Location: Genetic Improvement for Fruits & Vegetables Laboratory
2025 Annual Report
Objectives
Objective 1. Enhance genomic resources for blueberry and cranberry by increasing the number of reference genomes in these crops and related species, leveraging evolving genotyping platforms to develop well-saturated genetic maps, and mapping and utilizing QTL for marker development and gene discovery of selected traits.
Sub-objective 1a. Develop new reference genomes.
Sub-objective 1b. Develop a consensus genetic-physical map for cranberry.
Sub-objective 1c. Map QTL for selected traits in blueberry and cranberry.
Sub-objective 1d. Develop markers for key QTL and identify trait-associated candidate genes.
Sub-objective 1e. Develop and use virus-induced gene silencing (VIGs) and CRISPR gene editing to test target gene function in blueberry and cranberry.
Objective 2. Develop improved cranberry/blueberry pre-breeding and breeding methods that exploit high-dimensional genomic, phenomic, and environmental data, leading to the development of genetic stocks, improved breeding lines, and elite cultivars.
Sub-objective 2a. Develop and evaluate methods to effectively and reciprocally introgress rabbiteye (V. virgatum) and highbush (V. corymbosum) germplasm to produce improved and environmentallyadapted selections and cultivars.
Sub-objective 2b.Develop and evaluate methodology to utilize V. meridionale (Andean blueberry) to improve highbush blueberry (V. corymbosum), and to cross-transfer blueberry (V. corymbosum / V. virgatum), cranberry (V. macrocarpon), and lingonberry (V. vitis-idaea) germplasm.
Sub-objective 2c. Develop, validate, and implement genomic selection models to initiate a rapid recurrent selection cranberry pre-breeding pipeline.
Sub-objective 2d. Develop and deploy systems for image-based high-throughput phenotyping and trait discovery in cranberry and blueberry.
Objective 3. Apply ‘omics technologies to better understand fundamental cranberry/blueberry processes and plant-microbe interactions such as disease resistance and environmental stress tolerance.
Sub-objective 3a. Develop hyperspectral imaging techniques for the collection of phenotypic data of blueberry and cranberry, such as disease status, fruit wax, and stress response.
Sub-objective 3b. Use transcriptomics to characterize response of blueberries and cranberries to various treatments such as temperature stress and during various other processes/developmental stages.
Approach
Uncover genome variation in Vaccinium crops and related species, using long-read sequencing and advanced bioinformatics tools. Evaluate V. meridionale by highbush blueberry, by lingonberry, and by cranberry F1 progeny for quality traits and as pre-breeding material for the development of superior lines. Develop genomic selection (GS) models to enhance prediction accuracy for selection and breeding. Increase the use of imaging for high-throughput phenotyping and the procedures for image analysis. Employ RNAseq experiments to identify important target genes and create markers for their selection in progeny.
Progress Report
For Objective 1, our goal is to improve upon the modest genomic resources available in the cranberry and blueberry research communities by developing new reference genomes (Subobjective 1a), merging genetic and physical maps (Subobjective 1b), identifying genomic regions and potential candidate genes influencing quantitative traits (Subobjective 1c), developing practical markers for marker-assisted selection (Subobjective 1d), and testing gene function using virus induced gene silencing and gene editing (Subobjective 1e). This year, we advanced towards this goal by isolating DNA from Vaccinium elliottii for long-read whole-genome sequencing and genome assembly. We also merged existing and new cranberry genetic maps to create a consensus map; markers in this map were anchored to the ‘Ben Lear’ reference genome in order to determine local recombination rates. This or similar genetic maps were used to identify quantitative trait loci (QTL) for blueberry anthracnose resistance and cranberry fruit rot resistance using novel machine learning methods. We have collected additional data to expand upon or further investigate marker-trait associations by: 1) collecting additional phenotypic data from a bi-parental blueberry population to support QTL mapping for other fruit yield components and quality traits, and 2) whole-genome sequencing isolates of Phomopsis spp., a major cause of fruit rot in cranberry, to dissect the host-pathogen interactions at the fungal species level. To support the functional characterization of candidate genes discovered in QTL mapping studies, we constructed and deployed CRISPR gene editing vectors for gene silencing experiments in cranberry, and gene-edited plants will be evaluated in the next year to determine the impact of gene knockouts on phenotype expression.
