Volume 11, Issue 30 (9-2019)                   jcb 2019, 11(30): 168-177 | Back to browse issues page


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Safari P, Danyali S F, Rahimi M, Mahdavi Meyghan A. Application of Gibbs Variable Selection Approach to Study Genetic Control of Water Deficient Stress Tolerance in Wheat. jcb. 2019; 11 (30) :168-177
URL: http://jcb.sanru.ac.ir/article-1-776-en.html
Department of Biotechnology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran.
Abstract:   (234 Views)

Drought is the main abiotic stress seriously influencing wheat production and quality in Iran. Information about genetic controlling drought tolerance inheritance for grain yield is necessary to determine the type of breeding program as well as develop tolerant cultivars, enabling breeders to choose the most appropriate strategy to breeding trait of interest. In this study, Bayesian inference using Gibbs variable selection (GVS) approach used to identify the most important gene effects related to drought tolerance in context generation mean analysis. For this purpose, field experiments consist of two pairs of crosses with non-tolerant and tolerant cultivars and generations derived from them were carried out across two years as split plot designs based on RCBD with three replications in which main plots assigned to irrigation treatment consist of two levels (well watered and cessation of irrigation at pollination stage) and sub-plots given to the generations. To study the inheritance of any trait in generation mean analysis, joint scaling test is applied. Restrictions of degrees of freedom to number of parameters of model and over- or underestimation of the main and epistatic effects are disadvantages of this method. An alternative approach to obviate these limitations is to perform Bayesian inference and model selection strategies like GVS. GVS using estimation of posterior inclusion probabilities of effects identifies the most discriminant effects in the model. Since the additive, dominance and epistatic gene actions involved in drought tolerance inheritance, methods which utilize all type of gene effects, like recurrent selection followed by pedigree method may be useful for drought tolerance stress improvement. Also hybrid seed production, which utilizes all types of gene effects, may be useful in improving yield in wheat.
 

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Type of Study: Research | Subject: اصلاح نباتات، بیومتری
Received: 2017/06/12 | Revised: 2019/10/9 | Accepted: 2018/04/3 | Published: 2019/09/11

