Maize Genomics and Genetics 2025, Vol.16, No.5, 251-257 http://cropscipublisher.com/index.php/mgg 253 come from multiple growth cycles and locations, with the aim of capturing the differences in genotype and environmental interactions. In addition to the SPAD value and the number of green leaves, the visual scores during the development period should also be recorded, and even the yield traits should be taken into account. Evaluating this type of data cannot rely solely on intuition. Repeated field trials are standard. Statistically, mixed linear models or BLUP are generally run to estimate QTL effects and the heritability of traits (Li et al., 2020). The ultimate goal is to identify those NIL that remain stable and green in a variable environment. 3.3 Field evaluation standards and stay-green scoring system construction How does the field evaluate it? In fact, it's not that mysterious. Generally, a visual scoring system from 1 to 9 is used first, and then combined with the readings of the SPAD instrument and the records of the number of green leaves, to suppress the visual subjectivity. The scoring must of course be uniform, especially when it comes to the consistency of standards across years and locations; otherwise, the comparability will be lost (Zheng et al., 2024; Zhong et al., 2025). When this scoring method is combined with molecular markers, it can not only precisely locate QTLS but also improve the selection efficiency of NIL. It is particularly suitable for projects that pursue rapid breeding and precise positioning. 4 Preliminary QTL Mapping and Validation 4.1 Construction of QTL linkage maps and definition of target regions At the beginning, to identify the green-holding trait of corn, most studies start with constructing a linkage map. Not all graphs are complex, but there are also versions drawn with over 100 SSR markers, covering 10 linkage groups relatively comprehensively, with a total length even exceeding 1 400 cM (Trachsel et al., 2016). In this case, multiple QTLS at different developmental stages can be identified. The distribution locations are basically throughout the entire genome. Of course, some of the major QTLS themselves can explain about 13.5% of the phenotypic variations (Wang et al., 2012; Yang et al., 2017), which can be regarded as relatively "strong signal" segments. As for the target areas that are planned to be further precisely positioned later, they are usually placed between the markers close to both sides of the target QTL, which is more convenient for operation. 4.2 QTL effect validation in different genetic backgrounds Sometimes, a QTL that stands out in one material may not be "effective" when its genetic background is changed. Therefore, verification work cannot be carried out only on one group. Researchers often infuse principal QTLS into different strains, such as heterogeneous inbred lines, to see if they can still control the aging process or yield performance in other materials. QTLS related to NAC domain transcription factors like nac7 have been verified in multiple genetic contexts. After down-regulation, the leaves age more slowly and the yield increases instead (Zhang et al., 2019). Similar validations have also emerged in other materials and populations. Some QTLS have repeated results in different studies, indicating that their performance is not accidental (Belicuas et al., 2014). 4.3 Correlation analysis between preliminary QTL and stay-green phenotypes If QTL is truly useful for traits, it must be able to "bring out" some phenotypes, such as chlorophyll content, green leaf area, and yield. Many analyses have indeed shown that materials with green-preserving alleles have a significant advantage in these phenotypic indicators (Zheng et al., 2009; Wang et al., 2012). Interestingly, there is overlap between the regional and production-related QTLS of green conservation QTLS. This "collision" actually validates the value of these QTLS in turn (Bhadmus et al., 2022). Therefore, in breeding, this type of QTL is very suitable for use as a marker-assisted selection, which helps to simultaneously enhance stress resistance and yield stability. 5 Fine Mapping and Candidate Gene Identification 5.1 Development of high-density molecular markers and recombinant line screening To precisely lock the greenish retention trait of corn to a small chromosomal interval, relying solely on traditional mapping techniques is far from sufficient. Nowadays, a more common approach is to use high-density molecular markers such as SNPS and SSRS in combination with a hybrid separation strategy of next-generation sequencing (NGS) and BSA-seq to quickly lock onto the target region. As in a recent study, F2 populations of chlorogenic and non-chlorogenic parents were used for BSA-seq, and a large candidate region containing 778 genes was screened
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