Maize Genomics and Genetics 2025, Vol.16, No.6, 304-315 http://cropscipublisher.com/index.php/mgg 310 During the modeling process, an environmental similarity matrix was also introduced, and dimensionality reduction methods such as principal component analysis were adopted to alleviate the computational burden-this way, the main information could be retained without making the model run too "heavily". Ultimately, the team adopted cross-validation to test the model's performance and compared the results with those of the single-environment model and the main effect model to ensure that the overall results were not only stable but also understandable. 5.3 Key findings: predictive accuracy, trait heritability, and implications for breeding The results show that the MEGP model, which takes into account the genotype-environment interaction and environmental covariates, is significantly superior to traditional methods in both prediction accuracy and result stability. Taking grain yield and grain moisture content as examples, the prediction accuracy rates reached 0.33 and 0.73 respectively, and the coincidence degree between the predicted values and the measured superior hybrid varieties exceeded 50%. When additive effects, dominant effects and environmental variables are simultaneously incorporated into the model, the prediction effect is further enhanced-the prediction accuracy of certain traits even increases by 22%. Interestingly, in an environment with relatively ideal conditions, heritability estimates are generally higher, indicating that genetic differences are more prominent under high-quality ecological conditions (Tolley et al., 2023). These results, from one aspect, confirm the potential of the MEGP model in actual breeding: it can not only help identify excellent combinations that are stable across environments, but also guide resource allocation and selection strategies, thereby making corn breeding more efficient and targeted. 6 Enhancing MEGP Model Accuracy and Utility 6.1 Integration of high-throughput phenotyping and remote sensing data In the past, researchers mainly relied on field observations and genomic data to assess the performance of hybrids, but such information was often too "static". With the popularization of high-throughput phenotypic analysis (HTP) and remote sensing technology, the situation has changed. Continuous phenotypic data obtained through multispectral imaging, unmanned aerial vehicle (UAV) measurement and other methods enable the model to more truly reflect the dynamic changes of crops at different times and Spaces. These data often appear in the form of time series phenomics indicators, such as NDVI and other vegetation indices, which can more accurately characterize field heterogeneity. The results show that compared with relying solely on genomic data, the prediction accuracy of key traits such as flowering period and plant height can be improved by approximately 30% after incorporating phenomics information (Adak et al., 2023). More interestingly, this fusion can also reveal the dynamic relationship between genotypes and abiotic stress-for instance, which genes respond most significantly during droughts or high temperatures. Such methods not only enhance predictive performance but also help identify candidate genes and trait markers related to stress resistance, providing more intuitive biological clues for breeding. 6.2 Use of deep learning and bayesian frameworks for improved prediction In terms of modeling tools, the limitations of traditional linear models are gradually emerging. The introduction of deep learning has brought about a new breakthrough in predictive capabilities. It can automatically identify the complex nonlinear relationships among genotypes, environments, and traits without the need for manual explicit setting of G×E interaction terms. Studies have shown that deep learning models with multiple traits and multiple environments have prediction accuracy approximately 6% to 14% higher than traditional Bayesian or linear models in complex traits such as flowering time and grain yield (Mora-Poblete et al., 2023). However, the Bayesian method has not been "replaced" by deep learning. Models like BayesB and BMTME still perform quite robustably when considering the interaction between group structure or markers and the environment. Their strength does not lie in speed, but in enabling researchers to clearly understand the internal logic of the model-parameters are adjustable and explanations are intuitive. This makes them still frequently used in multitrait analysis or complex population studies (Yu et al., 2024).
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