Revisiting superiority and stability metrics of cultivar performances using genomic data: derivations of new estimators
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View abstract on PubMed
Summary
This summary is machine-generated.New genomic-based methods improve the selection of high-performing, stable plant genotypes by accurately estimating genotype-by-environment (GE) interactions, even in complex breeding program designs.
Area Of Science
- Plant breeding
- Quantitative genetics
- Genomic prediction
Background
- Genotype-by-environment (GE) interactions pose a challenge for selecting stable, high-yielding plant genotypes.
- Traditional GE interaction metrics rely solely on phenotypic data, limiting their use in unbalanced experimental designs.
Purpose Of The Study
- To develop and evaluate novel genomic-based estimators for key GE interaction metrics: Ecovalence, Environmental Variance, Finlay-Wilkinson regression coefficient, and Lin-Binns superiority measure.
- To address limitations of traditional methods in unbalanced experimental designs.
Main Methods
- Derived best linear unbiased prediction (BLUP) estimators for GE metrics using a multi-environment genomic prediction model.
- Incorporated both squared expectation and variance terms into the new estimators.
- Assessed estimator effectiveness through simulations with varying trait and environmental parameters.
Main Results
- Genomic-based estimators demonstrated superior accuracy compared to traditional phenotype-based methods.
- The inclusion of a variance term enhanced estimation precision, especially for Ecovalence under low heritability and high data sparseness.
- The GEmetrics R-package facilitates the application of these new methods.
Conclusions
- Genomic-based GE metric estimation is effective for unbalanced designs and predicting metrics for new genotypes.
- These advanced methods can significantly improve the selection efficiency of superior and stable genotypes in plant breeding programs.
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