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Assessing Genomic Selection Prediction Accuracy in a Dynamic Barley Breeding Population.

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Genomic selection (GS) prediction accuracy in barley breeding is reliable using parent data alone. Larger training populations combining parent and progeny sets offer minimal accuracy gains for predicting future progeny performance.

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Genomic selection

Background:

  • Genomic selection (GS) prediction accuracy is typically assessed via simulation and cross-validation.
  • Real-world validation using progeny performance in dynamic plant breeding programs remains less explored.
  • Understanding prediction accuracy over time is crucial for optimizing breeding strategies.

Purpose of the Study:

  • To evaluate prediction model performance using progeny data in a barley breeding population.
  • To investigate the impact of training population composition on prediction accuracy over time.
  • To identify factors influencing prediction accuracy in a dynamic breeding context.

Main Methods:

  • Evaluated multiple prediction models, including random regression best linear unbiased prediction (RR-BLUP), in a 647-line barley population.
  • Utilized 1536 single nucleotide polymorphism (SNP) markers across four traits with varying genetic architectures.
  • Trained models on a parent set and five consecutive progeny sets spanning a 5-year period.

Main Results:

  • Minimal differences in prediction accuracy were observed among models, with RR-BLUP performing consistently well.
  • Using only the parent set for training was largely sufficient for predicting progeny performance.
  • Larger training populations combining parent and progeny data yielded negligible accuracy improvements.
  • Prediction accuracy varied widely (0.03-0.99) across traits and progeny sets.
  • Marker allele frequency, population structure, and linkage disequilibrium influenced accuracy.
  • Reduced prediction accuracy for the DON trait correlated with marker fixation over time.
  • Higher trait heritability (H²) and simpler genetic architecture in training populations enhanced prediction accuracy.

Conclusions:

  • The parent set alone provides a robust basis for genomic prediction in this barley breeding program.
  • Expanding training populations with subsequent progeny offers limited benefits for prediction accuracy.
  • Trait heritability and genetic architecture are key determinants of successful genomic prediction.