Hostname: page-component-5c6d5d7d68-wbk2r Total loading time: 0 Render date: 2024-08-07T10:06:34.890Z Has data issue: false hasContentIssue false
  • English
  • Français

Selection of promising genotypes based on path and cluster analyses

Published online by Cambridge University Press:  23 November 2007

M. KOZAK ,
J. BOCIANOWSKI  and
W. RYBIŃSKI
M. KOZAK*
Affiliation:
Department of Biometry, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland
J. BOCIANOWSKI
Affiliation:
Department of Mathematical and Statistical Methods, August Cieszkowski Agricultural University, Wojska Polskiego 28, 60-637 Poznań, Poland
W. RYBIŃSKI
Affiliation:
Institute of Plant Genetics PAS, Strzeszyńska 34, 60-479 Poznań, Poland
*
*To whom all correspondence should be addressed. Email: m.kozak@omega.sggw.waw.pl
Get access Rights & Permissions [Opens in a new window]

Summary

The objective of the present paper was to propose a statistical approach to support selection of the most promising genotypes in a breeding programme. The approach is based on applying two state-of-the-art statistical methodologies, likelihood-based path analysis and model-based cluster analysis. The first method is applied to find a causal mechanism lying behind a biological process of development of final crop yield. These results are then used for weighting traits to be used in cluster analysis, which helps select genotypes possessing a desirable level of yield and yield-contributing traits. An application of the approach is presented for a 2-year study on 22 grasspea genotypes, two cultivars (Derek and Krab) and 20 mutants from those cultivars. Seed yield/plant and seven yield-related traits were studied. Among these, plant height, number of branches/plant, pod length and number of seeds/plant determined seed yield; number of pods/plant influenced seed yield only for 2002. These results were used for appropriate weighting in cluster analysis, which indicated that cultivar Krab and its two mutants, K3 and K64, had the best level of the traits and were the most stable genotypes.

