Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Genetic diversity analysis of North Dakota public soybean breeding program cultivars
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 22 January 2026

Genetic diversity analysis of North Dakota public soybean breeding program cultivars

  • Forrest Hanson  ORCID: orcid.org/0009-0003-2124-23081,
  • Benjamin Harms  ORCID: orcid.org/0009-0000-4694-60781,
  • Gustavo Kreutz   ORCID: orcid.org/0000-0001-6733-47401,
  • Anser Mahmood2,
  • Nonoy Bandillo  ORCID: orcid.org/0000-0002-5941-90471 nAff3,
  • Matthew Hudson  ORCID: orcid.org/0000-0002-4737-09364 &
  • …
  • Carrie Dottey1 

Scientific Reports , Article number:  (2026) Cite this article

  • 916 Accesses

  • 1 Altmetric

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Genotype
  • Plant breeding
  • Plant genetics
  • Population genetics
  • Sequencing

Abstract

Soybean [Glycine max (L.) Merr.] is a critical crop globally, valued for its protein and oil content. However, historical bottlenecks have constrained genetic diversity in soybean, particularly in high-latitude regions such as North Dakota, where environmental conditions necessitate maturity group (MG) 00 and 0 cultivars. This genetic diversity study examines the North Dakota State University (NDSU) soybean breeding program using pedigree, coefficient of parentage (CP), and SNP-based analyses. Pedigree tracing of 40 NDSU cultivars revealed a genetic base derived from 49 founders. CP analysis confirmed these findings, emphasizing dependence on limited germplasm, with the top ten founders accounting for over 70% of the genetic background and Mandarin (Ottawa) alone contributing 24%. SNP-based dendrograms and genetic relationship structures demonstrate the relationships among cultivars and founders. Notably, the specialty food grade natto cultivars formed a distinct cluster unrelated to commodity soybean. Population structure analyses emphasized the reliance on specific ancestral germplasm for breeding. This study underscores the need to diversify breeding materials to prevent genetic gain plateaus in MG 00 and 0 soybeans, thereby enhancing yield potential and adaptability in high-latitude regions.

Data availability

The genotypic datasets generated and/or analyzed during the current study are available in the NCBI Sequence Read Archive (SRA) repository: http://www.ncbi.nlm.nih.gov/bioproject/1235621.

References

  1. Fornari, H. D. The big change: cotton to soybeans. Agric. Hist. 53, 245–253 (1979). https://www.jstor.org/stable/3742873

    Google Scholar 

  2. Wilson, R. F. & Soybean market driven research needs. In Genetics and Genomics of Soybean. (eds. Stacey Gary) 3–15 (Springer Science + Business Media, 2008); https://doi.org/10.1007/978-0-387-72299-3_1

  3. Hartman, G. L., West, E. D. & Herman, T. K. Crops that feed the world 2. Soybean—worldwide production, use, and constraints caused by pathogens and pests. Food Secur. 3, 5–17. https://doi.org/10.1007/s12571-010-0108-x (2011).

    Google Scholar 

  4. Carter, T. E. Jr., Nelson, R. L., Sneller, C. H. & Cui, Z. Genetic diversity in soybean in Soybeans: Improvement, Production, and Uses. (eds. Boerma, H.R. & Specht, J.E.) 303–416 (American Society of Agronomy–Crop Science Society of America–Soil Science Society of America, ; (2004). https://doi.org/10.2134/agronmonogr16.3ed.c8

  5. Hyten, D. L. et al. Impacts of genetic bottlenecks on soybean genome diversity. Proc. Natl. Acad. Sci. U.S.A. 103, 16666–16671. https://doi.org/10.1073/pnas.0604379103 (2006).

    Google Scholar 

  6. Li, Y. H. et al. Genetic diversity in domesticated soybean (Glycine max) and its wild progenitor (Glycine soja) for simple sequence repeat and single-nucleotide polymorphism loci. New Phytol. 188, 242–253. https://doi.org/10.1111/j.1469-8137.2010.03344.x (2010).

