Invited review: Reproductive and genomic technologies to optimize breeding strategies for genetic progress in dairy cattle

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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Dairy genetic improvement through artificial insemination, performance recording and genetic evaluation

Canadian Journal of Animal Science ◽

10.4141/a03-023 ◽

2003 ◽

Vol 83 (3) ◽

pp. 385-392 ◽

Cited By ~ 3

Author(s):

B. J. Van Doormaal ◽

G. J. Kistemaker

Keyword(s):

Dairy Cattle ◽

Artificial Insemination ◽

Genetic Improvement ◽

Genetic Evaluation ◽

Production Traits ◽

Genetic Progress ◽

Genetic Gains ◽

Holstein Breed ◽

The Past ◽

Genetic Evaluations

Artificial insemination (AI) of dairy cattle in Canada was started more than half a century ago and today it is estimated that at least 75% of all dairy cattle nationally are bred using this common reproductive technology. A Best Linear Unbiased Prediction sire model for estimating genetic evaluations for production traits was introduced in 1975. The combination of extensive use of AI with genetic evaluations for bulls and cows has resulted in significant phenotypic and genetic gains over the past 20 yr. In the Holstein breed, mature equivalent yields have increased by an average of 200 kg milk, 7.0 kg fat and 6.3 kg protein per year since 1980. Genetically, the relative emphasis realized for production traits versus overall type during the past 5 yr has followed the 60:40 breeding goal represented in the Lifetime Profit Index, which has increased at an average rate of 0.28 standard units per year. Examination of the generation interval in the Canadian Holstein breed, associated with each of the four pathways for genetic improvement, indicates a 46% increase in the rate of annual genetic gain today compared to 20 yr ago. The increased accuracy and intensity of selection associated with the use of AI and genetic evaluations have also contributed to the rates of phenotypic and genetic progress achieved over the years. In the future , AI will continue to be a critical component of the genetic gains possible in dairy cattle breeding but it will be complemented by other reproductive technologies aimed at further reducing generation intervals and increasing the accuracy and selection of intensity, especially on the female side. Key words: Dairy cattle, artificial insemination, genetic progress, genetic evaluation

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The impact of genetic selection on greenhouse-gas emissions in Australian dairy cattle

Animal Production Science ◽

10.1071/an16510 ◽

2017 ◽

Vol 57 (7) ◽

pp. 1451 ◽

Cited By ~ 5

Author(s):

Jennie E. Pryce ◽

Matthew J. Bell

Keyword(s):

Dairy Cattle ◽

Milk Production ◽

Genetic Gain ◽

Genetic Improvement ◽

Genetic Selection ◽

Ghg Emissions ◽

Production Traits ◽

Milk Production Traits ◽

The Past ◽

Milk Solids

In Australia, dairy cattle account for ~12% of the nation’s agricultural greenhouse-gas (GHG) emissions. Genetic selection has had a positive impact, reducing GHG emissions from dairy systems mainly due to increased production per cow, which has led to (1) requiring fewer cows to produce the same amount of milk and (2) lowering emissions per unit of milk produced (emission intensity). The objective of the present study was to evaluate the consequences of previous and current genetic-selection practices on carbon emissions, using realised and predicted responses to selection for key traits that are included in the Australian national breeding objective. A farm model was used to predict the carbon dioxide equivalent (CO2-eq) emissions per unit change of these traits, while holding all other traits constant. Estimates of the realised change in annual CO2-eq emissions per cow over the past decade were made by multiplying predicted CO2-eq emissions per unit change of each trait under selection by the realised rates of genetic gain in each of those traits. The total impact is estimated to be an increase of 55 kg CO2-eq/cow.year after 10 years of selection. The same approach was applied to future CO2-eq emissions, except predicted rates of genetic gain assumed to occur over the next decade through selection on the Balanced Performance Index (BPI) were used. For an increase of AU$100 in BPI (~10 years of genetic improvement), we predict that the increase of per cow emissions will be reduced to 37 kg CO2-eq/cow.year. Since milk-production traits are a large part of the breeding goal, the GHG emitted per unit of milk produced will reduce as a result of improvements in efficiency and dilution of emissions per litre of milk produced at a rate estimated to be 35.7 g CO2-eq/kg milk solids per year in the past decade and is predicted to reduce to 29.5 g CO2-eq/kg milk solids per year after a conservative 10-year improvement in BPI (AU$100). In fact, cow numbers have decreased over the past decade and production has increased; altogether, we estimate that the net impact has been a reduction of CO2-eq emissions of ~1.0% in total emissions from the dairy industry per year. Using two future scenarios of either keeping the number of cows or amount of product static, we predict that net GHG emissions will reduce by ~0.6%/year of total dairy emissions if milk production remains static, compared with 0.3%/year, if cow numbers remain the same and there is genetic improvement in milk-production traits.

