Gupta Mamta, Choudhary Mukesh, Singh Alla, Sheoran Seema, Kumar Harish, Singla Deepak, Bohra Abhishek, Rakshit Sujay
ICAR-Indian Institute of Maize Research, PAU Campus, Ludhiana, Punjab, 141 004, India.
Punjab Agricultural University, Regional Research Station, Faridkot, Punjab, 151203, India.
Heliyon. 2024 Dec 14;11(1):e40984. doi: 10.1016/j.heliyon.2024.e40984. eCollection 2025 Jan 15.
Viral diseases severely impact maize yields, with occurrences of maize viruses reported worldwide. Deployment of genetic resistance in a plant breeding program is a sustainable solution to minimize yield loss to viral diseases. The meta-QTL (MQTL) has demonstrated to be a promising approach to pinpoint the most robust QTL(s)/candidate gene(s) in the form of an overlapping or common genomic region identified through leveraging on different research studies that independently report genomic regions significantly associated with the target traits. Here, we employed an MQTL approach by targeting 39 independent research investigations aimed at genetic dissection of the resistance in maize against 14 viral diseases. We could project 27 % (53) of the total 196 QTLs onto the maize genome. Our analysis found a robust set of 14 MQTLs on chromosomes 1, 3 and 10 that explain significant proportion of the variations for resistance against 11 viral diseases. Marker trait associations (MTAs) identified from genome-wide association studies (GWAS) provide evidence in support of the two MQTLs (MQTL3_2 and MQTL10_2) playing crucial roles in viral disease resistance (VDR) in maize. A total of 1,715 candidate genes underlie the identified MQTL regions, of which, we further examined the constitutively-expressed genes for their involvement in various metabolic pathways. The involvement of the identified genes in the antiviral resistance mechanism renders them a valuable genomic resource for allele mining and elucidating plant-virus interactions for maize research and breeding.
病毒病严重影响玉米产量，世界各地均有玉米病毒发生的报道。在植物育种计划中部署遗传抗性是将病毒病造成的产量损失降至最低的可持续解决方案。元QTL（MQTL）已被证明是一种很有前景的方法，可通过利用不同的研究来确定最稳健的QTL/候选基因，这些研究独立报告了与目标性状显著相关的基因组区域，并以重叠或共同基因组区域的形式呈现。在此，我们采用MQTL方法，针对39项独立研究展开调查，这些研究旨在对玉米抗14种病毒病的抗性进行遗传剖析。我们能够将总共196个QTL中的27%（53个）定位到玉米基因组上。我们的分析在1号、3号和10号染色体上发现了一组强大的14个MQTL，它们解释了对11种病毒病抗性变异的很大比例。从全基因组关联研究（GWAS）中确定的标记-性状关联（MTA）为两个MQTL（MQTL3_2和MQTL10_2）在玉米病毒病抗性（VDR）中发挥关键作用提供了证据。共有1715个候选基因位于已确定的MQTL区域，其中，我们进一步研究了组成型表达基因在各种代谢途径中的参与情况。所确定的基因参与抗病毒抗性机制，使其成为等位基因挖掘以及阐明玉米研究和育种中植物-病毒相互作用的宝贵基因组资源。