    Google Scholar 

  7. Larson, G. et al. Current perspectives and the future of domestication studies. Proc. Natl. Acad. Sci. U.S.A. 111, 6139–6146. https://doi.org/10.1073/pnas.1323964111 (2014).

    Google Scholar 

  8. Tang, H., Sezen, U. & Paterson, A. H. Domestication and plant genomes. Curr. Opin. Plant. Biol. 13, 160–166. https://doi.org/10.1016/j.pbi.2009.10.008 (2010).

    Google Scholar 

  9. Song, Q. et al. Fingerprinting soybean germplasm and its utility in genomic research. G3 5, 1999–2006. https://doi.org/10.1534/g3.115.019000 (2015).

    Google Scholar 

  10. Gizlice, Z., Carter, T. E. Jr. & Burton, J. Genetic base for North American public soybean cultivars released between 1947 and 1988. Crop Sci. 34, 1143–1151. https://doi.org/10.2135/cropsci1994.0011183X003400050001x (1994).

    Google Scholar 

  11. Xavier, A., Thapa, R., Muir, W. M. & Rainey, K. M. Population and quantitative genomic properties of the USDA soybean germplasm collection. Plant. Genetic Resour. 16, 513–523. https://doi.org/10.1017/S1479262118000102 (2018).

    Google Scholar 

  12. Mikel, M. A., Diers, B. W., Nelson, R. L. & Smith, H. H. Genetic diversity and agronomic improvement of North American soybean germplasm. Crop Sci. 50, 1219–1229. https://doi.org/10.2135/cropsci2009.08.0456 (2010).

    Google Scholar 

  13. Wilcox, J. R. Sixty years of improvement in publicly developed elite soybean lines. Crop Sci. 41, 1711–1716. https://doi.org/10.2135/cropsci2001.1711 (2001).

    Google Scholar 

  14. Rincker, K. et al. Genetic improvement of US soybean in maturity groups II, III, and IV. Crop Sci. 54, 1419–1432. https://doi.org/10.2135/cropsci2013.10.0665 (2014).

    Google Scholar 

  15. Bruce, R. W. et al. Genome-wide genetic diversity is maintained through decades of soybean breeding in Canada. Theor. Appl. Genet. 132, 3089–3100. https://doi.org/10.1007/s00122-019-03408-y (2019).

    Google Scholar 

  16. USDA-NASS USDA/NASS QuickStats AD-hoc Query Tool. United States Department of Agriculture—National Agriculture Statistics Service; (2024). https://quickstats.nass.usda.gov/

  17. Bangsund, D. A., Olson, F. E. & Leistritz, F. L. Economic contribution of the soybean industry to the North Dakota economy. Agribusiness and Applied Economics Report No. 678. (Department of Agricultural Economics, North Dakota State University, 2011); http://ageconsearch.umn.edu

  18. Specht, J. E. et al. Crop Science Society of America Special Publications,. Soybean in Yield Gains in Major US Field Crops. (eds. Smith, S., B. Diers, Specht, J. & Carver B.) 311–355 ; (2014). https://doi.org/10.2135/cssaspecpub33.c12

  19. Kandel, H. Soybean production field guide for North Dakota and Northwestern Minnesota. Agronomy Field Guide No. A-1172. (North Dakota State University Extension Service, North Dakota State University, (2010).

  20. Helms, T. C. & Halvorson, M. A. Registration of ‘Council’ soybean. Crop Sci. 36, 206 (1996).

    Google Scholar 

  21. Woodworth, C. M., Illini & Soybeans University of Illinois Agricultural Experiment Station Bulletin No. 335 (Urbana, 1929).

  22. USDA-AMS USDA Agricultural Marketing Service Plant Variety Protection (PVP) records. United States Department of Agriculture–Agricultural Marketing Service; (2024). https://www.ams.usda.gov/services/plant-variety-protection/

  23. Brown, A. V. et al. A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database. Nucleic Acids Res. 49, 1496–1501. https://doi.org/10.1093/nar/gkaa1107 (2021).

