Register      Login
Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
REVIEW (Open Access)

Genomic selection in French dairy cattle

D. Boichard A F , F. Guillaume A B , A. Baur C , P. Croiseau A , M. N. Rossignol D , M. Y. Boscher D , T. Druet E , L. Genestout D , J. J. Colleau A , L. Journaux C , V. Ducrocq A and S. Fritz C
+ Author Affiliations
- Author Affiliations

A INRA, UMR1313 Gabi, 78350 Jouy-en-Josas, France.

B Institut de l’Elevage, 78350 Jouy-en-Josas, France.

C UNCEIA, 149 Rue de Bercy, 75595 Paris, France.

D Labogena, 78350 Jouy-en-Josas, France.

E Liège University, Belgium.

F Corresponding author. Email: didier.boichard@jouy.inra.fr

Animal Production Science 52(3) 115-120 https://doi.org/10.1071/AN11119
Submitted: 21 June 2011  Accepted: 27 November 2011   Published: 6 March 2012

Journal Compilation © CSIRO Publishing 2012 Open Access CC BY-NC-ND

Abstract

Genomic selection is implemented in French Holstein, Montbéliarde, and Normande breeds (70%, 16% and 12% of French dairy cows). A characteristic of the model for genomic evaluation is the use of haplotypes instead of single-nucleotide polymorphisms (SNPs), so as to maximise linkage disequilibrium between markers and quantitative trait loci (QTLs). For each trait, a QTL-BLUP model (i.e. a best linear unbiased prediction model including QTL random effects) includes 300–700 trait-dependent chromosomal regions selected either by linkage disequilibrium and linkage analysis or by elastic net. This model requires an important effort to phase genotypes, detect QTLs, select SNPs, but was found to be the most efficient one among all tested ones. QTLs are defined within breed and many of them were found to be breed specific. Reference populations include 1800 and 1400 bulls in Montbéliarde and Normande breeds. In Holstein, the very large reference population of 18 300 bulls originates from the EuroGenomics consortium. Since 2008, ~65 000 animals have been genotyped for selection by Labogena with the 50k chip. Bulls genomic estimated breeding values (GEBVs) were made official in June 2009. In 2010, the market share of the young bulls reached 30% and is expected to increase rapidly. Advertising actions have been undertaken to recommend a time-restricted use of young bulls with a limited number of doses. In January 2011, genomic selection was opened to all farmers for females. Current developments focus on the extension of the method to a multi-breed context, to use all reference populations simultaneously in genomic evaluation.


References

Boichard D, Fritz S, Rossignol MN, Boscher MY, Malafosse A, Colleau JJ (2002) Implementation of marker-assisted selection in dairy cattle. In ‘Proceedings of the 7th world congress of genetics applied to livestock production, Montpellier, France’. Communication no. 22-03.

Boichard D, Fritz S, Rossignol MN, Guillaume F, Colleau JJ, Druet T (2006) Implementation of marker-assisted selection: practical lessons from dairy cattle. In ‘Proceedings of the 8th world congress of genetics applied to livestock production, Belo Horizonte, Brazil’. Communication no. 22-11.

Browning SR, Browning BL (2007) Rapid and accurate haplotype phasing and missing data inference for whole genome association studies using localized haplotype clustering. American Journal of Human Genetics 81, 1084–1097.
Rapid and accurate haplotype phasing and missing data inference for whole genome association studies using localized haplotype clustering.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXht1KmsL3M&md5=334109ceaf9982afbf5138f23f9b26d5CAS |

Colleau JJ, Fritz S, Guillaume F, Baur A, Dupassieux D, Boscher MY, Journaux L, Eggen A, Boichard D (2009) Simulating the potential of genomic selection in dairy cattle breeding. Rencontres Recherches Ruminants 16, 419 [in French].

Croiseau P, Legarra A, Guillaume F, Fritz S, Baur A, Colombani C, Robert-Granié C, Boichard D, Ducrocq V (2011) Fine tuning genomic evaluations in dairy cattle through SNP pre-selection with the Elastic-Net algorithm. Genetics Research 93, 409–417.
Fine tuning genomic evaluations in dairy cattle through SNP pre-selection with the Elastic-Net algorithm.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhs1KrsbfL&md5=3da12e73ba3bcd6b1fdf7bb71bc9d232CAS |

Daetwyler HD, Villanueva B, Bijma P, Woolliams JA (2007) Inbreeding in genome-wide selection. Journal of Animal Breeding and Genetics 124, 369–376.
Inbreeding in genome-wide selection.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD2sjjvFyhsQ%3D%3D&md5=563aee84967b25051aca608a0dd290a9CAS |

Dassonneville R, Brøndum RF, Druet T, Fritz S, Guillaume F, Guldbrandtsen B, Lund MS, Ducrocq V, Su G (2011) Impact of imputing markers from a low density chip on the reliability of genomic breeding values in Holstein populations. Journal of Dairy Science 94, 3679–3686.
Impact of imputing markers from a low density chip on the reliability of genomic breeding values in Holstein populations.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXnvFahtrw%3D&md5=96a7a47100085d4d8458f33f210348f8CAS |

