Location: Plant, Soil and Nutrition Research
2025 Annual Report
Objectives
Objective 1: Conduct research to simplify getting data into T3 and to expand T3 functionality for users.
Sub-objective 1.A: Sample tracking with the small grains genotyping labs.
Sub-objective 1.B: Data upload through Android Field Book, Coordinate, and Intercross apps.
Sub-objective 1.C: Spatial analysis of individual field trials.
Sub-objective 1.D: Target population of environments (TPE) determination.
Sub-objective 1.E: Determine the value to selection gain from weighting field evaluations by their genetic correlation to the long-run TPE average.
Sub-objective 1.F: Genomic mate selection (GMS).
Objective 2: Develop and incorporate analyses into T3 that strengthen initial data curation.
Sub-objective 2.A: Practical haplotype graph incorporation.
Objective 3: Strengthen T3 interoperability with other biological databases.
Sub-objective 3.A: Creation of private instances of T3.
Sub-objective 3.B: Ensure T3 remains compliant with the BrAPI specification
Objective 4: Assemble a T3 User Group to advise developers on advances in the breeding process and on data management feature needs.
Sub-objective 4.A: Host a meeting of the T3 Advisory User Group (TAUG) twice each year.
Approach
Research and development work on Objective 1 to simplify getting data into T3 and to expand T3 functionality includes some small subobjectives and some larger ones. We will develop interfaces to track samples coming from the small grains genotyping labs and to better interface with digital data acquisition devices like tablet apps. We will add functionality to run spatial analyses on phenotyping trials that have field layouts. These features will culminate in better data to determine target populations of environments (TPE) for breeding programs. With the TPE defined, we will seek to use multi-year and multi-location trail data to better weight data from trials in for selection. Finally, we will implement genomic mate selection.
Work on Objective 2 really focuses on harmonizing marker data across genotyping platforms or protocols. We will do this by integrating the practical haplotype graph into T3. Over time, we have come to acknowledge that breeders do not always want their data to be made public rapidly. In Objective 3 to avoid that putting data into T3 forces that, we will create private instance of T3. Nevertheless, to ensure ongoing T3 interoperability with open-source breeding data analysis packages, we will ensure that T3 remains compliant with the BrAPI specification.
Finally, we know T3 has faults that we are not fully aware of. To better appreciate and be able to address these faults, in Objective 4, we will assemble a T3 User Group to advise developers on advances in the breeding process and on data management feature needs. We will convene this group twice each year.
Progress Report
The Project Plan focuses on improving The Triticeae Toolbox (T3) as a service for United States public sector small grains breeders. Consistent with this focus, we document here innovations across three scales:
1. Small scale usability improvements
2. Intermediate scale functionality features
3. Big picture integrations that involve coordination across institutions
Small scale usability improvements
• Short trait name synonyms. To ensure trait name validity, T3 uses ontologies, but their names are clunky because they include trait ontology numbers. We have implemented synonyms that are one-to-one but that are shorter and display better.
• Better list management. Any analysis uses lists of accessions, trials, and traits. Often, we want to generate the union or intersection of multiple lists. That task now has a simple user interface.
• Consistent repeated measure storage and presentation. Particularly with high-throughput phenotyping some traits are measured multiple times within a trial. We have improved storage and download for that use case.
• Ability to display accession properties on Field Book. Breeders often need to look up properties like what genes are present at specific loci in an accession. Ideally those properties should be visible in electronic notebooks like Field Book. We are planning functionality for that.
• Ability to store and download calculated phenotypes. Calculated phenotypes are simple averages or ratios of raw phenotypes. We are planning functionality to store and display such calculated phenotypes.
Intermediate scale functionality features
• Factor analytic models applied to T3 data. Researchers in Ithaca, New York, have analyzed a number of datasets available on T3 using different factor analysis software and have chosen a package called MegaLMM. The factor analysis can provide a characterization of the environment that can be useful for developing a network of evaluation locations in a breeding program. This function of the factor analysis will be used in the project.
• Heat maps of spatial field variability. Spatial variability within fields is challenging for breeders who want to be able to visualize it. Such visualizations are planned.
