Project Details:

Title:
SOYGEN2: Increasing SB genetic gain for yield & seed composition by developing tools, know-how & community among public breeders in the NC US

Parent Project: Increasing the rate of genetic gain for yield in soybean breeding programs
Checkoff Organization:North Central Soybean Research Program
Categories:Breeding & genetics
Organization Project Code:GRT00060503
Project Year:2021
Lead Principal Investigator:Leah McHale (The Ohio State University)
Co-Principal Investigators:
Asheesh Singh (Iowa State University)
William Schapaugh (Kansas State University)
Dechun Wang (Michigan State University)
Katy M Rainey (Purdue University)
Brian Diers (University of Illinois at Urbana-Champaign)
Matthew Hudson (University of Illinois at Urbana-Champaign)
Nicolas Frederico Martin (University of Illinois at Urbana-Champaign)
Aaron Lorenz (University of Minnesota)
Pengyin Chen (University of Missouri)
Andrew Scaboo (University of Missouri)
George Graef (University of Nebraska)
David Hyten (University of Nebraska at Lincoln)
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Keywords: germplasm; database; genomic selection; Northern Uniform Soybean Trials; Soybase; imputation

Contributing Organizations

Funding Institutions

Information and Results

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Project Summary

The soybean research community has generated incredible public resources for soybean breeding, including collaborative yield trials such as the Northern Uniform Soybean Trials (NUST) which dates back to 1941 and commodity board funded genotypic data and genotyping platforms. However, these tools can be better leveraged to enhance gains for yield and seed composition in soybean. As part of our first objective, we propose to add value and utility to these resources through development of a breeding database that will be housed within SoyBase, the current community-supported USDA-ARS repository for soybean genetics and genomic data. We also propose the addition of environmental data to the NUST and addition of genotypic data to both the NUST and the SCN Regional Trials, both of which will facilitate breeding objectives for stability of both yield and seed composition.

Genomics-assisted breeding entails the use of genome-wide molecular marker data to aid in breeding decisions that make breeding programs more efficient and effective. Such applications range from the use of genomic selection, which can increase selection intensity and allow selection of parents earlier in a program, to the use of genomic data to optimally pair parents for creation of breeding populations containing more superior breeding lines and even possibly more favorable correlations between traits such as seed yield and protein. This latter application has been called “genomic mating”.

Numerous scientific articles have been published on the development and optimization of genomics-assisted plant breeding and, in part through our current NCSRP project, we have learned a lot about the optimal application of genomics-assisted breeding methods applied to soybean. The actual implementation of genomics-assisted breeding in the public plant breeding communities, however, has been minimal. Thus, Objective 2 is focused on the development and use of high-throughput genome-wide genotyping technologies that are of low cost with high-quality repeatable marker data, and making available tools for genomic data management and decisions that integrate genomic data and phenotypic data along with various analysis pipelines in a user-friendly form. The transfer and availability of these technologies to the public sector is critical to our ability to effectively train future soybean breeders, many of whom will be employed by private sector companies using these techniques.

Increases in soybean yield through breeding have been slower than growers expect. A collaborative study led by Diers of a historic set of MG II-IV varieties released from 1923 to 2008 revealed a recent rate of genetic gain of 0.43 bu/ac/yr, whereas reports of genetic gain in corn generally range from 1.0 to 1.2 bu/ac/yr. Moreover, this same study found that protein has decreased between these time periods by 1.7 percentage points, an undesirable outcome. Based on the mathematical formula for change resulting from selection, there are a number of possible targets for improving the rate of genetic gain. Objective 3 of this work focuses on the evaluation of different breeding methods each of which target one or more areas for improvement, such as selection intensity, accuracy, diversity, and the time required for each breeding cycle, and simultaneous improvement of traits that typically show negative correlations, such as yield and seed protein content. Breeders will implement and test the methods in their own breeding programs to determine which methods are most viable to improve genetic gains.

