K.A. Weigel*, P.M.VanRaden†, H.D. Norman‡, and H. Grosu§
*Department of Dairy Science, University of Wisconsin, Madison 53706
†Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD 20705
‡Council on Dairy Cattle Breeding, Bowie, MD 20716
§Institute for Biology and Animal Nutrition, Balotesti, Romania
2017 J. Dairy Sci. (?)
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In the early 1900s, breed society herdbooks had been established, and milk recording programs were in their infancy. Farmers were interested in improving the productivity of dairy cattle, but the foundations of population genetics, quantitative genetics, and animal breeding had not yet been laid. Like their 21st century counterparts, early animal breeders struggled to distinguish signal from noise while attempting to identify genetically superior families using performance records that were influenced heavily by local environmental conditions and herd-specific management practices. Daughter-dam comparisons were used for more than 30 years, and while genetic progress was minimal, the attention given to performance recording, genetic theory, and statistical methods would pay off in future years. Contemporary (herdmate) comparison methods allowed more accurate accounting for environmental factors and, when coupled with artificial insemination and progeny testing, genetic progress began to accelerate. Advances in computing facilitated the implementation of mixed linear models, such as the animal model, that used pedigree and performance data optimally and enabled accurate selection decisions. Sequencing of the bovine genome led to a revolution in dairy cattle breeding, and the pace of scientific discovery and genetic progress accelerated rapidly. Pedigree-based linear models have given way to whole genome prediction in every major dairy-producing country, and global trade of semen and embryos is the norm. Bayesian regression models and machine learning algorithms have joined mixed linear models in the toolbox of today’s dairy cattle breeder. Future developments will likely include elucidation of the mechanisms of genetic inheritance and epigenetic modification in key biological pathways, and genomic data will be used with data from on-farm sensors to facilitate precision management on modern dairy farms.
Keywords: genetic selection, dairy cattle, genomic selection, statistical models