Application of principal component analysis for rice f5 families characterization and evaluation

Authors

  • K. L. Y. Tejaswini Department of Genetics and Plant Breeding, Agriculture College, Bapatla, Guntur District, Andhra Pradesh, India
  • Srinivas Manukonda Andhra Pradesh Rice Research Institute & RARS, Maruteru, West Godavari, Andhra Pradesh, India
  • B. N. V. S. R. Ravi Kumar Andhra Pradesh Rice Research Institute & RARS, Maruteru, West Godavari, Andhra Pradesh, India
  • P. V. Ramana Rao Andhra Pradesh Rice Research Institute & RARS, Maruteru, West Godavari, Andhra Pradesh, India
  • Lal Ahamed M Department of Genetics and Plant Breeding, Agriculture College, Bapatla, Guntur District, Andhra Pradesh, India
  • S. Krishnam Raju Andhra Pradesh Rice Research Institute & RARS, Maruteru, West Godavari, Andhra Pradesh, India

DOI:

https://doi.org/10.7324/ELSR.2018.417284

Keywords:

D2 statistic, genetic divergence, principal component analysis

Abstract

The type and level of genetic variance in one hundred and fourteen F5 families of rice obtained from six different crosses was estimated along with their seven parents using Mahalanobis D2 – statistics by considering 10 characters. Mahalanobis D2 analysis revealed considerable amount of diversity in the material. The genotypes were grouped into twelve clusters. Cluster IX constituted maximum number of genotypes (26). The genotypes falling in cluster VII had the maximum divergence, which was closely followed by cluster VIII and cluster XI. The maximum inter cluster D2 values was observed between cluster X and XI (931.276) followed by cluster VIII and XI (814.784) suggesting that the genotypes constituted in these clusters may be used as parents for future hybridization programs. Principal component analysis revealed that families MTU 2462-1-5-2, MTU 2462-15-1-1, MTU 2468-30-2-2, MTU 2468-29-3-1, MTU 2462-1-9-2, MTU 2469-23-2-1, MTU 2469-6-2-1, MTU 2469-36-1-1, MTU 2469-32-2-1 were more divergent and hence, can be used in breeding programs. Hence, the results of cluster analysis were supported by principal component analysis.

References

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Published

2018-06-27

Issue

Vol 4, Issue 1, 2018

Section

Articles

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Copyright © The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original author(s) and source are properly credited, and the work is not modified or adapted.

How to Cite

Application of principal component analysis for rice f5 families characterization and evaluation. (2018). Emergent Life Sciences Research, 72-84. https://doi.org/10.7324/ELSR.2018.417284