Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: March 5, 2009
Publication Date: July 12, 2009
Citation: Van Raden, P.M., Sullivan, P.G. 2009. National and international genomic evaluations for dairy cattle. Journal of Dairy Science. 92(E-Suppl. 1):175(abstr. 200).
Genomic evaluations are rapidly replacing traditional evaluation systems used for dairy cattle selection. More than 35,000 dairy cattle worldwide have been genotyped for 50,000 markers. Reliabilities of 60-70% for young genotyped animals are now possible as compared to 35% for parent average. Gains depend on numbers of genotypes and are much less for Jerseys or Brown Swiss than for the large North American Holstein population. Accurate blending of genomic and non-genomic information is important because many animals are not genotyped. In U.S. evaluations, extra information from genotyped parents is transferred to non-genotyped descendants using the same formulas that adjust traditional evaluations for foreign parent data. Propagation from genotyped progeny to non-genotyped parents is more difficult because the extra information from genotyped progeny should not exceed the direct gain from genotyping the parent. Genomic information may be transferred across countries using simple conversion equations or by modifying multi-trait across-country evaluation (MACE) to account for correlated residuals and to account for genomic rather than pedigree relationships when deregressing national evaluations. In traditional MACE, residuals are independent because each daughter is measured in only one country. In genomic MACE, residuals may be correlated for two reasons: 1) multiple evaluation centers may include the same genomic and phenotypic data in national estimates of marker effects, and 2) genomic predictions act as repeated measures of the same portion of genetic merit rather than independent measures of total merit, especially for major gene marker(s) and low-density SNP panels. Marker effects in the U.S. and Canada are highly correlated because both countries share the same genomic data and include traditional MACE evaluations as input to their genomic equations. Residual correlations can be approximated using daughter equivalents from genomics as a fraction of the total in each country and proportions of common bulls shared. Economies of scale in genomics promote cooperation across country borders.