Genetics, Vol. 163, 347-365, January 2003, Copyright © 2003

On Marker-Assisted Prediction of Genetic Value: Beyond the Ridge

Daniel Gianolaa, Miguel Perez-Encisob, and Miguel A. Toroc
a Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706,
b Station d'Amelioration Génétique des Animaux, Institut National de la Recherche Agronomique, 31326 Castanet-Tolosan, France
c Departamento de Mejora Genética Animal, Instituto Nacional de Investigaciones Agrarias, 28040-Madrid, Spain

Corresponding author: Daniel Gianola, 1675 Observatory Dr., Madison, WI 53706., gianola{at}calshp.cals.wisc.edu (E-mail)

Communicating editor: J. B. WALSH

Marked-assisted genetic improvement of agricultural species exploits statistical dependencies in the joint distribution of marker genotypes and quantitative traits. An issue is how molecular (e.g., dense marker maps) and phenotypic information (e.g., some measure of yield in plants) is to be used for predicting the genetic value of candidates for selection. Multiple regression, selection index techniques, best linear unbiased prediction, and ridge regression of phenotypes on marker genotypes have been suggested, as well as more elaborate methods. Here, phenotype-marker associations are modeled hierarchically via multilevel models including chromosomal effects, a spatial covariance of marked effects within chromosomes, background genetic variability, and family heterogeneity. Lorenz curves and Gini coefficients are suggested for assessing the inequality of the contribution of different marked effects to genetic variability. Classical and Bayesian methods are presented. The Bayesian approach includes a Markov chain Monte Carlo implementation. The generality and flexibility of the Bayesian method is illustrated when a Lorenz curve is to be inferred.




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