Associations of marker panel scores with feed intake and efficiency traits in beef cattle using pre-selected single nucleotide polymorphisms - Mujibi, F.D.N.; Nkrumah, J.D.; Durunna, O.N.; Mah, J.; Wang, Z.; Basarab, J.; Plastow, G.; Crews, D.H.; Grant, J.R; Moore, S.S. (Oxford University Press, 2011)
Because of the moderate heritability and the expense associated with collection of feed intake data, effective selection for residual feed intake (RFI) would be enhanced if marker assisted evaluation were used for accurate estimation of genetic merit. In this study, a suite of genetic markers predictive of RFI, DMI and ADG were pre-selected using single marker regression analysis and the top 100 SNP analyzed further to provide prediction equations for the traits. The data used consisted of 728 spring born beef steers, offspring of a cross between a composite dam line and Angus, Charolais or University of Alberta hybrid bulls. Feed intake data was collected over a 5 yr period with 2 groups (Fall-Winter and Winter-Spring) tested every year. Training and validation data sets were obtained by splitting the data into 2 distinct sets as follows: by randomly splitting the data into training and testing sets based on sire family (split 1) in 5 replicates or by retaining all animals with no known pedigree relationships as the validation set (split 2). A total of 37,959 SNP were analyzed by single marker regression, of which only the top 100 that corresponded to a P-value < 0.002 were retained. The 100 SNP were then analyzed using random regression BLUP (RR-BLUP) and only SNP that were jointly significant (P < 0.05) were included in the final marker panels. The marker effects from the selected panels were used to derive the molecular breeding values (MBV), which were calculated as a weighted sum of the number of copies of the more frequent allele at each SNP locus, weights being the allele substitution effects. The correlation between MBV and phenotype represented the accuracy of prediction. For all traits evaluated, accuracy across breeds was low, ranging between 0.007 and 0.414. Accuracy was least in data split 2, where the validation individuals had no pedigree relationship with animals in the training data. Given the low predictive ability observed, a large number of individuals may be needed for prediction when using such an admixed population. Further, these results suggest that breed composition of the target population where the marker panels are likely to be used should be an important consideration when developing prediction equations across breeds, especially where an admixed population is used as the training dataset.