Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R

Ouma Victor Otieno; Onyango Nelson Owuor

doi:doi:10.11648/j.ijsd.20190502.11

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Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R

Ouma Victor Otieno, Onyango Nelson Owuor

Published in International Journal of Statistical Distributions and Applications (Volume 5, Issue 2)

Received: 6 May 2019 Accepted: 5 June 2019 Published: 26 June 2019

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Abstract

Complexity of Genotype by environment interaction (GxEI) in sugarcane multi-environmental trial (MET) requires further evaluation for genotypes performance determination. Genotype and genotype by environment (GGE) is one of the many statistical techniques for evaluating the interaction with emphasis on genotypes. Many statistical analysis tools for GGE exists with usage depending on cost and knowhow. R open source analytical software ensures availability and the knowledge on the necessary packages is required thus the objective of the paper on utilization of GGE using R software in the evaluation of genotypes with presence GxEI. The application used secondary data of Kenyan Mtwapa series of 96 and 97 preliminary varietal trial stage 4 established under randomized complete block design (RCBD), consisting of 15 test genotypes and three controls in the environments of SONYsugar, Mumias and KibosF9 with the plant crop and ratoon crop cycles as seasons. The 2-way GEI data was handled using singular value decomposition (SVD) through the R package; GGEbiplot programmed scripts and graphical user interface (GUI) were used in ranking genotypes and environments, determining genotypes performance overall and in each environment, determining stabilities and adaptability of the genotypes and identifying mega trial environments. GGEbiplot unpacked the GEI through the principle components (PC) 1 and 2 that sufficiently explained 85.37% of the variations.

Published in	International Journal of Statistical Distributions and Applications (Volume 5, Issue 2)
DOI	10.11648/j.ijsd.20190502.11
Page(s)	22-31
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Genotype by Environment Interaction, Genotype and Genotype by Environment, Singular Value Decomposition, R-software, GGEBiplot and Sugarcane

