Animal board invited review: The purebred-crossbred genetic correlation in poultry.

The purebred-crossbred genetic correlation (rpc) is a key parameter to determine whether the optimal selection of purebred animals to improve crossbred performance should rely on crossbred phenotypes, purebred phenotypes, or both. We reviewed published estimates of the rpc in poultry. In total, 19 studies were included, of which four were on broilers and 15 on laying hens, with 150 rpc estimates for nine different trait categories. Average reported rpc estimates were highest for egg weight, egg quality and egg colour (0.74-0.82), intermediate for BW, maturity and mortality (0.61-0.70) and egg number (0.58), and low for resilience (0.40) and body conformation (0.14). Most studies were based on measuring purebred and crossbred phenotypes in the same environment and thus did not capture the contribution of genotype by environment interactions to the rpc, suggesting that the presented average estimates may be higher than values that apply in practice. Nearly all studies were based on two-way crossbred animals. We hypothesised that rpc values for a two-way cross are good proxies for rpc of a four-way cross. Only eight out of 19 studies were published in the last 25 years, and only two of those used genomic data. We expect that more studies using genomic data may be published in the coming years, as the required data may be generated when implementing genomic selection for crossbred performance, which will lead to more accurate rpc estimates. Future studies that aim to estimate rpc are encouraged to capture the genotype by environment interaction component by housing purebred and crossbred animals differently as is done in practice. Moreover, there is a need for further studies that enable to explicitly estimate the magnitude of genotype by environment versus genotype by genotype interactions for multiple trait categories. Further, studies are advised to report: the specific housing conditions of the animals, any differences between measurements of purebred versus crossbred performance, and the heritabilities of purebred and crossbred performance.

Multibreed genomic prediction using multitrait genomic residual maximum likelihood and multitask Bayesian variable selection.

Genomic prediction is applicable to individuals of different breeds. Empirical results to date, however, show limited benefits in using information on multiple breeds in the context of genomic prediction. We investigated a multitask Bayesian model, presented previously by others, implemented in a Bayesian stochastic search variable selection (BSSVS) model. This model allowed for evidence of quantitative trait loci (QTL) to be accumulated across breeds or for both QTL that segregate across breeds and breed-specific QTL. In both cases, single nucleotide polymorphism effects were estimated with information from a single breed. Other models considered were a single-trait and multitrait genomic residual maximum likelihood (GREML) model, with breeds considered as different traits, and a single-trait BSSVS model. All single-trait models were applied to each of the 2 breeds separately and to the pooled data of both breeds. The data used included a training data set of 6,278 Holstein and 722 Jersey bulls, as well as 374 Jersey validation bulls. All animals had genotypes for 474,773 single nucleotide polymorphisms after editing and phenotypes for milk, fat, and protein yields. Using the same training data, BSSVS consistently outperformed GREML. The multitask BSSVS, however, did not outperform single-trait BSSVS, which used pooled Holstein and Jersey data for training. Thus, the rigorous assumption that the traits are the same in both breeds yielded a slightly better prediction than a model that had to estimate the correlation between the breeds from the data. Adding the Holstein data significantly increased the accuracy of the single-trait GREML and BSSVS in predicting the Jerseys for milk and protein, in line with estimated correlations between the breeds of 0.66 and 0.47 for milk and protein yields, whereas only the BSSVS model significantly improved the accuracy for fat yield with an estimated correlation between breeds of only 0.05. The relatively high genetic correlations for milk and protein yields, and the superiority of the pooling strategy, is likely the result of the observed admixture between both breeds in our data. The Bayesian model was able to detect several QTL in Holsteins, which likely enabled it to outperform GREML. The inability of the multitask Bayesian models to outperform a simple pooling strategy may be explained by the fact that the pooling strategy assumes equal effects in both breeds; furthermore, this assumption may be valid for moderate- to large-sized QTL, which are important for multibreed genomic prediction.

BOARD INVITED REVIEW: The purebred-crossbred correlation in pigs: A review of theory, estimates, and implications.

Pig and poultry production relies on crossbreeding of purebred populations to produce production animals. In those breeding schemes, selection takes place within the purebred population to improve crossbred performance (CB performance). The genetic correlation between purebred performance (PB performance) and CB performance () is, however, lower than unity for many traits. When is low, the use of CB performance in selection is required to achieve sizable genetic progress. The objectives of this paper were to describe the different components and importance of , and to review existing literature that report estimates in pigs. The has 3 components: 1) genotype by genotype interactions, 2) genotype by environment interactions, and 3) differences in trait measurements. We theoretically showed that direct selection for CB performance reduces the response to selection in purebreds for.

Across population genomic prediction scenarios in which Bayesian variable selection outperforms GBLUP.

BACKGROUND: The use of information across populations is an attractive approach to increase the accuracy of genomic prediction for numerically small populations. However, accuracies of across population genomic prediction, in which reference and selection individuals are from different populations, are currently disappointing. It has been shown for within population genomic prediction that Bayesian variable selection models outperform GBLUP models when the number of QTL underlying the trait is low. Therefore, our objective was to identify across population genomic prediction scenarios in which Bayesian variable selection models outperform GBLUP in terms of prediction accuracy. In this study, high density genotype information of 1033 Holstein Friesian, 105 Groningen White Headed, and 147 Meuse-Rhine-Yssel cows were used. Phenotypes were simulated using two changing variables: (1) the number of QTL underlying the trait (3000, 300, 30, 3), and (2) the correlation between allele substitution effects of QTL across populations, i.e. the genetic correlation of the simulated trait between the populations (1.0, 0.8, 0.4). RESULTS: The accuracy obtained by the Bayesian variable selection model was depending on the number of QTL underlying the trait, with a higher accuracy when the number of QTL was lower. This trend was more pronounced for across population genomic prediction than for within population genomic prediction. It was shown that Bayesian variable selection models have an advantage over GBLUP when the number of QTL underlying the simulated trait was small. This advantage disappeared when the number of QTL underlying the simulated trait was large. The point where the accuracy of Bayesian variable selection and GBLUP became similar was approximately the point where the number of QTL was equal to the number of independent chromosome segments (M e ) across the populations. CONCLUSION: Bayesian variable selection models outperform GBLUP when the number of QTL underlying the trait is smaller than M e . Across populations, M e is considerably larger than within populations. So, it is more likely to find a number of QTL underlying a trait smaller than M e across populations than within population. Therefore Bayesian variable selection models can help to improve the accuracy of across population genomic prediction.