Academic journal article Genetics

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

Academic journal article Genetics

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

Article excerpt

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.

(ProQuest: ... denotes formulae omitted.)

GENOMIC selection (GS) is a new technology that is expected to revolutionize animal breeding. It is distinct from traditional selection methods where phenotype and pedigree information is combined to predict breeding values and where at least one source is necessary for a prediction. Estimation of GS breeding value is based on the estimation of marker effects covering the whole genome and combines these estimates with the marker genotypes to obtain breeding value estimates. Given a sufficiently dense genomewide marker map, all the genetic variance is expected to be explained by the markers, and all quantitative trait loci (QTL) are in linkage disequilibrium (LD) with at least one marker (Calus et al. 2008). This allows GS to predict genomewide estimates of breeding values (GWEBV) without the need of phenotyping the selection candidates. A potential cost reduction of up to 90% can be achieved for a breeding program by GS (Schaeffer 2006), because only a moderate number of individuals are required to have both known marker genotypes and phenotypes. These individuals form a reference data set for the estimation of GW-EBV. The knowledge obtained from the reference data set can be applied to the 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 (Goddard and Hayes 2007).

For a successful application of GS, based on a reference data set, to a usually much larger population of selection candidates without phenotypic records, accuracy of the prediction is a key issue to consider (Goddard and Hayes 2009). Since GS was first proposed by Meuwissen et al. (2001), many research works using simulated data have been performed on this issue (Calus and Veerkamp 2007; Habier et al. 2007; Kolbehdari et al. 2007; Calus et al. 2008; Solberg et al. 2008). The recent availability of genomewide dense SNP marker maps has made GS with real data feasible. Studies of the accuracy of genomic predictions have emerged in some animal species, including mice (Lee et al. 2008; Legarra et al. 2008), chickens (Gonzalez- Recio et al. 2009), and cattle (Hayes et al. 2009), and in plant species [for example, barley (Zhong et al. 2009)]. For GS applied to dairy cattle, accuracies for the GWEBV have been reported in North American Holstein (VanRaden et al. 2009), Australian Holstein-Friesian (Hayes et al. 2009), and New Zealand Holstein-Friesian and Jersey dairy cattle (Harris et al. 2008).

In the present work we applied GS to Norwegian Red dairy cattle to investigate the accuracy and possible bias of GW-EBV prediction for the phenotypes of milk production, clinical mastitis, and calving ease, by using real bovine genotyping data. …

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