Genetics, Vol. 178, 2305-2313, April 2008, Copyright © 2008
doi:10.1534/genetics.107.084293

Nonparametric Methods for Incorporating Genomic Information Into Genetic Evaluations: An Application to Mortality in Broilers

Oscar González-Recio^*,,1, Daniel Gianola^,, Nanye Long, Kent A. Weigel, Guilherme J. M. Rosa and Santiago Avendaño

^* Departamento de Producción Animal, E.T.S.I. Agrónomos–Universidad Politécnica de Madrid, 28040 Madrid, Spain, Department of Dairy Science and Department of Animal Sciences, University of Wisconsin, Madison, Wisconsin 53706 and Aviagen Ltd., Newbridge EH28 8SZ, Scotland, United Kingdom

¹ Corresponding author: Department of Dairy Science, University of Wisconsin, 1465 Observatory Dr., Madison, WI 53706.
E-mail: ogonzalez2{at}wisc.edu

Four approaches using single-nucleotide polymorphism (SNP) information (F-metric model, kernel regression, reproducing kernel Hilbert spaces (RKHS) regression, and a Bayesian regression) were compared with a standard procedure of genetic evaluation (E-BLUP) of sires using mortality rates in broilers as a response variable, working in a Bayesian framework. Late mortality (14–42 days of age) records on 12,167 progeny of 200 sires were precorrected for fixed and random (nongenetic) effects used in the model for genetic evaluation and for the mate effect. The average of the corrected records was computed for each sire. Twenty-four SNPs seemingly associated with late mortality were included in three methods used for genomic assisted evaluations. One thousand SNPs were included in the Bayesian regression, to account for markers along the whole genome. The posterior mean of heritability of mortality was 0.02 in the E-BLUP approach, suggesting that genetic evaluation could be improved if suitable molecular markers were available. Estimates of posterior means and standard deviations of the residual variance were 24.38 (3.88), 29.97 (3.22), 17.07 (3.02), and 20.74 (2.87) for E-BLUP, the linear model on SNPs, RKHS regression, and the Bayesian regression, respectively, suggesting that RKHS accounted for more variance in the data. The two nonparametric methods (kernel and RKHS regression) fitted the data better, having a lower residual sum of squares. Predictive ability, assessed by cross-validation, indicated advantages of the RKHS approach, where accuracy was increased from 25 to 150%, relative to other methods.

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