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

Abstract

Abstract Viral diseases substantially impact global maize production. Genetic resistance is the most feasible approach to combat losses caused by viral diseases. Various researchers have mapped several QTLs for viral disease resistance (VDR) in maize. The meta-QTL (MQTL) analysis is the best way to leverage such independent studies to find overlapping or common genomic regions governing target traits. Towards this direction, in the present study, the MQTL analysis was carried out using a total of 39 QTL mapping investigations on resistance against 14 viral diseases. 53 (27.04%) out of 196 QTLs could be projected on the maize genome. The study discovered 14 MQTLs for resistance against 11 viral diseases on chromosomes 1, 3 and 10. Marker trait associations (MTAs) in genome-wide association studies confirmed the role of two MQTLs (MQTL3_2 and MQTL10_2) for VDR. The identified MQTL regions harboured 1715 candidate genes. Further, the genes expressed constitutively in the MQTLs identified for VDR were analyzed for their involvement in metabolic pathways. The identified genes were found to be implicated in antiviral resistance, and thus, constitute a good resource for allele mining to decipher the mechanism of plant-virus interactions.