Penalized factorial regression as a flexible and computationally attractive reaction norm model for prediction in the presence of GxE.

ABSTRACT:

Key message

Penalized factorial regression offers a computationally attractive alternative to kernel and deep learning methods for prediction of genotype by environment interactions. For two representative data sets on wheat and maize, prediction accuracies were comparable, while computing requirements and time were clearly lower. A longstanding challenge in plant breeding and genetics is the prediction of yield for new environments in the presence of genotype by environment interaction ( G×E ). The genotypes in this case are promising candidate varieties at an advanced stage of breeding programs or are part of statutory variety trials or post registration trials. The genotypes have been tested in a limited set of trials and the question is how these genotypes will perform in futur

SUBMITTER: Avagyan V 

PROVIDER: S-EPMC11953130 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature