Molecular Plant Breeding 2024, Vol.15, No.6, 340-350 http://genbreedpublisher.com/index.php/mpb 347 and Miedaner, 2022). The combination of QTL mapping and GPS analysis identified seven candidate genes linked to GER resistance (Yuan et al., 2022). Furthermore, GWAS and linkage mapping revealed 45 SNPs and 15 haplotypes significantly associated with FER resistance, with eight colocated loci on chromosomes 2, 3, 4, 5, 9, and 10 (Chen et al., 2016). These findings have enabled the development of maize varieties with improved disease resistance and yield, contributing to more sustainable maize production. 8.4 Lessons learned and future application of these techniques in maize breeding The successful application of QTL mapping and SNP marker integration in breeding programs has highlighted several key lessons. Firstly, the complexity of disease resistance traits necessitates the use of multiple genetic approaches to identify stable QTL and candidate genes (Akohoue and Miedaner, 2022). Secondly, the integration of advanced genomic tools, such as GWAS and GPS, enhances the precision and efficiency of identifying resistance loci (Yuan et al., 2022). Future applications of these techniques should focus on the continuous refinement of QTL and candidate gene identification, as well as the incorporation of these markers into genomics-assisted backcross breeding strategies to develop elite cultivars with enhanced resistance to multiple diseases (Chen et al., 2016; Akohoue and Miedaner, 2022). Additionally, exploring the genetic basis of resistance in diverse maize germplasm will further broaden the genetic pool for breeding programs, ensuring the development of robust and high-yielding maize varieties (Hou et al., 2024). 9 Challenges and Future Directions in QTL Mapping for Maize Resistance 9.1 Challenges in identifying stable QTLs across environments Identifying stable quantitative trait loci (QTLs) for resistance to ear rot in maize across different environments remains a significant challenge. Environmental factors such as climate, soil type, and pathogen variability can influence the expression of resistance traits, making it difficult to pinpoint QTLs that consistently confer resistance. For instance, studies have shown that QTLs identified in one environment may not be effective in another due to these environmental interactions (Akohoue and Miedaner, 2022). Additionally, the genetic architecture of resistance traits often involves multiple minor-effect QTLs, which further complicates the identification of stable QTLs (Zhou et al., 2021; Yuan et al., 2022). 9.2 Integrating multi-environment trials to refine QTL mapping To address the challenge of environmental variability, integrating data from multi-environment trials is crucial. This approach allows researchers to identify QTLs that are consistently expressed across different conditions, thereby refining the mapping of resistance traits. For example, multi-parent QTL mapping and joint multiple environments analysis have been employed to identify QTLs with additive effects that are stable across various environments (Yuan et al., 2022). Such methods enhance the reliability of QTL mapping by accounting for genotype-by-environment interactions, leading to more robust identification of resistance loci (Ding et al., 2008; Galiano-Carneiro et al., 2020). 9.3 Opportunities for improving the precision and accuracy of QTL-based breeding Advancements in genomic technologies and statistical methods offer new opportunities to improve the precision and accuracy of QTL-based breeding. Techniques such as meta-analysis and co-expression analysis can refine QTL mapping by integrating data from multiple studies, thereby identifying meta-QTLs that are more reliable for breeding programs (Akohoue and Miedaner, 2022). Additionally, combining QTL mapping with genome-wide association studies (GWAS) and RNA sequencing can uncover candidate genes within QTL regions, providing a more detailed understanding of the genetic basis of resistance (Chen et al., 2016; Xia et al., 2022). These integrated approaches can facilitate the development of molecular markers for marker-assisted selection, ultimately enhancing the efficiency of breeding programs aimed at improving resistance to ear rot in maize (Butrón et al., 2019; Wen et al., 2020). 10 Concluding Remarks Quantitative trait loci (QTL) mapping has been instrumental in identifying genetic regions associated with resistance to ear rot diseases in maize, such as Fusarium ear rot (FER) and Gibberella ear rot (GER). Several studies have successfully mapped QTLs that confer resistance to these diseases. For instance, a meta-analysis
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