Genetics, Vol. 178, 2289-2303, April 2008, Copyright © 2008
doi:10.1534/genetics.107.084285

Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits

* Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706, {dagger} Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, N-1432 Ås, Norway, {ddagger} Scienze Entomologiche, Fitopatologiche, Microbiologiche Agrarie e Zootecniche, Universitá degli Studi di Palermo, 90128 Palermo, Italy and § Istituto Zooprofilattico Sperimentale della Sicilia "A. Mirri," 90129 Palermo, Italy

1 Corresponding author: Department of Animal Sciences, University of Wisconsin, 1675 Observatory Dr., Madison, WI 53706.
E-mail: gianola{at}ansci.wisc.edu

Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components. Models for capturing different forms of interaction, e.g., chromosome-specific, are presented. Implementations can be carried out using software for likelihood-based or Bayesian inference.




This article has been cited by other articles:


Home page
J ANIM SCIHome page
G. de los Campos, D. Gianola, and G. J. M. Rosa
Reproducing kernel Hilbert spaces regression: A general framework for genetic evaluation
J Anim Sci, June 1, 2009; 87(6): 1883 - 1887.
[Abstract] [Full Text] [PDF]


Home page
GeneticsHome page
G. de los Campos, H. Naya, D. Gianola, J. Crossa, A. Legarra, E. Manfredi, K. Weigel, and J. M. Cotes
Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree
Genetics, May 1, 2009; 182(1): 375 - 385.
[Abstract] [Full Text] [PDF]


Home page
Crop Sci.Home page
E. L. Heffner, M. E. Sorrells, and J.-L. Jannink
Genomic Selection for Crop Improvement
Crop Sci., January 28, 2009; 49(1): 1 - 12.
[Abstract] [Full Text] [PDF]


Home page
J ANIM SCIHome page
N. Long, D. Gianola, G. J. M. Rosa, K. A. Weigel, and S. Avendano
Marker-assisted assessment of genotype by environment interaction: A case study of single nucleotide polymorphism-mortality association in broilers in two hygiene environments
J Anim Sci, December 1, 2008; 86(12): 3358 - 3366.
[Abstract] [Full Text] [PDF]


Home page
GeneticsHome page
O. Gonzalez-Recio, D. Gianola, N. Long, K. A. Weigel, G. J. M. Rosa, and S. Avendano
Nonparametric Methods for Incorporating Genomic Information Into Genetic Evaluations: An Application to Mortality in Broilers
Genetics, April 1, 2008; 178(4): 2305 - 2313.
[Abstract] [Full Text] [PDF]