Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-26T17:57:47.616Z Has data issue: false hasContentIssue false
  • English
  • Français

Design and comparison of gene-pyramiding schemes in animals

Published online by Cambridge University Press:  01 August 2009

F. P. Zhao ,
L. Jiang ,
H. J. Gao ,
X. D. Ding  and
Q. Zhang
F. P. Zhao
Affiliation:
State Key Laboratory for Agrobiotechnology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
L. Jiang
Affiliation:
State Key Laboratory for Agrobiotechnology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
H. J. Gao
Affiliation:
State Key Laboratory for Agrobiotechnology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China College of Animal Science and Technology, Northeast Agricultural University, Harbin, 100530, China
X. D. Ding
Affiliation:
State Key Laboratory for Agrobiotechnology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
Q. Zhang*
Affiliation:
State Key Laboratory for Agrobiotechnology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
*
†E-mail: [email protected]
Get access

Abstract

Marker-assisted gene pyramiding provides a promising way to develop new animal breeds or lines, in which genes responsible for certain favorable characters identified in different breeds or lines are incorporated. In consideration of features of animal populations, we proposed five schemes for pyramiding three genes, denoted Scheme A–E, and five schemes for pyramiding four genes, denoted Scheme F–J. These schemes are representative of the possible alternatives. We also provided an algorithm to compute the population sizes needed in each generation. We compared these schemes with respect to the total population size and the number of generations required under different situations. The results show that there is no scheme that is optimal in all cases. Among the schemes for pyramiding three genes from three lines (L1, L2 and L3), Scheme D (a three-way cross between the three lines are first performed, followed by a backcross to L1 and a subsequent intercross to obtain the desired genotype) has a significant advantage over the other schemes when the recombination rate between adjacent genes ranges from 0.1 to 0.4, while Scheme A (a two-way cross between L1 and L2 and a subsequent intercross are performed, followed by a cross with L3 and a subsequent intercross to obtain the desired genotype) is optimal when recombination rate is 0.5. Among schemes for pyramiding four genes from four lines (L1, L2, L3 and L4), Scheme I (seperately, a two-way cross between L1 and L2 (L3 and L4) followed by a backcross to L1 (L3) and a subsequent intercross are performed, then the offspring from the two sides are crossed and followed by a backcross to L1 and a subsequent intercross to obtain the desired genotype) is optimal when the recombination rate ranges from 0.1 to 0.4, while Scheme F (cross and subsequent intercross between the four lines are performed successively) is the optimal when the recombination rate is 0.5. We also disscuss how the animals’ reproductive capacity, the probabilities of obtaining the desired genotypes and genetic distance between adjacent genes would affect the design of an optimal scheme.

Keywords

animal populationsgene-pyramiding schemesreproductive capacity
Type
Full Paper
Information
animal , Volume 3 , Issue 8 , August 2009 , pp. 1075 - 1084
Copyright
Copyright © The Animal Consortium 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Dekkers, JC, Hospital, F 2002. The use of molecular genetics in the improvement of agricultural populations. Nature Reviews Genetics 3, 22–32.CrossRefGoogle ScholarPubMed
Hittalmani, S, Parco, A, Mew, TV, Huang, N 2000. Fine mapping and DNA marker-assisted pyramiding of the three major genes for blast resistance in rice. Theoretical and Applied Genetics 100, 1121–1128.Google Scholar
Hospital, F, Goldringer, I, Openshaw, S 2000. Efficient marker-based recurrent selection for multiple quantitative trait loci. Genetical Research 75, 357–368.CrossRefGoogle ScholarPubMed
Huang, N, Angeles, ER, Domingo, J, Magpantay, G, Singh, S, Zhang, G, Kumaravadivel, N, Bennett, J, Khush, GS 1997. Pyramiding of bacterial blight resistance genes in rice, marker-assisted selection using RFLP and PCR. Theoretical and Applied Genetics 95, 313–320.Google Scholar
Jiang, GH, Xu, CG, Tu, JM, Li, XH, He, YQ, Zhang, QF 2004. Pyramiding of insect- and disease-resistant genes into an elite indica, cytoplasm male sterile restorer line of rice, ‘Minghui 63’. Plant Breeding 123, 112–116.Google Scholar
Piyasatian, N, Fernando, RL, Dekkers, JCM 2008. Introgressing multiple QTL in breeding programmes of limited size. Journal of Animal Breeding and Genetics 125, 50–56.CrossRefGoogle ScholarPubMed
Saghai Marrof, MA, Jeong, SC, Gunduz, I, Tucker, DM, Buss, GR, Tolin, SA 2008. Pyramiding of soybean mosaic virus resistance genes by marker-assisted selection. Crop Science 48, 517–526.Google Scholar
Servin, B, Martin, OC, Mézard, M, Hospital, F 2004. Toward a theory of marker-assisted gene pyramiding. Genetics 168, 513–523.Google Scholar
Wang, XY, Chen, PD, Zhang, SZ 2001. Pyramiding and marker-assisted selection for powdery mildew resistance genes in common wheat. Acta Genetica Sinica 28, 640–646.Google Scholar
Weller, J, Soller, M 1981. Methods for production of multimarker strains. Theoretical and Applied Genetics 59, 73–79.CrossRefGoogle ScholarPubMed