|Van Tassell, Curtis - Curt|
Submitted to: American Association of Veterinary Parasitologists Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 7/14/2001
Publication Date: 7/14/2001
Citation: SONSTEGARD, T.S., GASBARRE, L.C., VAN TASSELL, C.P., PADILHA, T. GENOMIC TOOLS TO IMPROVE PARASITE RESISTANCE. AMERICAN ASSOCIATION OF VETERINARY PARASITOLOGISTS PROCEEDINGS. pp. 46, 2001. Interpretive Summary:
Technical Abstract: The natural genetic variability of the bovine immune system provides a feasible means to control gastro-intestinal (GI) parasite infection without anthelmintics. Typically, traditional selection has not been effectively applied to traits like parasite resistance due to the difficulty and expense of gathering accurate phenotypes. These characteristics make host traits related to GI nematode infection ideal candidates for genomics-base research. To initiate explanation of important allelic differences, economic trait loci (ETL) are being identified and mapped using a resource population of Angus cattle segregating for GI nematode resistance and susceptibility to the two most common nematode parasites of US cattle, Ostertagia ostertagi and Cooperia oncophora. The population is composed of 5 generations of half-sib progeny (N>300) with complete phenotypic records produced from controlled infections. Individual animals fall into three distinct phenotypic classes based on response: innately immune, acquired immune, and immunologically non-responsive. To detect the genomic locations of these traits, genotypes were generated for DNA markers (N=194) spaced at regular intervals (~20 cM intervals) throughout the entire genome (3,000 cM). Although initial ETL detection may be limited by half-sib family size, the unique structure of this population provides additional statistical power for refining map position of potential ETL. After allele frequency and contribution to phenotype are determined in this population, marker tests associated with ETL most beneficial for controlling parasite infection can be accurately used for selection. Map information will be utilized in further investigations to elucidate the genes underlying ETL.