Accuracy of genomic prediction for milk production traits in the Chinese Holstein population using a reference population consisting of cows

Journal of Dairy Science

Volumn 96, Issue 8, 2013, Pages 5315-5323

  Ding, X ^a   Zhang, Z ^a,b   Li, X ^a   Wang, S ^a   Wu, X ^a   Sun, D ^a   Yu, Y ^a   Liu, J ^a   Wang, Y ^a   Zhang, Y ^a,a   Zhang, S ^a   Zhang, Q ^a  

^a CHINA AGRICULTURAL UNIVERSITY   (China)

^b SOUTH CHINA AGRICULTURAL UNIVERSITY   (China)

Author keywords

Chinese Holstein; Cow; Cross validation; Genomic selection

Indexed keywords

ANIMAL; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; BREEDING; CATTLE; CHINA; CHINESE HOLSTEIN; COW; CROSS VALIDATION; DAIRYING; FEMALE; GENE FREQUENCY; GENETICS; GENOMIC SELECTION; GENOTYPE; LACTATION; MALE; METHODOLOGY; QUANTITATIVE TRAIT; REPRODUCIBILITY;

CHINESE HOLSTEIN; COW; CROSS VALIDATION; GENOMIC SELECTION;

ANIMALS; BAYES THEOREM; BREEDING; CATTLE; CHINA; DAIRYING; FEMALE; GENE FREQUENCY; GENOTYPE; LACTATION; MALE; MODELS, GENETIC; QUANTITATIVE TRAIT, HERITABLE; REPRODUCIBILITY OF RESULTS;

BOS; HUMAN HERPESVIRUS 4;

EID: 84880618683     PISSN: 00220302     EISSN: 15253198     Source Type: Journal    
DOI: 10.3168/jds.2012-6194     Document Type: Article

References (37)

