0.5, k1 = 0.5; "cousin", then k0 = 0.75, k1 = 0.25; "unrelated", then k0 = 1, k1 = 0.

Value

The value of log likelihood.

Author(s)

Xiuwen Zheng

References

Milligan BG. 2003. Maximum-likelihood estimation of relatedness. Genetics 163:1153-1167.

Weir BS, Anderson AD, Hepler AB. 2006. Genetic relatedness analysis: modern data and new challenges. Nat Rev Genet. 7(10):771-80.

Choi Y, Wijsman EM, Weir BS. 2009. Case-control association testing in the presence of unknown relationships. Genet Epidemiol 33(8):668-78.

See Also

snpgdsPairIBD, snpgdsIBDMLE, snpgdsIBDMLELogLik, snpgdsIBDMoM

Examples

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

YRI.id <- read.gdsn(index.gdsn(genofile, "sample.id"))[
    read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))=="YRI"]

# SNP pruning
set.seed(10)
snpset <- snpgdsLDpruning(genofile, sample.id=YRI.id, maf=0.05,
    missing.rate=0.05)
snpset <- unname(sample(unlist(snpset), 250))

# the number of samples
n <- 25

# specify allele frequencies
RF <- snpgdsSNPRateFreq(genofile, sample.id=YRI.id, snp.id=snpset,
    with.id=TRUE)
summary(RF$AlleleFreq)

subMLE <- snpgdsIBDMLE(genofile, sample.id=YRI.id[1:n], snp.id=RF$snp.id,
    allele.freq=RF$AlleleFreq)
subMoM <- snpgdsIBDMoM(genofile, sample.id=YRI.id[1:n], snp.id=RF$snp.id,
    allele.freq=RF$AlleleFreq)


# genotype matrix
mat <- snpgdsGetGeno(genofile, sample.id=YRI.id[1:n], snp.id=snpset,
    snpfirstdim=TRUE)


########################

rv <- NULL
for (i in 2:n)
{
    rv <- rbind(rv, snpgdsPairIBD(mat[,1], mat[,i], RF$AlleleFreq, "EM"))
    print(snpgdsPairIBDMLELogLik(mat[,1], mat[,i], RF$AlleleFreq,
        relatedness="unrelated", verbose=TRUE))
}
rv
summary(rv$k0 - subMLE$k0[1, 2:n])
summary(rv$k1 - subMLE$k1[1, 2:n])
# ZERO

rv <- NULL
for (i in 2:n)
    rv <- rbind(rv, snpgdsPairIBD(mat[,1], mat[,i], RF$AlleleFreq, "MoM"))
rv
summary(rv$k0 - subMoM$k0[1, 2:n])
summary(rv$k1 - subMoM$k1[1, 2:n])
# ZERO

# close the genotype file
snpgdsClose(genofile)

SNPRelate documentation built on Nov. 8, 2020, 5:31 p.m.