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| Evaluation of Breeding Programs Combining Genomic Information in Chinese Holstein |
| CHEN Jun, WANG Ya-chun, ZHANG Yi, SUN Dong-xiao, ZHANG Sheng-li , ZHANG Yuan |
| 1.College of Animal Science and Technology/National Engineering Laboratory for Animal Breeding/Key Laboratory of gricultural Animal Genetics and Breeding, China Agricultural University, Beijing 100193, P.R.China |
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摘要 Current study adopted gene flow theory and selection index method to compare the breeding efficiency of three breeding plans in the Chinese Holstein cattle using ZPLAN software. Simulated conventional progeny-testing program (PT) and young sire program (YS) were compared with breeding program using genomic selection (GS) taking parameters derived from Chinese Holstein breeding system. The results showed that, GS shortened generation interval by 1.5-2.2 years, and increased the genetic progress by 30-50%, comparing to PT and YS, respectively. Economic analysis showed that GS could obtain a higher breeding efficiency, being 119 and 97% higher than that of PT and YS, respectively; and GS was also powerful in improving functional traits with a low heritability. Main factors affecting breeding efficiency in GS were further discussed, including selection intensity, accuracy and the cost of SNP genotyping. Our finding provided references for future designing and implementing GS in Chinese dairy population.
Abstract Current study adopted gene flow theory and selection index method to compare the breeding efficiency of three breeding plans in the Chinese Holstein cattle using ZPLAN software. Simulated conventional progeny-testing program (PT) and young sire program (YS) were compared with breeding program using genomic selection (GS) taking parameters derived from Chinese Holstein breeding system. The results showed that, GS shortened generation interval by 1.5-2.2 years, and increased the genetic progress by 30-50%, comparing to PT and YS, respectively. Economic analysis showed that GS could obtain a higher breeding efficiency, being 119 and 97% higher than that of PT and YS, respectively; and GS was also powerful in improving functional traits with a low heritability. Main factors affecting breeding efficiency in GS were further discussed, including selection intensity, accuracy and the cost of SNP genotyping. Our finding provided references for future designing and implementing GS in Chinese dairy population.
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Received: 18 October 2010
Accepted:
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| Fund: This work was supported by the International S&T Cooperation Program (2008DFA31120), the National Importation of Agriculture Advanced Technology 948 Project of China (2010-C14), the Special Fund for Agro-Scientific Research in the Public Interest, China (nyhyzx07-36), the National Key Technologies R&D Program of China (2006BAD04A01), and the Earmarked Fund for Modern Agro-Industry Technology Research System, China (CARS-37). |
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Corresponding Authors:
Correspondence ZHANG Yuan, Tel/Fax: +86-10-62733687, E-mail: changy@cau.edu.cn
E-mail: changy@cau.edu.cn
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| About author: CHEN Jun, E-mail: chenjuncau@gmail.com |
Cite this article:
CHEN Jun, WANG Ya-chun, ZHANG Yi, SUN Dong-xiao, ZHANG Sheng-li , ZHANG Yuan .
2011.
Evaluation of Breeding Programs Combining Genomic Information in Chinese Holstein. Journal of Integrative Agriculture, 10(12): 1949-1957.
