rrBLUP-package: Ridge regression and other kernels for genomic selection
Description
This package has been designed for both genomic selection and association mapping. Some details of using the package for genomic selection have been published in the Plant Genome. The heart of the package is the function mixed.solve, which can be used to model marker effects as random effects or the genotypic values of the lines as random effects. In the latter case, the function A.mat is useful for calculating the additive relationship matrix and the prediction of breeding values. To include epistatic effects in the genotypic value predictions, use the function kinship.BLUP.Association mapping
Use function GWA for association mapping.Missing data
A number of improvements have been made concerning the handling of missing data since the original publication of the package. The functions mixed.solve, kinship.BLUP, and GWA will tolerate missing phenotypic data: those observations are simply not used. When genotypic data are missing, both kinship.BLUP (option "RR") and GWA rely on the EM algorithm in A.mat, which can also be used in conjunction with mixed.solve. The non-additive kernels in kinship.BLUP are based on dist, which uses pairwise complete observations.Parallel computing
For Mac, Linux, and UNIX users, R package multicore can be used in conjunction with rrBLUP to take advantage of multiple processors on a single machine. For large data sets, especially when there is missing data, I recommend trying this feature, which is available with kinship.BLUP, A.mat, and GWA. You need R >= 2.14.1 for this to work properly, and you must also use R from the command line (not the GUI).References
Endelman, J.B. 2011. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250-255.