Genome-Enabled Prediction Using the BLR (Bayesian Linear Regression) R-Package

  • Gustavo de los Campos
  • Paulino Pérez
  • Ana I. Vazquez
  • José Crossa
Part of the Methods in Molecular Biology book series (MIMB, volume 1019)


The BLR (Bayesian linear regression) package of R implements several Bayesian regression models for continuous traits. The package was originally developed for implementing the Bayesian LASSO (BL) of Park and Casella (J Am Stat Assoc 103(482):681–686, 2008), extended to accommodate fixed effects and regressions on pedigree using methods described by de los Campos et al. (Genetics 182(1):375–385, 2009). In 2010 we further developed the code into an R-package, reprogrammed some internal aspects of the algorithm in the C language to increase computational speed, and further documented the package (Plant Genome J 3(2):106–116, 2010). The first version of BLR was launched in 2010 and since then the package has been used for multiple publications and is being routinely used for genomic evaluations in some animal and plant breeding programs. In this article we review the models implemented by BLR and illustrate the use of the package with examples.

Key words

Genomic selection Whole-genome prediction Bayesian Shrinkage and genomic prediction 



de los Campos, Pérez, and Crossa acknowledge financial support from the International Maize and Wheat Improvement Center (CIMMYT). Pérez and de los Campos were also supported by NIH Grants R01GM101219-01 and R01GM099992-01A1.


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Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  • Gustavo de los Campos
    • 1
  • Paulino Pérez
    • 2
  • Ana I. Vazquez
    • 1
  • José Crossa
    • 3
  1. 1.Department of BiostatisticsUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.Statistics and Computer Science DepartmentsColegio de PostgraduadosMexicoMexico
  3. 3.Biometrics and Statistics Unit, Crop Research Informatics LabInternational Maize and Wheat Improvement Center (CIMMYT)MéxicoMexico

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