Description Usage Arguments Value Author(s) References Examples

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.

1 2 3 4 |

`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. |

Matrix with the marker-bases relationships between the individuals

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

*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.*

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
## 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)
``` |

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