Advancing the Implementation of Genomics-Assisted Breeding in a Public Soybean Breeding Program

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Advancing the Implementation of Genomics-Assisted Breeding in a Public Soybean Breeding Program

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The usefulness of genomic selection (GS) for improvement of complex traits has been demonstrated in plant and animal breeding. While fully adopted in the livestock sector and commercial plant breeding, the implementation of GS in public-sector plant breeding programs lags. Challenges such as redesign of the breeding program and cost of genotyping still remain, and gaps exist between research and practical application in public programs. To address these challenges, there are several relevant tools that can be assembled in a framework to enhance the rate of genetic gain. For instance, we demonstrated the usefulness of historical multi-environment trial data from the Northern Uniform Soybean Tests (NUST) for trait mapping and as a publicly available training population for genomic selection models. Characterization of this relevant elite germplasm revealed regions of genetic differentiation mainly driven by maturity group differences and the lack of strong population structure evidenced the germplasm sharing among the public programs encouraged by the NUST organizers to maximize genetic diversity and local adaptation. We also investigated two forms of GS for cultivar development with potential for near term adoption: genomic mating and GS in the progeny rows. Given the large number of potential crosses, prediction tools for parental selection and cross design are valuable to create populations more likely to generate progenies that will exceed current cultivars. We validated in soybeans a methodology to predict the family mean, genetic variance, superior progeny mean, and genetic correlation based on modeling the segregation and recombination of genome-wide marker effects. Lastly, the integration of low-cost high-throughput phenotyping on canopy coverage and genomic prediction to reduce genotyping costs and enhance progeny row selections showed significant yield improvement over random selection in the main evaluation region but the results were not consistent across regions. Overall, the findings and publicly available data from this research will contribute towards genomics-assisted breeding efforts in plant breeding programs.


University of Minnesota Ph.D. dissertation. 2023--. Major: Applied Plant Sciences. Advisor: Aaron Lorenz. 1 computer file (PDF); ix, 218 pages.

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Wartha, Cleiton Antonio. (2023). Advancing the Implementation of Genomics-Assisted Breeding in a Public Soybean Breeding Program. Retrieved from the University Digital Conservancy,

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