The genomic selection (GS) methodology has revolutionized plant breeding. This methodology makes predictions for genotyped candidate lines based on statistical machine learning algorithms that are trained with phenotypic and genotypic data of a reference population. GS can save significant resources in the selection of candidate individuals. However, plant breeders can face challenges when trying to implement it practically to make predictions for future seasons or new locations and/or environments. To help address this challenge, this study seeks to explore the use of the multi-trait partial least square (MT-PLS) regression methodology and to compare its performance with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method. A benchmarking process was performed with five actual data sets contained in this study. The results of the analysis are reported in the accompanying article.
