Bayesian inference to study genetic control of resistance to gray leaf spot in maize

Abstract

Gray leaf spot (GLS) is a major maize disease in Brazil that significantly affects grain production. We used Bayesian inference to investigate the nature and magnitude of gene effects related to GLS resistance by evaluation of contrasting lines and segregating populations. The experiment was arranged in a randomized block design with three replications and the mean values were analyzed using a Bayesian shrinkage approach. Additive-dominant and epistatic effects and their variances were adjusted in an over-parametrized model. Bayesian shrinkage analysis showed to be an excellent approach to handle complex models in the study of genetic control in GLS, since this approach allows to handle overparametrized models (main and epistatic effects) without using model-selection methods. Genetic control of GLS resistance was predominantly additive, with insignificant influence of dominance and epistasis effects.