Reproducing kernel Hilbert spaces regression: a general framework for genetic evaluation

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, 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.