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This Article Full Text Full Text (PDF) All Versions of this Article: jas.2008-1259v1 87/6/1883    most recent Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Google Scholar Articles by de los Campos, G. Articles by Rosa, G. J. M. PubMed PubMed Citation Articles by de los Campos, G. Articles by Rosa, G. J. M.

Reproducing kernel Hilbert spaces regression: A general framework for genetic evaluation¹

G. de los Campos^*,2, D. Gianola^*,, and G. J. M. Rosa

^* Departments of Animal Sciences, and Dairy Science, and Biostatistics and Medical Informatics, University of Wisconsin, Madison 53706

² Corresponding author: gdeloscampos{at}wisc.edu

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, and 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 marker • marker-assisted selection • reproducing kernel Hilbert spaces • sire model

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