1. Aguilar I., Misztal I., Johnson D.L., Legarra A., Tsuruta S., Lawlor T.J. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci 2010, 93:743-752.
2. Bijma P. Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations. J. Anim. Breed. Genet 2012, 129:345-358.
3. Browning B.L., Browning S.R. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet 2009, 84:210-223.
4. Christensen O.F., Lund M.S. Genomic prediction when some animals are not genotyped. Genet. Sel. Evol 2010, 42:2.
5. Cleveland M., Forni S., Garrick D., Deeb N. Prediction of genomic breeding values in a commercial pig population. Paper 266 in Proc. 9th World Congr. Genet. Appl. Livest. Prod., Leipzig, Germany 2010.
6. Edel C., Neuner S., Emmerling R., Götz K.U. A note on using 'forward prediction' to assess precision and bias of genomic predictions. Interbull Bull 2012, 46:16-19.
7. Gao, H., M. S. Lund, Y. Zhang, and G. Su. 2012. Accuracy of genomic prediction using different models and response variables in the Nordic Red Cattle population. Pages 11-29 in Comparison of statistical models for genomic predictions in Nordic Dairy cattle populations. PhD Thesis of H. Gao. Aarhus University, Foulum, Denmark.
8. Garrick D.J., Taylor J.F., Fernando R.L. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genet. Sel. Evol 2009, 41:55.
9. Goddard M.E., Hayes B.J. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat. Rev. Genet 2009, 10:381-391.
10. Goddard M.E., Hayes B.J., Meuwissen T.H. Using the genomic relationship matrix to predict the accuracy of genomic selection. J. Anim. Breed. Genet 2011, 128:409-421.
11. González-Recio O., Gianola D., Rosa G.J., Weigel K.A., Kranis A. Genome-assisted prediction of a quantitative trait measured in parents and progeny: Application to food conversion rate in chickens. Genet. Sel. Evol 2009, 41:3.
12. Grisart B., Farnir F., Karim L., Cambisano N., Kim J.J., Kvaze A., Mni M., Simon P., Frere J.M., Coppieters W., Georges M. Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition. Proc. Natl. Acad. Sci. USA 2004, 101:2398-2403.
13. Guo G., Lund M.S., Zhang Y., Su G. Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables. J. Anim. Breed. Genet 2010, 127:423-432.
14. Habier D., Tetens J., Seefried F.R., Lichtner P., Thaller G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet. Sel. Evol 2010, 42:5.
15. Hayes B.J., Visscher P.M., Goddard M.E. Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res. (Camb.) 2009, 91:47-60.
16. Heffner E.L., Sorrells M.E., Jannink J.-L. Genomic selection for crop improvement. Crop Sci 2009, 49:1-12.
17. Henderson C.R. Best linear unbiased estimation and prediction under a selection model. Biometrics 1975, 31:423-447.
18. König S., Simianer H., Willam A. Economic evaluation of genomic breeding programs. J. Dairy Sci 2009, 92:382-391.
19. Legarra A., Aguilar I., Misztal I. A relationship matrix including full pedigree and genomic information. J. Dairy Sci 2009, 92:4656-4663.
20. Legarra A., Robert-Granie C., Manfredi E., Elsen J.M. Performance of genomic selection in mice. Genetics 2008, 180:611-618.
21. Liu Z., Seefried F.R., Reinhardt F., Rensing S., Thaller G., Reents R. Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction. Genet. Sel. Evol 2011, 43:19.
22. Luan T., Woolliams J.A., Lien S., Kent M., Svendsen M., Meuwissen T.H.E. The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 2009, 183:1119-1126.
23. Lund M.S., Roos A.P.W.d., Vries A.G.d., Druet T., Ducrocq V., Fritz S., Guillaume F., Guldbrandtsen B., Liu Z., Reents R., Schrooten C., Seefried M., Su G. Improving genomic prediction by EuroGenomics collaboration. Paper 880 in Proc. 9th World Congr. Genet. Appl. Livest. Prod., Leipzig, Germany 2010.
24. Madsen P., Su G., Labouriau R., Christensen O.F. DMU-A package for analyzing multivariate mixed models. Paper 732 in Proc. 9th World Congr. Genet. Appl. Livest. Prod., Leipzig, Germany 2010.
25. Meuwissen T.H.E., Hayes B.J., Goddard M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001, 157:1819-1829.
26. Misztal I., Legarra A., Aguilar I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J. Dairy Sci 2009, 92:4648-4655.
27. Nielsen H.M., Sonesson A.K., Yazdi H., Meuwissen T.H.E. Comparison of accuracy of genome-wide and BLUP breeding value estimates in sib based aquaculture breeding schemes. Aquaculture 2009, 289:259-264.
28. Nieuwhof G.J., Beard K.T., Konstantinov K.V., Bowman P.J., Hayes B.J. Implementation of genomics in Australia. Interbull Bull 2010, 42:35-39.
29. Schaeffer L.R. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet 2006, 123:218-223.
30. Schaeffer L.R., Jamrozik J., Kistemaker G.J., Van Doormaal B.J. Experience with a test-day model. J. Dairy Sci 2000, 83:1135-1144.
31. Su G., Guldbrandtsen B., Gregersen V.R., Lund M.S. Preliminary investigation on reliability of genomic estimated breeding values in the Danish Holstein population. J. Dairy Sci 2010, 93:1175-1183.
32. Thomasen J.R., Guldbrandtsen B., Su G., Brøndum R.F., Lund M.S. Reliabilities of genomic estimated breeding values in Danish Jersey. Animal 2012, 6:789-796.
33. VanRaden P.M. Efficient methods to compute genomic predictions. J. Dairy Sci 2008, 91:4414-4423.
34. VanRaden P.M., Van Tassell C.P., Wiggans G.R., Sonstegard T.S., Schnabel R.D., Taylor J.F., Schenkel F.S. Invited review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci 2009, 92:16-24.
35. Verbyla K.L., Hayes B.J., Bowman P.J., Goddard M.E. Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle. Genet. Res. (Camb.) 2009, 91:307-311.
36. Zhang Z., Ding X., Liu J., Zhang Q., de Koning D.J. Accuracy of genomic prediction using low-density marker panels. J. Dairy Sci 2011, 94:3642-3650.
37. Zhong S., Dekkers J.C.M., Fernando R.L., Jannink J.L. Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: A barley case study. Genetics 2009, 182:355-364.