Soybean is the world’s leading source of vegetable protein and demand for its seed continues to grow. Breeders have successfully increased soybean yield, but the genetic architecture of yield and key agronomic traits is poorly understood. We developed a 40-mating soybean nested association mapping (NAM) population of 5,600 inbred lines that were characterized by single nucleotide polymorphism (SNP) markers and six agronomic traits in field trials in 22 environments. Analysis of the yield, agronomic, and SNP data revealed 23 significant marker-trait associations for yield, 19 for maturity, 15 for plant height, 17 for plant lodging, and 29 for seed mass. A higher frequency of estimated positive yield alleles was evident from elite founder parents than from exotic founders, although unique desirable alleles from the exotic group were identified, demonstrating the value of expanding the genetic base of US soybean breeding.

Genetic improvement toward optimized and stable agronomic performance of soybean genotypes is desirable for food security. Understanding how genotypes perform in different environmental conditions helps breeders develop sustainable cultivars adapted to target regions. Complex traits of importance are known to be controlled by a large number of genomic regions with small effects whose magnitude and direction are modulated by environmental factors. Knowledge of the constraints and undesirable effects resulting from genotype by environmental interactions is a key objective in improving selection procedures in soybean breeding programs. In this study, the genetic basis of soybean grain yield responsiveness to environmental factors was examined in a large soybean nested association population. For this, a genome-wide association to performance stability estimates generated from a Finlay-Wilkinson analysis and the inclusion of the interaction between marker genotypes and environmental factors was implemented. Genomic footprints were investigated by analysis and meta-analysis using a recently published multiparent model. Results indicated that specific soybean genomic regions were associated with stability, and that multiplicative interactions were present between environments and genetic background. Seven genomic regions in six chromosomes were identified as being associated with genotype-by-environment interactions. This study provides insight into genomic assisted breeding aimed at achieving a more stable agronomic performance of soybean, and documented opportunities to exploit genomic regions that were specifically associated with interactions involving environments and subpopulations.

1Herein we report the impacts of applying five selection methods across 40 cycles of recurrent 2 selection and identify interactions with other factors on genetic response using simulated families 3 of recombinant inbred lines derived from 21 homozygous soybean lines used for the Soybean 4 Nested Association Mapping study. The other factors we investigated included the number of 5 quantitative trait loci, broad sense heritability on an entry mean basis, selection intensity, and 6 training sets. Both the rates of genetic improvement in the early cycles and limits to genetic 7 improvement in the later cycles are affected by interactions among the factors. All genomic 8 selection methods provided greater rates of genetic improvement (per cycle) than phenotypic 9 selection, but phenotypic selection provided the greatest long term responses. Model updating 10 significantly improved prediction accuracy and genetic response for three parametric genomic 11 prediction models. Ridge Regression, if updated with training sets consisting of data from prior 12 cycles, achieved greater rates of response relative to BayesB and Bayes LASSO GP models. A 13 Support Vector Machine method, with a radial basis kernel, resulted in lowest prediction 14 accuracies and the least long term genetic response. Application of genomic selection in a closed 15 breeding population of a self-pollinated crop such as soybean will need to consider the impact of 16 these factors on trade-offs between short term gains and conserving useful genetic diversity in 17 the context of goals for the breeding program.18 19 4 Background 20 Plant breeding programs consist of 1) recurrent genetic improvement projects, 2) variety 21 development projects 3) trait introgression projects and 4) product placement projects (Fehr, 22 1991). Genetic improvement is assessed using realized genetic gain, which is an estimate of 23 change of the average genotypic value for traits of interest across cycles of selection and inter-24

Plant breeding is a decision-making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize the rate of genetic improvement and minimize the loss of useful genetic variance. For commercial plant breeders, competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast, public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing the loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short-term genetic gains from genomic selection are much greater than phenotypic selection, while phenotypic selection provides better long-term genetic gains because it retains useful genetic diversity during the early cycles of selection. With limited resources, must a soybean breeder choose between the two extreme responses provided by genomic selection or phenotypic selection? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs, and whether the breeding population should be organized as family islands. For breeding populations organized into islands, decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to 10 cycles using genomic selection and a hub network mating design, where the hub parents with the largest selection metric make large parental contributions. It also requires that the breeding populations be organized as fully connected family islands, where every island is connected to every other island, and migration rules allow the exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except that the mating design could be hub network, chain rule, or a multi-objective optimization method-based mating design. Weighted genomic selection applied to centralized populations also resulted in the realization of the greatest proportion of the genetic potential of the founders but required more cycles than the best compromise strategy.

