Genomic Bayesian Regression

The Genomic Bayesian Regression process builds predictive models using the Bayesian methods present in the BGLR package (Pérez and de los Campos, 2013¹). Genomic Bayesian regression is a form of regularized regression that allows for numerous, potentially correlated, predictors and shrinks them using a common variance component model. A complete guide to the BGLR package found at https://cran.r-project.org/web/packages/BGLR/vignettes/BGLR-extdoc.pdf.

What do I need?

You must have the latest version of base R installed on your machine. Refer to The R Project for Statistical Computing for downloads and more information. You must also download BGLR package. Use the R Package Manager to download and add this and other packages to your base R installation. You may also need to modify the sasv9.cfg file. Refer to the R Package Manager documentation for instructions.

One SAS data set is required: An input data set with one column per predictor variable (feature) and response variable (target).

Output/Results

The output generated by this process is summarized in a Tabbed report. Refer to the Genomic Bayesian Regression output documentation for detailed descriptions and guides to interpreting your results.

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1

Pérez P, de los Campos G. BGLR: a statistical package for whole genome regression and prediction. R package version 1. 0.2. 2013.