The proposed activities build on the current project funded to this group by NCSRP, “Increasing the rate of genetic gain for yield in soybean breeding programs.” One main objective in that project deals with extensive evaluation of diverse soybean genotypes from the USDA Soybean Germplasm Collection over four years and 30 environments to obtain high-quality phenotype and environment data. Completion and follow-up on that is detailed under Objective 4 in this project, and it provides foundational information for tool development and implementation here. Information from that study will be leveraged in this project for Objectives 1, 2, and 3. The entire set of 750 accessions, or some various subsets of those (i.e. exotic land races only, elite germplasm only, certain geographical regions only, etc.) can be used as training sets for prediction of yield, seed composition traits, maturity, and other traits for various objectives and for other programs.
Ultimately, in this project SOYGEN (Science Optimized Yield Gains across Environments) will leverage and build upon ongoing and previously funded work to increase soybean genetic gain for yield and seed composition by developing tools, know-how and community among public breeders in the north central US.

Project Objectives

Objective 1: Elevating collaborative field trials;
Objective 2: Development of a genomic breeding facilitation suite;
Objective 3: Evaluation of soybean breeding methods that increase gain
Objective 4: Characterization and use of the USDA Soybean Germplasm Collection, a foundation for future success

Project Deliverables

1.1) Database framework for agronomic, environmental, genotypic, meta and other trait data for collaborative trials.
1.2) Database populated with historical and current data from collaborative trials, including agronomic, environmental, genotypic, meta and other trait data.
1.3) Data from the uniform tests will become more useful as it will be connected to environmental and genotypic data.
1.4) Breeders will better understand how to weight data from different environments of the Uniform Tests to know how well it will predict performance.
2.1) Streamlined public genotyping service for the public soybean breeding sector at a low enough cost to afford genomic selection on a wide scale.
2.2) Workshop on genomic selection delivered to public soybean breeding community.
3.1) Methods to improve selection of progeny rows based on genomic selection with secondary traits and/or improved spatial statistics.
3.2) Understand the potential to improve the unfavorable correlation between yield and protein in soybean through genomic mating.
3.3) Application and limitations established for rapid cycling genomic selection in soybean.
3.4) Characterization of allelic effect of putative yield alleles and markers for their selection.
4.1) Comparison of sampling methods and effective ways to efficiently sample the genotype collection, particularly for improvement of quantitative traits like yield.
4.2) Means and variances for traits in the different sampling groups (so, effect of sampling method on those estimates).
4.3) Identify loci associated with yield and other traits in this diverse panel of accessions that represents the genetic diversity in the collection, so we may ID new loci and alleles that will be useful for commercial and public breeding programs.
4.4) Provide genomic predictions for yield (done), maturity, seed protein and oil %, and other traits as appropriate, for all untested accessions in the USDA Soybean Germplasm Collection.
4.5) Investigate genotype-environment interaction effects on traits, and evaluate stability of yield and composition traits across environments.
4.6) Use data/results in implementation in Objectives 1, 2, and 3 of this project (FY20-22).
4.7) Preliminary analysis of data from the validation set of 250 entries.

Progress of Work

Final Project Results

Benefit to Soybean Farmers

Ultimately, in this project SOYGEN (Science Optimized Yield Gains across Environments) will leverage and build upon ongoing and previously funded work to increase soybean genetic gain for yield and seed composition by developing tools, know-how and community among public breeders in the north central US. This work will result in greater genetic gains in soybean for yield, as well as any other targeted trait. This will translate to improved cultivars which achieve higher yields and higher quality.

Performance Metrics

Project Years

YearProject Title (each year)
2021SOYGEN2: Increasing SB genetic gain for yield & seed composition by developing tools, know-how & community among public breeders in the NC US
2021SOYGEN2: Increasing SB genetic gain for yield & seed composition by developing tools, know-how & community among public breeders in the NC US
2020Inceasing soybean genetic gain for yield by developing tools, know-how and community among public breeders in the north central US
2020Inceasing soybean genetic gain for yield by developing tools, know-how and community among public breeders in the north central US
2019Increasing the rate of genetic gain for yield in soybean breeding programs
2018Increasing the rate of genetic gain for yield in soybean breeding programs
2017Increasing the rate of genetic gain for yield in soybean breeding programs