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Mixed Model, AMMI and Eberhart-Russel Comparison via Simulation on Genotype × Environment Interaction Study in Sugarcane

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=48155#.VEXxFVfHRK0

Brazil is the world leader in sugarcane production and the largest sugar exporter. Developing new varieties is one of the main factors that contribute to yield increase. In order to select the best genotypes, during the final selection stage, varieties are tested in different environments (locations and years), and breeders need to estimate the phenotypic performance for main traits such as tons of cane yield per hectare (TCH) considering the genotype × environment interaction (GEI) effect. Geneticists and biometricians have used different methods and there is no clear consensus of the best method. In this study, we present a comparison of three methods, viz. Eberhart-Russel (ER), additive main effects and multiplicative interaction (AMMI) and mixed model (REML/BLUP), in a simulation study performed in the R computing environment to verify the effectiveness of each method in detecting GEI, and assess the particularities of each method from a statistical standpoint. In total, 63 cases representing different conditions were simulated, generating more than 34 million data points for analysis by each of the three methods. The results show that each method detects GEI differently in a different way, and each has some limitations. All three methods detected GEI effectively, but the mixed model showed higher sensitivity. When applying the GEI analysis, firstly it is important to verify the assumptions inherent in each method and these limitations should be taken into account when choosing the method to be used.
Cite this paper
Ferraudo, G. and Perecin, D. (2014) Mixed Model, AMMI and Eberhart-Russel Comparison via Simulation on Genotype × Environment Interaction Study in Sugarcane. Applied Mathematics, 5, 2107-2119. doi: 10.4236/am.2014.514205. 
 