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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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Interaction between genotype and geographical region for milk production traits of Iranian Holstein dairy cattle

Livestock Science ◽

10.1016/j.livsci.2014.08.010 ◽

2014 ◽

Vol 169 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Mehdi Bohlouli ◽

Jalil Shodja ◽

Sadegh Alijani ◽

Nasrollah Pirany

Keyword(s):

Dairy Cattle ◽

Milk Production ◽

Geographical Region ◽

Production Traits ◽

Milk Production Traits

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ARE TODAY’S DAIRY CATTLE BREEDING PROGRAMS SUITABLE FOR TOMORROW’S PRODUCTION REQUIREMENTS?

Canadian Journal of Animal Science ◽

10.4141/cjas80-034 ◽

1980 ◽

Vol 60 (2) ◽

pp. 253-264 ◽

Cited By ~ 4

Author(s):

A. J. McALLISTER

Keyword(s):

Dairy Cattle ◽

Genetic Improvement ◽

Herd Size ◽

Breeding Program ◽

Genetic Trend ◽

Genetic Progress ◽

Breeding Programs ◽

Holstein Breed ◽

Dairy Cattle Breeding ◽

National Production

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.

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Associations among milk production traits and glycosylated haemoglobin in dairy cattle; importance of lactose synthesis potential

Veterinary Research Communications ◽

10.1007/s11259-009-9324-2 ◽

2009 ◽

Vol 34 (1) ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Homayon Reza Shahbazkia ◽

Mahmoud Aminlari ◽

Atoosa Tavasoli ◽

Ahmad Reza Mohamadnia ◽

Alfredo Cravador

Keyword(s):

Dairy Cattle ◽

Milk Production ◽

Glycosylated Haemoglobin ◽

Production Traits ◽

Milk Production Traits ◽

Lactose Synthesis

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167 Genetic and genomic estimation for somatic cell score in relation with milk production traits of Russian Holstein dairy cattle

Journal of Animal Science ◽

10.2527/asasann.2017.167 ◽

2017 ◽

Vol 95 (suppl_4) ◽

pp. 82-83 ◽

Cited By ~ 1

Author(s):

A. A. Sermyagin ◽

E. A. Gladyr' ◽

A. A. Kharzhau ◽

K. V. Plemyashov ◽

E. N. Tyurenkova ◽

...

Keyword(s):

Dairy Cattle ◽

Somatic Cell ◽

Milk Production ◽

Somatic Cell Score ◽

Production Traits ◽

Milk Production Traits

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Confirmed effects of candidate variants for milk production, udder health, and udder morphology in dairy cattle

Genetics Selection Evolution ◽

10.1186/s12711-020-00575-1 ◽

2020 ◽

Vol 52 (1) ◽

Cited By ~ 1

Author(s):

Thierry Tribout ◽

Pascal Croiseau ◽

Rachel Lefebvre ◽

Anne Barbat ◽

Mekki Boussaha ◽

...

Keyword(s):

Dairy Cattle ◽

Candidate Genes ◽

Milk Production ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Production Traits ◽

Milk Production Traits ◽

Udder Health ◽

Gwas Approach

Abstract Background Over the last years, genome-wide association studies (GWAS) based on imputed whole-genome sequences (WGS) have been used to detect quantitative trait loci (QTL) and highlight candidate genes for important traits. However, in general this approach does not allow to validate the effects of candidate mutations or determine if they are truly causative for the trait(s) in question. To address these questions, we applied a two-step, within-breed GWAS approach on 15 traits (5 linked with milk production, 2 with udder health, and 8 with udder morphology) in Montbéliarde (MON), Normande (NOR), and Holstein (HOL) cattle. We detected the most-promising candidate variants (CV) using imputed WGS of 2515 MON, 2203 NOR, and 6321 HOL bulls, and validated their effects in three younger populations of 23,926 MON, 9400 NOR, and 51,977 HOL cows. Results Bull sequence-based GWAS detected 84 QTL: 13, 10, and 30 for milk production traits; 3, 0, and 2 for somatic cell score (SCS); and 8, 2 and 16 for udder morphology traits, in MON, NOR, and HOL respectively. Five genomic regions with effects on milk production traits were shared among the three breeds whereas six (2 for production and 4 for udder morphology and health traits) had effects in two breeds. In 80 of these QTL, 855 CV were highlighted based on the significance of their effects and functional annotation. The subsequent GWAS on MON, NOR, and HOL cows validated 8, 9, and 23 QTL for production traits; 0, 0, and 1 for SCS; and 4, 1, and 8 for udder morphology traits, respectively. In 47 of the 54 confirmed QTL, the CV identified in bulls had more significant effects than single nucleotide polymorphisms (SNPs) from the standard 50K chip. The best CV for each validated QTL was located in a gene that was functionally related to production (36 QTL) or udder (9 QTL) traits. Conclusions Using this two-step GWAS approach, we identified and validated 54 QTL that included CV mostly located within functional candidate genes and explained up to 6.3% (udder traits) and 37% (production traits) of the genetic variance of economically important dairy traits. These CV are now included in the chip used to evaluate French dairy cattle and can be integrated into routine genomic evaluation.

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