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Multivariate Genomic Hybrid Prediction with Kernels and Parental Information.

Osval A Montesinos-López1, José Crossa2,3, Carolina Saint Pierre2

  • 1Facultad de Telemática, Universidad de Colima, Colima 28040, Colima, Mexico.

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|September 28, 2023
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Summary
This summary is machine-generated.

Genomic selection (GS) improves hybrid prediction by incorporating parental data. Directly using parental phenotypic information (Pmean) slightly outperformed using breeding values (BV), enhancing hybrid breeding efficiency.

Keywords:
hybrid predictionintegrationmulti-traitparental information

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Genomics

Background:

  • Genomic selection (GS) is crucial for predicting hybrid performance and optimizing breeding strategies.
  • Leveraging genomic information enhances decision-making and success rates in hybrid breeding programs.
  • Current methods can be improved by integrating parental information to boost genomic prediction accuracy.

Purpose of the Study:

  • To explore the impact of incorporating parental phenotypic information as covariates within a multi-trait framework to improve genomic prediction of hybrid performance.
  • To compare two approaches: direct use of parental phenotypic information (Pmean) versus using estimated breeding values (BV).

Main Methods:

  • A multi-trait framework was employed to integrate parental information.
  • Two approaches were tested: Pmean (direct parental phenotypes) and BV (parental breeding values).
  • Prediction performance was evaluated using normalized root mean square error (NRMSE) and compared across approaches and kernel types (linear/nonlinear).

Main Results:

  • Both Pmean and BV approaches improved prediction performance, reducing NRMSE by at least 4.24%.
  • The Pmean approach, directly using parental phenotypic information, showed a slight but consistent advantage over the BV approach.
  • No significant performance gain was observed when using linear versus nonlinear kernels.

Conclusions:

  • Integrating parental phenotypic information significantly enhances the prediction performance of genomic selection for hybrids.
  • The Pmean approach offers a slightly more effective strategy for improving hybrid prediction compared to using breeding values alone.
  • These findings provide empirical evidence supporting the value of parental phenotypic data in optimizing hybrid breeding programs.