Multi-trait genome prediction of new environments with partial least squares - Montesinos-López, Osval A.; Montesinos-López, Abelardo; Bernal Sandoval, David Alejandro; Mosqueda-Gonzalez, Brandon Alejandro; Valenzo-Jiménez, Marco Alberto; Crossa, José (Frontiers in Genetics, 2022)
The genomic selection (GS) methodology proposed over 20 years ago by Meuwissen et al. (2001) has revolutionized plant breeding. A predictive methodology that trains statistical machine learning algorithms with phenotypic and genotypic data of a reference population and makes predictions for genotyped candidate lines, GS saves significant resources in the selection of candidate individuals. However, its practical implementation is still challenging when the plant breeder is interested in the prediction of future seasons or new locations and/or environments, also called the “leave one environment out” issue. However, because the distributions of the training and testing set do not match, most statistical machine learning methods struggle to produce moderate or reasonable prediction accuracies. For this reason, the main objective of this study was to explore the use of the multi-trait partial least square (MT-PLS) regression methodology for this specific task, and we benchmark its performance with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method. The benchmarking process was performed with five actual data sets. We found that in all data sets the MT-PLS method outperformed the popular MT-GBLUP method by 349.8% (under predictor E+G), 484.4% (under predictor E+G+GE) and 15.9% (under predictor G+GE) across traits. Our results display empirical evidence of the power MT-PLS methodology for the prediction of future seasons or new environments. Furthermore, the comparison between single univariate-trait (UT) versus MT for GBLUP and PLS gave an increase in prediction accuracy of UT-GLUP versus MT-GBLUP but not for UT-PLS versus MT-PLS.