Maize Genomics and Genetics 2025, Vol.16, No.5, 251-257 http://cropscipublisher.com/index.php/mgg 254 on chromosome 9 (Figure 1) (Zheng et al., 2023). Of course, when the area is large, it is not easy to operate. Therefore, researchers introduced weighted correlation network analysis (WGCNA) and RNA-seq data integration to further refine the candidate library. These combined methods are more precise and efficient in locating QTLS compared to a single technical approach. Figure 1 (A) The phenotype of visual stay-green trait in T01 and Xin3. (B) Frequency distribution of visual stay-green in the F2 population of maize. (C) The performance of visual stay-green in two extreme mixed pools of visual stay-green (Adopted from Zheng et al., 2023) 5.2 Recombination event analysis and interval narrowing of target regions The narrowing of candidate regions often depends not on luck but on whether one can seize those key recombination individual plants. In the analysis of recombination events, researchers rely on these "useful mutations" to gradually minimize the interval. Take the region of chromosome 9 for example. By integrating the BSA-seq and RNA-seq data, only three candidate genes of key concern remained in the end: Zm00001eb378880, Zm00001eb383680 and Zm00001eb384100 (Zheng et al., 2023). Moreover, this method does not rely solely on sequencing data. It also needs to be combined with phenotypic data to truly link functional sites with phenotypes, just as Fang et al. (2012) did, in order to achieve the goal of precise localization. 5.3 Functional annotation, expression analysis, and prediction of candidate genes After narrowing down the intervals, the next step is naturally to "dig out" each candidate gene one by one to see which one is more like the main cause. Researchers usually conduct functional annotations first, that is, by using methods such as gene ontology and homology comparison, to figure out what these genes usually do. Immediately after, the differences of these genes in green-preserving and non-green-preserving materials are examined through expression profile data (such as RNA sequencing). If a certain gene is "abnormally active" or obviously "silent" in the processes of photosynthesis, chlorophyll metabolism, and aging, then it is the key suspect. Like nac7, it has been proven to be a negative regulatory factor of senescence. As long as its expression is inhibited, leaf senescence slows down and yield increases (Zhang et al., 2019; Zhou and Liang, 2024). Strategies like this one that integrates "expression + annotation + functional prediction" have laid a solid foundation for subsequent marker-assisted selection and trait improvement. 6 Case Study: Fine Mapping of Stay-Green QTLs 6.1 Fine mapping of a stay-green QTL on chromosome 9 in maize Not all QTLS can be successfully located to specific genes, but there are exceptions. Taking a QTL related to greenness retention on chromosome 9 as an example, researchers gradually narrowed down the scope to a NAC domain transcription factor gene in a population with significantly different aging processes, which was later named nac7 as the target gene. In the transgenic corn experiment, after down-regulating the expression of nac7 through RNAi technology, leaf senescence was delayed, the biomass of the plants increased, and the nitrogen accumulation capacity was also strengthened. These changes basically indicate that nac7 is a typical negative
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