Abstract
Breeding programs around the world continually collect data on large numbers of individuals. To be able to combine data collected across regions, years, and experiments, research communities develop standard operating procedures for data collection and measurement. One such method is a crop ontology, or a standardized vocabulary for collecting data on commonly measured traits. The ontology is also computer readable to facilitate the use of data management systems such as databases. Blueberry breeders and researchers across the United States have come together to develop the first standardized crop ontology in blueberry (Vaccinium spp.). We provide an overview and report on the construction of the first blueberry crop ontology and the 178 traits and methods included within. Researchers of Vaccinium species—such as other blueberry species, cranberry, lingonberry, and bilberry—can use the described crop ontology to collect phenotypic data of greater quality and consistency, interoperability, and computer readability. Crop ontologies, as a shared data language, benefit the entire worldwide research community by enabling collaborative meta-analyses that can be used with genomic data for quantitative trait loci, genome-wide association studies, and genomic selection analysis.
Blueberries are an indigenous North American berry crop with strong cultural and economic significance globally. Despite the relatively small acreage of production, US blueberry growers produced 281,000 kg of berries, valued at USD 987 million in 2022 (US Department of Agriculture, National Agricultural Statistics Service 2023). Blueberries are grown across the US, with Washington and Oregon producing the most berries on 22,300 and 13,200 acres, respectively, in 2022 (US Department of Agriculture, National Agricultural Statistics Service 2023).
Different blueberry species are grown in different parts of the United States depending on climate adaptation. Northern highbush blueberries (Vaccinium corymbosum L.) and a few cultivars of half-high blueberries (V. corymbosum × Vaccinium angustifolium) are grown in the Pacific Northwest (PNW; which includes Washington and Oregon), Michigan, and New England. Southern highbush blueberries (V. corymbosum interspecific hybrids) and rabbiteye (Vaccinium virgatum, syn. Vaccinium ashei) are grown primarily in the southeastern United States and the Southwest, including California (Strik and Yarborough 2005). Lowbush blueberries, also known as wild blueberries (V. angustifolium or Vaccinium myrtilloides), are grown commercially in the northeastern United States, primarily Maine (Yarborough 2015). Blueberry types are categorized by the chilling requirement to set fruit, or the number of cumulative hours between 7 and 0 °C, with high-chill types needing 800 to 1000 h and low-chill types needing less than 800 h (Sharpe 1953; Sharpe and Darrow 1959). Blueberries of all types might be grown for the fresh market or processing, including frozen products, or U-pick agrotourism ventures.
Blueberry researchers and breeders conduct activities within public and private research programs, and are located primarily in the regions growing the most blueberries, including the PNW, Michigan, the Southwest, and along the East Coast. Although blueberry breeding programs differ in regional priorities, many of the core breeding objectives remain the same. For all blueberries, regardless of chill requirements, goals include fruit quality (firmness, flavor, and shelf life), machine harvestability (firmness, uniform ripening, bruise resistance), disease and arthropod resistance, and abiotic stress tolerance (heat and frost) (Gallardo et al. 2018; Olmstead and Finn 2014; The Northwest Center for Small Fruits Research 2023).
Phenotypic data in breeding and research are expensive to generate, published rarely as raw data, and difficult to translate between programs (Zamir 2013). Challenges arise when data are collected in different breeding programs and databases with inconsistent trait names and collection methods, which are often not fully explained (Andrés-Hernández et al. 2021). Ontologies provide a shared space to describe, render interoperable, and supply annotations to data such that trait knowledge becomes formalized and structured (Jonquet et al. 2018) in a community of practice grounded by a shared ontology, such as the Crop Ontology (https://cropontology.org/), which was created in 2008 by CGIAR (Montpellier, France). In the Blueberry Crop Ontology system, each crop has a specific crop ontology (CO) dataset for measured trait descriptors, with the goal of all researchers and breeders who are collecting data for that crop to use the trait descriptors in that CO, thus facilitating access, integration, comparison, and analysis of large phenomic and genomic datasets (Shrestha et al. 2010). All members of the community participate in contributing to a lexicon that matches their needs and defines relationships between traits.
By describing standardized definitions for traits, and logical relationships among them, a CO is designed to organize, aggregate, and retrieve large quantities of genomic and phenomic data, laying the foundation for large-scale genetic analysis, and enabling the discovery of common meaning between species or across taxa (Arnaud et al. 2012; Jaiswal 2011; Pan et al. 2019). As a semantic framework, CO facilitates communication and collaboration among researchers by providing a standardized and controlled vocabulary for traits (Walls et al. 2012). Standard nomenclature also provides equivalence links between trait descriptions, facilitating comparison of data among trials or between crops (Shrestha et al. 2012). In a crop-breeding context, databases store and name phenotypic data consistently, enabling sharing and interpretation of trait data across not only years and locations but also across programs. Crop ontologies have been built to support plant breeders of various crops, including maize (Zea mays), potato (Solanum tuberosum), squash (Cucurbita spp.), cassava (Manihot esculenta), sorghum (Sorghum spp.), pigeon pea (Cajanus cajan), rice (Oryza sativa), sweetpotato (Ipomoea batatas), soybean (Glycine max), wheat (Triticum aestivum), and lentil (Lens culinaris), among many others, to improve the consistency of measuring phenotypic traits across many research programs (Aminu et al. 2022; Cooper et al. 2013; Fernandez-Pozo et al. 2015; Jung et al. 2019; Oellrich et al. 2015; Shrestha et al. 2010; Zheng et al. 2019). The CO for all these crops is available to access and download on the Crop logy website (https://cropontology.org/). There is also a mechanism for breeders and researchers to suggest additions to each CO, and these requested changes are reviewed by curators who work with that crop before adding the changes to the Crop Ontology.
Crop Ontology has an established framework and provides guidelines and templates for new COs (https://cropontology.org/page/NewOntology). The Crop Ontology also encourages the use of the existing Plant Ontology and Agronomy Ontology, which are compliant with the Open Biological and Biomedical Foundry ontology principles, within the new CO terms to support interoperability. New COs that are submitted to the Crop Ontology website are reviewed to make sure the data comply with format and consistency. When the revisions are complete, the new CO becomes available on the Crop Ontology website. From there, users can explore the ontology through the website or download the ontology in either the Resource Description Framework/N-Triples or Excel formats. Information can also be retrieved using application programming interfaces (APIs), such as the Breeding API (https://brapi.org/) or the RDF (https://cropontology.org/api_help).
