snpgdsIBDKING: KING method of moment for the identity-by-descent (IBD)...

In zhengxwen/SNPRelate: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data

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=1L,
    useMatrix=FALSE, 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 (by default); "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
useMatrix	if TRUE, use Matrix::dspMatrix to store the output square matrix to save memory
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	a matrix for IBD coefficients, the probability of sharing zero IBD, if type="KING-homo"
k1	a matrix for IBD coefficients, the probability of sharing one IBD, if type="KING-homo"
IBS0	a matrix for the proportions of SNPs with zero IBS, if type="KING-robust"
kinship	a matrix for the estimated kinship coefficients, if type="KING-robust"

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)
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)")


# using Matrix
ibd.robust <- snpgdsIBDKING(genofile, sample.id=CEU.id, useMatrix=TRUE)
is(ibd.robust$IBS0)  # dspMatrix
is(ibd.robust$kinship)  # dspMatrix



####  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)")


# using Matrix
ibd.homo <- snpgdsIBDKING(genofile, sample.id=CEU.id, type="KING-homo",
    useMatrix=TRUE)
is(ibd.homo$k0)  # dspMatrix
is(ibd.homo$k1)  # dspMatrix


# close the genotype file
snpgdsClose(genofile)

zhengxwen/SNPRelate documentation built on Nov. 19, 2024, 1:02 p.m.