• Combining datasets of accessions that have data from different genotyping protocols. DNA marker technology is constantly changing. Many datasets have been typed with different protocols making it challenging to combine these datasets for joint, powerful analyses. There are two approaches to combining datasets:
o The first requires that the two datasets have overlapping accessions. Then separate relationship matrices are calculated on each dataset with their respective DNA marker protocol, and these are used to combine the datasets. This method makes minimal assumptions when combining.
o The second involves using each dataset to impute the genome-wide haplotype of each accession. This imputation is done with the Practical Haplotype Graph (PHG). Once this imputation step is accomplished, the datasets can be combined. This method does not require that the datasets have any overlap, but the imputation does introduce predicted haplotypes that may have error.
• The PHG has been in development by researchers in Ithaca, New York, for a number of years. It provides interesting outputs beyond its ability to enable datasets to be combined. Using the PHG for small grains has been challenging because of their very large genomes. That challenge is being tackled.
• Breeding data management features for intercropping. Intercropping is a practice in which two or more crops are grown together simultaneously in the field. Because of complementarities between crops, this practice often leads to agronomic benefits. Specific breeding efforts to maximize these benefits have historically not been made, however. Researchers in Ithaca, New York, are now developing breeding data management that can handle experiments with more than one.
Big picture integrations with coordination across institutions
The "bigger picture" progress given below describes T3 efforts to coordinate data across larger collaborations. Descriptions of effort are given for researcher coordinated nurseries and labs, a drone image repository and analysis hub (D2S). Such integration can simplify collaboration across programs in data generation, analysis, and use, thereby accelerating gain from selection broadly. This broad benefit makes T3 an endeavor worthy of USDA's ethic of public service.
Cooperative Nurseries USDA-coordinated cooperative nurseries test experimental lines from many USDA and Land Grant university breeding programs. They are a rich resource for understanding the adaptation of advanced experimental lines and exploring genotype by environment interaction. That exploration can only take place if the data are easily aggregated. We continue the effort to obtain and curate cooperative nursery data.
USDA-ARS researchers in Ithaca, New York, have created a list of the USDA-coordinated cooperative nurseries. A document has been written that serves as a guide for coordinators to improve uniformity across these nurseries. A T3 feature that enables summaries so that, for example, breeders can analyze trials from specific sets of locations that capture the environments that they are breeding for.
Coordination with the Small Grains Genotyping Labs
Many important traits for U.S. wheat producers are affected by major loci. Obvious examples are height, disease or pest resistance, photo period, and vernalization genes. Wheat breeders must routinely ensure that favorable alleles, or the right combinations of alleles, are present at these loci. When breeders observe an unexpected phenotype in the field (e.g., height or disease reaction) they need to quickly identify if it can be explained by alleles at major loci. Likewise, if a favorable allele is identified that should be introduced to a breeding population, breeders will want to identify breeding lines that will be the most advantageous sources. The USDA Small Grains Genotyping Labs (SGGLs) routinely generate genome-wide marker data for many cooperative nurseries across the U.S. to the identity of alleles at major loci. In collaboration with the SGGLs, The Triticeae Toolbox has developed data management and search functionalities to give breeders easy access to major locus allele information about all public breeding lines genotyped by the SGGLs through USDA-coordinated cooperative nurseries.
Coordination with the Data to Science (D2S) image repository and analysis hub
Unoccupied Aerial Systems (UAS) and sensor technology are now routinely used by plant breeders for high throughput quantitative phenotypic information. The massive volume of geospatial data generated by these imaging technologies is, however, challenging for breeders to deal with. Solutions for this challenge come from engineering. One example is the open-source online platform for big UAS HTP data management called D2S. D2S is investing effort into user-friendly tools to upload images from UAS flights over plant breeding trials and to extract phenotypes from those images. Traits that can be estimated from images include plot height, canopy cover, and various vegetation indices. What the D2S service lacks is data management for the plant breeding experiment side of the evaluations. Thus, D2S and T3 complement each other. USDA-ARS researchers in Ithaca, New York, are coordinating with D2S developers to improve interoperability between the platforms.
Password-protected instances of T3
The Wheat Coordinated Agricultural Project (WheatCAP) uses T3 as its central data management repository. WheatCAP data has a privacy period: any data can be kept private for two years, or longer if needed by the investigator to publish. Thus, all data uploaded to the WheatCAP instance of T3 is only moved over to the public production instance of T3 after two years and with approval of the data submitter. This privacy period makes breeders more comfortable submitting data to T3. To gain the benefit of this privacy period for the other crops that we work with (oat and barley) we have also set up password-protected instances of the oat and barley T3 databases.
