Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle
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Mujibi, F.D.N., Nkrumah, J.D., Durunna, O. N., Stothard, P., Mah, J., Wang, Z., Basarab, J., Plastow, G., Crews Jr., D.H. and Moore, S.S. 2011. Accuracy of genomic breeding values for residual feed intake in crossbred beef cattle. Journal of Animal Science 89(11): 3353-3361.
Permanent link to this item: http://hdl.handle.net/10568/5405
The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI and residual feed intake (RFI) for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing datasets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate datasets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panel with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed to polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of RFI in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the RR-BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may only be useful in populations of similar composition. Given the sample sized used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved.