Package 'SNPRelate'

Description

Genome-wide association studies are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed SNPRelate (R package for multi-core symmetric multiprocessing computer architectures) to accelerate two key computations on SNP data: principal component analysis (PCA) and relatedness analysis using identity-by-descent measures. The kernels of our algorithms are written in C/C++ and highly optimized.

Details

The genotypes stored in GDS format can be analyzed by the R functions in SNPRelate, which utilize the multi-core feature of machine for a single computer.

Webpage: https://github.com/zhengxwen/SNPRelate, http://corearray.sourceforge.net/

Tutorial: http://corearray.sourceforge.net/tutorials/SNPRelate/

Author(s)

Xiuwen Zheng [email protected]

References

Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A High-performance Computing Toolset for Relatedness and Principal Component Analysis of SNP Data. Bioinformatics (2012); doi: 10.1093/bioinformatics/bts610

Examples

####################################################################
# Convert the PLINK BED file to the GDS file
#

# PLINK BED files
bed.fn <- system.file("extdata", "plinkhapmap.bed.gz", package="SNPRelate")
fam.fn <- system.file("extdata", "plinkhapmap.fam.gz", package="SNPRelate")
bim.fn <- system.file("extdata", "plinkhapmap.bim.gz", package="SNPRelate")

# convert
snpgdsBED2GDS(bed.fn, fam.fn, bim.fn, "HapMap.gds")


####################################################################
# Principal Component Analysis
#

# open
genofile <- snpgdsOpen("HapMap.gds")

RV <- snpgdsPCA(genofile)
plot(RV$eigenvect[,2], RV$eigenvect[,1], xlab="PC 2", ylab="PC 1",
    col=rgb(0,0,150, 50, maxColorValue=255), pch=19)

# close the file
snpgdsClose(genofile)


####################################################################
# Identity-By-Descent (IBD) Analysis
#

# open
genofile <- snpgdsOpen(snpgdsExampleFileName())

RV <- snpgdsIBDMoM(genofile)
flag <- lower.tri(RV$k0)
plot(RV$k0[flag], RV$k1[flag], xlab="k0", ylab="k1",
    col=rgb(0,0,150, 50, maxColorValue=255), pch=19)
abline(1, -1, col="red", lty=4)

# close the file
snpgdsClose(genofile)


####################################################################
# Identity-By-State (IBS) Analysis
#

# open
genofile <- snpgdsOpen(snpgdsExampleFileName())

RV <- snpgdsIBS(genofile)
m <- 1 - RV$ibs
colnames(m) <- rownames(m) <- RV$sample.id
GeneticDistance <- as.dist(m[1:45, 1:45])
HC <- hclust(GeneticDistance, "ave")
plot(HC)

# close the file
snpgdsClose(genofile)


####################################################################
# Linkage Disequilibrium (LD) Analysis
#

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

snpset <- read.gdsn(index.gdsn(genofile, "snp.id"))[1:200]
L1 <- snpgdsLDMat(genofile, snp.id=snpset, method="composite", slide=-1)

# plot
image(abs(L1$LD), col=terrain.colors(64))

# close the file
snpgdsClose(genofile)

Description

A list object including the following components:

sample.id – a vector of sample ids;

snp.id – a vector of SNP ids;

snp.position – a vector of SNP positions;

snp.chromosome – a vector of chromosome indices;

snp.allele – a character vector of “reference / non-reference”;

genotype – a “# of SNPs” X “# of samples” genotype matrix.

Usage

hapmap_geno

Value

A list

Description

Plot the admixture proportions according to their ancestries.

Usage

snpgdsAdmixPlot(propmat, group=NULL, col=NULL, multiplot=TRUE, showgrp=TRUE,
    shownum=TRUE, ylim=TRUE, na.rm=TRUE)
snpgdsAdmixTable(propmat, group, sort=FALSE)

Arguments

Details

The minor allele frequency and missing rate for each SNP passed in snp.id are calculated over all the samples in sample.id.

Value

snpgdsAdmixPlot(): none.

snpgdsAdmixTable(): a list of data.frame consisting of group, num, mean, sd, min, max

Author(s)

Xiuwen Zheng

References

Zheng X, Weir BS. Eigenanalysis on SNP Data with an Interpretation of Identity by Descent. Theoretical Population Biology. 2015 Oct 23. pii: S0040-5809(15)00089-1. doi: 10.1016/j.tpb.2015.09.004.

See Also

snpgdsEIGMIX, snpgdsAdmixProp

Examples

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

# get population information
#   or pop_code <- scan("pop.txt", what=character())
#   if it is stored in a text file "pop.txt"
pop_code <- read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))

# get sample id
samp.id <- read.gdsn(index.gdsn(genofile, "sample.id"))

# run eigen-analysis
RV <- snpgdsEIGMIX(genofile)

# define groups
groups <- list(CEU = samp.id[pop_code == "CEU"],
    YRI = samp.id[pop_code == "YRI"],
    CHB = samp.id[is.element(pop_code, c("HCB", "JPT"))])

prop <- snpgdsAdmixProp(RV, groups=groups, bound=TRUE)

# draw
snpgdsAdmixPlot(prop, group=pop_code)

# use user-defined colors for the groups
snpgdsAdmixPlot(prop, group=pop_code, multiplot=FALSE, col=c(3,2,4))

snpgdsAdmixTable(prop, group=pop_code)

# close the genotype file
snpgdsClose(genofile)

Description

Estimate ancestral (admixture) proportions based on the eigen-analysis.

Usage

snpgdsAdmixProp(eigobj, groups, bound=FALSE)

Arguments

Details

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 matrix of ancestral proportions with rows for study individuals (rownames() is sample ID).

Author(s)

Xiuwen Zheng

References

Zheng X, Weir BS. Eigenanalysis on SNP Data with an Interpretation of Identity by Descent. Theoretical Population Biology. 2015 Oct 23. pii: S0040-5809(15)00089-1. doi: 10.1016/j.tpb.2015.09.004. [Epub ahead of print]

See Also

snpgdsEIGMIX, snpgdsPCA, snpgdsAdmixPlot

Examples

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

# get population information
#   or pop_code <- scan("pop.txt", what=character())
#   if it is stored in a text file "pop.txt"
pop_code <- read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))

# get sample id
samp.id <- read.gdsn(index.gdsn(genofile, "sample.id"))

# run eigen-analysis
RV <- snpgdsEIGMIX(genofile)

# eigenvalues
RV$eigenval

# make a data.frame
tab <- data.frame(sample.id = samp.id, pop = factor(pop_code),
    EV1 = RV$eigenvect[,1],    # the first eigenvector
    EV2 = RV$eigenvect[,2],    # the second eigenvector
    stringsAsFactors = FALSE)
head(tab)

# draw
plot(tab$EV2, tab$EV1, col=as.integer(tab$pop),
    xlab="eigenvector 2", ylab="eigenvector 1")
legend("bottomleft", legend=levels(tab$pop), pch="o", col=1:4)


# define groups
groups <- list(CEU = samp.id[pop_code == "CEU"],
    YRI = samp.id[pop_code == "YRI"],
    CHB = samp.id[is.element(pop_code, c("HCB", "JPT"))])

prop <- snpgdsAdmixProp(RV, groups=groups)
head(prop)

# draw
plot(prop[, "YRI"], prop[, "CEU"], col=as.integer(tab$pop),
    xlab = "Admixture Proportion from YRI",
    ylab = "Admixture Proportion from CEU")
abline(v=0, col="gray25", lty=2)
abline(h=0, col="gray25", lty=2)
abline(a=1, b=-1, col="gray25", lty=2)
legend("topright", legend=levels(tab$pop), pch="o", col=1:4)


# draw
snpgdsAdmixPlot(prop, group=pop_code)


# close the genotype file
snpgdsClose(genofile)

Description

Switch alleles according to the reference if needed.

