Accelerating improvement of livestock with genomic selection.

Three recent breakthroughs have resulted in the current widespread use of DNA information: the genomic selection (GS) methodology, which is a form of marker-assisted selection on a genome-wide scale, and the discovery of large numbers of single-nucleotide markers and cost effective methods to genotype them. GS estimates the effect of thousands of DNA markers simultaneously. Nonlinear estimation methods yield higher accuracy, especially for traits with major genes. The marker effects are estimated in a genotyped and phenotyped training population and are used for the estimation of breeding values of selection candidates by combining their genotypes with the estimated marker effects. The benefits of GS are greatest when selection is for traits that are not themselves recorded on the selection candidates before they can be selected. In the future, genome sequence data may replace SNP genotypes as markers. This could increase GS accuracy because the causative mutations should be included in the data.

[1]  Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. , 2002 .

[2]  T. Meuwissen,et al.  A comparison of dairy cattle breeding designs that use genomic selection. , 2011, Journal of dairy science.

[3]  I Misztal,et al.  A relationship matrix including full pedigree and genomic information. , 2009, Journal of dairy science.

[4]  J. Woolliams,et al.  The unified approach to the use of genomic and pedigree information in genomic evaluations revisited. , 2011, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

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

[6]  M. Goddard,et al.  Invited review: Genomic selection in dairy cattle: progress and challenges. , 2009, Journal of dairy science.

[7]  P. Lichtner,et al.  The impact of genetic relationship information on genomic breeding values in German Holstein cattle , 2010, Genetics Selection Evolution.

[8]  K. Weigel,et al.  Potential gains in lifetime net merit from genomic testing of cows, heifers, and calves on commercial dairy farms. , 2012, Journal of dairy science.

[9]  J. Dekkers,et al.  Prediction of response to marker-assisted and genomic selection using selection index theory. , 2007, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[10]  W. G. Hill,et al.  Linkage disequilibrium in finite populations , 1968, Theoretical and Applied Genetics.

[11]  Loretta Auvil,et al.  The yak genome and adaptation to life at high altitude , 2012, Nature Genetics.

[12]  L. D. Van Vleck,et al.  Genetic change in milk yield estimated from simultaneous genetic evaluation of bulls and cows. , 1986, Journal of dairy science.

[13]  M. Lund,et al.  Genomic prediction when some animals are not genotyped , 2010, Genetics Selection Evolution.

[14]  A Legarra,et al.  Genomic selection in the French Lacaune dairy sheep breed. , 2012, Journal of dairy science.

[15]  J. Vilkki,et al.  Birth of correctly genotyped calves after multiplex marker detection from bovine embryo microblade biopsies , 2007, Molecular reproduction and development.

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

[17]  J. Jensen,et al.  Partitioning additive genetic variance into genomic and remaining polygenic components for complex traits in dairy cattle , 2012, BMC Genetics.

[18]  Bruce Tier,et al.  A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers , 2009, Genetics Selection Evolution.

[19]  C. Wade,et al.  The sheep genome reference sequence: a work in progress. , 2010, Animal genetics.

[20]  M. Goddard,et al.  Novel strategies to minimize progeny inbreeding while maximizing genetic gain using genomic information. , 2012, Journal of dairy science.

[21]  Alvaro G. Hernandez,et al.  Whole-genome resequencing of two elite sires for the detection of haplotypes under selection in dairy cattle , 2012, Proceedings of the National Academy of Sciences.

[22]  P. Visscher,et al.  Common SNPs explain a large proportion of heritability for human height , 2011 .

[23]  G. Casella,et al.  The Bayesian Lasso , 2008 .

[24]  M Erbe,et al.  Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. , 2012, Journal of dairy science.

[25]  C. Schrooten,et al.  Effects of genomic selection on genetic improvement, inbreeding, and merit of young versus proven bulls. , 2011, Journal of dairy science.

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

[27]  Rohan L. Fernando,et al.  Extension of the bayesian alphabet for genomic selection , 2011, BMC Bioinformatics.

[28]  B. Larson,et al.  Growth, livability, and feed conversion of 1957 vs 1991 broilers when fed "typical" 1957 and 1991 broiler diets. , 1994, Poultry science.

[29]  Michel Georges,et al.  Velogenetics or the synergistic use of Marker Assisted Selection and Germ-Line Manipulation , 1991 .

[30]  M Grossman,et al.  Marker assisted selection using best linear unbiased prediction , 1989, Genetics Selection Evolution.

[31]  M. Goddard,et al.  LASSO with cross-validation for genomic selection. , 2009, Genetics research.

[32]  P. Keightley.  The distribution of mutation effects on viability in Drosophila melanogaster. , 1994, Genetics.

[33]  I Misztal,et al.  Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. , 2009, Journal of dairy science.

[34]  B. Guldbrandtsen,et al.  Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations. , 2011, Journal of dairy science.

[35]  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.

[36]  M. Goddard,et al.  Using the genomic relationship matrix to predict the accuracy of genomic selection. , 2011, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[37]  Sang Hong Lee,et al.  Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood , 2012, Bioinform..

[38]  M. Goddard,et al.  Accurate Prediction of Genetic Values for Complex Traits by Whole-Genome Resequencing , 2010, Genetics.

[39]  J. Woolliams,et al.  Genomic selection requires genomic control of inbreeding , 2012, Genetics Selection Evolution.

[40]  J E Pryce,et al.  Deterministic models of breeding scheme designs that incorporate genomic selection. , 2010, Journal of dairy science.

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

[42]  J. N. MacLeod,et al.  Genome Sequence, Comparative Analysis, and Population Genetics of the Domestic Horse , 2009, Science.

[43]  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.

[44]  Hans D. Daetwyler,et al.  Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach , 2008, PloS one.

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

[46]  P. VanRaden,et al.  Efficient methods to compute genomic predictions. , 2008, Journal of dairy science.