Optimizing training sets for genomic selection to identify superior genotypes across multiple environments
Journal
G3: Genes, Genomes, Genetics
Journal Volume
16
Journal Issue
4
Start Page
1
End Page
15
ISSN
2160-1836
Date Issued
2026-02-10
Author(s)
Liu, Zi-Jie
Editor(s)
de Koning, D-J
Abstract
Genomic selection (GS) is a promising strategy in plant breeding for identifying superior genotypes with high true breeding values (TBVs) across multiple environments. However, the relative performance of candidate genotypes often varies due to complex genotype-by-environment (G × E) interactions in multienvironment trials (METs). To address this challenge, we employed a GS prediction model incorporating fixed environment-specific means, random additive genetic effects, and random additive G × E interaction effects to develop training set optimization methods for GS in METs. Two optimization methods derived from the generalized coefficient of determination (CD) criterion—CDmean(v2) (Chen et al. 2024, equivalent to Rincent et al. 2012) and CDmean.MET (Rio et al. 2022)—were evaluated and compared with random sampling. Rather than relying on prediction accuracy–focused correlation metrics, we assessed training set performance using selection-focused ranking metrics, including normalized discounted cumulative gain, Spearman’s rank correlation, and rank sum ratio. Because TBVs are latent and unobservable, simulation experiments were conducted using real genotype data from diverse crop datasets, including rice (Oryza sativa L.), barley (Hordeum vulgare L.), and maize (Zea mays L.). Among the evaluated approaches, CDmean(v2) consistently showed high efficiency in identifying top-performing genotypes. In practice, CDmean(v2), implemented using the optimization algorithm provided in the TrainSel package (Akdemir et al. 2021), is recommended for GS-assisted breeding programs, as it produced superior training sets for identifying elite genotypes with reasonable computational cost. © The Author(s) 2026. Published by Oxford University Press on behalf of The Genetics Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Subjects
genomic prediction
genotype-by-environment interaction
multienvironment trial
plant breeding
training set optimization
Publisher
Oxford University Press (OUP)
Type
journal article