Abstract
BackgroundPig and poultry breeding programs aim at improving crossbred (CB) performance. Selection response may be suboptimal if only purebred (PB) performance is used to compute genomic estimated breeding values (GEBV) because the genetic correlation between PB and CB performance (r_{pc}) is often lower than 1. Thus, it may be beneficial to use information on both PB and CB performance. In addition, the accuracy of GEBV of PB animals for CB performance may improve when the breed-of-origin of alleles (BOA) is considered in the genomic relationship matrix (GRM). Thus, our aim was to compare scenarios where GEBV are computed and validated by using (1) either CB offspring averages or individual CB records for validation, (2) either a PB or CB reference population, and (3) a GRM that either accounts for or ignores BOA in the CB individuals. For this purpose, we used data on body weight measured at around 7 (BW7) or 35 (BW35) days in PB and CB broiler chickens and evaluated the accuracy of GEBV based on the correlation GEBV with phenotypes in the validation population (validation correlation).ResultsWith validation on CB offspring averages, the validation correlation of GEBV of PB animals for CB performance was lower with a CB reference population than with a PB reference population for BW35 (r_{pc} = 0.96), and about equal for BW7 (r_{pc} = 0.80) when BOA was ignored. However, with validation on individual CB records, the validation correlation was higher with a CB reference population for both traits. The use of a GRM that took BOA into account increased the validation correlation for BW7 but reduced it for BW35.ConclusionsWe argue that the benefit of using a CB reference population for genomic prediction of PB animals for CB performance should be assessed either by validation on CB offspring averages, or by validation on individual CB records while using a GRM that accounts for BOA in the CB individuals. With this recommendation in mind, our results show that the accuracy of GEBV of PB animals for CB performance was equal to or higher with a CB reference population than with a PB reference population for a trait with an r_{pc} of 0.8, but lower for a trait with an r_{pc} of 0.96. In addition, taking BOA into account was beneficial for a trait with an r_{pc} of 0.8 but not for a trait with an r_{pc} of 0.96.
Highlights
Pig and poultry breeding programs aim at improving crossbred (CB) performance
We argue that the benefit of using a CB reference population for genomic prediction of PB animals for CB performance should be assessed either by validation on CB offspring averages, or by validation on individual CB records while using a genomic relationship matrix (GRM) that accounts for breed-of-origin of alleles (BOA) in the CB individuals
With this recommendation in mind, our results show that the accuracy of genomic estimated breeding values (GEBV) of PB animals for CB performance was equal to or higher with a CB reference population than with a PB reference population for a trait with an rpc of 0.8, but lower for a trait with an rpc of 0.96
Summary
Pig and poultry breeding programs aim at improving crossbred (CB) performance. Selection response may be suboptimal if only purebred (PB) performance is used to compute genomic estimated breeding values (GEBV) because the genetic correlation between PB and CB performance ( rpc ) is often lower than 1. The pedigree-based relationship matrix can be replaced by a genomic relationship matrix (GRM) that is based on observed marker genotypes [9] This GRM enables breeders to use a reference population that consists of animals with phenotypes and genotypes to estimate genomic estimated breeding values (GEBV) of selection candidates that only have records on genotypes [10]. When pedigree information is available, replacing the pedigree-based relationship matrix by a GRM may increase the accuracy of estimated breeding values [11] As such, this method, called genomic prediction, allows breeders to use a CB reference population to compute GEBV for CB performance of PB selection candidates [4]
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