Usage

snpgdsAlleleSwitch(gdsobj, A.allele, verbose=TRUE)

Arguments

Value

A logical vector with TRUE indicating allele-switching and NA when it is unable to determine. NA occurs when A.allele = NA or A.allele is not in the list of alleles.

Author(s)

Xiuwen Zheng

Examples

# the file name of SNP GDS
(fn <- snpgdsExampleFileName())

# copy the file
file.copy(fn, "test.gds", overwrite=TRUE)

# open the SNP GDS file
genofile <- snpgdsOpen("test.gds", readonly=FALSE)

# allelic information
allele <- read.gdsn(index.gdsn(genofile, "snp.allele"))
allele.list <- strsplit(allele, "/")

A.allele <- sapply(allele.list, function(x) { x[1] })
B.allele <- sapply(allele.list, function(x) { x[2] })

set.seed(1000)
flag <- rep(FALSE, length(A.allele))
flag[sample.int(length(A.allele), 50, replace=TRUE)] <- TRUE

A.allele[flag] <- B.allele[flag]
A.allele[sample.int(length(A.allele), 10, replace=TRUE)] <- NA
table(A.allele, exclude=NULL)


# allele switching
z <- snpgdsAlleleSwitch(genofile, A.allele)

table(z, exclude=NULL)


# close the file
snpgdsClose(genofile)


# delete the temporary file
unlink("test.gds", force=TRUE)

Description

Randomly selects SNPs for which each pair is at least as far apart as the specified basepair distance.

Usage

snpgdsApartSelection(chromosome, position, min.dist=100000,
    max.n.snp.perchr=-1, verbose=TRUE)

Arguments

Value

A logical vector indicating which SNPs were selected.

Author(s)

Xiuwen Zheng

See Also

snpgdsLDpruning

Examples

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

chr <- read.gdsn(index.gdsn(genofile, "snp.chromosome"))
pos <- read.gdsn(index.gdsn(genofile, "snp.position"))

set.seed(1000)
flag <- snpgdsApartSelection(chr, pos, min.dist=250000, verbose=TRUE)
table(flag)

# close the genotype file
snpgdsClose(genofile)

Description

Convert a PLINK binary ped file to a GDS file.

Usage

snpgdsBED2GDS(bed.fn, fam.fn, bim.fn, out.gdsfn, family=FALSE,
    snpfirstdim=NA, compress.annotation="LZMA_RA", compress.geno="",
    option=NULL, cvt.chr=c("int", "char"), cvt.snpid=c("auto", "int"),
    verbose=TRUE)

Arguments

Details

GDS – Genomic Data Structures, the extended file name used for storing genetic data, and the file format is used in the gdsfmt package.

BED – the PLINK binary ped format.

The user could use option to specify the range of code for autosomes. For humans there are 22 autosomes (from 1 to 22), but dogs have 38 autosomes. Note that the default settings are used for humans. The user could call option = snpgdsOption(autosome.end=38) for importing the BED file of dog. It also allow define new chromosome coding, e.g., option = snpgdsOption(Z=27).

Value

Return the file name of GDS format with an absolute path.

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.

See Also

snpgdsOption, snpgdsPED2GDS, snpgdsGDS2PED

Examples

# PLINK BED files
bed.fn <- system.file("extdata", "plinkhapmap.bed.gz", package="SNPRelate")
fam.fn <- system.file("extdata", "plinkhapmap.fam.gz", package="SNPRelate")
bim.fn <- system.file("extdata", "plinkhapmap.bim.gz", package="SNPRelate")

# convert
snpgdsBED2GDS(bed.fn, fam.fn, bim.fn, "HapMap.gds")

# open
genofile <- snpgdsOpen("HapMap.gds")
genofile

# close
snpgdsClose(genofile)


# delete the temporary file
unlink("HapMap.gds", force=TRUE)

Description

Close the SNP GDS file

Usage

snpgdsClose(gdsobj)

Arguments

Details

It is suggested to call snpgdsClose instead of closefn.gds.

Value

None.

Author(s)

Xiuwen Zheng

See Also

snpgdsOpen

Examples

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

genofile

# close the file
snpgdsClose(genofile)

Description

To merge GDS files of SNP genotypes into a single GDS file

Usage

snpgdsCombineGeno(gds.fn, out.fn, method=c("position", "exact"),
    compress.annotation="ZIP_RA.MAX", compress.geno="ZIP_RA",
    same.strand=FALSE, snpfirstdim=FALSE, verbose=TRUE)

Arguments

Details

This function calls snpgdsSNPListIntersect internally to determine the common SNPs. Allele definitions are taken from the first GDS file.

Value

None.

Author(s)

Xiuwen Zheng

See Also

snpgdsCreateGeno, snpgdsCreateGenoSet, snpgdsSNPList, snpgdsSNPListIntersect

Examples

# get the file name of a gds file
fn <- snpgdsExampleFileName()

f <- snpgdsOpen(fn)
samp_id <- read.gdsn(index.gdsn(f, "sample.id"))
snp_id <- read.gdsn(index.gdsn(f, "snp.id"))
geno <- read.gdsn(index.gdsn(f, "genotype"), start=c(1,1), count=c(-1, 3000))
snpgdsClose(f)


# split the GDS file with different samples
snpgdsCreateGenoSet(fn, "t1.gds", sample.id=samp_id[1:10],
    snp.id=snp_id[1:3000])
snpgdsCreateGenoSet(fn, "t2.gds", sample.id=samp_id[11:30],
    snp.id=snp_id[1:3000])

# combine with different samples
snpgdsCombineGeno(c("t1.gds", "t2.gds"), "test.gds", same.strand=TRUE)
f <- snpgdsOpen("test.gds")
g <- read.gdsn(index.gdsn(f, "genotype"))
snpgdsClose(f)

identical(geno[1:30, ], g)  # TRUE


# split the GDS file with different SNPs
snpgdsCreateGenoSet(fn, "t1.gds", snp.id=snp_id[1:100])
snpgdsCreateGenoSet(fn, "t2.gds", snp.id=snp_id[101:300])

# combine with different SNPs
snpgdsCombineGeno(c("t1.gds", "t2.gds"), "test.gds")
f <- snpgdsOpen("test.gds")
g <- read.gdsn(index.gdsn(f, "genotype"))
snpgdsClose(f)

identical(geno[, 1:300], g)  # TRUE


# delete the temporary files
unlink(c("t1.gds", "t2.gds", "t3.gds", "t4.gds", "test.gds"), force=TRUE)

Description

To create a GDS file of genotypes from a matrix.

