Maize Genomics and Genetics 2025, Vol.16, No.2, 98-107 http://cropscipublisher.com/index.php/mgg 103 However, the complexity of genetic diversity cannot be ignored. Through quantitative trait loci mapping (QTL), studies have found that NCLB resistance is a trait controlled by multiple genes. In the hybrids of susceptible and resistant maize lines, three major QTLs on chromosomes 2, 5, and 8 were located, explaining a large part of the phenotypic differences (Figure 2) (Ranganatha et al., 2021). It can be seen that the accumulation of multiple resistance alleles is important for improving disease resistance. 5.2 Identification of key gene loci and their role in disease resistance Several key gene loci have been confirmed to play an important role in NCLB disease resistance. The Ht1 gene encodes a nucleotide-binding leucine-rich repeat (NLR) immune receptor and is one of the main disease resistance genes. Experiments have shown that after transgenic maize expresses Ht1, the disease symptoms are significantly reduced, indicating that it plays a significant role in the immune response (Thatcher et al., 2022). In addition, the Htn1 gene encodes a cell wall-associated receptor-like kinase (RLK), which provides quantitative resistance by delaying the formation of lesions, which is a key link in plant defense (Hurni et al., 2015). The study also found that the allelic variation of the ZmWAK-RLK1 kinase gene is related to the main resistance sites Ht2, Ht3, and Htn1, indicating that there is an allelic relationship between these genes and they may share similar disease resistance mechanisms (Yang et al., 2021). This is of great significance for breeding work because it simplifies the genetic structure of resistance and is conducive to the breeding of new disease-resistant varieties. 5.3 Study the impact of heterogeneity on the consistency of results Heterogeneity in maize genetic diversity and disease resistance research often affects the stability and consistency of results. Different experimental designs, environmental conditions, and genetic background differences may be the reasons. For example, different mapping populations or different phenotypic evaluation environments often lead to different NCLB resistance QTLs identified. A study using nested association mapping populations found a total of 29 QTLs, most of which had small effects, indicating that disease resistance is a complex and variable trait (Poland et al., 2011). 6 Discussion and Significance of Maize Disease Resistance Research 6.1 What is the use of these studies in disease resistance breeding? In actual breeding, genetic research on northern corn leaf spot (NCLB) is indeed not just a "paper talk" in the laboratory. Researchers have found some QTLs (quantitative trait loci) related to resistance, such as qNCLB7.02 and qNCLB5.04, which can explain many differences in traits (Chen et al., 2015; Wang et al., 2018). These results provide a directly available "tool" for marker-assisted selection (MAS). Of course, the value of such genetic markers ultimately depends on whether they can perform well in actual breeding. But it's not just these QTLs. Gene alleles like ZmWAK-RLK1 are directly related to major resistance loci such as Ht2, Ht3, and Htn1, and have become objects that can be "locked" in subsequent breeding (Hurni et al., 2015; Yang et al., 2021). They provide more stable genetic support for the next step of "directional resistance enhancement". As for technologies such as genome-wide association analysis (GWAS) and high-density SNP markers, although they sound a bit technical, their core purpose is one: to "fix" disease resistance more precisely. These methods have indeed promoted some inbred lines all the way to the F6 generation, and some materials have shown stable multi-disease resistance (Ranganatha et al., 2021). In other words, combining multiple resistance loci for "integrated breeding" has begun to show results. 6.2 Are there any flaws in the study? Of course there are Although there have been many advances in the research, there are also many problems, especially in terms of data consistency. Sometimes, the results of the same trait in different experimental environments are quite different. Environmental changes and different genetic backgrounds of materials will affect the identification of QTLs and SNPs. As a result, some sites are "useful" in one place, but have little effect in another place (Van
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