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Summary

This summary is machine-generated.

Genomic and pedigree models accurately predict wheat hybrid performance by accounting for genotype × environment interactions. This approach enables reliable prediction of untested hybrids, crucial for breeding programs.

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Area of Science:

Plant breeding
Quantitative genetics
Agricultural science

Background:

Predicting hybrid performance is essential for efficient crop improvement.
Genotype × environment interactions (G×E) significantly influence trait expression in crops.
Integrating genomic and pedigree data can enhance prediction accuracy.

Purpose of the Study:

To evaluate genotype × environment interactions (G×E) models for predicting hybrid performance in wheat.
To compare the accuracy of different genomic and pedigree models under various cross-validation schemes.
To assess the potential for predicting unobserved hybrids using G×E models.

Main Methods:

Utilized five genomic and pedigree models (M1-M5) incorporating G×E.
Employed four cross-validation schemes (T2FM, T1M, T1F, T0FM) to simulate different training/testing scenarios.
Assessed similarity using pedigree and molecular markers for lines, and environmental covariables for environments.
Tested models on 1888 wheat hybrids across three years.

Main Results:

The most complex model (M5) showed slightly higher prediction accuracy for grain yield under the T2FM scheme.
Prediction accuracies for grain yield and other traits ranged from 0.50 to 0.55 under the T1F scheme.
Model M3 achieved high accuracy for flowering traits (0.71) under T1M, while M5 excelled for grain yield (0.5).
Model M5 achieved a prediction accuracy of 0.61 for grain yield under the T0FM scheme.
High prediction accuracy was observed even with untested parents, leveraging both genomic and pedigree information.

Conclusions:

Genotype × environment interactions (G×E) models are effective for hybrid prediction in wheat.
Integrating genomic and pedigree data improves prediction accuracy, even for untested lines.
Modeling genomic general combining ability (GCA) and specific combining ability (SCA) with G×E interactions allows for the prediction of unobserved hybrids.