Accuracy of Predicted Genomic Breeding Values in Purebred and Crossbred Pigs

Abstract

Genomic selection has been widely implemented in dairy cattle breeding when the aim is to improve performance of purebred animals. In pigs, however, the final product is a crossbred animal. This may affect the efficiency of methods that are currently implemented for dairy cattle. Therefore, the objective of this study was to determine the accuracy of predicted breeding values in crossbred pigs using purebred genomic and phenotypic data. A second objective was to compare the predictive ability of SNPs when training is done in either single or multiple populations for four traits: age at first insemination (AFI); total number of piglets born (TNB); litter birth weight (LBW); and litter variation (LVR). We performed marker-based and pedigree-based predictions. Within-population predictions for the four traits ranged from 0.21 to 0.72. Multi-population prediction yielded accuracies ranging from 0.18 to 0.67. Predictions across purebred populations as well as predicting genetic merit of crossbreds from their purebred parental lines for AFI performed poorly (not significantly different from zero). In contrast, accuracies of across-population predictions and accuracies of purebred to crossbred predictions for LBW and LVR ranged from 0.08 to 0.31 and 0.11 to 0.31, respectively. Accuracy for TNB was zero for across-population prediction, whereas for purebred to crossbred prediction it ranged from 0.08 to 0.22. In general, marker-based outperformed pedigree-based prediction across populations and traits. However, in some cases pedigree-based prediction performed similarly or outperformed marker-based prediction. There was predictive ability when purebred populations were used to predict crossbred genetic merit using an additive model in the populations studied. AFI was the only exception, indicating that predictive ability depends largely on the genetic correlation between PB and CB performance, which was 0.31 for AFI. Multi-population prediction was no better than within-population prediction for the purebred validation set. Accuracy of prediction was very trait-dependent.

Evaluating the accuracy of genomic prediction of growth and wood traits in twoEucalyptusspecies and their F1hybrids

“…3). While similar results have been reported for animals [18,43] and crop species [9,36] across a number of traits, in forest trees prediction accuracies using genomic data have generally been similar or up to 10-30% lower than accuracies obtained using pedigree-estimated breeding values, including Eucalyptus [4], loblolly pine (Pinus taeda) [44], white spruce (Picea glauca) [45,46], interior spruce (Picea engelmannii × glauca) [47,48] and maritime pine (Pinus pinaster) [49]. Genomic predictions with lower accuracies than pedigree-based predictions could arise from insufficient marker density, such that not all casual variants are captured in the genomic estimate [41], or an overestimate of the pedigree-based prediction due to its inability of ascertaining the true genetic relationships in half-sib families [47].…”

“…The issue of genomic prediction in hybrid breeding has been investigated so far only within species and only for domestic animals, more specifically for bovine and pig breeding in which selection is carried out in pure breeds but with the aim to improve crossbred performance [43,59]. Results from simulations show that training on crossbred data provides good PAs by selecting purebred individuals for crossbred performance, although PAs drop with increasing distances between breeds [60].…”

Genomic selection in pigs: state of the art and perspectives

“…In contrast, other studies (i.e. Hidalgo et al 2014Hidalgo et al , 2015a suggested that multi-breeds training populations would not result in advantages in genomic selection implementations, as the across-breeds GEBV prediction yielded to null or low accuracies.…”

Goals and hurdles for a successful implementation of genomic selection in breeding programme for selected annual and perennial crops

“…Results on advanced testcrosses, bi-parental populations or diversity panels in crops are encouraging. Whether GS can be successfully applied across many generations in crossbred populations in livestock, such as most pig populations (Esfandyari, Sorensen, & Bijma, 2015;Hidalgo et al, 2015), where purebred animals are selected while phenotypes are recorded mainly based on crossbred offspring, has yet to be finally shown. Models for GS, selection of training populations and selection of candidate individuals will therefore depend on a well-designed breeding scheme, which has to be adapted to the reproduction but also production and selection requirements of each population.…”