Combination of linkage and association mapping with genomic prediction to infer QTL regions associated with gray leaf spot and northern corn leaf blight resistance in tropical maize - Omondi, Dennis O.; Dida, Mathews M.; Berger, Dave K.; Beyene, Yoseph; Nsibo, David L.; Juma, Collins; Mahabaleswara, Suresh L.; Gowda, Manje (Frontiers in Genetics, 2023)
Among the diseases threatening maize production in Africa are gray leaf spot (GLS) caused by Cercospora zeina and northern corn leaf blight (NCLB) caused by Exserohilum turcicum. The two pathogens, which have high genetic diversity, reduce the photosynthesizing ability of susceptible genotypes, and hence reduce the grain yield. To identify population-based quantitative trait loci (QTL) for GLS and NCLB resistance, a biparental population of 230 lines derived from the tropical maize parents CML511 and CML546, and an association mapping panel of 239 tropical and sub-tropical inbred lines were phenotyped across multi-environments in western Kenya. Based on 1,264 high-quality polymorphic SNPs in the biparental population, we identified 10 and 18 QTL which explained 64.2% and 64.9% of the total phenotypic variance for GLS and NCLB resistance, respectively. A major QTL for GLS, qGLS1_186 accounted for 15.2% of the phenotypic variance while qNCLB3_50 explained the most phenotypic variance at 8.8% for NCLB resistance. Association mapping with 230,743 markers revealed 11 and 16 SNPs significantly associated with GLS and NCLB resistance, respectively. Several of the SNPs detected in the association panel were colocalized with QTL identified in the biparental population, suggesting some consistent genomic regions across genetic backgrounds. These would be more relevant to use in field breeding to improve resistance to both diseases. Genomic prediction models trained on the biparental population data yielded average prediction accuracies of 0.66 to 0.75 for the disease traits when validated in the same population. Applying these prediction model to the association panel produced accuracies of 0.49 and 0.75 for GLS and NCLB, respectively. This research done in maize fields relevant to farmers in western Kenya has combined linkage and association mapping to identify new QTL and confirm previous QTL for GLS and NCLB resistance. Overall, our findings imply that genetic gain can be improved in maize breeding for resistance to multiple diseases including GLS and NCLB by using genomic selection.