snpgdsIBDMLE {SNPRelate} | R Documentation

## Maximum likelihood estimation (MLE) for the Identity-By-Descent (IBD) Analysis

### Description

Calculate the three IBD coefficients (k0, k1, k2) for non-inbred individual pairs by Maximum Likelihood Estimation.

### Usage

    snpgdsIBDMLE(gdsobj, sample.id=NULL, snp.id=NULL, autosome.only=TRUE,
        remove.monosnp=TRUE, maf=NaN, missing.rate=NaN, kinship=FALSE,
        kinship.constraint=FALSE, allele.freq=NULL,
        method=c("EM", "downhill.simplex", "Jacquard"), max.niter=1000L,
        reltol=sqrt(.Machine$double.eps), coeff.correct=TRUE,
        out.num.iter=TRUE, num.thread=1, verbose=TRUE)

### Arguments

`gdsobj` | an object of class `SNPGDSFileClass`, a SNP GDS file
`sample.id` | a vector of sample id specifying selected samples; if NULL, all samples are used
`snp.id` | a vector of snp id specifying selected SNPs; if NULL, all SNPs are used
`autosome.only` | if `TRUE`, use autosomal SNPs only; if it is a numeric or character value, keep SNPs according to the specified chromosome
`remove.monosnp` | if TRUE, remove monomorphic SNPs
`maf` | to use the SNPs with ">= maf" only; if NaN, no any MAF threshold
`missing.rate` | to use the SNPs with "<= missing.rate" only; if NaN, no any missing threshold
`kinship` | if `TRUE`, output the estimated kinship coefficients
`kinship.constraint` | if TRUE, constrict IBD coefficients ($k_0,k_1,k_2$) in the geneloical region ($2 k_0 k_1 >= k_2^2$)
`allele.freq` | to specify the allele frequencies; if NULL, determine the allele frequencies from `gdsobj` using the specified samples; if `snp.id` is specified, `allele.freq` should have the same order as `snp.id`
`method` | "EM", "downhill.simplex", "Jacquard", see details
`max.niter` | the maximum number of iterations
`reltol` | relative convergence tolerance; the algorithm stops if it is unable to reduce the value of log likelihood by a factor of $reltol * (abs(log likelihood with the initial parameters) + reltol)$ at a step.
`coeff.correct` | `TRUE` by default, see details
`out.num.iter` | if TRUE, output the numbers of iterations
`num.thread` | the number of (CPU) cores used; if `NA`, detect the number of cores automatically
`verbose` | if TRUE, show information

### Details

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

The PLINK moment estimates are used as the initial values in the algorithm of searching maximum value of log likelihood function. Two numeric approaches can be used: one is Expectation-Maximization (EM) algorithm, and the other is Nelder-Mead method or downhill simplex method. Generally, EM algorithm is more robust than downhill simplex method. `"Jacquard"` refers to the estimation of nine Jacquard's coefficients.

If `coeff.correct` is `TRUE`, the final point that is found by searching algorithm (EM or downhill simplex) is used to compare the six points (fullsib, offspring, halfsib, cousin, unrelated), since any numeric approach might not reach the maximum position after a finit number of steps. If any of these six points has a higher value of log likelihood, the final point will be replaced by the best one.

Although MLE estimates are more reliable than MoM, MLE is much more computationally intensive than MoM, and might not be feasible to estimate pairwise relatedness for a large dataset.

### Value

Return a `snpgdsIBDClass` object, which is a list:

`sample.id` | the sample ids used in the analysis
`snp.id` | the SNP ids used in the analysis
`afreq` | the allele frequencies used in the analysis
`k0` | IBD coefficient, the probability of sharing ZERO IBD, if `method="EM"` or `"downhill.simplex"`
`k1` | IBD coefficient, the probability of sharing ONE IBD, if `method="EM"` or `"downhill.simplex"`
`D1, ..., D8` | Jacquard's coefficients, if `method="Jacquard"`, D9 = 1 - D1 - ... - D8
`kinship` | the estimated kinship coefficients, if the parameter `kinship=TRUE`

### 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.

Jacquard, A. Structures Genetiques des Populations (Masson & Cie, Paris, 1970); English translation available in Charlesworth, D. & Chalesworth, B. Genetics of Human Populations (Springer, New York, 1974).

### See Also

`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"]
    YRI.id <- YRI.id[1:30]

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

    # select a set of pairs of individuals
    d <- snpgdsIBDSelection(mibd, kinship.cutoff=1/8)
    head(d)

    # log likelihood

    loglik <- snpgdsIBDMLELogLik(genofile, mibd)
    loglik0 <- snpgdsIBDMLELogLik(genofile, mibd, relatedness="unrelated")

    # likelihood ratio test
    p.value <- pchisq(loglik - loglik0, 1, lower.tail=FALSE)

    flag <- lower.tri(mibd$k0)
    plot(NaN, xlim=c(0,1), ylim=c(0,1), xlab="k0", ylab="k1")
    lines(c(0,1), c(1,0), col="red", lty=3)
    points(mibd$k0[flag], mibd$k1[flag])

    # specify the allele frequencies
    afreq <- snpgdsSNPRateFreq(genofile, sample.id=YRI.id,
        snp.id=snpset)$AlleleFreq
    subibd <- snpgdsIBDMLE(genofile, sample.id=YRI.id[1:25], snp.id=snpset,
        allele.freq=afreq)
    summary(c(subibd$k0 - mibd$k0[1:25, 1:25]))
    # ZERO
    summary(c(subibd$k1 - mibd$k1[1:25, 1:25]))
    # ZERO

    # close the genotype file
    snpgdsClose(genofile)

[Package SNPRelate version 1.14.0 Index]