Volume 9, Issue 20 (10-2018)                   rap 2018, 9(20): 123-128 | Back to browse issues page


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Mohammadi Y, Sattaei Mokhtari M. Accuracy of Genomic Breeding Values in Small Genotyped Populations-A Simulation Study . rap. 2018; 9 (20) :123-128
URL: http://rap.sanru.ac.ir/article-1-825-en.html
Abstract:   (908 Views)
In the present study two genetically connected small and large populations were simulated and the effect of different sources of information from foreign populations on the accuracy of predicted genomic breeding values of young animals of the small population was investigated. A large population consist of 200000 animals over 15 generations and a small population consist of 5000 animals over 3 generations were generated with QMSim simulation software in a such way that the small population was connected to the large one. Three scenarios were defined for estimating the accuracy of genomic evaluations based on various sources of available information. In the first scenario, the accuracy of genomic breeding values was estimated based on the genomic breeding values of individuals in the small population. In the second scenario, the accuracy of genomic breeding values of animals was estimated using the information of individuals in the small population accompanied with the genomic breeding values of male individuals from the large population. In the third scenario, phenotypic, genotypic and pedigree data of individuals from the large population were integrated to estimate the genomic breeding values of individuals in the small population. The averages for accuracy of estimated genomic breeding values were 0.34, 0.40 and 0.50 under the first, second and third defined scenarios, respectively. Furthermore, the averages for regression coefficient of prediction for genomic breeding values were 0.73, 0.83 and 0.93 under the first, second and third defined scenarios, respectively. The obtained results revealed that the integration of phenotypic, genotypic and pedigree information of both large and small populations had the most advantage for estimating the genomic breeding values of selected candidates in the small populations.
 
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Type of Study: Research | Subject: ژنتیک و اصلاح نژاد دام
Received: 2017/11/1 | Revised: 2018/10/3 | Accepted: 2018/06/17 | Published: 2018/10/3

