Simulations of genomic selection implementation pathways in common bean (Phaseolus vulgaris L.) using parametric and nonparametric models.

ABSTRACT: We conducted simulations of common bean (Phaseolus vulgaris L.) breeding programs to better understand the interplay between different choices a breeder must make when launching a genomic selection (GS) pipeline. We complement preceding studies on optimizing model parameters and training set makeup by exploring the practical implementation of GS in a common bean breeding program aimed at increasing seed yield. We simulated 24 GS implementation pathways on (1) what generation to train a new prediction model, (2) what generation to select parents for the next cycle, (3) which generation to collect training data, and (4) whether to use a parametric (ridge regression best linear unbiased predictor) or a nonparametric model (artificial neural network) for estimating breeding values. We found th

SUBMITTER: Chiaravallotti I 

PROVIDER: S-EPMC12547642 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature