snpgdsIBDMoM {SNPRelate} | R Documentation

## PLINK method of moment (MoM) for the Identity-By-Descent (IBD) Analysis

### Description

Calculate three IBD coefficients for non-inbred individual pairs by PLINK method of moment (MoM).

### Usage

    snpgdsIBDMoM(gdsobj, sample.id=NULL, snp.id=NULL, autosome.only=TRUE,
        remove.monosnp=TRUE, maf=NaN, missing.rate=NaN, allele.freq=NULL,
        kinship=FALSE, kinship.constraint=FALSE, 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 MAF threshold
`missing.rate` | to use the SNPs with "<= missing.rate" only; if NaN, no missing threshold
`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`
`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 ($2 k_0 k_1 >= k_2^2$)
`num.thread` | the number of (CPU) cores used; if `NA`, detect the number of cores automatically
`verbose` | if TRUE, show information

### Details

PLINK IBD estimator is a moment estimator, and it is computationally efficient relative to MLE method. In the PLINK method of moment, a correction factor based on allele counts is used to adjust for sampling. However, if allele frequencies are specified, no correction factor is conducted since the specified allele frequencies are assumed to be known without sampling.

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

### Value

Return a list:

`sample.id` | the sample ids used in the analysis
`snp.id` | the SNP ids used in the analysis
`k0` | IBD coefficient, the probability of sharing ZERO IBD
`k1` | IBD coefficient, the probability of sharing ONE IBD
`kinship` | the estimated kinship coefficients, if the parameter `kinship=TRUE`

### Author(s)

Xiuwen Zheng

### References

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ & Sham PC. 2007. PLINK: a toolset for whole-genome association and population-based linkage analysis. American Journal of Human Genetics, 81.

### See Also

`snpgdsIBDMLE`, `snpgdsIBDMLELogLik`

### Examples

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

    #########################################################
    # CEU population

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

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

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

    #########################################################
    # YRI population

    YRI.id <- read.gdsn(index.gdsn(genofile, "sample.id"))[
        read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))=="YRI"]
    pibd <- snpgdsIBDMoM(genofile, sample.id=YRI.id)
    flag <- lower.tri(pibd$k0)
    plot(NaN, xlim=c(0,1), ylim=c(0,1), xlab="k0", ylab="k1")
    lines(c(0,1), c(1,0), col="red", lty=3)
    points(pibd$k0[flag], pibd$k1[flag])

    # specify the allele frequencies
    afreq <- snpgdsSNPRateFreq(genofile, sample.id=YRI.id)$AlleleFreq
    aibd <- snpgdsIBDMoM(genofile, sample.id=YRI.id, allele.freq=afreq)
    flag <- lower.tri(aibd$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(aibd$k0[flag], aibd$k1[flag])

    # analysis on a subset
    subibd <- snpgdsIBDMoM(genofile, sample.id=YRI.id[1:25], allele.freq=afreq)
    summary(c(subibd$k0 - aibd$k0[1:25, 1:25]))
    # ZERO
    summary(c(subibd$k1 - aibd$k1[1:25, 1:25]))
    # ZERO

    # close the genotype file
    snpgdsClose(genofile)

[Package SNPRelate version 1.14.0 Index]