Skin Deep: Genetic Characterization And Prediction Of Russet Formation In ‘Honeycrisp’-Derived Apple Breeding Germplasm
2023
Loading...
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Skin Deep: Genetic Characterization And Prediction Of Russet Formation In ‘Honeycrisp’-Derived Apple Breeding Germplasm
Authors
Published Date
2023
Publisher
Type
Thesis or Dissertation
Abstract
Russet formation in apples (Malus domestica Borkh.) is a superficial skin disorder that detracts from fruit appearance and is a breeding target in many apple scion breeding programs. To-date, the understanding of the genetic basis of russet in apple has been limited to a relatively narrow set of germplasm. Additionally, no research on russet formation has been done in ‘Honeycrisp’, an important breeding parent and cultivar from the University of Minnesota’s apple breeding program. The goals of this dissertation were to examine the genetic basis of russet formation and to investigate the utility of genomewide prediction for russet formation in a pedigree-connected apple breeding germplasm set derived from ‘Honeycrisp’. Two previously reported russet formation QTLs, on linkage groups (LG) 2 and 6, were detected and characterized in this germplasm. Non-additive interactions were observed at and among LG2 and LG6 QTLs. Genomewide prediction was also investigated as a breeding approach for russet formation. Model type, training set, and relatedness between training and testing set were examined. Moderate predictive abilities for russet formation traits were estimated. Genomewide prediction models did not perform significantly different when using a random effects model vs. a model that included previously detected QTLs as fixed effects. Relatedness between training and testing set did not have a significant effect on predictive abilities in this germplasm. Genomewide prediction is a promising approach when targeting russet formation in apple breeding. The work reported in this dissertation will enable more informed parent selection and the development and deployment of predictive markers for russet formation. This work also demonstrates the utility of genomewide prediction as a breeding strategy for russet formation and will assist breeders to target russet formation in future efforts to breed for beautiful fruit. Future work should include validation of QTL and haplotype effects in other germplasm, development of trait-predictive DNA tests for use in apple breeding programs, and continued exploration of implementation of genomewide prediction in breeding programs for russet formation. Description of Supplementary FileExcel workbook with a ReadMe tab and 15 Supplementary Tables. Tables S1-S9 are discussed in Chapter 2 and tables S10-S15 are discussed in Chapter 3.
Description
University of Minnesota Ph.D. dissertation. 2023. Major: Applied Plant Sciences. Advisor: James Luby. 1 computer file (PDF); xiv, 83 pages + 1 supplementary table.
Related to
Replaces
License
Collections
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Other identifiers
Suggested citation
Powell, Ashley. (2023). Skin Deep: Genetic Characterization And Prediction Of Russet Formation In ‘Honeycrisp’-Derived Apple Breeding Germplasm. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/258882.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.