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Abstract In this study, a hierarchical threshold mixed model based on a cumulative t-link specification for the analysis of ordinal data or more, specifically, calving ease scores, was developed. The validation of this model and the Markov chain Monte Carlo (MCMC) algorithm was carried out on simulated data from normally and t4 (i.e. a t-distribution with four degrees of freedom) distributed populations using the deviance information criterion (DIC) and a pseudo Bayes factor (PBF) measure to validate recently proposed model choice criteria. The simulation study indicated that although inference on the degrees of freedom parameter is possible, MCMC mixing was problematic. Nevertheless, the DIC and PBF were validated to be satisfactory measures of model fit to data. A sire and maternal grandsire cumulative t-link model was applied to a calving ease dataset from 8847 Italian Piemontese first parity dams. The cumulative t-link model was shown to lead to posterior means of direct and maternal heritabilities (0.40 ± 0.06, 0.11 ± 0.04) and a direct maternal genetic correlation (-0.58 ± 0.15) that were not different from the corresponding posterior means of the heritabilities (0.42 ± 0.07, 0.14 ± 0.04) and the genetic correlation (-0.55 ± 0.14) inferred under the conventional cumulative probit link threshold model. Furthermore, the correlation (> 0.99) between posterior means of sire progeny merit from the two models suggested no meaningful rerankings. Nevertheless, the cumulative t-link model was decisively chosen as the better fitting model for this calving ease data using DIC and PBF.

Abstract Background The gilthead sea bream (Sparus aurata) has long been considered resistant to viral nervous necrosis (VNN), until recently, when significant mortalities caused by a reassortant nervous necrosis virus (NNV) strain were reported. Selective breeding to enhance resistance against NNV might be a preventive action. In this study, 972 sea bream larvae were subjected to a NNV challenge test and the symptomatology was recorded. All the experimental fish and their parents were genotyped using a genome-wide single nucleotide polymorphism (SNP) array consisting of over 26,000 markers. Results Estimates of pedigree-based and genomic heritabilities of VNN symptomatology were consistent with each other (0.21, highest posterior density interval at 95% (HPD95%): 0.1–0.4; 0.19, HPD95%: 0.1–0.3, respectively). The genome-wide association study suggested one genomic region, i.e., in linkage group (LG) 23 that might be involved in sea bream VNN resistance, although it was far from the genome-wide significance threshold. The accuracies (r) of the predicted estimated breeding values (EBV) provided by three Bayesian genomic regression models (Bayes B, Bayes C, and Ridge Regression) were consistent and on average were equal to 0.90 when assessed in a set of cross-validation (CV) procedures. When genomic relationships between training and testing sets were minimized, accuracy decreased greatly (r = 0.53 for a validation based on genomic clustering, r = 0.12 for a validation based on a leave-one-family-out approach focused on the parents of the challenged fish). Classification of the phenotype using the genomic predictions of the phenotype or using the genomic predictions of the pedigree-based, all data included, EBV as classifiers was moderately accurate (area under the ROC curve 0.60 and 0.66, respectively). Conclusions The estimate of the heritability for VNN symptomatology indicates that it is feasible to implement selective breeding programs for increased resistance to VNN of sea bream larvae/juveniles. Exploiting genomic information offers the opportunity of developing prediction tools for VNN resistance, and genomic models can be trained on EBV using all data or phenotypes, with minimal differences in classification performance of the trait phenotype. In a long-term view, the weakening of the genomic ties between animals in the training and test sets leads to decreased genomic prediction accuracies, thus periodical update of the reference population with new data is mandatory.

Abstract Background Susceptibility of European sea bass (Dicentrarchus labrax L.) to viral nervous necrosis (VNN) is well-known. Interest towards selective breeding as a tool to enhance genetic resistance in this species has increased sharply due to the major threat represented by VNN for farmed sea bass and limitations concerning specific therapeutical measures. A sea bass experimental population (N = 650) was challenged with nervous necrosis virus (NNV) to investigate genetic variation in VNN mortality. In addition, relationships of this trait with serum cortisol concentration after stress exposure, antibody titer against NNV antigens, and body weight at a fixed age were studied to identify potential indicator traits of VNN resistance. Results The estimate of heritability for VNN mortality was moderate and ranged from 0.15 (HPD95%, 95% highest posterior density interval: 0.02, 0.31) to 0.23 (HPD95%: 0.06, 0.47). Heritability estimates for cortisol concentration, antibody titer, and body weight were 0.19 (HPD95%: 0.07, 0.34), 0.36 (HPD95%: 0.16, 0.59) and 0.57 (HPD95%: 0.33, 0.84), respectively. Phenotypic relationships between traits were trivial and not statistically significant, except for the estimated correlation between antibody titer and body weight (0.24). Genetic correlations of mortality with body weight or antibody titer (− 0.39) exhibited a 0.89 probability of being negative. A negligible genetic correlation between mortality and cortisol concentration was detected. Antibody titer was estimated to be positively correlated with body weight (0.49). Conclusions Antibody titer against NNV offers the opportunity to use indirect selection to enhance resistance, while the use of cortisol concentration as an indicator trait in breeding programs for VNN resistance is questionable. The estimate of heritability for VNN mortality indicates the feasibility of selective breeding to enhance resistance to NNV and raises attention to the development of genomic prediction tools to simplify testing procedures for selection candidates.