References

[1]	Santos, A. D., Amaral Júnior, A. T. D., Kurosawa, R. D. N. F., Gerhardt, I. F. S., & Fritsche Neto, R. (2017). GGE Biplot projection in discriminating the efficiency of popcorn lines to use nitrogen. Ciência e Agrotecnologia, 41 (1), 22-31.
[2]	Akter, A., Hasan, M. J., Kulsum, U., Rahman, M. H., Khatun, M., & Islam, M. R. (2015). GGE biplot analysis for yield stability in multi-environment trials of promising hybrid rice (Oryza sativa L.). Bangladesh Rice Journal, 19 (1), 1-8.
[3]	Atnaf, M., Kidane, S., Abadi, S., & Fisha, Z. (2013). GGE biplots to analyze soybean multi-environment yield trial data in north Western Ethiopia. Journal of Plant Breeding and Crop Science, 5 (12), 245-254.
[4]	Donoso-Ñanculao, G., Paredes, M., Becerra, V., Arrepol, C., & Balzarini, M. (2016). GGE biplot analysis of multi-environment yield trials of rice produced in a temperate climate. Chilean journal of agricultural research, 76 (2), 152-157.
[5]	Yan, W., & Rajcan, I. (2002). Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Science, 42 (1), 11-20.
[6]	Yan, W., & Falk, D. E. (2002). Biplot analysis of host-by-pathogen data. Plant Disease, 86 (12), 1396-1401.
[7]	Yan, W., & Hunt, L. A. (2002). 19 Biplot Analysis of Multi-environment Trial Data.
[8]	Yan, W., & Tinker, N. A. (2005). An integrated biplot analysis system for displaying, interpreting, and exploring genotype× environment interaction. Crop Science, 45 (3), 1004-1016.
[9]	Dia, M., Wehner, T. C., & Arellano, C. (2016). Analysis of genotype× environment interaction (G× E) using SAS programming. Agronomy Journal, 108 (5), 1838-1852.
[10]	Minitab, I. (2014). MINITAB release 17: statistical software for windows. Minitab Inc, USA.
[11]	StataCorp, L. P. (2007). Stata data analysis and statistical Software. Special Edition Release, 10, 733.
[12]	SPSS, I. (2017). SPSS 17.0. Chicago, Ill, SPSS.
[13]	SAS Institute Inc 2009
[14]	StatSoft, P. L. (2009). STATISTICA®, version 9.0.
[15]	Udina, F. (2005). Interactive biplot construction. Journal of Statistical Software, 13 (5), 1-16.
[16]	Yan W, Kang MS (2006). GGEbiplot, Version 5. URL http://www.ggebiplot.com/.
[17]	Lipkovich, I., & Smith, E. P. (2002). Biplot and singular value decomposition macros for Excel. Journal of statistical software, 7 (5), 1-15.
[18]	VSN International Ltd (2017). Genstat for Windows 19th Edition. VSN International Ltd, Hemel Hempstead, UK. URL http://www.genstat.co.uk/.
[19]	Highland Statistics Ltd. (2009). Brodgar: Software Package for Data Exploration, Univariate Analysis, Multivariate Analysis and Time Series Analysis.
[20]	Lepš, J., & Šmilauer, P. (2003). Multivariate analysis of ecological data using CANOCO. Cambridge university press.
[21]	Ter Braak, C. J., & Smilauer, P. (2002). CANOCO reference manual and CanoDraw for Windows user's guide: software for canonical community ordination (version 4.5). www. canoco. com.
[22]	McCune, B., & Mefford, M. J. (1999). PC-ORD: multivariate analysis of ecological data; Version 4 for Windows; [User's Guide]. MjM software design.
[23]	Grandin, U. (2006). PC-ORD version 5: A user-friendly toolbox for ecologists. Journal of Vegetation Science, 17 (6), 843-844.
[24]	La Grange, A., le Roux, N., & Gardner-Lubbe, S. (2009). BiplotGUI: interactive biplots in R. Journal of Statistical Software, 30 (12), 1-37.
[25]	R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
[26]	Frutos, E., Galindo, M. P., Leiva, V. (2014). An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stoch. Environ. Res. Risk Assess. 28:1629-1641
[27]	Kevin Wright and Jean-Louis Laffont (2018). gge: Genotype Plus Genotype-by-Environment Biplots. R package version 1.4. https://CRAN.R-project.org/package=gge
[28]	Dia, M., Wehner, T. C., & Arellano, C. (2017). RGxE: an r program for genotype x environment interaction analysis. American Journal of Plant Sciences, 8 (07), 1672.
[29]	Yan, W., & Kang, M. S. (2002). GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC press.
[30]	Kang, M. S. (1997). Using genotype-by-environment interaction for crop cultivar development. In Advances in agronomy (Vol. 62, pp. 199-252). Academic Press.
[31]	Gauch Jr, H. G. (1992). Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Elsevier Science Publishers.
[32]	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.
[33]	Yan, W. (2001). GGEbiplot—a Windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agronomy journal, 93 (5), 1111-1118.
[34]	Aina, O. O., Dixon, A. G. O., Paul, I., & Akinrinde, E. A. (2009). G× E interaction effects on yield and yield components of cassava (landraces and improved) genotypes in the savanna regions of Nigeria. African Journal of Biotechnology, 8 (19).
[35]	Xu, F. F., TANG, F. F., SHAO, Y. F., CHEN, Y. L., Chuan, T., & BAO, J. S. (2014). Genotype× environment interactions for agronomic traits of rice revealed by association mapping. Rice Science, 21 (3), 133-141.
[36]	Kroonenberg, P. M. (1995). Introduction to biplots for G× E tables. Res. Rep. 51. Dep. of Mathematics, Univ. of Queensland, QLD, Australia. Introduction to biplots for G× E tables. Res. Rep. 51. Dep. of Mathematics, Univ. of Queensland, QLD, Australia.
[37]	Yan, W. (2002). Singular-value partitioning in biplot analysis of multienvironment trial data. Agronomy Journal, 94 (5), 990-996.
[38]	Gauch, H. G., & Zobel, R. W. (1996). AMMI analysis in yield trials. KANG, MS, GAUCH, HG (Ed) Genotype by environment interaction.

