Location: Plant Science Research
2024 Annual Report
Objectives
1. Identify and characterize genes and mechanisms underlying disease resistance and defense response in maize.
1A. Characterize quantitative trait loci (QTL) alleles underlying foliar disease resistance and multiple disease resistance in maize and identify the underlying causal genes.
1B. Characterize proteins produced by Peronosclerospora sorghi during infection of maize.
1C. Examine the role of genetic dominance and heterosis in disease resistance using the maize Intermated B73 x Mo17 (IBM) population.
2. Develop improved methods for prediction of yield in different environments and improved understanding of genotype response to environments by collaboration with the maize Genomes to Fields Project.
2A. Coordinate a public competition to predict environment-specific hybrid performance using Genomes to Fields data.
2B. Evaluate diverse landrace introgression stocks and progenies from Germplasm Enhancement of Maize (GEM) line crosses for yield potential across diverse Genomes to Fields environments.
3. Develop agronomically outstanding lines of maize from crosses between elite temperate and diverse exotic germplasm sources as part of the Germplasm Enhancement of Maize Project.
3A. Evaluate adapted and exotic maize genetic resources for maturity, yield, resistance to ear, stalk, and foliar diseases, and tolerance to environmental extremes. Record and disseminate evaluation data via the GEM website, GRIN-Global, and other data sources.
3B. Breed and release maize lines with primarily 50% exotic/50% temperate pedigrees which contribute to U.S. maize more diverse genetic resistance to diseases, tolerance to environmental extremes, higher yield, and other valuable new traits.
3C. Manage and coordinate the Southeastern component of a multi-year, multi-site, cooperative program of maize genetic resource evaluation and information sharing which will broaden the genetic base for U.S. maize.
4. Evaluate and improve corn populations for special culinary uses.
4A. Evaluate landraces of corn from the USA for relationships and utility for specialty corn production.
4B. Develop low protein corn varieties with acceptable agronomic performance.
Approach
We will identify candidate genes for disease resistance with linkage mapping and association mapping. Candidate genes will be targeted for knock-outs using gene editing technology. Knock-out stocks will be evaluated for disease resistance. Avirulence genes from rust fungus will be identified by genome sequence comparisons between avirulent and virulent strains. Avirulence genes will be validated by transformation into Nicotiana in combination with the resistance gene from maize, to determine if the combination of genes results in a hypersensitive response. Dominance gene action of resistance genes will be measured in comparison with additive gene action using field evaluations of testcrosses of recombinant inbred lines to both of their parents, generating all three possible genotypic classes at each genome region. Genotype performance across different environments will be measured in the Genomes to Fields project, using experimental designs that optimally balance testing of many hybrids across many environments. Different machine learning techniques for environment-specific hybrid prediction will be compared by conducting a public prediction competition using training and testing data from all available years of Genomes to Fields data. The effects of genomic introgressions from maize landraces will be tested on yield and other agronomic traits in crosses of introgression lines to a common tester and field evaluations in multiple environments. Outstanding Germplasm Enhancement of Maize inbred lines will be created using a multi-step breeding procedure starting with evaluation of exotic sources, crossing to elite commercial lines, selection during inbreeding for adaptation and disease resistance, and finally crossing to elite testers and multiple year yield trials. Sources of exotic germplasm will be evaluated for yield potential when crossed to each of the major breeding groups of USA maize, allowing them to be classified into heterotic groups. Germplasm Enhancement of Maize yield trials will be conducted in collaboration with private sector partners, with data summaries distributed publicly on an annual basis. Germplasm Enhancement of Maize lines will be screened for resistance to important disease and insect pests to identify new sources of resistance. Open-pollinated landraces of maize from the USA will be evaluated in multiple year field trials for a range of agronomic and seed quality traits and genotyped with sequencing to identify genetic and phenotypic relationships among them. Inbred lines with lower grain protein combined with acceptable agronomic performance will be created using pedigree breeding and phenotypic selection methods.
Progress Report
ARS researchers at Raleigh, North Carolina identified significant errors in the previously-generated genotypic dataset of a near isogenic population that we have been using for mapping and analysis. By selective regenotyping and re-analysis of the existing raw dataset we have corrected these errors.
We investigated heterosis for disease resistance in maize to common foliar diseases, southern corn leaf blight and gray leaf spot, in a population of recombinant inbred lines and their backcrosses to both parents. We found that the level of disease resistance heterosis depended on both the disease and the cross. Heterosis depends on non-additive gene action. We identified multiple forms of gene action (recessive, dominant, additive, over-dominant, and epistatic) among quantitative trait loci for disease resistance.
