| snpgdsIBDMoM {SNPRelate} | R Documentation |
Calculate three IBD coefficients for non-inbred individual pairs by PLINK method of moment (MoM).
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)
gdsobj |
an object of class |
sample.id |
a vector of sample id specifying selected samples;
if |
snp.id |
a vector of snp id specifying selected SNPs; if |
autosome.only |
if |
remove.monosnp |
if |
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 |
kinship |
if |
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 |
verbose |
if TRUE, show information |
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.
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
|
Xiuwen Zheng
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.
snpgdsIBDMLE, snpgdsIBDMLELogLik
# 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)