The Role of Prior-Induced Regularization in Accuracy and Stability of Genomic Prediction Across Unimodal and Multimodal Models

Abstract

In this study, we assessed the impact of prior-induced regularization using six real datasets from wheat, rice, and potato, spanning 107–758 genotypes, 2–12 environments, 1–18 traits, and 2744–108,024 molecular markers. Two modeling scenarios were evaluated: (i) unimodal genomic prediction based solely on marker information and (ii) multimodal (multi-component) prediction integrating genomic, environmental, and genotype-by-environment (G × E) effects. Predictive performance was evaluated using Pearson’s correlation (COR) and normalized root mean squared error (NRMSE) under 10 repeated random 50% training–50% testing partitions, representing prediction of untested lines in tested environments. Bayesian genomic prediction (BGP) relies on prior distributions to regulate shrinkage and stabilize inference in high-dimensional settings. We evaluated whether predictive performance was driven primarily by the type of Bayesian prior or by the presence of effective prior-induced regularization. Across most datasets, regularized Bayesian models achieved higher predictive correlations and markedly lower NRMSE than the weakly regularized or unregularized baseline. Differences among regularized prior families were generally modest, whereas weakening or removing regularization frequently produced unstable estimates and inflated prediction error. Predictive results were obtained for both winter-wheat datasets as well as for the rice, potato, and DMario datasets. In multimodal analyses, models with coherent regularization across genomic, environmental, and genotype-by-environment components were generally more accurate and stable than configurations in which regularization was absent or weakened in key components. Rice_Kim_2020 was an informative exception in which the baseline remained competitive. These results show that the principal empirical contrast is the presence versus absence of effective prior-induced regularization, rather than a universal ranking of Bayesian prior families. Appropriate regularization should therefore be treated as a central model-design decision in genomic prediction.