SNPRelate (version 1.6.4)

snpgdsGRM: Genetic Relationship Matrix (GRM) for SNP genotype data

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("Visscher", "Eigenstrat", "EIGMIX", "W&Z15"), num.thread=1L, with.id=TRUE, 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 "

method

"Visscher" -- genetic relationship matrix defined in CGTA; "Eigenstrat" -- genetic covariance matrix in EIGENSTRAT; "EIGMIX" -- coancestry matrix defined in Zheng & Weir (2015), "W&Z15" -- individual beta estimator

num.thread

the number of (CPU) cores used; if NA, detect the number of cores automatically

with.id

if TRUE, the returned value with sample.id and sample.id

verbose

if TRUE, show information

Value

Return a list if with.id = TRUE:

sample.id

the sample ids used in the analysis

snp.id

the SNP ids used in the analysis

grm

the genetic relationship matrix

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

References

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).

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

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]

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

See Also

snpgdsIndInb, snpgdsFst

Examples

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


rv <- snpgdsGRM(genofile, method="Visscher")
eig <- eigen(rv$grm)  # Eigendecomposition

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)