snpgdsGRM {SNPRelate} | R Documentation

## 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("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

`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
`method` | "GCTA" – genetic relationship matrix defined in CGTA; "Eigenstrat" – genetic covariance matrix in EIGENSTRAT; "EIGMIX" – two times coancestry matrix defined in Zheng&Weir (2016), "Weighted" – weighted GCTA, as the same as "EIGMIX", "Corr" – Scaled GCTA GRM (dividing each i,j element by the product of the square root of the i,i and j,j elements), "IndivBeta" – two times individual beta estimate relative to the minimum of beta; see details
`num.thread` | the number of (CPU) cores used; if `NA`, detect the number of cores automatically
`useMatrix` | if `TRUE`, use `Matrix::dspMatrix` to store the output square matrix to save memory
`out.fn` | NULL for no GDS output, or a file name
`out.prec` | double or single precision for storage
`out.compress` | the compression method for storing the GRM matrix in the GDS file
`with.id` | if `TRUE`, the returned value with `sample.id` and `sample.id`
`verbose` | if `TRUE`, show information

### 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`:

`sample.id` | the sample ids used in the analysis
`snp.id` | the SNP ids used in the analysis
`method` | characters, the method used
`grm` | the genetic relationship matrix; different methods might have different meanings and interpretation for estimates

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)

[Package SNPRelate version 1.26.0 Index]