- Volume 25 Issue 6
DOI QR Code
Optimal Design for Marker-assisted Gene Pyramiding in Cross Population
- Xu, L.Y. (Institute of Animal Science, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal) ;
- Zhao, F.P. (Institute of Animal Science, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal) ;
- Sheng, X.H. (Institute of Animal Science, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal) ;
- Ren, H.X. (Institute of Animal Science, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal) ;
- Zhang, L. (Institute of Animal Science, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal) ;
- Wei, C.H. (Institute of Animal Science, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal) ;
- Du, L.X. (Institute of Animal Science, Chinese Academy of Agricultural Sciences, National Center for Molecular Genetics and Breeding of Animal)
- Received : 2011.07.23
- Accepted : 2011.11.07
- Published : 2012.06.01
Marker-assisted gene pyramiding aims to produce individuals with superior economic traits according to the optimal breeding scheme which involves selecting a series of favorite target alleles after cross of base populations and pyramiding them into a single genotype. Inspired by the science of evolutionary computation, we used the metaphor of hill-climbing to model the dynamic behavior of gene pyramiding. In consideration of the traditional cross program of animals along with the features of animal segregating populations, four types of cross programs and two types of selection strategies for gene pyramiding are performed from a practical perspective. Two population cross for pyramiding two genes (denoted II), three population cascading cross for pyramiding three genes(denoted III), four population symmetry (denoted IIII-S) and cascading cross for pyramiding four genes (denoted IIII-C), and various schemes (denoted cross program-A-E) are designed for each cross program given different levels of initial favorite allele frequencies, base population sizes and trait heritabilities. The process of gene pyramiding breeding for various schemes are simulated and compared based on the population hamming distance, average superior genotype frequencies and average phenotypic values. By simulation, the results show that the larger base population size and the higher the initial favorite allele frequency the higher the efficiency of gene pyramiding. Parents cross order is shown to be the most important factor in a cascading cross, but has no significant influence on the symmetric cross. The results also show that genotypic selection strategy is superior to phenotypic selection in accelerating gene pyramiding. Moreover, the method and corresponding software was used to compare different cross schemes and selection strategies.
Supported by : National Natural Science Foundation of China
- Chen, M. and C. Kendziorski. 2007. A statistical framework for expression quantitative trait loci mapping. Genetics 177:761-771. https://doi.org/10.1534/genetics.107.071407
- David, E. G. 1989. Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Inc.
- Doerge, R. W. 2002. Mapping and analysis of quantitative trait loci in experimental populations. Nat. Rev. Genet. 3:43-52. https://doi.org/10.1038/nrg703
- Fadiel, A., I. Anidi and K. D. Eichenbaum. 2005. Farm animal genomics and informatics: an update. Nucleic Acids Res. 33: 6308-6318. https://doi.org/10.1093/nar/gki931
- Hu, X. S. 2007. A general framework for marker-assisted selection. Theor. Popul. Biol. 71:524-542. https://doi.org/10.1016/j.tpb.2007.02.001
- Huang, N., A. E. Domingo, J. Magpantay, G. S. Singh, G. Zhang, N. Kumaravadivel, J. Bennett and G. S. Khush. 1997. Pyramiding of bacterial blight resistance genes in rice, marker-assisted selection using RFLP and PCR. Theor. Appl. Genet. 95:313-320. https://doi.org/10.1007/s001220050565
- John, H. H. 1992. Adaptation in natural and artificial systems. MIT Press.
- Kameswara Rao, K., N. Lakshmi, M. Jena and K. Kshirod. 2010. Effective strategy for pyramiding three bacterial blight resistance genes into fine grain rice cultivar, Samba Mahsuri, using sequence tagged site markers. Springer, Heidelberg, ALLEMAGNE.
- Lande, R. and R. Thompson. 1990. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743-756.
- Lange, C. and J. C. Whittaker. 2001. On prediction of genetic values in marker-assisted selection. Genetics 159:1375-1381.
- Ljungberg, K., S. Holmgren and O. Carlborg. 2002. Efficient algorithms for quantitative trait loci mapping problems. J. Comput. Biol. 9:793-804. https://doi.org/10.1089/10665270260518272
- McCarthy, M. I., G. R. Abecasis, L. R. Cardon, D. B. Goldstein, J. Little, J. P. Ioannidis and Joel N. Hirschhorn. 2008. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9:356-369. https://doi.org/10.1038/nrg2344
- Moore, J. H., F. W. Asselbergs and S. M. Williams. 2010. Bioinformatics challenges for genome-wide association studies. Bioinformatics 26:445-455. https://doi.org/10.1093/bioinformatics/btp713
- Moreau, L., A. Charcosset, F. Hospital and A. Gallais. 1998. Marker-assisted selection efficiency in populations of finite size. Genetics 148:1353-1365.
- Muhlenbein, H. and D. S. Voosen. 1993. Predictive models for the breeder genetic algorithm. Evol. Comput. 1:25-49. https://doi.org/10.1162/evco.1922.214.171.124
- Pilcher, C. D., J. K. Wong and S. K. Pillai. 2008. Inferring HIV transmission dynamics from phylogenetic sequence relationships. PLoS Med 5:e69. https://doi.org/10.1371/journal.pmed.0050069
- Podlich, D. W. and M. Cooper. 1998. QU-GENE: a simulation platform for quantitative analysis of genetic models. Bioinformatics 14:632-653. https://doi.org/10.1093/bioinformatics/14.7.632
- Ruane, J. and J. J. Colleau. 1995. Marker assisted selection for genetic improvement of animal populations when a single QTL is marked. Genet. Res. 66:71-83. https://doi.org/10.1017/S0016672300034406
- Saghai Marrof, M. A., J. S. Jeong, I. Gunduz, D. M. Tucker, G. R. Buss and S. A. Tolin. 2008. Pyramiding of soybean mosaic virus resistance genes by marker-assisted selection. Crop. Sci. 48:517-526. https://doi.org/10.2135/cropsci2007.08.0479
- Servin, B., O. C. Martin, M. Mezard and F. Hospital. 2004. Toward a theory of marker-assisted gene pyramiding. Genetics 168:513-523. https://doi.org/10.1534/genetics.103.023358
- Singh, S., J. S. Sidhu, N. Huang, Y. Vikal, Z. Li, D. S. Brar, H. S. Dhaliwal and G. S. Khush. 2001. Pyramiding three bacterial blight resistance genes (xa5, xa13 and Xa21) using marker-assisted selection into indica rice cultivar PR106. TAG Theor. Appl. Genet. 102:1011-1015. https://doi.org/10.1007/s001220000495
- Wang, W. Y., B. J. Barratt, D. G. Clayton and J. A. Todd. 2005. Genome-wide association studies: theoretical and practical concerns. Nat. Rev. Genet. 6:109-118. https://doi.org/10.1038/nrg1522
- Zhao, F. P., L. Jiang, H. J. Gao, X. D. Ding and Q. Zhang. 2009. Design and comparison of gene-pyramiding schemes in animals. Animal 3:1075-1084. https://doi.org/10.1017/S1751731109004492
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