Autoregressive single-step model for genomic evaluation of longitudinal reproductive traits in portuguese holstein cattle.

We investigated the applicability of ssGBLUP methodology under the autoregressive model (H-AR) for genomic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle. The genotype data of 1,230 bulls and 1,645 cows were considered in our study. The reproductive traits evaluated were interval from calving to first service (ICF), calving interval (CI) and daughter pregnancy rate (DPR) measured during the first four parities. Reliability and rank correlation were used to compare the H-AR with the traditional pedigree-based autoregressive models (A-AR). In addition, a validation study was performed considering different scenarios. Higher genomic estimated breeding values (GEBV) reliabilities were obtained for genotyped bulls when evaluated under the H-AR model, with emphasis on bulls with less than 9 daughters. For this group, the averages of GEBV reliabilities corresponded to 0.62, 0.69 and 0.62 for ICF, CI and DPR, respectively, while the averages obtained by the A-AR model were 0.27, 0.15 and 0.16. The validation study was favourable to H-AR. The best results were observed in the scenario where genotyped cows were combined with contributing bulls (genotyped bulls with daughter or relationship information in the population). Overall, the results suggest that ssGBLUP methodology under the autoregressive model is a feasible and applicable approach to be used in genomic analyses of longitudinal reproductive traits in Portuguese Holstein cattle.

Autoregressive model for genetic evaluation of longitudinal reproductive traits in Brazilian Holstein cattle.

Reproductive efficiency is major determinant of the dairy herd profitability. Thus, reproductive traits have been widely used as selection objectives in the current dairy cattle breeding programs. We aimed to evaluate strategies to model days open (DO), calving interval (CI) and daughter pregnancy rate (DPR) in Brazilian Holstein cattle. These reproductive traits were analysed by the autoregressive (AR) model and compared with classical repeatability (REP) model using 127,280, 173,092 and 127,280 phenotypic records, respectively. The first three calving orders of cows from 1,469 Holstein herds were used here. The AR model reported lower values for Akaike Information Criteria and Mean Square Errors, as well as larger model probabilities, for all evaluated traits. Similarly, larger additive genetic and lower residual variances were estimated from AR model. Heritability and repeatability estimates were similar for both models. Heritabilities for DO, CI and DPR were 0.04, 0.07 and 0.04; and 0.05, 0.06 and 0.04 for AR and REP models, respectively. Individual EBV reliabilities estimated from AR for DO, CI and DPR were, in average, 0.29, 0.30 and 0.29 units higher than those obtained from REP model. Rank correlation between EBVs obtained from AR and REP models considering the top 10 bulls ranged from 0.72 to 0.76; and increased from 0.98 to 0.99 for the top 100 bulls. The percentage of coincidence between selected bulls from both methods increased over the number of bulls included in the top groups. Overall, the results of model-fitting criteria, genetic parameters estimates and EBV predictions were favourable to the AR model, indicating that it may be applied for genetic evaluation of longitudinal reproductive traits in Brazilian Holstein cattle.

GWAS and gene networks for milk-related traits from test-day multiple lactations in Portuguese Holstein cattle.

This study focused on the identification of QTL regions, candidate genes, and network related genes based on the first 3 lactations (LAC3) of milk, fat, and protein yields, and somatic cell score (SCS) in Portuguese Holstein cattle. Additionally, the results were compared with those from only first lactation (LAC1) data. The analyses were performed using the weighted single-step GWAS under an autoregressive test-day (TD) multiple lactations model. A total of 11,434,294 and 4,725,673 TD records from LAC3 and LAC1, respectively, including 38,323 autosomal SNPs and 1338 genotyped animals were used in GWAS analyses. A total of 51 (milk), 5 (fat), 24 (protein), and 4 (SCS) genes were associated to previously annotated relevant QTL regions for LAC3. The CACNA2D1 at BTA4 explained the highest proportion of genetic variance respectively for milk, fat, and protein yields. For SCS, the TRNAG-CCC at BTA14, MAPK10, and PTPN3 genes, both at BTA6 were considered important candidate genes. The accessed network refined the importance of the reported genes. CACNA2D1 regulates calcium density and activation/inactivation kinetics of calcium transport in the mammary gland; whereas TRNAG-CCC, MAPK10, and PTPN3 are directly involved with inflammatory processes widely derived from mastitis. In conclusion, potential candidate genes (TRNAG-CCC, MAPK10, and PTPN3) associated with somatic cell were highlighted, which further validation studies are needed to clarify its mechanism action in response to mastitis. Moreover, most of the candidate genes identified were present in both (LAC3 and LAC1) for milk, fat and protein yields, except for SCS, in which no candidate genes were shared between LAC3 and LAC1. The larger phenotypic information provided by LAC3 dataset was more effective to identify relevant genes, providing a better understanding of the genetic architecture of these traits over all lactations simultaneously.

Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle.

Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and -0.019 (-0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and -0.022 (-0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.

Random Regression Models Are Suitable to Substitute the Traditional 305-Day Lactation Model in Genetic Evaluations of Holstein Cattle in Brazil.

The aim of this study was to compare two random regression models (RRM) fitted by fourth (RRM4) and fifth-order Legendre polynomials (RRM5) with a lactation model (LM) for evaluating Holstein cattle in Brazil. Two datasets with the same animals were prepared for this study. To apply test-day RRM and LMs, 262,426 test day records and 30,228 lactation records covering 305 days were prepared, respectively. The lowest values of Akaike's information criterion, Bayesian information criterion, and estimates of the maximum of the likelihood function (-2LogL) were for RRM4. Heritability for 305-day milk yield (305MY) was 0.23 (RRM4), 0.24 (RRM5), and 0.21 (LM). Heritability, additive genetic and permanent environmental variances of test days on days in milk was from 0.16 to 0.27, from 3.76 to 6.88 and from 11.12 to 20.21, respectively. Additive genetic correlations between test days ranged from 0.20 to 0.99. Permanent environmental correlations between test days were between 0.07 and 0.99. Standard deviations of average estimated breeding values (EBVs) for 305MY from RRM4 and RRM5 were from 11% to 30% higher for bulls and around 28% higher for cows than that in LM. Rank correlations between RRM EBVs and LM EBVs were between 0.86 to 0.96 for bulls and 0.80 to 0.87 for cows. Average percentage of gain in reliability of EBVs for 305-day yield increased from 4% to 17% for bulls and from 23% to 24% for cows when reliability of EBVs from RRM models was compared to those from LM model. Random regression model fitted by fourth order Legendre polynomials is recommended for genetic evaluations of Brazilian Holstein cattle because of the higher reliability in the estimation of breeding values.

Population genetic structure in the Holstein breed in Brazil.

We evaluated the population genetic structure of the Holstein breed in Brazil through pedigree analysis with the aim of supporting genetic management of extant herds. We used data from genealogical records of 204,511 animals in farms from south and southeast Brazil. Pedigree records between 1943 and 2005 were divided into seven periods of 8 years to estimate the effective population size (N e ). N e varied during the study periods, ranging from 0.19 to 3016.25. There was an increase in the percentage of inbred animals over time, from 0.18 to 5.0 %. However, this figure may be an underestimate due to the low completeness of pedigree, primarily related to paternal pedigree. The effective number of founders (fe) was 473 animals and ancestors (fa) was 471. The genetic contribution of 260 ancestors (founders or not) accounted for 50 % of the genetic variability in the population. The average relatedness coefficient (AR) and inbreeding coefficient indicate that the Holstein breed in Brazil is being effectively managed, despite a moderate founder effect and the low number of animals that are responsible for the population variance.

Genetic parameters for linear type traits and milk, fat, and protein production in holstein cows in Brazil.

The objective of this study was to estimate genetic and phenotypic parameters for linear type traits, as well as milk yield (MY), fat yield (FY) and protein yield (PY) in 18,831 Holstein cows reared in 495 herds in Brazil. Restricted maximum likelihood with a bivariate model was used for estimation genetic parameters, including fixed effects of herd-year of classification, period of classification, classifier and stage of lactation for linear type traits and herd-year of calving, season of calving and lactation order effects for production traits. The age of cow at calving was fitted as a covariate (with linear and quadratic terms), common to both models. Heritability estimates varied from 0.09 to 0.38 for linear type traits and from 0.17 to 0.24 for production traits, indicating sufficient genetic variability to achieve genetic gain through selection. In general, estimates of genetic correlations between type and production traits were low, except for udder texture and angularity that showed positive genetic correlations (>0.29) with MY, FY, and PY. Udder depth had the highest negative genetic correlation (-0.30) with production traits. Selection for final score, commonly used by farmers as a practical selection tool to improve type traits, does not lead to significant improvements in production traits, thus the use of selection indices that consider both sets of traits (production and type) seems to be the most adequate to carry out genetic selection of animals in the Brazilian herd.

Factor analysis of linear type traits and their relation with longevity in brazilian holstein cattle.

In this study we aimed to evaluate the reduction in dimensionality of 20 linear type traits and more final score in 14,943 Holstein cows in Brazil using factor analysis, and indicate their relationship with longevity and 305 d first lactation milk production. Low partial correlations (-0.19 to 0.38), the medium to high Kaiser sampling mean (0.79) and the significance of the Bartlett sphericity test (p<0.001), indicated correlations between type traits and the suitability of these data for a factor analysis, after the elimination of seven traits. Two factors had autovalues greater than one. The first included width and height of posterior udder, udder texture, udder cleft, loin strength, bone quality and final score. The second included stature, top line, chest width, body depth, fore udder attachment, angularity and final score. The linear regression of the factors on several measures of longevity and 305 d milk production showed that selection considering only the first factor should lead to improvements in longevity and 305 milk production.

