Meta-QTL analysis for mining of candidate genes and constitutive gene network development for viral disease resistance in maize (Zea mays L.)

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.