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(A)cross-breed Genomic Prediction

Tuesday, August 19, 2014: 1:30 PM
Stanley Park Ballroom (The Westin Bayshore)
Mario P. L. Calus , Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Wageningen, Netherlands
Heyun Huang , Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Wageningen, Netherlands
Yvonne C.J. Wientjes , Animal Breeding and Genomics Centre, Wageningen University, Wageningen, Netherlands
Jan ten Napel , Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Lelystad, Netherlands
John W.M. Bastiaansen , Animal Breeding and Genomics Centre, Wageningen University, Wageningen, Netherlands
Matthew D, Price , Animal Breeding and Genomics Centre, Wageningen University, Wageningen, Netherlands
Roel F. Veerkamp , Animal Breeding and Genomics Centre, Wageningen University, Wageningen, Netherlands
Addie Vereijken , Hendrix Genetics, Boxmeer, Netherlands
Jack J Windig , Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Wageningen, Netherlands
Abstract Text: 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.

Keywords:

multibreed

genomic prediction