Effects of a mastitis J5 bacterin vaccination on the productive performance of dairy cows: An observational study using propensity score matching techniques.

Inferring causal effects between variables when utilizing observational data is challenging due to confounding factors not controlled through a randomized experiment. Propensity score matching can decrease confounding in observational studies and offers insights about potential causal effects of prophylactic management interventions such as vaccinations. The objective of this study was to determine potential causality and impact of vaccination with an Escherichia coli J5 bacterin on the productive performance of dairy cows applying propensity score matching techniques to farm-recorded (e.g., observational) data. Traits of interest included 305-d milk yield (MY305), 305-d fat yield (FY305), 305-d protein yield (PY305), and somatic cell score (SCS). Records from 6,418 lactations generated by 5,121 animals were available for the analysis. Vaccination status of each animal was obtained from producer-recorded information. Confounding variables considered were herd-year-season groups (56 levels), parity (5 levels: 1, 2, 3, 4, and ≥5), and genetic quartile groups (4 levels: top 25% through bottom 25%) derived from genetic predictions for MY305, FY305, PY305, and SCS, as well as for the genetic susceptibility to mastitis. A logistic regression model was applied to estimate the propensity score (PS) for each cow. Subsequently, PS values were used to form pairs of animals (1 vaccinated with 1 unvaccinated control), depending on their PS similarities (difference in PS values of cows within a match required to be <20% of 1 standard deviation of the logit of PS). After the matching process, 2,091 pairs of animals (4,182 records) remained available to infer the causal effects of vaccinating dairy cows with the E. coli J5 bacterin. Causal effects estimation was performed using 2 approaches: simple matching and a bias-corrected matching. According to the PS methodology, causal effects of vaccinating dairy cows with a J5 bacterin on their productive performance were identified for MY305. The simple matched estimator suggested that vaccinated cows produced 163.89 kg more milk over an entire lactation when compared with nonvaccinated counterparts, whereas the bias-corrected estimator suggested that such increment in milk production was of 150.48 kg. Conversely, no causal effects of immunizing dairy cows with a J5 bacterin were identified for FY305, PY305, or SCS. In conclusion, the utilization of PS matching techniques applied to farm-recorded data was feasible and allowed us to identify that vaccination with an E. coli J5 bacterin relates to an overall milk production increment without compromising milk quality.

Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle.

Several methods have been used for genome-enabled prediction (or genomic selection) of complex traits, for example, multiple regression models describing a target trait with a linear function of a set of genetic markers. Genomic selection studies have been focused mostly on single-trait analyses. However, most profitability traits are genetically correlated, and an increase in prediction accuracy of genomic breeding values for genetically correlated traits is expected when using multiple-trait models. Thus, this study was carried out to assess the accuracy of genomic prediction for carcass and meat quality traits in Nelore cattle, using single- and multiple-trait approaches. The study considered 15 780, 15 784, 15 742 and 526 records of rib eye area (REA, cm2), back fat thickness (BF, mm), rump fat (RF, mm) and Warner-Bratzler shear force (WBSF, kg), respectively, in Nelore cattle, from the Nelore Brazil Breeding Program. Animals were genotyped with a low-density single nucleotide polymorphism (SNP) panel and subsequently imputed to arrays with 54 and 777 k SNPs. Four Bayesian specifications of genomic regression models, namely, Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression; blending methods, BLUP; and single-step genomic best linear unbiased prediction (ssGBLUP) methods were compared in terms of prediction accuracy using a fivefold cross-validation. Estimates of heritability ranged from 0.20 to 0.35 and from 0.21 to 0.46 for RF and WBSF on single- and multiple-trait analyses, respectively. Prediction accuracies for REA, BF, RF and WBSF were all similar using the different specifications of regression models. In addition, this study has shown the impact of genomic information upon genetic evaluations in beef cattle using the multiple-trait model, which was also advantageous compared to the single-trait model because it accounted for the selection process using multiple traits at the same time. The advantage of multi-trait analyses is attributed to the consideration of correlations and genetic influences between the traits, in addition to the non-random association of alleles.

