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 Optimal strategies for the use of genomic selection in dairy cattle breeding programs

2011 ◽  
Vol 94 (8) ◽  
pp. 4140-4151 ◽  
Author(s):  
M. Wensch-Dorendorf ◽  
T. Yin ◽  
H.H. Swalve ◽  
S. König
Keyword(s):  
Dairy Cattle ◽  
Genomic Selection ◽  
Optimal Strategies ◽  
Cattle Breeding ◽  
Breeding Programs ◽  
Dairy Cattle Breeding

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.

Policy of selecting bulls to breed bulls

1987 ◽  
Vol 44 (1) ◽  
pp. 29-38 ◽  
Author(s):  
M. E. Goddard
Keyword(s):  
Dairy Cattle ◽  
Genetic Variance ◽  
Selection Response ◽  
Progeny Testing ◽  
Cattle Breeding ◽  
Sire Evaluation ◽  
Dairy Cattle Breeding ◽  
Breeding Programmes ◽  
Computer Simulation Program ◽  
Selection Of

ABSTRACTIn the breeding of dairy cattle the selection of bulls to breed young bulls for progeny testing is a crucial process. This paper compares several policies for making this selection based on the criteria-selection response, inbreeding depression, loss of genetic variance and variability of response. A number called the ‘effective number of new bulls to breed bulls selected each year’ (NBBe) is defined which is closely related to the last three of these criteria. Past studies of the design of dairy cattle breeding programmes have assumed that selection is within a group of bulls progeny tested in the same year (policy I). However, modern sire evaluation methods allow comparison of sires tested in different years. To evaluate the effect of selecting bulls to breed bulls from all available bulls (policy II) a computer simulation program was used. Policy II results in an increase in the response to selection but a substantial decrease in NBBe. When compared at the same NBBe, policy II results in a smaller selection response than policy I. A policy which allows the best bulls to be used for more than 1 year but which limits the maximum number of years for which they can be used, results in the best compromise. If bulls are to be used for several years there is little advantage to be gained from making more matings within each year to more high-rated bulls or to older, more reliably evaluated bulls.

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 Relevance of genomic evaluation for egg quality traits in layers

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.

scholarly journals Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing.

Genetics ◽  
1995 ◽  
Vol 139 (2) ◽  
pp. 907-920 ◽  
Author(s):  
M Georges ◽  
D Nielsen ◽  
M Mackinnon ◽  
A Mishra ◽  
R Okimoto ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Dairy Cattle ◽  
Milk Production ◽  
Quantitative Trait ◽  
Bovine Genome ◽  
Progeny Testing ◽  
Cattle Breeding ◽  
Trait Loci ◽  
Dairy Cattle Breeding ◽  
Autosomal Microsatellites

Abstract We have exploited "progeny testing" to map quantitative trait loci (QTL) underlying the genetic variation of milk production in a selected dairy cattle population. A total of 1,518 sires, with progeny tests based on the milking performances of > 150,000 daughters jointly, was genotyped for 159 autosomal microsatellites bracketing 1645 centimorgan or approximately two thirds of the bovine genome. Using a maximum likelihood multilocus linkage analysis accounting for variance heterogeneity of the phenotypes, we identified five chromosomes giving very strong evidence (LOD score > or = 3) for the presence of a QTL controlling milk production: chromosomes 1, 6, 9, 10 and 20. These findings demonstrate that loci with considerable effects on milk production are still segregating in highly selected populations and pave the way toward marker-assisted selection in dairy cattle breeding.

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