Breeding Scheme Development and Optimization During Neo-Domestication and Wide Hybridization
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2023
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Global food systems are under increasing pressure. There is an increasingly stochastic climate with future projections showing declines in major food crop production across the major growing locales. In the future, the geographic regions that are projected to be suitable for staple production are expected to shift, which may lead to the need to abandon current production regions and/or shift to new species for cultivation. Many of the potential new species to replace current systems or fill novel niches include ∼30,000 edible plants worldwide; however, currently ∼150 are cultivated at large scale across the world. These domestic and semi-domestic plant species span 160 taxonomic families with a total of ∼2,500 species having undergone some extent of domestication. Domestication is a process by which a wild organism shifts to a form more adapted for human use, typically through the acquisition and subsequent fixation of traits, commonly termed the domestication syndrome. Neo-domestication is the attempt to re-domesticate or newly domesticate wild and semi-wild species leveraging modern breeding techniques in a strategic framework. These programs use large phenotypic and/orgenotypic data sets to efficiently select breeding parents to maximize progeny gain of typically quantitative traits. Neo-domestication foci include fixation of simple traits, via variance reduction, that typically hinder cultivation and breeding for complex traits that improve marketability. The increasing pressure on food systems and breadth of options creates a situation of strategic uncertainty, specifically two questions:
1) Which species do we choose?2) How do we increase the adaptability of said species to the agroecosystem?
This dissertation aims to systematically answer the second question to understand the impact of breeding schemes on the pace of adaptation. Specifically breeding scheme development encompasses the parametrization in breeding cycle components (Crossing, Evaluation, Selection) by leveraging empirical data to train stochastic models. First, empirical data are integrated with simulation output to test specific use-cases, this is followed by optimization of genetic gain. The empirical case study chapters (1-3) rely upon the concepts of population improvement through artificial selection: an iterative process of generational selection to increase in favorable alleles in the population. The goal is to increase the probability of extracting a superior cultivar from the population. The increase in favorable allelesdrives genetic gain, which is a product of additive genetic variation within the population, selection intensity, and selection accuracy. Optimizing breeding cycle components has beneficial effects on the genetic gain components. However, this framework has not been applied to wild and semi-domesticated breeding towards domestication. The last chapter (4) integrates the effects of these case studies to create a framework for understanding the expected rate of gain for potential species with known biological characteristics.
In Chapter 1, the first breeding cycle component addressed is crossing. Crossing includes the following parameters: number of parents, number of crosses, number of progeny, type of cross, and mate allocation. To parse parametric effects in crossing on gain, the wild-endemic tree species Acacia koa (koa) was used. Koa is an excellent system for understanding crossing parameters of number of parents and number of progeny effects on gain in seedling vigor with disease resistance constraint. The koa system provides a clear set of experiments to validate the influence of breeding population size and number of progeny on phenotypic gain in simulation results of seedling vigor when constrained by varying levels of disease.
The second chapter focuses on the next component of the breeding cycle, evaluation. This component includes the following parameters: number of locations, levels of replication, number of checks, experimental design, and subsampling. To parse parametric effects in evaluation on gain, the tropical tree species Theobroma cacao (cacao) is used. Cacao production spans the globe, but production varieties vary from developed (hybrids) to semi-domesticated (open-pollinated landrace). The cacao system provides a clear set of experiments to understand the precision necessary to observe significant differences between varieties within different domestication status groups. The investigation into the deviation of phenotypic measurements from the most precise (full sub-sampling of plot) to least precise (single sample of plot) will facilitate an understanding of the appropriate level of precision needed when using different types of germplasm in a neo-domestication breeding program.
The last aspect of the breeding cycle, selection, is addressed in chapter 3. This component includes the following parameters: percentage selected (intensity), selection method (culling, index), selection unit (families, lines, parents), and selection criteria (phenotypic, genotypic, breeding values, index). To parse parametric effects in selection on gain, the subtropical herbaceous shrub species Stevia rebaudiana (stevia) is used. Stevia production spans the globe, but production varieties are considered non-adaptable to the agroecosystem (semi-domesticate) and lack classic domestication syndrome traits, including reduced dormancy, branching, and photoperiod sensitivity. The stevia system provides a clear set of experiments to understand the influence of phenotypic recurrent selection on domestication traits across multiple generations.
Although these systems differ by life-history and domesticated status, they provide key insight into the components of the breeding cycle with varying trait complexities. The simulation varying crossing parameters can take the empirical estimates from Koa and expand intuition around other parameters during crossing (e.g., varying number of crosses). The simulation varying evaluation parameters can take the empirical estimates from Cacao and expand intuition around other parameters during evaluation (e.g., level of replication or environmental evaluation). The simulation varying selection parameters can take the empirical estimates from Stevia and expand intuition around other parameters during selection (e.g., alternative selection criteria). Each variable is therefore grounded in crucial empirical estimates derived from experiments in each case-study crop. The input for these case studies provides the knowledge and intuition for the final chapter, which synthesizes these expectations into species agnostic neo-domestication breeding schemes. This synthesis uses replicated and varying stochastic simulations across the breeding cycle components to provide an estimate of potential gain for any domestication scenario. These new breeding schemes are returned to reality by applying known cost structures to key parameter decisions (e.g., cost of phenotyping versus genotyping; cost of subsampling versus plot level analysis). Mixed-model analysis is then applied to estimate breeding cycle component parameter values which maximize the return on investment for given traits and targets.
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Agriculture, Genetics, Statistics, Breeding, Genetic Gain, Neo-Domestication, Simulation, Wide Hybridization
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91 pages
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