Multi-trait genome prediction of new environments with partial least squares.

ABSTRACT: 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 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, which is called the "leave one environment out" issue. Furthermore, because the distributions of the training and testing set do not match, most statistical machine learning methods struggle to produce moderate or reasonable pr

SUBMITTER: Montesinos-Lopez OA 

PROVIDER: S-EPMC9483856 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature