An examination of diversity and interrelationships among traits in a winter wheat (Triticum aestivum L.) germplasm collection by multivariate methods. Part II. Principal component analysis using the phenotypic and genotypic correlation matrix

Joanna Ukalska

joanna_ukalska@sggw.edu.pl
Katedra Biometrii, Szkoła Główna Gospodarstwa Wiejskiego, Warszawa (Poland)

Krzysztof Ukalski


Katedra Biometrii, Szkoła Główna Gospodarstwa Wiejskiego, Warszawa (Poland)

Tadeusz Śmiałowski


Instytut Hodowli i Aklimatyzacji Roślin, Zakład Roślin Zbożowych w Krakowie (Poland)

Wiesław Mądry


Katedra Doświadczalnictwa i Bioinformatyki, Szkoła Główna Gospodarstwa Wiejskiego, Warszawa (Poland)

Abstract

In the paper, the diversity among quantitative traits in the winter wheat germplasm working collection using Principal Component Analysis (PCA) was studied. Fifty-one genotypes (cultivars and clones) from the Plant Breeding and Acclimatization Institute, Department of Cereal Crops in Cracow, were evaluated in the years 1999–2002. Two approaches of the PCA were applied. The first, classical approach, involved the phenotypic correlation matrix, i.e. correlation coefficients between across year phenotypic means. In the second, new approach, application of genotypic correlation matrix, i.e. correlation coefficients matrix between the unobservable genotypic effects for considered traits in PCA, has been proposed. Coincident results were obtained using the above methods. However, the first three principal components obtained using the genotypic correlation matrix compared to the classical PCA accounted for 15% more of the overall variation among genotypes. Moreover, higher absolute values of correlation coefficients between principal components and the evaluated traits were recorded.


Keywords:

genotypic correlation matrix, germplasm collection, phenotypic correlation matrix, principal component analysis, winter wheat

Abdi A., Bejkele E., Asfaw Z., Teshome A. 2002. Patterns of morphological variation of sorghum (Sorghum bicolor (L.) Moench) landraces in qualitative characters in North Shewa and South Welo, Ethiopia. Hereditas 137: 161 — 172.
Google Scholar

Ayana A., Bekele E. 1999. Multivariate analysis of morphological variation in sorghum (Sorghum bicolor (L.) Moench) germplasm from Ethiopia and Eritrea. Gen. Res. Crop Evol. 46: 273 — 284.
Google Scholar

Errikson L., Johansson E., Kettaneh-Wold N., Wold S. 1999. Introduction to multi- and megavariate data analysis using projection methods (PCA & PLS). Umetrics AB, Umea, Sweden.
Google Scholar

Everitt B. S., Dunn G. 1992. Applied Multivariate Data Analysis. Oxford University Press, New York.
Google Scholar

Flores F., Gutierrez J. C., Lopez J., Moreno M. T., Cuberto J. I. 1997. Multivariate analysis approach to evaluate a germplasm collection of Hedysarum coronarium L. Gen. Res. Crop Evol. 44: 545 — 555.
Google Scholar

Hotteling H. 1933. Analysis of a Complex of Statistical Variables into Principal Components. Journal of Educational Psychology 24: 417 — 441, 498 — 520.
Google Scholar

Hotteling H. 1936. Simplified Computation of Principal Components. Psychometrica 1:27 — 35.
Google Scholar

Iezzoni A. F., Pritts M. P. 1991. Application of Principal Components Analysis to Horticultural Research. Hort. Sci. 26(4):334 — 338.
Google Scholar

Jolliffe I. T. 1986. Principal components analysis. Springer-Verlag, New York.
Google Scholar

Khattree R., Naik D. N. 2000. Multivariate data reduction and discrimination with SAS software. SAS Institute Inc., Cary, NC.
Google Scholar

Krzanowski W. J. 1988. Principles of multivariate analysis: a users’s perspective. Oxford University Press, Oxford. 563 pp.
Google Scholar

Leguizamon J., Badenes M. L. 2003. Multivariate analysis as a tool for germplasm studies, Example of Analysis of Gerplasm Loquat Data. Acta Hort. 606: 29 — 35.
Google Scholar

Littell R. C., Milliken G. A., Stroup W.W., Wolfinger R. D. 1996. SAS system for mixed models. SAS Institute Inc., Cary, NC
Google Scholar

Mars M., Marrakchi M. 1999. Diversity of pomegrante (Punica granatum L.) germplasm in Tunisia. Genet. Resour. Crop. Evol. 46: 461 — 467.
Google Scholar

Martinez-Calvo J. 2007. Study of a germplasm collection of loquat (Eriobotrya japonica Lindl.) by multivariate analysis. Genet. Resour. Crop Evol. 55(5):695 — 703.
Google Scholar

Mądry W. 2007. Metody statystyczne do oceny różnorodności fenotypowej dla cech ilościowych w kolekcjach roślinnych zasobów genowych. Zesz. Probl. Post. Nauk Rol. 517: 21 — 41.
Google Scholar

Mohammadi S.A., Prasanna B.M. 2003. Analysis of genetic diversity in crop plants-Salient statistical tools and considerations. Crop Sci. 43:1235 — 1248.
Google Scholar

Pearson K. 1901. On lines planes of closest fit to a system of points in space. Philosophical Magazine 2:557 — 572.
Google Scholar

Perez-Gonzalez S. 1992. Associations among morphological and phenological characters representing apricot germplasm in Central Mexico. J. Am. Soc. Hort. Sci. 117:486 — 490.
Google Scholar

Rao C.R. 1964. The Use and Interpretation of Principal Components in Applied Research. Sankhya A26:329 — 358.
Google Scholar

Rojas W., Barriga P., Figueroa H. 2000. Multivariate analysis of the genetic diversity of Bolivian quinoa germplasm. Plant Genetic Resources Newsletter 122: 16 — 23.
Google Scholar

Rotondi A., Magli M., Ricciolini C., Baldoni L. 2003. Morphological and molecular analyses for the characterization of a group of Italian olive cultivars. Euphytica 132: 129 — 137.
Google Scholar

SAS/STAT User's Guide, Version 8.2. 2002. SAS Institute, Cary NC.
Google Scholar

Skinner D.Z., Barchan G.R., Auricht G., Hughes S. 1999. A method for the efficient management and utilization of large germplasm collections. Crop Sci. 39:1237 — 1242.
Google Scholar

Ukalska J., Mądry W., Ukalski K., Masny A., 2007. Wielowymiarowa ocena różnorodności fenotypowej w kolekcji zasobów genowych truskawki (Fragaria × ananassa Duch.) Część I: analiza zmienności. Zesz. Probl. Post. Nauk Rol. 517: 749 — 758.
Google Scholar

Ukalski K., Ukalska J., Śmiałowski T., Mądry W. 2008. Badanie zmienności i współzależności cech użytkowych w kolekcji roboczej pszenicy ozimej (Triticum aestivum L.) za pomocą metod wielowymiarowych. Część I. Korelacje fenotypowe i genotypowe. Biul. IHAR 249: 35 — 43.
Google Scholar

Upadhyaya H. D., Mallikarjuna Swamy B. P., Kenchana Goudar P. V., Kullaiswamy B.Y., Singh S. 2005. Identification of diverse groundnut germplasm through multienvironment evaluation of a core collection for Asia. Field Crops Research 93: 293 — 299.
Google Scholar


Published
2008-09-30

Cited by

Ukalska, J. (2008) “An examination of diversity and interrelationships among traits in a winter wheat (Triticum aestivum L.) germplasm collection by multivariate methods. Part II. Principal component analysis using the phenotypic and genotypic correlation matrix”, Bulletin of Plant Breeding and Acclimatization Institute, (249), pp. 45–57. doi: 10.37317/biul-2008-0032.

Authors

Joanna Ukalska 
joanna_ukalska@sggw.edu.pl
Katedra Biometrii, Szkoła Główna Gospodarstwa Wiejskiego, Warszawa Poland

Authors

Krzysztof Ukalski 

Katedra Biometrii, Szkoła Główna Gospodarstwa Wiejskiego, Warszawa Poland

Authors

Tadeusz Śmiałowski 

Instytut Hodowli i Aklimatyzacji Roślin, Zakład Roślin Zbożowych w Krakowie Poland

Authors

Wiesław Mądry 

Katedra Doświadczalnictwa i Bioinformatyki, Szkoła Główna Gospodarstwa Wiejskiego, Warszawa Poland

Statistics

Abstract views: 180
PDF downloads: 20


License

Copyright (c) 2008 Joanna Ukalska, Krzysztof Ukalski, Tadeusz Śmiałowski, Wiesław Mądry

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Upon submitting the article, the Authors grant the Publisher a non-exclusive and free license to use the article for an indefinite period of time throughout the world in the following fields of use:

  1. Production and reproduction of copies of the article using a specific technique, including printing and digital technology.
  2. Placing on the market, lending or renting the original or copies of the article.
  3. Public performance, exhibition, display, reproduction, broadcasting and re-broadcasting, as well as making the article publicly available in such a way that everyone can access it at a place and time of their choice.
  4. Including the article in a collective work.
  5. Uploading an article in electronic form to electronic platforms or otherwise introducing an article in electronic form to the Internet or other network.
  6. Dissemination of the article in electronic form on the Internet or other network, in collective work as well as independently.
  7. Making the article available in an electronic version in such a way that everyone can access it at a place and time of their choice, in particular via the Internet.

Authors by sending a request for publication:

  1. They consent to the publication of the article in the journal,
  2. They agree to give the publication a DOI (Digital Object Identifier),
  3. They undertake to comply with the publishing house's code of ethics in accordance with the guidelines of the Committee on Publication Ethics (COPE), (http://ihar.edu.pl/biblioteka_i_wydawnictwa.php),
  4. They consent to the articles being made available in electronic form under the CC BY-SA 4.0 license, in open access,
  5. They agree to send article metadata to commercial and non-commercial journal indexing databases.

Most read articles by the same author(s)

1 2 3 4 5 > >> 

Similar Articles

<< < 38 39 40 41 42 43 44 45 46 47 48 49 50 > >> 

You may also start an advanced similarity search for this article.