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

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.

[1]  M. Goddard,et al.  Quantitative Trait Locus-by-Environment Interaction for Milk Yield Traits on Bos taurus Autosome 6 , 2008, Genetics.

[2]  M. Goddard,et al.  Genomic selection. , 2007, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[3]  M. Goddard,et al.  The distribution of the effects of genes affecting quantitative traits in livestock , 2001, Genetics Selection Evolution.

[4]  M. Goddard,et al.  Prediction of total genetic value using genome-wide dense marker maps. , 2001, Genetics.

[5]  N. Wray,et al.  Increasing long-term response to selection , 1994, Genetics Selection Evolution.

[6]  William G. Hill,et al.  Estimation of effective population size from data on linkage disequilibrium , 1981 .

[7]  Shizhong Xu Estimating polygenic effects using markers of the entire genome. , 2003, Genetics.

[8]  M. Goddard,et al.  Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data , 2004, Genetics Selection Evolution.

[9]  P. Donnelly,et al.  Association mapping in structured populations. , 2000, American journal of human genetics.

[10]  Michel Georges,et al.  Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[11]  M. Goddard Genomic selection: prediction of accuracy and maximisation of long term response , 2009, Genetica.

[12]  Shizhong Xu,et al.  Genomewide Analysis of Epistatic Effects for Quantitative Traits in Barley , 2007, Genetics.

[13]  M. Goddard Mapping genes for quantitative traits using linkage disequilibrium , 1991, Genetics Selection Evolution.

[14]  Robin Thompson,et al.  ASREML user guide release 1.0 , 2002 .

[15]  R. Fernando,et al.  Genomic-Assisted Prediction of Genetic Value With Semiparametric Procedures , 2006, Genetics.

[16]  B. J. Hayes,et al.  Genomic selection: Genomic selection , 2007 .

[17]  I Misztal,et al.  Technical note: Computing strategies in genome-wide selection. , 2008, Journal of dairy science.

[18]  J. Woolliams,et al.  Inbreeding in genome-wide selection. , 2007, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[19]  M. Goddard,et al.  Technical note: prediction of breeding values using marker-derived relationship matrices. , 2008, Journal of animal science.

[20]  G. Gregory,et al.  Characterization of the DGAT1 gene in the New Zealand dairy population. , 2002, Journal of dairy science.

[21]  M. Georges,et al.  Haplotype diversity of the myostatin gene among beef cattle breeds , 2003, Genetics Selection Evolution.

[22]  M. Calus,et al.  Accuracy of Genomic Selection Using Different Methods to Define Haplotypes , 2008, Genetics.

[23]  M. Goddard,et al.  Linkage Disequilibrium and Persistence of Phase in Holstein–Friesian, Jersey and Angus Cattle , 2008, Genetics.

[24]  J. Gibson Short-term gain at the expense of long-term response with selection of identified loci. , 1994 .

[25]  Shah Ebrahim,et al.  Common variants in the GDF5-UQCC region are associated with variation in human height , 2008, Nature Genetics.

[26]  J. Beckmann,et al.  Genetic polymorphism in varietal identification and genetic improvement , 1983, Theoretical and Applied Genetics.

[27]  P. VanRaden,et al.  Invited review: reliability of genomic predictions for North American Holstein bulls. , 2009, Journal of dairy science.

[28]  L R Schaeffer,et al.  Strategy for applying genome-wide selection in dairy cattle. , 2006, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[29]  A. Robertson,et al.  The Isolation of Polygenic Factors Controlling Bristle Score in Drosophila Melanogaster. II. Distribution of Third Chromosome Bristle Effects within Chromosome Sections. , 1988, Genetics.

[30]  W. Muir,et al.  Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. , 2007, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[31]  R. Spelman,et al.  Genomic selection in New Zealand and the implications for national genetic evaluation. , 2009 .

[32]  R. Fernando,et al.  The Impact of Genetic Relationship Information on Genome-Assisted Breeding Values , 2007, Genetics.

[33]  Paul Scheet,et al.  A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. , 2006, American journal of human genetics.

[34]  Charles Smith Improvement of metric traits through specific genetic loci , 1967 .

[35]  A. Malafosse,et al.  Implementation of marker-assisted selection in French dairy cattle. , 2002 .

[36]  Technical note: Computing options for genetic evaluation with a large number of genetic markers. , 2008, Journal of animal science.