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

Albrecht E Melchinger1,2, Rohan Fernando3, Andreas J Melchinger4

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Summary

Optimizing selection response in plant breeding requires careful consideration of candidate proportions and population parameters. Using a uniform threshold for Best Linear Unbiased Predictors (BLUPs) maximizes selection response across diverse genetic groups.

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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.