Genetic parameters and selection of maize cultivars using Bayesian inference in a multi-trait linear model

Variance components must be obtained to estimate genetic parameters and predict breeding values. In studies which take many traits into account, it is reasonable to use the Bayesian approach for the estimation of genetic parameters. The main goal of the present research was not only to consider the genetic correlations of the examined traits, but above all to estimate unknown genetic parameters and to gain profits from the selection. Bayesian inference was also useful for the selection of the best maize varieties. It was applied to predict genetic values in the multi-traits linear model. Thirteen maize cultivars representing the traits of our interest were studied by means of Bayesian inference. The traits are the number of plants before harvest, the grain yield, the length of the ears, the mass of leaves and the number of ears. The experiment involved a randomised block design with four replications and ten plants per plot. The highest correlation estimates were found between the number of plants before harvest and the number of ears, jointly with the grain yield and the number of ears. Lower correlation estimates were found between the length of the ears and the number of ears as well as the grain yield and the length of the ears. The research confirms that the best varieties to be grown are: Clarica, NK Cooler, Drim and PR 39K13. The Bayesian approach proved to be useful in selection studies, which can further be used to improve the studied genotypes.