The objective of the study was to develop a genomic evaluation for French beef cattle breeds and assess accuracy and bias of prediction for different genomic selection strategies. Based on a reference population of 2,682 Charolais bulls and cows, genotyped or imputed to a high-density SNP panel (777K SNP), we tested the influence of different statistical methods, marker densities (50K versus 777K), and training population sizes and structures on the quality of predictions. Four different training sets containing up to 1,979 animals and a unique validation set of 703 young bulls only known on their individual performances were formed. BayesC method had the largest average accuracy compared to genomic BLUP or pedigree-based BLUP. No gain of accuracy was observed when increasing the density of markers from 50K to 777K. For a BayesC model and 777K SNP panels, the accuracy calculated as the correlation between genomic predictions and deregressed EBV (DEBV) divided by the square root of heritability was 0.42 for birth weight, 0.34 for calving ease, 0.45 for weaning weight, 0.52 for muscular development, and 0.27 for skeletal development. Half of the training set constituted animals having only their own performance recorded, whose contribution only represented 5% of the accuracy. Using DEBV as a response brought greater accuracy than using EBV (+5% on average). Considering a residual polygenic component strongly reduced bias for most of the traits. The optimal percentage of polygenic variance varied across traits. Among the methodologies tested to implement genomic selection in the French Charolais beef cattle population, the most accurate and less biased methodology was to analyze DEBV under a BayesC strategy and a residual polygenic component approach. With this approach, a 50K SNP panel performed as well as a 777K panel.

Selection for disease resistance is a powerful way to improve the health status of herds and to reduce the use of antibiotics. The objectives of this study were to estimate 1) the genetic parameters for simple visually assessed disease syndromes and for a composite trait of resistance to infectious disease including all syndromes and 2) their genetic correlations with production traits in a rabbit population. Disease symptoms were recorded in the selection herds of 2 commercial paternal rabbit lines during weighing at the end of the test (63 and 70 d of age, respectively). Causes of mortality occurring before these dates were also recorded. Seven disease traits were analyzed: 3 elementary traits visually assessed by technicians on farm (diarrhea, various digestive syndromes, and respiratory syndromes), 2 composite traits (all digestive syndromes and all infectious syndromes), and 2 mortality traits (digestive mortality and infectious mortality). Each animal was assigned only 1 disease trait, corresponding to the main syndrome ( = 153,400). Four production traits were also recorded: live weight the day before the end of test on most animals ( = 137,860) and cold carcass weight, carcass yield, and perirenal fat percentage of the carcass on a subset of slaughtered animals ( = 13,765). Records on both lines were analyzed simultaneously using bivariate linear animal models after validation of consistency with threshold models applied to logit-transformed traits. The heritabilities were low for disease traits, from 0.01 ± 0.002 for various digestive syndromes to 0.04 ± 0.004 for infectious mortality, and moderate to high for production traits. The genetic correlations between digestive syndromes were high and positive, whereas digestive and respiratory syndromes were slightly negatively correlated. The genetic correlations between the composite infectious disease trait and digestive or respiratory syndromes were moderate. Genetic correlations between disease and production traits were favorable. Our results indicate that it is possible to select rabbits using visually assessed disease syndromes without the need for a trade-off between health and production traits. Using a composite criterion that includes all infectious syndromes is easy to implement and heritable and is, therefore, a promising way to improve the general disease resistance in livestock species.