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Related Experiment Video

Updated: Feb 27, 2026

Scalable Transfection of Maize Mesophyll Protoplasts

Scalable Transfection of Maize Mesophyll Protoplasts

Published on: June 23, 2023

Omics-based hybrid prediction in maize.

Matthias Westhues¹, Tobias A Schrag¹, Claas Heuer^2,3

¹Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik

|June 26, 2017

Summary

This summary is machine-generated.

Integrating transcriptomic and genomic data improves prediction of complex agronomic traits in maize hybrids. Transcriptomic data alone surpassed genomic data for predicting dry matter yield, highlighting its value in hybrid breeding.

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Area of Science:

Plant breeding
Genomics
Systems biology

Background:

Accurate prediction of complex agronomic traits is vital for crop and animal breeding.
Whole-genome prediction has limitations in capturing epistatic interactions.
Downstream 'omics' data offer potential to improve trait prediction by integrating biological strata.

Purpose of the Study:

To evaluate the predictive power of genomic, transcriptomic, and metabolic data for hybrid combinations in maize.
To determine if combining 'omics' data enhances prediction accuracy for complex traits.
To investigate the role of transcriptomic data in capturing parent-to-progeny trait inheritance.

Main Methods:

Collected genomic, transcriptomic, and metabolic data from maize parent lines.
Applied various 'omics' predictors to forecast hybrid performance.
Conducted eQTL analysis to understand transcriptomic data's genomic integration.

Main Results:

Integration of transcriptomic and genomic data significantly increased prediction success rates for untested maize hybrid combinations.
Transcriptomic data alone outperformed genomic data and other predictors for dry matter yield, a complex heterotic trait.
eQTL analysis indicated that transcriptomic data capture genomic information from both proximal and distal sites.

Conclusions:

Downstream 'omics' predictors, particularly transcriptomic data, capture physiological epistasis transmitted from parents to offspring.
Combining genomic data with transcriptomic data enhances the prediction of complex agronomic traits in hybrid breeding.
Utilizing downstream 'omics' data provides valuable information beyond structural genomics to improve hybrid breeding efficiency.