The Effect of Dams of Sire Path Management on Genetic and Economic Parameters in a Simulated Genomic Selection Program
Subject Areas : Camelس. عزیزیان 1 , ع.ا. شادپرور 2 , س. جوئزی-شکالگورابی 3 , ن. قوی حسین-زاده 4
1 - Department of Animal Science, Faculty of Agricultural Science, University of Guilan, Rasht, Iran
2 - Department of Animal Science, Faculty of Agricultural Science, University of Guilan, Rasht, Iran
3 - Department of Animal Science, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran
4 - Department of Animal Science, Faculty of Agricultural Science, University of Guilan, Rasht, Iran
Keywords: Economic, genetic, Efficiency, Holstein, genomic selection,
Abstract :
A deterministic model based on the gene flow method, considering the features of Iranian Holstein cattle population, was implemented in this study to evaluate the effect of altering the number of age-classes in the dams of future sire (DS) path and the number of dams required for breeding a young bull (YB), to be evaluated as future sire, on genetic gain and resultant economic efficiency of a genomic selection program for milk production as a selection goal. Based on the simulation, changing the number of age-classes from 10 to 1 resulted in higher replacement rate of DS path (from 0.22 to 1) and shorter generation interval. Consequently, the economic efficiency of the program increased up to a maximum point and then a descending trend was observed. The maximum economic efficiency (25.68) was obtained when 7 age-classes in DS path was assumed. By chaining the number of dams required for breeding a YB from 7 to 1, the genetic gain in selection goal increased from 0.0232 to 0.0264 kg per dairy cattle and therefore, the economic efficiency rose from 25.42 to 28.52. The results revealed that a decrease in generation interval does not necessarily result in maximum economic efficiency and there is an optimum level for generation interval. Less number of required dams per YB could result in higher economic efficiency and therefore should be considered as an effective management strategy to improve the economic efficiency of a genomic selection program for milk production.
Calus M.P.L., Bijma P. and Veerkamp R.F.J. (2015). Evaluation of genomic selection for replacement strategies using selection index theory. J. Dairy Sci. 98, 1-11.
Dickerson G.E. (1978). Animal size and efficiency: basic concepts. Anim. Prod. 27, 367-379.
Erbe M., Gredler B., Seefried F.R., Bapst B. and Simianer H. (2013). A function accounting for training set size and marker density to model the average accuracy of genomic prediction. PLoS ONE. 8(12), 1-9.
Falconer D.S. and Mackay T.F.C. (1996). Introduction to Quantitative Genetics. Longman Group, Ltd, Harlow, UK.
Hill W.G. (1974). Prediction and evaluation of response to selection with overlapping generations. Anim. Prod. 18, 174-189.
Hjorto L., Ettema J.F., Kargo M. and Sorensen A.C. (2015). Genomic testing interacts with reproductive surplus in reducing genetic lag and increasing economic net return. J. Dairy Sci. 98, 646-658.
Hozé C., Fritz S., Phocas F., Boichard D., Ducrocq V. and Croiseau P. (2014). Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population. J. Dairy Sci. 97, 3918-3929.
Humblot P. (2011). Reproductive technologies and epigenetics: their implications for genomic selection in cattle. Acta Sci. Vet. 39, 253-262.
Joezy-Shekalgorabi S., Shadparvar A.A., Vaez Torshizi R. and Moradi Shahrebabak M. (2010a). Age distribution and generation interval corresponding to four pathways of Holstein cattle in Iran. Iranian J. Anim. Sci. 41, 223-229.
Joezy-Shekalgorabi S., Shadparvar A.A., Vaez Torshizi R., Moradi Shahrebabak M. and Jorjani H. (2010b). Investigation of asymptotic phase of genetic improvement in different selection pathways in Iranian Holstein. Pp. 23-28 in Proc. 4th Cong. Anim. Sci. Karaj, Iran.
Jonas E. and Koning D.J. (2015). Genomic selection needs to be carefully assessed to meet specific requirements in livestock breeding programs. Front. Genet. 6, 1-8.
Kӧnig S., Simianer H. and Willam A. (2009). Economic evaluation of genomic breeding programs. J. Dairy Sci. 92, 382-391.
Kӧnig S. and Swalve H.H. (2009). Application of selection index calculations to determine selection strategies in genomic breeding programs. J. Dairy Sci. 92, 5292-5303.
Mc Hugh N., Meuwissen T.H., Cromie A.R. and Sonesson A.K. (2011). Use of female information in dairy cattle genomic breeding programs. J. Dairy Sci. 94, 4109-4118.
Meuwissen T.H.E., Hayes B. and GoddardM.E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157, 1819-1829.
Ponsart C., Le Bourhis D., Knijn H., Fritz S., Guyader-Joly C., Otter T., Lacaze S., Charreaux F., Schibler L., Dupassieux D. and Mullaart E. (2014). Reproductive technologies and genomic selection in dairy cattle. Reprod. Fertil. Dev. 26, 12-21.
Schaeffer L.R. (2006). Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet. 123, 218-223.
Thomasen J.R., Egger-Danner C., Willam A., Guldbrandtsen B., Lund M.S. and Sørensen A.C. (2014). Genomic selection strategies in a small dairy cattle population evaluated for genetic gain and profit. J. Dairy Sci. 97, 458-470.