Maize Genomics and Genetics 2025, Vol.16, No.5, 239-250 http://cropscipublisher.com/index.php/mgg 240 al., 2024). Ultimately, the combination of GS and ML is not for showing off skills, but to truly find a way to deal with yield prediction in complex environments-especially under the increasingly normalized stress condition of drought (Wu et al., 2024; He et al., 2025). This study is not intended to propose a new method, but rather to systematically review the existing achievements: This includes the challenges encountered in predicting corn yields under drought conditions, the application logic and development progress of GS and ML in breeding, several real cases verifying the effectiveness of their combination, as well as future directions worth paying attention to, such as how to build data infrastructure and how policies can support to truly promote the implementation of these technologies in agricultural practice. 2 Genomic Selection in Maize Breeding 2.1 Principles of genomic selection (GS) On the surface, GS is a modern breeding tool that "predicts traits using whole-genome molecular markers". It sounds grand and sophisticated, but in essence, it's about using all the genotype information you have at hand, regardless of whether it has a significant or minor impact, and then putting it all into the model for training-the aim is to estimate the potential of each corn material in a certain trait, such as drought resistance. This is quite different from the previous practice of selecting seeds by relying on a few principal QTLS. In the past, methods focused on key points, but GS adheres to the principle of "leaving no one unchecked". Not only potent sites but also those with minor effects are included. In this way, we can obtain the so-called genomic estimated breeding value (GEBV), and then we do not have to rely entirely on phenotypes when making subsequent seed selection decisions. Of course, there are many types of models, such as ridge regression, Bayes A/B, and random forest methods, all of which are used to train prediction accuracy (Shikha et al., 2017; Nepolean et al., 2018). However, which model to choose actually depends on the characteristics of the data. Sometimes, there is no "universal answer". 2.2 Advantages over conventional breeding To be honest, when it comes to drought-resistant breeding, traditional methods are indeed not very easy. For decades, breeding experts have been struggling with the old problems of "low heritability, significant environmental interference and slow progress", especially when it comes to complex traits, they find it even more difficult to take steps forward. But the emergence of GS has torn a new knot in this deadlock. It doesn't wait for the corn to grow out to observe its performance, but rather determines in advance-at the seed stage or even earlier-whether this plant is worth continuing to cultivate through genetic information (Chen, 2024). The benefits of this "early judgment" are obvious: it saves time, reduces experimentation, and also avoids wasting resources on materials with little potential. For those who are in a hurry to shorten the breeding cycle, GS is more like a speed-up tool. However, GS is not just about being fast. It can also be "multi-functional"-not only considering drought resistance, but also taking into account other traits simultaneously, such as yield, plant type, and even quality. This kind of multi-objective improvement is almost impossible to achieve in traditional methods, or rather, it is extremely inefficient. Some studies simply rely on figures: under drought conditions, using the GS method can increase the yield of corn by approximately 7.3% (Vivek et al., 2017; Das et al., 2021). This extent, when placed in actual production, is already a considerable gain-not a theoretical improvement, but a real increase in money. 2.3 Application to drought tolerance When it comes to application, the performance of GS in drought-resistant breeding of corn is obvious to all. Especially in those regions with intense climate change and frequent droughts, GS has significantly improved the efficiency of seed selection. The efficiency of trying one piece at a time as in the past is far from sufficient. Nowadays, researchers have been able to predict in advance the performance of certain corn strains under drought by modeling whole-genome SNP data. Not only that, these models can also identify key genetic factors related to the drought resistance response mechanism, such as root development, stomatal regulation, and even hormone signals (Figure 1) (Liu and Qin, 2021; Sheoran et al., 2022). More detailed approaches also include the introduction of multi-environmental test data, taking into account both additive and dominant effects to adapt to
RkJQdWJsaXNoZXIy MjQ4ODYzNA==