Genome-wide association studies (GWAS) and genome-enabled prediction (GP) in plants serve as an important starting point for breeding programs. To maximize their potential, it is necessary to evaluate phenotypes that are relevant in target field environments. Thus, complex experiments with a large environmental influence are often required. However, these experiments produce noisy data that may obscure the contribution of genetic variation. Most modern statistical methods for GWAS and GP have been developed for animals, where each genetically distinct individual is evaluated only once and the confounding effects of population structure are not strong. These methods are not adequate for most applications in rice and other crop species. We have developed a general Bayesian multi-trait approach that accounts for noise in the data, including low heritability, unbalanced designs, missingness, outlier observations, and systematic experimental bias. We also implemented steps to correct for deep population structure (e.g., differentiation between O. s. indica and japonica). We illustrate our methods on simulated data. A C++ library, MuGen, that efficiently implements our techniques is freely available on GitHub (https://github.com/tonymugen/MuGen).
