scholarly journals Use of female information in dairy cattle genomic breeding programs

2011 ◽  
Vol 94 (8) ◽  
pp. 4109-4118 ◽  
Author(s):  
N. Mc Hugh ◽  
T.H.E. Meuwissen ◽  
A.R. Cromie ◽  
A.K. Sonesson
Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 599
Author(s):  
Miguel A. Gutierrez-Reinoso ◽  
Pedro M. Aponte ◽  
Manuel Garcia-Herreros

Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.


1980 ◽  
Vol 60 (2) ◽  
pp. 253-264 ◽  
Author(s):  
A. J. McALLISTER

In the last decade the dairy cattle population has declined to a level of 1.9 million cows in 1978 with about 56% of these cows bred AI and nearly 20% of the population enrolled in a supervised milk recording program. The decline in cow numbers has been accompanied by an increase in herd size and production per cow. The current breeding program of the dairy industry is a composite of breeding decisions made by AI organizations, breeders who produce young bulls for sampling and all dairymen who choose the sires and dams of their replacement heifers. Estimates of genetic trend from 1958–1975 for milk production in the national milk recorded herd range from 21 to 55 kg per year for the four dairy breeds with Holsteins being 41 kg per year. Both differential use of superior proven sires and improved genetic merit of young bulls entering AI studs contribute to this genetic improvement. Various national production and marketing alternatives were examined. Selection is a major breeding tool in establishing a breeding program to meet national production requirements for milk and milk products once the selection goal is defined. AI and young sire sampling programs will continue to be the primary vehicle for genetic improvement through selection regardless of the selection goal. The current resources of milk-recorded cows bred AI is not being fully utilized to achieve maximum genetic progress possible from young sire sampling indicate that the number of young bulls sampled annually in the Holstein breed could be tripled with the existing milk-recorded and AI bred dairy cow population. Expanded milk recording and AI breeding levels could increase the potential for even further genetic improvement. The potential impact of selection for other traits, crossbreeding and the use of embryo transfer of future breeding programs is highlighted.


2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Floor Biemans ◽  
Mart C. M. de Jong ◽  
Piter Bijma

Abstract Background For infectious diseases, the probability that an animal gets infected depends on its own susceptibility, and on the number of infectious herd mates and their infectivity. Together with the duration of the infectious period, susceptibility and infectivity determine the basic reproduction ratio of the disease ($$ R_{0} $$R0). $$ R_{0} $$R0 is the average number of secondary cases caused by a typical infectious individual in an otherwise uninfected population. An infectious disease dies out when $$ R_{0} $$R0 is lower than 1. Thus, breeding strategies that aim at reducing disease prevalence should focus on reducing $$ R_{0} $$R0, preferably to a value lower than 1. In animal breeding, however, $$ R_{0} $$R0 has received little attention. Here, we estimate the additive genetic variance in host susceptibility, host infectivity, and $$ R_{0} $$R0 for the endemic claw disease digital dermatitis (DD) in Holstein Friesian dairy cattle, and estimate genomic breeding values (GEBV) for these traits. We recorded DD disease status of both hind claws of 1513 cows from 12 Dutch dairy farms, every 2 weeks, 11 times. The genotype data consisted of 75,904 single nucleotide polymorphisms (SNPs) for 1401 of the cows. We modelled the probability that a cow got infected between recordings, and compared four generalized linear mixed models. All models included a genetic effect for susceptibility; Models 2 and 4 also included a genetic effect for infectivity, while Models 1 and 2 included a farm*period interaction. We corrected for variation in exposure to infectious herd mates via an offset. Results GEBV for $$ R_{0} $$R0 from the model that included genetic effects for susceptibility only had an accuracy of ~ 0.39 based on cross-validation between farms, which is very high given the limited amount of data and the complexity of the trait. Models with a genetic effect for infectivity showed a larger bias, but also a slightly higher accuracy of GEBV. Additive genetic standard deviation for $$ R_{0} $$R0 was large, i.e. ~ 1.17, while the mean $$ R_{0} $$R0 was 2.36. Conclusions GEBV for $$ R_{0} $$R0 showed substantial variation. The mean $$ R_{0} $$R0 was only about one genetic standard deviation greater than 1. These results suggest that lowering DD prevalence by selective breeding is promising.


2012 ◽  
Vol 52 (3) ◽  
pp. 107 ◽  
Author(s):  
J. E. Pryce ◽  
H. D. Daetwyler

High rates of genetic gain can be achieved through (1) accurate predictions of breeding values (2) high intensities of selection and (3) shorter generation intervals. Reliabilities of ~60% are currently achievable using genomic selection in dairy cattle. This breakthrough means that selection of animals can happen at a very early age (i.e. as soon as a DNA sample is available) and has opened opportunities to radically redesign breeding schemes. Most research over the past decade has focussed on the feasibility of genomic selection, especially how to increase the accuracy of genomic breeding values. More recently, how to apply genomic technology to breeding schemes has generated a lot of interest. Some of this research remains the intellectual property of breeding companies, but there are examples in the public domain. Here we review published research into breeding scheme design using genomic selection and evaluate which designs appear to be promising (in terms of rates of genetic gain) and those that may have unfavourable side-effects (i.e. increasing the rate of inbreeding). The schemes range from fairly conservative designs where bulls are screened genomically to reduce numbers entering progeny testing, to schemes where very large numbers of bull calves are screened and used as sires as soon as they reach sexual maturity. More radical schemes that incorporate the use of reproductive technologies (in juveniles) and genomic selection in nucleus herds are also described. The models used are either deterministic and more recently tend to be stochastic, simulating populations of cattle. A key driver of the rate of genetic gain is the generation interval, which could range from being similar to that in conventional testing (~5 years), down to as little as 1.5 years. Generally, the rate of genetic gain is between 12% and 100% more than in conventional progeny testing, while the rate of inbreeding tends to be lower per generation than in progeny testing because Mendelian sampling terms can be estimated more accurately. However, short generation intervals can lead to higher rates of inbreeding per year in genomic breeding programs.


2018 ◽  
Vol 61 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Allison Fleming ◽  
Emhimad A. Abdalla ◽  
Christian Maltecca ◽  
Christine F. Baes

Abstract. Dairy cattle breeders have exploited technological advances that have emerged in the past in regards to reproduction and genomics. The implementation of such technologies in routine breeding programs has permitted genetic gains in traditional milk production traits as well as, more recently, in low-heritability traits like health and fertility. As demand for dairy products increases, it is important for dairy breeders to optimize the use of available technologies and to consider the many emerging technologies that are currently being investigated in various fields. Here we review a number of technologies that have helped shape dairy breeding programs in the past and present, along with those potentially forthcoming. These tools have materialized in the areas of reproduction, genotyping and sequencing, genetic modification, and epigenetics. Although many of these technologies bring encouraging opportunities for genetic improvement of dairy cattle populations, their applications and benefits need to be weighed with their impacts on economics, genetic diversity, and society.


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