Genomic prediction for crossbred performance using metafounders

Future genomic evaluation models to be used routinely in breeding programs for pigs and poultry need to be able to optimally use information of crossbred animals to predict breeding values for crossbred performance of purebred selection candidates. Important challenges in the commonly used single-step genomic best linear unbiased prediction (ssGBLUP) model, are the definition of relationships between the different line compositions and the definition of the base generation per line. The use of metafounders (MFs) in ssGBLUP has been proposed to overcome these issues. When relationships between lines are known to be different from 0, the use of MFs generalizes the concept of genetic groups relying on the genotype data. Our objective was to investigate the effect of using MFs in genomic prediction for crossbred performance on estimated variance components, and accuracy and bias of genomic estimated breeding values. This was studied using stochastic simulation to generate data representing a three-way crossbreeding scheme in pigs. Results show that using MFs, the variance components should be interpreted with caution, especially when comparing them to estimates obtained with e.g. a pedigree based model. The accuracies of genomic estimated breeding values that were obtained using MFs were similar to accuracies without using MFs, regardless whether the lines involved in the crossbred were closely related or unrelated. The use of MFs resulted in a model that had similar or somewhat better convergence properties compared to other models. We recommend the use of MFs in ssGBLUP for genomic evaluations in crossbreeding schemes.