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Journal of Animal Science Abstract - Animal Genetics

Technical Note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding1


This article in JAS

  1. Vol. 91 No. 8, p. 3522-3531
    Received: Dec 07, 2012
    Accepted: Apr 16, 2013
    Published: November 25, 2014

    2 Corresponding author(s):

  1. P. Pérez-Rodríguez 2,
  2. D. Gianola†‡§,
  3. K. A. Weigel,
  4. G. J. M. Rosa†§ and
  5. J. Crossa#
  1. Colegio de Postgraduados, Km. 36.5 Carretera Mexico-Texcoco, C.P. 56230
    Department of Animal Sciences
    Department of Dairy Science
    Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, 53706
    Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico D.F., Mexico


In recent years, several statistical models have been developed for predicting genetic values for complex traits using information on dense molecular markers, pedigrees, or both. These models include, among others, the Bayesian regularized neural networks (BRNN) that have been widely used in prediction problems in other fields of application and, more recently, for genome-enabled prediction. The R package described here (brnn) implements BRNN models and extends these to include both additive and dominance effects. The implementation takes advantage of multicore architectures via a parallel computing approach using openMP (Open Multiprocessing) for the computations. This note briefly describes the classes of models that can be fitted using the brnn package, and it also illustrates its use through several real examples.

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