Location: Plant Genetics Research2021 Annual Report
Objective 1: Identify genetic and physiological mechanisms controlling growth under drought in maize, wheat, and related species. • Sub-objective 1.1: Characterize the genetic regulation of maize root growth responses to soil water-deficit stress. • Sub-objective 1.2: Determine the roles of plant hormones abscisic acid (ABA) and gibberellins (GA) in the regulation of wheat root responses to water deficit. • Sub-objective 1.3: Characterize the genetic networks that link transcription factor expression and metabolism central to cellular protection during dehydration in a C4 resurrection grass. Objective 2: Characterize corn for natural rootworm resistance, rootworm larvae for Bt tolerance, and artificial diets for improved understanding of rootworm biology and management. • Sub-objective 2.1: Systematically screen exotic and Germplasm Enhancement of Maize (GEM) germplasm, identify potential sources of western corn rootworm (WCR) resistance, verify resistance, and move into adapted germplasm. • Sub-objective 2.2: Characterize heritability and other traits of rootworm larvae with Bt tolerance. • Sub-objective 2.3: Evaluate northern corn rootworm (NCR) development on larval Diabrotica diets and develop a diet toxicity assay for NCR. Objective 3: Identify genetic and physiological mechanisms governing response to artificial selection in cereals and related species. • Sub-objective 3.1: Develop an experimental evolution maize population to characterize adaptation to selective pressures at the genomic level in maize and related species. • Sub-objective 3.2: Quantify the importance of epistasis with novel Epistasis Mapping Populations. • Sub-objective 3.3: Develop, implement, and validate statistical methods to better understand traits controlled by multiple genes acting in concert. Objective 4: Develop and characterize germplasm to elucidate the genetic mechanisms underlying nutritional and food traits in maize. • Sub-objective 4.1: Screen and develop maize germplasm for traits important in food-grade corn. Objective 5: Identify genetic and physiological mechanisms underlying maize adaptation to the environment to enhance its productivity. • Sub-objective 5.1: Develop and evaluate germplasm segregating for adaptation to high elevation. • Sub-objective 5.2: Evaluate diverse maize hybrids in multi-location trials as part of the Genomes To Fields Genotype x Environment Project.
Conduct genome-wide association analysis of water-stress root growth using high-throughput maize root phenotyping to link transcription factor (TF) expression with root growth phenotypes under stress. Characterize water deficit growth and hormone responses in wheat roots, and interrogate the gene expression profiles (RNAseq) for the root growth zone. Use chromatin immunoprecipitation-sequencing to establish the role of transcription and TF targets in the response of both wheat and maize roots to water deficits. Develop gene network maps for dehydration TFs in the resurrection grass Sporobolus stapfianus. Evaluate 75 new sources of maize germplasm each year for resistance to Western Corn Rootworm (WCR) larval feeding in replicated field trials. Develop an artificial diet for Northern Corn Rootworm (NCR) and conduct toxicity assays for all available Bt proteins. Expose NCR populations to current industry Bt corn in plant assays and measure the effect on insect development. Evaluate the inheritance of Bt resistance in WCR. Conduct five cycles of selection for high and low plant height in the Shoepeg maize landrace population, followed by genotyping and selection mapping. Phenotype an Epistasis Mapping Population and conduct statistical tests for epistatic effects. Screen 100 heirloom maize varieties for adaptation to the southern Corn Belt and make selections based on agronomic performance and kernel composition traits. Create and release modified open pollinated varieties with improved performance and food characteristics. Conduct quantitative trait locus (QTL) mapping of traits related to highland adaptation in maize populations grown at low, mid, and high elevations. Compare QTLs identified in a Mexican and South American germplasm. Identify candidate genes based on traits related to adaptation and fitness at varying elevation. Participate in multi-location yield trials to evaluate diverse maize hybrids across the US.
