Bayesian inference to study genetic control of water deficit stress tolerance in wheat by LASSO method

Document Type : Research Paper

Authors

1 Ph. D. Candidate, Dept. of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz,Iran

2 Graduated Ph.D., Dept. of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

3 Assist. Prof., Dept. of Biotechnology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran

Abstract

Drought is the main abiotic stress seriously influencing wheat production and quality in Iran. Information about genetic controlling drought tolerance inheritance is necessary to determine the type of breeding program as well as develop tolerant cultivars. In this study, Bayesian inference using LASSO method 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. Bayesian inference is an alternative approach which combines available prior knowledge (prior distribution) with the information contained in the data. The result is the posterior distribution containing all information to interpret genetic structure. LASSO is an effective method to apply shrinkage and selection on model variables. Non-important effects in the model shrunk toward zero and excluded from the model. While for important effects, less shrinkage will be achieved. 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.

Keywords

References

Balestre, M., Von Pinho, R. G. and Brito, A. H. 2012.Bayesian inference to study genetic control of resistance to gray leaf spot in maize. Genetics and Molecular Research 11(1): 17-29.##Bartlett, M. S. 1957. Measles periodicity and community size. Journal of the Royal Statistical Society. Series A (General) 120(1): 48-70.##Blasco, A. 2001.The Bayesian controversy in animal breeding. Journal of Animal Science 79(8): 2023-2046.##Chowdhry, M. A., Rafiq, M. and Alam, K. 1992.Genetic architecture of grain yield and certain other traits in bread wheat. Pakistan Journal of Agricultural Research 13(3): 216-220.##Dellaportas, P., Forster, J. J. and Ntzoufras, I. 2002.On Bayesian model and variable selection using MCMC. Statistics and Computing 12(1): 27-36.##Fotokian, M. H., Ahmadi, J. and F. Orang, S. 2008. Genetic assay of some traits in wheat (Triticumaestivum L.) under drought stress conditions using generation mean analysis. Iranian Journal of Biology 22(3): 431-441. (In Persian with English Abstract).##Gelfand, A. E. and Smith, A. F. 1990.Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85(410): 398-409.##Geman,S.andGeman,D.1984.Stochasticrelaxation,Gibbsdistributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6: 721-741.##Gomez, K. A. and Gomez, A. A. 1984. Statistical procedures for agricultural research.John Wiley & Sons.##Hastings, W. K. 1970.Monte Carlo sampling methods using Markov chains and their applications. Biometrika57(1): 97-109.##Ijaz, U. S. and Kashif, M. 2013. Genetic study of quantitative traits in spring wheat through generation means analysis. American-Eurasian Journal of Agricultural and Environmental Sciences 13(2): 191-197.##Kearsey, M. J. and Pooni, H. S. 1998. The genetical analysis of quantitative traits.Stanley Thornes Publishers, Ltd.##Khattab, S. A. M., Esmail, R. M. and Al-Ansary, A. M. F. 2010.Genetical analysis of somequantitative traits in bread wheat (Triticumaestivum L.).New York Science Journal 3: 152-157.##Kuo, L. and Mallick, B. 1998.Variable selection for regression models. Sankhyā: The Indian Journal of Statistics,Series B: 65-81.##Lindley, D.V. 1957.A statistical paradox. Biometrika 44(1-2): 187-192.##Lykou, A. and Ntzoufras, I. 2011.WinBUGS: A tutorial. Wiley Interdisciplinary Reviews: Computational Statistics 3(5): 385-396.##Lykou, A. and Ntzoufras, I. 2013. On Bayesian lasso variable selection and the specification of the shrinkage parameter. Statistics and Computing 23: 361-390.##Lynch, S. M. 2007. Introduction to applied Bayesian statistics and estimation for social scientists.Springer Science and Business Media.##Mather, K. and Jinks, J. L. 1971.Biometrical genetics. Cornell University Press, Ithaca, N.Y.##Mathew, B., Bauer, A. M., Koistinen, P., Reetz, T. C., Léon, J. and Sillanpää, M. J. 2012.Bayesian adaptive Markov Chain Monte Carlo estimation of genetic parameters. Heredity 109(4): 235-245.##Metropolis,N.,Rosenbluth,A.W.,Rosenbluth,M.N.,Teller,A.H.andTeller,E.1953. Equation of statecalculationsbyfastcomputingmachines. TheJournalofChemicalPhysics 21:1087-1092.##Mettle, F. O., Asiedu, L., Quaye, E. N. and Asare-Kumi, A. A. 2016. Comparison of least squares method and Bayesian with multivariate normal prior in estimating multiple regression parameters. British Journal of Mathematics and Computer Science 15(1): 1-8.##Munir, M., Chowdhry, M. A. and Ahsan, M. 2007.Generation means studies in bread wheat under drought condition. International Journal of Agriculture and Biology 9 (2): 282-286.##Nezhadahmadi, A., Prodhan, Z. H. and Faruq, G. 2013.Drought tolerance in wheat. The Scientific World Journal 2013: 1-12.##Novoselovic, D., Baric, M., Drezner, G., Gunjaca, J. and Lalic, A. 2004.Quantitative inheritance of some wheat plant traits. Genetics and Molecular Biology 27(1): 92-98.##Ntzoufras, I. 2011. Bayesian modeling using WinBUGS. Vol. 698.John Wiley & Sons.##SAS Institute. 2002. SAS user's guide: Statistics version 9 for windows. SAS Institute., Carry, NC.##Spiegelhalter, D. J., Thomas, A., Best, N. G. and Lunn, D. 2003.WinBUGS user manual. MRC Biostatistics Unit, Cambridge.##Tibshirani, R. 1996.Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B (Methodological): 267-288.##Waldmann, P., Hallander, J., Hoti, F. and Sillanpää, M. J. 2008.Efficient Markov Chain Monte Carlo implementation of Bayesian analysis of additive and dominance genetic variances in noninbred pedigrees. Genetics 179(2): 1101-1112.##Wang, D., El-Basyoni, I.S., Baenziger, P.S., Crossa, J., Eskridge, K.M. and Dweikat, I. 2012.Prediction of genetic values of quantitative traits with epistatic effects in plant breeding populations. Heredity 109(5): 313-319.##Wang, D., Eskridge, K.M. and Crossa, J. 2011.Identifying QTLs and epistasis in structured plant populations using adaptive mixed LASSO. Journal of Agricultural, Biological and Environmental Statistics 16(2): 170-184.##Xu, S. 2003.Estimating polygenic effects using markers of the entire genome. Genetics, 163(2): 789-801.##Xu, S. 2007.An empirical Bayes method for estimating epistatic effects of quantitative trait loci. Biometrics 63(2): 513-521.
Cereal Research
Volume 8, Issue 1
June 2018
Pages 57-72

Files

Share

How to cite

Statistics