VIGoR: Variational Bayesian Inference for Genome-Wide Regression. Issue 1 (4th April 2016)

• Record Type: Journal Article
• Authors: Onog, Akio; Iwata, Hiroyoshi
• Is Part Of: Journal of open research software. Volume 4:Issue 1(2016)
• Publication Date: 2016-04-04
• Subjects: Genome-wide association study -- GWAS -- genomic selection -- whole-genome prediction; Computer software -- Reusability -- Periodicals; Open source software -- Periodicals; 005
• DOI: 10.5334/jors.80
• Languages: English
• ISSNs: 2049-9647

Abstract

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 athttps://github.com/Onogi/VIGoR . The R package is also available athttps://cran.r-project.org/web/packages/VIGoR/index.html .