Usage

snpgdsCreateGeno(gds.fn, genmat, sample.id=NULL, snp.id=NULL, snp.rs.id=NULL,
    snp.chromosome=NULL, snp.position=NULL, snp.allele=NULL, snpfirstdim=TRUE,
    compress.annotation="ZIP_RA.max", compress.geno="", other.vars=NULL)

Arguments

Details

There are possible values stored in the variable genmat: 0, 1, 2 and other values. “0” indicates two B alleles, “1” indicates one A allele and one B allele, “2” indicates two A alleles, and other values indicate a missing genotype.

If snpfirstdim is TRUE, then genmat should be “# of SNPs X # of samples”; if snpfirstdim is FALSE, then genmat should be “# of samples X # of SNPs”.

The typical variables specified in other.vars are “sample.annot” and “snp.annot”, which are data.frame objects.

Value

None.

Author(s)

Xiuwen Zheng

See Also

snpgdsCreateGenoSet, snpgdsCombineGeno

Examples

# load data
data(hapmap_geno)

# create a gds file
with(hapmap_geno, snpgdsCreateGeno("test.gds", genmat=genotype,
    sample.id=sample.id, snp.id=snp.id, snp.chromosome=snp.chromosome,
    snp.position=snp.position, snp.allele=snp.allele, snpfirstdim=TRUE))

# open the gds file
genofile <- snpgdsOpen("test.gds")

RV <- snpgdsPCA(genofile)
plot(RV$eigenvect[,2], RV$eigenvect[,1], xlab="PC 2", ylab="PC 1")

# close the file
snpgdsClose(genofile)

Description

To create a GDS file of genotypes from a specified GDS file.

Usage

snpgdsCreateGenoSet(src.fn, dest.fn, sample.id=NULL, snp.id=NULL,
    snpfirstdim=NULL, compress.annotation="ZIP_RA.max", compress.geno="",
    verbose=TRUE)

Arguments

Value

None.

Author(s)

Xiuwen Zheng

See Also

snpgdsCreateGeno, snpgdsCombineGeno

Examples

# open an example dataset (HapMap)
(genofile <- snpgdsOpen(snpgdsExampleFileName()))
# +    [  ] *
# |--+ sample.id   { VStr8 279 ZIP(29.9%), 679B }
# |--+ snp.id   { Int32 9088 ZIP(34.8%), 12.3K }
# |--+ snp.rs.id   { VStr8 9088 ZIP(40.1%), 36.2K }
# |--+ snp.position   { Int32 9088 ZIP(94.7%), 33.6K }
# |--+ snp.chromosome   { UInt8 9088 ZIP(0.94%), 85B } *
# |--+ snp.allele   { VStr8 9088 ZIP(11.3%), 4.0K }
# |--+ genotype   { Bit2 279x9088, 619.0K } *
# \--+ sample.annot   [ data.frame ] *
#    |--+ family.id   { VStr8 279 ZIP(34.4%), 514B }
#    |--+ father.id   { VStr8 279 ZIP(31.5%), 220B }
#    |--+ mother.id   { VStr8 279 ZIP(30.9%), 214B }
#    |--+ sex   { VStr8 279 ZIP(17.0%), 95B }
#    \--+ pop.group   { VStr8 279 ZIP(6.18%), 69B }

set.seed(1000)
snpset <- unlist(snpgdsLDpruning(genofile))
length(snpset)
# 6547

# close the file
snpgdsClose(genofile)

snpgdsCreateGenoSet(snpgdsExampleFileName(), "test.gds", snp.id=snpset)

####################################################
# check

(gfile <- snpgdsOpen("test.gds"))
# +    [  ] *
# |--+ sample.id   { Str8 279 ZIP_ra(31.2%), 715B }
# |--+ snp.id   { Int32 6547 ZIP_ra(34.9%), 8.9K }
# |--+ snp.rs.id   { Str8 6547 ZIP_ra(41.5%), 27.1K }
# |--+ snp.position   { Int32 6547 ZIP_ra(94.9%), 24.3K }
# |--+ snp.chromosome   { Int32 6547 ZIP_ra(0.45%), 124B }
# |--+ snp.allele   { Str8 6547 ZIP_ra(11.5%), 3.0K }
# \--+ genotype   { Bit2 279x6547, 446.0K } *

# close the file
snpgdsClose(gfile)


unlink("test.gds", force=TRUE)

Description

To determine sub groups of individuals using a specified dendrogram from hierarchical cluster analysis

Usage

snpgdsCutTree(hc, z.threshold=15, outlier.n=5, n.perm = 5000, samp.group=NULL,
    col.outlier="red", col.list=NULL, pch.outlier=4, pch.list=NULL,
    label.H=FALSE, label.Z=TRUE, verbose=TRUE)

Arguments

Details

The details will be described in future.

Value

Return a list:

Author(s)

Xiuwen Zheng

See Also

snpgdsHCluster, snpgdsDrawTree, snpgdsIBS, snpgdsDiss

Examples

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

pop.group <- as.factor(read.gdsn(index.gdsn(
    genofile, "sample.annot/pop.group")))
pop.level <- levels(pop.group)

diss <- snpgdsDiss(genofile)
hc <- snpgdsHCluster(diss)

# close the genotype file
snpgdsClose(genofile)



###################################################################
# cluster individuals
#

set.seed(100)
rv <- snpgdsCutTree(hc, label.H=TRUE, label.Z=TRUE)

# the distribution of Z scores
snpgdsDrawTree(rv, type="z-score", main="HapMap Phase II")

# draw dendrogram
snpgdsDrawTree(rv, main="HapMap Phase II",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"))


###################################################################
# or cluster individuals by ethnic information
#

rv2 <- snpgdsCutTree(hc, samp.group=pop.group)

# cluster individuals by Z score, specifying 'clust.count'
snpgdsDrawTree(rv2, rv$clust.count, main="HapMap Phase II",
    edgePar = list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
    labels = c("YRI", "CHB/JPT", "CEU"), y.label=0.1)
legend("bottomleft", legend=levels(pop.group), col=1:nlevels(pop.group),
    pch=19, ncol=4, bg="white")



###################################################################
# zoom in ...
#

snpgdsDrawTree(rv2, rv$clust.count, dend.idx = c(1),
    main="HapMap Phase II -- YRI",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
    y.label.kinship=TRUE)

snpgdsDrawTree(rv2, rv$clust.count, dend.idx = c(2,2),
    main="HapMap Phase II -- CEU",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
    y.label.kinship=TRUE)

snpgdsDrawTree(rv2, rv$clust.count, dend.idx = c(2,1),
    main="HapMap Phase II -- CHB/JPT",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"),
    y.label.kinship=TRUE)

Description

Calculate the individual dissimilarities for each pair of individuals.

Usage

snpgdsDiss(gdsobj, sample.id=NULL, snp.id=NULL, autosome.only=TRUE,
    remove.monosnp=TRUE, maf=NaN, missing.rate=NaN, num.thread=1, verbose=TRUE)

Arguments

Details

The minor allele frequency and missing rate for each SNP passed in snp.id are calculated over all the samples in sample.id.

snpgdsDiss() returns 1 - beta_ij which is formally described in Weir&Goudet (2017).

Value

Return a class "snpgdsDissClass":

Author(s)

Xiuwen Zheng

References

Zheng, Xiuwen. 2013. Statistical Prediction of HLA Alleles and Relatedness Analysis in Genome-Wide Association Studies. PhD dissertation, the department of Biostatistics, University of Washington.

Weir BS, Zheng X. SNPs and SNVs in Forensic Science. 2015. Forensic Science International: Genetics Supplement Series.

