Bayesian Discrete Lognormal Regression Model for Genomic Prediction

Abstract

Genomic selection is a powerful tool in modern breeding programs that uses genomic information to predict the performance of individuals and select those with desirable traits. It has revolutionized animal and plant breeding, as it allows breeders to identify the best candidates without labor-intensive and time-consuming phenotypic evaluations. While several statistical models have been developed, most of them have been for quantitative continuous traits and only a few for count responses. In this paper, we propose a discrete lognormal regression model in the Bayesian context, developed using the inference by Gibbs sampler to explore the corresponding posterior distribution and make the predictions. A data set of resistance disease is used in the wheat crop and is then evaluated against the traditional Gaussian model and a lognormal model over the located response. The results indicate the proposed model is a competitive and natural model for predicting count genomic traits.