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ARS Home » Midwest Area » Madison, Wisconsin » Vegetable Crops Research » Research » Publications at this Location » Publication #353746

Research Project: Cranberry Genetics and Insect Management

Location: Vegetable Crops Research

Title: Image-based phenotyping for identification of QTL determining fruit shape and size in American cranberry (Vaccinium macrocarpon L.)

Author
item Diaz-garcia, Luis - University Of Wisconsin
item Covarrubias-pazaran, Giovanny - University Of Wisconsin
item Schlautman, Brandon - The Land Institute
item Grygleski, Edward - Valley Corporation
item Zalapa, Juan

Submitted to: PeerJ
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/26/2018
Publication Date: 8/15/2018
Citation: Diaz-Garcia, L., Covarrubias-Pazaran, G., Schlautman, B., Grygleski, E., Zalapa, J.E. 2018. Image-based phenotyping for identification of QTL determining fruit shape and size in American cranberry (Vaccinium macrocarpon L.). PeerJ. https://doi.org/10.7717/peerj.5461.
DOI: https://doi.org/10.7717/peerj.5461

Interpretive Summary: The growing consumer demand for sweetened dried cranberries (SDC) has increased the need for new cranberry varieties that produce large, round, and uniformly shaped fruit. The cranberry industry has used fruit sizers to separate large fruit, which are sold at a premium for SDC processing compared with small fruit, which are used for cranberry juices. Most cranberry breeding programs still use manual caliper measurements to gather data about basic cranberry shape and size attributes, and sometimes use visual assessments to categorize complex shapes of interest. In this study, we developed a massive phenotyping approach based on digital imaging to rapidly acquire data for basic cranberry shape descriptors such as length, width, and area in breeding programs. We also implemented a newly developed persistent homology methodology to comprehensively quantify complex shape features that are difficult to quantify with traditional descriptors. Moreover, we used data gathered using such computer-vision methodologies in a cranberry population to map genetic regions governing fruit shape and size. To fully exploit the current cranberry supply, fill niche markets (e.g., such as sweetened and dried fruit), this research will be useful for cranberry breeding programs to focus on fruit shape and firmness cranberry characteristics and that provide value-added incentives to growers and processors.

Technical Abstract: Image-based phenotyping methodologies are powerful tools to determine quality parameters for fruit breeders and processors. Fruit size and shape are particularly important characteristics that determine processing value and potential end-use products (e.g., juice vs. sweetened dried cranberries) in American cranberry (Vaccinium macrocarpon L.). Cranberry fruit size and shape attributes can be difficult and time consuming for breeders and processors to measure, especially when relying on manual measurements and visual ratings. Therefore, in this study, we implemented image-based phenotyping techniques for gathering data regarding basic cranberry fruit parameters such as length, width, length-to-width ratio, and eccentricity. Additionally, we applied a persistent homology algorithm to better characterize complex shape parameters. With this high-throughput artificial vision approach, we characterized fruit harvested for 351 progeny from a full-sib cranberry population over three field seasons. Using a covariate analysis to maximize the identification of well-supported quantitative trait loci (QTL), we found 252 single QTL in a three-year period for cranberry size and shape descriptors from which 20% were consistently found in all years. The present study highlights the potential for the identified QTL and the image-based methods to serve as a basis for future explorations of the genetic architecture of fruit size and shape in cranberry and other fruit crops.