hibayes: An R Package to Fit Individual-Level, Summary-Level and Single-Step Bayesian Regression Models for Genomic Prediction and Genome-Wide Association Studies

Abstract

With the rapid development of sequencing technology, the costs of individual genotyping have been reduced dramatically, leading to genomic prediction and genome-wide association studies being widely promoted and used to predict the unknown phenotypes and to locate candidate genes for animal and plant economic traits and, increasingly, for human diseases. Developing new advanced statistical models to improve prediction accuracy and location precision for the traits with various genetic architectures has always been a hot topic in those two research domains. The Bayesian regression model (BRM) has played a crucial role in the past decade, and it has been used widely in relevant genetic analyses owing to its flexible model assumptions on the unknown genetic architecture of complex traits. To fully utilize the available data from either a self-designed experimental population or a public database, statistical geneticists have constantly extended the fitting capacity of BRM, and a series of new methodologies have been proposed for different application scenarios. Here we introduce the R package hibayes, a software tool that can be used to fit individual-level, summary-level, and single-step Bayesian regression models. Including also the richest methods achieved thus far, it covers most of the functionalities involved in the field of genomic prediction and genome-wide association studies, potentially helping to address a wide range of research problems, while retaining an easy-to-learn and flexible-to-use experience. We believe that package hibayes will facilitate the academic research and practical application of statistical genetics for humans, plants, and animals.