Maize Genomics and Genetics 2025, Vol.16, No.6, 316-324 http://cropscipublisher.com/index.php/mgg 321 Figure 2 Schematic diagram showing heat stress response in maize at the cellular level (Adopted from Djalović et al., 2023) 7.2 Integration of MAS, GS, and gene editing for improved silk emergence To make good use of these sites in actual breeding, it depends on whether the tools at hand are precise enough. MAS is still quite useful in the early stage, especially for those major QTLS with obvious additive effects, which can be easily introduced into strains. However, traits like the filament stage are often not explained by just one or two large QTLS, but rather by the combined effect of many small effect sites. At this point, GS comes in handy - it can integrate these scattered genetic effects and enhance the predictive ability (Budhlakoti et al., 2022). As for gene editing, it is no longer just a theory now. Tools like CRISPR/Cas9 have been able to directly target QTL regions for modification. Some teams have even begun to attempt multi-gene editing, targeting the entire gene network to improve the overall expression of complex traits. MAS, GS and gene editing - none of these three tools can be lacking. A combination of them is the key to success. 7.3 Establishing genetic improvement models for heat-adapted silk emergence traits Breeding cannot be accomplished merely by relying on a single good QTL; a model is needed to connect these scattered pieces of information. To construct an improved strategy adapted to heat stress, it is necessary to consider the relationship among genes, phenotypes and the environment simultaneously. For instance, MAS can focus on major loci, but GS models have more advantages in multi-environment predictions, especially for those traits that are greatly influenced by the environment (Cerrudo et al., 2018). Nowadays, many teams are beginning to attempt to combine the two, along with high-throughput phenotyping and genotyping technologies, to increase the "hit rate" of selection. The ultimate goal is to establish a continuously operating breeding pipeline: in the early stage, breakthroughs are sought through QTL localization; in the middle stage, multi-site combinations are predicted by GS; and in the later stage, details are corrected by gene editing (Leng et al., 2022). The climate is becoming increasingly uncontrollable, and the window of opportunity for breeders to make mistakes is getting smaller and smaller. 8 Conclusion and Future Perspectives Some QTLS related to the occurrence time of filaments under high-temperature stress, although often regarded as having the potential to improve the adaptability of corn to rising temperatures, have not always been smoothly applied in practice. Meta-QTL analysis has indeed screened out some regions and candidate genes that are relatively stable in multiple environments, among which there are no shortage of transcription factor families involved in stress responses (which can be used for marker-assisted selection and functional studies), but the
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