scholarly journals Designing dairy cattle breeding schemes under genomic selection: a review of international research

2012 ◽  
Vol 52 (3) ◽  
pp. 107 ◽  
Author(s):  
J. E. Pryce ◽  
H. D. Daetwyler
Keyword(s):  
Dairy Cattle ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Reproductive Technologies ◽  
Progeny Testing ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Scheme Design ◽  
Dairy Cattle Breeding ◽  
Breeding Schemes

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.

scholarly journals Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping

2021 ◽  
Vol 12 ◽  
Author(s):  
Jana Obšteter ◽  
Janez Jenko ◽  
Gregor Gorjanc
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Breeding Program ◽  
Progeny Testing ◽  
Initial Training ◽  
Training Population ◽  
Breeding Programs ◽  
Use Of Resources ◽  
Dairy Cattle Breeding ◽  
Close Relatives

This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal available resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario collected 11 phenotypic records per lactation. In genomic selection scenarios, we reduced phenotyping to between 10 and 1 phenotypic records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional selection scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic selection scenarios expectedly increased accuracy for young non-phenotyped candidate males and females, but also proven females. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximize return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.

scholarly journals 189 Choices ahead for dairy cattle breeding programs

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 22-23
Author(s):  
Michael M Lohuis
Keyword(s):  
Dairy Cattle ◽  
Genomic Selection ◽  
Gene Editing ◽  
New Technologies ◽  
Value Added ◽  
Progeny Testing ◽  
Cattle Breeding ◽  
Breeding Programs ◽  
Dairy Cattle Breeding ◽  
Selection For

Abstract Dairy cattle breeding programs have been transformed from conventional progeny-testing schemes to genomic selection paired with nucleus herd breeding in the span of one decade. This was spurred by the simultaneous advances in low-cost SNP genotyping, genomic selection methodology and reproductive biotechnologies. The rates of genetic progress have approximately doubled in this time but so have increases in inbreeding levels. This was driven by intense competition between AI studs and farmer adherence to common selection indices which has concentrated selection on very elite segments of juvenile age groups. This has led to speculation on the need for alternative indices and selection for novel traits in order to differentiate breeding programs and customize selection for unique farm conditions. This will be made more possible by the advent of on-farm sensor technology and artificial intelligence algorithms. Large commercial dairies are increasingly experimenting with crossbreeding with varying levels of success and this will require a new approach by breeding programs to focus both on purebred and crossbred performance. In addition, the potential exists for use of gene-editing to further enable value-added traits to be added into breeding programs. In parallel with breeding program advancements, consumer trends are also changing to include more interest in specialty dairy products with implied differences in digestibility, health or environmental impacts. Identifying technologies and traits that will add value either on the farm as well as at the consumer level will be a challenge for today’s breeders and producers. Some new technologies, such as gene editing, can pose consumer acceptance challenges if they are perceived to be used carelessly or for the wrong reasons. Careful choices will need to be made to continue to improve profitability, functionality and health of dairy cattle while also meeting higher consumer standards for animal welfare, health and the environment.

scholarly journals Intensified Use of Reproductive Technologies and Reduced Dimensions of Breeding Schemes Put Genetic Diversity at Risk in Dairy Cattle Breeds

Animals ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1903
Author(s):  
Anna-Charlotte Doublet ◽  
Gwendal Restoux ◽  
Sébastien Fritz ◽  
Laura Balberini ◽  
Guillaume Fayolle ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Dairy Cattle ◽  
Genetic Gain ◽  
Reproductive Technologies ◽  
Vitro Fertilization ◽  
Cattle Breeds ◽  
Reduced Dimensions ◽  
Recent Trends ◽  
Breeding Schemes

In the management of dairy cattle breeds, two recent trends have arisen that pose potential threats to genetic diversity: the use of reproductive technologies (RT) and a reduction in the number of bulls in breeding schemes. The expected outcome of these changes, in terms of both genetic gain and genetic diversity, is not trivial to predict. Here, we simulated 15 breeding schemes similar to those carried out in large French dairy cattle breeds; breeding schemes differed with respect to their dimensions, the intensity of RT use, and the type of RT involved. We found that intensive use of RT resulted in improved genetic gain, but deteriorated genetic diversity. Specifically, a reduction in the interval between generations through the use of ovum pick-up and in vitro fertilization (OPU-IVF) resulted in a large increase in the inbreeding rate both per year and per generation, suggesting that OPU-IVF could have severe adverse effects on genetic diversity. To achieve a given level of genetic gain, the scenarios that best maintained genetic diversity were those with a higher number of sires/bulls and a medium intensity of RT use or those with a higher number of female donors to compensate for the increased intensity of RT.

Genetic correlations between 305-day and monthly test day milk yield records in primiparous Iranian Holsteins

2001 ◽  
Vol 2001 ◽  
pp. 219-219
Author(s):  
H. Farhangfar ◽  
P. Rowlinson ◽  
M.B. Willis
Keyword(s):  
Dairy Cattle ◽  
Genetic Gain ◽  
Genetic Correlations ◽  
Cattle Breeding ◽  
Breeding Values ◽  
Dairy Heifers ◽  
The Past ◽  
Dairy Cattle Breeding ◽  
Adjustment Factors ◽  
Breeding Programmes

Traditionally, in most dairy cattle breeding programmes genetic evaluation of dairy sires and cows has been primarily based on 305-day lactation yield. To provide 305-day lactation yields many partial lactations have to be extended by adjustment factors resulting in overestimation or underestimation of 305-day yields which in turn leads to biased prediction of breeding values. Over the past decade there has been a considerable interest in using monthly test day records instead of 305-day lactation yield to predict breeding values of dairy cattle as early as possible and also to increase genetic gain through reducing generation interval. The main objective of present research was to estimate the genetic correlations between 305-day and monthly test day milk yields in Iranian Holstein dairy heifers.

scholarly journals Simulation studies to optimize genomic selection in honey bees

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Richard Bernstein ◽  
Manuel Du ◽  
Andreas Hoppe ◽  
Kaspar Bienefeld
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Honey Bees ◽  
Reference Population ◽  
Breeding Program ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Genomic Breeding ◽  
Breeding Values

Abstract Background With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees. Methods Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ. Results Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required. Conclusions This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

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