References
1. Ahmadian, S., S.M. Mortazavian, M. Ebrahimi, F. Amini, M. Ghorbani Javid and B. Foghi. 2017. Genetic Analysis of some Morphological Traits in Wheat using Generation Mean Analysis under Normal and Drought Stress Conditions. Journal of Crop Breeding, 8(20): 182-175 (In Persian).
2. Balestre, M., R.G. Von Pinho and A.H. Brito. 2012. Bayesian inference to study genetic control of resistance to gray leaf spot in maize. Genetics and Molecular Research, 11(1): 17-29. [DOI:10.4238/2012.January.9.3]
3. Barbieri, M.M. and J.O. Berger. 2004. Optimal predictive model selection. Annals of Statistics, 870-897. [DOI:10.1214/009053604000000238]
4. Carlin, B.P. and S. Chib. 1995. Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, 473-484. [DOI:10.1111/j.2517-6161.1995.tb02042.x]
5. Chowdhry, M.A., M. Rafiq and K. Alam. 1992. Genetic architecture of grain yield and certain other traits in bread wheat. Pakistan Journal of Agricultural Research, 13(3): 216-220.
6. Clyde, M., H. Desimone and G. Parmigiani. 1996. Prediction via orthogonalized model mixing. Journal of the American Statistical Association, 91(435): 1197-1208. [DOI:10.1080/01621459.1996.10476989]
7. Dellaportas, P., J.J. Forster and I. Ntzoufras. 2000. Bayesian Variable Selection Using the Gibbs Sampler, In: Dey, D.K., S.K. Ghosh and B.K. Mallick (eds.) Generalized Linear Models: A Bayesian Perspective, CRC Press, New York, 271-286.
8. Dellaportas, P., J.J. Forster and I. Ntzoufras. 2002. On Bayesian model and variable selection using MCMC. Statistics and Computing, 12(1): 27-36. [DOI:10.1023/A:1013164120801]
9. Eftekhari, A., A. Baghizadeh, R. Abdoshahi and M.M. Yaghoubi. 2017. Estimation of Genes Effect and Combining Ability of Agronomic Traits in Some Bread Wheat Varieties under Drought Stress. Journal of Crop Breeding, 9(22): 98-108 (In Persian).
10. Fotokian, M.H., J. Ahmadi, and S.F. Orang. 2008. Genetic assay of some traits in wheat (Triticum aestivum L.) under drought stress condition using generation mean analysis. Iranian Journal of Biology, 22(3): 431-441 (In Persian).
11. Gelman, A., J.B. Carlin, H.S. Stern and D.B. Rubin. 2004. Bayesian data analysis. Boca Raton, FL, USA: Chapman & Hall/CRC.
12. Geman, S. and D. Geman. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6: 721-741. [DOI:10.1109/TPAMI.1984.4767596]
13. George, E.I. and R.E. McCulloch. 1993. Variable selection via Gibbs sampling. Journal of the American Statistical Association, 88(423): 881-889. [DOI:10.1080/01621459.1993.10476353]
14. Gomez, K.A. and A.A. Gomez. 1984. Statistical Procedures for Agricultural Research. John Wiley & Sons.
15. Green, P.J. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 711-732. [DOI:10.1093/biomet/82.4.711]
16. Hallauer, A.R., M.J. Carena and J.B. Miranda Filho. 2010. Quantitative Genetics in Maize Breeding. Springer, New York. [DOI:10.1007/978-1-4419-0766-0_12]
17. Hayman, B.I. 1960. The separation of epistatic from additive and dominance variation in generation means. Heredity, 12: 371-390. [DOI:10.1038/hdy.1958.36]
18. Ijaz, U.S. and M. Kashif. 2013. Genetic study of quantitative traits in spring wheat through generation means analysis. American-Eurasian Journal of Agricultural & Environmental Sciences, 13(2): 191-197.
19. Khattab, S.A.M., R.M. Esmail and A.M.F. Al-Ansary. 2010. Genetical analysis of some quantitative traits in bread wheat (Triticum aestivum L.). New York Science Journal, 3(11): 152-157.
20. Kuo, L. and B. Mallick. 1998. Variable selection for regression models. Sankhyā: The Indian Journal of Statistics, Series B: 65-81.
21. Mather, K. and J.L. Jinks. 1971. Biometrical Genetics. Cornell University Press, Ithaca, N.Y. [DOI:10.1007/978-1-4899-3404-8]
22. Munir, M., M.A. Chowdhry and M. Ahsan. 2007. Generation means studies in bread wheat under drought condition. International Journal of Agriculture and Biology, (9)2: 282-286.
23. Nezhadahmadi, A., Z.H. Prodhan and G. Faruq. 2013. Drought tolerance in wheat. The Scientific World Journal, 12pp. [DOI:10.1155/2013/610721]
24. Novoselovic, D., M. Baric, G. Drezner, J. Gunjaca and A. Lalic. 2004. Quantitative inheritance of some wheat plant traits. Genetics and Molecular Biology, 27(1): 92-98. [DOI:10.1590/S1415-47572004000100015]
25. Ntzoufras, I. 2002. Gibbs variable selection using BUGS. Journal of statistical software, 7(7): 1-19. [DOI:10.18637/jss.v007.i07]
26. Ntzoufras, I. 2011. Bayesian modeling using WinBUGS. John Wiley & Sons, 698pp.
27. Raftery, A.E., D. Madigan and J.A. Hoeting. 1997. Bayesian model averaging for linear regression models. Journal of the American Statistical Association, 92(437): 179-191. [DOI:10.1080/01621459.1997.10473615]
28. SAS Institute. 2002. SAS user's guide: Statistics version 9 for windows. SAS Institute, Carry, NC.
29. Siani, H.S. and D. Aspinall. 1981. Effects of water deficit on sporogensis in wheat. Annals of Botany, 43: 623-633. [DOI:10.1093/oxfordjournals.aob.a086170]
30. Spiegelhalter, D.J., A. Thomas, N.G. Best and D. Lunn. 2003. WinBUGS user manual. MRC Biostatistics Unit, Cambridge.
31. Viana, J. and M. Soriano. 2000. Generation mean analysis in relation to polygenic systems with epistasis and fixed genes. Pesquisa Agropecuária Brasileira, 35(6): 1159-1167. [DOI:10.1590/S0100-204X2000000600012]
32. Xu, S. 2003. Estimating polygenic effects using markers of the entire genome. Genetics, 163(2): 789-801.
33. Yi, N., B.S. Yandell, G.A. Churchill, D.B. Allison, E.J. Eisen and D. Pomp. 2005. Bayesian model selection for genome-wide epistatic quantitative trait loci analysis. Genetics, 170(3): 1333-1344. [DOI:10.1534/genetics.104.040386]
34. Yi, N., D. Shriner, S. Banerjee, T. Mehta, D. Pomp and B.S. Yandell. 2007. An efficient Bayesian model selection approach for interacting quantitative trait loci models with many effects. Genetics, 176(3): 1865-1877. [DOI:10.1534/genetics.107.071365]

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