Genomic prediction and genetic correlations estimated for milk production and fatty acid traits in Walloon Holstein cattle using random regression models.

The aims of this study were to: (1) estimate genetic correlation for milk production traits (milk, fat and protein yields and fat and protein contents) and fatty acids (FA: C16:0, C18:1 cis-9, LCFA, SFA, and UFA) over days in milk, (2) investigate the performance of genomic predictions using single-step GBLUP (ssGBLUP) based on random regression models (RRM), and (3) identify the optimal scaling and weighting factors to be used in the construction of the H matrix. A total of 302 684 test-day records of 63.875 first lactation Walloon Holstein cows were used. Positive genetic correlations were found between milk yield and fat and protein yield (rg from 0.46 to 0.85) and between fat yield and milk FA (rg from 0.17 to 0.47). On the other hand, negative correlations were estimated between fat and protein contents (rg from -0.22 to -0.59), between milk yield and milk FA (rg from -0.22 to -0.62), and between protein yield and milk FA (rg from -0.11 to -0.19). The selection for high fat content increases milk FA throughout lactation (rg from 0.61 to 0.98). The test-day ssGBLUP approach showed considerably higher prediction reliability than the parent average for all milk production and FA traits, even when no scaling and weighting factors were used in the H matrix. The highest validation reliabilities (r2 from 0.09 to 0.38) and less biased predictions (b1 from 0.76 to 0.92) were obtained using the optimal parameters (i.e., ω = 0.7 and α = 0.6) for the genomic evaluation of milk production traits. For milk FA, the optimal parameters were ω = 0.6 and α = 0.6. However, biased predictions were still observed (b1 from 0.32 to 0.81). The findings suggest that using ssGBLUP based on RRM is feasible for the genomic prediction of daily milk production and FA traits in Walloon Holstein dairy cattle.

Autoregressive single-step test-day model for genomic evaluations of Portuguese Holstein cattle.

The multiple-lactation autoregressive test-day (AR) model is the adopted model for the national genetic evaluation of dairy cattle in Portugal. Under this model, animals' permanent environment effects are assumed to follow a first-order autoregressive process over the long (auto-correlations between parities) and short (auto-correlations between test-days within lactation) terms. Given the relevance of genomic prediction in dairy cattle, it is essential to include marker information in national genetic evaluations. In this context, we aimed to evaluate the feasibility of applying the single-step genomic (G)BLUP to analyze milk yield using the AR model in Portuguese Holstein cattle. In total, 11,434,294 test-day records from the first 3 lactations collected between 1994 and 2017 and 1,071 genotyped bulls were used in this study. Rank correlations and differences in reliability among bulls were used to compare the performance of the traditional (A-AR) and single-step (H-AR) models. These 2 modeling approaches were also applied to reduced data sets with records truncated after 2012 (deleting daughters of tested bulls) to evaluate the predictive ability of the H-AR. Validation scenarios were proposed, taking into account young and proven bulls. Average EBV reliabilities, empirical reliabilities, and genetic trends predicted from the complete and reduced data sets were used to validate the genomic evaluation. Average EBV reliabilities for H-AR (A-AR) using the complete data set were 0.52 (0.16) and 0.72 (0.62) for genotyped bulls with no daughters and bulls with 1 to 9 daughters, respectively. These results showed an increase in EBV reliabilities of 0.10 to 0.36 when genomic information was included, corresponding to a reduction of up to 43% in prediction error variance. Considering the 3 validation scenarios, the inclusion of genomic information improved the average EBV reliability in the reduced data set, which ranged, on average, from 0.16 to 0.26, indicating an increase in the predictive ability. Similarly, empirical reliability increased by up to 0.08 between validation tests. The H-AR outperformed A-AR in terms of genetic trends when unproven genotyped bulls were included. The results suggest that the single-step GBLUP AR model is feasible and may be applied to national Portuguese genetic evaluations for milk yield.

