Genetic Evaluation of Dairy Cattle with Test-Day Models with Autoregressive Covariance Structures and with a 305-d Model

Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from -0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.

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.

Autoregressive repeatability model for genetic evaluation of longitudinal reproductive traits in dairy cattle

We investigated the efficiency of the autoregressive repeatability model (AR) for genetic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle and compared the results with those from the conventional repeatability model (REP). The data set comprised records taken during the first four calving orders, corresponding to a total of 416, 766, 872 and 766 thousand records for interval between calving to first service, days open, calving interval and daughter pregnancy rate, respectively. Both models included fixed (month and age classes associated to each calving order) and random (herd-year-season, animal and permanent environmental) effects. For AR model, a first-order autoregressive (co)variance structure was fitted for the herd-year-season and permanent environmental effects. The AR outperformed the REP model, with lower Akaike Information Criteria, lower Mean Square Error and Akaike Weights close to unity. Rank correlations between estimated breeding values (EBV) with AR and REP models ranged from 0.95 to 0.97 for all studied reproductive traits, when the total bulls were considered. When considering only the top-100 selected bulls, the rank correlation ranged from 0.72 to 0.88. These results indicate that the re-ranking observed at the top level will provide more opportunities for selecting the best bulls. The EBV reliabilities provided by AR model was larger for all traits, but the magnitudes of the annual genetic progress were similar between two models. Overall, the proposed AR model was suitable for genetic evaluations of longitudinal reproductive traits in dairy cattle, outperforming the REP model.

Lactation and test day random regression models for genetic evaluation of Murrah buffaloes

In this study the variance components, genetic parameters and breeding values for 305 day or less milk yield were estimated using lactation model and random regression models for the first three lactations in Murrah buffaloes. Random regression model were taken both as random regression model with homogeneous residual variance (RRMHOM) and heterogeneous residual variance (RRM-HET). The estimates of additive genetic variance using RRM were higher than lactation model in all the three lactations. RRM-HET gave higher estimates of additive genetic variance than RRM-HOM in first lactation while RRM-HOM gave higher estimates in second and third lactation. From RRM, it was possible to account for permanent environmental variance arising due to individual milk yield variations during lactation. The heritability estimates were comparable in all the three models. However, in first lactation, the heritability estimates from lactation model RRM-HOM and RRM-HET were 0.319, 0.296 and 0.305, respectively. Likewise in second and third lactations these estimates were 0.004, 0.137, 0.135 and 0.520, 0.315, 0.264, respectively. The breeding value rank correlation was high in all the lactations. More sires were common for each model among the top 10 ranked sires in all three lactations. In conclusion, RRM can be an alternative to lactation model owing to high accuracy, early evaluation, high additive genetic variance, comparable heritability and high rank correlation for breeding values.

Modelos para avaliação genética da produção de leite no dia do controle nas três primeiras lactações

RESUMO Objetivou-se verificar se a utilização do modelo autorregressivo (MAR) é adequada para obtenção de parâmetros genéticos para produção de leite no dia do controle (PLDC) de bovinos leiteiros da raça Gir. Foram analisados 125.191 registros de produções diárias, nas três primeiras lactações, por meio dos modelos de repetibilidade (MREP) e MAR. No MREP, foi considerado o efeito de ambiente de curto prazo; no MAR, foi considerado, também, o efeito de ambiente de longo prazo. Os modelos foram comparados por meio do logaritmo da função de máxima verossimilhança ( − 2 log L ). A herdabilidade estimada pelo MREP foi 0,18; no caso do MAR, as estimativas para primeira, segunda e terceira lactações foram 0,32, 0,28 e 0,26, respectivamente. A estimativa de autocorrelação dos componentes de variância de longo prazo foi próxima de zero, e as de curto prazo foram de alta magnitude para primeira (0,79), segunda (0,79) e terceira (0,81) lactações. Logo, a influência do ambiente de curto prazo dentro de cada lactação não é a mesma. O valor de − 2 log L mais próximo de zero foi obtido para o MAR (-294.884,7778) em relação ao MREP (-329.266,4810). Assim, o MAR é adequado para obtenção de estimativas de parâmetros genéticos para PLDC nas três primeiras lactações de bovinos leiteiros.