Objective 1. ARS researchers in Columbia, Missouri finished last year’s milestone of re-tooling, repairing, troubleshooting, and testing the high-throughput root phenotyping robot, “Rootbot” (Sub-objective 1.1). The phenotyping experiments were initiated and, at present, 92/282 (33%) of the inbred lines have been phenotyped. Objective 2. Over the past year, we have made significant progress on all Objective 2 Sub-objectives. For Sub-objective 2.1, we have again planted the 100 maize lines from the Plant Introduction Station in Ames, Iowa. For Sub-objective 2.2a, we have completed the evaluation of the northern corn rootworm laboratory colony on all current Bt toxins currently targeting rootworm in plant assays and have begun assays with wild northern corn rootworm (NCR). For Sub-objective 2.2b, we have evaluated Cry34/35Ab1-selected colonies after removal from selection and documented for the first time that resistance in western corn rootworm can disappear after the selection pressure is removed. For other traits, western corn rootworm has maintained resistance after selection pressure is removed. Reciprocal cross experiments have been initiated now that we have access to protein. Finally, for sub-objective 2.3, we have completed diet assays for all current Bt toxins on the laboratory northern corn rootworm colony. The manuscript on baseline susceptibility of the northern corn rootworm to all Bt toxins in plant and diet assays has been published. We are now completing diet-toxicity assays with wild NCR. Objective 4. We have made significant progress in the maize heirloom improvement activities of Sub-objective 4.1. A number of heirlooms were chosen for follow-up studies from a previous experiment in a collaboration between ARS researchers at Columbia, Missouri, and Raleigh, North Carolina. Twelve heirlooms have undergone two cycles of selection for improved agronomic performance in both locations, and an additional seven populations have undergone a single cycle of selection in Missouri. In 2021, the original heirloom accessions and the selected populations were planted in replicated trials in Columbia and Raleigh in order to measure the response to selection for the target agronomic traits and to verify that the desirable traits in each population have not changed. Data collection is ongoing. Approximately 200 landrace/heirloom accessions were chosen based on food properties described in the “Races of Maize” books and were sent to winter nurseries to create population hybrids affectionately referred to as “corny combos.” This project complements the efforts outlined in Objective 4 by exploring the potential of landrace hybrids for unique kernel properties useful in food corn breeding. A second year of replicated trials containing 400 landraces and corny combos was planted in May 2021 in Columbia, Missouri, and data collection is ongoing for agronomic, adaptation, and productivity traits. Objective 5. We have nearly completed the genotypic analysis of the highland by lowland landrace populations from both Mexico and South America in Sub-objective 5.1 and as part of the grant-funded, “The Genetics of Highland Adaptation in Maize” project (subordinate project 5070-21000-041-06R). Leaf tissue of the parental materials was genotyped with ~50,000 single nucleotide polymorphisms (SNPs) in order to design the custom SNP array for the progeny families. Leaf tissue for the 720 F2 families has been submitted for genotypic analysis using this custom SNP array and data are expected at the end of the summer. The 2020 Genomes to Fields (G2F) trial of 1,658 yield plots was completed at the end FY 2021 (Sub-objective 5.2). The main experiment was comprised of three hybrid sets with two replicates each, and three additional smaller experiments. The three main hybrid sets represent stiff stalk doubled haploids crossed with three non-stiff stalk testers planted at up to 30 locations (Missouri is only one location) to study how these hybrids respond to and interact with the environment. Stand counts were poor due to extremely heavy rains after planting. Phenotype data was collected by hand, Unoccupied Aerial Vehicle (UAV), and a phenotyping robot. Protocols and pipelines were developed for storing, sharing, analyzing, and archiving high throughput phenotyping data (Sub-objective 5.2). The trial was combine harvested in October 2020 and data were submitted to the G2F Project. Both in house and external analysis is ongoing. The 2021 G2F experiments were only partially planted due extremely wet weather in spring 2021 (Sub-objective 5.2). The smaller G2F location was planted at the Hinkson Valley Farm on May 14, 2021. It contained 500 yield plots of the same experiment from 2020 but with only one of the testers, as well as two additional smaller experiments. The larger G2F location of >2,000 plots comprised of six small projects that was planned for Bradford Farm was abandoned due to wet field conditions through early June; however, several of the smaller embedded projects totaling 504 plots were salvaged by planting them on June 4, 2021 at Genetics Farm where a smaller field was dry enough to plant the smaller subsets. Data collection is ongoing. UAV and field rover phenotyping is ongoing within the plots. Convolutional Neural Networks (CNN), and Crop Growth Models (CGM) were developed and refined for predicting yield in maize (Sub-objective 5.3). Scripts and methods for mining, comparing and combining public wheat phenotype and genotype data were developed with the goal of using that data for phenotype prediction within CNN and CGM frameworks. The publicly available wheat data proved to poorly annotated for details like planting date, density, fertilizer, and other management details. Evaluation of the data’s utility for modeling is ongoing. New maize tetraploid lines were induced during the end of FY20 and the beginning of FY21 (Sub-objective 5.4). Screening and making crosses between the lines will be initiated in the coming months. Phenotyping protocols were developed to screen 282 inbred lines for sensitivity to auxin treatment (Sub-objective 5.5). Optimization of an assay in an incubator was conducted to grow seedlings for 5.5 days at 29 °C in the dark. This allows for identification of significant differences between auxin treatment and mock treatment in sensitive inbred lines. We developed an image acquisition protocol using a flatbed scanner to efficiently acquire images of seedlings roots for measurement. To measure the primary root length, researchers developed a high-throughput script to automatically measure root length utilizing publicly available software. We are currently phenotyping the first replicate of the 282 inbred lines and the second replicate is being prepared for phenotyping. The optimization of a gene editing protocol to create constructs for transformation into maize has been developed (Sub-objective 5.6). Two sets of guide ribonucleic acids to specifically target a gibberellic acid transcription factor gene in maize were created. Two sets of guide ribonucleic acids to specifically target two different sets of two brassinosteroid transcription factors were created. We have ligated the double stranded guide ribonucleic acids into the entry vectors and recombined them into a destination vector for transformation into maize. These different constructs will be used to target the desired loci and cause knock-outs of these genes to reduce plant height and alter plant architecture.
1. Prediction of corn yield from genetic, environment, management, and historical data using machine learning. Predicting how new cultivars will perform in new environments is a longstanding challenge in agriculture with potential to increase the effectiveness of breeding and the profitability of farmers. ARS researchers in Columbia, Missouri, and Ithaca, New York, developed computational methods (known as machine learning, deep learning, or artificial intelligence) that increase prediction accuracy by 7 percentage points compared to current state of the art methods. These results will potentially allow ARS and its partners and stakeholders to more quickly develop breeding lines and cultivars for specific environments.
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