bWGR: Bayesian whole-genome regression

Abstract

Motivation: Whole-genome regressions methods represent a key framework for genome-wide prediction, cross-validation studies and association analysis. The bWGR offers a compendium of Bayesian methods with various priors available, allowing users to predict complex traits with different genetic architectures. Results: Here we introduce bWGR, an R package that enables users to efficient fit and cross-validate Bayesian and likelihood whole-genome regression methods. It implements a series of methods referred to as the Bayesian alphabet under the traditional Gibbs sampling and optimized expectation-maximization. The package also enables fitting efficient multivariate models and complex hierarchical models. The package is user-friendly and computational efficient.