A Blueberry (Vaccinium L.) Crop Ontology to Enable Standardized Phenotyping for Blueberry Breeding and Research

• Published in: HortScience Vol. 59; no. 10; pp. 1433 - 1442
• Main Authors: Hislop, Lillian M., Luby, Claire H., Loarca, Jenyne, Humann, Jodi, Hummer, Kim E., Bassil, Nahla, Zhao, Dongyan, Sheehan, Moira J., Casa, Alexandra M., Billings, Grant T., Echeverria, Daniella M., Ashrafi, Hudson, Babiker, Ebrahiem, Edger, Patrick, Ehlenfeldt, Mark K., Hancock, Jim, Finn, Chad, Iorizzo, Massimo, Mackey, Ted, Muñoz, Patricio R., Olmstead, James, Rowland, Lisa J., Sandefur, Paul, Spencer, Jessica A., Stringer, Stephen, Vorsa, Nicholi, Wagner, Adam, Hulse-Kemp, Amanda M.
• Format: Journal Article
• Language: English
• Published: American Society for Horticultural Science (ASHS) 01.10.2024
• Subjects: blueberry; controlled vocabulary; phenotype; plant ontology; trait ontology
• ISSN: 0018-5345; 2327-9834
• DOI: 10.21273/HORTSCI17676-23

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.