Maize Genomics and Genetics 2025, Vol.16, No.2, 70-79 http://cropscipublisher.com/index.php/mgg 76 established - all of which are to preserve these precious genetic diversity for a long time. Interestingly, although these technologies have their own characteristics, their purpose is the same: to leave a "backup" for future breeding work. The international community also attaches great importance to this matter. Frameworks such as the Convention on Biological Diversity and the Global Plan of Action (Mahato et al., 2018) particularly emphasize the importance of protecting agricultural biodiversity. However, protection alone is not enough, and these resources must be available to everyone. The current global sharing of germplasm has indeed made it easier for researchers to obtain more diverse genetic materials to improve crops. Although it may be a bit complicated to operate, it is indeed critical for breeding work. 7 Research Challenges and Future Prospects 7.1 Key Issues in current sweet corn germplasm resource research There is a headache in studying fresh corn germplasm now - the genes between varieties are too similar. Ferreira et al. found in 2018 (Ferreira et al., 2018) that the genetic diversity of fresh corn mainly exists within a single variety, and there is little difference between different varieties. This is troublesome. Think about it, the genetic differentiation between inbred lines is not high, and coupled with this situation, it is particularly difficult for breeders to introduce new traits into corn. Although the internal variation of a single variety is relatively rich, this "dominant" diversity pattern is indeed a considerable obstacle to the breeding of new and improved varieties. There is a paradoxical phenomenon in corn breeding: there are many good things in the germplasm bank, but few are actually used in commercial breeding. The reason is that it is too troublesome to use these resources well. Just think about it, just to figure out their genotypes and phenotypes, you have to do a lot of testing and evaluation work. Although the diversity in the resource library is quite rich (Ferreira et al., 2018), in actual operations, breeders often still focus on the few commonly used varieties. This is easier said than done, resulting in many potential excellent genes being idle and failing to play their due value. 7.2 Potential of interdisciplinary approaches in genetic diversity analysis Now, the old methods alone are no longer enough to study the genetic diversity of fresh corn. Interestingly, the combination of new methods such as molecular biology and bioinformatics with traditional statistical genetics has surprisingly good results. For example, high-throughput sequencing technology such as GBS can obtain massive amounts of SNP data at once, making genetic variation clearly visible. But then again, it is not enough to just look at genes, and we have to evaluate them in combination with actual performance in the field. Although these new technologies are a bit complicated to use, they do allow us to have a more comprehensive understanding of the genetic characteristics of fresh corn. After all, to truly understand these corn varieties, we have to take into account everything from laboratory data to field performance. New tools for studying fresh corn are becoming more and more interesting. Take artificial neural networks, for example. When combined with GT biplot analysis (Mehta et al., 2017), the relationship between genotypes and traits can be more accurately found. Although these methods sound high-tech, to put it bluntly, they help us find good varieties faster. Interestingly, through these analyses, breeders can lock in those genotypes that may have high yields and high quality in advance, without having to look for a needle in a haystack as before. Of course, these tools are not omnipotent, but they do provide new ideas for breeding better fresh corn varieties. 7.3 Applications of data integration and multivariate analysis in future research In the future, the key to studying fresh corn germplasm resources is to integrate various data. You see, molecular data such as SNP and microsatellites are not enough. They must be analyzed in combination with field performance data and environmental factors (Diwan et al., 2015). Only in this way can we fully understand the practical role of genetic diversity. In fact, multivariate statistical methods such as PCoA and cluster analysis are quite useful (Kumar et al., 2022), which can help us clarify the genetic background of different corn varieties. Although the operation is a bit complicated, the parent materials selected in this way are indeed more reliable, and the new varieties bred are more adaptable. But then again, it is also a big challenge for researchers to handle so much data.
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