Genomic analysis and predictive modeling in the Northern Uniform Soybean Tests

Abstract The Northern Uniform Soybean Tests (NUST) are a regional field trial network coordinated by the United States Department of Agriculture to evaluate experimental soybean ( Glycine max L.) strains developed by public institutions. Historical data from the NUST compiled, curated, and reported herein comprise a valuable multi‐environment trial dataset including relevant elite soybean germplasm from maturity groups 00 to IV evaluated over 28 years in 199 locations, totaling 1652 environments. Our aim was to characterize the genetic structure of the NUST experimental strains, perform genome‐wide association studies using historical phenotypic data, and assess the usefulness of these historical data for genomic prediction model training. Molecular marker information was collected on 2544 unique NUST experimental strains using the BARCSoySNP6K assay. High fixation index values between early and later maturity groups were observed in a region on chromosome 10 near the known soybean maturity gene E2 . We failed to find strong genetic divergence between strains from different breeding programs, reflecting the germplasm sharing common among public programs. Genome‐wide association analyses on important agronomic traits identified marker‐trait associations, many of which overlap with quantitative trait loci previously reported in the literature. Genomic prediction models trained using the historical NUST data produced moderate to high predictive abilities in most cases, suggesting these data could make useful contributions to training sets. We have made these data publicly available as a data resource for others to study genotype–phenotype relationships within elite public soybean germplasm and develop predictive models for advancement and implementation of genomics‐assisted breeding.

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Publication Details

Journal
Crop Science
Published
2025-08-29
DOI
https://doi.org/10.1002/csc2.70138
Citations
1
Primary Topic
Soybean genetics and cultivation
Type
article
Field-Weighted Citation Impact
1.75

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Genomic analysis and predictive modeling in the Northern Uniform Soybean Tests

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Genomic analysis and predictive modeling in the Northern Uniform Soybean Tests

João Paulo Gomes Viana, Carrie Miranda, Matthew E. Hudson, Andrew Scaboo, Asheesh Kumar Singh, Cleiton Antônio Wartha, Leah K. McHale, George L. Graef, Milad Eskandari, Katy Martin Rainey, Liana Nice, David L. Hyten, Nicolás F. Martín, Aaron J. Lorenz, Vishnu Ramasubramanian, Alistair J. Brock, Dechun Wang, Istvan Rajcan, Guohong Cai, Rex T. Nelson, W. T. Schapaugh, Adam L. Mahan, Benjamin W. Campbell, E. Dominguez
article en
1 citations

Abstract

Abstract The Northern Uniform Soybean Tests (NUST) are a regional field trial network coordinated by the United States Department of Agriculture to evaluate experimental soybean ( Glycine max L.) strains developed by public institutions. Historical data from the NUST compiled, curated, and reported herein comprise a valuable multi‐environment trial dataset including relevant elite soybean germplasm from maturity groups 00 to IV evaluated over 28 years in 199 locations, totaling 1652 environments. Our aim was to characterize the genetic structure of the NUST experimental strains, perform genome‐wide association studies using historical phenotypic data, and assess the usefulness of these historical data for genomic prediction model training. Molecular marker information was collected on 2544 unique NUST experimental strains using the BARCSoySNP6K assay. High fixation index values between early and later maturity groups were observed in a region on chromosome 10 near the known soybean maturity gene E2 . We failed to find strong genetic divergence between strains from different breeding programs, reflecting the germplasm sharing common among public programs. Genome‐wide association analyses on important agronomic traits identified marker‐trait associations, many of which overlap with quantitative trait loci previously reported in the literature. Genomic prediction models trained using the historical NUST data produced moderate to high predictive abilities in most cases, suggesting these data could make useful contributions to training sets. We have made these data publicly available as a data resource for others to study genotype–phenotype relationships within elite public soybean germplasm and develop predictive models for advancement and implementation of genomics‐assisted breeding.

Crop ScienceVol. 65(5)
University of Nebraska–Lincoln (US), University of Minnesota (US), Agricultural Research Service (US), University of Illinois Urbana-Champaign (US), Iowa State University (US), Kansas State University (US), Purdue University West Lafayette (US), The Ohio State University (US), North Dakota State University (US), University of Missouri (US), University of Guelph (CA), Michigan State University (US)
National Science Foundation, North Central Soybean Research Program
Openalex Percentile: Top 13%
Soybean genetics and cultivation
1.75
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