Maize Genomics and Genetics 2025, Vol.16, No.6, 304-315 http://cropscipublisher.com/index.php/mgg 304 Feature Review Open Access Multi-Environment Genomic Prediction Models for Hybrid Maize Performance Hongpeng Wang, Minghua Li Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China Corresponding author: minghua.li@jicat.org Maize Genomics and Genetics, 2025, Vol.16, No.6 doi: 10.5376/mgg.2025.16.0028 Received: 28 Sep., 2025 Accepted: 15 Nov., 2025 Published: 30 Nov., 2025 Copyright © 2025 Wang and Li, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Wang H.P., and Li M.H., 2025, Multi-environment genomic prediction models for hybrid maize performance, Maize Genomics and Genetics, 16(6): 304-315 (doi: 10.5376/mgg.2025.16.0028) Abstract Hybrid maize breeding relies on accurate prediction of hybrid performance across diverse environments to enhance yield stability and adaptability. This study focused on developing and evaluating Multi-Environment Genomic Prediction (MEGP) models to improve the predictive accuracy of hybrid maize performance under variable environmental conditions. We first examined the principles of genomic prediction and the limitations of single-environment models before implementing MEGP frameworks that integrate genotype-by-environment (G×E) interactions through reaction norm and factor analytic approaches. Environmental variation was quantified using spatial and temporal covariates, while envirotyping provided additional insights into environmental effects on hybrid performance. A multi-year hybrid maize trial was conducted to assess the MEGP models, integrating genotypic, phenotypic, and environmental data. Results demonstrated that MEGP models significantly outperformed single-environment models in predictive accuracy and heritability estimates, highlighting their potential for more robust selection decisions. The study also explored the integration of high-throughput phenotyping, remote sensing, and machine learning techniques to further enhance model performance. Overall, MEGP models present a promising framework for accelerating hybrid maize breeding, improving climate resilience, and supporting global breeding networks through data-driven decision-making. Keywords Genomic prediction; Hybrid maize; Multi-environment models; G×E interactions; Breeding optimization 1 Introduction When it comes to hybrid corn breeding, what often comes to mind first are high yield and stable yield. But this outcome is actually not accidental. Since the 20th century, hybrid breeding has been regarded as one of the most core approaches to crop improvement. Based on the principle of "heterosis", researchers have continuously improved the parent combination and eventually cultivated a batch of corn varieties that have performed outstandingly in commercial production-they can not only achieve high yields but also maintain stability under different adverse conditions (Zhao et al., 2025). Of course, the process is not as simple as it sounds. Breeding work often begins with the selection of suitable inbred lines, which should have ideal "compatibility", taking into account both general and special aspects. Afterwards, these combinations will be placed in multi-environment trials (METs) for verification. There are significant differences in location, climate and soil. A variety may perform extremely well in one place but be unremarkable in another environment. It is precisely this complex relationship between genotypes and the environment (GEI) that makes breeding work full of challenges. To evaluate these interaction effects more accurately, researchers have to rely on more sophisticated statistical tools and computational models (Yu et al., 2020; Supriadi et al., 2024; Popa et al., 2025). The emergence of genomic prediction (GP) has changed all of this. Results that used to take years of experimentation to obtain can now be "rehearsed" in advance through data simulation. GP uses whole-genome molecular markers to estimate an individual's breeding value. In other words, it uses data to replace field trials and run through them first (Wang, 2025). Compared with traditional marker-assisted selection, it is better at handling complex traits influenced by multiple genes, such as yield and stress resistance. Even for genotypes that have never been tested, the model can provide relatively accurate predictions. This means that the breeding cycle has shortened, costs have decreased, and the pace of genetic improvement has also accelerated. Nowadays, GP has
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