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Related Experiment Video

Updated: Oct 28, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.
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High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

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Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review.

Roberto Fritsche-Neto1, Giovanni Galli1, Karina Lima Reis Borges1

  • 1Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil.

Frontiers in Plant Science
|July 19, 2021
PubMed
Summary

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Statistics and data science unlock the predictive power of quantitative genetics.

Frontiers in plant science·2026
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Multimodal genomic prediction is not a buzzword: why modern plant breeding must integrate genomics, enviromics, and phenomics.

G3 (Bethesda, Md.)·2026
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Genomic language model-based genomic prediction in plant breeding.

Trends in plant science·2026
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Comparing statistical 'phenomic prediction' models for remote-sensing-based phenotyping of maize susceptibility to common rust.

Plant phenomics (Washington, D.C.)·2026
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Correction: Multi-trait and multi-environment genomic prediction enhances yield components improvement in durum wheat.

Frontiers in plant science·2026
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Multimodal deep learning improves cross-environment prediction of durum wheat yield components.

BMC plant biology·2026

Genomic prediction (GP) strategies improve tropical maize hybrid breeding, especially for small-scale, low-budget programs. Research highlights cost-effective methods using open-source tools and advanced data for accurate performance prediction.

Area of Science:

  • Plant Breeding
  • Genetics
  • Agricultural Science

Background:

  • Genomic prediction (GP) is crucial for animal and plant breeding, particularly in maize hybrid development for enhanced adaptation and productivity.
  • Its application in small-scale, low-budget breeding programs, common in developing countries, requires tailored strategies for tropical environments.

Approach:

  • This research focuses on improving GP accuracy in tropical maize by optimizing germplasm characterization, mating designs, and training population selection.
  • It examines factors influencing prediction accuracy, including population structure, non-additive genetic effects (dominance, epistasis), molecular marker types, and genotype-environment interactions.

Key Points:

  • Strategies for cost-effective GP in tropical maize include using open-source software for marker quality control and envirotyping pipelines.
Keywords:
R packagesaccuracybreeding schemesgenomic selectionquantitative genomics

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  • The study highlights outcomes from the University of São Paulo (USP) demonstrating improved prediction accuracy for tropical maize hybrids.
  • Seven years of research in a public maize hybrid breeding program under tropical conditions provide valuable insights and results.

    • Conclusions:

    • Exploring new models integrating high-throughput phenotyping and large-scale envirotyping can enhance the resolution and realism of genotype performance predictions.
    • GP platforms, despite initial genotyping costs, offer a cost-effective approach for predicting maize hybrid performance across diverse growing conditions when combined with advanced data sources.