|Da silva, Marcos|
|Dos santos, Daniel|
|Do carmo, Adriana|
|Van tassell, Curtis - Curt|
Submitted to: Livestock Science
Publication Type: Peer reviewed journal
Publication Acceptance Date: 5/16/2014
Publication Date: 8/1/2014
Publication URL: http://handle.nal.usda.gov/10113/60899
Citation: Da Silva, M., Dos Santos, D.J., Boison, S.A., Utsunomiya, A.T., Do Carmo, A.S., Sonstegard, T.S., Cole, J.B., Van Tassell, C.P. 2014. The development of genomics applied to dairy breeding. Livestock Science. 166:66-75. Interpretive Summary: The introduction of low-cost technology for simultaneously genotyping large numbers of DNA markers permitted the development of statistical methods for using those data in genetic improvement programs for dairy cattle. This article reviews the principal aspects of genomic selection in dairy cattle, including the most popular methods for estimation of genetic merit, use of high-density data to fill-in missing marker information from low-density genotypes, and the use of genomic information to identify changes in DNA that have undesirable effects on fertility.
Technical Abstract: Genomic selection (GS) has profoundly changed dairy cattle breeding in the last decade and can be defined as the use of genomic breeding values (GEBV) in selection programs. The GEBV is the sum of the effects of dense DNA markers across the whole genome, capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. This technology was successfully implemented in many countries including the United States, Canada, New Zealand, Australia and many others with very promising results, such as the EuroGenomics consortium. The GEBV reliability depends on estimation procedures and models. The different methodologies to estimate SNP effect and GEBV have been extensively tested by many research groups with very promising results. Although the GS has been a success, many challenges still remain, including integration of GEBV into genetic evaluation programs and increasing GEBV reliability. The aim of this review is to discuss the main aspects involved with GS, including different methodologies of imputation, SNP effect estimation, and the most important impacts of GS implementation in dairy cattle.