Effects of a breeding scheme combined by genomic pre‐selection and progeny testing on annual genetic gain in a dairy cattle population

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.

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Wrong and Missing Sire Information Affects Genetic Gain in the Angeln Dairy Cattle Population

Journal of Dairy Science ◽

10.3168/jds.s0022-0302(06)72096-3 ◽

2006 ◽

Vol 89 (1) ◽

pp. 315-321 ◽

Cited By ~ 38

Author(s):

K. Sanders ◽

J. Bennewitz ◽

E. Kalm

Keyword(s):

Dairy Cattle ◽

Genetic Gain ◽

Cattle Population

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Genetic and Financial Considerations of Progeny Testing Programs in an Artificial Dairy Cattle Population

Journal of Dairy Science ◽

10.3168/jds.s0022-0302(74)85045-9 ◽

1974 ◽

Vol 57 (10) ◽

pp. 1245-1253 ◽

Cited By ~ 4

Author(s):

P.A. Oltenacu ◽

C.W. Young

Keyword(s):

Dairy Cattle ◽

Progeny Testing ◽

Cattle Population

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Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit

Journal of Dairy Science ◽

10.3168/jds.2013-6599 ◽

2014 ◽

Vol 97 (1) ◽

pp. 458-470 ◽

Cited By ~ 32

Author(s):

J.R. Thomasen ◽

C. Egger-Danner ◽

A. Willam ◽

B. Guldbrandtsen ◽

M.S. Lund ◽

...

Keyword(s):

Dairy Cattle ◽

Genomic Selection ◽

Genetic Gain ◽

Cattle Population ◽

Selection Strategies

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A deterministic model for the optimization of dairy cattle breeding based on BLUP breeding value estimates

Animal Science ◽

10.1017/s0003356100032311 ◽

1989 ◽

Vol 49 (2) ◽

pp. 193-202 ◽

Cited By ~ 32

Author(s):

T. H. E. Meuwissen

Keyword(s):

Steady State ◽

Dairy Cattle ◽

Genetic Gain ◽

Variance Reduction ◽

Deterministic Model ◽

Age Distribution ◽

Breeding Value ◽

Progeny Testing ◽

Age Classes ◽

Testing Scheme

ABSTRACTA deterministic model was developed to examine the optimization of open nucleus breeding schemes in order to maximize the rate of genetic response in dairy cattle. By changing the parameters, the model was able to simulate both a closed nucleus and a progeny testing scheme. The model implicitly optimized the generation interval and the selection across tiers by means of truncation across age classes and tiers respectively. The effects of size of the progeny test group and the nucleus size were assessed by comparing alternative plans. It is possible to optimize a breeding plan given the reproduction rates of the animals, the availability of different sources of information, the age distribution of the animals (survival rates) and the phenotypic and genetic parameters of the trait.The steady state selection response was assessed by calculating the genetic progress year after year until it stabilized. The genetic gain was corrected for the effects of reduced variances due to previous selections and increased variances due to genetic differences between parental age classes.In an example, the model was used to predict the improvement in milk yield in a closed artificial insemination breeding scheme. The genetic gain of a conventional progeny testing scheme was about one-third lower than the genetic gain of the optimized breeding plan. The variance reduction due to selection decreased the steady state genetic gain by a factor 0·3

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Progeny Testing of Dairy Cattle Bulls Used in Artificial Insemination

Proceedings of the British Society of Animal Production ◽

10.1017/s036985210000124x ◽

1953 ◽

Vol 1953 (2) ◽

pp. 17-35 ◽

Cited By ~ 1

Author(s):

D. B. Brown

Keyword(s):

Dairy Cattle ◽

Artificial Insemination ◽

Progeny Testing ◽

Cattle Population ◽

Clearing House ◽

England And Wales ◽

Beef Bulls ◽

Set Up ◽

Milk Marketing ◽

Lactation Record

In 1945 the Milk Marketing Board (M.M.B.) of England and Wales undertook responsibility for the development of an A.I. service to cover large areas of the two countries.By 1951, a total of 24 main Centres had been set up, housing some 600 dairy and beef bulls and supplying semen to 77 Sub-Centres. During the year ending 31st March, 1952, approximately 707,000 first inseminations were carried out, representing 25% of the available cattle population. Further progress continues to be made and it is estimated that this proportion will be raised to 30% for the corresponding year ending in 1953.From the outset it has been agreed that the rate of cattle improvement through A.I. will be largely dependent upon the use made of the older progeny-recorded bulls available to the scheme. Here it should be noted that the M.M.B. took over responsibility for milk recording under ‘ National Milk Records ‘ in 1943. By adopting a system based upon lactation record cards, it was possible to establish in 1947 a central clearing house for milk records of animals registered with Breed Societies, viz. the Bureau of Records. One of the main services operated by the Bureau is that of progeny recording for sires and summaries of bulls fulfilling certain requirements are now published annually.

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Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review

Animals ◽

10.3390/ani11030599 ◽

2021 ◽

Vol 11 (3) ◽

pp. 599

Author(s):

Miguel A. Gutierrez-Reinoso ◽

Pedro M. Aponte ◽

Manuel Garcia-Herreros

Keyword(s):

Dairy Cattle ◽

Production Efficiency ◽

Association Studies ◽

Phenotypic Traits ◽

Progeny Testing ◽

Genome Wide Association Studies ◽

Genetic Progress ◽

Breeding Programs ◽

Increased Risk ◽

Selection Of

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.

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