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      Nonparametric methods for incorporating genomic information into genetic evaluations: an application to mortality in broilers.

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          Abstract

          Four approaches using single-nucleotide polymorphism (SNP) information (F(infinity)-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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          Most cited references 21

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          Robust Locally Weighted Regression and Smoothing Scatterplots

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            On Estimating Regression

             E. Nadaraya (1964)
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              Best linear unbiased estimation and prediction under a selection model.

              Mixed linear models are assumed in most animal breeding applications. Convenient methods for computing BLUE of the estimable linear functions of the fixed elements of the model and for computing best linear unbiased predictions of the random elements of the model have been available. Most data available to animal breeders, however, do not meet the usual requirements of random sampling, the problem being that the data arise either from selection experiments or from breeders' herds which are undergoing selection. Consequently, the usual methods are likely to yield biased estimates and predictions. Methods for dealing with such data are presented in this paper.
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                Author and article information

                Journal
                Genetics
                Genetics
                Genetics Society of America
                0016-6731
                0016-6731
                Apr 2008
                : 178
                : 4
                Affiliations
                [1 ] Departamento de Producción Animal, E.T.S.I. Agrónomos-Universidad Politécnica de Madrid, 28040 Madrid, Spain. ogonzalez2@wisc.edu
                Article
                178/4/2305
                10.1534/genetics.107.084293
                2323817
                18430951

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