title: Genome-enabled prediction using the BLR (Bayesian Linear Regression) R-package.
abstract: 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.
authors.affiliation: Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA.
authors.forename: Gustavo
authors.initials: G
authors.lastname: de Los Campos
authors.forename: Paulino
authors.initials: P
authors.lastname: Pérez
authors.forename: Ana I
authors.initials: AI
authors.lastname: Vazquez
authors.forename: José
authors.initials: J
authors.lastname: Crossa
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001947:Breeding; D016678:Genome; D005838:Genotype; D016014:Linear Models; D008957:Models, Genetic; D010375:Pedigree; D010641:Phenotype; D010944:Plants / Q000235:genetics*; D011381:Programming Languages; D019655:Quantitative Trait, Heritable