    Google Scholar 

  24. USDA-ARS Germplasm Resources Information Network – Global (GRIN-Global). United States Department of Agriculture—Agriculture Research Service Germplasm Resources Information Network; (2024). https://www.ars-grin.gov/

  25. Chan, Y. O. et al. The allele catalog tool: a web-based interactive tool for allele discovery analysis. BMC Genom. 24, 107. https://doi.org/10.1186/s12864-023-09161-3 (2023).

    Google Scholar 

  26. Danecek, P. et al. Twelve years of samtools and BCFtools. Gigascience 10, giab008. https://doi.org/10.1093/gigascience/giab008 (2021).

    Google Scholar 

  27. Grant, D., Nelson, R. T., Cannon, S. B., Shoemaker, R. C. & SoyBase The USDA-ARS soybean genetics and genomics database. Nucleic Acids Res. 38, D843–D846 https://doi.org/10.1093/nar/gkp798 (2010). 

  28. Bradbury, P. J. et al. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635. https://doi.org/10.1093/bioinformatics/btm308 (2007).

    Google Scholar 

  29. Money, D. et al. LinkImpute: fast and accurate genotype imputation for nonmodel organisms. G3. 5, 2383–2390 . https://doi.org/10.1534/g3.115.021667 (2015).

  30. Amadeu, R. R., Garcia, A. A. F. & Munoz, P. R. Ferrão L.F.V. AGHmatrix: genetic relationship matrices in R. Bioinformatics 39, 1–4. https://doi.org/10.1093/bioinformatics/btad445 (2023).

    Google Scholar 

  31. R Core Team R. A Language and Environment for Statistical Computing. (2023). https://www.R-project.org/

  32. Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328. https://doi.org/10.1093/bioinformatics/bts606 (2012).

    Google Scholar 

  33. Yu, G. Data Integration, Manipulation and Visualization of Phylogenetic Trees (CRC, 2022). https://doi.org/10.1201/9781003279242

  34. VanRaden, P. M. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414–4423. https://doi.org/10.3168/jds.2007-0980 (2008).

    Google Scholar 

  35. Zhao, S., Yin, L., Guo, Y., Sheng, Q. & Shyr, Y. heatmap3: An Improved Heatmap Package. (2021). https://CRAN.R-project.org/package=genetic relationship map3.

  36. Frichot, E. & François, O. L. E. A. An R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929. https://doi.org/10.1111/2041-210X.12382 (2015).

    Google Scholar 

  37. Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. Cluster: Cluster analysis basics and extensions. (2019). https://doi.org/10.32614/CRAN.package.cluster

  38. Tibshirani, R., Walther, G. & Hastie, T. Estimating the number of clusters in a data set via the gap statistic. J. Royal Stat. Soc. Ser. B: Stat. Methodol. 63, 411–423. https://doi.org/10.1111/1467-9868.00293 (2001).

    Google Scholar 

  39. Kassambara, A., Mund, F. & Factoextra Extract and visualize the results of multivariate data analyses. (2017). https://doi.org/10.32614/CRAN.package.factoextra

  40. Viana, J. P. G. et al. Impact of multiple selective breeding programs on genetic diversity in soybean germplasm. Theor. Appl. Genet. 135, 1591–1602. https://doi.org/10.1007/s00122-022-04056-5 (2022).

    Google Scholar 

  41. Hymowitz, T. & Bernard, R. Origin of the soybean and germplasm introduction and development in North America in Use of Plant Introductions in Cultivar Development Part 1. (eds. Shands H.L. & Wiesner L.E.) 147–164 (Crop Science Society of America Special Publications, 1991); https://doi.org/10.2135/cssaspecpub17.c9

  42. Stoa, T. Growing soybeans in North Dakota. Bimon. Bull. North. Dak. Agricultural Exp. Stn. 12, 131 (1950).

    Google Scholar 

  43. Bernard, R. L., Juvik, G. A., Hartwig, E. E. & Edwards, C. J. Jr Origins and pedigrees of public soybean varieties in the United States and Canada. USDA Technical Bulletin. US Department of Agriculture. 1746, (1988).

