Osval A. Montesinos-López, Abelardo Montesinos-López, Kismiantini, Armando Roman-Gallardo, Keith Gardner, Morten Lillemo, Roberto Fritsche-Neto, José Crossa
In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as “leave one environment out,” is of paramount importance to increase the genetic gain in breeding programs and contribute to food and nutrition security worldwide. Genomic selection (GS) has the potential to increase the accuracy of future seasons or new locations because it is a predictive methodology. However, most statistical machine learning methods used for the task of predicting a new environment or season struggle to produce moderate or high prediction accuracies. For this reason, in this study we explore the use of the partial least squares (PLS) regression methodology for this specific task, and we benchmark its performance with the Bayesian Genomic Best Linear Unbiased Predictor (GBLUP) method. The benchmarking process was done with 14 real datasets. We found that in all datasets the PLS method outperformed the popular GBLUP method by margins between 0% (in the Indica data) and 228.28% (in the Disease data) across traits, environments, and types of predictors. Our results show great empirical evidence of the power of the PLS methodology for the prediction of future seasons or new environments. Copyright © 2022 Montesinos-López, Montesinos-López, Kismiantini, Roman-Gallardo, Gardner, Lillemo, Fritsche-Neto and Crossa.
Facultad de Telemática, Universidad de Colima, Colima, Mexico; Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Mexico; Statistics Study Program, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia; International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico; Department of Plant Sciences, Norwegian University of Life Sciences, IHA/CIGENE, Ås, Norway; Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil; Colegio de Postgraduados, Montecillo, Mexico
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