USING MULTIVARIATE ANALYSIS OF COVARIANCE IN GENETIC PARAMETER ESTIMATION

Abstract

In this study, it is aimed to use multivariate analysis of covariance (MANCOVA) as an alternative approach for estimating genetic parameters and to define the efficiency of the MANCOVA method in the estimation of variance components by comparing the results to the ML and ANOVA methods. The data are collected by simulation. A mixed model, in which both fixed and random factors exist, is employed. For the MANCOVA model, the dependent variables are defined as milk yield and birth weight, and the covariances are determined as dry period and dam weight. In comparing the variance components derived from different analysis methods, the study is based on the criteria that the environmental variance should be low and that the ratio of the environmental variance to the total variance should be low. In the comparison of the methods, the lowest environmental variances for the dependent variables are obtained from MANCOVA as 0.27317 and 21.58371 for milk yield and birth weight, respectively. Comparing the ratio of the environmental variance to the total variance, both for milk yield (0.2647) and birth weight (0.9634), the lowest values are obtained from the MANCOVA method. In mixed models and in the case of balanced and normally distributed data, MANCOVA is ascertained to be an alternative to ML and ANOVA for estimating genetic parameters.