Molecular Plant Breeding 2024, Vol.15, No.6, 340-350 http://genbreedpublisher.com/index.php/mpb 348 identified 40 meta-QTLs (MQTLs) associated with multiple traits related to FER and GER resistance, with 29 MQTLs linked to 2~5 traits each. Another study mapped 11 QTLs for GER resistance, with a novel QTL, qGER4.09, showing potential for multi-pathogen resistance. Additionally, linkage mapping and genome-wide association studies (GWAS) have identified numerous QTLs and SNPs associated with yield-related traits and disease resistance, further elucidating the genetic basis of these complex traits. Single nucleotide polymorphism (SNP) markers have played a crucial role in enhancing maize resistance to ear rot diseases and improving yield. SNP markers facilitate high-resolution mapping and the identification of candidate genes associated with disease resistance. For example, the use of dense genome-wide SNPs enabled the identification of stable MQTLs and candidate genes for FER and GER resistance. Similarly, SNP arrays have been used to genotype recombinant inbred line (RIL) populations, leading to the discovery of significant QTLs for GER resistance. The integration of SNP markers with QTL mapping and GWAS has also revealed genomic loci associated with yield-related traits, providing valuable information for marker-assisted selection in breeding programs. Future research should focus on the following priorities to further improve maize resistance to ear rot diseases and enhance yield. Continued efforts are needed to validate and fine-map identified QTLs to narrow down the candidate regions and identify the underlying resistance genes. This will facilitate the development of more precise markers for breeding programs. Combining QTL mapping with transcriptomics, proteomics, and metabolomics can provide a comprehensive understanding of the molecular mechanisms underlying disease resistance and yield traits. This integrative approach will help identify key regulatory networks and pathways involved in these complex traits. Implementing genomic selection strategies that incorporate identified QTLs and SNP markers can accelerate the breeding of maize varieties with enhanced resistance and yield. This approach will enable the selection of individuals with favorable alleles for multiple traits, improving overall breeding efficiency. Expanding the genetic diversity of breeding populations by incorporating diverse germplasm can uncover novel resistance alleles and enhance the genetic base of cultivated maize. This will provide new opportunities for improving disease resistance and yield stability. Given the impact of climate change on disease prevalence and crop performance, future research should also focus on developing maize varieties that are resilient to changing environmental conditions. This includes identifying QTLs and genes associated with stress tolerance and integrating them into breeding programs. By addressing these research priorities, the application of QTL mapping and genomic tools will continue to advance the development of high-yielding, disease-resistant maize varieties, contributing to global food security and agricultural sustainability. Acknowledgments The authors appreciate the anonymous peer reviewers for their suggestions on the manuscript of this study. Funding This study was supported by the project of "QTL Localization of Maize Resistance to Ear Rot Based on SNP Markers and Cultivation of New Varieties with High Yield, Disease Resistance and Good Harvest" (20240303017NC) of Jilin Provincial Department of Science and Technology. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Akohoue F., and Miedaner T., 2022, Meta-analysis and co-expression analysis revealed stable QTL and candidate genes conferring resistances to Fusarium and Gibberella ear rots while reducing mycotoxin contamination in maize, Frontiers in Plant Science, 13: 1050891. https://doi.org/10.3389/fpls.2022.1050891 PMid:36388551 PMCid:PMC9662303
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