By using the existing Crop Ontology system, new crop-specific ontologies are compliant with findable, accessible, interoperable, and reusable principles (Arnaud et al. 2020; Harper et al. 2018), and are part of a semantic framework that enables computers to understand these meanings and to find relationships among them through semantic reasoners (Matteis et al. 2013). A shared lexicon supports data integration and interoperability, which enable scientific discoveries through the merging of diverse datasets (Goble and Stevens 2008). These datasets could originate from different sources such as private collections, breeding programs, and public collections, such as that of the US Department of Agriculture-Agricultural Research Service’s (USDA-ARS) Grin Global (2023) collection preserved in Corvallis, Oregon, USA, for blueberry and its relatives, and with data that are available through the Germplasm Resource Information Network (GRIN)-Global database (https://www.grin-global.org/). These semantic frameworks allow the use of tools such as the Breeding Information Management System (BIMS), which is an open-source breeding platform for storing and analyzing breeding data (Jung et al. 2021) available through crop-specific databases such as the Genome Database for Vaccinium (GDV, https://www.vaccinium.org/) (Humann et al. 2023).
To integrate phenotypic data from the various blueberry breeding programs and collections, our objective was to develop a CO that systematizes the traits measured in blueberry and provides a shared vocabulary and phenotyping methodology for each trait that is also computer readable. We describe the CO that we developed for blueberry. Our Blueberry Crop Ontology (BCO) reflects a consensus understanding of traits being captured across blueberry among 14 US blueberry breeders and researchers, representing most major blueberry programs in the United States. We use our CO to refer to a comprehensive collection of traits measured, quantified, or observed in blueberry research, with one ontological entry per method of measurement.
Materials and Methods
We surveyed breeders and researchers in public and private blueberry breeding programs in the United States (N = 14) in 2019 regarding the traits they measure at different phenological stages. The respondents were from a variety of institutions, including university, government, and private breeding programs that represented all regions of US blueberry production except for Maine: USDA-ARS Beltsville Agricultural Research Center, the USDA-ARS Horticultural Crops Production and Genetic Improvement Research Unit, the USDA-ARS Thad Cochran Southern Horticultural Research Center, Michigan State University, North Carolina State University, Rutgers University, University of Florida, and industry partners (Driscoll’s, Watsonville, CA, USA; Fall Creek Farm and Nursery, Lowell, OR, USA; and Oregon Blueberry, Salem, OR, USA) (Fig. 1). Indicated in Fig. 1, but not included in the survey as a result of transitions in the program on the survey date, are the University of Georgia and Auburn University blueberry breeding programs.
Blueberry research and breeding programs. The legend indicates the primary type of research being conducted at the institution and the number of acres of blueberry production in each state. BARC = Beltsville Agricultural Research Center; GBRU = Genomics and Bioinformatics Research Unit; HCPGIRU = Horticultural Crops Production and Genetic Improvement Research Unit; NCGR = National Clonal Germplasm Repository; SHRC = Southern Horticultural Research Center; USDA = US Department of Agriculture.
Citation: HortScience 59, 10; 10.21273/HORTSCI17676-23
In the survey, we included all current GRIN-Global descriptors for blueberries for the USDA-ARS NCGR collection (accessed in 2019) (Postman et al. 2010) as well as additional traits collected in USDA-ARS programs. We circulated a list of 103 blueberry traits and asked each program on which traits they collect data. Programs were also asked to provide traits on which they collect data that were not included the initial list. From each program, information collected about each scored trait included descriptions, phenotyping method, method class, and measurement scale (Table 1).
Classification components of each trait collected for the curated blueberry crop ontology, including classification name, whether it is required, a brief description, relevant notes on the classification, and a relevant example.
We compiled traits from all blueberry programs and determined which traits were overlapping. For these overlapping traits, we determined whether programs were using the same method for measurement. If they were, that measurement was coded as the standard method for that trait. If different methods were used for a given trait, we described the unique method and gave each entry a unique name.
The collated data from each plant breeder were entered into a standardized template, with required components outlined by Crop Ontology (Table 1, Supplemental Table 1). During that process, new traits were compared with existing traits in the GDV database, and common abbreviations and ontological terms were used to facilitate future integration into the GDV database. In addition, metadata were entered for each trait, such as trait class, trait description, method details, method reference, and rating scale information. After an additional round of curation, the curated traits were deposited in multiple locations. The curated traits were made publicly accessible in Crop Ontology as well as through the GDV database (Humann et al. 2023; Matteis et al. 2013).
Results
We found wide variability in the number of traits measured by program, ranging from 11 to 83 traits, with an average of 49.1 traits (standard deviation, σ = 18.8). Two traits were found to be measured by all respondents: berry firmness and fruit soluble solids (degrees Brix). Other commonly measured traits (n = 13 respondents) included fruit epicuticular wax (bloom), berry color, subjective flavor (recorded most frequently on a point scale, indicating hedonic liking and mouthfeel), fruit size, picking scar, and titratable acidity. We determined 178 unique ontological entries among the phenotypic traits and their various methodologies (Fig. 2). Different programs have different research objectives and questions, so not every program measures or prioritizes the same traits.
Example of traits in the blueberry crop ontology and the seasonal stage at which they might be measured. SWD = Spotted-wing Drosophila.
Citation: HortScience 59, 10; 10.21273/HORTSCI17676-23
Traits were organized by classification into nine groups based on Trait class, which is why they are measured, or the traits that are commonly measured together. These classifications included abiotic stress, agronomic, biochemical, biotic stress, fertility, morphological, phenological, physiological, and quality, with biochemical being the largest group with 51 traits (Fig. 3).
Traits in the blueberry crop ontology (n = 178) classified by trait grouping.
