Literature DB >> 19164653

Invited review: Genomic selection in dairy cattle: progress and challenges.

B J Hayes1, P J Bowman, A J Chamberlain, M E Goddard.   

Abstract

A new technology called genomic selection is revolutionizing dairy cattle breeding. Genomic selection refers to selection decisions based on genomic breeding values (GEBV). The GEBV are calculated as the sum of the effects of dense genetic markers, or haplotypes of these markers, across the entire genome, thereby potentially capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. The QTL effects, inferred from either haplotypes or individual single nucleotide polymorphism markers, are first estimated in a large reference population with phenotypic information. In subsequent generations, only marker information is required to calculate GEBV. The reliability of GEBV predicted in this way has already been evaluated in experiments in the United States, New Zealand, Australia, and the Netherlands. These experiments used reference populations of between 650 and 4,500 progeny-tested Holstein-Friesian bulls, genotyped for approximately 50,000 genome-wide markers. Reliabilities of GEBV for young bulls without progeny test results in the reference population were between 20 and 67%. The reliability achieved depended on the heritability of the trait evaluated, the number of bulls in the reference population, the statistical method used to estimate the single nucleotide polymorphism effects in the reference population, and the method used to calculate the reliability. A common finding in 3 countries (United States, New Zealand, and Australia) was that a straightforward BLUP method for estimating the marker effects gave reliabilities of GEBV almost as high as more complex methods. The BLUP method is attractive because the only prior information required is the additive genetic variance of the trait. All countries included a polygenic effect (parent average breeding value) in their GEBV calculation. This inclusion is recommended to capture any genetic variance not associated with the markers, and to put some selection pressure on low-frequency QTL that may not be captured by the markers. The reliabilities of GEBV achieved were significantly greater than the reliability of parental average breeding values, the current criteria for selection of bull calves to enter progeny test teams. The increase in reliability is sufficiently high that at least 2 dairy breeding companies are already marketing bull teams for commercial use based on their GEBV only, at 2 yr of age. This strategy should at least double the rate of genetic gain in the dairy industry. Many challenges with genomic selection and its implementation remain, including increasing the accuracy of GEBV, integrating genomic information into national and international genetic evaluations, and managing long-term genetic gain.

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Year:  2009        PMID: 19164653     DOI: 10.3168/jds.2008-1646

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  460 in total

1.  Genetic variances of SNP loci for milk yield in dairy cattle.

Authors:  Petr Pešek; Josef Přibyl; Luboš Vostrý
Journal:  J Appl Genet       Date:  2014-11-16       Impact factor: 3.240

2.  Evaluation of genome-wide selection efficiency in maize nested association mapping populations.

Authors:  Zhigang Guo; Dominic M Tucker; Jianwei Lu; Venkata Kishore; Gilles Gay
Journal:  Theor Appl Genet       Date:  2011-09-22       Impact factor: 5.699

3.  Accuracy of genomic selection in European maize elite breeding populations.

Authors:  Yusheng Zhao; Manje Gowda; Wenxin Liu; Tobias Würschum; Hans P Maurer; Friedrich H Longin; Nicolas Ranc; Jochen C Reif
Journal:  Theor Appl Genet       Date:  2011-11-11       Impact factor: 5.699

4.  Impact of selective genotyping in the training population on accuracy and bias of genomic selection.

Authors:  Yusheng Zhao; Manje Gowda; Friedrich H Longin; Tobias Würschum; Nicolas Ranc; Jochen C Reif
Journal:  Theor Appl Genet       Date:  2012-04-06       Impact factor: 5.699

5.  A non-parametric mixture model for genome-enabled prediction of genetic value for a quantitative trait.

Authors:  Daniel Gianola; Xiao-Lin Wu; Eduardo Manfredi; Henner Simianer
Journal:  Genetica       Date:  2010-08-25       Impact factor: 1.082

6.  The impact of genetic architecture on genome-wide evaluation methods.

Authors:  Hans D Daetwyler; Ricardo Pong-Wong; Beatriz Villanueva; John A Woolliams
Journal:  Genetics       Date:  2010-04-20       Impact factor: 4.562

7.  Graph-based data selection for the construction of genomic prediction models.

Authors:  Steven Maenhout; Bernard De Baets; Geert Haesaert
Journal:  Genetics       Date:  2010-05-17       Impact factor: 4.562

8.  A two-stage approximation for analysis of mixture genetic models in large pedigrees.

Authors:  D Habier; L R Totir; R L Fernando
Journal:  Genetics       Date:  2010-04-09       Impact factor: 4.562

9.  Sensitivity of genomic selection to using different prior distributions.

Authors:  Klara L Verbyla; Philip J Bowman; Ben J Hayes; Michael E Goddard
Journal:  BMC Proc       Date:  2010-03-31

10.  Modeling Epistasis in Genomic Selection.

Authors:  Yong Jiang; Jochen C Reif
Journal:  Genetics       Date:  2015-07-27       Impact factor: 4.562

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