Using the Animal Model to Accelerate Response to Selection in a Self-Pollinating Crop

Area of Science:

• Plant breeding
• Quantitative genetics
• Agricultural science

Background:

• Recurrent selection is vital for improving traits in self-pollinating crops.
• Integrating self-progeny data into pedigrees presents analytical challenges.
• Accurate prediction of breeding values is crucial for effective selection.

Purpose of the Study:

• To apply the animal model for the first time in recurrent selection of a self-pollinating crop (Pisum sativum).
• To incorporate both self and cross progeny data into the pedigree for enhanced accuracy.
• To assess the model's effectiveness in predicting breeding values for ascochyta blight resistance.

Main Methods:

• Utilized an animal model incorporating phenotypic and relationship data from self and cross progeny.
• Applied best linear unbiased prediction (BLUP) for ascochyta blight resistance over two selection cycles.
• Included additive and nonadditive genetic covariances, fixed effects, and random effects in the full model.

Main Results:

• Achieved narrow-sense heritability of 0.305 (cycle 1) and 0.352 (cycle 2) for ascochyta blight resistance.
• Reported a high correlation (0.82) of predicted breeding values across cycles.
• Forecasted a 11.2% response to selection in the next cycle with a 20% selection proportion.

Conclusions:

• The animal model is effective for cyclic selection in heterozygous populations of selfing plants.
• This approach can be integrated with genomic selection for trait improvement.
• The model is suitable for traits measured on bulked progeny, such as grain yield.