The Accuracy of Genomic Selection in Norwegian Red Cattle Assessed by Cross-Validation

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The Accuracy of Genomic Selection in Norwegian Red Cattle Assessed by Cross-Validation Powered By Docstoc
					Copyright Ó 2009 by the Genetics Society of America
DOI: 10.1534/genetics.109.107391



     The Accuracy of Genomic Selection in Norwegian Red Cattle Assessed
                            by Cross-Validation

       Tu Luan,*,1 John A. Woolliams,*,† Sigbjørn Lien,‡ Matthew Kent,*,‡ Morten Svendsen§
                                  and Theo H. E. Meuwissen*
       *Department of Animal and Aquacultural Sciences and ‡Centre for Integrative Genetics, Norwegian University of Life Sciences,
                ˚
        N-1432 As, Norway, †The Roslin Institute (Edinburgh), Royal (Dick) School of Veterinary Studies, University of Edinburgh,
              Roslin, Midlothian EH25 9PS, United Kingdom and §Geno Breeding and Artificial Insemination Association,
                                                                ˚
                                                          1432 As, Norway
                                                         Manuscript received July 15, 2009
                                                      Accepted for publication August 12, 2009


                                                                 ABSTRACT
                Genomic Selection (GS) is a newly developed tool for the estimation of breeding values for quantitative
             traits through the use of dense markers covering the whole genome. For a successful application of GS,
             accuracy of the prediction of genomewide breeding value (GW-EBV) is a key issue to consider. Here we
             investigated the accuracy and possible bias of GW-EBV prediction, using real bovine SNP genotyping (18,991
             SNPs) and phenotypic data of 500 Norwegian Red bulls. The study was performed on milk yield, fat yield,
             protein yield, first lactation mastitis traits, and calving ease. Three methods, best linear unbiased prediction
             (G-BLUP), Bayesian statistics (BayesB), and a mixture model approach (MIXTURE), were used to estimate
             marker effects, and their accuracy and bias were estimated by using cross-validation. The accuracies of the
             GW-EBV prediction were found to vary widely between 0.12 and 0.62. G-BLUP gave overall the highest
             accuracy. We observed a strong relationship between the accuracy of the prediction and the heritability of the
             trait. GW-EBV prediction for production traits with high heritability achieved higher accuracy and also lower
             bias than health traits with low heritability. To achieve a similar accuracy for the health traits probably more
             records will be needed.




G    ENOMIC selection (GS) is a new technology that
       is expected to revolutionize animal breeding. It
is distinct from traditional selection methods where
                                                                            calculation of GW-EBV for the selection candidates on
                                                                            the basis of their marker genotypes, with an accuracy
                                                                            that is found in the validation of the prediction
phenotype and pedigree information is combined to                           (Goddard and Hayes 2007).
predict breeding values and where at least one source is                       For a successful application of GS, based on a ref-
necessary for a prediction. Estimation of GS breeding                       erence data set, to a usually much larger population
value is based on the estimation of marker effects cover-                   of selection candidates without phenotypic records,
ing the whole genome and combines these estimates                           accuracy of the prediction is a key issue to consider
with the marker genotypes to obtain breeding value                          (Goddard and Hayes 2009). Since GS was first pro-
estimates. Given a sufficiently dense genomewide marker                      posed by Meuwissen et al. (2001), many research works
map, all the genetic variance is expected to be ex-                         using simulated data have been performed on this
plained by the markers, and all quantitative trait loci                     issue (Calus and Veerkamp 2007; Habier et al. 2007;
(QTL) are in linkage disequilibrium (LD) with at least                      Kolbehdari et al. 2007; Calus et al. 2008; Solberg et al.
one marker (Calus et al. 2008). This allows GS to pre-                      2008). The recent availability of genomewide dense
dict genomewide estimates of breeding values (GW-                           SNP marker maps has made GS with real data feasible.
EBV) without the need of phenotyping the selection                          Studies of the accuracy of genomic predictions have
candidates. A potential cost reduction of up to 90% can                     emerged in some animal species, including mice (Lee
be achieved for a breeding program by GS (Schaeffer                         et al. 2008; Legarra et al. 2008), chickens (Gonzalez-
2006), because only a moderate number of individuals                        Recio et al. 2009), and cattle (Hayes et al. 2009), and in
are required to have both known marker genotypes and                        plant species [for example, barley (Zhong et al. 2009)].
phenotypes. These individuals form a reference data set                     For GS applied to dairy cattle, accuracies for the GW-
for the estimation of GW-EBV. The knowledge obtained                        EBV have been reported in North American Holstein
from the reference data set can be applied to the                           (VanRaden et al. 2009), Australian Holstein–Friesian
                                                                            (Hayes et al. 2009), and New Zealand Holstein–Friesian
  1
                                                                            and Jersey dairy cattle (Harris et al. 2008).
   Corresponding author: Department of Animal and Aquacultural Scien-
ces, Norwegian University of Life Sciences, Box 5003, N-1432 As,  ˚            In the present work we applied GS to Norwegian Red
Norway. E-mail: tu.luan@umb.no                                              dairy cattle to investigate the accuracy and possib
				
DOCUMENT INFO
Description: Genomic Selection (GS) is a newly developed tool for the estimation of breeding values for quantitative traits through the use of dense markers covering the whole genome. For a successful application of GS, accuracy of the prediction of genomewide breeding value (GW-EBV) is a key issue to consider. Here we investigated the accuracy and possible bias of GW-EBV prediction, using real bovine SNP genotyping (18,991 SNPs) and phenotypic data of 500 Norwegian Red bulls. The study was performed on milk yield, fat yield, protein yield, first lactation mastitis traits, and calving ease. Three methods, best linear unbiased prediction (G-BLUP), Bayesian statistics (BayesB), and a mixture model approach (MIXTURE), were used to estimate marker effects, and their accuracy and bias were estimated by using cross-validation. The accuracies of the GW-EBV prediction were found to vary widely between 0.12 and 0.62. G-BLUP gave overall the highest accuracy. We observed a strong relationship between the accuracy of the prediction and the heritability of the trait. GW-EBV prediction for production traits with high heritability achieved higher accuracy and also lower bias than health traits with low heritability. To achieve a similar accuracy for the health traits probably more records will be needed. [PUBLICATION ABSTRACT]
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