AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data

Open access

Summary

The presence of genotype-environment interaction (GEI) influences production making the selection of cultivars in a complex process. The two most used methods to analyze GEI and evaluate genotypes are AMMI and GGE Biplot, being used for the analysis of multi environment trials data (MET). Despite their different approaches, both models complement each other in order to strengthen decision making. However, both models are based on biplots, consequently, biplot-based interpretation doesn’t scale well beyond two-dimensional plots, which happens whenever the first two components don’t capture enough variation. This paper proposes an approach to such cases based on cluster analysis combined with the concept of medoids. It also applies AMMI and GGE Biplot to the adjusted data in order to compare both models. The data is provided by the International Maize and Wheat Improvement Center (CIMMYT) and comes from the 14th Semi-Arid Wheat Yield Trial (SAWYT), an experiment concerning 50 genotypes of spring bread wheat (Triticum aestivum) germplasm adapted to low rainfall. It was performed in 36 environments across 14 countries. The analysis provided 25 genotypes clusters and 6 environments clusters. Both models were equivalent for the data’s evaluation, permitting increased reliability in the selection of superior cultivars and test environments.

Annicchiarico P. (1997): Additive Main Effects and Multiplicative Interaction (AMMI) Analysis of Genotype-location Interaction in Variety Trials Repeated over Years. Teor. Appl. Genet. 94: 1072-1077.

Annicchiarico P. (2002): Genotype × environment interaction: Challenges and opportunities for plant breeding and cultivar recommendations. Food & Agriculture Org 174.

Akbarpour O., Dehghani H., Sorkhi B., Gauch Jr. H.G. (2014): Evaluation of Genotype × Environment Interaction in Barley (Hordeum Vulgare L.) Based on AMMI model Using Developed SAS Program. J. Agr. Sci. Tech. 16: 909-920.

Barroso L.P. (2003): Análise Multivariada. Lavras: UFLA, 151p.

Camargo-Buitrago I., Intire E.Q.M., Gorddón-Mendoza R., (2011): Identificación de mega-ambientes para potenciar el uso de genótipos superiores de arroz em Panamá. Pesquisa Agropecuária Brasileira 46(9): 1061-1069.

Crossa J. (1990): Statistical Analyses of Multilocation Trials. Adv. Agron. 44: 55-85.

Datta S., Datta S. (2003): Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19(4): 459-466.

Gabriel K.R. (1971): The biplot graphic display of matrices with application to principal component analysis. Biometrika 58(3): 453-467.

Gauch H.G. (1992): Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Elsevier, Amsterdam.

Gauch H.G., Zobel R.W. (1996): AMMI analysis in yield trials. KANG, M. S., GAUCH, H. G. (Ed) Genotype by environment interaction. New York: CRC Press: 416-428.

Gauch H.G. (2006): Statistical analysis of yield trials by AMMI and GGE. Crop Science 46: 1488-1500.

Gauch H.G., Piepho H.P., Annicchiarico P., (2008): Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations. Crop Science 48: 866-889.

Gauch H.G. (2013): A Simple Protocol for AMMI Analysis of Yield Trials. Crop Science (in press).

Gollob H.F. (1968): A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33: 73-115.

Hartigan J.A., Wong M.A. (1979): K-means clustering algorithm. Journal of the Royal Statistical Society 28(1): 100-108.

Hongyu K., Penña M.G., Araújo L.B., Dias C.T.S. (2014): Statistical analysis of yield trials by AMMI analysis of genotype × environment interaction. Biometrical Letters 51(2): 89-102.

Hongyu K., Silva F.L., Oliveira A.C.S., Sarti D.A, Araújo L.C., Dias C.T.S. (2015): Comparação entre os modelos AMMI e GGE Biplot para os dados de ensaios multi-ambientais. Rev. Bras. Biom., São Paulo 33(2): 139-155.

Johnson R.A., Wichern D. (1998): Multivariate Analysis. Wiley StatsRef: Statistics Reference Online.

Kang M.S. (2002): Genotype-environment Interaction: Progress and Prospects. In: “Quantitative Genetics, Genomics and Plant Breeding”. CAB International, Wallingford, England: 221-243.

Kaufman L., Rousseeuw P. (1990): Partitioning around medoids (program pam). Finding groups in data: an introduction to cluster analysis: 68-125.

Mahalanobis P.C. (1936): On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India: 49-55.

Miranda G.V., Souza L.V., Guimarães L.J.M., Namorato H., Oliveira L.R., Soares M.O. (2009): Multivariate analyses of genotype x environment interaction of popcorn. Pesq. agropec. bras., Brasília 44(1): 45-50.

Neisse A.C., Hongyu K. (2016): Application of Principal Components and Factor Analysis to Crime Data From 26 US States. Pesq. agropec. bras., Brasília 44(1): 45-50.

Pacheco R.M., Duarte J.B., Vencovsky R., Pinheiro J.B., Oliveira A.B. (2005): Use of supplementary genotypes in AMMI analysis. Theor Appl Genet 110: 812-818.

Pearson K. (1901): On lines and planes of closest fit to systems of points in space. Philos. Mag. 6(2): 559-572.

R DEVELOPMENT CORE TEAM (2017): R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2017. URL https://www.R-project.org/.

Rodrigues P.C., Malosetti M., Gauch H. G., Van Eeuwijk F.A. (2014): A weighted AMMI algorithm to study genotype-by-environment interaction and QTLby-environment interaction. Crop Science 54(4) : 1555-1570.

Xu R., Wunsch D.C. (2008): Recent advances in cluster analysis. International Journal of Intelligent Computing and Cybernetics 1(4) : 484-508.

Yan W., Hunt L.A., Sheng Q., Szlavnics Z. (2000): Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science 40(3) : 597-605.

Yan W., Kang M.S. (2003): G GE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. CRC Press, Boca Raton, FL, USA, 271p.

Yan W., Tinker N.A. (2005): An Integrated Biplot Analysis System for Displaying, Interpreting, and Exploring Genotype × Environment Interaction. Crop Science 45 : 1004-1016.

Yan W., Tinker N.A. (2006): Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86(3) : 623-645.

Yan W., Kang M.S., Ma B., Woods S., Cornelius P.L. (2007): GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47 : 643-655.

Yan W. (2011): GGE Biplot vs. AMMI Graphs for the Genotype-by-Environment Data Analysis. Journal of the Indian Society of Agricultural Statistics 65(2): 181-193.

Biometrical Letters

The Journal of Polish Biometric Society

Journal Information

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 228 228 87
PDF Downloads 197 197 67