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- Title
bWGR: Bayesian whole-genome regression.
- Authors
Xavier, Alencar; Muir, William M; Rainey, Katy M
- 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. Availability and implementation bWGR is an R package available in the CRAN repository. It can be installed in R by typing: install.packages('bWGR'). Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects
GIBBS sampling; FORECASTING
- Publication
Bioinformatics, 2020, Vol 36, Issue 6, p1957
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btz794