Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 19, 2011
Publication Date: June 1, 2011
Citation: Kuehn, L.A., Keele, J.W., Bennett, G.L., Mcdaneld, T.G., Smith, T.P., Snelling, W.M., Sonstegard, T.S., Thallman, R.M. 2011. Predicting breed composition using breed frequencies of 50,000 markers from the U.S. Meat Animal Research Center 2,000 bull project. Journal of Animal Science. 89:1742-1750. Interpretive Summary: Knowledge of breed composition of beef cattle can be useful for a variety of applications. Breed compositions of animals can be used as a sorting criteria in feedlots and feeding trials when one breed is known to have a competitive advantage (e.g., rate of gain) relative to others. Some potential herds of origin can be excluded as sources of animals or animal parts in traceback applications when the breed composition of candidate herds is known. We demonstrate a method to predict the breed composition of animals using high-density marker sets that are commercially available based on allele frequencies from a reference set of several beef cattle breeds. This method accurately predicted the pedigree-derived breed composition of animals as long as the reference breeds are in the reference pool of allele frequencies. Some related breeds were difficult to distinguish correctly from one another (i.e., Angus, Red Angus). In addition to the prediction method, the allele frequencies from samples of Angus, Beefmaster, Brahman, Brangus, Braunvieh, Charolais, Chiangus, Gelbvieh, Hereford, Limousin, Maine Anjou, Salers, Santa Gertrudis, Shorthorn, Simmental, Red Angus beef breeds and 3 prominent dairy breeds (Brown Swiss, Holstein, Jersey) are reported.
Technical Abstract: Knowledge of breed composition can be useful in multiple aspects of cattle production, and can be critical for analyzing the results of whole genome wide association studies (GWAS) currently being conducted around the world. We examine the feasibility and accuracy of using genotype data from the most prevalent bovine GWAS platform, the Illumina BovineSNP50 array, to estimate breed composition for individual breed cattle. First, allele frequencies (of Illumina defined allele B) of SNP on the array for each of 16 beef cattle breeds were defined by genotyping a large set of over 2,000 bulls selected in cooperation with the respective breed associations to be representative of their breed. Using these breed specific allele frequencies, the breed composition of approximately 2,000 2-, 3-, and 4-way cross (of eight breeds) cattle produced at the U.S. Meat Animal Center (USMARC) were predicted using a simple multiple regression technique or Mendel, and then compared with pedigree-based estimates of breed composition using genotypes from the Illumina BovineSNP50 array. Accuracy of marker-based breed composition estimates was 89% using either estimation method for all breeds except Angus and Red Angus (averaged 79%) based on comparing estimates to pedigree based average breed composition. Accuracy increased to approximately 88% when these two breeds were combined into an aggregate Angus group. Additionally, we used a subset of these markers, approximately 3,000 that populate the Illumina Bovine3K, to see whether breed composition could be estimated with similar accuracy using this reduced panel of SNP makers. When breed composition was estimated using only SNP in common with the Bovine 3K array, accuracy was slightly reduced to 83%. These results suggest that SNP data from these arrays could be used to estimate breed composition in most U.S. beef cattle in situations where pedigree is not known (e.g., multiple sire natural service matings, non-source verified animals in feedlots or at slaughter). This approach can aid analyses that depend on knowledge of breed composition, including identification and adjustment of breed-based population stratification when performing GWAS on populations with incomplete pedigrees. In addition, SNP-based breed composition estimates may facilitate fitting cow germplasm to the environment, managing cattle in the feedlot, and tracing disease cases back to geographic region or farm of origin.