snpgdsPairIBDMLELogLik {SNPRelate} | R Documentation

## Log likelihood for MLE method in the Identity-By-Descent (IBD) Analysis

### 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)

[Package SNPRelate version 1.20.1 Index]