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Journal of Integrative Agriculture
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Leveraging disease-resistant gene mining to enhance genomic predictability for southern leaf blight in maize

Youyu Zhao1, 2, Sen Xie1, 2, Guanhua He2, Dengfeng Zhang2, Zhenju Li2, Chunhui Li2, Yu Li2, Tianyu Wang2, Xuyang Liu2#, Yuncai Lu1#, Yongxiang Li2#

1 College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin 150080, China

2 State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China

 Highlights 

Multi-model genome-wide association study (GWAS) in 2,108 maize lines identified 325 quantitative trait nucleotides (QTNs) and 83 candidate genes, delineating southern leaf blight (SLB) resistance genetic architecture.

A compact set of 83 Tag-SNPs achieved prediction accuracy comparable to full genome-wide markers panel.

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摘要  

小斑病(southern leaf blight)是威胁玉米生产的主要病害之一。基因组选择复杂性状预测和改良的有效手段,利用功能信息明确的少量SNP标记进行表型预测,可降低成本和提升模型的生物学可解释性。本研究构建包含2108多样性玉米自交系的关联群体,采用多模型全基因组关联分析框架鉴定出325与小斑病抗性显著关联的QTN83个候选基因。这些候选基因功能显著富集于植物免疫应答通路,包含了已知的玉米抗病基因ChSK1ZmMM1新发现的抗病候选基因ZmCNGC2ZmAGC1.8等,其优异等位基因主要富集于热带种质等特定亚群,而在其他亚群中尚未得到充分利用,显示出巨大的遗传改良潜力。针对以上候选基因,建立83个标签SNP的全基因组选择标记集。在交叉验证和独立验证中,该标签SNP标记集展示出与完整的全基因组标记相当的预测精度。本研究解析了玉米小斑病抗性的遗传结构,并为玉米抗病育种提供了可应用分子标记



Abstract  

Southern leaf blight (SLB) is a significant and persistent threat to global maize production. While genomic selection (GS) offers promise for improving complex traits, strategies leveraging functionally informed SNPs with reduced marker sets can enhance model efficiency, cost-effectiveness, and biological interpretability. Here, we established a large association panel comprising 2,108 diverse inbred lines and employed a multi-model genome-wide association study (GWAS) framework. Through this approach, we identified 325 quantitative trait nucleotides (QTNs) and resolved 83 candidate genes. These candidate genes were functionally enriched in plant immune responses and included known disease resistance gene ChSK1 and ZmMM1. Haplotype analysis revealed that favorable alleles of novel candidate genes including ZmCNGC2 and ZmAGC1.8 are predominantly enriched in specific subgroups such as tropical lines but remain underutilized in other modern breeding materials, indicating significant potential for genetic improvement. Leveraging these genetic insights, we developed a compact set of 83 GWAS Tag-SNPs. This compact marker set achieved genomic prediction accuracy comparable to a full genome-wide markers while reducing marker density by 99.7%. In independent validation, the compact SNP set maintained robust predictive ability, which could be further enhanced by incorporating population structure as covariates. Our study provides a comprehensive dissection of the genetic architecture of SLB resistance and offers a cost-effective and biologically interpretable framework for disease resistance breeding in maize.

Keywords:  maize       southern leaf blight              genome-wide association study              genomic selection  
Online: 10 March 2026  
Fund: 

This research was supported by funding from National Key Research and Development Program of China (2021YFD1200700), China Agriculture Research System (CARS-02-04), Innovation Program of Chinese Academy of Agricultural Sciences (CAAS-CSCB-202403), Rural Revitalization Agricultural Science and Technology Project of Beijing (NY2401030324) and Basic Research Funds for Higher Education Institutions in Heilongjiang Province (2024-KYYWF-0119).

About author:  #Correspondence Xuyang Liu, E-mail: liuxuyang@caas.cn; Yuncai Lu, E-mail: luyuncai@hlju.edu.cn; Yongxiang Li, E-mail: liyongxiang@caas.cn

Cite this article: 

Youyu Zhao, Sen Xie, Guanhua He, Dengfeng Zhang, Zhenju Li, Chunhui Li, Yu Li, Tianyu Wang, Xuyang Liu, Yuncai Lu, Yongxiang Li. 2026. Leveraging disease-resistant gene mining to enhance genomic predictability for southern leaf blight in maize. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2026.03.026

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