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.
This study aimed to verify if random regression models using linear splines (RRMLS) are suitable for identifying genetic parameters in multiple-breed populations and also to investigate whether an ...interaction exists between the breeding value (BV) of sires and their progeny breed group. Ten populations were simulated by crossing 2 breeds with distinct genetic variance and nonzero segregation variance. To obtain the genetic parameters, 2 models were used: a multiple-trait model (MULT), in which the trait was considered distinct when evaluated in each group (1/2P1 + 1/2P2, 5/8P1 + 3/8P2, and 3/4P1 + 1/4P2), and a RRMLS with the spline polynomial knots adjusted to these same groups. The genetic parameters estimated through MULT and RRMLS did not differ from the simulated values. The correlations between BV (simulated and estimated) of animals were high and varied from 0.74 to 0.76, which indicates the efficiency of using MULT and RRMLS for predicting BV. Using field data, the traits age at first calving (AFC), first lactation length (LL), and 305-d milk yield (MY-305) from a multiple-breed population of Holstein-Gyr cattle were analyzed. The BV of animals were modeled through RRMLS with 3, 5, and 7 knots, distributed in accordance with the fraction of Holstein breed in each progeny breed group. It was verified that RRMLS with 7 knots for adjusting mean trajectories and genetic effects, with homogeneous residual variance, best fit AFC and LL. For MY-305, the best fit for mean trajectory and genetic effects was the RRMLS with 5 knots and with homogeneous residual variance. The posterior means of heritability varied from 0.21 to 0.48, 0.21 to 0.38, and 0.10 to 0.33 for AFC, LL, and MY-305, respectively. Estimates from genetic parameters obtained by using RRMLS with field data showed that this model is a useful tool for genetic evaluations of populations formed by a great number of breed groups. An interaction occurred between the BV of sires and their progeny breed group, and the genetic parameters for AFC, LL, and MY-305 traits from a multiple-breed population depend on breed composition of the progeny from which the evaluations are based.
The effect of ractopamine (RAC) supplementation on growth, carcass, and meat quality traits of finishing pigs was studied using a meta-analytical approach. The database was composed of 57 studies ...published from 2004 to 2016. The dependent variables extracted for the meta-analysis included final BW, ADG, ADFI, feed:gain ratio, HCW, dressing percentage, carcass length, lean yield, back fat thickness, loin muscle area, loin depth, postmortem pH, meat brightness, redness, and yellowness. The studies were grouped by similarity in 3 clusters (C1, C2, and C3) by hierarchical clustering on principle components. The main differences observed between clusters were those of animal initial weight, which increased from C1 through C3. Linear mixed models were used to analyze the data, where studies were assumed as random effect, whereas the total amount of RAC in the diet, cluster, and sex category were considered fixed effects. The interactions between cluster and sex category (barrows, gilts, and mixed sex) and RAC level were also evaluated. Dietary RAC was effective in improving final weight ( < 0.0001), ADG ( < 0.0001), and feed:gain ratio ( < 0.0001) and had a positive effect on HCW ( < 0.0001), lean yield ( = 0.0081), loin muscle area ( = 0.0190), and loin depth ( < 0.0001). In addition, a relatively limited effect on pork quality was observed in the current study. The RAC supplementation was more effective, mainly when pigs started supplementation with higher initial weight, although different responses were observed according to sex category ( < 0.05). There is ample indication that growth and carcass traits could be improved by dietary RAC supplementation. Ractopamine supplementation did not influence the pork quality.
The effect of vitamin E supplementation on the growth performance, carcass traits, meat quality, and immune response of male broiler chickens was studied using a meta-analysis. The database was ...consisted of 51 scientific papers published in peer-reviewed journals. The dependent variables for meta-analysis included final body weight, average daily gain, daily feed intake, feed conversion ratio, vitamin E concentration in the muscle, tissue polyunsaturated fatty acid concentration, lipid peroxidation value, post mortem pH, heterophil to lymphocyte ratio, and total immunoglobulins. Linear mixed models were used to analyze the data. Vitamin E supplementation did not influence growth performance, as the estimated slopes were not different from zero, with P-values equal to 0.92 for final body weight, 0.81 for average daily gain, 0.31 for daily feed intake, and 0.83 for feed conversion ratio. Dietary vitamin E supplementation increased the vitamin E content in the muscle (P = 0.001), did not change the polyunsaturated fatty acid concentration, and decreased the lipid peroxidation (P = 0.01). The immune response was improved, the heterophil to lymphocyte ratio was constant, and the total immunoglobulins were increased (P = 0.037) by dietary vitamin E supplementation. With regard to broiler chicken performance, there seemed to be no relationship between dietary vitamin E supplementation and growth performance. There is ample indication that meat quality and immune response could be improved by dietary vitamin E supplementation.