Estimation of genetic parameters for electrical conductivity of milk for Holstein breed using random regression / Estimativa de parâmetros genéticos para condutividade elétrica do leite para a raça holandesa utilizando regressão aleatória

The electrical conductivity of milk is an indirect method of mastitis diagnosis and can be used as selection criterion in breeding programs to obtain resistant animals to infection. For the present study data from 9,302 milk electrical conductivity measurements in the morning (ECM), from 1,129 Holstein cows in first lactation, calving between 2001 and 2011, belonging to eight herds in the Southeast of Brazil, obtained from automated milking equipment WESTFALIA® with system management "Dairyplan" was utilized. Classes of ECM were formed at weekly intervals, representing a total of 42 classes. The model included direct additive genetic, permanent environmental and residual effects as random and the fixed effects of contemporary group (herd - year and season of the control), age at calving as a covariate (linear and quadratic). Mean trends were modeled by an orthogonal Legendre polynomial with three coefficients of days in milk. The residual variance was considered homogeneous throughout lactation. Variance components were estimated by restricted maximum likelihood method (REML), using the statistical package Wombat (Meyer, 2006). The mean and standard deviation of the electrical conductivity of milk were 4.799 ± 0.543 ms/cm. The heritability for ECM were increased from the beginning to the middle of lactation (154 days), when it reached the maximum value (0.44), decreasing therea

O artigo não apresenta resumo em português.

Estimation of genetic parameters for electrical conductivity of milk for Holstein breed using random regression / Estimativa de parâmetros genéticos para condutividade elétrica do leite para a raça holandesa utilizando regressão aleatória

The electrical conductivity of milk is an indirect method of mastitis diagnosis and can be used as selection criterion in breeding programs to obtain resistant animals to infection. For the present study data from 9,302 milk electrical conductivity measurements in the morning (ECM), from 1,129 Holstein cows in first lactation, calving between 2001 and 2011, belonging to eight herds in the Southeast of Brazil, obtained from automated milking equipment WESTFALIA® with system management "Dairyplan" was utilized. Classes of ECM were formed at weekly intervals, representing a total of 42 classes. The model included direct additive genetic, permanent environmental and residual effects as random and the fixed effects of contemporary group (herd - year and season of the control), age at calving as a covariate (linear and quadratic). Mean trends were modeled by an orthogonal Legendre polynomial with three coefficients of days in milk. The residual variance was considered homogeneous throughout lactation. Variance components were estimated by restricted maximum likelihood method (REML), using the statistical package Wombat (Meyer, 2006). The mean and standard deviation of the electrical conductivity of milk were 4.799 ± 0.543 ms/cm. The heritability for ECM were increased from the beginning to the middle of lactation (154 days), when it reached the maximum value (0.44), decreasing therea

O artigo não apresenta resumo em português.

Genetic parameters for milk, fat and protein yields in Murrah buffaloes (Bubalus bubalis Artiodactyla, Bovidae).

The objective of the present study was to estimate genetic parameters for test-day milk, fat and protein yields and 305-day-yields in Murrah buffaloes. 4,757 complete lactations of Murrah buffaloes were analyzed. Co-variance components were estimated by the restricted maximum likelihood method. The models included additive direct genetic and permanent environmental effects as random effects, and the fixed effects of contemporary group, milking number and age of the cow at calving as linear and quadratic covariables. Contemporary groups were defined by herd-year-month of test for test-day yields and by herd-year-season of calving for 305-day yields. The heritability estimates obtained by two-trait analysis ranged from 0.15 to 0.24 for milk, 0.16 to 0.23 for protein and 0.13 to 0.22 for fat, yields. Genetic and phenotypic correlations were all positive. The observed population additive genetic variation indicated that selection might be an effective tool in changing population means in milk, fat and protein yields.

Genetic parameters for milk, fat and protein yields in Murrah buffaloes (Bubalus bubalis Artiodactyla, Bovidae)

The objective of the present study was to estimate genetic parameters for test-day milk, fat and protein yields and 305-day-yields in Murrah buffaloes. 4,757 complete lactations of Murrah buffaloes were analyzed. Co-variance components were estimated by the restricted maximum likelihood method. The models included additive direct genetic and permanent environmental effects as random effects, and the fixed effects of contemporary group, milking number and age of the cow at calving as linear and quadratic covariables. Contemporary groups were defined by herd-year-month of test for test-day yields and by herd-year-season of calving for 305-day yields. The heritability estimates obtained by two-trait analysis ranged from 0.15 to 0.24 for milk, 0.16 to 0.23 for protein and 0.13 to 0.22 for fat, yields. Genetic and phenotypic correlations were all positive. The observed population additive genetic variation indicated that selection might be an effective tool in changing population means in milk, fat and protein yields.