Genetic evaluation for persistency of lactation in Holstein cows using a random regression model

A model for analyzing test day records including both fixed and random coefficients was applied to the genetic evaluation of first lactation data for Holstein cows. Data comprising 87045 test-day milk yield records from calving between 1997 and 2001 from Holstein herds in 10 regions of the Brazilian state of Minas Gerais. Six persistency of lactation measures were evaluated using breeding values obtained by random regression analyses. The Wilmink function was used to model the additive genetic and permanent environmental effects. Residual variance was constant throughout lactation. Ranking for animals did not change among criteria for persistency measurements, but ranking changes were observed when the estimated breeding value (EBV) for persistency of lactation was contrasted with those estimated for 305-day milk yield (305MY). The rank correlation estimates for persistency of lactation and 305MY were practically the same for sire and cows, and ranged from -0.45 to 0.69. The EBVs for milk yield during lactation for sires producing daughters with superior 305MY indicate genetic differences between sires regarding their ability to transmit desirable persistency of lactation traits. This suggests that selection for total lactation milk yield does not identify sires or cows that are genetically superior in regard to persistency of lactation. Genetic evaluation for persistency of lactation is important for improving the efficiency of the milk production capacity of Holstein cows.

Estimation of genetic parameters for test-day milk yield in Holstein cows using a random regression model

Test-day milk yield records of 11,023 first-parity Holstein cows were used to estimate genetic parameters for milk yield during different lactation periods. (Co)variance components were estimated using two random regression models, RRM1 and RRM2, and the restricted maximum likelihood method, compared by the likelihood ratio test. Additive genetic variances determined by RRM1 and additive genetic and permanent environmental variances estimated by RRM2 were described, using the Wilmink function. Residual variance was constant throughout lactation for the two models. The heritability estimates obtained by RRM1 (0.34 to 0.56) were higher than those obtained by RRM2 (0.15 to 0.31). Due to the high heritability estimates for milk yield throughout lactation and the negative genetic correlation between test-day yields during different lactation periods, the RRM1 model did not fit the data. Overall, genetic correlations between individual test days tended to decrease at the extremes of the lactation trajectory, showing values close to unity for adjacent test days. The inclusion of random regression coefficients to describe permanent environmental effects led to a more precise estimation of genetic and non-genetic effects that influence milk yield.

An investigation into heterogeneity of variance for milk and fat yields of Holstein cows in brazilian herd environments

Registros de produçäo de leite e de gordura de primeira lactaçäo de vacas da raça Holandesa foram utilizados para estudar a heterogeneidade de variância entre rebanhos, os quais foram classificados em dois grupos (baixo e alto) com base no desvio padräo fenotípico da produçäo de leite por rebanho-ano (HYSD). Componentes de (co)variância dos efeitos de reprodutor e resíduo, heritabilidade e correlaçäo genética entre grupos de baixo e alto HYSD foram estimados através de análises bivariadas, usando-se um modelo reprodutor e o método da máxima verossimilhança restrita com um algoritmo livre de derivaçöes. Os componentes de variância de reprodutor e de resíduo para a produçäo de leite (produçäo de gordura) nos rebanhos de baixo HYSD foram respectivamente 79 por cento e 57 por cento (67 por cento e 60 por cento) dos valores estimados para os rebanhos de alto HYSD. As estimativas de heritabilidade para a produçäo de leite e de gordura foram maiores (0,30 e 0,22) nos rebanhos de baixo HYSD que nos rebanhos de alto HYSD (0,23 e 0,20). As correlaçöes genéticas para a produçäo de leite e de gordura entre os dois grupos de rebanhos foram respectivamente 0,997 e 0,985. A resposta correlacionada esperada nos rebanhos de baixo HYSD, usando-se informaçöes dos rebanhos de alto HYSD, foram 0,89 kg/kg para a produçäo de leite e 0,80 kg/kg para a produçäo de gordura. As avaliaçöes genéticas no Brasil devem considerar a heterogeneidade de variância para aumentar a precisäo das avaliaçöes e a eficiência da seleçäo para a produçäo de leite e de gordura na raça Holandesa. A resposta à seleçäo é menor nos rebanhos de baixo HYSD do que nos rebanhos de alto HYSD devido à reduçäo das diferenças na resposta da progênie e nos valores genéticos dos reprodutores nos rebanhos de baixo HYSD. Os investimentos na seleçäo de reprodutores para aumento da produçäo têm maior probabilidade de sucesso nos rebanhos de alto HYSD do que nos rebanhos de baixo HYSD.