Multitrait Bayesian shrinkage and variable selection models with the BGLR-R package |
| |
Authors: | Paulino Pé rez-Rodrí guez,Gustavo de los Campos |
| |
Affiliation: | Colegio de Postgraduados, Montecillo, Estado de México 56230, Mexico;Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA;Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA;Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA |
| |
Abstract: | The BGLR-R package implements various types of single-trait shrinkage/variable selection Bayesian regressions. The package was first released in 2014, since then it has become a software very often used in genomic studies. We recently develop functionality for multitrait models. The implementation allows users to include an arbitrary number of random-effects terms. For each set of predictors, users can choose diffuse, Gaussian, and Gaussian–spike–slab multivariate priors. Unlike other software packages for multitrait genomic regressions, BGLR offers many specifications for (co)variance parameters (unstructured, diagonal, factor analytic, and recursive). Samples from the posterior distribution of the models implemented in the multitrait function are generated using a Gibbs sampler, which is implemented by combining code written in the R and C programming languages. In this article, we provide an overview of the models and methods implemented BGLR’s multitrait function, present examples that illustrate the use of the package, and benchmark the performance of the software. |
| |
Keywords: | Bayesian high-dimensional regression multivariate models multitrait models Gibbs sampling genomic regressions pedigree regressions Genomic Prediction GenPred Shared Data Resource |
|
|