Simulation study of linear mixed models with contaminated normal distribution in animal breeding

Objetivou-se com esse trabalho comparar estimativas de componentes de vari?ncias obtidas por meio de modelos lineares mistos Gaussianos e Robustos, via Amostrador de Gibbs, em dados simulados. Foram simulados 50 arquivos de dados com 1.000 animais cada um, distribu?dos em cinco gera??es, em dois n?veis de efeito fixo e tr?s valores fenot?picos distintos para uma caracter?stica hipot?tica, com diferentes n?veis de contamina??o. Exceto para os dados sem contamina??o, quando os modelos foram iguais, o modelo Robusto apresentou melhores estimativas da vari?ncia residual. As estimativas de herdabilidade foram semelhantes em todos os modelos, mas as an?lises de regress?o mostraram que os valores gen?ticos preditos com uso do modelo Robusto foram mais pr?ximos dos valores gen?ticos verdadeiros. Esses resultados sugerem que o modelo linear normal contaminado oferece uma alternativa flex?vel para estima??o robusta em melhoramento gen?tico animal.Funda??o de Amparo ? Pesquisa do Estado de Minas Gerais (FAPEMIG)Empresa de Pesquisa Agropecu?ria de Minas Gerais (EPAMIG)Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq)Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)The objective of this study was to compare Gaussian and Robust linear mixed models for the estimation of variance components by REML and Gibbs Sampling, using data from fifty simulated populations consisting of 1,000 animals distributed in 5 generations. Two levels of fixed effect and three hypothetical phenotypic values for a trait, with different levels of contamination were used in the simulations. Additive and residual variance estimates were similar for both REML and Bayesian inference using the Gaussian and Robust model. The best estimates of residual variance in the presence of contaminants were obtained by the Robust model. Estimates of heritability were similar for all models, but regression analyses indicated that predicted genetic values obtained by the robust model were more similar to real breeding values. These results suggest that the contaminated normal linear model is a flexible alternative for robust estimation in animal breeding

Generalized linear mixed models for the genetic evaluation of binary reproductive traits: a simulation study

Funda??o de Amparo ? Pesquisa do Estado de Minas Gerais (FAPEMIG)Empresa de Pesquisa Agropecu?ria de Minas Gerais (EPAMIG)Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq)Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)The objective of this study was to evaluate the use of probit and logit link functions for the genetic evaluation of early pregnancy using simulated data. The following simulation/analysis structures were constructed: logit/logit, logit/probit, probit/logit, and probit/probit. The percentages of precocious females were 5, 10, 15, 20, 25 and 30% and were adjusted based on a change in the mean of the latent variable. The parametric heritability (h?) was 0.40. Simulation and genetic evaluation were implemented in the R software. Heritability estimates (h^2) were compared with h? using the mean squared error. Pearson correlations between predicted and true breeding values and the percentage of coincidence between true and predicted ranking, considering the 10% of bulls with the highest breeding values (TOP10) were calculated. The mean h^? values were under- and overestimated for all percentages of precocious females when logit/probit and probit/logit models used. In addition, the mean squared errors of these models were high when compared with those obtained with the probit/probit and logit/logit models. Considering (h^2), probit/probit and logit/logit were also superior to logit/probit and probit/logit, providing values close to the parametric heritability. Logit/probit and probit/logit presented low Pearson correlations, whereas the correlations obtained with probit/probit and logit/logit ranged from moderate to high. With respect to the TOP10 bulls, logit/probit and probit/logit presented much lower percentages than probit/probit and logit/logit. The genetic parameter estimates and predictions of breeding values of the animals obtained with the logit/logit and probit/probit models were similar. In contrast, the results obtained with probit/logit and logit/probit were not satisfactory. There is need to compare the estimation and prediction ability of logit and probit link functions