Profitability in dairy cattle production system is determined by several production and reproduction traits. But milk production was the most important economic trait for many decades in the past century. In 1970s, the theory of genetic evaluation using Best Linear Unbiased Predictions (BLUP) was proposed. Thanks to artificial insemination, the genetic progress for milk production resulted in global improvement for this trait very quickly. However, BLUP evaluation have helped the breeders not only in dairy cattle but in many other species. These methods, applying pedigree-based genetic relationship, solve mixed model equations and predict the breeding value for each animals.
With advent of huge genotyping technology in 2000, a new genetic evaluation called "genomic selection" was proposed. The idea is to dissect the black box of the genome and use of DNA variation in the population to overcome the limitations of BLUP evaluation and to improve the rate of genetic gain in dairy cattle. In 2009, a very efficient method of genetic evaluation was introduced in which uses available information on trait recordings, pedigree, and genotypes at the same time, and results in predicting Genomic Estimated Breeding Value (GEBV). This method is so-called Single-Step Genomic Best Unbiased Linear Prediction, "ssGBLUP" which is very efficient in both accuracy and computation.
In our project, we are going to launch the Genomic Selection breeding program for Holstein Dairy Cattle in Russia. We will use all available pedigree, genomic and phenotype information in the country and international database to predict the GEBVs for important economic traits. The results will be implemented immediately in the involved farms. Genetics, phenotypes, economics and inbreeding is monitored over the years of the project.