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Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America

Author

Listed:
  • Marco Lopez-Cruz

    (Michigan State University
    Michigan State University)

  • Fernando M. Aguate

    (Michigan State University
    Michigan State University)

  • Jacob D. Washburn

    (University of Missouri)

  • Natalia Leon

    (University of Wisconsin)

  • Shawn M. Kaeppler

    (University of Wisconsin
    University of Wisconsin)

  • Dayane Cristina Lima

    (University of Wisconsin)

  • Ruijuan Tan

    (Michigan State University)

  • Addie Thompson

    (Michigan State University
    Michigan State University)

  • Laurence Willard Bretonne

    (University of Wisconsin)

  • Gustavo los Campos

    (Michigan State University
    Michigan State University
    Michigan State University)

Abstract

Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations and years. The Genomes-to-Fields (G2F) Initiative has tested maize hybrids in more than 130 year-locations in North America since 2014. Here, we curate and expand this data set by generating environmental covariates (using a crop model) for each of the trials. The resulting data set includes DNA genotypes and environmental data linked to more than 70,000 phenotypic records of grain yield and flowering traits for more than 4000 hybrids. We show how this valuable data set can serve as a benchmark in agricultural modeling and prediction, paving the way for countless G×E investigations in maize. We use multivariate analyses to characterize the data set’s genetic and environmental structure, study the association of key environmental factors with traits, and provide benchmarks using genomic prediction models.

Suggested Citation

  • Marco Lopez-Cruz & Fernando M. Aguate & Jacob D. Washburn & Natalia Leon & Shawn M. Kaeppler & Dayane Cristina Lima & Ruijuan Tan & Addie Thompson & Laurence Willard Bretonne & Gustavo los Campos, 2023. "Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42687-4
    DOI: 10.1038/s41467-023-42687-4
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    References listed on IDEAS

    as
    1. Frank Technow & Carlos D Messina & L Radu Totir & Mark Cooper, 2015. "Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-20, June.
    2. John Novembre & Toby Johnson & Katarzyna Bryc & Zoltán Kutalik & Adam R. Boyko & Adam Auton & Amit Indap & Karen S. King & Sven Bergmann & Matthew R. Nelson & Matthew Stephens & Carlos D. Bustamante, 2008. "Genes mirror geography within Europe," Nature, Nature, vol. 456(7219), pages 274-274, November.
    3. John Novembre & Toby Johnson & Katarzyna Bryc & Zoltán Kutalik & Adam R. Boyko & Adam Auton & Amit Indap & Karen S. King & Sven Bergmann & Matthew R. Nelson & Matthew Stephens & Carlos D. Bustamante, 2008. "Genes mirror geography within Europe," Nature, Nature, vol. 456(7218), pages 98-101, November.
    4. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    5. Gustavo de los Campos & Paulino Pérez-Rodríguez & Matthieu Bogard & David Gouache & José Crossa, 2020. "A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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