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Journal of Animal Science Abstract - Animal Genetics

Technical note: An R package for fitting generalized linear mixed models in animal breeding1


This article in JAS

  1. Vol. 88 No. 2, p. 497-504
    Received: Mar 10, 2009
    Accepted: Oct 02, 2009
    Published: December 4, 2014

    2 Corresponding author(s):

  1. A. I. Vazquez 2,
  2. D. M. Bates,
  3. G. J. M. Rosa*,
  4. D. Gianola*‡ and
  5. K. A. Weigel*
  1. Department of Dairy Science,
    Department of Statistics, and
    Department of Animal Sciences, University of Wisconsin, Madison 53706



Mixed models have been used extensively in quantitative genetics to study continuous and discrete traits. A standard quantitative genetic model proposes that the effects of levels of some random factor (e.g., sire) are correlated accordingly with their relationships. For this reason, routines for mixed models available in standard packages cannot be used for genetic analysis. The pedigreemm package of R was developed as an extension of the lme4 package, and allows mixed models with correlated random effects to be fitted for Gaussian, binary, and count responses. Following the method of Harville and Callanan (1989), a correlation between levels of the grouping factor (e.g., sire) is induced by post-multiplying the incidence matrix of the levels of this random factor by the Cholesky factor of the corresponding (co)variance matrix (e.g., the numerator relationship matrix between sires). Estimation methods available in pedigreemm include approximations to maximum likelihood and REML. This note describes the classes of models that can be fitted using pedigreemm and presents examples that illustrate its use.

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