Genomic prediction holds the promise to use information of other populations to improve prediction accuracy. Thus far, empirical evaluations showed limited benefit of multi-breed compared to single breed genomic prediction. We compared prediction accuracy of different models based on two closely related and one unrelated line of layer chickens. Multi-breed genomic prediction may be successful when lines are closely related, and when the number of training animals of the additional line is large compared to the line itself. Multi-breed genomic prediction requires models that are flexible enough to use beneficial and ignore detrimental sources of information in the training data. Combining linear and non-linear models may lead to small increases in accuracy of multibreed genomic prediction. Multitrait models, modelling a separate trait for each breed, appear especially beneficial when relationships between breeds are very low, or when the genetic correlation between breeds is negative.

Mario PL Calus, Heyun Huang, Yvonne CJ Wientjes, Jan ten Napel, John WM Bastiaansen, Matthew D Price, Roel F Veerkamp, Addie Vereijken, Jack J Windig

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Genetic Improvement Programs: Selection using molecular information, , 064, 2014
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