Weir BS, Goudet J. A Unified Characterization of Population Structure and Relatedness. Genetics. 2017 Aug;206(4):2085-2103. doi: 10.1534/genetics.116.198424.

See Also

snpgdsHCluster

Examples

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

pop.group <- as.factor(read.gdsn(index.gdsn(
    genofile, "sample.annot/pop.group")))
pop.level <- levels(pop.group)

diss <- snpgdsDiss(genofile)
diss
hc <- snpgdsHCluster(diss)
names(hc)
plot(hc$dendrogram)

# close the genotype file
snpgdsClose(genofile)


# split
set.seed(100)
rv <- snpgdsCutTree(hc, label.H=TRUE, label.Z=TRUE)

# draw dendrogram
snpgdsDrawTree(rv, main="HapMap Phase II",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"))

Description

To draw a dendrogram or the distribution of Z scores

Usage

snpgdsDrawTree(obj, clust.count=NULL, dend.idx=NULL,
    type=c("dendrogram", "z-score"), yaxis.height=TRUE, yaxis.kinship=TRUE,
    y.kinship.baseline=NaN, y.label.kinship=FALSE, outlier.n=NULL,
    shadow.col=c(rgb(0.5, 0.5, 0.5, 0.25), rgb(0.5, 0.5, 0.5, 0.05)),
    outlier.col=rgb(1, 0.50, 0.50, 0.5), leaflab="none",
    labels=NULL, y.label=0.2, ...)

Arguments

Details

The details will be described in future.

Value

None.

Author(s)

Xiuwen Zheng

See Also

snpgdsCutTree

Examples

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

pop.group <- as.factor(read.gdsn(index.gdsn(
    genofile, "sample.annot/pop.group")))
pop.level <- levels(pop.group)

diss <- snpgdsDiss(genofile)
hc <- snpgdsHCluster(diss)

# close the genotype file
snpgdsClose(genofile)


# split
set.seed(100)
rv <- snpgdsCutTree(hc, label.H=TRUE, label.Z=TRUE)

# draw dendrogram
snpgdsDrawTree(rv, main="HapMap Phase II",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"))

Description

Eigen-analysis on IBD matrix based SNP genotypes.

Usage

snpgdsEIGMIX(gdsobj, sample.id=NULL, snp.id=NULL, autosome.only=TRUE,
    remove.monosnp=TRUE, maf=NaN, missing.rate=NaN, num.thread=1L,
    eigen.cnt=32L, diagadj=TRUE, ibdmat=FALSE, verbose=TRUE)
## S3 method for class 'snpgdsEigMixClass'
plot(x, eig=c(1L,2L), ...)

Arguments

Value

Return a snpgdsEigMixClass object, and it is a list:

Author(s)

Xiuwen Zheng

References

Zheng X, Weir BS. Eigenanalysis on SNP Data with an Interpretation of Identity by Descent. Theoretical Population Biology. 2016 Feb;107:65-76. doi: 10.1016/j.tpb.2015.09.004

See Also

snpgdsAdmixProp, snpgdsAdmixPlot, snpgdsPCA, snpgdsPCASNPLoading, snpgdsPCASampLoading

Examples

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

# get population information
#   or pop_code <- scan("pop.txt", what=character())
#   if it is stored in a text file "pop.txt"
pop_code <- read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))

# get sample id
samp.id <- read.gdsn(index.gdsn(genofile, "sample.id"))

# run eigen-analysis
RV <- snpgdsEIGMIX(genofile)
RV

# eigenvalues
RV$eigenval

# make a data.frame
tab <- data.frame(sample.id = samp.id, pop = factor(pop_code),
    EV1 = RV$eigenvect[,1],    # the first eigenvector
    EV2 = RV$eigenvect[,2],    # the second eigenvector
    stringsAsFactors = FALSE)
head(tab)

# draw
plot(tab$EV2, tab$EV1, col=as.integer(tab$pop),
    xlab="eigenvector 2", ylab="eigenvector 1")
legend("topleft", legend=levels(tab$pop), pch="o", col=1:4)


# define groups
groups <- list(CEU = samp.id[pop_code == "CEU"],
    YRI = samp.id[pop_code == "YRI"],
    CHB = samp.id[is.element(pop_code, c("HCB", "JPT"))])

prop <- snpgdsAdmixProp(RV, groups=groups)

# draw
plot(prop[, "YRI"], prop[, "CEU"], col=as.integer(tab$pop),
    xlab = "Admixture Proportion from YRI",
    ylab = "Admixture Proportion from CEU")
abline(v=0, col="gray25", lty=2)
abline(h=0, col="gray25", lty=2)
abline(a=1, b=-1, col="gray25", lty=2)
legend("topright", legend=levels(tab$pop), pch="o", col=1:4)


# close the genotype file
snpgdsClose(genofile)

Description

Return the last error message.

Usage

snpgdsErrMsg()

Value

Characters

Author(s)

Xiuwen Zheng

Examples

snpgdsErrMsg()

Description

Return the file name of example data

Usage

snpgdsExampleFileName()

Details

A GDS genotype file was created from a subset of HapMap Phase II dataset consisting of 270 individuals and duplicates.

Value

Characters

Author(s)

Xiuwen Zheng

Examples

snpgdsExampleFileName()

Description

A SNPGDSFileClass object provides access to a GDS file containing genome-wide SNP data. It extends the class gds.class in the gdsfmt package.

Author(s)

Xiuwen Zheng

See Also

snpgdsOpen, snpgdsClose

Examples

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

class(genofile)
# "SNPGDSFileClass" "gds.class"

# close the file
snpgdsClose(genofile)

Description

Calculate relatedness measures F-statistics (also known as fixation indices) for given populations

Usage

snpgdsFst(gdsobj, population, method=c("W&C84", "W&H02"), sample.id=NULL,
    snp.id=NULL, autosome.only=TRUE, remove.monosnp=TRUE, maf=NaN,
    missing.rate=NaN, with.id=FALSE, verbose=TRUE)

Arguments

Details

The minor allele frequency and missing rate for each SNP passed in snp.id are calculated over all the samples in sample.id.

The "W&H02" option implements the calculation in Buckleton et. al. 2016.

Value

Return a list:

Author(s)

Xiuwen Zheng

References

Weir, BS. & Cockerham, CC. Estimating F-statistics for the analysis of population structure. (1984).

Weir, BS. & Hill, WG. Estimating F-statistics. Annual review of genetics 36, 721-50 (2002).

Population-specific FST values for forensic STR markers: A worldwide survey. Buckleton J, Curran J, Goudet J, Taylor D, Thiery A, Weir BS. Forensic Sci Int Genet. 2016 Jul;23:91-100. doi: 10.1016/j.fsigen.2016.03.004.

Examples

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

group <- as.factor(read.gdsn(index.gdsn(
    genofile, "sample.annot/pop.group")))

# Fst estimation
v <- snpgdsFst(genofile, population=group, method="W&C84")
v$Fst
v$MeanFst
summary(v$FstSNP)

# or
v <- snpgdsFst(genofile, population=group, method="W&H02")
v$Fst
v$MeanFst
v$Beta
summary(v$FstSNP)

# close the genotype file
snpgdsClose(genofile)

Description

Convert a GDS file to a PLINK binary ped (BED) file.

