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The Optimization of Introgression Projects for Plant Genetic Improvement John Nicholas Cameron Iowa State University

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The Optimization of Introgression Projects for Plant Genetic Improvement John Nicholas Cameron Iowa State University Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2017 The optimization of introgression projects for plant genetic improvement John Nicholas Cameron Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Genetics Commons, Operational Research Commons, and the Statistics and Probability Commons Recommended Citation Cameron, John Nicholas, "The optimization of introgression projects for plant genetic improvement" (2017). Graduate Theses and Dissertations. 16080. https://lib.dr.iastate.edu/etd/16080 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. The optimization of introgression projects for plant genetic improvement by John N. Cameron A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Plant Breeding Program of Study Committee: Bill Beavis, Major Professor Alicia Carriquiry Asheesh Singh Lizhi Wang Jianming Yu The student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a degree is conferred. Iowa State University Ames, Iowa 2017 Copyright © John N. Cameron, 2017. All rights reserved. ii DEDICATION This dissertation is dedicated to Dr. Stephen Hull for his guidance and friendship. iii TABLE OF CONTENTS Page ABSTRACT ............................................................................................................................. iv CHAPTER 1. GENERAL INTRODUCTION .........................................................................1 CHAPTER 2. SYSTEMATIC DESIGN OF TRAIT INTROGRESSION PROJECTS ............8 Appendix 1: Simulation ...............................................................................................30 Appendix 2: Recombination Models ...........................................................................36 Appendix 3: Supplementary Tables .............................................................................39 CHAPTER 3. OPTIMIZATION OF SINGLE SOURCE, MULTIPLE ALLELE INTROGRESSION ..................................................................................................................47 Appendix: Supplementary Tables ................................................................................67 CHAPTER 4. OPTIMIZATION OF MULTIPLE SOURCE, MULTIPLE ALLELE INTROGRESSION ..................................................................................................................70 CHAPTER 5. GENERAL CONCLUSIONS ...........................................................................87 REFERENCES ........................................................................................................................89 iv ABSTRACT We approach the problem of trait introgression as an optimization challenge with clearly defined objectives in 3 different scenarios that largely capture the introgression problem in a diploid outcrossing selfing-tolerant species: 1) the introgression of a single alleles into a recipient background via backcrossing, 2) the introgression of multiple alleles from one line into a recipient line, and 3) the introgression of several alleles from multiple lines into the background of a recipient line. For each of the 3 cases, we present a mathematical formulation, based on optimization principles from Operations Research (OR), defining the objectives to be optimized, decision variables and constraints of the introgression problem. We then use simulation, with genome size and reproductive biology based on maize, to estimate the probability of achieving a set of breeding goals. Algorithms from OR and combinatorics are used to optimize selections. Finally, Pareto response surfaces are presented for each of the 3 cases to concisely show the tradeoffs between objectives. With this systematic approach of defining quantifiable objectives, translating the objectives into a mathematical model, then building simulation models that allow for analysis of the tradeoffs between objectives, we show a way forward where plant breeding has a deeper engagement with applied mathematics. 1 CHAPTER 1. GENERAL INTRODUCTION Introgression is the transfer of alleles from one genomic background into another. Historically, this has been carried out by crossing the progeny of a cross between a donor line and recipient line back to the recipient line each generation, selecting those progeny to cross that show the desired phenotype for a specific trait displayed by the donor line. The goal is to obtain a line that expresses the desirable phenotype of the donor in the genomic background of the recipient line. Harlan and Pope (1922) were the first to recommend the value of using introgression via backcrossing to improve crop plants after observing that backcrossing had been used in animal breeding for many years to transfer desirable traits to breeding populations. During the Green Revolution introgression was used to develop high-yielding, short (or semi- dwarf), inbred varieties of wheat and rice which provided lodging tolerance under high fertilization regimes, leading to an increase in grain yield delaying the Malthusian prediction of massive famines in developing countries (Borlaug 1968). Advances in biotechnology have led to a growing catalogue of available transgenic traits in crop species, from herbicide and insect resistances (Castle, Wu et al. 2006) to reducing the amount of non-digestible fiber in forage species like switch grass (Shen, Poovaiah et al. 2013). Because of regulatory and intellectual property regulations in many countries, transgenic traits have to be introgressed into cultivars at the end of breeding for quantitative traits, thus introgression is an ever-present process for cultivar development. The size of the seed-corn market is roughly 9 billion dollars in the U.S.A (USDA-ERS, 2016). To illustrate the economic significance of introgression, take for example the adoption of herbicide tolerant (HT) or Bacillus thuringiensis (BT) corn in the United States. In the year 2000, only about 25% of all corn acreage in the U.S. had at least one of these traits. By 2015, over 95% of the corn grown in the U.S. had at least one of these traits. Thus, the optimization of introgression with respect to cost and time are critical in a competitive environment where first to market can rapidly change market share and corporate earnings. 2 Adoption of GM maize in the U.S. 100 BT HT Stacked 80 All GM 60 40 % GM acreage 20 0 2000 2005 2010 2015 Year Figure 1 Percentage of U.S. corn acreage growing GM varieties including: Herbicide tolerant (HT), Bacillus thuringiensis (BT), the two traits stacked together, and the cumulative sum of all 3 types of GM corn (USDA-ERS, 2016) Exotic germplasm from centers of origin of crop species hold a wealth of genetic diversity, and have been shown to possess useful alleles for herbicide resistance, grain quality, disease and insect resistances, and tolerance to abiotic stress such as heat and drought (Lewis and Goodman 2003). As disease pressures change with climate changes, the alleles conferring resistances in exotic accessions could become highly important to global food security. Transferring desirable alleles from these populations as quickly and cost effectively as possible while not losing agronomic performance in target locations will be necessary to increase and protect yield potentials of major crops (Evans and Fischer 1999). Optimization is the action of making the best or most effective use of a situation or resource. Operations Research is a discipline in applied mathematics that uses analytical methods 3 to quantify outcomes involving trade-offs among competing objectives, so that decision makers can optimize the design of their projects. Operations Researchers use mathematical models to model a system or a process, algorithms to solve the mathematical models, and computer solvers to implement the algorithms. Operations Research arose as a field in the British Military during the second world war out of the need for quantitative reasoning behind decision-making. It found its way into Agriculture shortly after, as soldiers who studied and worked in the field came back home. Pesek (1954) presented a linear programming approach to the optimization of land allocation to certain crops on a single farm under water and subsidy constraints to maximize overall profit. He used the simplex algorithm, with his hand as the solver implementing it. Heady and Pesek (1954) used a response surface method to optimize fertilizer application and maximize bushels per acre of maize. Robertson (1957) used a linear programming method to determine optimum group size in progeny testing and family selection in animal breeding. Although agriculture and genetics seemed to have had an early start with Operations Research, there was a dearth of research on optimization in agriculture in the decades following the initial work. However, the last few years have seen Operations Research bring real improvement to plant breeding. In 2015,
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