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| [1]Ansari-Mahyari S, Sørensen A C, Lund M S, Thomsen H, Berg P. 2008. Across-family marker-assisted selection using selective genotyping strategies in dairy cattle breeding schemes. Journal of Dairy Science, 91, 1628-1639. [2]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. [3]Dekkers J C M. 2004. Commercial application of marker and gene-assisted selection in livestock: strategies and lessons. Journal of Animal Science, 82, E313-E328. Gianola D, Fernando R L, Stella A. 2006. Genomic assisted prediction of genetic value with semi-parametric procedures. Genetics, 173, 1761-1776. [4]Goddard M E, Hayes B J. 2009. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nature Review Genetics, 10, 381-391. [5]Graser H U, Nitter G, Barwick S A. 1994. Evaluation of advanced industry breeding schemes for Australian beef cattle. II. Selection on combinations of growth, reproduction and carcass criteria. Australian Journal of Agricultural Research, 45, 1657-1669. [6]Habier D, Fernando R L, Dekkers J C M. 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics, 177, 2389-2397. [7]Habier D, Fernando R L, Dekkers J C M. 2009. Genomic selection using low-density marker panels. Genetics, 182, 343-353. [8]Hazel L N. 1943. The genetic basis for constructing selection indexes. Genetics, 28, 476-490. [9]Hayes B J, Bowman P J, Chamberlain A J, Goddard M E. 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science, 92, 433-443. [10]Hill W G. 1974. Prediction and evaluation of response to selection with overlapping generations. Animal Production, 18, 117- 139. Kasonta J S, Nitter G. 1990. Efficiency of nucleus breeding schemes in dual-purpose cattle of Tanzania. Animal Production, 50, 245-251. [11]König S, Simianer H, Willam A. 2009. Economic evaluation of genomic breeding programs. Journal of Dairy Science, 92, 382-391. [12]Kominakis A, Nitter G, Fewson D, Rogdakis E. 1997. Evaluation of the efficiency of alternative selection schemes and breeding objectives in dairy sheep of greece. Animal Science, 64, 453- 461. [13]Kosgey I S, Kahi A K, van Arendonk J A M. 2005. Evaluation of closed adult nucleus multiple ovulation and embryo transfer and conventional progeny testing breeding schemes for milk production in tropical crossbred cattle. Journal of Dairy Science, 88, 1582-1594. [14]Lande R, Thompson R. 1990. The efficiency of marker assisted selection in dairy cattle breeding schemes. Genetics, 124, 743-753. [15]Luo W Z, Wang Y C, Zhang Y. 2008. Simulation study on the efficiencies of MOET nucleus breeding schemes applying marker assisted selection in dairy cattle. Science in China (Series C-Life Sciences), 38, 1056-1065. [16]Meuwissen T H E, Hayes B, Goddard M E. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157, 1819-1829. [17]Muir W M. 2007. 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. [18]Nicholas F W, Smith C. 1983. Increased rates of genetic change in dairy cattle by embryo transfer and splitting. Animal Production, 36, 341-353. [19]Nitter G, Bartenschlager H, Karras K, Niebel E, Graser H U. 2007. ZPLAN: a PC-Program to Optimize Livestock Selection Schemes. University of Hohenheim, Germany and University of New England, Armidale, Australia. Norman H D, Powell R L, Wright J R, Sattler C G. 2004. Overview of progeny-test programs of artificial-insemination organizations in the United States. Journal of Dairy Science, 84, 1899-1912. [20]Powell R L, Norman H D, Sanders A H. 2003. Progeny testing and selection intensity for Holstein bulls in different countries. Journal of Dairy Science, 86, 3386-3393. [21]van Raden P M. 2008. Efficient methods to compute genomic predictions. Journal of Dairy Science, 91, 4414-4423. [22]vanRaden P M, van Tassell 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. [23]de Roos A P W, Hayes B J, Goddard M E. 2009. Reliability of genomic predictions across multiple populations. Genetics, 183, 1545-1553. [24]Schaeffer L R. 2006. Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics, 123, 218-223. [25]Stella A, Lohuis M, Pagnacco G, Jansen G B. 2002. Strategies for continual application of marker-assisted selection in an open nucleus population. Journal of Dairy Science, 85, 2358-2367. [26]Wünsch U, Nitter G, Schüler L. 1999. Genetic and economic evaluation of genetic improvement schemes in pigs. I. Methodology with an application to a three-way crossbreeding scheme. Archiv Tierzucht, 42, 571-582. [27]Xu S Z. 2003. Estimating polygenic effects using markers of the entire genome. Genetics, 163, 789-801. [28]Zhang Y. 2000. Animal Breeding Plan. China Agricultural University Press, Beijing. pp. 149-178. (in Chinese) |
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