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Originally published as Genetics Published Articles Ahead of Print on April 28, 2006.
Genetics, Vol. 173, 1761-1776, July 2006, Copyright © 2006
doi:10.1534/genetics.105.049510
Genomic-Assisted Prediction of Genetic Value With Semiparametric Procedures
Daniel Gianola*,
,
,1,
Rohan L. Fernando
and
Alessandra Stella
* Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706, Parco Tecnologico Padano, 26900 Lodi, Italy, Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, N-1432 Ås, Norway and Department of Animal Science, Iowa State University, Ames, Iowa 50011
1 Corresponding author: Department of Animal Sciences, 1675 Observatory Dr., Madison, WI 53706.
E-mail: gianola{at}ansci.wisc.edu
Semiparametric procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are presented. The methods focus on the treatment of massive information provided by, e.g., single-nucleotide polymorphisms. It is argued that standard parametric methods for quantitative genetic analysis cannot handle the multiplicity of potential interactions arising in models with, e.g., hundreds of thousands of markers, and that most of the assumptions required for an orthogonal decomposition of variance are violated in artificial and natural populations. This makes nonparametric procedures attractive. Kernel regression and reproducing kernel Hilbert spaces regression procedures are embedded into standard mixed-effects linear models, retaining additive genetic effects under multivariate normality for operational reasons. Inferential procedures are presented, and some extensions are suggested. An example is presented, illustrating the potential of the methodology. Implementations can be carried out after modification of standard software developed by animal breeders for likelihood-based or Bayesian analysis.
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