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Last data update: 2014.03.03

R: PLINK method of moment (MoM) for the Identity-By-Descent...

snpgdsIBDMoM

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.

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(SNPRelate)
Loading required package: gdsfmt
SNPRelate -- supported by Streaming SIMD Extensions 2 (SSE2)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/SNPRelate/snpgdsIBDMoM.Rd_%03d_medium.png", width=480, height=480)
> ### Name: snpgdsIBDMoM
> ### Title: PLINK method of moment (MoM) for the Identity-By-Descent (IBD)
> ###   Analysis
> ### Aliases: snpgdsIBDMoM
> ### Keywords: GDS GWAS
> 
> ### ** 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)
IBD analysis (PLINK method of moment) on SNP genotypes:
Excluding 365 SNPs on non-autosomes
Excluding 1217 SNPs (monomorphic: TRUE, < MAF: NaN, or > missing rate: NaN)
Working space: 92 samples, 7506 SNPs
	using 1 (CPU) core
PLINK IBD:	the sum of all selected genotypes (0, 1 and 2) = 702139
Wed Jul  6 05:34:43 2016    (internal increment: 65536)
 [>.................................................]  0%, ETC: NA     [==================================================] 100%, completed  
Wed Jul  6 05:34:43 2016    Done.
> names(pibd)
[1] "sample.id" "snp.id"    "afreq"     "k0"        "k1"       
> 
> 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$k1[flag])
> 
> # select a set of pairs of individuals
> d <- snpgdsIBDSelection(pibd, kinship.cutoff=1/8)
> head(d)
      ID1     ID2          k0        k1   kinship
1 NA07034 NA07048 0.000000000 1.0000000 0.2500000
2 NA07055 NA07048 0.000000000 0.9780063 0.2554984
3 NA12814 NA12802 0.004482185 0.9808720 0.2525409
4 NA10847 NA12239 0.000000000 1.0000000 0.2500000
5 NA10847 NA12146 0.000000000 1.0000000 0.2500000
6 NA12056 NA10851 0.002237194 0.9935560 0.2504924
> 
> 
> #########################################################
> # YRI population
> 
> YRI.id <- read.gdsn(index.gdsn(genofile, "sample.id"))[
+     read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))=="YRI"]
> pibd <- snpgdsIBDMoM(genofile, sample.id=YRI.id)
IBD analysis (PLINK method of moment) on SNP genotypes:
Excluding 365 SNPs on non-autosomes
Excluding 563 SNPs (monomorphic: TRUE, < MAF: NaN, or > missing rate: NaN)
Working space: 93 samples, 8160 SNPs
	using 1 (CPU) core
PLINK IBD:	the sum of all selected genotypes (0, 1 and 2) = 755648
Wed Jul  6 05:34:43 2016    (internal increment: 65536)
 [>.................................................]  0%, ETC: NA     [==================================================] 100%, completed  
Wed Jul  6 05:34:44 2016    Done.
> flag <- lower.tri(pibd$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)$AlleleFreq
> aibd <- snpgdsIBDMoM(genofile, sample.id=YRI.id, allele.freq=afreq)
IBD analysis (PLINK method of moment) on SNP genotypes:
Excluding 365 SNPs on non-autosomes
Excluding 563 SNPs (monomorphic: TRUE, < MAF: NaN, or > missing rate: NaN)
Working space: 93 samples, 8160 SNPs
	using 1 (CPU) core
Specifying allele frequencies, mean: 0.500, sd: 0.315
*** A correction factor based on allele count is not used, since the allele frequencies are specified.
PLINK IBD:	the sum of all selected genotypes (0, 1 and 2) = 755648
Wed Jul  6 05:34:44 2016    (internal increment: 65536)
 [>.................................................]  0%, ETC: NA     [==================================================] 100%, completed  
Wed Jul  6 05:34:44 2016    Done.
> flag <- lower.tri(aibd$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)
IBD analysis (PLINK method of moment) on SNP genotypes:
Excluding 365 SNPs on non-autosomes
Excluding 563 SNPs (monomorphic: TRUE, < MAF: NaN, or > missing rate: NaN)
Working space: 25 samples, 8160 SNPs
	using 1 (CPU) core
Specifying allele frequencies, mean: 0.500, sd: 0.315
*** A correction factor based on allele count is not used, since the allele frequencies are specified.
PLINK IBD:	the sum of all selected genotypes (0, 1 and 2) = 203285
Wed Jul  6 05:34:44 2016    (internal increment: 65536)
 [>.................................................]  0%, ETC: NA     [==================================================] 100%, completed  
Wed Jul  6 05:34:44 2016    Done.
> summary(c(subibd$k0 - aibd$k0[1:25, 1:25]))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0       0       0       0 
> # ZERO
> summary(c(subibd$k1 - aibd$k1[1:25, 1:25]))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0       0       0       0 
> # ZERO
> 
> 
> # close the genotype file
> snpgdsClose(genofile)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>