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Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program.

Sikiru Adeniyi Atanda1, Nonoy Bandillo2

  • 1Agricultural Data Analytics Unit, North Dakota State University, Fargo, ND, 58105-6050, USA. sikiru.atanda@ndsu.edu.

Plant Methods
|September 1, 2024
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Summary
This summary is machine-generated.

Genomic selection can decrease genetic variance, but a new index selection approach balances genetic gain and variance. This method optimizes crosses for improved progeny performance and sustained genetic diversity in breeding programs.

Keywords:
Breeding cycleGenetic driftGenetic gainGenomic estimated breeding valueGenomic predictionOptimal haploid valueQuantitative trait nucleotideSelection indexStochastic simulationUsefulness criterion

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Genomic selection

Background:

  • Genomic selection offers rapid genetic improvement but risks long-term additive genetic variance reduction.
  • Balancing short-term genetic gain with long-term genetic variance is crucial for sustainable breeding programs.

Purpose of the Study:

  • To develop an integrated index selection approach within the genomic inferred cross-selection (GCS) framework.
  • To maximize genetic gain across multiple traits while retaining additive genetic variance.

Main Methods:

  • Utilized a stochastic simulated recurrent breeding program over 40 years.
  • Evaluated different GCS methods, including posterior mean variance, usefulness criterion, and others.
  • Assessed the impact of parent, cross, and progeny numbers on genetic gain.

Main Results:

  • The posterior mean variance approach consistently enhanced genetic gain compared to other methods.
  • Identified optimal crosses that maximize progeny performance and maintain genetic variance.
  • Provided strategies for optimizing parent, cross, and progeny numbers for maximal short- and long-term gain.

Conclusions:

  • The integrated index selection within GCS effectively balances genetic gain and variance.
  • Posterior mean variance is a superior method for enhancing genetic gain in breeding programs.
  • Optimizing breeding program parameters is key to maximizing both short- and long-term genetic gain.