In Objective 2, our goal is to create and deploy new ‘omics-enabled approaches for cranberry and blueberry pre- breeding to more rapidly develop improved germplasm and elite cultivars. We made progress towards this goal by establishing a randomized and partially replicated field trial of 437 heterogeneous cranberry individuals along with cycle 1 pre-breeding germplasm in Chatsworth, New Jersey. Separately, the parents and other relatives of the pre- breeding populations were phenotyped for multiple traits to support training genomic prediction models. Individuals in the newly established field trial will be phenotyped over the next 5 years for a common suite of traits to validate and optimize genomic prediction models. Additionally, we upgraded our proximal sensing cart to support image- based high-throughput phenotyping within cranberry field plots using color and thermal sensors, and upgrades to our postharvest fruit imaging platform added the ability to measure the percentage of rotten fruit in a sample of cranberries using color images and an AI object detection model. In the next year, we anticipate integrating high- throughput phenotyping data into our genomic prediction models to enable more rapid and accurate selection.
Our goals for Objective 3 are to apply various ‘omics technologies to understand cranberry and blueberry growth, development, and interactions with biotic and abiotic stresses. This year, we advanced towards this goal in three ways. First, we refined hyperspectral imaging techniques to detect systemic diseases (viruses and bacterial diseases) in the leaves of blueberry and cranberry. In the next year, we anticipate deploying this technique in the field using a global positioning system (GPS)-guided robot. Second, we obtained shotgun metagenome sequencing data from blueberry rhizosphere soils as an important step in identifying and understanding plant- microbe interactions and the effects of microbial composition on blueberry plant health. Third, we developed methods for rapid detection of a serious bacterial disease of cranberry. We will deploy these methods to better understand the host-pathogen system of this disease and to screen for resistance in inoculated cranberry pre- breeding experiments.
Accomplishments
1. Bacterial infection of cranberry plants makes them tastier to insect herbivores. Crop plant defenses must be improved to reduce attacks by pathogens, insects and other pests. This is needed to reduce losses from on the farm all the way to the consumer. ARS researchers in Chatsworth, New Jersey, studied plant-insect interactions on cranberries that were infected with a bacterial disease vs. healthy plants. The study revealed that the plant’s natural defenses are reduced while insect herbivore attraction and performance are increased due to the bacterial infection. This research represents an important step in understanding the complex plant-pathogen-insect interactions and how these interactions impact expression of plant defenses. This advances our research toward increasing plant natural defenses to reduce the need for chemical control of pathogens and insects in crop plants. This will not only decrease crop production losses and expenses for farmers but will also minimize the negative impacts of chemical control on the environment and consumer health.
2. Accurate and low-cost DNA profiling to accelerate cranberry breeding. Cranberry breeding is a time-consuming process that could be made more efficient by selecting superior varieties using information from DNA markers. Existing marker systems in cranberry are either too expensive for routine use or suffer from high rates of missing data. To address this, ARS scientists in Chatsworth, New Jersey in collaboration with Breeding Insight (Cornell University) and other university partners developed a DNA fingerprinting array with 3,000 markers for cranberry. This array was created using gene information from a representative sample of 50 cranberry clones, ensuring that the array will capture important genetic diversity within the crop. DNA marker profile information from a set of 376 clones showed high reproducibility (>99%) and low missing data rates (< 15%). At about $15 per sample, this array is an affordable and accurate tool for breeders to profile new cranberry clones for markers linked to specific and desirable traits, allowing earlier selection of high- performance varieties for crop producers.