Citation: HortScience 59, 10; 10.21273/HORTSCI17676-23
Ontologies that measured the same traits, but used different methods or scales, were given distinct names and distinct identifiers. One example of this is the Plant vigor trait, for which two methods exist, and as such, each method was assigned a unique, computer-readable identifier: VIG and VIG2. Both methods assess plant health and resilience, but use different scales: VIG uses a 1−9 scale (1 = low, 5 = average, 9 = high), while VIG2 uses truncated 1–3 scale for the same levels. Both methods are useful and appropriate depending on variation between genotypes and among environments. The identifier serves as a unique, computer-readable abbreviation for the longer trait name. Eight different methods were recorded for spotted-wing Drosophila (Drosophila suzukii) (SWD) resistance phenotyping, which was the most methods for any trait.
Because this survey reached most known blueberry researchers and breeders in the United States, it captured comprehensively the traits of interest in modern US blueberry research at the time of the survey. The researchers surveyed work with all existing types of blueberries including Southern highbush, Northern highbush, half-high, rabbiteye, and wild Vaccinium spp. used as breeding germplasm (Fig. 1).
The ontologies also make note of the portion of the plant to which the trait is related (e.g., stem, fruit, bud), trait attribute being measured, whether the trait is being used actively by a research program, collection method, method class, and method description. Method class includes calculation, estimation, count, and measurement (Fig. 4). It also includes the scale or unit used to record the trait (e.g., binary yes/no, date, percentage), the scale class (e.g., numerical, ordinal), and any relevant numerical limits (e.g., 0–100 for percentages, 0–14 for pH measurements). Measurement was the most used Method class, with 94 entries, followed by estimation with 65 entries.
Traits in blueberry crop ontology (n = 178) classified by collection method.
Citation: HortScience 59, 10; 10.21273/HORTSCI17676-23
This curated BCO has been published via the Crop Ontology and GDV websites (Humann et al. 2023; Matteis et al. 2013). Best practice would be to use the Crop Ontology website to view and download the most current BCO, under code 371 by searching with the crop name or the accession number CO_371 (Fig. 5) (https://cropontology.org/term/CO_371:ROOT). The Crop Ontology database enables visualization of the ontology through Ontology explorer tools (Fig. 5B–E).
Screen captures of the blueberry ontology from the Crop Ontology database. The homepage for the ontology is shown in (A), which can then be downloaded or viewed (B) by clicking icons to the right. The Ontology explorer (C) clusters features and highlights by class that can be explored further, such as CO_371:Biotic stress (D), and shows all traits using the same method of collection (E).
Citation: HortScience 59, 10; 10.21273/HORTSCI17676-23
The BCO can also be found in the GDV database for researchers using that database (a GDV curator is part of the Crop Ontology BCO management team (Fig. 5A) and will sync the CO from Crop Ontology with the GDV database. The trait descriptors have been loaded into the GDV database and can be searched using the Trait Descriptor Search (https://www.vaccinium.org/search/trait_descriptor). For breeders using the BIMS tool (https://www.vaccinium.org/bims), the trait descriptors can be selected and imported into breeding programs for use in data collection. Phenotypic data added to the GDV database are also associated with the trait descriptors in the BCO to facilitate comparison of data between multiple studies.
Discussion
The traits evaluated in this study include those recognized as priority traits for blueberry breeding in the 2016 crop vulnerability statement (resistance to fungal diseases, bacterial diseases, insect pests, arthropod pests) (US Department of Agriculture, Small Fruit Crop Germplasm Committee 2016). It also includes priority traits identified more recently (plant characteristics, fruit characteristics) (Gallardo et al. 2018; US Department of Agriculture, Small Fruit Crop Germplasm Committee 2020). We describe how we developed a CO for blueberry by surveying breeding programs around the United States. We invite and encourage researchers and plant breeders of blueberry and other Vaccinium crops around the globe to begin using this ontology, and have outlined best practices of use. Using the ontology will allow integration, comparison, and communication of phenotypic data across research programs.
This ontology intends to promote collaboration across programs and increase the usefulness of valuable phenotypic data. This BCO will enable efficient analysis of existing and future blueberry data more effectively, as well as that from other Vaccinium. Future expansion of this ontology would benefit from the inclusion of the International Union for the Protection of New Varieties of Plants (UPOV) Vaccinium trait list. These traits were not included in the presented ontology, as we focused on traits measured by breeders and researchers in the United States. However, the traits found in the UPOV are measured for the protection and classification of new cultivars internationally, and are standardized, which meshes well with the purpose of the Crop Ontology, and is highly valuable given blueberry’s strong presence in the global marketplace. In future cultivar releases, it would be beneficial to consider both trait lists for standardized data collection. Additional improvements to the BCO could include expanding and standardizing phenological stages. Additional improvements to the system could also include the implementation of automatic curation or automatic reasoning, each of which has been suggested to be an important factor for bridging biological levels such as through the Ontology of Biological Attributes computational traits for the life sciences (Laporte et al. 2016; Stefancsik et al. 2023).
We encourage use of the Crop Ontology database across programs. The BCO has begun to be used across US programs, allowing incorporation of data collection through the use of the Field Book app which replaces hard-copy field notes and Excel spreadsheets with digital data collection, enhancing the speed of data collection (Morales et al. 2022; Rife and Poland 2014). Systems such as Field Book allow breeders and researchers to evaluate plants in the field with access to precise trait information, including the standard protocol and scales (Rife and Poland 2014). To use the BCO in this system, users only need to download the database and upload the traits to Field Book, along with a field map of their materials, and then can begin digital data collection immediately.
While the ontology is available to the public through the Crop Ontology and GDV databases, enhanced use requires implementation on database systems to be functional for research and breeding programs. To fit this need, the BCO has not only been implemented in the Crop Ontology database, but has also been made available to users through services such as BIMS and BreedBase (https://breedbase.org/) (Jung et al. 2021; Morales et al. 2022). In this manner, collected data that are associated with the BCO can be uploaded, stored, and managed. The GDV curator is part of the Crop Ontology management team (Fig. 5A) and will ensure continuity when changes are made in the database, so that those changes are also reflected in the other places that the BCO is available. These systems will also allow for automated data transfer and extraction, which supports continuity, through the Breeding API (https://brapi.org/), enabling further streamlining and future use of the BCO (Selby et al. 2019).
