Since its discovery in 1969, Goss's wilt, a foliar blight and vascular wilt disease caused by the Gram‐positive bacterium Clavibacter michiganensis (Smith) Davis et al. subsp. nebraskensis (Vidaver & Mandel) Davis et al. (Cmn), has emerged as one of the top four diseases of maize (Zea mays L.) in the United States and Ontario, Canada. No source of complete resistance has been described for Goss's wilt, and little is known about the genetic and mechanistic basis of host resistance to Cmn. Our objective was to perform linkage mapping on three populations to uncover the genomic regions associated with Goss's wilt resistance. We evaluated the intermated B73 × Mo17 population and two corresponding disease‐resistant introgression line populations: B73(4) × Mo17 and Mo17(4) × B73. We identified putative quantitative trait loci (QTLs) in bins 1.05 to 1.06, 2.06, 7.01 to 7.02, 8.05, and 10.04, both confirming previous findings and identifying novel resistance QTLs. The QTL on chromosome 1, designated qGW1.06, was identified in multiple environments and overlaps with a known multiple disease resistance locus. The QTL in bin 8.05 represents a novel region associated with Goss's wilt. Using the data from this study and previous studies, we found that Goss's wilt resistance was correlated with northern leaf blight [Setosphaeria turcica (Luttr.) K.J. Leonard & Suggs], but not gray leaf spot (Cercospora spp.) or southern leaf blight [Cochliobolus heterostrophus (Drechsler) Drechsler]. These results offer a deeper understanding of the genetic basis of resistance to Goss's wilt in maize that may facilitate breeding for resistance, and qGW1.06 is a strong candidate for further characterization and use.
Crops are hosts to numerous plant pathogenic microorganisms. Maize has several major disease issues; thus, breeding multiple disease resistant (MDR) varieties is critical. While the genetic basis of resistance to multiple fungal pathogens has been studied in maize, less is known about the relationship between fungal and bacterial resistance. In this study, we evaluated a disease resistance introgression line (DRIL) population for the foliar disease Goss's bacterial wilt and blight (GW) and conducted quantitative trait locus (QTL) mapping. We identified a total of ten QTL across multiple environments. We then combined our GW data with data on four additional foliar diseases (northern corn leaf blight, southern corn leaf blight, gray leaf spot, and bacterial leaf streak) and conducted multivariate analysis to identify regions conferring resistance to multiple diseases. We identified 20 chromosomal bins with putative multiple disease effects. We examined the five chromosomal regions (bin 1.05, 3.04, 4.06, 8.03, and 9.02) with the strongest statistical support. By examining how each haplotype effected each disease, we identified several regions associated with increased resistance to multiple diseases and three regions associated with opposite effects for bacterial and fungal diseases. In summary, we identified several promising candidate regions for multiple disease resistance in maize and specific DRILs to expedite interrogation.
Core Ideas Goss's wilt is a complex, polygenic trait with no resistance genes or large‐effect QTL. Genomic prediction accuracy of 0.69 achieved for Goss's wilt in panel of diverse inbred lines. Association mapping using a diverse panel of maize lines revealed no significant SNPs. Goss's bacterial wilt and leaf blight is one of the most important foliar diseases of maize (Zea mays L.). To date, neither large‐effect resistance genes, nor practical chemical controls exist to manage the disease. Thus, the importance of discovering durable host resistance necessitates additional genetic mapping for this disease. Unfortunately, because of the biology of the pathogen and the highly significant genotype‐by‐environment interaction effect observed with Goss's wilt, consistent phenotyping across multiple years poses a hurdle for genetic studies and conventional breeding methods. The objective of this study was to perform a genome‐wide association study (GWAS) to identify regions of the genome associated with Goss's wilt resistance as well as to use genomic prediction models to evaluate the utility of genomic selection (GS) in predicting Goss's wilt phenotypes in a panel of diverse maize lines. Using genome‐wide association mapping, we were unable to identify any variants significantly associated with Goss's wilt. However, using genomic prediction we were able to train a model with an accuracy of 0.69. Taken together, this suggests that resistance to Goss's wilt is highly polygenic. In addition, when evaluating the accuracy of our prediction model under reduced marker density, it was shown that only 10,000 single nucleotide polymorphisms (SNPs), or ∼20% of our total marker set, was necessary to achieve prediction accuracies similar to the full marker set. This is the first report of genomic prediction for a bacterial disease of maize, and these results highlight the potential of GS for disease resistance in maize.
The logistic mixed model (LMM) is well-suited for the genome-wide association study (GWAS) of binary agronomic traits because it can include fixed and random effects that account for spurious associations. The recent implementation of a computationally efficient model fitting and testing approach now makes it practical to use the LMM to search for markers associated with such binary traits on a genome-wide scale. Therefore, the purpose of this work was to assess the applicability of the LMM for GWAS in crop diversity panels. We dichotomized three publicly available quantitative traits in a maize diversity panel and two quantitative traits in a sorghum diversity panel, and them performed a GWAS using both the LMM and the unified mixed linear model (MLM) on these dichotomized traits. Our results suggest that the LMM is capable of identifying statistically significant marker-trait associations in the same genomic regions highlighted in previous studies, and this ability is consistent across both diversity panels. We also show how subpopulation structure in the maize diversity panel can underscore the LMM’s superior control for spurious associations compared to the unified MLM. These results suggest that the LMM is a viable model to use for the GWAS of binary traits in crop diversity panels and we therefore encourage its broader implementation in the agronomic research community.
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