Características do pelame de vacas Holandesas em ambiente tropical: um estudo genético e adaptativo

As características do pelame (espessura da capa, comprimento médio dos pêlos, número de pêlos por unidade de área, densidade de massa dos pêlos, ângulo de inclinação dos pêlos com respeito a superfície da epiderme e diâmetro médio do pêlos) foram avaliadas em 973 vacas da raça Holandesa, entre novembro de 2000 e abril de 2001, numa área localizada 20cm abaixo da coluna vertebral, no centro do tronco, tanto nas malhas brancas como nas negras. As amostras de pêlos foram obtidas com um alicate comum adaptado. O método da Máxima Verossimilhança Restrita (REML) foi usado para estimar os componentes de variância e covariância sob modelo animal, sendo empregado o sistema MTDFREML. Os resultados mostraram que as características do pelame preto são diferentes das do branco, quando os animais são criados em ambiente tropical. O pelame preto apresentou-se menos denso, com pêlos mais curtos e grossos devido à maior necessidade de perder calor, enquanto que, o pelame branco é mais denso e com pêlos mais compridos, oferecendo uma melhor proteção contra à radiação solar direta. A seleção de vacas predominantemente negras pode ser uma boa escolha para aumentar a resistência do gado Holandês às condições do ambiente tropical, principalmente à radiação solar, quando esses animais são criados a campo, devido a que a epiderme sob esse tipo de malhas é altamente pigmentada. Tal seleção pode ser facilmente realizada, considerando a alta herdabilidade (h²=0,75) para a proporção de malhas negras. Esta seleção deve ser realizada no sentido de um pelame menos denso, com pêlos curtos e grossos favorecendo as perdas de calor sensível e calor latente.

Hair coat characteristics and sweating rate of Braford cows in Brazil

This paper deals with the effects of hair coat characteristics on the sweating rate of Brazilian Braford cows and estimation of heritabilities and genetic correlations of these traits. Data (n=1607) on hair length, coat thickness, hair diameter, number of hairs per unit area, coat reflectance and sweating rate were recorded from heifers and cows of a commercial herd managed on range under extensive system. The data were analyzed considering the following effects on the model for hair coat traits: classes of sires and contemporary groups; linear effects of month and genotype; linear and quadratic effects of age. The effect of sire was important (P<0.05) for all hair coat traits, except for number of hairs; contemporary groups affected (P<0.05) all hair coat traits; the effect of sampling month was important (P<0.05) for hair length and reflectance; genotype affected (P<0.05) hair length, diameter and coat reflectance; the quadratic effect of age was important (P<0.05) only for coat reflectance. Two models were used to analyze the sweating rate. The first model considered the following fixed effects: classes of contemporary groups and sires; linear effect of genotype, coat thickness, hair length, hair diameter, number of hairs, coat reflectance; linear and quadratic effects of time of day, age, air temperature, partial vapour pressure and radiant heat load. The second model used for the sweating rate considered the same fixed effects for the first model, except that the hair coat characteristics were adjusted for important effects used in the models to analyze hair coat traits. All meteorological factors and contemporary groups were important (P<0.05) on variation of sweating rate in both models. The Restricted Maximum. Likelihood (REML) method was used to estimate variance and covariance components under the sire model. Results included heritability estimates in narrow (h(2)) and broad (H) sense for single-trait analyzes: hair thickness (h(2)=0.16; H-2=0.26); hair length (h(2)=0.18; H-2=0.39); number of hairs (h(2)=0.08 +/- 0.07; H-2=0.08 +/- 0.07); hair diameter (h(2)=0.12 +/- 0.07; H-2=0.12 +/- 0.07); coat reflectance (h(2)=0.30; H-2=0.42); and sweating rate (h(2)=0.10 +/- 0.07; H-2=0.10 +/- 0.07). In general, the genetic correlations between the adaptive traits were favorable as for the direction to select for adaptation in tropical environment; however, they presented high standard errors. The results of this study imply that hair coat characteristics and sweating ability are important for the adaptability to heat stress and they must be better studied and further considered for selection for genetic progress of adaptation in tropical environment. (C) 2007 Elsevier B.V All rights reserved.

Fatores genéticos e de meio no intervalo de partos de caprinos leiteiros

The kidding intervals (IDP) of dairy goats raised in Southeastern Brazil were studied to quantify the influence of environmental factors and to estimate genetic parameters by means of least squares (MMQ) and restricted maximum likelihood (REML). The data analyzed were obtained from five farms and three breeds (Alpine, Saanen, and Toggenburg). The overall mean and standard error of IDP, as estimated by MMQ, were 339 +/- 12.70 days. The interaction of year x season of parturition influenced IDP. In two of the years studied, goats kidding at the end of the kidding season showed a shorter IDP when compared to those that were kidding after the end of the season. A quadratic trend of IDP over years was observed across the three kidding periods. For the three seasons. IDP increased from 1986 until mid-1989 and decreased thereafter. The heritability and repeatability of IDP, as estimated by MMQ and REML, were: 0.046 +/- 0.071 and 0.103 +/- 0.043, and 0.00026 and 0.08411, respectively. These estimates indicate that little genetic gain can be expected from selection for this trait.

