Strategies for implementing genomic selection for feed efficiency in dairy cattle breeding schemes

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.

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Optimising multistage dairy cattle breeding schemes including genomic selection using decorrelated or optimum selection indices

Genetics Selection Evolution ◽

10.1186/1297-9686-44-1 ◽

2012 ◽

Vol 44 (1) ◽

Cited By ~ 16

Author(s):

Vinzent Börner ◽

Norbert Reinsch

Keyword(s):

Dairy Cattle ◽

Genomic Selection ◽

Cattle Breeding ◽

Optimum Selection ◽

Selection Indices ◽

Dairy Cattle Breeding ◽

Breeding Schemes

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Impact of heterogeneous within herd variances on dairy cattle breeding schemes: a simulation study

Livestock Production Science ◽

10.1016/0301-6226(93)90236-b ◽

1993 ◽

Vol 33 (1-2) ◽

pp. 31-41 ◽

Cited By ~ 22

Author(s):

T.H.E. Meuwissen ◽

J.H.J. van der Werf

Keyword(s):

Dairy Cattle ◽

Simulation Study ◽

Cattle Breeding ◽

Dairy Cattle Breeding ◽

Breeding Schemes

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Genetic Progress in Multistage Dairy Cattle Breeding Schemes Using Genetic Markers

Journal of Dairy Science ◽

10.3168/jds.s0022-0302(05)72826-5 ◽

2005 ◽

Vol 88 (4) ◽

pp. 1569-1581 ◽

Cited By ~ 26

Author(s):

C. Schrooten ◽

H. Bovenhuis ◽

J.A.M. van Arendonk ◽

P. Bijma

Keyword(s):

Dairy Cattle ◽

Genetic Markers ◽

Genetic Progress ◽

Cattle Breeding ◽

Dairy Cattle Breeding ◽

Breeding Schemes

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

Animal Production Science ◽

10.1071/an11098 ◽

2012 ◽

Vol 52 (3) ◽

pp. 107 ◽

Cited By ~ 67

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.

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Relevance of genomic evaluation for egg quality traits in layers

10.1101/704742 ◽

2019 ◽

Author(s):

David Picard Druet ◽

Amandine Varenne ◽

Florian Herry ◽

Frédéric Hérault ◽

Sophie Allais ◽

...

Keyword(s):

Dairy Cattle ◽

Genomic Selection ◽

Egg Quality ◽

Pure Line ◽

Genetic Evaluation ◽

Evaluation Methodology ◽

Quality Traits ◽

Genomic Evaluation ◽

Cattle Breeding ◽

Dairy Cattle Breeding

AbstractBackgroundGenomic evaluation, based on thousands of genetic markers, has become the standard evaluation methodology in dairy cattle breeding programs over the past few years. Despite the many differences between dairy cattle breeding and poultry breeding, genomic selection seems very promising for the avian sector, and studies are currently being conducted to optimize avian selection schemes. In this optimization perspective, one of the key parameters is to properly predict the accuracy of genomic evaluation in pure line layers.MethodsBoth genetic evaluation and genomic evaluation were performed on three candidate populations (male and female), using different sizes of phenotypic records on five egg quality traits and at two different ages. The methodologies used were BLUP & ssGBLUP, and variance-covariance matrices were estimated through REML. To estimate evaluation accuracy, the LR method was implemented. Four statistics were used to assess the relative accuracy of the estimated breeding values of candidates, their bias and dispersion, as well as the differences between genetic evaluation and genomic evaluation.ResultsIt was observed that genomic evaluation, whether performed on males or females, always proved more accurate than genetic evaluation. The gain was higher when phenotypic information was narrowed and an augmentation of the size of the reference population led to an increase in accuracy prediction, for what regards genomic evaluation. By taking into account the increase of selection intensity and the decrease of the generation interval induced by genomic selection, the expected annual genetic gain would be higher with ancestry-based genomic evaluation of male candidates than with genetic evaluation based on collaterals. This advantage of genomic selection over genetic selection requires to be studied in more details for female candidates.ConclusionsIn conclusion, in the population studied, genomic evaluation for egg quality traits of breeding birds at birth seems a promising strategy, at least for what regards males selection.

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Across-Family Marker-Assisted Selection Using Selective Genotyping Strategies in Dairy Cattle Breeding Schemes

Journal of Dairy Science ◽

10.3168/jds.2007-0613 ◽

2008 ◽

Vol 91 (4) ◽

pp. 1628-1639 ◽

Cited By ~ 9

Author(s):

S. Ansari-Mahyari ◽

A.C. Sørensen ◽

M.S. Lund ◽

H. Thomsen ◽

P. Berg

Keyword(s):

Dairy Cattle ◽

Marker Assisted Selection ◽

Selective Genotyping ◽

Cattle Breeding ◽

Dairy Cattle Breeding ◽

Breeding Schemes

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Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle

Canadian Journal of Animal Science ◽

10.1139/cjas-2019-0193 ◽

2020 ◽

Vol 100 (4) ◽

pp. 587-604 ◽

Cited By ~ 1

Author(s):

Luiz F. Brito ◽

Hinayah R. Oliveira ◽

Kerry Houlahan ◽

Pablo A.S. Fonseca ◽

Stephanie Lam ◽

...

Keyword(s):

Dairy Cattle ◽

Genomic Selection ◽

Feed Efficiency ◽

Genetic Selection ◽

Complex Trait ◽

Feed Utilization ◽

Successful Implementation ◽

Genetic Progress ◽

Selection For ◽

Indicator Traits

The economic importance of genetically improving feed efficiency has been recognized by cattle producers worldwide. It has the potential to considerably reduce costs, minimize environmental impact, optimize land and resource use efficiency, and improve the overall cattle industry’s profitability. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. The main objective of this review paper is to present an overview of the main genetic and physiological mechanisms underlying feed utilization in ruminants and the process towards implementation of genomic selection for feed efficiency in dairy cattle. In summary, feed efficiency can be improved via numerous metabolic pathways and biological mechanisms through genetic selection. Various studies have indicated that feed efficiency is heritable, and genomic selection can be successfully implemented in dairy cattle with a large enough training population. In this context, some organizations have worked collaboratively to do research and develop training populations for successful implementation of joint international genomic evaluations. The integration of “-omics” technologies, further investments in high-throughput phenotyping, and identification of novel indicator traits will also be paramount in maximizing the rates of genetic progress for feed efficiency in dairy cattle worldwide.

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