[1] Perecin, D., Landell, M.G.A., Xavier, M.A., Anjos, I.A., Bidoia, M.A.P. and Silva, D.N. (2009) Agronomic and Genetic Progress in Sugar-Cane Breeding Program. Revista Brasileira de Biometria, 27, 279-287.
http://jaguar.fcav.unesp.br/RME/fasciculos/v27/v27_n2/Perecin.pdf.
[2] Ramburan, S., Zhou, M. and Labuschagne, M. (2011) Interpretation of Genotype × Environment Interactions of Sugarcane: Identifying Significant Environmental Factors. Field Crops Research, 124, 392-399.
http://dx.doi.org/10.1016/j.fcr.2011.07.008
[3] Yan, W., and Tinker, N.A. (2006) Biplot Analysis of Multi-Environment Trial Data: Principles and Applications. Canadian Journal of Plant Science, 86, 623-645.
http://dx.doi.org/10.4141/P05-169
[4] Eberhart, S.A., and Russel, W.A. (1966) Stability Parameters for Comparing Varieties. Crop Science, 6, 36-40.
http://dx.doi.org/10.2135/cropsci1966.0011183X000600010011x
[5] Gollob, H.F. (1968) A Statistical Model Which Combines Features of Factor Analytic and Analysis of Variance Techniques. Psychometrika, 33, 73-115.
http://dx.doi.org/10.1007/BF02289676
[6] Gabriel, K.R. (1971) The Biplot Graphic Display of Matrices with Application to Principal Component Analysis. Biometrika, 58, 453-467.
http://dx.doi.org/10.1093/biomet/58.3.453
[7] Mandel, J. (1971) A New Analysis of Variance Model for Non-Additive Data. Technometrics, 13, 1-18.
http://dx.doi.org/10.1080/00401706.1971.10488751
[8] Gauch, H.G. (1988) Model Selection and Validation for Yield Trials with Interaction. Biometrics, 44, 705-715.
http://dx.doi.org/10.2307/2531585
[9] Gauch, H.G. (2006) Statistical Analysis of Yield Trials by AMMI and GGE. Crop Science, 46, 1488-1500.
http://dx.doi.org/10.2135/cropsci2005.07-0193
[10] Zobel, R.W., Wright, M.J. and Gauch, H.G. (1988) Statistical Analysis of a Yield Trial. Agronomy Journal, 80, 388-393.
http://dx.doi.org/10.2134/agronj1988.00021962008000030002x
[11] van Eeuwijk, F.A. (1995) Linear and Bilinear Models for the Analysis of Multi-Environment Trials: I. An Inventory of Models. Euphytica, 84, 1-7. http://dx.doi.org/10.1007/BF01677551
[12] Dias, C.T. and Krzanowski, W.J. (2003) Model Selection and Cross Validation in Additive Main Effects and Multiplicative Interaction Models. Crop Science, 43, 865-873.
http://dx.doi.org/10.2135/cropsci2003.8650
[13] Henderson, C.R. (1975) Best Linear Estimation and Prediction under a Selection Model. Biometrics, 31, 423-447.
http://dx.doi.org/10.2307/2529430
[14] Smith, A.B., Cullis, B.R. and Thompson, R. (2005) The Analysis of Crop Cultivar Breeding and Evaluation Trials: An Overview of Current Mixed Model Approaches. Journal of Agricultural Science, 143, 449-462.
http://dx.doi.org/10.1017/S0021859605005587
[15] Resende, M.D.V. (2007) Estimação e predição em modelos lineares mistos. In: Resende, M.D.V., Ed., Matemática e estatística na análise de experimentos e no melhoramento genético, Embrapa Florestas, Colombo, 101-170.
[16] Piepho, H.-P., Möhring, J., Melchinger, A.E. and Büchse, A. (2008) BLUP for Phenotypic Selection in Plant Breeding and Variety Testing. Euphytica, 161, 209-228.
http://dx.doi.org/10.1007/s10681-007-9449-8
[17] Bajpai, P.K. and Kumar, R. (2005) Comparison of Methods for Studying Genotype × Environment Interaction in Sugarcane. Sugar Tech, 7, 129-135.
http://dx.doi.org/10.1007/BF02950597
[18] Bastos, I.T., Barbosa, M.H.P., Resende, M.D.V., Peternelli, L.A., Silveira, L.C.I., Donda, L.R., Fortunato, A.A., Costa, P.M.A. and Figueiredo, I.C.R. (2007) Evaluation of Genotype versus Environment Interaction in Sugarcane Using Mixed Models. Pesquisa Agropecuária Tropical, 37, 195-203.
[19] Silva, M.A. (2008) Genotype × Environment Interaction and Phenotypic Stability in Sugarcane under a Twelve-Months Planting Cycle. Bragantia, 67, 109-117.
[20] Guerra, E.P., Oliveira, R.A., Daros, E., Zambon, J.L.C., Ido, O.T. and Bespalhok Filho, J.C. (2009) Stability and Adaptability of Early Maturing Sugarcane Clones by AMMI Analysis. Crop Breeding and Applied Biotechnology, 9, 260-267.
http://dx.doi.org/10.12702/1984-7033.v09n03a08
[21] Piepho, H.-P. (1994) Best Linear Unbiased Prediction (BLUP) for Regional Yield Trials: A Comparison to Additive Main Effects and Multiplicative Interaction (AMMI) Analysis. Theoretical and Applied Genetics, 89, 647-654.
http://dx.doi.org/10.1007/BF00222462
[22] Annicchiarico, P. (1997) Joint Regression vs AMMI Analysis of Genotype-Environment Interactions for Cereals in Italy. Euphytica, 94, 53-62.
http://dx.doi.org/10.1023/A:1002954824178
[23] Duarte, J. B. and Vencovsky, R. (1999) Interação genótipos × ambientes: Uma introdução à análise “AMMI”. Sociedade Brasileira de Genética, Ribeirão Preto.
[24] Yang, R.-C., Crossa, J., Cornelius, P.L. and Burgueño, J. (2009) Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution. Crop Science, 49, 1564-1576.
http://dx.doi.org/10.2135/cropsci2008.11.0665
[25] Malosetti, M., Ribaut, J.M. and van Eeuwijk, F.A. (2013) The Statistical Analysis of Multi-Environment Data: Modeling Genotype-by-Environment Interaction and Its Genetic Basis. Frontiers in Physiology, 4, 1-17.
http://dx.doi.org/10.3389/fphys.2013.00044
[26] Yates, F. and Cochran, W.G. (1938) The Analysis of Groups of Experiments. Journal of Agricultural Science, 28, 556-580.
http://dx.doi.org/10.1017/S0021859600050978
[27] Hogg, R.V. and Craig, A.T. (1965) Some Special Distributions. In: Hogg, R.V. and Craig, A.T., Eds., Introduction to Mathematical Statistics, 2nd Edition, Macmillan, New York, 103-104.
[28] Bernardo, R. (2010) Genotype × Environment Interaction. In: Bernardo, R., Ed., Breeding for Quantitative Traits in Plants, Stemma Press, Woodbury, 177-203.
[29] Crossa, J. (1990) Statistical Analyses of Multilocation Trials. Advances in Agronomy, 44, 55-85.
http://dx.doi.org/10.1016/S0065-2113(08)60818-4
[30] Lavoranti, O.J., Dias, C.T.S. and Krzanowski, W.J. (2007) Phenotypic Stability via AMMI Model with Bootstrap Re-Sampling. Pesquisa Florestal Brasileira, 54, 45-52.
[31] Balzarini, M. (2001) Applications of Mixed Models in Plant Breeding. In: Kang, M.S., Ed., Quantitative Genetics, Genomics and Plant Breeding, CABI Publishing, New York, 353-363.
http://dx.doi.org/10.1079/9780851996011.0353
[32] Lynch, M. and Walsh, B. (1997) Genotype × Environment Interaction. In: Lynch, M. and Walsh, B., Eds., Genetics and Analysis of Quantitative Traits, Sinauer, Sunderland, 657-683.
[33] R Core Team (2013) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.
http://www.R-project.org/
[34] Mendiburu, F. (2013). Agricolae: Statistical Procedures for Agricultural Research. R Package Version 1.1-4.
http://CRAN.R-project.org/package=agricolae
[35] Bates, D., Maechler, M. and Bolker, B. (2013) lme4: Linear Mixed-Effects Models Using S4 Classes. R Package Version 0.999999-2.
http://CRAN.R-project.org/package=lme4
[36] Crossa, J., Fox, P.N., Pfeifer, W.H., Rajaram, J. and Gauch, H.G. (1991) AMMI Adjustment for Statistical Analysis of an International Wheat Yield Trial. Theoretical and Applied Genetics, 81, 27-37.
http://dx.doi.org/10.1007/BF00226108                    eww141021lx
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