rrBLUP-package: Ridge regression and other kernels for genomic selection
In rrBLUP: Ridge Regression and Other Kernels for Genomic Selection
rrBLUP-package R Documentation
Ridge regression and other kernels for genomic selection
Description
This package has been developed primarily for genomic prediction with mixed models (but it can also do genome-wide association mapping with GWAS). The heart of the package is the function mixed.solve, which is a general-purpose solver for mixed models with a single variance component other than the error. Genomic predictions can be made by estimating marker effects (RR-BLUP) or by estimating line effects (G-BLUP). In Endelman (2011) I made the poor choice of using the letter G to denotype the genotype or marker data. To be consistent with Endelman (2011) I have retained this notation in kinship.BLUP. However, that function has now been superseded by kin.blup and A.mat, the latter being a utility for estimating the additive relationship matrix (A) from markers. In these newer functions I adopt the usual convention that G is the genetic covariance (not the marker data), which is also consistent with the notation in Endelman and Jannink (2012).
Vignettes illustrating some of the features of this package can be found at https://potatobreeding.cals.wisc.edu/software/.
References
Endelman, J.B. 2011. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250-255. <doi:10.3835/plantgenome2011.08.0024>
Endelman, J.B., and J.-L. Jannink. 2012. Shrinkage estimation of the realized relationship matrix. G3:Genes, Genomes, Genetics 2:1405-1413. <doi:10.1534/g3.112.004259>
rrBLUP documentation built on May 29, 2024, 8:02 a.m.
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| rrBLUP-package | R Documentation |
Ridge regression and other kernels for genomic selection
Description
This package has been developed primarily for genomic prediction with mixed models (but it can also do genome-wide association mapping with GWAS). The heart of the package is the function mixed.solve, which is a general-purpose solver for mixed models with a single variance component other than the error. Genomic predictions can be made by estimating marker effects (RR-BLUP) or by estimating line effects (G-BLUP). In Endelman (2011) I made the poor choice of using the letter G to denotype the genotype or marker data. To be consistent with Endelman (2011) I have retained this notation in kinship.BLUP. However, that function has now been superseded by kin.blup and A.mat, the latter being a utility for estimating the additive relationship matrix (A) from markers. In these newer functions I adopt the usual convention that G is the genetic covariance (not the marker data), which is also consistent with the notation in Endelman and Jannink (2012).
Vignettes illustrating some of the features of this package can be found at https://potatobreeding.cals.wisc.edu/software/.
References
Endelman, J.B. 2011. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250-255. <doi:10.3835/plantgenome2011.08.0024>
Endelman, J.B., and J.-L. Jannink. 2012. Shrinkage estimation of the realized relationship matrix. G3:Genes, Genomes, Genetics 2:1405-1413. <doi:10.1534/g3.112.004259>
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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