J. Anim Sci.
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Published online first on February 11, 2009
J. Anim Sci. 1910. doi:10.2527/jas.2008-1259
© 2009 American Society of Animal Science
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Reproducing Kernel Hilbert Spaces Regression: a General Framework for Genetic Evaluation

Gustavo de los Campos*, Daniel Gianola*,{dagger},{ddagger} and Guilherme J. M. Rosa{dagger}

* Departments of Animal Sciences, University of Wisconsin-Madison , {dagger} Dairy Science, University of Wisconsin-Madison {ddagger} Biostatistics and Medical Informatics, University of Wisconsin-Madison

gdeloscampos{at}wisc.edu

Abstract

Reproducing Kernel Hilbert Spaces (RKHS) methods are widely used for statistical learning in many areas of endeavor. Recently, these methods have been suggested as a way of incorporating dense markers into genetic models. This note argues that RKHS regression provides a general framework for genetic evaluation that can be used either for pedigree or marker based regressions and under any genetic model infinitesimal or not, additive or not. Most of the standard models for genetic evaluation, such as infinitesimal animal or sire models, and marker-assisted selection models appear as special cases of RKHS methods.

Key Words: animal model • dense markers • marker-assisted selection • MAS • reproducing kernel Hilbert spaces • sire model




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 GeneticsHome page
D. Gianola, G. de los Campos, W. G. Hill, E. Manfredi, and R. Fernando
Additive Genetic Variability and the Bayesian Alphabet
Genetics, September 1, 2009; 183(1): 347 - 363.
[Abstract] [Full Text] [PDF]


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