- THIS ARTICLE
- Full Text
- Full Text (PDF)
- Alert me when this article is cited
- Alert me if a correction is posted
- SERVICES
- Email this article to a friend
- Similar articles in this journal
- Similar articles in PubMed
- Alert me to new issues of the journal
- Download to citation manager
- Reprints & Permissions
- CITING ARTICLES
- Citing Articles via HighWire
- Citing Articles via Google Scholar
- GOOGLE SCHOLAR
- Articles by Gianola, D.
- Articles by van Kaam, J. B. C. H. M.
- Search for Related Content
- PUBMED
- PubMed Citation
- Articles by Gianola, D.
- Articles by van Kaam, J. B. C. H. M.
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
Daniel Gianola*,
,
,1 and
Johannes B. C. H. M. van Kaam
* Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706, Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, N-1432 Ås, Norway, 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:
 |
|
 |
|
  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]
|
|
|
|
 |
|
 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]
|
|
|
|
 |
|
 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]
|
|
|
|
|
|
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]
|
|
|
|
|
|
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]
|
|