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 reed genomic prediction. We compared prediction accuracy of different models based on two losely 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 lexible 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 elationships between breeds are very low, or when the genetic correlation between breeds is negative.
|Publication status||Published - 2014|
|Event||10th WCGALP, Vancouver, Canada - |
Duration: 17 Aug 2014 → 22 Aug 2014
|Conference||10th WCGALP, Vancouver, Canada|
|Period||17/08/14 → 22/08/14|