Genomic Assisted Prediction of Genetic Value with Semi-parametric Procedures

¹ University of Wisconsin-Madison
² Iowa State University
³ Parco Tecnologico Padano

^* To whom correspondence should be addressed. E-mail: gianola{at}ansci.wisc.edu.

Submitted on August 14, 2005
Revised on October 13, 2005
Accepted on 18 April 2006

	Abstract

Semi-parametric procedures for prediction of total genetic value for quantitative traits, that make use of phenotypic and genomic data simultaneously, are presented. The methods focus on the challenge posed by 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 the application of non-parametric procedures attractive. Kernel regression and reproducing kernel Hilbert spaces regression procedures are embedded in the context of the standard mixed effects linear model, retaining the additive genetic effect under multivariate normality for operational reasons. Inferential procedures are presented, and some extensions are suggested. Implementations can be carried out with standard software developed by animal breeders for likelihood-based or Bayesian analysis.

Key Words: Bayesian methods, kernel regression, marker assisted selection, quantitative genetics, semi-parametric procedures

This article has been cited by other articles:

				D. Gianola and J. B. C. H. M. van Kaam Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits Genetics, April 1, 2008; 178(4): 2289 - 2303. [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]