•Vitamin E supplementation (0–960mg/kg) did not influence growth performance.•Vitamin E content of ingredients in feed may be sufficient to ensure performance 39–49d).•Dietary vitamin E supplementation increased its muscle content.•Dietary vitamin E supplementation decreased the lipid peroxidation.•Immune response was improved by dietary vitamin E supplementation.
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.
The effect of ractopamine (RAC) supplementation on growth, carcass, and meat quality traits of finishing pigs was studied using a meta-analytical approach. The database was composed of 57 studies ...published from 2004 to 2016. The dependent variables extracted for the meta-analysis included final BW, ADG, ADFI, feed:gain ratio, HCW, dressing percentage, carcass length, lean yield, back fat thickness, loin muscle area, loin depth, postmortem pH, meat brightness, redness, and yellowness. The studies were grouped by similarity in 3 clusters (C1, C2, and C3) by hierarchical clustering on principle components. The main differences observed between clusters were those of animal initial weight, which increased from C1 through C3. Linear mixed models were used to analyze the data, where studies were assumed as random effect, whereas the total amount of RAC in the diet, cluster, and sex category were considered fixed effects. The interactions between cluster and sex category (barrows, gilts, and mixed sex) and RAC level were also evaluated. Dietary RAC was effective in improving final weight ( < 0.0001), ADG ( < 0.0001), and feed:gain ratio ( < 0.0001) and had a positive effect on HCW ( < 0.0001), lean yield ( = 0.0081), loin muscle area ( = 0.0190), and loin depth ( < 0.0001). In addition, a relatively limited effect on pork quality was observed in the current study. The RAC supplementation was more effective, mainly when pigs started supplementation with higher initial weight, although different responses were observed according to sex category ( < 0.05). There is ample indication that growth and carcass traits could be improved by dietary RAC supplementation. Ractopamine supplementation did not influence the pork quality.
The objective was to use various nonlinear models to describe scrotal circumference (SC) growth in Guzerat bulls on three farms in the state of Minas Gerais, Brazil. The nonlinear models were: Brody, ...Logistic, Gompertz, Richards, Von Bertalanffy, and Tanaka, where parameter A is the estimated testis size at maturity, B is the integration constant, k is a maturating index and, for the Richards and Tanaka models, m determines the inflection point. In Tanaka, A is an indefinite size of the testis, and B and k adjust the shape and inclination of the curve. A total of 7410 SC records were obtained every 3 months from 1034 bulls with ages varying between 2 and 69 months (<240 days of age = 159; 241–365 days = 451; 366–550 days = 1443; 551–730 days = 1705; and >731 days = 3652 SC measurements). Goodness of fit was evaluated by coefficients of determination (R2), error sum of squares, average prediction error (APE), and mean absolute deviation. The Richards model did not reach the convergence criterion. The R2 were similar for all models (0.68–0.69). The error sum of squares was lowest for the Tanaka model. All models fit the SC data poorly in the early and late periods. Logistic was the model which best estimated SC in the early phase (based on APE and mean absolute deviation). The Tanaka and Logistic models had the lowest APE between 300 and 1600 days of age. The Logistic model was chosen for analysis of the environmental influence on parameters A and k. Based on absolute growth rate, SC increased from 0.019 cm/d, peaking at 0.025 cm/d between 318 and 435 days of age. Farm, year, and season of birth significantly affected size of adult SC and SC growth rate. An increase in SC adult size (parameter A) was accompanied by decreased SC growth rate (parameter k). In conclusion, SC growth in Guzerat bulls was characterized by an accelerated growth phase, followed by decreased growth; this was best represented by the Logistic model. The inflection point occurred at approximately 376 days of age (mean SC of 17.9 cm). We inferred that early selection of testicular size might result in smaller testes at maturity.
ABSTRACT
The effect of ractopamine (RAC) supplementation on growth, carcass, and meat quality traits of finishing pigs was studied using a meta-analytical approach. The database was composed of 57 ...studies published from 2004 to 2016. The dependent variables extracted for the meta-analysis included final BW, ADG, ADFI, feed:gain ratio, HCW, dressing percentage, carcass length, lean yield, back fat thickness, loin muscle area, loin depth, postmortem pH, meat brightness, redness, and yellowness. The studies were grouped by similarity in 3 clusters (C1, C2, and C3) by hierarchical clustering on principle components. The main differences observed between clusters were those of animal initial weight, which increased from C1 through C3. Linear mixed models were used to analyze the data, where studies were assumed as random effect, whereas the total amount of RAC in the diet, cluster, and sex category were considered fixed effects. The interactions between cluster and sex category (barrows, gilts, and mixed sex) and RAC level were also evaluated. Dietary RAC was effective in improving final weight (P < 0.0001), ADG (P < 0.0001), and feed:gain ratio (P < 0.0001) and had a positive effect on HCW (P < 0.0001), lean yield (P = 0.0081), loin muscle area (P = 0.0190), and loin depth (P < 0.0001). In addition, a relatively limited effect on pork quality was observed in the current study. The RAC supplementation was more effective, mainly when pigs started supplementation with higher initial weight, although different responses were observed according to sex category (P < 0.05). There is ample indication that growth and carcass traits could be improved by dietary RAC supplementation. Ractopamine supplementation did not influence the pork quality.
