Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding
continues to grow. However, it is very well documented that its success strongly depends on …

Multi-trait and multi-environment data are common in animal and plant breeding programs.
However, what is lacking are more powerful statistical models that can exploit the correlation …

In this paper we propose a Bayesian multi-output regressor stacking (BMORS) model that is
a generalization of the multi-trait regressor stacking method. The proposed BMORS model …

When information on multiple genotypes evaluated in multiple environments is recorded, a
multi-environment single trait model for assessing genotype× environment interaction (G× E) …

The phenomenon of genotype× environment (G× E) interaction in plant breeding decreases
selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction …

Genomic-enabled prediction models are of paramount importance for the successful
implementation of genomic selection (GS) based on breeding values. As opposed to animal …

When a plant scientist wishes to make genomic-enabled predictions of multiple traits
measured in multiple individuals in multiple environments, the most common strategy for …

When multitrait data are available, the preferred models are those that are able to account
for correlations between phenotypic traits because when the degree of correlation is …

Background Genomic selection is an effective tool for animal and plant breeding, allowing
effective individual selection without phenotypic records through the prediction of genomic …

The genomic selection (GS) methodology proposed over 20 years ago by Meuwissen et
al.(Genetics, 2001) has revolutionized plant breeding. A predictive methodology that trains …