Accomplishments
1. 01 Using crop growth models (CGMs) in resource-constrained plant breeding programs. USDA-ARS scientists in Ithaca, New York, worked on improving a computer crop growth model (CGM) that predicts how Cassava plants grow. They used data from 67 different types of cassava that were tested in 16 different locations in Nigeria from 2017 to 2020. The CGM was calibrated using automated and manual steps which improved model accuracy substantially, reducing error for root yield prediction from 21 to 5 t/ha. They found that the model could use data from regular plant breeding tests and therefore did not require special experiments. This makes the model more useful and easier to apply in real-world breeding programs especially in places with fewer resources. Overall, their work showed that crop models like this one can help breeders predict which plant varieties will do well in new environments that have weather-related stress. Improving these predictions will lead to the release of more resilient varieties, ultimately yielding more stable production to the benefit of farmers.
2. Plant immune-system activation: a novel approach to disease resistance breeding. USDA-ARS scientists in Ithaca, New York, studied how a chemical that acts like a natural plant signal that a pathogen is present can help oats fight off diseases. They tested 24 different types of oats to see how they reacted when sprayed with a product called ASM that tells the plant to activate its defenses. The researchers wanted to know if all oat types responded the same way. The answer was no—some types showed strong reactions, like turning on special defense genes and growing a little shorter. But others hardly responded at all. They also found that the plant's reaction depended on the environment, for example whether it was growing in a greenhouse or a field. This research shows that some oat varieties are better at switching on their natural defenses when they get a signal. Plant breeders could use this information to pick varieties that are good at defending themselves, helping farmers grow crops that need fewer chemical sprays. In short, this study helps us find ways to grow healthier, more resilient oats that use their own defense systems to fight off threats—in a way that’s good for both the environment and farmers.
3. Identifying mechanisms of crosstalk between carotenoid and starch pathways. USDA-ARS scientists in Ithaca, New York, reviewed studies to understand how the levels of two important nutrients: starches (carbohydrates) and carotenoids (like vitamin A) affect each other. The studies included research on crops like corn, wheat, carrots, and potatoes to see how the levels of these two nutrients are connected. The review revealed that in many plants, when there's more of one (like carotenoids), there’s often less of the other (like starch). This pattern showed up again and again, even across very different crops, meaning it might be a common rule in how plants grow. Consequently, sets of genes that affected one trait often affected the other in an opposite direction. This could mean that the same genes control both traits or that genes affecting the traits in opposite directions are located close together in the genome. The review highlights that if a breeder wants to grow crops with more vitamin A, they might accidentally reduce the amount of starch in the plant, which could lower yields. The review also highlighted the value of searching for special plant types that can make both nutrients well, breaking the usual pattern. Breeders with this information will be better able to accomplish this task. Thus, this research will help breeders create crops that are both nutritious and productive which will benefit farmers growing the crops and people eating them.
Review Publications
Nandudu, L., Strock, C., Ogbonna, A., Kawuki, R., Jannink, J. 2024. Genetic analysis of cassava brown streak disease root necrosis using image analysis and genome-wide association studies. Frontiers in Plant Science. 15. https://doi.org/10.3389/fpls.2024.1360729.
Bakare, M.A., Kayondo, S., Kulakow, P., Rabbi, I., Jannink, J. 2023. Evaluating breeding for broad versus narrow adaptation for cassava in Nigeria using stochastic simulation. Crop Science. 64(2):603-616. https://doi.org/10.1002/csc2.21170.
Nandudu, L., Sheat, S., Winter, S., Ogbonna, A., Kawuki, R., Jannink, J. 2024. Genetic complexity of cassava brown streak disease: insights from qPCR-based viral titer analysis and genome-wide association studies. Frontiers in Plant Science. 15. https://doi.org/10.3389/fpls.2024.1365132.
Sandro, P., Bhatta, M., Bower, A., Carlson, S., Jannink, J., Waring, D.J., Birkett, C.L., Smith, K., Wiersma, J., Caffe, M., Kleinjan, J., Mcmullen, M.S., English, L., Gutierrez, L. 2024. Genomic prediction for targeted populations of environments in oat (Avena sativa). Crop and Pasture Science. 75. https://doi.org/10.1071/CP23126.