GENOME WIDE ASSOCIATION STUDIES FOR MILK PRODUCTION TRAITS IN DAIRY CATTLE: A REVIEW

ALOK KUMAR YADAV¹*, ANUPAMA MUKHERJEE², JITENDRA SINGH^3
1Division of Dairy Cattle Breeding, ICAR-National Dairy Research Institute, Karnal, 132001, Haryana, India
²Division of Dairy Cattle Breeding, ICAR-National Dairy Research Institute, Karnal, 132001, Haryana, India
³Department of Animal Husbandry, Lucknow, Uttar Pradesh
* Corresponding Author : alokvet1000@gmail.com

Received : 27-02-2018     Accepted : 10-03-2018     Published : 30-03-2018
Volume : 10     Issue : 2       Pages : 339 - 342
Genetics 10.2 (2018):339-342
DOI : http://dx.doi.org/10.9735/0975-2862.10.2.339-342

Keywords : Genomic selection, Accuracy of selection, Genetic gain, Genomic estimated breeding values
Conflict of Interest : None declared
Acknowledgements/Funding : Author express sense of gratitude towards the ICAR for Financial assistance in the form of fellowship and gratefully acknowledged Author are thankful to ICAR-National Dairy Research Institute, Karnal, 132001, Haryana. Author also thankful to Dr R R B Singh, Director, ICAR-National Dairy Research Institute, Karnal, 132001, Haryana
Author Contribution : All author equally contributed

The selection of animals using molecular information is more reliable with increased accuracy of selection and higher genetic gain. Hence, there is need to use selection methods that are based on genomic studies. Genomic selection (GS) is a variant of marker-assisted selection method used for predicting genomic breeding values (GEBVs) of animals using high density genetic markers, such as single nucleotide polymorphisms (SNPs). The utility of genomic information in dairy cattle breeding schemes has now reached the level of accuracy that enables dramatic changes and improvements to breeding schemes. GS can increase the accuracy of selection, shorten the generation interval by selecting individuals at the early stage of life, and accelerate genetic progress. The application of GS in dairy cattle has been reported in many countries, including USA, Canada, Australia, Norway, New Zealand, Netherland, Denmark, Germany and Ireland with very promising results. Published results indicates that for dairy cattle approximately 1000 bulls are required in the reference population to obtain GEBVs with accuracies that compete with the accuracies of EBVs based on progeny testing for all traits. Use of genomically evaluated young bulls can accelerate the breeding cycle and increase genetic gain per unit time beyond what is possible with phenotypic selection. With denser marker panels, more sophisticated statistical tools and in the longer term, sequencing, it is expected that the accuracy of GEBVs will continue to improve and breeding schemes will utilize genomic information further at the expense of progeny testing. Current application of genomic selection is only the start of the genomic era in livestock production. To fully capitalize on the benefits provided by GS, breeding programmes may need to be redesigned substantially.

1. Bouquet A. and Juga J. (2013) Animal, 7(5), 705-713
2. Meuwissen T., Hayes B.J. and Goddard M.E. (2001) Genetics, 157(4), 1819-1829
3. Schaeffer L.R. (2006) Journal of Animal Breeding and Genetics, 123, 218-223.
4. De Roos A.P.W., Schrooten C. and Mullaart E. (2009) Interbull Bulletin, 39, 47–50.
5. Van Tassell C.P., Smith T.P., Matukumalli L.K., Taylor J.F., Schnabel R.D., Lawley C.T., Haudenschild C. D., Moore S. S., Warren W. C. and Sonstegard T. S. (2008) Nat Methods, 5(3), 247-52.
6. Wiggans G.R., Van Raden P.M. and Cooper T.A. (2011) Journal of Dairy Science, 94, 3202–3211.
7. Meuwissen T., Hayes B. and Goddard M.E. (2013) Annu. Rev. Animal Biosciences, 1, 221-237.
8. Calus M.P.L. and Veerkamp R.F. (2007) Journal of Animal Breeding and Genetics, 124, 362-368.
9. Loberg A. and Dürr J. (2009) Interbull survey on the use of genomic information. Proc. Interbull international workshop, Uppsala, Sweden, p. 3-14.
10. Van Raden P.M., Van Tassell C.P. and Wiggans G.R. (2009) Journal of Dairy Science, 92, 16–24.
11. Pryce J.E., Hayes B.J. and Goddard M.E. (2012) Journal of Dairy Science, 95, 377-388.
12. Eggen A. (2012) Animal Front, 2(1), 10–15.
13. Pryce J.E. and Daetwyler H.D. (2012) Animal Production Science, 52, 107-114.
14. Jorjani H., Jakobsen J., Hjerpe E. Palucci V. and Durr J. (2012) Interbull Bulletin, 46. Cork, Ireland.
15. Harris B.L., Winkelman A.M. and Johnson D.L. (2013) Interbull Bulletin, 47, 23–25.
16. Szyda J. and Komisarek J. (2007) Journal of Dairy Science, 90(6), 2971–2979.
17. Bionaz M. and Loor J.J. (2008) BMC Genomics, 9, 366.
18. Hayes B.J., Bowman P.J., Chamberlain A.J. and Goddard M.E. (2009) Journal of Dairy Science, 92, 433-443.
19. Beecher C., Daly M., Childs S., Berry D.P., Magee D.A., McCarthy T.V. and Giblin L. (2010) BMC Genetics, 11, 99.
20. Jiang L., Liu J., Sun D., Ma P., Ding X., Yu Y. and Zhang Q. (2010) PLoS One, 5(10), 13661
21. Pryce J. E., Bolormaa S., Chamberlain A. J., Bowman P. J., Savin K., Goddard M. E. and Hayes B.J. (2010) Journal of Dairy Science, 93(7), 3331–3345.
22. Bouwman A.C., Bovenhuis H., Marleen H.P.W., Visker J.A.M. and Arendonk V. (2011) BMC Genetics, 12, 43.
23. Olsen H.G., Hayes B. J., Kent M. P., Nome T., Svendsen M., Larsgard A.G. and Lien S. (2011) Animal Genetics, 42(5), 466-74.
24. Fuyong L. (2012) Genome-wide association analysis for 305-day milk production traits in dairy cattle Thesis animal breeding and genetics (Department Of Sustainable Agricultural Systems Division of Livestock Sciences) University of Natural Resources and Life Sciences, Vienna
25. Gray K.A., Cassady J.P., Huang Y. and Maltecca C. (2012) Genetics Selection Evolution, 44, 24.
26. Maxa J., Neuditschko M., Russ I., Forster M. and Medugorac I. (20120) Journal of Dairy Science, 95(9), 5357-64.
27. Meredith B. K., Kearney F. J., Finlay E. K., Bradley D. G., Fahey A. G., Berry D. P. and Lynn D.J. (2012) BMC Genetics, 13, 21.
28. Crepaldi P., Nicoloso L., Coizet B., Milanesi E., Pagnacco G., Fresi P., Dimauro C. and Macciotta N.P.P. (2013) Journal of Dairy Science, 96(3), 1856–1864.
29. Fang M., Fu W., Jiang D., Zhang Q., Sun D., Ding X. and Jianfeng L. (2014) PLoS One, 9(8), 99544.
30. Mai M.D., Sahana G., Christiansen F.B. and Guldbrandtsen B. (2014) Journal of Animal Science, 8(11), 3522-3528.