Modelagem das trajetórias médias do peso vivo e do rendimento de carcaça de tilápias do Nilo (Oreochromis niloticus) por meio de funções B-spline / Modeling the mean trajectories of live body weight and carcass yield of Nile tilapia (Oreochromis niloticus) by B-spline functions

Empregando o método dos quadrados mínimos e polinômios B-spline quadráticos, diferentes modelos estatísticos foram testados para identificar o mais apropriado para modelar as trajetórias médias do peso vivo e do rendimento de carcaça de tilápias do Nilo (Oreochromis niloticus). Dados de peso vivo (8.758) e de rendimento de carcaça (2.042) de tilápias com idades entre 106 e 245 dias foram obtidos de 72 famílias provenientes de 36 machos e 72 fêmeas. As variáveis sexo e tanque de criação foram consideradas como classificatórias, e os coeficientes dos polinômios B-spline quadráticos com dois a cinco intervalos de mesmo tamanho foram utilizados como covariáveis. Segundo a maioria dos critérios de ajuste utilizados, os modelos com polinômio B-spline quadrático com cinco intervalos de mesmo tamanho apresentaram os melhores ajustes. O aumento do número de intervalos do polinômio B-spline melhorou o ajuste dos polinômios aos dados. A inclusão dos efeitos classificatórios de sexo, tanque de criação, interação entre esses efeitos e polinômio B-spline quadrático aninhado a essa interação indicou que, com o decorrer do tempo, cada sexo, cultivado em diferente tanque, apresentou trajetória média diferente, sendo necessária a inclusão do aninhamento do tempo na interação sexo x tanque de criação para que, em programas de melhoramento genético da espécie, os valores genéticos dos candidatos à seleção não sejam sub ou superestimados.

Employing the method of least squares and quadratic B-spline polynomials, different statistical models were tested to identify the most appropriate to model the mean trajectories of live weight and carcass yield of Nile tilapia (Oreochromis niloticus). Data of live weight (8,758) and carcass yield (2,042) of tilapias with ages between 106 and 245 days were obtained from 72 families derived from 36 males and 72 females. The sex and tank variables were considered as classificatory and the coefficients of quadratic polynomials B-spline of two to five intervals of the same size were used as covariables. According to most fit criteria used, the models with quadratic B-spline polynomial with five intervals of the same size presented the best adjustments. The increase in number of intervals of B-spline polynomial improved the fit of the polynomials to the data. The inclusion of classificatory effects from sex, tank, the interaction of these effects and the quadratic polynomial B-spline nested in this interaction indicated that, over time, each sex, grown in different tank, presented different mean trajectory, necessitating the inclusion of nesting time in the interaction sex x tank in order to avoid the under or overestimation of breeding values of the selection candidates in breeding programs of this species.

Genotype × environment interaction in individual performance and progeny tests in beef cattle.

The study reported here evaluated genotype × environment interaction in individual performance and progeny tests in beef cattle. Genetic parameters for final weight (FW), ADG, and scrotal circumference (SC) of 33,013 Nellore young bulls tested on pasture or in feedlots were analyzed. The posterior means (and highest posterior density interval with 90% of samples [HPD90]) of heritability for traits measured on pasture-raised and feedlot-raised animals were 0.44 (HPD90 = 0.40 to 0.48) and 0.50 (HPD90 = 0.43 to 0.56) for FW, 0.26 (HPD90 = 0.23 to 0.29) and 0.26 (HPD90 = 0.20 to 0.32) for ADG, and 0.53 (HPD90 = 0.48 to 0.59) and 0.65 (HPD90 = 0.55 to 0.74) for SC, respectively. The posterior means (and HPD90) of genetic correlations for FW, ADG, and SC on pasture and in feedlots were 0.75 (HPD90 = 0.66 to 0.87), 0.49 (HPD90 = 0.31 to 0.66), and 0.89 (HPD90 = 0.83 to 0.97), respectively. When the selection intensity was kept the same for both the environments, the greatest direct responses for FW and ADG were exhibited by the animals reared and selected in feedlots. The correlated responses relative to production on pasture and based on selection in feedlots were similar to the direct responses, whereas the correlated responses for production in feedlots and based on selection on pasture were lower than the direct responses. When the selection intensity on pasture was higher than the selection intensity in feedlots, the responses to direct selection were similar for both the environments and correlated responses obtained in feedlots by selection on pasture were similar to the direct responses in feedlots. Analyses of few or poor indicators of genotype × environment interaction result in incorrect interpretations of its existence and implications. The present work demonstrated that traits with lower heritability are more susceptible to genotype × environment interaction and that selection intensity plays an important role in the study of genotype × environment interaction in beef cattle.