3. Use of crop wild relatives for environmental stress resistance in cranberry. Improvement of crop plants response to environmental stress is needed to maintain production during less-than-ideal environmental weather conditions. Crop wild relatives (CWR) represent a valuable resource for capturing traits for introduction in cultivated crops through traditional breeding. A wild relative of cranberry was crossed with cultivated cranberry to produce hybrids. To advance this research, ARS scientists in Chatsworth, New Jersey in collaboration with a non-profit research institute evaluated the performance of the hybrids and their parents for stress responses to high and low temperature shock. Utility of using CWR as source of new genes that help mitigate the impact of adverse temperature conditions and other stresses was demonstrated. The research supports the collection and maintenance of CWR as a valuable resource for crop improvement and is useful to researchers that are breeding for improved cranberry and other small fruit crops. This research also contributes to the goal of maintaining and improving crop production during exposure to environmental and other stresses.
4. Discovering cranberry terroir by analyzing genotype-environment interactions. Crop varieties respond differently to their local growing environment, and the best variety for crop producers to grow may change depending on the location. Identifying these superior variety-location combinations is impossible without understanding genotype-environment interactions, and in the case of cranberry, current knowledge about these interactions is limited. To address this gap and develop improved methods for identifying the best cranberry varieties for individual growing regions, ARS scientists in Chatsworth, New Jersey and university collaborators analyzed data from a multi-environment trial of 80 advanced cranberry clones planted and evaluated in four locations across the United States and Canada. New models were developed to handle the complexity of repeated measurements from the same plant across multiple years, a complication that is common to woody perennial crops. These models also enable the prediction of the performance of all 80 clones in all four locations, even if a particular clone was not grown in a particular location. Interestingly, locations that were geographically close did not show similar performance levels among the clones, emphasizing the impact of local environment. This research is being used by ARS and university breeders and extension specialists to design future cranberry evaluation trials, and the statistical models developed will help support grower stakeholders in deciding what cranberry cultivar to grow to maximize profitability.
5. Rapid detection of cranberry fruit rot using advanced computer modeling. Improvement of plant traits necessitates that plants moving through the development pipeline are carefully assessed for the desired traits. This step is very time consuming and error prone. To speed evaluation of fruit samples and increase accuracy, ARS scientists in Chatsworth, New Jersey, developed a computer modeling system, called Convolutional Neural Network to accurately identify sound vs. rotten cranberries in mixed batches using near-infrared imaging. Using this system, evaluation for fruit rot was nearly as accurate as hand counting and time for evaluation was dramatically (50%) reduced. This allows the assessment of many more samples per season and will ultimately speed the release of improved varieties that will benefit growers and consumers.
Review Publications
Halpin-Mccormick, A., Campbell, Q., Negrao, S., Morrell, P., Hubner, S., Neyhart, J.L., Kantar, M. 2025. Environmental genomic selection to leverage polygenic local adaptation in barley landraces. Communications Biology. https://doi.org/10.1038/s42003-025-08045-4.
Loarca, J., Wiesner-Hanks, T., Lopez-Moreno, H., Maule, A.F., Liou, M., Torres-Meraz, M.A., Diaz-Garcia, L., Johnson-Cicalese, J., Neyhart, J.L., Polashock, J., Sideli, G.M., Strock, C.F., Beil, C.T., Sheehan, M., Iorizzo, M., Atucha, A., Zalapa, J.E. 2024. BerryPortraits: Phenotyping of ripening traits in cranberry (Vaccinium macrocarpon Ait.)with YOLOv8. Plant Methods. https://doi.org/10.1186/s13007-024-01285-1.
Harnly, J.M., Geng, P., Polashock, J.J., Chen, P., Vorsa, N., Johnson, J. 2025. Impact of genetics and environment on cranberry fruit metabolites. Journal of AOAC International. Article qsaf056. https://doi.org/10.1093/jaoacint/qsaf056.
Rodriguez-Saona, C., Salazar-Mendoza, P., Holdcraft, R., Polashock, J.J. 2024. Phytoplasma infection renders cranberries more susceptible to above- and belowground insect herbivores. Insect Science. 32(3):957-972. https://doi.org/10.1111/1744-7917.13444.