The developed Crop Ontology system is only a starting point and should be maintained by the community and updated as new technologies are developed and traits are collected in different improved ways. Traits can continue to be added by users who cannot find a method that suits their needs, and traits can be updated as techniques are refined. If researchers would like to request that traits be added or adjusted in the ontology, they submit their request through https://trait-requests.planteome.org/. Users can submit new traits, request that changes be made to a trait, or suggest trait synonyms. For the greater good of the community, we encourage all Vaccinium breeders and researchers to evaluate the Crop Ontology database, use methods that are standardized with others as possible, and, if the trait to be measured does not exist currently, to add it to the BCO.
Phenotypic data generated from the Crop Ontology can be leveraged in pangenome studies. The USDA-funded Vaccinium Coordinated Agriculture Project accomplished the assembly of blueberry and cranberry pangenomes, a robust high-density genotyping platform for GWA studies that supports breeding efforts for blueberry, cranberry, and related Vaccinium of commerce (Iorizzo et al. 2022; Yocca et al. 2023). Despite an estimated divergence between 5 and 10 million years between blueberry and cranberry species, 93% of the blueberry genome is syntenic and collinear with cranberry, indicating that large-scale genome differentiation has not occurred between these species during their evolution. This suggests that DNA sequence information between blueberry and cranberry is highly transferable (Schlautman et al. 2018). This is confirmed by the pangenome, which found more than 10,000 positionally conserved core genes in cranberry and blueberry, more than 90% of which are present in related Vaccinium species (Yocca et al. 2023). Using a pangenome renders genome-wide association studies (GWAS) more powerful, in that additional candidate loci influencing traits of interest can and have been identified (Lei et al. 2021; Tao et al. 2019; Tay Fernandez et al. 2022; Zhou et al. 2015). Combined with the BCO, the positionally conserved core genes can serve as markers to identify quantitative trait loci (QTL) for priority traits across commercial Vaccinium species from data in multiple environments. As the first CO in Vaccinium, this blueberry ontology sets the groundwork for other Vaccinium COs, such as the forthcoming cranberry (Vaccinium macrocarpon) crop ontology. Comparison of traits, genes, and gene products across species is especially relevant because blueberry and cranberry share a high amount of synteny (Schlautman et al. 2018) and common breeding priority traits, such as pathogen resistance (Phytophthora, Fusarium, Macrophomina), insect resistance (root weevil), disease resistance (fruit rot), fruit quality (fruit size), and plant characteristics (frost tolerance) (Gallardo 2018; US Department of Agriculture, Small Fruit Crop Germplasm Committee 2020).
Our combined efforts not only unify genetic and phenotypic data from blueberry and cranberry, but also lay the groundwork for genetic studies and efficient introgression within and between other commercial Vaccinium species, such as lingonberry, bilberry, and other blueberry species. The interest in exploring hybridization and fertility among Vaccinium species is well documented, and includes crosses between blueberry (Vaccinium darrowii) × cranberry (Vorsa et al. 2009), lingonberry (Vaccinium vitis-idaea L.) × cranberry (Zeldin and McCown 1997), Andean blueberry (Vaccinium meridionale) × lingonberry (Ehlenfeldt et al. 2022), highbush blueberry × sparkleberry (Vaccinium arboreum) (Lyrene 2011), lingonberry × bilberry (Vaccinium uliginosum) (Morozov 2007), rabbiteye blueberry × deerberry (Vaccinium stamineum) (Lyrene 2021), Andean blueberry × highbush blueberry (Ehlenfeldt and Luteyn 2021), highbush blueberry × Madeira blueberry (Vaccinium padifolium) (Ehlenfeldt and Polashock 2014) and V. darrowii × lingonberry (Ehlenfeldt et al. 2024). This collaboration supports breeders interested in broadening gene pools for cultivar development and opens frontiers in novel crop development, as some commercial and underused species can serve in secondary or tertiary gene pools for one another.
Leveraging quality phenotyping data and the blueberry draft genome as a reference (Gupta et al. 2015), QTLs and GWAS have identified loci associated with phenology traits, flavor, terpenes, phytonutrient pathways, anthocyanin biosynthesis, pH, soluble solids, phenolic acids, fruit abscission, weight, and fruit ripening (Colle et al. 2019; Ferrão et al. 2020, 2022; Guo et al. 2023; Herniter et al. 2023; Li et al. 2022; Mengist et al. 2021, 2022; Nagasaka et al. 2022; Wang et al. 2023a, 2023b). The enthusiastic use of the draft genome points to the obvious need for a high-quality reference genome for blueberry (Benevenuto et al. 2019). In the interim, this blueberry draft genome can be used with the phenotypic data generated from the BCO, enabling collaborators to perform GWAS, genomic prediction using phenotype data from multiple years, traits, and locations to identify QTLs or genetic markers that are robust across multiple environments.
Our efforts to develop this BCO have created a path toward standardization in blueberry phenotyping, allowing researchers around the world to speak the same research language and compare results directly across years, locations, and environments. The Crop Ontology system will allow easier integration of phenotypic data with genotypic and environmental information. Our blueberry trait ontology complements and can be used in concert with larger genomic analyses, such as QTL analysis, GWA analyses, and genomic prediction to advance future blueberry breeding efforts. This will aid in the integration of phenomics with software, genomic tools, and data management, with the ultimate goal of leading to greater genetic gains across all breeding programs. The results of our survey will benefit blueberry and Vaccinium researchers, breeders, and, consequently, growers and consumers, by supporting national and international collaboration among research groups working toward improving blueberry production.
References Cited
Aminu EF, Oyefolahan IO, Abdullahi MB, Salaudeen MT. 2022. MaCOnto: A robust maize crop ontology based on soils, fertilizers and irrigation knowledge. Intelligent Syst Appl. 16:200125. https://doi.org/10.1016/j.iswa.2022.200125.
