Browsing by Subject "genomic prediction"

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    Developing genomic tools to breed for climate-adapted plant varieties
    (2023-03) Della Coletta, Rafael
    Climate change is a major threat to global food security, as current plant varieties used by farmers may not adapt to new growing environments. To mitigate this problem, plant breeders must use all available tools to speed up the development and release of new climate-adapted varieties. In this dissertation, I discuss how the recent advances in crop genomics due to improvements in sequencing technology, genome assembly methods, and computational resources are revolutionizing plant breeding. Particularly, I argue that the analysis of the complete catalog of genetic variation of a crop can provide useful information for plant breeders. I demonstrate that modeling this pan-genome information can increase the accuracy of multi- environment genomic prediction models, a tool widely used by breeders to develop new plant varieties. I also show how utilizing prior information on genetic variants associated with certain phenotypes can help simulate traits that are more realistic and relevant for breeders using digital breeding, a tool where breeders can test many different experiments before deployment in their breeding programs. Finally, I developed a new tool that identifies genetic variants associated with specific environmental factors via network analysis of common datasets available to plant breeders.
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    Skin Deep: Genetic Characterization And Prediction Of Russet Formation In ‘Honeycrisp’-Derived Apple Breeding Germplasm
    (2023) Powell, Ashley
    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.