Accuracy of genomic prediction of purebreds for cross bred performance in pigs

Abstract

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.

Improving production efficiency in the presence of genotype by environment interactions in pig genomic selection breeding programmes

“…In a study using similar breeding structure, where there was more than 90% genetic correlation between nucleus and production environments, the accuracy of selection was higher for production traits when the reference population was designed using only nucleus animals (Hidalgo et al . ). In such cases, we expect less benefit by including animals from production environment into the reference population (Mulder et al .…”

“…This is a typical breeding scheme used in practice and such reference population designs would be ideal for populations where there is little interaction between genotypes and the environments. In a study using similar breeding structure, where there was more than 90% genetic correlation between nucleus and production environments, the accuracy of selection was higher for production traits when the reference population was designed using only nucleus animals (Hidalgo et al 2016). In such cases, we expect less benefit by including animals from production environment into the reference population (Mulder et al 2006).…”

Genetics of crossbreeding

“…This result was, however, only found for scenarios where the CB reference population was at least of the same size as the PB reference population, and the PB selection candidates had similar relationships to the PB and CB reference populations. A study with real data from pigs reported an of ~0.9, and lower accuracies of EBV with a CB versus a PB reference population (Hidalgo et al 2016). In this study, the CB reference population was smaller than the PB reference population, and the PB reference population had weaker relationships with the selection candidates than the CB reference population.…”

“…In contrast to this result, Lopes et al (2017) found higher accuracies of EBV for CB performance with a CB versus a PB reference population for a trait with an of ~0.9. It should be noted, however, that the studies of Hidalgo et al (2016) and Lopes et al (2017) differed in how accuracies were obtained (i.e. how estimated BV for CB performance were validated).…”

Genomic selection on breeding time in a wild bird population

“…Predicting GEBVs across populations, while ignoring this in the model, assumes that the genetic architecture of the trait is identical, that is that the same loci segregate in both populations. Cross-breed prediction of GEBVs in domesticated populations has been attempted but accuracies are much lower than typical within-breed accuracies (Moghaddar et al 2014;Hidalgo et al 2016).…”