  44. LeRoy, A. R., Fehr, W. R. & Cianzio, S. R. Introgression of genes for small seed size from Glycine Soja into G. max. Crop Sci. 31, 693–697. https://doi.org/10.2135/cropsci1991.0011183X003100030029x (1991).

    Google Scholar 

  45. Escamilla, D. M., Rosso, M. L., Holshouser, D. L., Chen, P. & Zhang, B. Improvement of soybean cultivars for Natto production through the selection of seed morphological and physiological characteristics and seed compositions: a review. Plant. Breed. 138, 131–139. https://doi.org/10.1111/pbr.12678 (2019).

    Google Scholar 

  46. Dietz, N. et al. Geographic distribution of the E1 family of genes and their effects on reproductive timing in soybean. BMC Plant Biol. 21, 441. https://doi.org/10.1186/s12870-021-03197-x (2021).

    Google Scholar 

  47. Bandillo, N. et al. A population structure and genome-wide association analysis on the USDA soybean germplasm collection. The Plant Genome. 8, 1–13; (2015). https://doi.org/10.3835/plantgenome2015.04.0024 (2015).

Download references

Acknowledgements

Thank you to the Bilyeu lab at the USDA in Columbia, Missouri, for preparing the tissue and DNA samples for whole genome sequencing. Thank you to Brian Diers and Rex Nelson for their advice on data interpretation and historical soybean semantics.

Funding

The NDSU Soybean Breeding program and cultivars created are supported by the North Dakota Soybean Council and the USDA Hatch Project Grant/Award Number: ND01505. The author Forrest Hanson was supported by the North Central Soybean Research Program project “SOYGEN3: Building capacity to increase soybean genetic gain for yield and composition through combining genomics-assisted breeding with characterization of future environments”.

Author information

Author notes
  1. Nonoy Bandillo

    Present address: Crop and Soil Sciences Deptartment, North Carolina State University, 840 Method Rd Unit 3 Rm 219, NC, Raleigh , USA

Authors and Affiliations

  1. Department of Plant Sciences, North Dakota State University, ND, Fargo, 58108, USA

    Forrest Hanson, Benjamin Harms, Gustavo Kreutz , Nonoy Bandillo & Carrie Dottey

  2. Division of Plant Science and Technology, University of Missouri, 52 Agriculture Lab, Columbia, 65211, USA

    Anser Mahmood

  3. Department of Crop Sciences, University of Illinois, 1109 IGB, 1206 W Gregory Dr, Urbana, IL, 61801, USA

    Matthew Hudson

Authors
  1. Forrest Hanson
    View author publications

    Search author on:PubMed Google Scholar

  2. Benjamin Harms
    View author publications

    Search author on:PubMed Google Scholar

  3. Gustavo Kreutz
    View author publications

    Search author on:PubMed Google Scholar

  4. Anser Mahmood
    View author publications

    Search author on:PubMed Google Scholar

  5. Nonoy Bandillo
    View author publications

    Search author on:PubMed Google Scholar

  6. Matthew Hudson
    View author publications

    Search author on:PubMed Google Scholar

  7. Carrie Dottey
    View author publications

    Search author on:PubMed Google Scholar

Contributions

CD conceptualized the research; CD, NB, MH proposed and established methodology; FH, BH, GK, AM were responsible for data curation; FH, BH, GK analyzed the data and generated figures and tables; FH, BH, GK, CD wrote the manuscript; FH, BH, GK, NB, MH, AM, CD revised the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Carrie Dottey.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hanson, F., Harms, B., Kreutz , G. et al. Genetic diversity analysis of North Dakota public soybean breeding program cultivars. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35464-y

Download citation

  • Received: 27 February 2025

  • Accepted: 06 January 2026

  • Published: 22 January 2026

  • DOI: https://doi.org/10.1038/s41598-026-35464-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Soybeans
  • Pedigree
  • Coefficient of parentage
  • Population structure
  • Genetic diversity
  • SNP-based dendrogram
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on Twitter
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com sitemap

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research