Gmatrix {AGHmatrix} | R Documentation

## Construction of Relationship Matrix G

### Description

Given a matrix (individual x markers), a method, a missing value, and a maf threshold, return a additive or non-additive relationship matrix. For diploids, the methods "Yang" and "VanRaden" for additive relationship matrices, and "Su" and "Vitezica" for non-additive relationship matrices are implemented. For autopolyploids, the method "VanRaden" for additive relationship, method "Slater" for full-autopolyploid model including non-additive effects, and pseudo-diploid parametrization are implemented.

### Usage

    Gmatrix(SNPmatrix = NULL, method = "VanRaden", missingValue = -9,
      maf = 0, thresh.missing = 1, verify.posdef = FALSE, ploidy = 2,
      pseudo.diploid = FALSE, integer = TRUE, ratio = FALSE,
      impute.method = FALSE, ratio.check = TRUE)

### Arguments

`SNPmatrix` | matrix (n x m), where n is is individual names and m is marker names (coded inside the matrix as 0, 1, 2, ..., ploidy, and, missingValue).

`method`

"Yang" or "VanRaden" for marker-based additive relationship matrix. "Su" or "Vitezica" for marker-based dominance relationship matrix. "Slater" for full-autopolyploid model including non-additive effects. "Endelman" for autotetraploid dominant (digentic) relationship matrix. "MarkersMatrix" for a matrix with the amount of shared markers between individuals (3). Default is "VanRaden", for autopolyploids will be computed a scaled product (similar to Covarrubias-Pazaran, 2006).

`missingValue` | missing value in data. Default=-9.
`maf` | max of missing data accepted to each marker. Default=0.05.
`thresh.missing` | threshold on missing data, SNPs below of this frequency value will be maintained, if equal to 1, no threshold and imputation is considered. Default = 1.
`verify.posdef` | verify if the resulting matrix is positive-definite. Default=FALSE.
`ploidy` | data ploidy (an even number between 2 and 20). Default=2.
`pseudo.diploid` | if TRUE, uses pseudodiploid parametrization of Slater (2016).
`integer` | if FALSE, not check for integer numbers. Default=TRUE.
`ratio` | if TRUE, molecular data are considered ratios and its computed the scaled product of the matrix (as in "VanRaden" method).
`impute.method` | FALSE to not impute missing data, "mean" to impute the missing data by the mean, "mode" to impute the missing data my the mode. Default = FALSE.
`ratio.check` | if TRUE, run snp.check with ratio data.

### Value

Matrix with the marker-bases relationships between the individuals

### Author(s)

Rodrigo R Amadeu rramadeu@gmail.com, Marcio Resende Jr, Letícia AC Lara, and Ivone Oliveira

### References

Covarrubias-Pazaran G., 2016. Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6):1-15.

Slater, A.T., et al., 2016. Improving genetic gain with genomic selection in autotetraploid potato. The Plant Genome 9(3), pp.1-15.

Su, G., et al., 2012. Estimating additive and non-additive genetic variances and predicting genetic merits using genome-wide dense single nucleotide polymorphism markers. PloS one, 7(9), p.e45293.

VanRaden, P.M., 2008. Efficient methods to compute genomic predictions. Journal of dairy science, 91(11), pp.4414-4423.

Vitezica, Z.G., Varona, L. and Legarra, A., 2013. On the additive and dominant variance and covariance of individuals within the genomic selection scope. Genetics, 195(4), pp.1223-1230.

Yang, J., et al., 2010. Common SNPs explain a large proportion of the heritability for human height. Nature genetics, 42(7), pp.565-569.

Endelman, J. B., et al., 2018. Genetic variance partitioning and genome-wide prediction with allele dosage information in autotetraploid potato. Genetics, 209(1) pp. 77-87.

### Examples

    ## Not run:
    ## Diploid Example
    data(snp.pine)
    #Verifying if data is coded as 0,1,2 and missing value.
    str(snp.pine)
    #Build G matrices
    Gmatrix.Yang <- Gmatrix(snp.pine, method="Yang", missingValue=-9, maf=0.05)
    Gmatrix.VanRaden <- Gmatrix(snp.pine, method="VanRaden", missingValue=-9, maf=0.05)
    Gmatrix.Su <- Gmatrix(snp.pine, method="Su", missingValue=-9, maf=0.05)
    Gmatrix.Vitezica <- Gmatrix(snp.pine, method="Vitezica", missingValue=-9, maf=0.05)

    ## Autetraploid example
    #Generating fake data
    data(snp.sol)
    #Build G matrices
    Gmatrix.VanRaden <- Gmatrix(snp.sol, method="VanRaden", ploidy=4)
    Gmatrix.Endelman <- Gmatrix(snp.sol, method="Endelman", ploidy=4)
    Gmatrix.Slater <- Gmatrix(snp.sol, method="Slater", ploidy=4)
    Gmatrix.Pseudodiploid <- Gmatrix(snp.sol, method="VanRaden", ploidy=4, pseudo.diploid=TRUE)

    ## End(Not run)

[Package AGHmatrix version 1.0.2 Index]