Andrés-Hernández L, Halimi RA, Mauleon R, Mayes S, Baten A, King GJ. 2021. Challenges for FAIR-compliant description and comparison of crop phenotype data with standardized controlled vocabularies. Database. 2021:baab028. https://doi.org/10.1093/DATABASE/BAAB028.
Arnaud E, Cooper L, Shrestha R, Menda N, Nelson RT, Matteis L, Skofic M, Bastow R, Jaiswal P, Mueller L, McLaren G. 2012. Towards a reference plant trait ontology for modeling knowledge of plant traits and phenotypes. Proc Int Conf Knowl Eng Ontology Dev. 220–225. https://doi.org/10.5220/0004138302200225.
Arnaud E, Laporte MA, Kim S, Aubert C, Leonelli S, Miro B, Cooper L, Jaiswal P, Kruseman G, Shrestha R, Buttigieg PL, Mungall CJ, Pietragalla J, Agbona A, Muliro J, Detras J, Hualla V, Rathore A, Das RR, Dieng I, Bauchet G, Menda N, Pommier C, Shaw F, Lyon D, Mwanzia L, Juarez H, Bonaiuti E, Chiputwa B, Obileye O, Auzoux S, Yeumo ED, Mueller LA, Silverstein K, Lafargue A, Antezana E, Devare M, King B. 2020. The ontologies community of practice: A CGIAR initiative for big data in agrifood systems. Patterns. 1(7): 100105. https://doi.org/10.1016/j.patter.2020.100105.
Benevenuto J, Ferrão LFV, Amadeu RR, Munoz P. 2019. How can a high-quality genome assembly help plant breeders? Gigascience. 8(6): giz068. https://doi.org/10.1093/gigascience/giz068.
Colle M, Leisner CP, Wai CM, Ou S, Bird KA, Wang J, Wisecaver JH, Yocca AE, Alger EI, Tang H, Xiong Z, Callow P, Ben-Zvi G, Brodt A, Baruch K, Swale T, Shiue L, Song G, Childs KL, Schilmiller A, Vorsa N, Buell CR, VanBuren R, Jiang N, Edger PP. 2019. Haplotype-phased genome and evolution of phytonutrient pathways of tetraploid blueberry. Gigascience. 8(3):giz012. https://doi.org/10.1093/GIGASCIENCE/GIZ012.
Cooper L, Walls RL, Elser J, Gandolfo MA, Stevenson DW, Smith B, Preece J, Athreya B, Mungall CJ, Rensing S, Hiss M, Lang D, Reski R, Berardini TZ, Li D, Huala E, Schaeffer M, Menda N, Arnaud E, Shrestha R, Yamazaki Y, Jaiswal P. 2013. The Plant Ontology as a tool for comparative plant anatomy and genomic analyses. Plant Cell Physiol. 54(2):e1. https://doi.org/10.1093/pcp/pcs163.
Ehlenfeldt MK, Luteyn JL. 2021. Fertile intersectional F1 hybrids of 4x Vaccinium meridionale (section Pyxothamnus) and highbush blueberry, V. corymbosum (section Cyanococcus). HortScience. 56(3):318–323. https://doi.org/10.21273/HORTSCI15523-20.
Ehlenfeldt MK, Polashock JJ. 2014. Highly fertile intersectional blueberry hybrids of Vaccinium padifolium section Hemimyrtillus and V. corymbosum section Cyanococcus. J Am Soc Hortic Sci. 139(1):30–38. https://doi.org/10.21273/JASHS.139.1.30.
Ehlenfeldt MK, Polashock JJ, Rowland LJ, Ogden E, Luteyn JL. 2022. Fertile intersectional hybrids of 4x Andean blueberry (Vaccinium meridionale) and 2x lingonberry (V. vitis-idaea). HortScience. 57(4):525–531. https://doi.org/10.21273/HORTSCI16285-21.
Ehlenfeldt MK. Bassil N, King R, Zalapa J, de la Torre F, Luteyn JL. 2024. Intersectional Hybrids between Darrow’s Blueberry (V. darrowii Camp) and Lingonberry (V. vitis-idaea L.). Plants. 13(11):1572.
Fernandez-Pozo N, Menda N, Edwards JD, Saha S, Tecle IY, Strickler SR, Bombarely A, Fisher-York T, Pujar A, Foerster H, Yan A, Mueller LA. 2015. The Sol Genomics Network (SGN)—From genotype to phenotype to breeding. Nucl Acids Res. 43(D1):D1036–D1041. https://doi.org/10.1093/nar/gku1195.
Ferrão LFV, Johnson TS, Benevenuto J, Edger PP, Colquhoun TA, Munoz PR. 2020. Genome-wide association of volatiles reveals candidate loci for blueberry flavor. New Phytol. 226(6):1725–1737. https://doi.org/10.1111/nph.16459.
Ferrão LFV, Sater H, Lyrene P, Amadeu RR, Sims CA, Tieman DM, Munoz PR. 2022. Terpene volatiles mediates the chemical basis of blueberry aroma and consumer acceptability. Food Res Int. 158:111468. https://doi.org/10.1016/j.foodres.2022.111468.
Gallardo RK, Zhang Q, Dossett M, Polashock JJ, Rodriguez-Saona C, Vorsa N, Edger PP, Ashrafi H, Babiker E, Finn CE, Iorizzo M. 2018. Breeding trait priorities of the blueberry industry in the United States and Canada. HortScience. 53(7):1021–1028. https://doi.org/10.21273/HORTSCI1296418.
Goble C, Stevens R. 2008. State of the nation in data integration for bioinformatics. J Biomed Inform. 41(5):687–693. https://doi.org/10.1016/j.jbi.2008.01.008.
GRIN-Global. 2023. Germplasm resources information network - global, USDA-ARS (National Plant Germplasm System). https://www.grin-global.org/. [accessed 31 Jul 2023].
Guo X, Hu J, Yang S, Wang D, Wang J. 2023. Genome-wide analysis of the F3′5′H gene family in blueberry (Vaccinium corymbosum L.) provides insights into the regulation of anthocyanin biosynthesis. Phython. 92(9):2683–2697. https://doi.org/10.32604/phyton.2023.029820.
