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1. The aim of this study was to investigate the associations between several carcass, performance and meat quality traits in broilers through factor analysis and use the latent variables (i.e. factors) as pseudo-phenotypes in genetic evaluations. 2. Factors were extracted using the principal components method and varimax rotation algorithm. Genetic parameters were estimated via Bayesian inference under a multiple-trait animal model. 3. All factors taken together explained 71% of the original variance of the data. The first factor, denominated as 'weight', was associated with carcass and body weight traits; and the second factor, defined as 'tenderness', represented traits related to water-holding capacity and shear force. The third factor, 'colour', was associated with traits related to meat colour, whereas the fourth, referenced as 'viscera', was related to heart, liver and abdominal fat. 4. The four biological factors presented moderate to high heritability (ranging from 0.35 to 0.75), which may confer genetic gains in this population. 5. In conclusion, it seems possible to reduce the number of traits in the genetic evaluation of broilers using latent variables derived from factor analysis. [ABSTRACT FROM AUTHOR]

Summary: Genome‐wide association studies (GWASes) have been performed to search for genomic regions associated with residual feed intake (RFI); however inconsistent results have been obtained. A meta‐analysis may improve these results by decreasing the false‐positive rate. Additionally, pathway analysis is a powerful tool that complements GWASes, as it enables identification of gene sets involved in the same pathway that explain the studied phenotype. Because there are no reports on GWAS pathways‐based meta‐analyses for RFI in beef cattle, we used several GWAS results to search for significant pathways that may explain the genetic mechanism underlying this trait. We used an efficient permutation hypothesis test that takes into account the linkage disequilibrium patterns between SNPs and the functional feasibility of the identified genes over the whole genome. One significant pathway (valine, leucine and isoleucine degradation) related to RFI was found. The three genes in this pathway—methylcrotonoyl‐CoA carboxylase 1 (MCCC1), aldehyde oxidase 1 (AOX1) and propionyl‐CoA carboxylase alpha subunit (PCCA)—were found in three different studies. This same pathway was also reported in a transcriptome analysis from two cattle populations divergently selected for high and low RFI. We conclude that a GWAS pathway‐based meta‐analysis can be an appropriate method to uncover biological insights into RFI by combining useful information from different studies. [ABSTRACT FROM AUTHOR]

Genomic selection is applied to dairy cattle breeding to improve the genetic progress of purebred (PB) animals, whereas in pigs and poultry the target is a crossbred (CB) animal for which a different strategy appears to be needed. The source of information used to estimate the breeding values, i.e., using phenotypes of CB or PB animals, may affect the accuracy of prediction. The objective of our study was to assess the direct genomic value (DGV) accuracy of CB and PB pigs using different sources of phenotypic information. Data used were from 3 populations: 2,078 Dutch Landrace-based, 2,301 Large White-based, and 497 crossbreds from an F1 cross between the 2 lines. Two female reproduction traits were analyzed: gestation length (GLE) and total number of piglets born (TNB). Phenotypes used in the analyses originated from offspring of genotyped individuals. Phenotypes collected on CB and PB animals were analyzed as separate traits using a single-trait model. Breeding values were estimated separately for each trait in a pedigree BLUP analysis and subsequently deregressed. Deregressed EBV for each trait originating from different sources (CB or PB offspring) were used to study the accuracy of genomic prediction. Accuracy of prediction was computed as the correlation between DGV and the DEBV of the validation population. Accuracy of prediction within PB populations ranged from 0.43 to 0.62 across GLE and TNB. Accuracies to predict genetic merit of CB animals with one PB population in the training set ranged from 0.12 to 0.28, with the exception of using the CB offspring phenotype of the Dutch Landrace that resulted in an accuracy estimate around 0 for both traits. Accuracies to predict genetic merit of CB animals with both parental PB populations in the training set ranged from 0.17 to 0.30. We conclude that prediction within population and trait had good predictive ability regardless of the trait being the PB or CB performance, whereas using PB population(s) to predict genetic merit of CB animals had zero to moderate predictive ability. We observed that the DGV accuracy of CB animals when training on PB data was greater than or equal to training on CB data. However, when results are corrected for the different levels of reliabilities in the PB and CB training data, we showed that training on CB data does outperform PB data for the prediction of CB genetic merit, indicating that more CB animals should be phenotyped to increase the reliability and, consequently, accuracy of DGV for CB genetic merit. [ABSTRACT FROM AUTHOR]