View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by University of Minnesota Digital Conservancy Genomewide Selection in Apple: Prediction and Postdiction in the University of Minnesota Apple Breeding Program A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Elizabeth Nicole Blissett IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Dr. Rex Bernardo & Dr. James Luby October 2019 © Elizabeth Nicole Blissett 2019 Acknowledgements While I have been at the University of Minnesota, it has been my sincere pleasure to be a part of two outstanding labs and departments. My advisors, Dr. Rex Bernardo (Department of Agronomy and Plant Genetics) and Dr. James Luby (Department of Horticultural Sciences), have truly been a privilege to work with. Their guidance, advice, and constant support have been invaluable to me. I would like to thank the members of my committee, Dr. Matthew Clark, Dr. Neil Anderson, and Dr. Aaron Lorenz, for their insights and patience as I moved through the steps of completing my research and dissertation. Additionally, I would like to offer my sincerest gratitude to Dr. Emily Hoover for her mentorship, advice, and constant willingness to lend an ear. I would like to extend a warm thank you to Jack Tillman, whose seemingly bottomless knowledge on apple breeding, fruit research, and lab and field techniques has helped me to grow as a researcher. I would also like to thank all of the members of both of my labs Seth Wannemuehler, Dr. Sofía Brandariz, Emmanuel Adeyemo, Dr. Nick Ames, Dr. Tola Oyetunde, Dr. Sushan Ru, Ashley Powell, Baylee Miller, Laise Moreira, Lu Yin and my APS friends, who are all more like family now. I would finally like the thank my friends and family. My parents and siblings, especially my sister Emily, have helped me through the challenges and obstacles I’ve faced, and shared in my successes. I cannot put into words how grateful I am for all of the support of everyone mentioned, and many more. i Abstract Although marker assisted breeding is now considered routine in apple breeding programs, the adoption of genomewide selection is still in its infancy. Genomewide selection offers the potential to be a valuable tool to apple breeders. The first aim of this research was to assess the predictive ability of genomewide selection for fruit traits by testing an additive prediction model, a model fitting heterozygote effects, and a model fitting fixed effects for major QTL. The second aim of this research was to assess the utility of genomewide selection for fruit traits in the University of Minnesota apple breeding program. This comprised two main objectives, a comparison of selections based on genomewide predictions to selections made based on phenotypic selection and an analysis of the impact on predictive ability when full-sibs are included in the training data. This research finds that in general, a simple linear model is the most efficient choice for genomewide selection in apple unless major effect QTL are known, in which case including them as fixed effects may improve predictive abilities. We also confirmed that predictions made based on genomewide selection to be consistent with selections based on traditional phenotypic selection and that including five to 15 full-sibs from the test population in the training population data can improve predictive ability. i Table of Contents Page Genomewide Selection in Apple: Prediction and Postdiction in the University of Minnesota Apple Breeding Program ........................................... i Acknowledgements ............................................................................................. i Abstract ................................................................................................................ i Table of Contents ................................................................................................ ii List of Tables ...................................................................................................... v Chapter 1 – Literature Review ........................................................................... 8 1.1 Domestication of apple ............................................................................. 8 1.2 The Rosaceae crop family ....................................................................... 8 1.3 Domesticated apple ................................................................................. 9 1.4 The genome of domesticated apple ........................................................ 9 1.5 Marker informed breeding in Rosaceae and apple ................................ 10 1.6 Genomewide selection ........................................................................... 11 1.7 Challenges associated with apple breeding ........................................... 12 1.8 Traits ...................................................................................................... 13 ii Chapter 2 – Fitting heterozygotic and major QTL effects in apple (Malus domestica Borkh.) ............................................................................................ 15 2.1 Synopsis ................................................................................................ 15 2.2 Introduction ............................................................................................ 16 2.3 Materials and Methods ........................................................................... 20 2.3.1 Germplasm ......................................................................................... 20 2.3.2 Trait data and harvest procedure in Dataset 2 .................................... 21 2.3.3 Marker data and genomewide selection procedures ........................... 24 2.4 Results ................................................................................................... 30 2.4.1 Model fitting additive effects ................................................................ 28 2.4.2 Model fitting heterozygote effects ....................................................... 29 2.4.3 Model fitting fixed effects for major QTL .............................................. 30 2.5 Discussion .............................................................................................. 34 2.6 Application ............................................................................................. 37 Tables ............................................................................................................. 39 iii Chapter 3 – Potential applications for genomewide selection in the University of Minnesota apple breeding program ........................................ 47 3.1 Synopsis ................................................................................................ 47 3.2 Introduction ............................................................................................ 48 3.3 Materials and Methods ........................................................................... 52 3.3.1 Retrospective analysis ........................................................................ 52 3.3.2 Comparianison of varying levels of relatedness between a training and test population .............................................................................................. 55 3.4 Results ................................................................................................... 56 3.5 Discussion .............................................................................................. 58 Tables ............................................................................................................. 65 Literature Cited ................................................................................................. 72 Appendix ........................................................................................................... 85 iv List of Tables Page Chapter 2 Table 2.1. Number of seedlings in each cross for Dataset 1 and Dataset 2 ....... 40 Table 2.2. Summary of total snps and snps per chromosome for dataset 1 and dataset 2. .............................................................................................. 41 Table 2.3. Traits and criteria for inclusion as fixed effects in models. ................ 42 Table 2.4. Number of QTL detected and number of QTL included in model fitting fixed effects for each trait in Dataset 1 and Dataset 2. ....................... 43 Table 2.5. Predictive abilities for Dataset 1 from each model tested and using the untested-family procedure. .................................................................... 44 Table 2.6. Predictive abilities for Dataset 1 from each model tested and using the untested-seedling procedure. ................................................................ 45 Table 2.7. Predictive abilities for Dataset 2 from each model tested and using the untested-family procedure ..................................................................... 46 Table 2.8. Predictive abilities for Dataset 2 from each model tested and using the untested-seedling procedure ................................................................. 47 v Chapter 3 Table 3.1. Number of seedlings in each cross for Dataset 1 and Dataset 2 ....... 66 Table 3.2. Dataset 1 minimum acceptable trait value(s) for culling test ............. 67 Table 3.3. Dataset 2 minimum acceptable trait value(s) for culling test ............. 68 Table 3.4. Number of seedlings culled for each trait evaluated