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  3. Predicting Complex Phenotypes Using Multi-omics Data In Maize.
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  3. Predicting Complex Phenotypes Using Multi-omics Data In Maize.

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Predicting complex phenotypes using multi-omics data in maize.

Maddy Creach1,2,3, Brandon Webster1,2, Linsey Newton1,3

  • 1Plant Resilience Institute, Michigan State University, East Lansing, MI 48824, USA.

The Plant Cell
|June 15, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Integrating multi-omics data, including genomics, transcriptomics, and phenomics, significantly improves the prediction of complex maize plant traits. This approach enhances understanding of genotype-by-environment interactions and genetic architecture for better crop breeding.

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Area of Science:

  • Plant genetics and breeding
  • Systems biology
  • Agricultural biotechnology

Background:

  • Predicting complex plant traits is challenging due to interactions between genetics, regulation, and environment.
  • Accurate trait prediction and identification of genetic elements are crucial for crop improvement and biotechnology.

Purpose of the Study:

  • To evaluate if multi-omic datasets (genomic, transcriptomic, phenomic) improve predictive accuracy for diverse maize phenotypes across multiple environments.
  • To compare the performance of linear and nonlinear models using single- and multi-omics inputs.

Main Methods:

  • Utilized genomic markers, field-based transcriptomic data, and drone-derived phenomic data for 129 maize phenotypes across nine environments.
  • Trained and compared linear (rrBLUP) and nonlinear (support vector regression) models with single- and multi-omics data.
  • Analyzed model feature weights to understand the distribution of predictive signals.

    • Main Results:

    • Multi-omics models consistently outperformed single-omics models in predicting maize traits.
    • Transcriptomic data enabled accurate cross-environment trait prediction and captured genotype-by-environment interactions.
    • Phenomic data showed lower predictive power alone but improved predictions for specific traits like root architecture.

    Conclusions:

    • Integrating transcriptomic and phenomic data with genotypes enhances trait prediction accuracy and model generalizability across environments.
    • This multi-omics approach provides deeper insights into the genetic and regulatory architecture of agriculturally important maize traits.
    • Complex traits arise from coordinated, network-level genetic processes rather than a few dominant loci.