Open Access

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


Cite

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: 1072-1077.10.1007/s001220050517Search in Google Scholar

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

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.Search in Google Scholar

Barroso L.P. (2003): Análise Multivariada. Lavras: UFLA, 151p.Search in Google Scholar

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): 1061-1069.10.1590/S0100-204X2011000900013Search in Google Scholar

Crossa J. (1990): Statistical Analyses of Multilocation Trials. Adv. Agron. 44: 55-85.10.1016/S0065-2113(08)60818-4Search in Google Scholar

Datta S., Datta S. (2003): Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19(4): 459-466.10.1093/bioinformatics/btg02512611800Search in Google Scholar

Gabriel K.R. (1971): The biplot graphic display of matrices with application to principal component analysis. Biometrika 58(3): 453-467.10.1093/biomet/58.3.453Search in Google Scholar

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

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: 416-428.10.1201/9781420049374Search in Google Scholar

Gauch H.G. (2006): Statistical analysis of yield trials by AMMI and GGE. Crop Science 46: 1488-1500.10.2135/cropsci2005.07-0193Search in Google Scholar

Gauch H.G., Piepho H.P., Annicchiarico P., (2008): Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations. Crop Science 48: 866-889.10.2135/cropsci2007.09.0513Search in Google Scholar

Gauch H.G. (2013): A Simple Protocol for AMMI Analysis of Yield Trials. Crop Science (in press).10.2135/cropsci2013.04.0241Search in Google Scholar

Gollob H.F. (1968): A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33: 73-115.10.1007/BF022896765239571Search in Google Scholar

Hartigan J.A., Wong M.A. (1979): K-means clustering algorithm. Journal of the Royal Statistical Society 28(1): 100-108.10.2307/2346830Search in Google Scholar

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): 89-102.10.2478/bile-2014-0007Search in Google Scholar

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.Search in Google Scholar

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

Kang M.S. (2002): Genotype-environment Interaction: Progress and Prospects. In: “Quantitative Genetics, Genomics and Plant Breeding”. CAB International, Wallingford, England: 221-243.10.1079/9780851996011.0221Search in Google Scholar

Kaufman L., Rousseeuw P. (1990): Partitioning around medoids (program pam). Finding groups in data: an introduction to cluster analysis: 68-125.10.1002/9780470316801.ch2Search in Google Scholar

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

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): 45-50.10.1590/S0100-204X2009000100007Search in Google Scholar

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.Search in Google Scholar

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: 812-818.10.1007/s00122-004-1822-615690176Search in Google Scholar

Pearson K. (1901): On lines and planes of closest fit to systems of points in space. Philos. Mag. 6(2): 559-572.10.1080/14786440109462720Search in Google Scholar

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/.Search in Google Scholar

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) : 1555-1570.10.2135/cropsci2013.07.0462Search in Google Scholar

Xu R., Wunsch D.C. (2008): Recent advances in cluster analysis. International Journal of Intelligent Computing and Cybernetics 1(4) : 484-508.10.1108/17563780810919087Search in Google Scholar

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) : 597-605.10.2135/cropsci2000.403597xSearch in Google Scholar

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.10.1201/9781420040371Search in Google Scholar

Yan W., Tinker N.A. (2005): An Integrated Biplot Analysis System for Displaying, Interpreting, and Exploring Genotype × Environment Interaction. Crop Science 45 : 1004-1016.10.2135/cropsci2004.0076Search in Google Scholar

Yan W., Tinker N.A. (2006): Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86(3) : 623-645.10.4141/P05-169Search in Google Scholar

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 : 643-655.10.2135/cropsci2006.06.0374Search in Google Scholar

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.Search in Google Scholar

eISSN:
1896-3811
Language:
English
Publication timeframe:
2 times per year
Journal Subjects:
Life Sciences, Bioinformatics, other, Mathematics, Probability and Statistics, Applied Mathematics