Druet T, Georges M (2010) A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping. Genetics 184, 789–798.
A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhsFOnurvN&md5=18bb77ec25c91292a97809f342a86c64CAS |

Gautier M, Faraut T, Moazami-Goudarzi K, Navratil V, Foglio M, Grohs C, Boland A, Garnier JG, Boichard D, Lathrop GM, Gut IG, Eggen A (2007) Genetic and haplotypic structure in 14 European and African cattle breeds. Genetics 177, 1059–1070.
Genetic and haplotypic structure in 14 European and African cattle breeds.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtlyrs7rK&md5=d95b8fc7c596048e8a57c4c16a26a004CAS |

Grisart B, Coppieters W, Farnir F, Karim L, Ford C, Berzi P, Cambisano N, Mni M, Reid S, Simon P, Spelman R, Georges M, Snell R (2002) Positional candidate cloning of a QTL in dairy cattle: identication of a missense mutation in the bovine DGAT1 gene with a major effect on milk yield and composition. Genome Research 12, 222–231.
Positional candidate cloning of a QTL in dairy cattle: identication of a missense mutation in the bovine DGAT1 gene with a major effect on milk yield and composition.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XhtlKksrs%3D&md5=101173298a98503d2e18b1ec8d71a0feCAS |

Guillaume F, Fritz S, Boichard D, Druet T (2008a) Estimation by simulation of the efficiency of the French marker-assisted selection program in dairy cattle. Genetics, Selection, Evolution 40, 91–102.
Estimation by simulation of the efficiency of the French marker-assisted selection program in dairy cattle.Crossref | GoogleScholarGoogle Scholar |

Guillaume F, Fritz S, Boichard D, Druet T (2008b) Correlations of marker-assisted breeding values with progeny-test breeding values for eight hundred ninety-nine French Holstein bulls. Journal of Dairy Science 91, 2520–2522.
Correlations of marker-assisted breeding values with progeny-test breeding values for eight hundred ninety-nine French Holstein bulls.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXmsVWmurY%3D&md5=80cca5f9064f9b60516035ced93f2b31CAS |

Hayes BJ, Chamberlain AJ, McPartlan H, MacLeod I, Sethuraman L, Goddard ME (2007) Accuracy of marker-assisted selection with single markers and marker haplotypes in cattle. Genetical Research 89, 215–220.
Accuracy of marker-assisted selection with single markers and marker haplotypes in cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXovV2nsQ%3D%3D&md5=40a7173cb6eda32b2092f5ecce7abc0cCAS |

Lund MS, de Roos APW, de Vries AG, Druet T, Ducrocq V, Fritz S, Guillaume F, Guldbrandtsen B, Liu Z, Reents R, Schrooten C, Seefried M, Su G (2011) Common reference of four European Holstein populations increases reliability of genomic predictions. Genetics, Selection, Evolution 43, 43
Common reference of four European Holstein populations increases reliability of genomic predictions.Crossref | GoogleScholarGoogle Scholar |

Meuwissen THE, Goddard ME (2000) Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci. Genetics 155, 421–430.

Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.

Pryce JE, Goddard ME, Raadsma HW, Hayes BJ (2010) Deterministic models of breeding scheme designs that incorporate genomic selection. Journal of Dairy Science 93, 5455–5466.
Deterministic models of breeding scheme designs that incorporate genomic selection.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXnvVSjtQ%3D%3D&md5=4324442e41a5cdb6838940b03b371fdaCAS |

Schaeffer LR (2006) Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123, 218–223.
Strategy for applying genome-wide selection in dairy cattle.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BD28vlvFCitA%3D%3D&md5=f2323721cc79f7ab1ccb3d6057ddefe7CAS |

Sørensen MK, Sørensen AC (2009) Inbreeding rates in breeding programs with different strategies for using genomic selection. In ‘Interbull meeting, Barcelona, Spain’, 21–24 August 2009. Available at http://www-interbull.slu.se/bulletins/bulletin40/Sorensen.pdf [Verified 8 December 2011]

Sørensen MK, Voergaard J, Pedersen LD, Berg P, Sørensen AC (2011) Genetic gain in dairy cattle populations is increased using sexed semen in commercial herds. Journal of Animal Breeding and Genetics 128, 267–275.
Genetic gain in dairy cattle populations is increased using sexed semen in commercial herds.Crossref | GoogleScholarGoogle Scholar |

Stoop M, de Jong G, van Pelt M, van der Linde C (2010) Implementation of a claw health index in The Netherlands. In ‘Interbull meeting, Riga, Latvia’, 31 May – 4 June 2010. Available at http://www.interbull.org/images/stories/Stoop_Claw_20100601.pdf [Verified 8 December 2011]

Yang JA, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM (2010) Common SNPs explain a large proportion of the heritability for human height. Nature Genetics 42, 565–569.
Common SNPs explain a large proportion of the heritability for human height.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXns1GisL8%3D&md5=c9db73a48ec56a558783f98f92da10efCAS |