Bayesian estimation of genotypic and phenotypic correlations from crop variety trials

Abstract

Genotypic and phenotypic correlations are necessary for constructing indirect selection indices. Bayesian analysis, therefore, was applied to obtain posterior distributions of the correlations, and the estimates were compared with those under a frequentist approach. Three a priori distributions for standard deviation components based on uniform distribution, positive values from t- distribution, and positive values from normal distribution were examined, while a priori distribution for correlation was taken as a uniform distribution. The prior based on uniform was best found using the deviation information criterion. Data from sorghum genotypes evaluated in complete blocks in 2010-2011 in Northern Kordofan, Sudan, resulted in a posterior mean of 0.48 for genotypic correlation between seed yield and seed weight with posterior standard deviation of 0.24. Due to a wider inference base and the fact that it makes use of prior information, we recommend the Bayesian approach in estimation of genotypic correlations.