Project Details:

Title:
SOYGEN2: Increasing soybean genetic gain for yield and seed composition by developing tools, know-how and community among public breeders in the north central 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:
Project Year:2022
Lead Principal Investigator:Leah McHale (The Ohio State University)
Co-Principal Investigators:
Keywords:

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 date back to 1941 and commodity board funded-genotypic data and genotyping platforms. However, these tools can be better leveraged to enhance genetic gains for yield and improvement for seed composition in soybean. As part of our first objective, we are adding value and utility to these resources through a breeding database housed within SoyBase, the current community-supported USDA-ARS repository for soybean genetics and genomic data. In addition to the agronomic, resistance, and composition data normally collected in the NUST, we have added GPS coordinates in order to access environmental data for the NUST and have added genotypic data to both the NUST and the SCN Regional Trials, this information will facilitate breeding 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. 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 prior NCSRP project, we have learned 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. While we are making these tools and technologies widely available, the transfer and availability 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. 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. Compiling data across breeding programs will provide power and confidence in our findings.
The proposed activities build on the previous project funded to this group by NCSRP. One main objective in that project dealt 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. Information from that study will be leveraged in this project for Objectives 1, 2, and 3. The entire set of 750 accessions evaluated in the project, or 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, maturity, and other traits for various objectives and for other programs.

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

Objective 1:
(1) Database framework for agronomic, environmental, genotypic, meta and other trait data for collaborative trials.
(2) Database populated with historical and current data from collaborative trials, including agronomic, environmental, genotypic, meta and other trait data.
(3) Data from the uniform tests will become more useful as it will be connected to environmental and genotypic data.
(4) Breeders will better understand how to weigh data from different environments of the NUST understand where new cultivars be more likely to be adapted and tested successfully.

Objective 2: Development of a genomic breeding facilitation suite
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) Workshops on genomic selection delivered to public soybean breeding community.

Objective 3:
(1) Methods to improve selection of progeny rows based on genomic selection with secondary traits and/or improved spatial statistics.
(2) Understand the potential to improve the unfavorable correlation between yield and protein in soybean through genomic mating.
(3) Application and limitations established for rapid cycling genomic selection in soybean.
(4) Characterization of allelic effect of putative yield alleles and markers for their selection.

Objective 4:
(1) Include high-throughput phenotype data in the analyses and models to identify important relationships and potential future focus areas for HTP data collection and use.
(2) Include weather and environment data in analyses and models to ID significant factors, better define environment and genotype-environment interaction effects, and evaluate contributions to prediction models.
(3) Complete submission of additional publications with complete set of data, image and environment data.

Progress of Work

Updated April 19, 2022:
In the last reporting period, 7975 breeding lines from eight breeding programs were genotyped with the 1000 SNP genotyping panel at UNL. DNA has been isolated for an additional 2520 lines which will be genotyped. In total, DNA has been isolated from 17,259 breeding lines and 14739 have been genotyped with the 1000 SNP genotyping panel.

Using the genotype data collected from the 1000 SNP panel at UNL as well as the genotypic and phenotypic data continually collected from the Northern Uniform Regional Trial entries we calculated genomic estimated breeding values (GEBVs) of progeny rows. In March 2022, we calculated GEBVs for a total of 9357 progeny rows from four different breeding programs. GEBVs were calculated from a training set which included 7700 unique genotypes with phenotypic and genotypic data.

This data has been used in combination with or comparison to canopy coverage and yield or other breeders' choice methodology, respectively, to make selections. Outcomes of those selection methods will be evaluated by field trials in 2022.

Final Project Results

Updated June 8, 2022:

Benefit to Soybean Farmers

This SOYGEN (Science Optimized Yield Gains across Environments) project leverages and builds 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. Ultimately, this will lead to faster development of improved (yield and seed quality) soybean cultivars, which will provide farmers with increased production and increase the competitiveness of US soybean in the global market.

Performance Metrics

Project Years

YearProject Title (each year)
2022SOYGEN2: Increasing soybean genetic gain for yield and seed composition by developing tools, know-how and community among public breeders in the north central US
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