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

Pryce, J.E.; Daetwyler, Hans D.

doi:10.1071/an11098

Cited by 108 publications

(98 citation statements)

References 33 publications

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“…Therefore, a reliability of 99% is very close to the true breeding value. Reliabilities of milk-production traits evaluated using genomic selection and recently surveyed (April 2011) are currently~60% (Pryce and Daetwyler 2012). For a trait with a heritability of 0.3, a reliability of 60% is approximately the same as proof based on phenotypes from 20 daughters.…”

Section: Selection Of Siresmentioning

confidence: 87%

“…The application of genomic selection to dairy cows has enabled breeding companies to redesign their breeding schemes (Pryce and Daetwyler 2012). More than two times the rate of genetic gain achieved through conventional progeny testing is feasible if bulls are used at a young age and large numbers of young bulls are screened (de Roos et al 2011).…”

Section: Introductionmentioning

confidence: 99%

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A review of how dairy farmers can use and profit from genomic technologies

Pryce¹,

Hayes

2012

Anim. Prod. Sci.

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Abstract. New genomic technologies can help farmers to (1) achieve higher annual rates of genetic gain through using genomically tested bulls in their herds, (2) select for 'difficult' to measure traits, such as feed conversion efficiency, methane emissions and energy balance, (3) select the best heifers to become herd replacements, (4) sell pedigree heifers at a premium, (5) use mating plans to optimise rates of genetic gain while controlling inbreeding, (6) achieve certainty in parentage of individual cows and (7) avoid genetic defects that could arise from mating cows to bulls that are known carriers of genetic diseases that are the result of a single lethal mutation. The first use does not require genotyping females and could approximately double the net income per cow that arises due to genetic improvement, mainly through a reduction in generation interval. On the basis of current rates of genetic gain, the net profit from using genotyped bulls could be worth AU $20/cow per year and is permanent and cumulative. One of the most powerful uses of genomic selection is to select for economically important, yet difficult-or expensive-to-measure traits, such as residual feed intake or energy balance. Provided the accuracy of genomic breeding values is high enough (i.e. correlation between the true and estimated breeding values), these traits lend themselves well to genomic selection. For selecting replacement heifers, if genotyping costs are AU$50/cow, the net profit of genotyping 40 heifers to select the top 20 as replacements (per 100 cows) would be worth approximately AU$41 per cow. However, using parent average estimated breeding-value information is free and can already be used to select replacement heifers. So, genotyping costs would need to be very low to be more profitable than selecting on parent average estimated breeding value. However, extra value from genotyping can also be captured by using other strategies. For example, mating plans that use genomic relationships rather than pedigree relationships to capture inbreeding are superior in terms of reducing progeny inbreeding at a desired level of genetic gain, although pedigree does an adequate job. So, again, the benefits of genotyping are small (

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Section: Selection Of Siresmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

A review of how dairy farmers can use and profit from genomic technologies

Pryce¹,

Hayes

2012

Anim. Prod. Sci.

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“…Their estimate is also inflated by sequencing errors (possibly 1-1.5 million errors) and potentially has an upward bias from population substructure because they included black and white Holsteins from North America, Europe, and Australia as well as a subpopulation of red and white Holsteins. We chose a dairy cattle breed demography because a number of countries are already implementing genomic prediction in dairy cattle populations (Lund et al 2011;VanRaden et al 2011;Pryce and Daetwyler 2012). This is likely to reflect one of the more extreme livestock breeds in terms of the very sharp recent reduction in N e , because the widespread use of artificial insemination enables popular bulls to sire tens of thousands of daughters.…”

Section: Model Populationsmentioning

confidence: 99%

The Effects of Demography and Long-Term Selection on the Accuracy of Genomic Prediction with Sequence Data

