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Genomic prediction for crossbred performance using metafounders.

Elizabeth M van Grevenhof¹, Jérémie Vandenplas¹, Mario P L Calus¹

¹Wageningen University & Research Animal Breeding and Genomics, Wageningen, The Netherlands.

Journal of Animal Science

|November 14, 2018

Summary

This summary is machine-generated.

Metafounders improve genomic evaluations in crossbreeding programs. Using metafounders (MFs) in single-step genomic best linear unbiased prediction (ssGBLUP) models helps define relationships and base generations for crossbred (CB) animals, enhancing genomic prediction accuracy.

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Area of Science:

Animal breeding and genetics
Quantitative genetics
Genomic selection

Background:

Genomic evaluation models must effectively utilize crossbred (CB) animal data for predicting breeding values in purebred (PB) selection candidates.
Single-step genomic best linear unbiased prediction (ssGBLUP) faces challenges in defining inter-line relationships and base generations.
Metafounders (MFs) offer a potential solution to these ssGBLUP challenges by generalizing genetic groups using genotype data.

Purpose of the Study:

To investigate the impact of using MFs in genomic prediction for CB performance.
To assess the effects on estimated variance components, accuracy, and bias of genomic estimated breeding values (GEBV).

Main Methods:

Stochastic simulation was employed to generate data for a three-way crossbreeding scheme in pigs.
Simulated parental lines were either closely related or unrelated.
The study compared ssGBLUP models with and without MFs.

Main Results:

Appropriate scaling of variance components is necessary when using MFs, particularly if derived from pedigree-based models.
Accuracies of GEBV were comparable between models with and without MFs, irrespective of the relatedness of parental lines.
Models incorporating MFs demonstrated similar or improved convergence properties.

Conclusions:

Metafounders (MFs) are recommended for use in ssGBLUP within crossbreeding schemes.
MFs effectively generalize genetic group definitions, leveraging genotype data for improved genomic evaluations.
The study provides valuable insights for optimizing genomic prediction models in complex breeding programs.