Predictive performances of animal models using different multibreed relationship matrices in systems with rotational crossbreeding

BACKGROUND: In livestock breeding, selection for some traits can be improved with direct selection for crossbred performance. However, genetic analyses with phenotypes from crossbred animals require methods for multibreed relationship matrices; especially when some animals are rotationally crossbred. Multiple methods for multibreed relationship matrices exist, but there is a lack of knowledge on how these methods compare for prediction of breeding values with phenotypes from rotationally crossbred animals. Therefore, the objective of this study was to compare models that use different multibreed relationship matrices in terms of ability to predict accurate and unbiased breeding values with phenotypes from two-way rotationally crossbred animals. METHODS: We compared four methods for multibreed relationship matrices: numerator relationship matrices (NRM), García-Cortés and Toro’s partial relationship matrices (GT), Strandén and Mäntysaari’s approximation to the GT method (SM), and one NRM with metafounders (MF). The methods were compared using simulated data. We simulated two phenotypes; one with and one without dominance effects. Only crossbred animals were phenotyped and only purebred animals were genotyped. RESULTS: The MF and GT methods were the most accurate and least biased methods for prediction of breeding values in rotationally crossbred animals. Without genomic information, all methods were almost equally accurate for prediction of breeding values in purebred animals; however, with genomic information, the MF and GT methods were the most accurate. The GT, MF, and SM methods were the least biased methods for prediction of breeding values in purebred animals. CONCLUSIONS: For prediction of breeding values with phenotypes from rotationally crossbred animals, models using the MF method or the GT method were generally more accurate and less biased than models using the SM method or the NRM method.