References
1. Aguilar, I., I. Misztal, D.L. Johnson, A. Legarra, S. Tsuruta and T.J. Lawlor. 2010. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal Dairy Science, 93: 743-752. [DOI:10.3168/jds.2009-2730]
2. Andonov, S., D.A.L. Lourenco, B.O. Fragomeni, Y. Masuda, I. Pocrnic, S. Tsuruta and I. Misztal. 2017. Accuracy of breeding values in small genotyped populations using different sources of external information-A simulation study. Journal Dairy Science, 100: 395-401. [DOI:10.3168/jds.2016-11335]
3. Cromie, A.R., D.P. Berry, B. Wickham, J.F. Kearney, J. Pena, J.B.C.H. Van Kaam, N. Gengler, J. Szyda, U. Schnyder, M. Coffey, B. Moster, K. Hagiya, J.I. Weller, D. Abernethy and R. Spelman. 2010. International genomic co-operation: Who, what, when, where, why and how? Interbull Bull, 42: 72-78.
4. Gao, H., O.F. Christensen, P. Madsen, U.S. Nielsen, Y. Zhang, M.S. Lund and G. Su. 2012. Comparison on genomic predictions using three GBLUP methods and two single step blending methods in the Nordic Holstein population. Genetics Selection Evolution, 44: 1-8. [DOI:10.1186/1297-9686-44-8]
5. Goddard, M.E. 2009. Genomic selection: Prediction of accuracy and maximization of long term response. Genetica, 136: 245-257. [DOI:10.1007/s10709-008-9308-0]
6. Habier, D., R.L. Fernando and J.C.M. Dekkers. 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics, 177: 2389-2397. [DOI:10.1534/genetics.107.081190]
7. Hayes, B.J., P.J. Bowman, A.C. Chamberlain, K. Verbyla and M.E. Goddard. 2009. Accuracy of genomic breeding values in multi breed dairy cattle populations. Genetic Selection Evolution, 41(1): 51. [DOI:10.1186/1297-9686-41-51]
8. Interbull. 2014. Interbull routine genetic evaluation for production traits, April 2014. Accessed Mar. 19, 2016.
9. Legarra, A., J.K. Bertrand, T. Strabel, R.L. Sapp, J.P. Sanchez and I. Misztal. 2007. Multi-breed genetic evaluation in a Gelbvieh population. Journal Animal Breeding Genetics, 124: 286-295. [DOI:10.1111/j.1439-0388.2007.00671.x]
10. Lund, M.S., I. van den Berg, P. Ma, R.F. Brøndum and G. Su. 2016. Review: How to improve genomic predictions in small dairy cattle populations. Animal, 1042-1049. [DOI:10.1017/S1751731115003031]
11. Lund, M.S., A.P.W. de Roos, A.G. de Vries, T. Druet, V. Ducrocq, S. Fritz, F. Guillaume, B. Guldbrandtsen, Z. Liu, R. Reents, C. Schrooten, F. Seefried and G. Su. 2011. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genetic Selection Evolution, 43(1): 43. [DOI:10.1186/1297-9686-43-43]
12. Meuwissen, T.H., B.J. Hayes and M.E. Goddard. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157: 1819-1829.
13. Misztal, I., S. Tsuruta, D. Lourenco, I. Aguilar, A. Legarra and Z.Vitezica. 2015. Manual for BLUPF90 family of programs. Accessed Mar. 19, 2016.
14. Olson, K.M., P.M. VanRaden, M.E. Tooker and T.A. Cooper. 2011. Differences amongmethods to validate genomic evaluations for dairy cattle. Journal of Dairy Science, 94: 2613-2620. [DOI:10.3168/jds.2010-3877]
15. Patry, G. 2015. Euro-Genomics for reliable cattle breeding: How international collaboration fostered an efficient use of the genomics for a reliable cattle breeding. Session: Industry uptake of national (GEBV) and international (GMACE) genomic evaluations. Interbull Industry Meeting, Feb. 25, 2015, Verden, Germany. Accessed Mar. 19, 2016.
16. Pribyl, J., J. Bauer, P. Pešek, J. Pribylova, L. Vostry and L. Zavadlova. 2014. Domestic and Interbull information in the single step genomic evaluation of Holstein milk production. Czech Journal Animal Science, 59: 409-415. [DOI:10.17221/7652-CJAS]
17. Sargolzaei, M. and F.S. Schenkel. 2009. QMSim: a large-scale genome simulator for livestock. Bioinformatics, 25: 680-681. [DOI:10.1093/bioinformatics/btp045]
18. Schaeffer, L.R. 1994. Multiple-country comparison of dairy sires. Journal Dairy Science, 77: 2671-2678. [DOI:10.3168/jds.S0022-0302(94)77209-X]
19. Schaeffer, L.R. 2006. Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics, 123: 218-223. [DOI:10.1111/j.1439-0388.2006.00595.x]
20. Schenkel, F., M. Sargolzaei, G. Kistemaker, G. Jansen, P. Sullivan, B.J. Van Doormaal, P.M. Vanraden and G. R. Wiggans. 2009. Reliability of genomic evaluation of Holstein cattle in Canada. Interbull Bulletin 39, 51-58. Strandén I and Mäntysaari EA 2010. A recipe for multiple trait de-regression. Inter bull Bulletin, 42: 21-24.
21. Vandenplas, J., F.G. Frederic and N. Gengler. 2014. Unified method to integrate and bled several, potentially related, sources of information for genetic evaluation. Genetic Selection Evolution, 46(1): 59. [DOI:10.1186/s12711-014-0059-3]
22. Vandenplas, J. and N. Gengler. 2012. Comparison and improvements of different Bayesian procedures to integrate external information into genetic evaluations. Journal Dairy Science, 95: 1513-1526. [DOI:10.3168/jds.2011-4322]
23. VanRaden, P.M., C.P. Van Tassell, G.R. Wiggans, T.S. Sonstegard, R.D. Schnabel, J.F. Taylor and F.S. Schenkel. 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal Dairy Science, 92: 16-24. [DOI:10.3168/jds.2008-1514]
24. Wiggans, G.R., P.M. VanRaden and T.A. Cooper. 2011. The genomic evaluation system in the United States: Past, present, future. Journal Dairy Science, 94: 3202-3211. [DOI:10.3168/jds.2010-3866]
25. Zhang, Z.W., R.L. Quaas and E.J. Pollak. 2002. Simulation study on the effects of incorporating external genetic evaluation results. Common. 20-14 in Proc. 7th World Congress Genetics Applied Livestock Production, Montpellier, France. INRA, Castanet-Tolosan, Cedex, France.

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