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APA Style

Ouma Victor Otieno, Onyango Nelson Owuor. (2019). Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R. International Journal of Statistical Distributions and Applications, 5(2), 22-31. https://doi.org/10.11648/j.ijsd.20190502.11

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ACS Style

Ouma Victor Otieno; Onyango Nelson Owuor. Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R. Int. J. Stat. Distrib. Appl. 2019, 5(2), 22-31. doi: 10.11648/j.ijsd.20190502.11

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AMA Style

Ouma Victor Otieno, Onyango Nelson Owuor. Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R. Int J Stat Distrib Appl. 2019;5(2):22-31. doi: 10.11648/j.ijsd.20190502.11

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@article{10.11648/j.ijsd.20190502.11,
  author = {Ouma Victor Otieno and Onyango Nelson Owuor},
  title = {Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {5},
  number = {2},
  pages = {22-31},
  doi = {10.11648/j.ijsd.20190502.11},
  url = {https://doi.org/10.11648/j.ijsd.20190502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20190502.11},
  abstract = {Complexity of Genotype by environment interaction (GxEI) in sugarcane multi-environmental trial (MET) requires further evaluation for genotypes performance determination. Genotype and genotype by environment (GGE) is one of the many statistical techniques for evaluating the interaction with emphasis on genotypes. Many statistical analysis tools for GGE exists with usage depending on cost and knowhow. R open source analytical software ensures availability and the knowledge on the necessary packages is required thus the objective of the paper on utilization of GGE using R software in the evaluation of genotypes with presence GxEI. The application used secondary data of Kenyan Mtwapa series of 96 and 97 preliminary varietal trial stage 4 established under randomized complete block design (RCBD), consisting of 15 test genotypes and three controls in the environments of SONYsugar, Mumias and KibosF9 with the plant crop and ratoon crop cycles as seasons. The 2-way GEI data was handled using singular value decomposition (SVD) through the R package; GGEbiplot programmed scripts and graphical user interface (GUI) were used in ranking genotypes and environments, determining genotypes performance overall and in each environment, determining stabilities and adaptability of the genotypes and identifying mega trial environments. GGEbiplot unpacked the GEI through the principle components (PC) 1 and 2 that sufficiently explained 85.37% of the variations.},
 year = {2019}
}

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TY  - JOUR
T1  - Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R
AU  - Ouma Victor Otieno
AU  - Onyango Nelson Owuor
Y1  - 2019/06/26
PY  - 2019
N1  - https://doi.org/10.11648/j.ijsd.20190502.11
DO  - 10.11648/j.ijsd.20190502.11
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 22
EP  - 31
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20190502.11
AB  - Complexity of Genotype by environment interaction (GxEI) in sugarcane multi-environmental trial (MET) requires further evaluation for genotypes performance determination. Genotype and genotype by environment (GGE) is one of the many statistical techniques for evaluating the interaction with emphasis on genotypes. Many statistical analysis tools for GGE exists with usage depending on cost and knowhow. R open source analytical software ensures availability and the knowledge on the necessary packages is required thus the objective of the paper on utilization of GGE using R software in the evaluation of genotypes with presence GxEI. The application used secondary data of Kenyan Mtwapa series of 96 and 97 preliminary varietal trial stage 4 established under randomized complete block design (RCBD), consisting of 15 test genotypes and three controls in the environments of SONYsugar, Mumias and KibosF9 with the plant crop and ratoon crop cycles as seasons. The 2-way GEI data was handled using singular value decomposition (SVD) through the R package; GGEbiplot programmed scripts and graphical user interface (GUI) were used in ranking genotypes and environments, determining genotypes performance overall and in each environment, determining stabilities and adaptability of the genotypes and identifying mega trial environments. GGEbiplot unpacked the GEI through the principle components (PC) 1 and 2 that sufficiently explained 85.37% of the variations.
VL  - 5
IS  - 2
ER  -

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Author Information

Ouma Victor Otieno

School of Mathematics, University of Nairobi (UoN), Nairobi, Kenya
Onyango Nelson Owuor