Avaliação genética do tamanho de leitegada em suínos das raças Landrace e Large White / Genetic evaluation of litter size in swine of Landrace and Large White breeds

Objetivou-se com este estudo estimar parâmetros genéticos para o número total de leitões nascidos (NTLN), número de leitões nascidos vivos (NLNV) e número de leitões vivos aos cinco dias de idade (NLV5) com modelos de regressão aleatória e averiguar melhor modelagem da variância residual na avaliação das trajetórias genéticas do tamanho da leitegada de fêmeas Landrace e Large White. Os dados utilizados foram provenientes de uma granja de melhoramento genético de suínos e continham 2.388 observações de fêmeas Landrace e 2.325 de Large White. Os modelos de melhor ajuste para o NTLN e NLV5 foram os que consideraram a variância residual homogênea e, para NLNV, o modelo com quatro classes de variâncias residuais foi o mais adequado (BIC). Para Landrace, o efeito materno não foi significativo. O modelo que incluiu o efeito materno e quatro classes de variância residual foi o que apresentou melhor ajuste para NTLN na raça Large White, sendo os modelos sem efeito materno e com variância residual homogênea os mais adequados para NLNV e NLV5. As herdabilidades estimadas variaram de baixas a altas (0,08-0,34; 0,04-0,29 e 0,05-0,21 na raça Landrace e 0,16-0,30; 0,10-0,37 e 0,09-0,32 na Large White, para NTLN, NLNV e NLV5, respectivamente). A alta correlação de posto entre os valores genéticos do NLNV e NLV5 sugere que não há necessidades do controle do NLV5 nesse programa de melhoramento genético. Maiores ganhos podem ser obtidos pela seleção no NLNV de fêmeas primíparas, em função da diminuição do intervalo de gerações.

This study aimed to estimate genetic parameters for total number of piglets born (NTLN), number of piglets born alive (NLNV) and number of piglets alive at five days of age (NLV5) using random regression models and to evaluate the best way for modelling the residual variance in the description of the genetic trajectories of litter size in Landrace and Large White breeds. The data came from a swine breed improvement program, and a total of 2388 and 2325 litter size records of Landrace and Large White, respectively were used in the analyses. The models considering homogenous residual variance showed the best goodness of fit for NTLN and NLV5 and the model with four classes of residual variances was most appropriate for NLNV (BIC). In the Landrace breed the maternal effect was not significant. The model including maternal effect and four classes of residual variance adequately described NTLN of Large White breed and the models without maternal effect and with homogeneous residual variance were most appropriate to describe NLNV and NLV5. The estimated heritability for NTLN, NLNV and NLV5 ranged from low to high (0.08-0.34, 0.04-0.29 and 0.05-0.21 in Landrace breed and 0.16-0.30, 0.10-0.37, 0.09-0.32 in Large White breed.). The magnitude of the rank correlations between breeding values of NLNV and NLV5 suggests that the recording of NLV5 is not necessary in this breed improvement program. High genetic gains can be obtained by selecting NLNV of primiparous females, due to the reduction in the generation interval.

Polynomials to model the growth of young bulls in performance tests.

The use of polynomial functions to describe the average growth trajectory and covariance functions of Nellore and MA (21/32 Charolais+11/32 Nellore) young bulls in performance tests was studied. The average growth trajectories and additive genetic and permanent environmental covariance functions were fit with Legendre (linear through quintic) and quadratic B-spline (with two to four intervals) polynomials. In general, the Legendre and quadratic B-spline models that included more covariance parameters provided a better fit with the data. When comparing models with the same number of parameters, the quadratic B-spline provided a better fit than the Legendre polynomials. The quadratic B-spline with four intervals provided the best fit for the Nellore and MA groups. The fitting of random regression models with different types of polynomials (Legendre polynomials or B-spline) affected neither the genetic parameters estimates nor the ranking of the Nellore young bulls. However, fitting different type of polynomials affected the genetic parameters estimates and the ranking of the MA young bulls. Parsimonious Legendre or quadratic B-spline models could be used for genetic evaluation of body weight of Nellore young bulls in performance tests, whereas these parsimonious models were less efficient for animals of the MA genetic group owing to limited data at the extreme ages.