VIGoR: Variational Bayesian Inference for Genome-Wide Regression

• Main Authors: Akio Onogi, Hiroyoshi Iwata
• Format: Article
• Language: English
• Published: Ubiquity Press 2016-04-01
• Series: Journal of Open Research Software
• Subjects: Linear regression, variational Bayesian inference, genome-wide association, genomic prediction, variable selection
• Online Access: http://openresearchsoftware.metajnl.com/articles/80

Genome-wide regression using a number of genome-wide markers as predictors is now widely used for genome-wide association mapping and genomic prediction. We developed novel software for genome-wide regression which we named VIGoR (variational Bayesian inference for genome-wide regression). Variational Bayesian inference is computationally much faster than widely used Markov chain Monte Carlo algorithms. VIGoR implements seven regression methods, and is provided as a command line program package for Linux/Mac, and as a cross-platform R package. In addition to model fitting, cross-validation and hyperparameter tuning using cross-validation can be automatically performed by modifying a single argument. VIGoR is available at https://github.com/Onogi/VIGoR. The R package is also available at https://cran.r-project.org/web/packages/VIGoR/index.html.