PopVar includes two functions useful for genome-based breeding:
pop.predict uses phenotypic and genotypic data from a set of individuals known as a training population (TP) and a set of candidate parents, which may or may not be included in the TP, to predict the mean (\(\mu\)), genetic variance (V_G), and superior progeny value (\(\mu\)_sp) of the half-diallel, or a defined set of pairwise bi-parental crosses between parents. When multiple traits are provided pop.predict will also predict the correlated responses and correlation between all pairwise traits. See Mohammadi, Tiede, and Smith (2015) for further details.
x.val performs cross-validation (CV) to estimate the accuracy of genome-wide prediction (otherwise known as genomic selection) for a specific training population (TP), i.e. a set of individuals for which phenotypic and genotypic data is available. Cross-validation can be conducted via one of two methods, see Details in x.val documentation for more information.
The dataset think_barley.rda, previously described in Sallam et al. (2014), is provided as an example of the proper formatting of input files and also for users to become familiar with the functions within PopVar.
Mohammadi M., T. Tiede, and K.P. Smith. 2015. PopVar: A genome-wide procedure for predicting genetic variance and correlated response in bi-parental breeding populations. Crop Sci. Accepted.
Sallam, A.H., J.B. Endelman, J-L. Jannink, and K.P. Smith. 2015. Assessing Genomic Selection Prediction Accuracy in a Dynamic Barley Breeding Population. Plant Gen. 8(1)