Plant breeding is a decision making discipline based on understanding project objectives. Genetic improvement projects can have two competing objectives: maximize rate of genetic improvement and minimize loss of useful genetic variance. For commercial plant breeders competition in the marketplace forces greater emphasis on maximizing immediate genetic improvements. In contrast public plant breeders have an opportunity, perhaps an obligation, to place greater emphasis on minimizing loss of useful genetic variance while realizing genetic improvements. Considerable research indicates that short term genetic gains from Genomic Selection (GS) are much greater than Phenotypic Selection (PS), while PS provides better long term genetic gains because PS retains useful genetic diversity during the early cycles of selection. With limited resources must a soybean breeder choose between the two extreme responses provided by GS or PS? Or is it possible to develop novel breeding strategies that will provide a desirable compromise between the competing objectives? To address these questions, we decomposed breeding strategies into decisions about selection methods, mating designs and whether the breeding population should be organized as family islands. For breeding populations organized into islands decisions about possible migration rules among family islands were included. From among 60 possible strategies, genetic improvement is maximized for the first five to ten cycles using GS, a hub network mating design in breeding populations organized as fully connected family islands and migration rules allowing exchange of two lines among islands every other cycle of selection. If the objectives are to maximize both short-term and long-term gains, then the best compromise strategy is similar except a genomic mating design, instead of a hub networked mating design, is used. This strategy also resulted in realizing the greatest proportion of genetic potential of the founder populations. Weighted genomic selection applied to both non-isolated and island populations also resulted in realization of the greatest proportion of genetic potential of the founders, but required more cycles than the best compromise strategy.

Soybean is grown primarily for the protein and oil extracted from its seed and its value is influenced by these components. The objective of this study was to map marker‐trait associations (MTAs) for the concentration of seed protein, oil, and meal protein using the soybean nested association mapping (SoyNAM) population. The composition traits were evaluated on seed harvested from over 5000 inbred lines of the SoyNAM population grown in 10 field locations across 3 years. Estimated heritabilities were at least 0.85 for all three traits. The genotyping of lines with single nucleotide polymorphism markers resulted in the identification of 107 MTAs for the three traits. When MTAs for the three traits that mapped within 5 cM intervals were binned together, the MTAs were mapped to 64 intervals on 19 of the 20 soybean chromosomes. The majority of the MTA effects were small and of the 107 MTAs, 37 were for protein content, 39 for meal protein, and 31 for oil content. For cases where a protein and oil MTAs mapped to the same interval, most (94%) significant effects were opposite for the two traits, consistent with the negative correlation between these traits. A coexpression analysis identified candidate genes linked to MTAs and 18 candidate genes were identified. The large number of small effect MTAs for the composition traits suggest that genomic prediction would be more effective in improving these traits than marker‐assisted selection.

I would like to dedicate this dissertation to my parents and colleagues at Iowa State University. I can't thank Dr. Beavis and his lab members enough for their constant support. I need to thank Dr. Beavis for his constant support in the midst of all kinds of challenges. Through all these years, I've been with his research group, he has always been an extremely welcoming person and patient with everything. I could not have done anything without Dr. Beavis' guidance at every stage of this dissertation project, as Applied Plant Breeding and Quantitative Genetics were mostly a new field of study for me. He was also extremely patient in explaining all the concepts and encouraged independent work. I would also like to thank my POS committee members Dr. Alicia Carriquiry, Dr. Jack Dekkers, Dr. Karin Dorman and Dr. Lizhi Wang for their helpful suggestions and guidance throughout the dissertation project.I must also thank the research group members Danielle, John, Haley, Reka, and Bongsong. I've never seen colleagues more patient and understanding than this group and they often worked hard to make it a welcoming team. I must also thank the larger ISU community for their support and making ISU a great campus town. Their engagement and involvement in affairs of the campus and larger community completely changed my perception of life in a community.Having lived with only small groups of friends and family with very little direct engagement with the larger community, I found campus life in Ames to be a completely different experience that provided a sense of community engagement. I believe this experience will stay with me as long as I can remember things. Thanks to the ISU community and the larger Ames community for that. I would also like to thank my parents for giving me the freedom to choose my career and a scholarly way of life. Their support has been a constant source of encouragement since high v school to pursue higher education. I would also like to thank my cousins and their families for encouraging me to pursue higher education in the US.