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

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

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

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