Random regression test-day milk yield models as a suitable alternative to the traditional 305-day lactation model for genetic evaluation of Sahiwal cattle

In the present study, first three lactation 305-day milk yield variance components, genetic parameters and breeding value were estimated using test-day milk yield and 305-day actual milk yield data of Sahiwal cattle. The estimates were obtained using three methods viz. random regression model (RRM) with homogeneous residual variance (RRM-HOM), RRM with heterogeneous residual variance (RRM-HET) and univariate animal model. The additive genetic variance of 305-day milk yield estimated from RRM was higher compared to univariate animal model for all lactation. From RRM, it was possible to account for permanent environmental effects due to individual milk yield variations during lactation. The heritability estimates were low for first (0.072 to 0.079) and third lactation (0.087 to 0.112) 305-day milk yield from all three methods. For second lactation, low heritability estimate from univariate animal model (0.144) and moderate estimate from different RRM (0.206 to 0.219) were obtained. For all lactation, breeding value rank correlation was more than 0.78 between lactation model and random regression testday model. The same bull was identified as top ranking bull from all three methods. It can be concluded that random regression test-day models can replace conventional 305-day lactation model for genetic evaluation as it resulted in higher additive genetic variance estimates, gave similar/higher heritability value and moderate to high rank correlation estimates for breeding values.

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.

Use of repeated measures analysis for evaluation of genetic background of dairy cattle behavior in automatic milking systems

Milking frequencies measured at official test days were used with repeated measurement analysis to reveal the environmental and genetic impact on the milking frequency of cows in automatic milking systems. Repeated measurements were 3 test-day observations per cow within days in milk (DIM) classes, with 1,216 cows in DIM class 1 (d 0 to 99), from 1,112 cows in DIM class 2 (d 100 to 199), and from 1,004 cows in DIM class 3 (d 200 to 299) kept in 15 farms. Selection criteria for models analyzing repeated measurements were Akaike and Schwarz Bayesian values, which favored the autoregressive [AR(1)] covariance structure over the compound symmetry model. Results from the AR(1) model indicated a significant impact of fixed herd and parity effects. Milking frequencies decreased with increasing parities and were greatest for first-parity cows. High daily milk yield was associated with higher milking frequencies. Heritabilities for milking frequency were 0.16, 0.19, and 0.22 in DIM classes 1, 2, and 3, respectively, from the AR(1) model. Higher heritabilities in the later stage of lactation were due to a substantial reduction of the residual variance. Genetic correlations between test-day milk yield and daily milking frequency were in the range of 0.46 to 0.57 for all DIM classes and between milking frequency and somatic cell score were near zero. For verification of results, milking frequencies of the same cows obtained from herd management programs were averaged within DIM classes. Heritabilities were slightly above the values from the AR(1) model. In conclusion, heritabilities for milking frequency in automatic milking systems are moderate enough to incorporate this behavioral trait in a combined breeding goal. The inevitable improvement of labor efficiency in dairy cattle farming demands such cows going easily and voluntarily in automatic milking systems.

Genetic and non-genetic parameters of several characteristics of production and semen quality in young bucks

The objective of this study was to evaluate genetic and non-genetic factors influencing characteristics of young buck semen production using a multivariate model that takes into account the longitudinal structure of data. Data were collected from 1989 to 2002 at two French A.I. centres. The data corresponded to 13151 and 9206 ejaculates of 758 Alpine and 535 Saanen bucks respectively, collected at the beginning of the first breeding season (September-December). The semen volume, the total number of spermatozoa, the concentration, the motility score of spermatozoa after freezing and the percentage of motile spermatozoa after freezing were registered for each ejaculate. Within-breed heritabilities and repeatabilities were estimated using a multivariate animal model using a power spatial covariance structure for environmental effect. For all characteristics and the two breeds, the main source of variation was the year-month interaction that interacted with the centre. We observed a decrease in years of motility score after freezing. Age and frequency of collection had a significant effect on semen volume and number of spermatozoa for both breeds, and on concentration of spermatozoa for the Alpine breed. No effect of these factors was found on the characteristics observed after freezing. Heritabilities for concentration, number of spermatozoa, semen volume, motility score after freezing and percentage of motile spermatozoa after freezing per ejaculate were respectively, 0.32, 0.15, 0.25, 0.12 and 0.05 for the Saanen breed and 0.34, 0.25, 0.29, 0.17 and 0.03 for the Alpine breed. Genetic correlations between volume and number of spermatozoa were respectively, 0.74 for the Alpine breed and 0.86 for the Saanen breed. Further study is required to compare the semen characteristics of young bucks with their mature production.