Abordagens freqüentista e bayesiana para avaliação genética de bovinos da raça Canchim para características de crescimento

This study aimed to: a) to compare the covariance components obtained by Restricted Maximum Likelihood (REML) and by bayesian inference (BI): b) to run genetic evaluations for weights of Canchim cattle measured at weaning (W240) and at eighteen months of age (W550), adjusted or not to 240 and 550 days of age, respectively, using the mixed model methodology with covariance components obtained by REML or by BI; and c) to compare selection decisions from genetic evaluations using observed or adjusted weights and by REML or BI. Covariance components, heritabilities and genetic correlation for W240 and W550 were estimated and the predicted breeding values were used to select 10% and 50% of the best bulls and cows, respectively. The covariance components obtained by REML were smaller than the a posteriori means obtained by Bl. Selected animals from both procedures were not the same, probably because the covariance components and genetic parameters were different. The inclusion of age of animal at weighing as a covariate in the statistical model fitted by BI did not change the selected bulls and cows.

Inferência bayesiana na predição de valores genéticos do peso aos 365 dias de bovinos de corte

Data from purebred Simmental, Nellore and Canchim cattle breeds obtained from the respective Brazilian Associations of Breeders were used to estimate variance components and to predict genetic values for 365 days weight. The results obtained by Bayesian inference were compared to those from Restricted Maximum Likelihood (REML) and Best Linear Unbiased Prediction (BLUP), which are the most commonly used methods of estimation and prediction in animal breeding. The two methods presented similar point estimates but the study of the marginal posterior distributions in the Bayesian approach yields more detailed information about the parameters and other unknowns in the model.

Genetic relationship between reproductive traits in Nellore cattle

Pós-graduação em Genética e Melhoramento Animal - FCAV

Estimativas de parâmetros genéticos para características de crescimento e de carcaça em ovinos de corte

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Efeito de marcadores moleculares sobre características de crescimento e reprodutivas de bovinos da raça Canchim

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Avaliação dos benefícios da adoção de boas práticas de manejo no bem-estar de bezerros leiteiros

Pós-graduação em Zootecnia - FCAV

Association of apolipoprotein B and adiponectin receptor 1 genes with carcass, bone integrity and performance traits in a paternal broiler line

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Effect of nonnormal random components on level and power in group-randomized trials

The use of group-randomized trials is particularly widespread in the evaluation of health care, educational, and screening strategies. Group-randomized trials represent a subset of a larger class of designs often labeled nested, hierarchical, or multilevel and are characterized by the randomization of intact social units or groups, rather than individuals. The application of random effects models to group-randomized trials requires the specification of fixed and random components of the model. The underlying assumption is usually that these random components are normally distributed. This research is intended to determine if the Type I error rate and power are affected when the assumption of normality for the random component representing the group effect is violated. ^ In this study, simulated data are used to examine the Type I error rate, power, bias and mean squared error of the estimates of the fixed effect and the observed intraclass correlation coefficient (ICC) when the random component representing the group effect possess distributions with non-normal characteristics, such as heavy tails or severe skewness. The simulated data are generated with various characteristics (e.g. number of schools per condition, number of students per school, and several within school ICCs) observed in most small, school-based, group-randomized trials. The analysis is carried out using SAS PROC MIXED, Version 6.12, with random effects specified in a random statement and restricted maximum likelihood (REML) estimation specified. The results from the non-normally distributed data are compared to the results obtained from the analysis of data with similar design characteristics but normally distributed random effects. ^ The results suggest that the violation of the normality assumption for the group component by a skewed or heavy-tailed distribution does not appear to influence the estimation of the fixed effect, Type I error, and power. Negative biases were detected when estimating the sample ICC and dramatically increased in magnitude as the true ICC increased. These biases were not as pronounced when the true ICC was within the range observed in most group-randomized trials (i.e. 0.00 to 0.05). The normally distributed group effect also resulted in bias ICC estimates when the true ICC was greater than 0.05. However, this may be a result of higher correlation within the data. ^

Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical Study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which tender the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

Log-modified Weibull regression models with censored data: Sensitivity and residual analysis

This paper proposes a regression model considering the modified Weibull distribution. This distribution can be used to model bathtub-shaped failure rate functions. Assuming censored data, we consider maximum likelihood and Jackknife estimators for the parameters of the model. We derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes and we also present some ways to perform global influence. Besides, for different parameter settings, sample sizes and censoring percentages, various simulations are performed and the empirical distribution of the modified deviance residual is displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for a martingale-type residual in log-modified Weibull regression models with censored data. Finally, we analyze a real data set under log-modified Weibull regression models. A diagnostic analysis and a model checking based on the modified deviance residual are performed to select appropriate models. (c) 2008 Elsevier B.V. All rights reserved.