Research HighlightsThe SOYGEN Project: Nearly a Decade of Advancing Soybean Genetic Gain
Highlights:
- Soy Checkoff research backed by the North Central Soybean Research Program is helping scientists at 10 universities improve soybean genetic gain.
- The team of researchers across the region have been making strides in soybean yield improvement for nearly a decade.
- The tools and database information the team has developed aid them in faster, more effective prediction of soybean yield within breeding lines.
By Carol Brown
Delivering soybean genetic gain to a farmer’s planter box from the scientist’s lab lies a lengthy, complicated, but important road. Genetic gain is the improvement of yield due to breeding and technology exclusive of factors such as fertilizers or good weather. And it’s behind the soybean seed’s performance each spring.
Through Soy Checkoff investments, geneticists and breeders are working to improve soybean genetic gain. Specifically, research funded by the North Central Soybean Research Program has focused on soybean genetic gain for nearly a decade. The SOYGEN3 project is wrapping up the final year of the three-year funding cycle. Aaron Lorenz, University of Minnesota soybean breeding and genetics professor, is leading the current project.
The NCSRP-supported research project began in 2017 and those involved named it SOYGEN (Science-Optimized Yield Gains across ENvironments) in 2018. In addition to Lorenz, scientists in breeding programs at nine universities are part of the project and most have been involved since the first iteration.
“Our ultimate goal is to develop resources, methods and know-how to help us better predict how each individual soybean breeding line performs in an individual environment,” Lorenz says. “If we can improve that process, then we can be more effective and increase the efficiency of our breeding programs. Ultimately, breeders like me want to predict yield without having to conduct field trials; predicting yield based on the soybean’s DNA would make our process of developing new varieties much faster and more effective.”
The science isn’t to that level yet, but through this project the team is advancing the speed and accuracy of the soybean yield prediction process. By standardizing and validating tools, increasing access to data, and improving communication between breeders, new soybean lines that will perform better for farmers can be developed more quickly.
The SOYGEN3 team has created a publicly accessible soybean dataset that will increase successful genomic prediction, which was one of the team’s goals. Included in the dataset will be breeding line genotype-by-environment interaction, or GxE.
“GxE interaction is how the soybean interacts with the environment in a unique way,” Lorenz explains. “Some soybean varieties perform well in certain environments but relatively poorly in others. The plant may have taken advantage of the soil composition, or dealt with prevalent pests or weather conditions.”
The definition of “environment” could be as simple as the same field in different years, or the same crop year but grown in different locations — as each growing season and location sees a unique combination of rain, temperature, pests or other factors. Including environmental information with the genotype will give breeders an idea of how the soybean variety performs within their prediction models. By the end of this project, the team will have data on 1,200 breeding lines from 60 different environments across the north central region.
“Researchers can use this powerful dataset to ask and answer questions as well as assist with the breeding process in terms of making better predictions on soybean line performance before anything goes into the field,” says Lorenz.
Another SOYGEN3 goal is to continue genotyping all the material entered into the USDA Northern Uniform Soybean Tests (NUST) regional trials. Genotyping shows the presence or absence of genes. The team relies on David Hyten, Haskins Professor in Plant Genetics at the University of Nebraska, to sequence the soybean DNA, or “fingerprint” the genotypes. This past year, Hyten’s lab genotyped more than 500 new breeding lines for the SOYGEN team.
Through the project, more than 4,000 advanced soybean breeding lines entered in the NUST were genotyped, generating an impressive resource for current and future soybean breeders. In support of that, the team recently developed a software application and a streamlined workflow so genomic prediction can be done more easily and faster, Lorenz says.
A component of the recent work is looking at changes at the genome level that affect plant phenotype, or its characteristics. Understanding the soybean DNA variations, or structural variants, will impact variables like protein content and yield.
“When we refer to structural variants at the DNA level, an individual may have one copy of a gene and another has multiple copies of that gene. These types of differences among breeding lines can have a large impact on phenotype,” Lorenz explains. “For example, we know structural variance has an important role with soybean cyst nematode resistance. The main soybean gene that controls resistance to SCN is a structural variant. Increasing our understanding of structural variants will be illuminating. Matt Hudson, a genomic biology professor at the University of Illinois, is leading this portion of the project.”
The SOYGEN project has created more than great breeding line information. It’s brought this talented group of scientists together from across the north central region.
“We’re working as closely together now as we ever have,” Lorenz comments. “Because of this one common project, we can coordinate shared trials and conduct trials that have a scale and scope much larger than any one of us could do individually.”
Additional Resources
Breeders Continue to Improve Tools for Soybean Genetic Gain – SRIN article
SOYGEN2 Project Identifies Opportunities for Genetic Gain – SRIN article
Increasing Soybean Genetic Gain for Yield – SRIN article
Progress of a Genomic Selection ‘Pipeline’ – SRIN article
Yield Potential, Yield Protection and Genetic Gain – YouTube video
Meet the researcher: Aaron Lorenz University profile | University Soybean Breeding and Genetics Program webpage
The Soybean Research & Information Network (SRIN) is funded by the Soy Checkoff and the North Central Soybean Research Program. For more information about soybean research, visit the National Soybean Checkoff Research Database.
Published: Jun 1, 2026