Usage

snpgdsGDS2BED(gdsobj, bed.fn, sample.id=NULL, snp.id=NULL, snpfirstdim=NULL,
    verbose=TRUE)

Arguments

Details

GDS – Genomic Data Structures, the extended file name used for storing genetic data, and the file format used in the gdsfmt package.

BED – the PLINK binary ped format.

Value

None.

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.

http://corearray.sourceforge.net/

See Also

snpgdsBED2GDS, snpgdsGDS2PED

Examples

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

snpset <- snpgdsSelectSNP(genofile, missing.rate=0.95)
snpgdsGDS2BED(genofile, bed.fn="test", snp.id=snpset)

# close the genotype file
snpgdsClose(genofile)


# delete the temporary files
unlink(c("test.bed", "test.bim", "test.fam"), force=TRUE)

Description

Convert a GDS file to an EIGENSTRAT file.

Usage

snpgdsGDS2Eigen(gdsobj, eigen.fn, sample.id=NULL, snp.id=NULL, verbose=TRUE)

Arguments

Details

GDS – Genomic Data Structures, the extended file name used for storing genetic data, and the file format used in the gdsfmt package.

Eigen – the text format used in EIGENSTRAT.

Value

None.

Author(s)

Xiuwen Zheng

References

Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genetics 2:e190.

Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 38, 904-909.

http://corearray.sourceforge.net/

See Also

snpgdsGDS2PED

Examples

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

snpset <- snpgdsSelectSNP(genofile, missing.rate=0.95)
snpgdsGDS2Eigen(genofile, eigen.fn="tmpeigen", snp.id=snpset)

# close the genotype file
snpgdsClose(genofile)


# delete the temporary files
unlink(c("tmpeigen.eigenstratgeno", "tmpeigen.ind", "tmpeigen.snp"), force=TRUE)

Description

Convert a GDS file to a PLINK text ped file.

Usage

snpgdsGDS2PED(gdsobj, ped.fn, sample.id=NULL, snp.id=NULL, use.snp.rsid=TRUE,
    format=c("A/G/C/T", "A/B", "1/2"), verbose=TRUE)

Arguments

Details

GDS – Genomic Data Structures, the extended file name used for storing genetic data, and the file format used in the gdsfmt package.

PED – the PLINK text ped format.

Value

None.

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.

http://corearray.sourceforge.net/

See Also

snpgdsGDS2BED

Examples

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

# GDS ==> PED
snpgdsGDS2PED(genofile, ped.fn="tmp")

# close the GDS file
snpgdsClose(genofile)

Description

Convert an Oxford GEN file (text format) to a GDS file.

Usage

snpgdsGEN2GDS(gen.fn, sample.fn, out.fn, chr.code=NULL,
    call.threshold=0.9, version=c(">=2.0", "<=1.1.5"),
    snpfirstdim=FALSE, compress.annotation="ZIP_RA.max", compress.geno="",
    verbose=TRUE)

Arguments

Details

GDS – Genomic Data Structures, the extended file name used for storing genetic data, and the file format is used in the gdsfmt package.

NOTE : the sample file format (sample.fn) has changed with the release of SNPTEST v2. Specifically, the way in which covariates and phenotypes are coded on the second line of the header file has changed. version has to be specified, and the function uses ">=2.0" by default.

Value

Return the file name of GDS format with an absolute path.

Author(s)

Xiuwen Zheng

References

https://code.enkre.net/bgen

See Also

snpgdsBED2GDS, snpgdsVCF2GDS

Examples

cat("running snpgdsGEN2GDS ...\n")
## Not run: 
snpgdsGEN2GDS("test.gen", "test.sample", "output.gds", chr.code=1)

## End(Not run)

Description

To get a genotype matrix from a specified GDS file

Usage

snpgdsGetGeno(gdsobj, sample.id=NULL, snp.id=NULL, snpfirstdim=NA,
    .snpread=NA, with.id=FALSE, verbose=TRUE)

Arguments

Value

The function returns an integer matrix with values 0, 1, 2 or NA representing the number of reference allele when with.id=FALSE; or list(genotype, sample.id, snp.id) when with.id=TRUE. The orders of sample and SNP IDs in the genotype matrix are actually consistent with sample.id and snp.id in the GDS file, which may not be as the same as the arguments sampel.id and snp.id specified by users.

Author(s)

Xiuwen Zheng

Examples

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

set.seed(1000)
snpset <- sample(read.gdsn(index.gdsn(genofile, "snp.id")), 1000)

mat1 <- snpgdsGetGeno(genofile, snp.id=snpset, snpfirstdim=TRUE)
dim(mat1)
# 1000  279
table(c(mat1), exclude=NULL)

mat2 <- snpgdsGetGeno(genofile, snp.id=snpset, snpfirstdim=FALSE)
dim(mat2)
# 279 1000
table(c(mat2), exclude=NULL)

identical(t(mat1), mat2)
# TRUE

# close the file
snpgdsClose(genofile)

Description

Calculate Genetic Relationship Matrix (GRM) using SNP genotype data.

Usage

snpgdsGRM(gdsobj, sample.id=NULL, snp.id=NULL,
    autosome.only=TRUE, remove.monosnp=TRUE, maf=NaN, missing.rate=NaN,
    method=c("GCTA", "Eigenstrat", "EIGMIX", "Weighted", "Corr", "IndivBeta"),
    num.thread=1L, useMatrix=FALSE, out.fn=NULL, out.prec=c("double", "single"),
    out.compress="LZMA_RA", with.id=TRUE, verbose=TRUE)

Arguments

Details

"GCTA": the genetic relationship matrix in GCTA is defined as $G_ij = avg_l [(g_il - 2*p_l)*(g_jl - 2*p_l) / 2*p_l*(1 - p_l)]$ for individuals i,j and locus l;

"Eigenstrat": the genetic covariance matrix in EIGENSTRAT $G_ij = avg_l [(g_il - 2*p_l)*(g_jl - 2*p_l) / 2*p_l*(1 - p_l)]$ for individuals i,j and locus l; the missing genotype is imputed by the dosage mean of that locus.

"EIGMIX" / "Weighted": it is the same as '2 * snpgdsEIGMIX(, ibdmat=TRUE, diagadj=FALSE)$ibd': $G_ij = [sum_l (g_il - 2*p_l)*(g_jl - 2*p_l)] / [sum_l 2*p_l*(1 - p_l)]$ for individuals i,j and locus l;

"IndivBeta": 'beta = snpgdsIndivBeta(, inbreeding=TRUE)' (Weir&Goudet, 2017), and beta-based GRM is $grm_ij = 2 * (beta_ij - beta_min) / (1 - beta_min)$ for $i!=j$, $grm_ij = 1 + (beta_i - beta_min) / (1 - beta_min)$ for $i=j$. It is relative to the minimum value of beta estimates.

Value

Return a list if with.id = TRUE:

If with.id = FALSE, this function returns the genetic relationship matrix (GRM) without sample and SNP IDs.

Author(s)

Xiuwen Zheng

References

Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. American journal of human genetics 88, 76-82 (2011).

