Maize Genomics and Genetics 2025, Vol.16, No.6, 304-315 http://cropscipublisher.com/index.php/mgg 305 almost become the "standard configuration" of modern breeding systems, supporting key links such as rapid cycling, optimized training population and multi-omics data integration (Beyene et al., 2019; Alemu et al., 2024; Zhang et al., 2025). However, even the most advanced algorithms have blind spots. If environmental differences are ignored, model predictions are very likely to "go off track". Introducing multi-environment data into genomic prediction is the key to improving the accuracy of prediction. Practical experience has shown that models that can take into account environmental covariates, spatial variations or label-environment interactions often perform better than single-environment models. It is reported that the prediction accuracy of the main traits of corn can be increased by an average of 12% to 20%. For breeders, this is not merely a numerical advancement, but rather an opportunity to truly screen out more stable and reliable varieties. Especially in today's increasingly intense climate change, the importance of stability has been infinitely magnified. Meanwhile, the introduction of deep learning and multimodal data enables complex GEI patterns to be presented more precisely. The goal of breeding is also quietly changing, from a single "high yield" to "high yield and wide adaptability", laying the foundation for cultivating corn hybrids with greater environmental adaptability (Xu et al., 2022; Gao et al., 2025; Zou et al., 2025). 2 Fundamentals of Genomic Prediction in Maize 2.1 Basic principles of genomic selection and prediction accuracy In corn breeding, genomic selection (GS) is no longer a novelty. Its core idea is actually quite straightforward-through whole-genome molecular markers, the genetic potential of individuals can be judged in advance, thus enabling the identification of "good signs" at an early stage of breeding. However, the accuracy of this process is not constant. Marker density, the size and composition of the training population, trait heritability, genetic structure, and the type of model used all have an impact on the prediction results (Zhang et al., 2019). Generally speaking, the denser the markers, the larger the population, and the more diverse the genetic differences, the more reliable the prediction results will be, especially for traits with strong heritability. Interestingly, the information captured by different models also varies. Models like GBLUP, BayesB, and RKHS not only look at the additive effect but also take the non-additive part into account, which makes them perform more stability in the prediction of hybrid maize traits (Kaler et al., 2022). Furthermore, if environmental factors or the interaction between genotype and environment (G×E) are added to the model, the prediction accuracy often reaches a new level, especially for traits that are greatly affected by the environment, such as grain yield and drought resistance, the improvement is particularly significant. 2.2 Role of marker-based models in predicting hybrid performance In corn breeding, marker-based prediction models are almost the mainstay. They estimate the genomic breeding values (GEBV) of hybrid combinations that have not yet undergone field trials through SNP markers or other genotyping platforms. However, merely having markers is not enough. Incorporating dominant effects, superposition effects and population structure into the model together will make the prediction results closer to reality, especially for traits that have a significant impact on non-additive inheritance (Luo et al., 2024). Interestingly, some studies have mentioned that when the model incorporates both additive and dominant effects, the prediction accuracy of corn grain yield can increase by approximately 30% (Ferrão et al., 2020). However, if one goes further and combines the functional markers screened out in the GWAS analysis with the trait specific markers, the prediction results tend to be more stable and closer to the true performance (Yu et al., 2022). For breeders, the significance of such improvements does not lie in the technology itself, but in saving experimental costs. In the past, it took a lot of time and resources to conduct hundreds of field validations. Now, with the help of these models, more promising combinations can be selected before the experiment begins. In other words, it has transformed breeding from "trying it out" to "calculating it out", accelerating the pace of genetic improvement.
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