Partial Least Squares Enhances Genomic Prediction of New Environments.

ABSTRACT: 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 Lin

SUBMITTER: Montesinos-Lopez OA 

PROVIDER: S-EPMC9608852 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature