Submitted to: Cornell Nutrition Conference for Feed Manufacturers
Publication Type: Proceedings
Publication Acceptance Date: September 27, 2011
Publication Date: October 18, 2011
Citation: Van Tassell, C.P., Wiggins, G.R., Vanraden, P.M., Cooper, T.A., Sonstegard, T.S. 2011. Applications of Genomics to Genetic Improvement of Dairy Cattle. Cornell Nutrition Conference for Feed Manufacturers. p. 57-68.
Implementation of genomic evaluation has caused profound changes in dairy cattle breeding. All young bulls bought by major artificial-insemination (AI) organizations now are selected based on such evaluations. Evaluation reliability can reach about 75% for yield traits, which is adequate for marketing semen of 2-yr-old bulls. Shortened generation interval from using genomic evaluations is the most important factor in increasing rates of genetic improvement. Current genomic evaluations are based on 42,503 single nucleotide polymorphisms (SNP) genotyped with technology that became available in 2007. The first unofficial USDA genomic evaluations were released in 2008 and became official for Holsteins, Jerseys, and Brown Swiss in 2009. Evaluation accuracy has increased steadily from including additional bulls with genotypes and traditional evaluations (predictor animals). Some of that increase occurs as a result of young bulls with genotypes receiving a progeny-test evaluation at 5 yr of age. Cow contribution to evaluation accuracy is increased by reducing mean and variance of their evaluations so that they are similar to bull evaluations. Integration of US and Canadian genotype databases was critical to achieving acceptable initial accuracy and continues to benefit both countries. Genotype exchange with other countries added predictor bulls for Brown Swiss and will add bulls for Holstein. In 2010, a low-density chip with 2,900 SNP and a high-density chip with 777,962 SNP were released. The low-density chip has increased greatly the number of animals genotyped and is expected to replace microsatellites in parentage verification. The high-density chip can increase evaluation accuracy by better tracking of loci responsible for genetic differences. To integrate information from chips of various densities, a method to impute missing genotypes was developed based on splitting each genotype into its maternal and paternal haplotypes and tracing their inheritance through the pedigree. The same method is used to impute genotypes of nongenotyped dams based on genotyped progeny and mates. Reliability of resulting evaluations is discounted to reflect errors inherent in the process. Further increases in evaluation accuracy are expected because of added predictor animals and more SNP. The large population of existing genotypes can be used to evaluate new traits; however, phenotypic observations must be obtained for enough animals to allow estimation of SNP effects with sufficient accuracy for application to the general population.