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Assessing the response to genomic selection by simulation.

Harimurti Buntaran1, Angela Maria Bernal-Vasquez2, Andres Gordillo3

  • 1Biostatistics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstraße 23, 70599, Stuttgart, Germany.

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Summary
This summary is machine-generated.

This study introduces a simulation method for genomic selection in plant breeding. It helps breeders determine how many top entries to select to ensure a high probability of identifying the best performing varieties.

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Area of Science:

  • Plant breeding and genetics
  • Quantitative genetics
  • Genomic prediction

Background:

  • Maximizing genetic gain is crucial for plant breeding programs.
  • Genomic prediction (GP) utilizes dense markers for estimating genomic breeding values (GBV).
  • Classical selection response metrics do not fully inform selection decisions in GP.

Purpose of the Study:

  • To develop a simulation approach for genomic selection in a multi-environment framework.
  • To provide breeders with tools to determine optimal selection strategies.
  • To answer key breeder questions regarding selection probability and top entry identification.

Main Methods:

  • Employed simulation based on a fitted mixed model for genomic prediction.
  • Incorporated a multi-environment framework to account for genotype-by-environment interactions.
  • Calculated the number of entries needed for a defined probability of selecting the best entry.

Main Results:

  • Quantified the number of entries required to achieve a specific probability of selecting the best entry.
  • Determined the probability of including top-performing entries when a fixed number are selected.
  • Validated the simulation approach for practical application in breeding programs.

Conclusions:

  • The proposed simulation approach effectively computes response to genomic selection.
  • Provides breeders with critical information for optimizing selection decisions in multi-environment trials.
  • Enhances the efficiency of identifying superior genotypes for crop improvement.