We have characterized a protein derived from Peronosclerospora sorghi that induces resistance-gene dependent defense responses
We worked with ARS and university collaborators to coordinate a public competition to predict environment-specific hybrid performance using Genomes to Fields data. Our primary role was to curate trait data from 8 years of historical data into a single comprehensive training data set, and to harmonize nomenclature between trait and genotype data sets. The competition was conducted and a winner declared. Topcross hybrids of landrace introgression lines were created for yield testing. Experimental designs for the whole Genomes 2 Fields evaluation of Germplasm Enhancement of Maize (GEM) line x ex-plant variety protection line hybrids was created and the experiment planted locally and at many cooperator sites.
We coordinated 14,000 yield plots from Raleigh, North Carolina with 8,400 planted in North Carolina by the USDA-ARS GEM project, 1900 planted in North Carolina in cooperation with the North Carolina Official Variety testing program, and the rest planted by five cooperators at various locations throughout the Southeast and Midwest. The results of second year trials will determine which entries are recommended to the GEM project cooperators. Disease evaluation continues in 2024 for resistance foliar diseases in North Carolina and ear rots in Mississippi. As well, 100 new breeding crosses were observed for agronomic traits of interest. Seventy-five semi-exotic populations were evaluated and pollinated the summer 2024 nursery in Clayton, North Carolina.
Working with ARS colleagues in Missouri and at the USDA Plant Introduction station, we identified a set of 1,000 open-pollinated maize varieties from the USA, Canada, and Mexico in the USDA seed bank available for evaluation. We created an experimental design to evaluate these varieties in partially replicated trials across four environments. The first year of field evaluations was planted in North Carolina and Missouri. Leaf tissue was sampled for DNA and RNA analysis. We planted progenies of crosses between lower protein lines in the field for selfing and phenotypic selection.
Accomplishments
1. Corn hybrid yield prediction competition. Crop variety yield prediction based on historical yield trials and genetic and weather data would help breeders and farmers identify best varieties to plant in specific environments. ARS researchers at Raleigh, North Carolina helped coordinate a public competition to predict corn hybrid yields across more than 20 environments as part of the Genomes to Fields project. The competition attracted 128 teams from 19 countries. Most of the best performing teams submitted their analysis code for evaluation to identify methods associated with improved prediction algorithms.
2. Germplasm Enhancement of Maize (GEM) Germplasm releases. The GEM project released four new semi-exotic germplasm lines for GEM cooperators to utilize in 2024. Three of the releases are from the stiff stalk heterotic group and contain 25-37% exotic germplasm, while the other is from the non-stiff stalk heterotic group and contains 50% exotic germplasm. The lines have shown good yield in trials and adaptation to the southeast and southern Corn belt and represent new diverse germplasm for incorporation into cooperator's breeding programs.
Review Publications
Qiu, Y., Adhikari, P., Balint Kurti, P.J., Jamann, T. 2023. Identification of loci conferring resistance to four foliar diseases of maize. G3, Genes/Genomes/Genetics. 2023:jkad275. https://doi.org/10.1093/g3journal/jkad275.
Zhong, T., Zhu, M., Zhang, Q., Zhang, Y., Deng, S., Guo, C., Xu, L., Jiu, T., Li, Y., Bi, Y., Fan, X., Balint Kurti, P.J., Xu, M. 2024. The ZmWAKL–ZmWIK–ZmBLK1–ZmRBOH4 module provides quantitative resistance to gray leaf spot in maize. Nature. 56:315-326. https://doi.org/10.1038/s41588-023-01644-z.
Banah, H., Balint Kurti, P.J., Houdinet, G., Hawkes, C., Kudenov, M. 2024. The quantification of southern corn leaf blight disease using deep UV fluorescence spectroscopy and autoencoder anomaly detection techniques. PLOS ONE. 19(5). Article e0301779. https://doi.org/10.1371/journal.pone.0301779.
Hudson, A., Mullens, A., Hind, S., Jamann, T., Balint Kurti, P.J. 2024. Natural variation in the pattern-triggered immunity response in plants: investigations, implications and applications. Molecular Plant Pathology. 25(3). Article e13445. https://doi.org/10.1111/mpp.13445.
Krafft, D., Scarboro, C.G., Hsieh, W., Doherty, C.J., Balint Kurti, P.J., Kudenov, M.W. 2024. Mitigating illumination-, leaf-, and view-angle dependencies in hyperspectral imaging using polarimetry. Plant Phenomics. 6:0157. https://doi.org/10.34133/plantphenomics.0157.
Dobbs, A., Sousa-Ortega, C., Holland, J.B., Snyder, L., Leon, R. 2024. Variability structure and heritability of germination timing in Capsella bursa-pastoris (L.) Medik (Shepherd’s purse). Weed Research. 64(1):1-7. https://doi.org/10.1111/wre.12605.
Woore, M.S., Flint Garcia, S.A., Holland, J.B. 2024. Characterization of southeastern United States open-pollinated maize landraces. Crop Science. 64:772-787. https://doi.org/10.1002/csc2.21198.