Skip to main content
ARS Home » Research » Publications at this Location » Publication #260081
Title: The genomic evaluation system in the United States: Past, present, future

Author
item Wiggans, George
item Vanraden, Paul
item Cooper, Tabatha

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/4/2011
Publication Date: 6/1/2011
Citation: Wiggans, G.R., Van Raden, P.M., Cooper, T.A. 2011. The genomic evaluation system in the United States: Past, present, future. Journal of Dairy Science. 94(6):3202-3211.

Interpretive Summary: The implementation of genomic evaluation for dairy cattle has caused profound changes in dairy cattle breeding. The reliability of those evaluations can reach around 75% for yield traits. This technology, which resulted from an international consortium of government, university, and industry cooperators, became available in December 2007, and the first unofficial USDA evaluations based on SNP genotypes were released in April 2008. A steady increase in evaluation accuracy has resulted from including additional bulls with genotypes and traditional evaluations. Exchange of genotypes with other countries has added predictor bulls and collaborations are expected to increase in the future.

Technical Abstract: The implementation of genomic evaluation for dairy cattle has caused profound changes in dairy cattle breeding. All young bulls purchased by major artificial-insemination (AI) organizations are selected based on genomic evaluations. The reliability of those evaluations can reach around 75% for yield traits, which is adequate for wide marketing of semen for 2-yr-old bulls. The shortened generation interval from using genomic evaluations is the most important factor in increasing the rate of genetic improvement. Genomic evaluations are based on 43,382 single nucleotide polymorphisms (SNP) generated from the Illumina BovineSNP50 BeadChip. This technology, which resulted from an international consortium of government, university, and industry cooperators, became available in December 2007, and the first unofficial USDA evaluations based on SNP genotypes were released in April 2008. Genomic evaluations became official for Holsteins and Jerseys in January 2009 and for Brown Swiss in August 2009. A steady increase in evaluation accuracy has resulted from including additional bulls with genotypes and traditional evaluations. Much of the increase occurs automatically as bulls genotyped as young bulls receive a traditional evaluation at 5 yr of age. Cow evaluations also contribute to evaluation accuracy, and that contribution is increased by adjusting their evaluations to be more like bull evaluations. However, that adjustment reduces the evaluations of most cows. Full integration of U.S. and Canadian genotype databases provided a critical source of genotypes to achieve acceptable accuracy initially and continued benefits for both countries. Exchange of genotypes with other countries has added predictor bulls for Brown Swiss, and collaboration for other breeds is expected. In July 2010, Illumina released two new genotyping chips: the 3K chip with 2,900 SNP and the high density (HD) chip with 777,962 SNP. The 3K chip is expected to increase greatly the number of animals genotyped and replace microsatellites in parentage verification. The HD chip can provide more accurate genomic evaluations by better tracking of the loci responsible for genetic differences. To integrate multiple chips, a method to impute missing genotypes was developed. That method is based on splitting each genotype into its maternal and paternal haplotypes and tracing their inheritance. The same method is used to impute genotypes of nongenotyped dams based on their genotyped progeny and mates. The reliability of the resulting evaluations is appropriately discounted to reflect errors inherent in the process. Increases in evaluation accuracy are expected to continue because of added predictor animals and more SNP. The large population of existing genotypes can be used for the evaluation of new traits; however, the challenge is to measure the new traits for enough animals to allow estimation of SNP effects with sufficient accuracy for application to the general population.