Identification of Quantitative Trait Loci for Goss's Wilt of Maize

Cooper, Julian; Balint‐Kurti, Peter; Jamann, Tiffany M.

doi:10.2135/cropsci2017.10.0618

Cited by 16 publications

(29 citation statements)

References 38 publications

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“…The heritability of GW resistance in this population was relatively high and on par with previous GW studies ( Ngong-Nassah 1992 ; Singh et al 2016 ; Cooper et al 2018 ). High heritability has been reported for the three fungal diseases for this population, namely 0.76 for SCLB, 0.75 for NCLB, and 0.59 for GLS ( Lopez-Zuniga et al 2019 ), indicating that progress can be made from inbred line evaluations in breeding for these diseases.…”

Section: Discussionsupporting

confidence: 79%

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Identification of Loci That Confer Resistance to Bacterial and Fungal Diseases of Maize

Qiu

Cooper

Kaiser

et al. 2020

G3 Genes|Genomes|Genetics

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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.

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Section: Discussionsupporting

confidence: 79%

“…Clavibacter nebraskensis isolate 16Cmn001 was used for the GW inoculations ( Cooper et al 2018 ). We inoculated the plants twice, once at the V4 stage and a second time at the V7 stage ( Abendroth et al 2011 ), as described by Cooper et al (2018) . Two inoculations improved the differentiation between lines.…”

Section: Methodsmentioning

confidence: 99%

Identification of Loci That Confer Resistance to Bacterial and Fungal Diseases of Maize

Qiu

Cooper

Kaiser

et al. 2020

G3 Genes|Genomes|Genetics

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“…Inoculations were performed twice, 1 wk apart, between the V4 and V7 stages using a modified pinprick inoculation method to simulate mechanical damage (Blanco et al, 1977; Chang et al, 1977). Disease ratings were performed three to four times, approximately every 2 wk after initial inoculation using the methods described in Cooper et al (2018). Inbreds were scored on a per‐plot basis using a 0‐to‐100% scale with 5% intervals meaning there were 20 possible rating categories (Cooper et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Disease ratings were performed three to four times, approximately every 2 wk after initial inoculation using the methods described in Cooper et al (2018). Inbreds were scored on a per‐plot basis using a 0‐to‐100% scale with 5% intervals meaning there were 20 possible rating categories (Cooper et al, 2018). Ratings represented the total percentage of infected leaf area, with 0% representing no symptoms and 100% denoting complete plant death (Poland and Nelson, 2011).…”

Section: Methodsmentioning

confidence: 99%

Genome‐Wide Analysis and Prediction of Resistance to Goss's Wilt in Maize

et al. 2019

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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.

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“…In maize, the relationship between fungal and bacterial diseases has been explored. Previously, Cooper, Balint‐Kurti, and Jamann (2018) observed a significant correlation among Goss's bacterial wilt, leaf blight, and NCLB, but not among Goss's bacterial wilt, leaf blight, and GLS or SCLB. The lack of correlation between disease phenotypes may be due to different diseases being controlled by different genetic mechanisms.…”

Section: Discussionmentioning

confidence: 92%

Identification of quantitative trait loci associated with maize resistance to bacterial leaf streak

et al. 2020

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Bacterial leaf streak (BLS), a foliar disease of maize (Zea mays L.) caused by Xanthomonas vasicola pv. vasculorum, recently emerged in the Americas as a disease of major importance. Little is known about the disease cycle, and consequently, management is difficult. No chemical control is available. Host resistance will likely play a major role in controlling the disease, but to date, no data regarding the resistance of maize germplasm to X. vasicola pv. vasculorum have been published. The objective of this study was to examine the genetic architecture of resistance to BLS. We conducted quantitative trait locus (QTL) mapping for BLS resistance in three maize populations: the Z022 (B73 × Oh43 recombinant inbred line) nested association mapping (NAM) population, the Z023 (B73 × Oh7B recombinant inbred line) NAM population, and the DRIL78 (NC344 × Oh7B chromosome segment substitution line) population. A total of five QTL were detected across two of the mapping populations. Of the QTL detected, one conferred a moderate effect, whereas the others conferred small effects. We also examined the relationship between resistance to BLS and resistance to three foliar diseases of maize, which had been mapped previously. The only significant correlation we found for BLS was with northern corn leaf blight [caused by Exserohilum turcicum (Pass.) K. J. Leonard & Suggs] in one of the populations, although two of the five BLS regions were involved in resistance to other diseases. These data will be useful for developing maize varieties resistant to BLS to mitigate the impact of bacterial leaf streak on maize production.

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Identification of Quantitative Trait Loci for Goss's Wilt of Maize

Cited by 16 publications

References 38 publications

Identification of Loci That Confer Resistance to Bacterial and Fungal Diseases of Maize

Identification of Loci That Confer Resistance to Bacterial and Fungal Diseases of Maize

Genome‐Wide Analysis and Prediction of Resistance to Goss's Wilt in Maize

Identification of quantitative trait loci associated with maize resistance to bacterial leaf streak

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