Short communication: Genomic selection using a multi-breed, across-country reference population

Pryce, J.E.; Gredler, Birgit; Bolormaa, Sunduimijid; Bowman, P.J.; Egger-Danner, C.; Fuerst, Christian; Emmerling, Reiner; Sölkner, Johann; Goddard, Michael E.; Hayes, Ben J.

doi:10.3168/jds.2010-3719

Cited by 82 publications

(89 citation statements)

References 12 publications

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“…A number of studies (Hayes et al, 2009;Pryce et al, 2011;Erbe et al, 2012;Olson et al, 2012) report on the effect of (Pryce et al, 2011). Generally, no improvements are observed in the accuracies of GEBV for HF when Jersey animals are added to the reference population, and for Jersey animal results are similar or worse when using 54 k data and GBLUP methods (Hayes et al, 2009;Erbe et al, 2012).…”

Section: Genomic Prediction In Small Populationsmentioning

confidence: 99%

Review: How to improve genomic predictions in small dairy cattle populations

Lund

Berg

et al. 2016

Animal

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This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components.

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Section: Genomic Prediction In Small Populationsmentioning

confidence: 99%

Review: How to improve genomic predictions in small dairy cattle populations

Lund

Berg

et al. 2016

Animal

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show abstract

“…One way to increase the reference population size for selection would be to use a breed with more genotyped individuals in the reference population for a breed with fewer candidates. It was found that for Holsteins and Jerseys, one breed could not be directly used as the sole reference population for selection in the other breed using the 50k marker panel, but results showed more promise when both breeds were combined into a common population (Pryce et al, 2011). For genomic selection to be effective, markers and QTL need to be in the same linkage phase across the populations, and must be consistent from the reference population to the validation population.…”

Section: Introductionmentioning

confidence: 99%

Extent of linkage disequilibrium, consistency of gametic phase, and imputation accuracy within and across Canadian dairy breeds

Larmer

Sargolzaei

Schenkel

2014

Journal of Dairy Science

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Some dairy breeds have too few animals genotyped for within breed genomic selection to be carried out with sufficient accuracy. As such, the level of linkage disequilibrium within each breed as well as consistency of gametic phase across breeds was studied.High correlations of phase (>0.9) were found between all breed pairs at this same SNP density. The efficacy of imputing animals genotyped on lower density (6k and 50k) panels was then explored in order to increase the size of the reference population with 777k genotypes in a cost-effective manner. These results showed high accuracies (>0.92) in all imputation scenarios studies, using both a within breed and a multi-breed reference population for imputation. It was concluded that given the results of both of these studies, pooling breeds into a common reference population for genomic selection should be a viable option for accurate genomic selection in breeds with few genotyped individuals.iii ACKNOWLEDGEMENTS

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“…Unfortunately, they generally have lower population sizes and may be spread over several geographic regions. This has limited the size of reference populations and the resulting predictions are less accurate (Pryce et al 2011b). In addition, some of these breeds have higher effective population sizes than do Holsteins (e.g.…”

Section: Implementation In Other Dairy Cattle Breedsmentioning

confidence: 99%

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

Pryce¹,

Daetwyler²

2012

Anim. Prod. Sci.

108

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Abstract. 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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Short communication: Genomic selection using a multi-breed, across-country reference population

Cited by 82 publications

References 12 publications

Review: How to improve genomic predictions in small dairy cattle populations

Review: How to improve genomic predictions in small dairy cattle populations

Extent of linkage disequilibrium, consistency of gametic phase, and imputation accuracy within and across Canadian dairy breeds

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

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