Genomic selection for crossbred performance accounting for breed-specific effects

Abstract

BackgroundBreed-specific effects are observed when the same allele of a given genetic marker has a different effect depending on its breed origin, which results in different allele substitution effects across breeds. In such a case, single-breed breeding values may not be the most accurate predictors of crossbred performance. Our aim was to estimate the contribution of alleles from each parental breed to the genetic variance of traits that are measured in crossbred offspring, and to compare the prediction accuracies of estimated direct genomic values (DGV) from a traditional genomic selection model (GS) that are trained on purebred or crossbred data, with accuracies of DGV from a model that accounts for breed-specific effects (BS), trained on purebred or crossbred data. The final dataset was composed of 924 Large White, 924 Landrace and 924 two-way cross (F1) genotyped and phenotyped animals. The traits evaluated were litter size (LS) and gestation length (GL) in pigs.ResultsThe genetic correlation between purebred and crossbred performance was higher than 0.88 for both LS and GL. For both traits, the additive genetic variance was larger for alleles inherited from the Large White breed compared to alleles inherited from the Landrace breed (0.74 and 0.56 for LS, and 0.42 and 0.40 for GL, respectively). The highest prediction accuracies of crossbred performance were obtained when training was done on crossbred data. For LS, prediction accuracies were the same for GS and BS DGV (0.23), while for GL, prediction accuracy for BS DGV was similar to the accuracy of GS DGV (0.53 and 0.52, respectively).ConclusionsIn this study, training on crossbred data resulted in higher prediction accuracy than training on purebred data and evidence of breed-specific effects for LS and GL was demonstrated. However, when training was done on crossbred data, both GS and BS models resulted in similar prediction accuracies. In future studies, traits with a lower genetic correlation between purebred and crossbred performance should be included to further assess the value of the BS model in genomic predictions.

Genetics of crossbreeding

“…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.…”

“…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).…”

“…These results are in line with studies based on simulated (Esfandyari et al 2015a;Van Grevenhof and Van Der Werf 2015) and real data (Hidalgo et al 2016), thus confirming that the benefit of a CB reference population is larger for smaller values of . However, with validation on individual CB records, the validation correlation was higher with a CB reference population, regardless of the (i.e., for both traits), which agrees with Lopes et al (2017), who analysed traits with an of about 0.9 and also validated on CB offspring records. In addition, two other studies have shown that genotyping CB animals improves the accuracy of CB offspring GEBV using single-step genomic best linear unbiased prediction (GBLUP) (Lourenco et al 2016;Xiang et al 2016).…”

“…Simulation studies indeed suggested that considering BOA can improve the response to selection, but only for scenarios where the CB reference population was large, the number of markers was small, and the parental lines of the CB animals were distantly related (Ibañez-Escriche et al 2009;Esfandyari et al 2015a). Empirical studies in pigs indicate that considering BOA may yield more accurate EBV than ignoring BOA, but only when the heritability and of the studied trait are low (Lopes et al 2017;Sevillano et al 2017). To my knowledge, there have been no studies on the benefits of considering the BOA for estimation of , or for genomic prediction in poultry.…”

“…However, this benefit of considering BOA was only observed when the CB reference population was large (4000), the number of markers was small (500), and the parental lines of CB animals were distantly related. Moreover, empirical studies on pigs suggested that taking BOA into account may increase the accuracy of GEBV only when and heritability are low (Lopes et al 2017;Sevillano et al 2017).…”