Submitted to: Animal Genetics International Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: June 20, 2008
Publication Date: July 18, 2008
Citation: Da Silva, M., Sonstegard, T.S., Van Tassell, C.P., Matukumalli, L.K., Schroeder, S.G., Gasbarre, L.C. 2008. A WHOLE-GENOME ASSOCIATION STUDY OF MAJOR DETERMINANTS FOR PARASITIC INFECTION IN ANGUS BREED. Animal Genetics International Conference Proceedings.
In most cattle-producing areas of the world, infection by helminth parasites (particularly gastrointestinal nematodes) is considered to be a primary cause of production loss. QTL for parasite indicator traits in cattle are ideal targets for study of marker assisted selection. Fecal egg count (FEC) data recorded for 410 animals between 1992 and 2003 from an Angus herd were transformed using an extension of the Box-Cox transformation to approach normality. DNA for genetic analysis has been acquired for all animals from the resource population and over 70 sires in the historic pedigree, in total of 595 animals. Genotyping was performed using Illumina’s BovineSNP50. For GWA analysis, SNP with a call rate (<93%), departure from Hardy-Weinberg equilibrium (exact test p<0.01), and minor allele frequency below 5 percent were excluded from the final analysis (26,158 markers retained). All the statistical analyses were carried out with scripts in the R environment and Fortran. Empirical p-values were corrected for genome-wide testing and maximization across genetic models, and a genome-wide significance level of 0.05 (two-sided) and Bonferroni corrected level of 0.01 were used. Five genetic models (codominant, dominant, recessive, overdominant and log-additive) were analyzed. The five most significant SNP were found on Chr 6 localized within a single linkage disequilibrium (LD) block. These findings provide the first evidence of biomarkers that contribute to early disease detection and primary prevention strategies for parasite infection in cattle and suggest new molecular targets for disease-modifying therapies (secondary prevention). Correlation studies of genotypes to prognostic outcomes may predict the effect of treatments targeting these genes and their proteins; thus, reducing costs to identify animals most likely to benefit from treatment ("personalized therapy").