Validation of genomic predictions for body weight in broilers using crossbred information and considering breed-of-origin of alleles

Abstract

Background 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 ( ) 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). Results With 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 ( = 0.96), and about equal for BW7 ( = 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. Conclusions 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 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 of 0.8, but lower for a trait with an of 0.96. In addition, taking BOA into account was beneficial for a trait with an of 0.8 but not for a trait with an of 0.96. Electronic supplementary material The online version of this article (10.1186/s12711-019-0481-7) contains supplementary material, which is available to authorized users.

The Genetic Basis of Tomato Aroma

“…Genomic prediction is particularly useful for traits for which phenotyping is expensive, difficult or time consuming, since no phenotyping is needed for selection, once a good prediction model based on data of a representative training population is available. This technique has been widely applied in animal selection [83][84][85], while its practical application in plant breeding is still limited to major crops, such as maize and wheat [82,86,87], in which QTLs for important traits, such as yield, have already been fixed in the elite germplasm [88], or to tree crops, where early selection is very useful and cost-effective [89].…”

Optimizing genomic reference populations to improve crossbred performance

“…A crossbred reference population benefits from expressing the breeding goal trait [ 14 , 25 ], but suffers from a lower genetic relatedness with the purebred selection candidates than a purebred reference population [ 16 – 18 ]. The balance between these two factors determines whether a crossbred or purebred reference population is beneficial for predicting breeding values for crossbred performance of purebred selection candidates.…”

“…For crossbred animals, only the alleles coming from the sire (two-way crossbred) or the paternal grand sire (four-way crossbred) were simulated. This implicitly assumes that the line-origin of the alleles in the crossbreds could be traced back without error and that genomic information from the other lines was not helpful to predict breeding values for the line of interest, as generally observed in practice [ 24 , 25 ]. The alleles coming from the other lines were set to missing, and their contributions to the phenotypes were simulated as for a polygenic trait.…”

Assessment of sire contribution and breed-of-origin of alleles in a three-way crossbred broiler dataset

“…The dataset used in our study was designed to enable estimating the PB-CB correlation for body weight (Duenk et al., 2019a), and to validate genomic prediction for CB performance, using either PB or CB performance for training the genomic prediction model (Duenk et al., 2019b). To ensure sufficient power to estimate the PB-CB correlation, the offspring generation was sired by a limited number of sires with both PB and CB offspring (Bijma and Bastiaansen, 2014).…”