Zheng X, Weir BS. Eigenanalysis on SNP Data with an Interpretation of Identity by Descent. Theoretical Population Biology. 2016 Feb;107:65-76. doi: 10.1016/j.tpb.2015.09.004

Weir BS, Zheng X. SNPs and SNVs in Forensic Science. Forensic Science International: Genetics Supplement Series. 2015. doi:10.1016/j.fsigss.2015.09.106

Weir BS, Goudet J. A Unified Characterization of Population Structure and Relatedness. Genetics. 2017 Aug;206(4):2085-2103. doi: 10.1534/genetics.116.198424.

See Also

snpgdsPCA, snpgdsEIGMIX, snpgdsIndivBeta, snpgdsIndInb, snpgdsFst, snpgdsMergeGRM

Examples

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

rv <- snpgdsGRM(genofile, method="GCTA")
eig <- eigen(rv$grm)  # Eigen-decomposition

# output to a GDS file
snpgdsGRM(genofile, method="GCTA", out.fn="test.gds")


pop <- factor(read.gdsn(index.gdsn(genofile, "sample.annot/pop.group")))
plot(eig$vectors[,1], eig$vectors[,2], col=pop)
legend("topleft", legend=levels(pop), pch=19, col=1:4)


# close the file
snpgdsClose(genofile)

# delete the temporary file
unlink("test.gds", force=TRUE)

Description

Perform hierarchical cluster analysis on the dissimilarity matrix.

Usage

snpgdsHCluster(dist, sample.id=NULL, need.mat=TRUE, hang=0.25)

Arguments

Details

Call the function hclust to perform hierarchical cluster analysis, using method="average".

Value

Return a list (class "snpgdsHCClass"):

Author(s)

Xiuwen Zheng

See Also

snpgdsIBS, snpgdsDiss, snpgdsCutTree

Examples

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

pop.group <- read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))
pop.group <- as.factor(pop.group)
pop.level <- levels(pop.group)

diss <- snpgdsDiss(genofile)
hc <- snpgdsHCluster(diss)
rv <- snpgdsCutTree(hc)
rv

# call 'plot' to draw a dendrogram
plot(rv$dendrogram, leaflab="none", main="HapMap Phase II")


# the distribution of Z scores
snpgdsDrawTree(rv, type="z-score", main="HapMap Phase II")

# draw dendrogram
snpgdsDrawTree(rv, main="HapMap Phase II",
    edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"))


# close the file
snpgdsClose(genofile)

Description

Calculate the p-values for the exact SNP test of Hardy-Weinberg Equilibrium.

Usage

snpgdsHWE(gdsobj, sample.id=NULL, snp.id=NULL, with.id=FALSE)

Arguments

Value

If with.id=FALSE, return a vector of numeric values (p-value); otherwise, return a list with three components "pvalue", "sample.id" and "snp.id".

Author(s)

Xiuwen Zheng, Janis E. Wigginton

References

Wigginton, J. E., Cutler, D. J. & Abecasis, G. R. A note on exact tests of Hardy-Weinberg equilibrium. Am. J. Hum. Genet. 76, 887-93 (2005).

See Also

snpgdsSNPRateFreq

Examples

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

# Japanese samples
sample.id <- read.gdsn(index.gdsn(genofile, "sample.id"))
pop <- read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))
(samp.sel <- sample.id[pop=="JPT"])
samp.sel <- samp.sel[nchar(samp.sel) == 7]

# chromosome 1
snp.id <- snpgdsSelectSNP(genofile, sample.id=samp.sel, autosome.only=1L)

# HWE test
p <- snpgdsHWE(genofile, sample.id=samp.sel, snp.id=snp.id)
summary(p)

# QQ plot
plot(-log10((1:length(p))/length(p)), -log10(p[order(p)]),
	xlab="-log10(expected P)", ylab="-log10(observed P)", main="QQ plot")
abline(a=0, b=1, col="blue")

# close the genotype file
snpgdsClose(genofile)

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

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:

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)

Description

Calculate the three IBD coefficients (k0, k1, k2) for non-inbred individual pairs by Maximum Likelihood Estimation.

Usage

snpgdsIBDMLE(gdsobj, sample.id=NULL, snp.id=NULL, autosome.only=TRUE,
    remove.monosnp=TRUE, maf=NaN, missing.rate=NaN, kinship=FALSE,
    kinship.constraint=FALSE, allele.freq=NULL,
    method=c("EM", "downhill.simplex", "Jacquard"), max.niter=1000L,
    reltol=sqrt(.Machine$double.eps), coeff.correct=TRUE,
    out.num.iter=TRUE, num.thread=1, verbose=TRUE)

Arguments

Details

The minor allele frequency and missing rate for each SNP passed in snp.id are calculated over all the samples in sample.id.

The PLINK moment estimates are used as the initial values in the algorithm of searching maximum value of log likelihood function. Two numeric approaches can be used: one is Expectation-Maximization (EM) algorithm, and the other is Nelder-Mead method or downhill simplex method. Generally, EM algorithm is more robust than downhill simplex method. "Jacquard" refers to the estimation of nine Jacquard's coefficients.

If coeff.correct is TRUE, the final point that is found by searching algorithm (EM or downhill simplex) is used to compare the six points (fullsib, offspring, halfsib, cousin, unrelated), since any numeric approach might not reach the maximum position after a finit number of steps. If any of these six points has a higher value of log likelihood, the final point will be replaced by the best one.

Although MLE estimates are more reliable than MoM, MLE is much more computationally intensive than MoM, and might not be feasible to estimate pairwise relatedness for a large dataset.

Value

Return a snpgdsIBDClass object, which is a list:

Author(s)

Xiuwen Zheng

References

Milligan BG. 2003. Maximum-likelihood estimation of relatedness. Genetics 163:1153-1167.

Weir BS, Anderson AD, Hepler AB. 2006. Genetic relatedness analysis: modern data and new challenges. Nat Rev Genet. 7(10):771-80.

Choi Y, Wijsman EM, Weir BS. 2009. Case-control association testing in the presence of unknown relationships. Genet Epidemiol 33(8):668-78.

Jacquard, A. Structures Genetiques des Populations (Masson & Cie, Paris, 1970); English translation available in Charlesworth, D. & Chalesworth, B. Genetics of Human Populations (Springer, New York, 1974).

See Also

snpgdsIBDMLELogLik, snpgdsIBDMoM

Examples

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

YRI.id <- read.gdsn(index.gdsn(genofile, "sample.id"))[
    read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))=="YRI"]
YRI.id <- YRI.id[1:30]

# SNP pruning
set.seed(10)
snpset <- snpgdsLDpruning(genofile, sample.id=YRI.id, maf=0.05,
    missing.rate=0.05)
snpset <- sample(unlist(snpset), 250)
mibd <- snpgdsIBDMLE(genofile, sample.id=YRI.id, snp.id=snpset)
mibd

# select a set of pairs of individuals
d <- snpgdsIBDSelection(mibd, kinship.cutoff=1/8)
head(d)


# log likelihood

loglik <- snpgdsIBDMLELogLik(genofile, mibd)
loglik0 <- snpgdsIBDMLELogLik(genofile, mibd, relatedness="unrelated")

# likelihood ratio test
p.value <- pchisq(loglik - loglik0, 1, lower.tail=FALSE)


flag <- lower.tri(mibd$k0)
plot(NaN, xlim=c(0,1), ylim=c(0,1), xlab="k0", ylab="k1")
lines(c(0,1), c(1,0), col="red", lty=3)
points(mibd$k0[flag], mibd$k1[flag])

# specify the allele frequencies
afreq <- snpgdsSNPRateFreq(genofile, sample.id=YRI.id,
    snp.id=snpset)$AlleleFreq
subibd <- snpgdsIBDMLE(genofile, sample.id=YRI.id[1:25], snp.id=snpset,
    allele.freq=afreq)
summary(c(subibd$k0 - mibd$k0[1:25, 1:25]))
# ZERO
summary(c(subibd$k1 - mibd$k1[1:25, 1:25]))
# ZERO


# close the genotype file
snpgdsClose(genofile)

Description

Calculate the log likelihood values from maximum likelihood estimation.

