snpgdsDiss: Individual dissimilarity analysis

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

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

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
num.thread	the number of (CPU) cores used; if NA, detect the number of cores automatically
verbose	if TRUE, show information

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":

sample.id	the sample ids used in the analysis
snp.id	the SNP ids used in the analysis
diss	a matrix of individual dissimilarity

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"))

SNPRelate documentation built on Nov. 8, 2020, 5:31 p.m.