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.

Background In pig and poultry breeding programs, the breeding goal is to improve crossbred (CB) performance, whereas selection in the purebred (PB) lines is often based on PB performance. Thus, response to selection may be suboptimal, because the genetic correlation between PB and CB performance ( ) is generally lower than 1. Accurate estimates of the are needed, so that breeders can decide if they should collect data from CB animals. can be estimated either from pedigree or genomic relationships, which may produce different results. With genomic relationships, the estimate could be improved when relationships between purebred and crossbred animals are based only on the alleles that originate from the PB line of interest. This work presents the first comparison of estimated and variance components of body weight in broilers, using pedigree-based or genotype-based models, where the breed-of-origin of alleles was either ignored or considered. We used genotypes and body weight measurements of PB and CB animals that have a common sire line. Results Our results showed that the estimates depended on the relationship matrix used. Estimates were 5 to 25% larger with genotype-based models than with pedigree-based models. Moreover, estimates were similar (max. 7% difference) regardless of whether the model considered breed-of-origin of alleles or not. Standard errors of estimates were smaller with genotype-based than with pedigree-based methods, and smaller with models that ignored breed-of-origin than with models that considered breed-of-origin. Conclusions We conclude that genotype-based models can be useful for estimating , even when the PB and CB animals that have phenotypes are closely related. Considering breed-of-origin of alleles did not yield different estimates of , probably because the parental breeds of the CB animals were distantly related. Electronic supplementary material The online version of this article (10.1186/s12711-019-0447-9) contains supplementary material, which is available to authorized users.

Accuracy of genomic predictions is an important component of the selection response. The objectives of this research were: 1) to investigate trends for prediction accuracies over time in a broiler population of accumulated phenotypes, genotypes, and pedigrees; 2) to test if data from distant generations are useful to maintain prediction accuracies in selection candidates. The data contained 820K phenotypes for a growth trait (GT), 200K for two feed efficiency traits (FE1 and FE2), and 42K for a carcass yield trait (CY). The pedigree included 1,252,619 birds hatched over seven years, of which 154,318 from the last four years were genotyped. Training populations were constructed adding one year of data sequentially, persistency of accuracy over time was evaluated using predictions from birds hatched in the three generations following or in the years after the training populations. In the first generation, before genotypes became available for the training populations (first three years of data), accuracies remained almost stable with successive additions of phenotypes and pedigree to the accumulated dataset. The inclusion of one year of genotypes in addition to four years of phenotypes and pedigree in the training population led to increases in accuracy of 54% for GT, 76% for FE1, 110% for CY, and 38% for FE2; on average, 74% of the increase was due to genomics. Prediction accuracies declined faster without than with genomic information in the training populations. When genotypes were unavailable, the average decline in prediction accuracy across traits was 41% from the first to the second generation of validation, and 51% from the second to the third generation of validation. When genotypes were available, the average decline across traits was 14% from the first to the second generation of validation, and 3% from the second to the third generation of validation. Prediction accuracies in the last three generations were the same when the training population included five or two years of data, and a decrease of ~7% was observed when the training population included only one year of data. Training sets including genomic information provided an increase in accuracy and persistence of genomic predictions compared to training sets without genomic data. The two most recent years of pedigree, phenotypic and genomic data were sufficient to maintain prediction accuracies in selection candidates. Similar conclusions were obtained using validation populations per year.

The introduction of animals from a different environment or population is a common practice in commercial livestock populations. In this study we modeled the inclusion of a group of external birds into a local broiler chicken population for the purpose of genomic evaluations. The pedigree was composed by 242,413 birds and genotypes were available for 107,216 birds. A five-trait model that included one growth, two yield, and two efficiency traits was used for the analyses. The strategies to model the introduction of external birds were to include a fixed effect representing the of origin of parents and to use UPG or metafounders. Genomic estimated breeding values (GEBV) were obtained with single-step GBLUP (ssGBLUP) using the Algorithm for Proven and Young (APY). Bias, dispersion, and accuracy of GEBV for the validation birds, i.e., from the most recent generation, were computed. The bias and dispersion were estimated with the LR-method, whereas accuracy was estimated by the LR-method and predictive ability. When fixed UPG were fit without estimated inbreeding, the model did not converge. In contrast, models with fixed UPG and estimated inbreeding or random UPG converged and resulted in similar GEBV. The inclusion of an extra fixed effect in the model made the GEBV unbiased and reduced the inflation. Genomic predictions with metafounders were slightly biased and inflated due to the unbalanced number of observations assigned to each metafounder. When combining local and external populations, the greatest accuracy can be obtained by adding an extra fixed effect to account for origin of parents plus UPG with estimated inbreeding or random UPG. To estimate the accuracy, the LR-method is more consistent among scenarios, whereas predictive ability greatly depends on the model specification.

In this study, we investigated the performance of different machine learning (ML) methods for predicting withdrawing events (culled or dead animals) according to feeding behaviors (FB) time series. The raw data comprised a total of 1,492,482 daily observations for six FB traits from 55,400 birds allocated into 88 trials. After data editing, the overall class distribution was 17:1 (44,352 healthy animals and 2,689 culled or dead). The event classification (0 or 1) was performed one day in advance, by treating healthy birds randomly as control groups for each day. The FB daily trends were used for extracting 21 time-series features per trait, generating a structured feature dataset (day before the event + 126 time-series features). The trained ML algorithms were the gradient boosting machine (GBM), multilayer perceptron neural network (MLP), naïve Bayes (NB), random forest (RF), and support vector machine (SVM). The models were compared based on the area under the ROC (AUC) and precision-recall (AUPRC) curves, computed with 20-fold cross-validation. The performance metrics ranged from 0.76 to 0.85 for AUC and from 0.32 to 0.48 for AUPRC, so that models achieved considerable superior performance than that expected for a random classifier (0.50 for AUC and 0.06 for AUCPR). The better classifier in terms of AUC was the RF (0.85±0.02), although not statistically different from the average performance obtained with the GBM and SVM (0.84±0.02 in both cases). The MLP achieved the greatest AUPRC (0.48±0.05), while the NB performed poorly considering this criterion (0.32±0.02), indicating that a low precision-recall gain is expected for this model. Broiler barns presenting a critical number of animals with high-risk scores could be indicative of a disease outbreak or management failures. Hence, the proposed approach offers a potential tool for real-time monitoring of health status and welfare in broilers.