Accuracy of genomic prediction of purebreds for cross bred performance in pigs. (17th April 2016)

• Record Type: Journal Article
• Authors: Hidalgo, A.M.; Bastiaansen, J.W.M.; Lopes, M.S.; Calus, M.P.L.; de Koning, D.J.
• Is Part Of: Journal of animal breeding and genetics. Volume 133:Number 6(2016)
• Page Start: 443
• Page End: 451
• Publication Date: 2016-04-17
• Subjects: Cross breeding -- genomic selection -- reproduction traits -- within‐population prediction; Livestock -- Breeding -- Periodicals; Livestock -- Genetics -- Periodicals; 636.0820
• DOI: 10.1111/jbg.12214
• Languages: English
• ISSNs: 0931-2668

Abstract

Summary: In pig breeding, as the final product is a cross bred (CB) animal, the goal is to increase the CB performance. This goal requires different strategies for the implementation of genomic selection from what is currently implemented in, for example dairy cattle breeding. A good strategy is to estimate marker effects on the basis of CB performance and subsequently use them to select pure bred (PB) breeding animals. The objective of our study was to assess empirically the predictive ability (accuracy) of direct genomic values of PB for CB performance across two traits using CB and PB genomic and phenotypic data. We studied three scenarios in which genetic merit was predicted within each population, and four scenarios where PB genetic merit for CB performance was predicted based on either CB or a PB training data. Accuracy of prediction of PB genetic merit for CB performance based on CB training data ranged from 0.23 to 0.27 for gestation length (GLE), whereas it ranged from 0.11 to 0.22 for total number of piglets born (TNB). When based on PB training data, it ranged from 0.35 to 0.55 for GLE and from 0.30 to 0.40 for TNB. Our results showed that it is possible to predict PB genetic merit for CB performance using CB training data, but predictive ability was lower than training using PB training data. This result is mainly due to the structure of our data, which had small‐to‐moderate size of the CB training data set, low relationship between the CB training and the PB validation populations, and a high genetic correlation (0.94 for GLE and 0.90 for TNB) between the studied traits in PB and CB individuals, thus favouring selection on the basis of PB data.