|Chitko Mckown, Carol|
|Thallman, Richard - Mark|
Submitted to: Journal of Animal Science Supplement
Publication Type: Abstract Only
Publication Acceptance Date: 11/5/2010
Publication Date: 9/6/2011
Citation: Kuehn, L.A., Casas, E., Bennett, G.L., Chitko Mckown, C.G., Keele, J.W., McDaneld, T.G., Snelling, W.M., Thallman, R.M. 2011. Genomics of bovine respiratory disease complex at USMARC [Invited abstract]. Journal of Animal Science. 89(E-Suppl. 2):61-62. Abstract #43. Interpretive Summary:
Technical Abstract: Selection for genetic resistance/resilience bovine respiratory disease complex (BRDC) would significantly increase the efficiency of beef production in the U.S. through decreased treatment costs, productivity, and death loss. Unfortunately, selection for resistance to BRDC is challenging to implement for multiple reasons: treatment records are not absolute (sick animals undiagnosed, healthy animals treated, etc.), multiple pathogens cause the same complex, exposure to pathogens is inconsistent, and records generally do not exist for breeding animals to apply standard genetic evaluation approaches. Genetic markers are an alternative to traditional selection, but detection requires large phenotypic databases. The U.S. Meat Animal Research Center (USMARC) has implemented a multi-faceted approach to implement discovery of genetic markers associated with resistance to BRDC. Regions of interest were identified using treatment records from steers in a genome wide association study using the Illumina BovineSNP50. These results have been used in fine mapping studies to identify markers on BTA 1, 2, 6, 20, and 26. A subset (n = 2,200 as of 2010) of the USMARC germplasm evaluation program has been dedicated to collecting indicative phenotypes for risk of acquiring BRDC (vaccination response, stress indicators) and for undiagnosed infections (lung lesions at slaughter, complete blood counts, pathogen identification at treatment). Last, DNA pooling strategies (pools of high vs. low disease incidence) have been employed as a tool to detect marker frequency differences in large samples. This strategy has been applied for detecting resistance to BRDC on large portions of USMARC treated animals and on industry animals of unknown origin based on lung lesion differences at slaughter or treatment records at cooperating feedlots. The combination of fine mapping, extensive phenotyping, and larger data sets will support discovery of genomic tools useful for selection.