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 causal or candidate genes for animal and plant economic traits and, increasingly, for human diseases. Developing newly advanced statistical models to improve accuracy in predicting and locating 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 hibayes, which is the only one tool that can implement three types of Bayesian regression models. With the richest methods achieved thus far, it covers almost all the functions involved in genetic analyses for genomic prediction and genome-wide association studies, potentially addressing a wide range of research problems, while retaining an easy-to-use and transparent interface. We believe that hibayes will facilitate the researches conducted by human geneticists, as well as plant and animal breeders. The hibayes package is freely available from CRAN at https://cran.r-project.org/package=hibayes.