Effects of different strategies for selection of animals as reference population on the accuracy of genomic evaluation for moderate heritability traits in dairy cattle

Document Type : Research Paper

Authors

1 Assistant Professor, Department of Animal Science, Moghan College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Iran

2 Assistant Professor, Department of Biotechnology, Institute of Science and High Technology and Environmental Science, Graduate University of Advanced Technology, Kerman, Iran

3 Associate Professor, Department of Animal Science, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

4 Assistant Professor, Department of Animal Science, Faculty of Agriculture Science and Natural Resources, University of Mohaghegh Ardabili, Iran

Abstract

An important question about genomic evaluation is the effectiveness of using superior animals as reference population, on the accuracy of estimated breeding values of selection candidates. In this research, the accuracy of genomic evaluation is selecting superior animals as reference population (strategy1) was compared to situations in which the animals in reference population were a random sample of population (strategy2) and superior and inferior animals (strategy3). Best linear unbiased prediction method was used to estimate marker effects. The results showed that using only superior animals as reference population would decrease the accuracy of genomic evaluation. If the ratio of animals in the reference group is low (for example 10%) the difference between strategy1 and the other strategies would be more than the situation in which this ratio is high (for example 50%). For example in situation that the generation before validation set (generation four) was used as reference population, the accuracy of strategy1 was about 0.34 lower than strategy3 when 10% of animals were used as reference population but this difference was decreased to 0.04 when 50% of animals were used as reference population. These results showed that genotyping and using some of no superior animals in the reference population, beside to superior animals with high accurate traditional estimated breeding values, could lead to increase in the accuracy of genomic evaluation.

Keywords

References

  1. Boligon, A. A., Long, N., Albuquerque, L. G., Weigel, K. A., Gianola, D. & Rosa, G. J. M. (2012) Comparison of selective genotyping strategies for prediction of breeding values in a population undergoing selection. Journal of Animal Science, 90, 4716-4722.
  2. Brøndum, R. F., Rius-Vilarrasa, E., Strandén, I., Su, G., Guldbrandtsen, B., Fikse, W. F. & Lund, M. S. (2011). Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations. Journal Dairy Science, 94, 4700-4707
  3. Calus, M. P. L., Meuwissen, T. H. E., De Roos, A. P. W. & Veerkamp, R. F. (2008). Accuracy of genomic selection using different methods to define haplotypes. Genetics, 178, 553-561.
  4. Calus, M. P. L. & Veerkmp, R. F. (2007). Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimating with a maker density of one SNP per CM. Journal of Animal Breeding and Genetics, 124, 362-368.
  5. Daetwyler, H. D., Villanueva, B. & Woolliams, J. A. (2008). Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE, 3, e3395.
  6. Dassonneville, R., Baur, A., Fritz, S., Boichard, D. & Ducrocq, V. (2012). Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment. Genetics Selection Evolution, 44(1), 40.
  7. Goddard, M. (2009). Genomic selection: Prediction of accuracy and maximisation of long term response. Genetica, 136, 245-257.
  8. Hayes, B. J. (2007). QTL mapping, MAS and genomic selection. A short course organized by Animal Breeding and Genetics, department of Animal Science, Iowa State University.
  9. Hayes, B. J., Bowman, P. J., Chamberlain, A. C., Verbyla, K. & Goddard, M. E. (2009). Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution, 41, 51.
  10. Ibañez-Escriche, N. & Gonzalez-Recio, O. (2011). Review. Promises, pitfalls and challenges of genomic selection in breeding programs. Spanish Journal of Agricultural Research, 9(2), 404-413.
  11. Khansefid, M. (2010). Genetic evaluation of animals with genotypes of bull animals for dense markers by simulation. MS thesis, University of Tehran, Iran. (in Farsi)
  12. Lourenco, D. A. L., Misztal, I., Tsuruta, S., Aguilar, I., Ezra, E., Ron, M., Shirak, A. & Weller, J. I. (2014). Methods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses. Journal of Dairy Science, 97, 1742-1752.
  13. Meuwissen, T. H. E., Hayes, B. J.  & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819-1829.
  14. Muir, W. M. (2007). Comparison of genomic and traditional BLUP estimated breeding value accuracy and selection response under alternative trait and genomic parameters.  Journal of Animal Breeding and Genetics, 124, 342-355.
  15. Saatchi, M. (2009). Estimation of breeding values using dense marker information in dairy cattle population. Ph.D. thesis, University of Tehran, Iran. (in Farsi)
  16. Schaeffer, L. R. (2006). Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics, 123, 1-6.
  17. VanRaden, P. M., Van Tassel, C. P., Wiggans, G. R., Sonstegard, T. S., Schnabel, R. D., Taylor, J. F. & Schenkel, F. S. (2009). Invited review: Reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science, 92, 16-24.
  18. VanRaden, P. M. & Wiggans, G. R. (1991). Derivation, calculation and use of national animal model information. Journal of Dairy Science, 74, 2737-2746.
  19. Wientjes, Y. C. J., Veerkamp, F. R. & Calus, M. P. L. (2013). The Effect of Linkage Disequilibrium and Family Relationships on the Reliability of Genomic Prediction. Genetics, 193, 621-631.
  20. Zhou, L., Ding, X., Zhang, Q., Wang, Y., Lund, M. S. & Su, G. (2013). Consistency of linkage disequilibrium between Chinese and Nordic Holsteins and genomic prediction for Chinese Holsteins using a joint reference population. Genetics Selection Evolution, 45(1), 7.

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History

  • Receive Date: 10 February 2015
  • Revise Date: 15 November 2016
  • Accept Date: 16 November 2016

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