26. Estimation of Breeding Values
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since 17 October 1995 |
Updated notes
Shayle Searle has just published some excellent notes (around 250 pages) that are a mathematical supplement to Henderson's classic book Applications of linear models in animal breeding. These are rather detailed and are quite informative.
Dr. L. R. Schaeffer (Department of Animal & Poultry Science, University of Guelph, Ontario) has an outstanding set on notes on advanced topics in
Animal Models .
Programs
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MTGSAM (Multiple Trait Gibbs Sampler for Animal Models) is a set of Fortran programs for Gibbs sampling in animal models. The programs support a flexible set of multiple trait models; the programs support the same model options as the MTDFREML programs. The programs generate the Gibbs sampling information and the posterior mean estimates for a variety
of parameters including (co)variance components, heritabilities, and
correlations. The programs were developed by Curt Van Tassell at the USDA Meat Animal Research Center and the University of Nebraska.
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MTDFREML (Multiple Trait Derivative Free REstricted Maximum Likelihood) is a flexible set of FORTRAN programs which can be used to
estimate variance components using a derivative free restricted maximum
likelihood algorithm. The programs were developed by Keith Boldman, Lisa
Kriese, Dale Van Vleck, Curt Van Tassell, and Steve Kachman at the USDA
Meat Animal Research Center and the University of Nebraska. The programs
are designed to be used with animal breeding data where an animal genetic
effect is used for each trait, however it is often straightforward to apply
the programs to non-animal breeding data. The programs can handle an arbitrary number of fixed effects, uncorrelated random effects and covariates for a number of traits. The program also generates BLUP solutions to the mixed model equations, contrasts of solutions, prediction error variances of solutions and contrasts, and calculates expected values of solutions.
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Created 25 February 1995, last updated 25 April 1999
Bruce Walsh. jbwalsh@u.arizona.edu .
Comments welcome.