Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters

Area of Science:

• Quantitative genetics
• Plant breeding
• Genomic selection

Background:

• Plant breeding programs often utilize multiple families from diverse populations.
• These populations can vary in genetic parameters like means, variances, and prediction accuracy.
• Traditional selection methods need adaptation for complex multi-population scenarios.

Purpose of the Study:

• To extend the breeder's equation for truncation selection to multiple sets of genotypes.
• To investigate the impact of post-selection proportions on overall selection response.
• To determine optimal selection threshold strategies for different prediction methods (BLUPs and BLUEs).

Main Methods:

• Mathematical extension of the classical breeder's equation.
• Analysis of selection response under truncation selection across multiple genotype sets.
• Derivation of formulas for candidate origin and proportions before and after selection.

Main Results:

• Selection response depends on within-set responses and post-selection proportions.
• Uniform thresholds maximize selection response for Best Linear Unbiased Predictors (BLUPs) across all sets.
• Optimal thresholds for Best Linear Unbiased Estimators (BLUEs) are set-specific and depend on population parameters.

Conclusions:

• Post-selection proportions can significantly differ from initial proportions, especially for inferior sets.
• Results inform resource allocation for training and prediction sets in genomic selection.
• Strategic selection proportions in parent lines can enhance hybrid breeding when population variances or accuracies differ.