Gupta V, Estrada AD, Blakley I, Reid R, Patel K, Meyer MD, Andersen SU, Brown AF, Lila MA, Loraine AE. 2015. RNA-Seq analysis and annotation of a draft blueberry genome assembly identifies candidate genes involved in fruit ripening, biosynthesis of bioactive compounds, and stage-specific alternative splicing. GigaScience. 4:5. https://doi.org/10.1186/s13742-015-0046-9.
Harper L, Campbell J, Cannon EKS, Jung S, Poelchau M, Walls R, Andorf C, Arnaud E, Berardini TZ, Birkett C, Cannon S, Carson J, Condon B, Cooper L, Dunn N, Elsik CG, Farmer A, Ficklin SP, Grant D, Grau E, Herndon N, Hu Z-L, Humann J, Jaiswal P, Jonquet C, Laporte M-A, Larmande P, Lazo G, McCarthy F, Menda N, Mungall CJ, Munoz-Torres MC, Naithani S, Nelson R, Nesdill D, Park C, Reecy J, Reiser L, Sanderson L-A, Sen TZ, Staton M, Subramaniam S, Tello-Ruiz MK, Unda V, Unni D, Wang L, Ware D, Wegrzyn J, Williams J, Woodhouse M, Yu J, Main D. 2018. AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture. Database. 2018:bay008. https://doi.org/10.1093/DATABASE/BAY088.
Herniter IA, Kim Y, Wang Y, Havill JS, Johnson-Cicalese J, Muehlbauer GJ, Iorizzo M, Vorsa N. 2023. Trait mapping of phenolic acids in an interspecific (Vaccinium corymbosum var. caesariense × V. darrowii) diploid blueberry population. Plants. 12(6):1346. https://doi.org/10.3390/plants12061346.
Humann JL, Cheng C-H, Lee T, Buble K, Jung S, Yu J, Zheng P, Hough H, Crabb J, Frank M, Scott K, Iorizzo M, Main D. 2023. Using the Genome Database for Vaccinium for genetics, genomics, and breeding research. Acta Hortic. 1357:115–122. https://doi.org/10.17660/ActaHortic.2023.1357.17.
Iorizzo M, Lila MA, Perkins-Veazie P, Luby C, Vorsa N, Edger P, Bassil N, et al. 2022. VacciniumCAP, a community-based project to develop advanced genetic tools to improve fruit quality in blueberry and cranberry, vol. 1362, p 71–80. In: XXXI International Horticultural Congress (IHC2022): International Symposium on Breeding and Effective Use of Biotechnology Molecular Tools in Horticultural Crops
Jaiswal P. 2011. Gramene Database: A hub for comparative plant genomics. In: Pereira A (ed). Plant reverse genetics: Methods in molecular biology (vol 678). Humana Press, Totowa, NJ, USA. https://doi.org/10.1007/978-1-60761-682-5_18.
Jonquet C, Toulet A, Arnaud E, Aubin S, Dzalé Yeumo E, Emonet V, Graybeal J, Laporte M-A, Musen MA, Pesce V, Larmande P. 2018. AgroPortal: A vocabulary and ontology repository for agronomy. Comput Electron Agric. 144:126–143. https://doi.org/10.1016/j.compag.2017.10.012.
Jung S, Lee T, Cheng C-H, Buble K, Zheng P, Yu J, Humann J, Ficklin SP, Gasic K, Scott K, Frank M, Ru S, Hough H, Evans K, Peace C, Olmstead M, DeVetter LW, McFerson J, Coe M, Wegrzyn JL, Staton ME, Abbott AG, Main D. 2019. 15 Years of GDR: New data and functionality in the Genome Database for Rosaceae. Nucl Acids Res. 47(D1):D1137–D1145. https://doi.org/10.1093/nar/gky1000.
Jung S, Lee T, Gasic K, Campbell BT, Yu J, Humann J, Ru S, Edge-Garza D, Hough H, Main D. 2021. The Breeding Information Management System (BIMS): An online resource for crop breeding. Database. 2021:baab054. https://doi.org/10.1093/DATABASE/BAAB054.
Laporte MA, Valette L, Cooper L, Mungall C, Meier A, Jaiswal P, Arnaud E. 2016. Comparison of ontology mapping techniques to map plant trait ontologies. InICBO/BioCreative.
Lei L, Goltsman E, Goodstein D, Wu GA, Rokhsar DS, Vogel JP. 2021. Plant pan-genomics comes of age. Annu Rev Plant Biol. 72(1):411–435. https://doi.org/10.1146/annurev-arplant-080720-105454.
Li T, Wang X, Elango D, Zhang W, Li M, Zhang F, Pan Q, Wu Y. 2022. Genome-wide identification, phylogenetic and expression pattern analysis of Dof transcription factors in blueberry (Vaccinium corymbosum L.). PeerJ. 10:e14087. https://doi.org/10.7717/peerj.14087.
Lyrene PM. 2011. First report of Vaccinium arboreum hybrids with cultivated highbush blueberry. HortScience. 46(4):563–566. https://doi.org/10.21273/HORTSCI.46.4.563.
Lyrene PM. 2021. Breeding cultivars from blueberry × deerberry hybrids: Progress and prospects. HortScience. 56(4):439–446. https://doi.org/10.21273/HORTSCI15619-20.
Matteis L, Chibon PY, Espinosa H, Skofic M, Finkers HJ, Bruskiewich R, Hyman JM, Arnoud E. 2013. Crop ontology: Vocabulary for crop-related concepts. https://edepot.wur.nl/264505. [accessed 28 Aug 2023].
Mengist MF, Bostan H, Young E, Kay KL, Gillitt N, Ballington J, Kay CD, Ferruzzi MG, Ashrafi H, Lila MA, Iorizzo M. 2021. High-density linkage map construction and identification of loci regulating fruit quality traits in blueberry. Hortic Res. 8:169. https://doi.org/10.1038/s41438-021-00605-z.
