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

Abstract

In the early 1900s, breed society herdbooks had been established and milk-recording programs were in their infancy. Farmers wanted to improve the productivity of their cattle, but the foundations of population genetics, quantitative genetics, and animal breeding had not been laid. Early animal breeders struggled to identify genetically superior families using performance records that were influenced by local environmental conditions and herd-specific management practices. Daughter-dam comparisons were used for more than 30 yr and, although genetic progress was minimal, the attention given to performance recording, genetic theory, and statistical methods paid off in future years. Contemporary (herdmate) comparison methods allowed more accurate accounting for environmental factors and genetic progress began to accelerate when these methods were coupled with artificial insemination and progeny testing. Advances in computing facilitated the implementation of mixed linear models 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 models have given way to whole-genome prediction, and Bayesian regression models and machine learning algorithms have joined mixed linear models in the toolbox of modern animal breeders. 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.

Prenatal programming of later performance in dairy cattle

“…The primary aim was to breed towards genetically improved livestock. Since then, a rapid evolution in genetic selection towards high milk yield has taken place (Weigel et al, 2017). However, heifers with a genetic potential for high milk yield do not always turn out to be the highest yielding cows, as the phenotype is a result of both genotype and environment.…”

Co‐inheritance of sea age at maturity and iteroparity in the Atlantic salmon vgll3 genomic region

“…When correlated characters have contrasting fitness trajectories in the adaptive landscape, the climb towards the local fitness peak can be restricted, and result in suboptimal fitness of populations (Lande, ). The principles of the multivariate theory of evolution have been successfully applied to many fields, such as animal and plant breeding, multi‐trait artificial selection (Careau, Reale, Humphries, & Thomas, ; Chen et al., ; Kadarmideen, Thompson, Coffey, & Kossaibati, ; Kause, Quinton, Airaksinen, Ruohonen, & Koskela, ; Weigel, VanRaden, Norman, & Grosu, ) and epidemiology (Bulik‐Sullivan et al., ; Gratten & Visscher, ; Hammerschlag et al., ; Lee, Yang, Goddard, Visscher, & Wray, ; Sanchez‐Guillen, Wellenreuther, & Cordero Rivera, ; Schnurr et al., ).…”

Genetic correlations among selected traits in Canadian Holsteins

“…These results further confirm the importance of considering genetic correlations when selecting for multiple traits. Weigel et al (2017) defined these correlations by how the genetic superiority for one trait tends to be inherited with genetic superiority or inferiority for another trait. The cause of a genetic correlation may be found at the genomic level, due to linkage or pleiotropy among the regions influencing the considered traits (Rauw et al 1998).…”