snpgdsIBDKING {SNPRelate} | R Documentation

## KING method of moment for the identity-by-descent (IBD) analysis

### Description

Calculate IBD coefficients by KING method of moment.

### Usage

    snpgdsIBDKING(gdsobj, sample.id=NULL, snp.id=NULL, autosome.only=TRUE,
        remove.monosnp=TRUE, maf=NaN, missing.rate=NaN,
        type=c("KING-robust", "KING-homo"), family.id=NULL,
        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
`type` | `"KING-robust"` – relationship inference in the presence of population stratification; `"KING-homo"` – relationship inference in a homogeneous population
`family.id` | if `NULL`, all individuals are treated as singletons; if family id is given, within- and between-family relationship are estimated differently. If `sample.id=NULL`, `family.id` should have the same length as "sample.id" in the GDS file, otherwise `family.id` should have the same length and order as the argument `sample.id`
`num.thread` | the number of (CPU) cores used; if `NA`, detect the number of cores automatically
`verbose` | if TRUE, show information

### Details

KING IBD estimator is a moment estimator, and it is computationally efficient relative to MLE method. The approaches include `"KING-robust"` – robust relationship inference within or across families in the presence of population substructure, and `"KING-homo"` – relationship inference in a homogeneous population.

With `"KING-robust"`, the function would return the proportion of SNPs with zero IBS (`IBS0`) and kinship coefficient (`kinship`). With `"KING-homo"` it would return the probability of sharing one IBD (`k1`) and the probability of sharing zero IBD (`k0`).

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
`IBS0` | proportion of SNPs with zero IBS
`kinship` | the estimated kinship coefficients, if the parameter `kinship=TRUE`

### Author(s)

Xiuwen Zheng

### References

Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010 Nov 15;26(22):2867-73.

### See Also

`snpgdsIBDMLE`, `snpgdsIBDMoM`

### Examples

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

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

    ####  KING-robust:
    ####  relationship inference in the presence of population stratification
    ####               robust relationship inference across family

    ibd.robust <- snpgdsIBDKING(genofile, sample.id=CEU.id, family.id=NULL)
    names(ibd.robust)
    # [1] "sample.id" "snp.id"    "afreq"     "IBS0"      "kinship"

    # select a set of pairs of individuals
    dat <- snpgdsIBDSelection(ibd.robust, 1/32)
    head(dat)

    plot(dat$IBS0, dat$kinship, xlab="Proportion of Zero IBS",
        ylab="Estimated Kinship Coefficient (KING-robust)")

    ####  KING-robust:
    ####  relationship inference in the presence of population stratification
    ####               within- and between-family relationship inference

    # incorporate with pedigree information
    family.id <- read.gdsn(index.gdsn(genofile, "sample.annot/family.id"))
    family.id <- family.id[match(CEU.id, samp.id)]

    ibd.robust2 <- snpgdsIBDKING(genofile, sample.id=CEU.id, family.id=family.id)
    names(ibd.robust2)

    # select a set of pairs of individuals
    dat <- snpgdsIBDSelection(ibd.robust2, 1/32)
    head(dat)

    plot(dat$IBS0, dat$kinship, xlab="Proportion of Zero IBS",
        ylab="Estimated Kinship Coefficient (KING-robust)")

    ####  KING-homo: relationship inference in a homogeneous population

    ibd.homo <- snpgdsIBDKING(genofile, sample.id=CEU.id, type="KING-homo")
    names(ibd.homo)
    # "sample.id" "snp.id"    "afreq"     "k0"        "k1"

    # select a subset of pairs of individuals
    dat <- snpgdsIBDSelection(ibd.homo, 1/32)
    head(dat)

    plot(dat$k0, dat$kinship, xlab="Pr(IBD=0)",
        ylab="Estimated Kinship Coefficient (KING-homo)")

    # close the genotype file
    snpgdsClose(genofile)

[Package SNPRelate version 1.14.0 Index]