snpgdsIBDKING: KING method of moment for the identity-by-descent (IBD)...
In SNPRelate: Parallel Computing Toolset for Genome-Wide Association Studies (GWAS)

Description Usage Arguments Details Value Author(s) References See Also Examples

Calculate IBD coefficients by KING method of moment.

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)

`gdsobj`	a GDS file object (`gds.class`)
`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
`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
`verbose`	if TRUE, show information

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.

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`

Xiuwen Zheng

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. doi: 10.1093/bioinformatics/btq559. Epub 2010 Oct 5.

snpgdsIBDMLE, snpgdsIBDMoM

# open an example dataset (HapMap)
genofile <- openfn.gds(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
closefn.gds(genofile)