Mengist MF, Grace MH, Mackey T, Munoz B, Pucker B, Bassil N, Luby C, Ferruzzi M, Lila MA, Iorizzo M. 2022. Dissecting the genetic basis of bioactive metabolites and fruit quality traits in blueberries (Vaccinium corymbosum L.). Front Plant Sci. 13:964656. https://doi.org/10.3389/fpls.2022.964656.
Morales N, Ogbonna AC, Ellerbrock BJ, Bauchet GJ, Tantikanjana T, Tecle IY, Powell AF, Lyon D, Menda N, Simoes CC, Saha S, Hosmani P, Flores M, Panitz N, Preble RS, Agbona A, Rabbi I, Kulakow P, Peteti P, Kawuki R, Esuma W, Kanaabi M, Chelangat DM, Uba E, Olojede A, Onyeka J, Shah T, Karanja M, Egesi C, Tufan H, Paterne A, Asfaw A, Jannink J-L, Wolfe M, Birkett CL, Waring DJ, Hershberger JM, Gore MA, Robbins KR, Rife T, Courtney C, Poland J, Arnaud E, Laporte M-A, Kulembeka H, Salum K, Mrema E, Brown A, Bayo S, Uwimana B, Akech V, Yencho C, De Boeck B, Campos H, Swennen R, Edwards JD, Mueller LA. 2022. Breedbase: A digital ecosystem for modern plant breeding. G3. 12(7):jkac078. https://doi.org/10.1093/g3journal/jkac078.
Morozov OV. 2007. The prospects for using Vaccinium uliginosum L. × Vaccinium vitis-idaea L. hybrid in breeding. Int J Fruit Sci. 6(4):43–56. https://doi.org/10.1300/J492v06n04_05.
Nagasaka K, Nishiyama S, Fujikawa M, Yamane H, Shirasawa K, Babiker E, Tao R. 2022. Genome-wide identification of loci associated with phenology-related traits and their adaptive variations in a highbush blueberry collection. Front Plant Sci. 12:793679. https://doi.org/10.3389/fpls.2021.793679.
Olmstead JW, Finn CE. 2014. Breeding highbush blueberry cultivars adapted to machine harvest for the fresh market. HortTechnology. 24(3):290–294. https://doi.org/10.21273/HORTTECH.24.3.290.
Oellrich A, Walls RL, Cannon EKS, Cannon SB, Cooper L, Gardiner J, Gkoutos GV, Harper L, He M, Hoehndorf R, Jaiswal P., Kalberer, S.R., Lloyd, J.P., Meinke, D., Menda, N., Moore, L., Nelson, R.T., Pujar, A., Lawrence, C.J., Huala, E. 2015. An ontology approach to comparative phenomics in plants. Plant Methods. 11:10. https://doi.org/10.1186/s13007-015-0053-y.
Pan Q, Wei J, Guo F, Huang S, Gong Y, Liu H, Liu J, Li L. 2019. Trait ontology analysis based on association mapping studies bridges the gap between crop genomics and phenomics. BMC Genomics. 20(1):443. https://doi.org/10.1186/s12864-019-5812-0.
Postman J, Hummer K, Bretting P, Kinard G, Bohning M, Emberland G, Sinnott Q, Cyr P, Millard M, Gardner C, Guarino L, Weaver B. 2010. GRIN-Global: An international project to develop a global plant genebank information management system. Acta Hortic. 859:49–56. https://doi.org/10.17660/ACTAHORTIC.2010.859.4.
Rife TW, Poland JA. 2014. Field Book: An open‐source application for field data collection on Android. Crop Sci. 54:1624–1627. https://doi.org/10.2135/cropsci2013.08.0579.
Schlautman B, Diaz-Garcia L, Covarrubias-Pazaran G, Schlautman N, Vorsa N, Polashock J, Ogden EL, Brown A, Lin Y-C, Bassil N, Buck EJ, Wiedow C, McCallum S, Graham J, Iorizzo M, Rowland LJ, Zalapa J. 2018. Comparative genetic mapping reveals synteny and collinearity between the American cranberry and diploid blueberry genomes. Mol Breed. 38(1):9. https://doi.org/10.1007/s11032-017-0765-y.
Selby P, Abbeloos R, Backlund JE, Basterrechea Salido M, Bauchet G, Benites-Alfaro OE, Birkett C, Calaminos VC, Carceller P, Cornut G, Vasques Costa B, Edwards JD, Finkers R, Yanxin Gao S, Ghaffar M, Glaser P, Guignon V, Hok P, Kilian A, König P, Lagare JEB, Lange M, Laporte M-A, Larmande P, LeBauer DS, Lyon DA, Marshall DS, Matthews D, Milne I, Mistry N, Morales N, Mueller LA, Neveu P, Papoutsoglou E, Pearce B, Perez-Masias I, Pommier C, Ramírez-González RH, Rathore A, Raquel AM, Raubach S, Rife T, Robbins K, Rouard M, Sarma C, Scholz U, Sempéré G, Shaw PD, Simon R, Soldevilla N, Stephen G, Sun Q, Tovar C, Uszynski G, Verouden M. 2019. BrAPI: An application programming interface for plant breeding applications. Bioinformatics. 35(20):4147–4155. https://doi.org/10.1093/BIOINFORMATICS/BTZ190.
Sharpe R. 1953. Horticultural development of Florida blueberries. Proc Annu Meet Fla State Hort Soc. 66:188–190.
Sharpe R, Darrow G. 1959. Breeding blueberries for the Florida climate. Proc Annu Meet Fla State Hort Soc. 72:308–311.
Shrestha R, Arnaud E, Mauleon R, Senger M, Davenport GF, Hancock D, Morrison N, Bruskiewich R, McLaren G. 2010. Multifunctional crop trait ontology for breeders’ data: Field book, annotation, data discovery and semantic enrichment of the literature. AoB Plants. 2010:plq008. https://doi.org/10.1093/AOBPLA/PLQ008.
Shrestha R, Matteis L, Skofic M, Portugal A, McLaren G, Hyman G, Arnaud E. 2012. Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice. Front Plant Physiol. 3:326. https://doi.org/10.3389/fphys.2012.00326.