School of Mathematics, University of Nairobi (UoN), Nairobi, Kenya

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APA Style

Ouma Victor Otieno, Onyango Nelson Owuor. (2019). Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R. International Journal of Statistical Distributions and Applications, 5(2), 22-31. https://doi.org/10.11648/j.ijsd.20190502.11

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ACS Style

Ouma Victor Otieno; Onyango Nelson Owuor. Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R. Int. J. Stat. Distrib. Appl. 2019, 5(2), 22-31. doi: 10.11648/j.ijsd.20190502.11

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AMA Style

Ouma Victor Otieno, Onyango Nelson Owuor. Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R. Int J Stat Distrib Appl. 2019;5(2):22-31. doi: 10.11648/j.ijsd.20190502.11

Copy | Download

@article{10.11648/j.ijsd.20190502.11,
  author = {Ouma Victor Otieno and Onyango Nelson Owuor},
  title = {Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {5},
  number = {2},
  pages = {22-31},
  doi = {10.11648/j.ijsd.20190502.11},
  url = {https://doi.org/10.11648/j.ijsd.20190502.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20190502.11},
  abstract = {Complexity of Genotype by environment interaction (GxEI) in sugarcane multi-environmental trial (MET) requires further evaluation for genotypes performance determination. Genotype and genotype by environment (GGE) is one of the many statistical techniques for evaluating the interaction with emphasis on genotypes. Many statistical analysis tools for GGE exists with usage depending on cost and knowhow. R open source analytical software ensures availability and the knowledge on the necessary packages is required thus the objective of the paper on utilization of GGE using R software in the evaluation of genotypes with presence GxEI. The application used secondary data of Kenyan Mtwapa series of 96 and 97 preliminary varietal trial stage 4 established under randomized complete block design (RCBD), consisting of 15 test genotypes and three controls in the environments of SONYsugar, Mumias and KibosF9 with the plant crop and ratoon crop cycles as seasons. The 2-way GEI data was handled using singular value decomposition (SVD) through the R package; GGEbiplot programmed scripts and graphical user interface (GUI) were used in ranking genotypes and environments, determining genotypes performance overall and in each environment, determining stabilities and adaptability of the genotypes and identifying mega trial environments. GGEbiplot unpacked the GEI through the principle components (PC) 1 and 2 that sufficiently explained 85.37% of the variations.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Multivariate Genotype and Genotype by Environment Interaction Biplot Analysis of Sugarcane Breeding Data Using R
AU  - Ouma Victor Otieno
AU  - Onyango Nelson Owuor
Y1  - 2019/06/26
PY  - 2019
N1  - https://doi.org/10.11648/j.ijsd.20190502.11
DO  - 10.11648/j.ijsd.20190502.11
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 22
EP  - 31
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20190502.11
AB  - Complexity of Genotype by environment interaction (GxEI) in sugarcane multi-environmental trial (MET) requires further evaluation for genotypes performance determination. Genotype and genotype by environment (GGE) is one of the many statistical techniques for evaluating the interaction with emphasis on genotypes. Many statistical analysis tools for GGE exists with usage depending on cost and knowhow. R open source analytical software ensures availability and the knowledge on the necessary packages is required thus the objective of the paper on utilization of GGE using R software in the evaluation of genotypes with presence GxEI. The application used secondary data of Kenyan Mtwapa series of 96 and 97 preliminary varietal trial stage 4 established under randomized complete block design (RCBD), consisting of 15 test genotypes and three controls in the environments of SONYsugar, Mumias and KibosF9 with the plant crop and ratoon crop cycles as seasons. The 2-way GEI data was handled using singular value decomposition (SVD) through the R package; GGEbiplot programmed scripts and graphical user interface (GUI) were used in ranking genotypes and environments, determining genotypes performance overall and in each environment, determining stabilities and adaptability of the genotypes and identifying mega trial environments. GGEbiplot unpacked the GEI through the principle components (PC) 1 and 2 that sufficiently explained 85.37% of the variations.
VL  - 5
IS  - 2
ER  -

Copy | Download