A 100-year review: Methods and impact of genetic selection in dairy cattle—From daughter–dam comparisons to deep learning algorithms

Authors: WEIGEL, KENT - University Of Wisconsin; Vanraden, Paul; NORMAN, H - Council On Dairy Cattle Breeding; GROSU, H - Collaborator

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/16/2017
Publication Date: 12/1/2017
Citation: Weigel, K.A., Van Raden, P.M., Norman, H.D., Grosu, H. 2017. A 100-year review: Methods and impact of genetic selection in dairy cattle—From daughter–dam comparisons to deep learning algorithms. Journal of Dairy Science. 100(12):10234-10250. https://doi.org/10.3168/jds.2017-12954.
DOI: https://doi.org/10.3168/jds.2017-12954

Interpretive Summary: The past 100 years have seen a remarkable evolution of genetic selection 1 programs for dairy cattle. Initial efforts based on daughter-dam comparisons resulted in minimal genetic progress, but the parallel growth in milk recording programs laid the foundation for future success. Contemporary comparison methods, coupled with artificial insemination and progeny testing, led to significant genetic improvements. Advances in computing power allowed the implementation of mixed linear (animal) models that featured optimal use of pedigree and performance data and enabled precise selection decisions. In the past decade, after sequencing of the bovine genome, the pace of scientific discovery and genetic progress has accelerated. Pedigree-based linear models have given way to genome-based prediction methods, including Bayesian regression models and machine learning algorithms.

Technical Abstract: 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.