Stefancsik R, Balhoff JP, Balk MA, Ball RL, Bello SM, Caron AR, Chesler EJ, de Souza V, Gehrke S, Haendel M, Harris LW. 2023. The Ontology of Biological Attributes (OBA)—Computational traits for the life sciences. Mamm Genome. 34(3):364–378. https://doi.org/10.1007/s00335-023-09992-1.
Strik BC, Yarborough D. 2005. Blueberry production trends in North America, 1992 to 2003, and predictions for growth. HortTechnology. 15(2):391–398. https://doi.org/10.21273/HORTTECH.15.2.0391.
Tao Y, Zhao X, Mace E, Henry R, Jordan D. 2019. Exploring and exploiting pan-genomics for crop improvement. Mol Plant. 12(2):156–169. https://doi.org/10.1016/j.molp.2018.12.016.
Tay Fernandez CG, Nestor BJ, Danilevicz MF, Gill M, Petereit J, Bayer PE, Finnegan PM, Batley J, Edwards D. 2022. Pangenomes as a resource to accelerate breeding of under-utilised crop species. Int J Mol Sci. 23(5):2671. https://doi.org/10.3390/ijms23052671.
The Northwest Center for Small Fruits Research. 2023. Research priorities. https://nwsmallfruits.org/wp-content/uploads/2023/12/2023-2024_NCSFR-Priorities.pdf. [accessed 21 Mar 2024].
US Department of Agriculture, National Agricultural Statistics Service. 2023. Noncitrus fruits and nuts 2022 summary. https://downloads.usda.library.cornell.edu/usda-esmis/files/zs25x846c/zk51wx21m/k356bk214/ncit0523.pdf. [accessed 28 Aug 2023].
US Department of Agriculture, Small Fruit Crop Germplasm Committee. 2016. USDA Vaccinium crop vulnerability statement 2016 section 1: Blueberries. https://www.ars-grin.gov/npgs/cgc_reports/Blueberry%20Vulnerability%20Statement%202023.docx. [accessed 28 Aug 2023].
US Department of Agriculture, Small Fruit Crop Germplasm Committee. 2020. Small fruits evaluation priorities. https://www.ars-grin.gov/npgs/cgc_reports/Evaluation%20Priority%20List%202020.doc. [accessed 28 Aug 2023].
Vaccinium Coordinated Agriculture Project. https://www.vacciniumcap.org/. [accessed 28 Aug 2023].
Vorsa N, Johnson-Cicalese J, Polashock J. 2009. A blueberry by cranberry hybrid derived from a Vaccinium darrowii × (V. macrocarpon × V. oxycoccos) intersectional cross. Acta Hortic. 810:187–190. https://doi.org/10.17660/ActaHortic.2009.810.24.
Walls R, Athreya B, Cooper L, Elser J, Gandolfo M. 2012. Ontologies as integrative tools for plant science. Am J Bot. 99(8):1263–1275. https://doi.org/10.3732/ajb.1200222.
Wang X, Huang Q, Shen Z, Baron G, Li X, Lu X, Li Y, Chen W, Xu L, Lu J, Li W. 2023a. Genome-wide identification and analysis of the MADS-Box transcription factor genes in blueberry (Vaccinium spp.) and their expression pattern during fruit ripening. Plants. 12(7):1424. https://doi.org/10.3390/plants12071424.
Wang Y, Xu Y, Liao F, Li T, Li X, Wu B, Hong S-B, Xu K, Zang Y, Zheng W. 2023b. Genome-wide identification of GH9 gene family and the assessment of its role during fruit abscission zone formation in Vaccinium ashei. Plant Cell Rep. 42(10):1589–1609. https://doi.org/10.1007/s00299-023-03049-y.
Yarborough D. 2015. Wild blueberry fact sheet: Wild blueberry culture in Maine. https://www.wildblueberries.com/wp-content/uploads/2017/04/UMaineWildBlueberryFact-Sheet.pdf. [accessed 28 Feb 2024].
Yocca AE, Platts A, Alger E, Teresi S, Mengist MF, Benevenuto J, Ferrão LFV, Jacobs M, Babinski M, Magallanes-Lundback M, Bayer P, Golicz A, Humann JL, Main D, Espley RV, Chagné D, Albert NW, Montanari S, Vorsa N, Polashock J, Díaz-Garcia L, Zalapa J, Bassil NV, Munoz PR, Iorizzo M, Edger PP. 2023. Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts. Hortic Res. 10(11):uhad202. https://doi.org/10.1093/hr/uhad202.
Zamir D. 2013. Where have all the crop phenotypes gone? PLoS Biol. 11(6):e1001595. https://doi.org/10.1371/JOURNAL.PBIO.1001595.
Zeldin EL, McCown BH. 1997. Intersectional hybrids of lingonberry (Vaccinium vitis-idaea, section Vitis-idaea) and cranberry (V. macrocarpon, section Oxycoccus) to Vaccinium reticulatum (section Macropelma). Acta Hortic. 446:235–238. https://doi.org/10.17660/ActaHortic.1997.446.34.
Zheng Y, Wu S, Bai Y, Sun H, Jiao C, Guo S, Zhao K, Blanca J, Zhang Z, Huang S, Xu Y, Weng Y, Mazourek MK, Reddy U, Ando K, McCreight JD, Schaffer AA, Burger J, Tadmor Y, Katzir N, Tang X, Liu Y, Giovannoni JJ, Ling K-S, Wechter WP, Levi A, Garcia-Mas J, Grumet R, Fei Z. 2019. Cucurbit Genomics Database (CuGenDB): A central portal for comparative and functional genomics of cucurbit crops. Nucl Acids Res. 47(D1):D1128–D1136. https://doi.org/10.1093/nar/gky944.
Zhou Z, Jiang Y, Wang Z, Gou Z, Lyu J, Li W, Yu Y, Shu L, Zhao Y, Ma Y, Fang C, Shen Y, Liu T, Li C, Li Q, Wu M, Wang M, Wu Y, Dong Y, Wan W, Wang X, Ding Z, Gao Y, Xiang H, Zhu B, Lee S-H, Wang W, Tian Z. 2015. Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. Nat Biotechnol. 33(4):408–414. https://doi.org/10.1038/nbt.3096.