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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics and Animal Breeding » Research » Publications at this Location » Publication #271277

Title: Accuracy of genomic breeding values in multibreed beef cattle populations derived from deregressed breeding values and phenotypes

item Thallman, Richard - Mark
item Keele, John
item Snelling, Warren
item Bennett, Gary
item Smith, Timothy - Tim
item McDaneld, Tara
item Kuehn, Larry

Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/19/2012
Publication Date: 12/1/2012
Citation: Weber, K.L., Thallman, R.M., Keele, J.W., Snelling, W.M., Bennett, G.L., Smith, T.P., McDaneld, T.G., Allan, M.F., Van Eenennaam, A.L., Kuehn, L.A. 2012. Accuracy of genomic breeding values in multibreed beef cattle populations derived from deregressed breeding values and phenotypes. Journal of Animal Science. 90(12):4177-4190.

Interpretive Summary: Genomic predictions that are useful in multiple breeds and crossbreds could improve the rate of genetic improvement in beef cattle. Using two large cattle populations generated at the U.S. Meat Animal Research Center and through collaboration with beef breed associations, genotyped with the Illumina Bovine SNP50 BeadChip Assay (San Diego, CA), genomic breeding values for growth and carcass traits were developed and cross-validated across multi-breed populations and within each of several influential beef breeds. Both phenotypes and deregressed breeding values were successfully implemented for prediction and evaluation of genomic breeding values. Accuracy of genomic prediction was generally low and variable between traits and breeds. It is expected that greater accuracy that is consistent across many breeds will require larger populations and greater genotypic density.

Technical Abstract: Genomic selection involves the assessment of genetic merit through prediction equations that allocate genetic variation with dense marker genotypes. It has the potential to provide accurate breeding values for selection candidates at an early age and facilitate selection for expensive or difficult to measure traits. Accurate across-breed prediction would allow genomic selection to be applied on a larger scale in the beef industry, but the limited availability of large populations for the development of prediction equations has delayed researchers from providing genomic predictions that are accurate across multiple beef breeds. In this study, the accuracy of genomic predictions for 6 growth and carcass traits were derived and evaluated using 2 multibreed beef cattle populations: 3,358 crossbred cattle of the U.S. Meat Animal Research Center Germplasm Evaluation Program (USMARC_GPE) and 1,834 high accuracy bull sires of the 2,000 Bull Project (2000_BULL) representing influential breeds in the U.S. beef cattle industry. The 2000_BULL EPD were deregressed, scaled, and weighted to adjust for between- and within-breed heterogeneous variance before use in training and validation. Molecular breeding values (MBV) trained in each multibreed population and in Angus and Hereford purebred sires of 2000_BULL were derived using the GenSel BayesC*pi* function (Fernando and Garrick, 2009) and cross-validated. Less than 10% of large effect loci were shared between prediction equations trained on (USMARC_GPE) relative to 2000_BULL although locus effects were moderately to highly correlated for most traits and the traits themselves were highly correlated between populations. Prediction of MBV accuracy was low and variable between populations. For growth traits, MBV accounted for up to 18% of genetic variation in a pooled, multibreed analysis and up to 28% in single breeds. For carcass traits, MBV explained up to 8% of genetic variation in a pooled, multibreed analysis and up to 42% in single breeds. Prediction equations trained in multibreed populations were more accurate for Angus and Hereford subpopulations because those were the breeds most highly represented in the training populations. Accuracies were less for prediction equations trained in a single breed due to the smaller number of records derived from a single breed in the training populations.