2014

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The use of dense SNPs to predict the genetic value of an individual for a complex trait is often referred to as "genomic selection" in livestock and crops, but is also relevant to human genetics to predict, for example, complex genetic disease risk. The accuracy of prediction depends on the strength of linkage disequilibrium (LD) between SNPs and causal mutations. If sequence data were used instead of dense SNPs, accuracy should increase because causal mutations are present, but demographic history and longterm negative selection also influence accuracy. We therefore evaluated genomic prediction, using simulated sequence in two contrasting populations: one reducing from an ancestrally large effective population size (N e ) to a small one, with high LD common in domestic livestock, while the second had a large constant-sized N e with low LD similar to that in some human or outbred plant populations. There were two scenarios in each population; causal variants were either neutral or under long-term negative selection. For large N e , sequence data led to a 22% increase in accuracy relative to 600K SNP chip data with a Bayesian analysis and a more modest advantage with a BLUP analysis. This advantage increased when causal variants were influenced by negative selection, and accuracy persisted when 10 generations separated reference and validation populations. However, in the reducing N e population, there was little advantage for sequence even with negative selection. This study demonstrates the joint influence of demography and selection on accuracy of prediction and improves our understanding of how best to exploit sequence for genomic prediction. M ETHODOLOGY has been developed to predict genetic value for polygenic traits in livestock and crops by exploiting high-density genome-wide SNP genotypes that are fitted simultaneously in an analytical model (Meuwissen et al. 2001). The same methodology can be applied in human genetics, for example, to predict complex disease risk (reviewed in De los Campos et al. 2010). In livestock and plants this analytical approach is often referred to as "genomic selection" because the genomic predictions are used for selection decisions. First, a large "reference population" with genotypes and phenotypes is required to jointly estimate genome-wide SNP effects. Then the accuracy of prediction using the estimated SNP effects is reevaluated in an independent "validation population," before the genomic prediction equation is routinely applied on individuals with genotypes but no phenotypes.Genomic prediction (GP) methods generally use dense genome-wide SNP genotypes and therefore rely on exploiting linkage disequilibrium (LD) between these SNPs and unknown causative mutations or quantitative trait loci (QTL). The lower the LD is between the SNP and causal mutations, the lower the accuracy will be of GP. As the number of generations separating the reference and validation populations increases, the LD between SNPs and causative mutations is further eroded by recombination and t...

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“…Genomic selection refers to selection decisions based on the genomic estimated breeding values (GEBVs) (Meuwissen et al 2001). The GEBVs are calculated as the sum of the effects of dense genetic markers that are approximately equally spaced across the entire genome, thereby potentially capturing most of the quantitative trait loci that contribute to variation in a trait (Pryce et al 2012). Genomic enabled selection is a method of marker-assisted selection (MAS) based on linkage disequilibrium (LD) between the markers and quantitative traits loci (QTL).…”

Section: Application Of High Throughput Snps Genotyping Methods In Famentioning

confidence: 99%

SNPs genotyping technologies and their applications in farm animals breeding programs: review

Koopaee

Koshkoiyeh

2014

Braz. arch. biol. technol.

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Single nucleotide polymorphisms (SNPs) are DNA sequence variations that occur when a single nucleotide: adenine (A), thymine (T), cytosine (C) or guanine (G) in the genome sequence is altered. Traditional and high throughput methods are two main strategies for SNPs genotyping. The SNPs genotyping technologies provide powerful resources for animal breeding programs.Genomic selection using SNPs is a new tool for choosing the best breeding animals. In addition, the high density maps using SNPs can provide useful genetic tools to study quantitative traits genetic variations. There are many sources of SNPs and exhaustive numbers of methods of SNP detection to be considered. For many traits in farm animals, the rate of genetic improvement can be nearly doubled when SNPs information is used compared to the current methods of genetic evaluation. The goal of this review is to characterize the SNPs genotyping methods and their applications in farm animals breeding.

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

Cited by 108 publications

References 33 publications

A review of how dairy farmers can use and profit from genomic technologies

A review of how dairy farmers can use and profit from genomic technologies

The Effects of Demography and Long-Term Selection on the Accuracy of Genomic Prediction with Sequence Data

SNPs genotyping technologies and their applications in farm animals breeding programs: review

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