Usage

snpgdsIBDMLELogLik(gdsobj, ibdobj, k0 = NaN, k1 = NaN,
    relatedness=c("", "self", "fullsib", "offspring",
    "halfsib", "cousin", "unrelated"))

Arguments

Details

If (relatedness == "") and (k0 == NaN or k1 == NaN), then return the log likelihood values for each (k0, k1) stored in ibdobj. \ If (relatedness == "") and (k0 != NaN) and (k1 != NaN), then return the log likelihood values for a specific IBD coefficient (k0, k1). \ If relatedness is: "self", then k0 = 0, k1 = 0; "fullsib", then k0 = 0.25, k1 = 0.5; "offspring", then k0 = 0, k1 = 1; "halfsib", then k0 = 0.5, k1 = 0.5; "cousin", then k0 = 0.75, k1 = 0.25; "unrelated", then k0 = 1, k1 = 0.

Value

Return a n-by-n matrix of log likelihood values, where n is the number of samples.

Author(s)

Xiuwen Zheng

References

Milligan BG. 2003. Maximum-likelihood estimation of relatedness. Genetics 163:1153-1167.

Weir BS, Anderson AD, Hepler AB. 2006. Genetic relatedness analysis: modern data and new challenges. Nat Rev Genet. 7(10):771-80.

Choi Y, Wijsman EM, Weir BS. 2009. Case-control association testing in the presence of unknown relationships. Genet Epidemiol 33(8):668-78.

See Also

snpgdsIBDMLE, snpgdsIBDMoM

Examples

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

YRI.id <- read.gdsn(index.gdsn(genofile, "sample.id"))[
    read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))=="YRI"]
YRI.id <- YRI.id[1:30]

# SNP pruning
set.seed(10)
snpset <- snpgdsLDpruning(genofile, sample.id=YRI.id, maf=0.05,
    missing.rate=0.05)
snpset <- sample(unlist(snpset), 250)
mibd <- snpgdsIBDMLE(genofile, sample.id=YRI.id, snp.id=snpset)
names(mibd)

# select a set of pairs of individuals
d <- snpgdsIBDSelection(mibd, kinship.cutoff=1/8)
head(d)


# log likelihood

loglik <- snpgdsIBDMLELogLik(genofile, mibd)
loglik0 <- snpgdsIBDMLELogLik(genofile, mibd, relatedness="unrelated")

# likelihood ratio test
p.value <- pchisq(loglik - loglik0, 1, lower.tail=FALSE)


flag <- lower.tri(mibd$k0)
plot(NaN, xlim=c(0,1), ylim=c(0,1), xlab="k0", ylab="k1")
lines(c(0,1), c(1,0), col="red", lty=3)
points(mibd$k0[flag], mibd$k1[flag])

# specify the allele frequencies
afreq <- snpgdsSNPRateFreq(genofile, sample.id=YRI.id,
    snp.id=snpset)$AlleleFreq
subibd <- snpgdsIBDMLE(genofile, sample.id=YRI.id[1:25], snp.id=snpset,
    allele.freq=afreq)
summary(c(subibd$k0 - mibd$k0[1:25, 1:25]))
# ZERO
summary(c(subibd$k1 - mibd$k1[1:25, 1:25]))
# ZERO


# close the genotype file
snpgdsClose(genofile)

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=1L, useMatrix=FALSE,
    verbose=TRUE)

Arguments

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:

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.

See Also

snpgdsIBDMLE, snpgdsIBDMLELogLik

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)

Description

Return a data frame with IBD coefficients.

Usage

snpgdsIBDSelection(ibdobj, kinship.cutoff=NaN, samp.sel=NULL)

Arguments

Value

Return a data.frame:

Author(s)

Xiuwen Zheng

See Also

snpgdsIBDMLE, snpgdsIBDMoM, snpgdsIBDKING

Examples

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

# 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])

# close the genotype file
snpgdsClose(genofile)

# IBD coefficients
dat <- snpgdsIBDSelection(pibd, 1/32)
head(dat)
#       ID1     ID2          k0        k1    kinship
# 1 NA19152 NA19154 0.010749154 0.9892508 0.24731271
# 2 NA19152 NA19093 0.848207777 0.1517922 0.03794806
# 3 NA19139 NA19138 0.010788047 0.9770181 0.25035144
# 4 NA19139 NA19137 0.012900661 0.9870993 0.24677483
# 5 NA18912 NA18914 0.008633077 0.9913669 0.24784173
# 6 NA19160 NA19161 0.008635754 0.9847777 0.24948770

Description

Calculate the fraction of identity by state for each pair of samples

Usage

snpgdsIBS(gdsobj, sample.id=NULL, snp.id=NULL, autosome.only=TRUE,
    remove.monosnp=TRUE, maf=NaN, missing.rate=NaN, num.thread=1L,
    useMatrix=FALSE, verbose=TRUE)

Arguments

Details

The minor allele frequency and missing rate for each SNP passed in snp.id are calculated over all the samples in sample.id.

The values of the IBS matrix range from ZERO to ONE, and it is defined as the average of 1 - | g_{1,i} - g_{2,i} | / 2 across the genome for the first and second individuals and SNP i.

Value

Return a list (class "snpgdsIBSClass"):

Author(s)

Xiuwen Zheng

See Also

snpgdsIBSNum

Examples

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

# perform identity-by-state calculations
ibs <- snpgdsIBS(genofile)

# perform multidimensional scaling analysis on
# the genome-wide IBS pairwise distances:
loc <- cmdscale(1 - ibs$ibs, k = 2)
x <- loc[, 1]; y <- loc[, 2]
race <- as.factor(read.gdsn(index.gdsn(genofile, "sample.annot/pop.group")))
plot(x, y, col=race, xlab = "", ylab = "", main = "cmdscale(IBS Distance)")
legend("topleft", legend=levels(race), text.col=1:nlevels(race))

# close the file
snpgdsClose(genofile)

Description

Calculate the number of SNPs for identity by state for each pair of samples.

