Calving ease scores from Holstein dairy cattle in the Walloon Region of Belgium were analysed using univariate linear and threshold animal models. Variance components and derived genetic parameters were estimated from a data set including 33,155 calving records. Included in the models were season, herd and sex of calf × age of dam classes × group of calvings interaction as fixed effects, herd × year of calving, maternal permanent environment and animal direct and maternal additive genetic as random effects. Models were fitted with the genetic correlation between direct and maternal additive genetic effects either estimated or constrained to zero. Direct heritability for calving ease was approximately 8% with linear models and approximately 12% with threshold models. Maternal heritabilities were approximately 2 and 4%, respectively. Genetic correlation between direct and maternal additive effects was found to be not significantly different from zero. Models were compared in terms of goodness of fit and predictive ability. Criteria of comparison such as mean squared error, correlation between observed and predicted calving ease scores as well as between estimated breeding values were estimated from 85,118 calving records. The results provided few differences between linear and threshold models even though correlations between estimated breeding values from subsets of data for sires with progeny from linear model were 17 and 23% greater for direct and maternal genetic effects, respectively, than from threshold model. For the purpose of genetic evaluation for calving ease in Walloon Holstein dairy cattle, the linear animal model without covariance between direct and maternal additive effects was found to be the best choice.

Assignment of individual cattle to a specific breed can often not rely on pedigree information. This is especially the case for local breeds for which the development of genomic assignment tools is required to allow individuals of unknown origin to be included to their herd books. A breed assignment model can be based on two specific stages: (a) the selection of breed‐informative markers and (b) the assignment of individuals to a breed with a classification method. However, the performance of combination of methods used in these two stages has been rarely studied until now. In this study, the combination of 16 different SNP panels with four classification methods was developed on 562 reference genotypes from 12 cattle breeds. Based on their performances, best models were validated on three local breeds of interest. In cross‐validation, 14 models had a global cross‐validation accuracy higher than 90%, with a maximum of 98.22%. In validation, best models used 7,153 or 2,005 SNPs, based on a partial least squares‐discriminant analysis (PLS‐DA) and assigned individuals to breeds based on nearest shrunken centroids. The average validation sensitivity of the first two best models for the three local breeds of interest were 98.33% and 97.5%. Moreover, results reported in this study suggest that further studies should consider the PLS‐DA method when selecting breed‐informative SNPs.

The segregation of the causal mutation () in the muscular hypertrophy gene in dual-purpose Belgian Blue (dpBB) cattle is considered to result in greater calving difficulty (dystocia). Establishing adapted genetic evaluations might overcome this situation through efficient selection. However, the heterogeneity of dpBB populations at the locus implies separating the major gene and other polygenic effects in complex modeling. The use of mixed inheritance models may be an interesting option because they simultaneously assume both influences. A genetic evaluation in dpBB based on a mixed inheritance model was developed for birth and conformation traits: gestation length (GL), calving difficulty (CD), birth weight (BiW), and body conformation score (BC). A total of 27,362 animals having records were used for analyses. The total number of animals in the pedigree used to build the numerator relationship matrix was 62,617. Genotypes at the locus were available for 2,671 animals. Missing records at this locus were replaced with genotype probabilities. A total of 13,221 (48.3%) were registered as dpBB, 1,287 (4.7%) as beef Belgian Blue, and 12,854 (47.0%) were unknown. From those 13,221 dpBB animals, 650, 849, and 534 had double or single copies or no copy, respectively, of the causal mutation () in the muscular hypertrophy gene, whereas 11,188 had missing genotypes. This heterogeneity at the locus may be the reason for high variability in the studied traits, that is, high heritability estimates of 0.33, 0.30, 0.38, and 0.43 for GL, CD, BiW, and BC, respectively. In general, additive ( < 0.05) and dominance ( < 0.001) allele substitution for calves and dams had significant impact for all traits. The moderate coefficient of genetic variation (27.80%) and high direct heritability (0.28) for CD suggested genetic variability in dpBB and possible genetic improvement through selection. This variability has allowed dpBB breeders to successfully apply mass selection in the past. Genetic trend means from 1988 to 2016 showed that sire selection for CD within genotype was progressively applied by breeders. The selection intensity was more important for CD in double-muscled lines than in segregated lines. Our study illustrated the possible confusion caused by the use of major genes in selection and the importance of fitting appropriate models such as mixed inheritance models that combine polygenic and gene content information.

Based on a Bayesian view of linear mixed models, several studies showed the possibilities to integrate estimated breeding values (EBV) and associated reliabilities (REL) provided by genetic evaluations performed outside a given evaluation system into this genetic evaluation. Hereafter, the term "internal" refers to this given genetic evaluation system, and the term "external" refers to all other genetic evaluations performed outside the internal evaluation system. Bayesian approaches integrate external information (i.e., external EBV and associated REL) by altering both the mean and (co)variance of the prior distributions of the additive genetic effects based on the knowledge of this external information. Extensions of the Bayesian approaches to multivariate settings are interesting because external information expressed on other scales, measurement units, or trait definitions, or associated with different heritabilities and genetic parameters than the internal traits, could be integrated into a multivariate genetic evaluation without the need to convert external information to the internal traits. Therefore, the aim of this study was to test the integration of external EBV and associated REL, expressed on a 305-d basis and genetically correlated with a trait of interest, into a multivariate genetic evaluation using a random regression test-day model for the trait of interest. The approach we used was a multivariate Bayesian approach. Results showed that the integration of external information led to a genetic evaluation for the trait of interest for, at least, animals associated with external information, as accurate as a bivariate evaluation including all available phenotypic information. In conclusion, the multivariate Bayesian approaches have the potential to integrate external information correlated with the internal phenotypic traits, and potentially to the different random regressions, into a multivariate genetic evaluation. This allows the use of different scales, heritabilities, variance components, measurement units, or trait definitions for external and internal traits. However, one possible issue for implementing multivariate Bayesian approaches could be the availability or estimation of genetic correlations between external and internal traits.

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