SOYGEN3: Building Capacity to Increase Soybean Genetic Gain for Future Environments
Project Overview

SOYGEN3 is a three-year initiative aimed at enhancing soybean genetic gain by integrating genomics-assisted breeding with environmental characterization. This initiative, in its third and final year, is designed to address the challenges of genotype-by-environment interactions, improve yield stability, and develop predictive models for future environments. The project involves a collaboration of multiple universities and institutions across the North Central region.

Project Justification and Rationale
Soybean is a critical crop with high global demand driven by its use in food, feed, and renewable fuel production. Since the 1940s, scientific breeding efforts have significantly improved yield, expanded production regions, and developed varieties with defensive traits. However, genotype-by-environment interactions complicate breeding efforts, requiring broader field testing across diverse environmental conditions. The SOYGEN initiative seeks to address these challenges by leveraging genomics, phenomics, and environmental data to enhance predictive breeding methodologies.

Key Objectives
1. Enhancing Genomics-Assisted Breeding:
o Develop and implement genomic selection tools in public breeding programs.
o Utilize genome-wide markers for genotyping advanced breeding lines.
o Integrate low-pass sequencing technology to generate cost-effective genomic data.
o Establish user-friendly software applications for genomic selection.

2. Predicting Cultivar Performance in Future Environments:
o Conduct multi-environment trials with 1,200 diverse breeding lines.
o Characterize environmental conditions and model genotype-by-environment interactions.
o Utilize UAV imagery to assess canopy development and growth rates.
o Develop predictive models connecting genotype, phenotype, and environmental data.

3. Structural Variant Analysis for Genomic Prediction:
o Sequence 41 SoyNAM founder lines to identify structural variants.
o Evaluate their influence on seed yield, composition, and adaptability.
o Improve genomic prediction models by incorporating structural variant data.

Progress to Date
Significant advancements have been made over the first two years, including:
• Genotyping and Data Management:
o Over 4,000 breeding lines genotyped with genome-wide markers.
o Development of a public genomic selection application integrated with SoyBase.
• Yield Trials and Environmental Modeling:
o Multi-location yield trials initiated with 1,200 elite lines.
o Collection of environmental data to refine predictive models.
• Structural Variant Discovery:
o Identification of over 470,000 structural variants using advanced sequencing tools.
o Initiation of pangenome sequencing for key soybean lines.

Expected Outcomes and Deliverables
• Publicly available genomic selection tools for soybean breeding programs.
• New knowledge on genotype-by-environment interactions and improved predictive models.
• Identification of structural variants impacting yield and seed composition.
• Development of superior soybean germplasm adapted to future environmental conditions.

Economic Impact
This project supports U.S. soybean competitiveness by ensuring continued genetic gain, improving yield stability, and equipping future breeders with cutting-edge genomic tools. The integration of advanced genomic and environmental modeling approaches will enhance breeding efficiency and profitability for soybean producers.

Budget Considerations
The project leverages existing breeding infrastructure and funding sources from multiple institutions. The final year will focus on optimizing resource use to complete genomic analysis, conduct large-scale trials, and refine predictive models for future breeding applications.

1. Continue to develop and enhance genomics-assisted breeding resources and tools to facilitate routine application in public breeding programs.
2. Develop and test methods for predicting cultivar performance in future target environments through genomics-assisted breeding models, phenomics, and environment characterization.
3. Discover structural variants and test whether modelling structural variants improves genomic predictions for yield and seed composition.

The following will be delivered upon completion of this three-year project:
1. Publicly available resources and tools for soybean breeders to implement cost-effective genomic prediction in their programs.
2. Publicly available knowledge on genetic control of genotype-by-environment interaction in soybean, and improved models for prediction of breeding line performance in new environments. Knowledge will be made available through open-access publications, presentations at scientific meetings, and presentations to the seed industry.
3. Identification of important structural variants that control seed yield and composition, and publication of knowledge on any benefit into explicitly modeling structural variants for predicting breeding line performance.
4. Enhanced germplasm and superior varieties developed through adoptions of genomics-assisted breeding techniques better adapted to future environmental conditions.

Soybean breeding has a large impact on the efficiency and profitability of agriculture through the development of high yielding new varieties with critical defensive traits and enhanced seed composition. Ensuring that such programs (both private and public) are using state-of-the-art technologies to drive genetic gain in the face of changing environments and narrowing genetic diversity will contribute to continual development and release of ever better varieties. Additionally, these efforts help to educate future agricultural scientists and soybean breeders that are best prepared to enter the seed industry and develop impactful future products for farmers, keeping the North Central region competitive in soybean production.