•Worms and ticks cause considerable economic and productive losses in beef cattle.•The choice of statistical model can impact genetic analysis of parasite infestation.•Heritability estimates were low ...to moderate for both worm and tick infestation.•A better fit was observed for models employing non-Gaussian distributions.•Genetic selection and parasite control practices should be carried out together.
Worms and ticks are important parasites in beef cattle, especially in tropical areas, causing significant economic and production losses. Understanding animal-to-animal variation on infestation for these parasites might guide genetic selection and improvement of management practices to attenuate its detrimental effects. Statistical models used to analyze such traits usually assume a Gaussian distribution for the observed data. However, this assumption is quite often inappropriate for counting data. Therefore, the objectives of this study were: 1) Estimate genetic parameters for worms and tick infestations in Nellore cattle, and 2) To compare the overall performance of six data analysis approaches for worm and tick infestation in Nellore cattle, using different specifications of generalized linear mixed models (GLMM) and response variables. Data consisted of presence/absence of parasites as well as counting observations for both worms and ticks in a Nellore herd in Brazil. The binary data were analysed with both Gaussian and Threshold models, whereas the counting data were studied using Gaussian models on the original and logarithmic scales, as well as Poisson and Zero-Inflated Poisson (ZIP) models. All models included the systematic effects of contemporary group and age, as well as the random additive genetic and residual effects. Models were compared using four criteria: Deviance Information Criterion (DIC), Spearman's correlation between predicted breeding values from different models, the agreement on the 5 and 50% top-ranked animals across models, and the Mean Squared Error of Prediction (MSEP) assessed via Monte Carlo Cross-Validation (MCCV). The MCCV was performed using parallel computing through the Center for High Throughput Computing (CHTC) at the University of Wisconsin-Madison. The estimates of heritability ranged from 0.15 to 0.40 for worms and from 0.08 to 0.25 for ticks. According to the DIC, non-Gaussian models displayed the best goodness of fit compared to Gaussian models. DIC's results excluded Gaussian models on the logarithmic scale because fairer comparisons involve phenotypes on the same scale. Spearman's correlation and the percentage of agreement on the 5% and 50% top-ranked animals suggested some re-ranking of animals depending upon the model used. Monte Carlos Cross-Validation showed that all models presented similar MSEP with average values of 0.20 (binary data; worms), 0.18 (binary data; ticks), 15.69 (count data; worms), and 14.19 (count data; ticks). Moreover, performing MCCV in parallel had the benefit to deliver results for all models in about 2 days. Heritability estimates indicate that the selection of high merit animals for worms and tick infestation is possible feasible and can potentially contribute to the genetic progress. Furthermore, genetic selection should be performed concomitantly with traditional parasite control approaches. Overall, non-Gaussian models seem to be better suitable for genetic analysis of worm and tick infestation in beef cattle, because such models have lower DIC values with similar predictive performance compared to Gaussian models.
•Different statistical models for analyzing genetic parameters for longitudinal traits.•Simulation processes to validate field data analyzed by single trait model.•Sampling records influence over ...estimation of genetic parameters.
The comprehensive analyses of longitudinal traits under sequential selection could improve genetic parameters estimates and lead to more accurate selection decisions. The objective of this study was to evaluate statistical models for analyzing longitudinal traits under sequential selection. We used single trait (STM), multiple trait (MTM) and random regression model with linear splines polynomials (RRM) to estimate genetic parameters for body weight records of Nellore young bulls. First, we used a complete dataset (DS100) with 60,550 body weight records of 12,110 young bulls. Two additional datasets were also obtained from DS100. They were obtained with a sequential selection of 85% (DS85) and 70% (DS70) of heaviest animals. In addition, some datasets with the same number of records as DS85 and DS70 were also obtained with random sampling of 85% (RS85) and 70% (RS70) of body weight records at each age. Body weights were standardized at 330, 385, 440, 495 and 550 days of age for STM and MTM analysis. In RRM, the knots of linear splines were fitted at 250, 330, 385, 440, 495, 550 and 597 days of age. The estimates of additive genetic, residual and phenotypic variances from STM analysis of DS85 and DS70 were lower than the corresponding estimates from STM analysis of DS100. However, the estimates of genetic and environmental parameters from MTM and RRM analysis of DS100, DS85 and DS70 were similar. The reduction of dataset size with random sampling (RS85 and RS70) did not affect the estimates of genetic and environmental parameters from STM, MTM and RRM analysis. MTM and RRM are adequate for genetic evaluation of the longitudinal traits under sequential selection, but RRM presents some advantages over MTM. RRM with linear splines does not need previous adjustments of the body weights for standard ages and it also provides estimates of genetic and environmental parameters directly at the same points as the corresponding traits in MTM.
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