Usage

snpgdsIBSNum(gdsobj, sample.id = NULL, snp.id = NULL, autosome.only = TRUE,
    remove.monosnp = TRUE, maf = NaN, missing.rate = NaN, num.thread = 1L,
    verbose = TRUE)

Arguments

Details

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 (n is the number of samples):

Author(s)

Xiuwen Zheng

See Also

snpgdsIBS

Examples

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

RV <- snpgdsIBSNum(genofile)
pop <- read.gdsn(index.gdsn(genofile, "sample.annot/pop.group"))
L <- order(pop)
image(RV$ibs0[L, L]/length(RV$snp.id))

# close the genotype file
snpgdsClose(genofile)

Description

To calculate individual inbreeding coefficients using SNP genotype data

Usage

snpgdsIndInb(gdsobj, sample.id=NULL, snp.id=NULL,
    autosome.only=TRUE, remove.monosnp=TRUE, maf=NaN, missing.rate=NaN,
    method=c("mom.weir", "mom.visscher", "mle", "gcta1", "gcta2", "gcta3"),
    allele.freq=NULL, out.num.iter=TRUE, reltol=.Machine$double.eps^0.75,
    verbose=TRUE)

Arguments

Details

The method can be: "mom.weir": a modified Visscher's estimator, proposed by Bruce Weir; "mom.visscher": Visscher's estimator described in Yang et al. (2010); "mle": the maximum likelihood estimation; "gcta1": F^I in GCTA, avg [(g_i - 2p_i)^2 / (2*p_i*(1-p_i)) - 1]; "gcta2": F^II in GCTA, avg [1 - g_i*(2 - g_i) / (2*p_i*(1-p_i))]; "gcta3": F^III in GCTA, the same as "mom.visscher", avg [g_i^2 - (1 + 2p_i)*g_i + 2*p_i^2] / (2*p_i*(1-p_i)).

Value

Return estimated inbreeding coefficient.

Author(s)

Xiuwen Zheng

References

Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM. 2010. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 42(7):565-9. Epub 2010 Jun 20.

Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. American journal of human genetics 88, 76-82 (2011).

Examples

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

rv <- snpgdsIndInb(genofile, method="mom.visscher")
head(rv$inbreeding)
summary(rv$inbreeding)

# close the genotype file
snpgdsClose(genofile)

Description

To calculate an individual inbreeding coefficient using SNP genotype data

Usage

snpgdsIndInbCoef(x, p, method = c("mom.weir", "mom.visscher", "mle"),
    reltol=.Machine$double.eps^0.75)

Arguments

Details

The method can be: "mom.weir": a modified Visscher's estimator, proposed by Bruce Weir; "mom.visscher": Visscher's estimator described in Yang et al. (2010); "mle": the maximum likelihood estimation.

Value

Return estimated inbreeding coefficient.

Author(s)

Xiuwen Zheng

References

Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM. 2010. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 42(7):565-9. Epub 2010 Jun 20.

Examples

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

chr1 <- read.gdsn(index.gdsn(genofile, "snp.id"))[
    read.gdsn(index.gdsn(genofile, "snp.chromosome"))==1]
chr1idx <- match(chr1, read.gdsn(index.gdsn(genofile, "snp.id")))

AF <- snpgdsSNPRateFreq(genofile)
g <- read.gdsn(index.gdsn(genofile, "genotype"), start=c(1,1), count=c(-1,1))

snpgdsIndInbCoef(g[chr1idx], AF$AlleleFreq[chr1idx], method="mom.weir")
snpgdsIndInbCoef(g[chr1idx], AF$AlleleFreq[chr1idx], method="mom.visscher")
snpgdsIndInbCoef(g[chr1idx], AF$AlleleFreq[chr1idx], method="mle")

# close the genotype file
snpgdsClose(genofile)

Description

Calculate individual inbreeding and relatedness estimation (beta estimator) using SNP genotype data.

Usage

snpgdsIndivBeta(gdsobj, sample.id=NULL, snp.id=NULL, autosome.only=TRUE,
    remove.monosnp=TRUE, maf=NaN, missing.rate=NaN, method=c("weighted"),
    inbreeding=TRUE, num.thread=1L, with.id=TRUE, useMatrix=FALSE, verbose=TRUE)
snpgdsIndivBetaRel(beta, beta_rel, verbose=TRUE)

Arguments

Value

Return a list if with.id = TRUE:

If with.id = FALSE, this function returns the genetic relationship matrix without sample and SNP IDs.

Author(s)

Xiuwen Zheng

References

Weir BS, Zheng X. SNPs and SNVs in Forensic Science. Forensic Science International: Genetics Supplement Series. 2015. doi:10.1016/j.fsigss.2015.09.106

Weir BS, Goudet J. A Unified Characterization of Population Structure and Relatedness. Genetics. 2017 Aug;206(4):2085-2103. doi: 10.1534/genetics.116.198424.

See Also

snpgdsGRM, snpgdsIndInb, snpgdsFst

Examples

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

b <- snpgdsIndivBeta(genofile, inbreeding=FALSE)
b$beta[1:10, 1:10]

z <- snpgdsIndivBetaRel(b, min(b$beta))

# close the file
snpgdsClose(genofile)

Description

Return a LD matrix for SNP pairs.

Usage

snpgdsLDMat(gdsobj, sample.id=NULL, snp.id=NULL, slide=250L,
    method=c("composite", "r", "dprime", "corr", "cov"), mat.trim=FALSE,
    num.thread=1L, with.id=TRUE, verbose=TRUE)

Arguments

Details

Four methods can be used to calculate linkage disequilibrium values: "composite" for LD composite measure, "r" for R coefficient (by EM algorithm assuming HWE, it could be negative), "dprime" for D', and "corr" for correlation coefficient. The method "corr" is equivalent to "composite", when SNP genotypes are coded as: 0 – BB, 1 – AB, 2 – AA.

If slide <= 0, the function returns a n-by-n LD matrix where the value of i row and j column is LD of i and j SNPs. If slide > 0, it returns a m-by-n LD matrix where n is the number of SNPs, m is the size of sliding window, and the value of i row and j column is LD of j and j+i SNPs.

Value

Return a list:

Author(s)

Xiuwen Zheng

References

Weir B: Inferences about linkage disequilibrium. Biometrics 1979; 35: 235-254.

Weir B: Genetic Data Analysis II. Sunderland, MA: Sinauer Associates, 1996.

Weir BS, Cockerham CC: Complete characterization of disequilibrium at two loci; in Feldman MW (ed): Mathematical Evolutionary Theory. Princeton, NJ: Princeton University Press, 1989.

See Also

snpgdsLDpair, snpgdsLDpruning

Examples

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

# missing proportion and MAF
ff <- snpgdsSNPRateFreq(genofile)

# chromosome 15
snpset <- read.gdsn(index.gdsn(genofile, "snp.id"))[
    ff$MissingRate==0 & ff$MinorFreq>0 &
    read.gdsn(index.gdsn(genofile, "snp.chromosome"))==15]
length(snpset)


# LD matrix without sliding window
ld.noslide <- snpgdsLDMat(genofile, snp.id=snpset, slide=-1, method="composite")
# plot
image(t(ld.noslide$LD^2), col=terrain.colors(16))

# LD matrix with a sliding window
ld.slide <- snpgdsLDMat(genofile, snp.id=snpset, method="composite")
# plot
image(t(ld.slide$LD^2), col=terrain.colors(16))


# close the genotype file
snpgdsClose(genofile)

Description

Return a LD value between snp1 and snp2.

Usage

snpgdsLDpair(snp1, snp2, method = c("composite", "r", "dprime", "corr"))

Arguments

Details

Four methods can be used to calculate linkage disequilibrium values: "composite" for LD composite measure, "r" for R coefficient (by EM algorithm assuming HWE, it could be negative), "dprime" for D', and "corr" for correlation coefficient. The method "corr" is equivalent to "composite", when SNP genotypes are coded as: 0 – BB, 1 – AB, 2 – AA.

Value

Return a numeric vector:

if method = "r" or "dprime",

Author(s)