Type
Crops and Soils
Information
The Journal of Agricultural Science , Volume 146 , Issue 1 , February 2008 , pp. 85 - 92
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ahmad, Q., Rana, M. A. & Siddiqui, S. U. H. (1991). Sunflower seed yield as influenced by some agronomic and seed characters. Euphytica 56, 137–142.CrossRefGoogle Scholar
Allkin, R., McFarlane, T. D., White, R. J., Bisby, F. A. & Adey, M. E. (1983). Names and Synonyms of Species and Subspecies in the Vicieae. Issue 2 of the Vicieae Database Project. Publication No. 2. Southampton, UK: University of Southampton.Google Scholar
Baye, T. & Becker, H. C. (2005). Genetic variability and interrelationship of traits in the industrial oil crop Vernonia galamensis. Euphytica 142, 119–129.CrossRefGoogle Scholar
Bridge, J. (2000). Nematodes of bananas and plantains in Africa: research trends and management strategies relating to the small-scale farmer. Acta Horticulturae 540, 391–408.CrossRefGoogle Scholar
Campbell, C. G., Mehra, R. B., Agrawal, S. K., Chen, Y. Z., El Moneim, A. M. A., Khawaja, H. I. T., Yadov, C. R., Tay, J. U. & Araya, W. A. (1994). Current status and future strategy in breeding grasspea (Lathyrus sativus L.). Euphytica 37, 167–175.Google Scholar
Diz, D. A., Wofford, D. S. & Schank, S. C. (1994). Correlation and path-coefficient analyses of seed-yield components in pearl millet×elephantgrass hybrids. Theoretical and Applied Genetics 89, 112–115.CrossRefGoogle ScholarPubMed
Everitt, B. S., Landau, S. & Leese, M. (2001). Cluster Analysis, 4th edn.London: Edward Arnold.Google Scholar
Fox, J. (2006). Structural equation modeling with the sem package in R. Structural Equation Modeling 13, 465–486.CrossRefGoogle Scholar
Fraley, C. & Raftery, A. E. (1998). How many clusters? Which clustering method? Answers via model-based cluster analysis. The Computational Journal 41, 578–588.CrossRefGoogle Scholar
Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97(458), 611–631.CrossRefGoogle Scholar
Fraley, C. & Raftery, A. E. (2006). Model-based methods of classification: using the mclust software in chemometrics. Journal of Statistical Software 18(6).Google Scholar
Gozdowski, D., Kozak, M., Kang, M. S. & Wyszyński, Z. (2007). Dependence of grain weight of spring barley genotypes on traits of individual stems. Journal of Crop Improvement 21(1/2), 223–233.CrossRefGoogle Scholar
Gravois, K. A. (1998). Optimizing selection for rough rice yield, head rice, and total milled rice. Euphytica 101, 151–156.CrossRefGoogle Scholar
Gűler, M., Adak, M. S. & Ulukan, H. (2001). Determining relationships among yield and some yield components using path coefficient analysis in chickpea (Cicer arietinum L.). European Journal of Agronomy 14, 161–166.CrossRefGoogle Scholar
Jiao, C.-J., Xu, Q.-L., Wang, C.-Y., Li, F.-M., Li, Z.-X. & Wang, Y.-F. (2006). Accumulation pattern of toxin β-ODAP during lifespan and effect of nutrient elements on β-ODAP content in Lathyrus sativus seedlings. Journal of Agricultural Science, Cambridge 144, 369–375.CrossRefGoogle Scholar
Kozak, M. & Kang, M. S. (2006). Note on modern path analysis in application to crop science. Communications in Biometry and Crop Science 1, 32–34.Google Scholar
Kozak, M. & Mądry, W. (2006). Note on yield component analysis. Cereal Research Communications 34, 933–940.CrossRefGoogle Scholar
Lambein, F. & Kuo, Y. H. (1997). Lathyrus sativus, a neolithic crop with a modern future? An overview of a present situation. In Lathyrus sativus – Cultivation and Nutritive Value in Animals and Human (Eds Zwierząt, Instytut & Rolniczego, Ośrodek), pp. 6–11. Poland: Lublin-Radom.Google Scholar
Milczak, M., Pędziński, M., Mnichowska, H., Szwed-Urbaś, K. & Rybiński, W. (2001). Creative breeding of grasspea (Lathyrus sativus L.) in Poland. Lathyrus Lathyrism Newsletter 2, 85–88.Google Scholar
Mohammadi, S. A., Prasanna, B. M. & Singh, N. N. (2003). Sequential path model for determining interrelationships among grain yield and related characters in maize. Crop Science 43, 1690–1697.CrossRefGoogle Scholar
Quinn, G. P. & Keough, M. J. (2002). Experimental Design and Data Analysis for Biologists. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Rabiei, B., Valizadeh, M., Ghareyazie, B. & Moghaddam, M. (2004). Evaluation of selection indices for improving rice grain shape. Field Crops Research 89, 359–367.CrossRefGoogle Scholar
R Development Core Team (2006). R: a Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available online at http://www.et.bs.ehu.es/soft/fullrefman.pdf (verified 20/8/07).Google Scholar
Rybiński, W. (2003). Mutagenesis as a tool for improvement of traits in grasspea (Lathyrus sativus L.). Lathyrus Lathyrism Newsletter 3, 27–31.Google Scholar
Rybiński, W. & Bocianowski, J. (2006). Variability of morphological traits and yield structure of grasspea mutants (Lathyrus sativus L.). Biuletyn Instytutu Hodowli i Aklimatyzacji Roslin 240/241, 291–297 (in Polish).Google Scholar
Rybiński, W. & Pokora, L. (2002). The influence of helium-neon laser and chemomutagen (MNU) on variability of traits in M1 progeny of grasspea (Lathyrus sativus L.). Acta Agrophysica 62, 127–134.Google Scholar
Shapiro, S. S. &. Wilk, M. B (1965). An analysis of variance test for normality (complete samples). Biometrika 52, 591–611.CrossRefGoogle Scholar
Shipley, B. (1997). Exploratory path analysis with applications in ecology and evolution. American Naturalist 149, 1113–1138.CrossRefGoogle ScholarPubMed
Shipley, B. (2002). Cause and Correlation in Biology. A User's Guide to Path Analysis, Structural Equations and Causal Inference. Cambridge, UK: Cambridge University Press.Google Scholar
Singh, B. U., Padmaja, P. G. & Seetharama, N. (2004). Stability of biochemical constituents and their relationships with resistance to shoot fly, Atherigona soccata (Rondani) in seedling sorghum. Euphytica 136, 279–289.CrossRefGoogle Scholar
Topal, A., Aydin, C., Akgűn, N. & Babaoglu, M. (2004). Diallel cross analysis in durum wheat (Triticum durum Desf.): identification of best parents for some kernel physical features. Field Crops Research 87, 1–12.CrossRefGoogle Scholar
Vargas, M., Crossa, J., Reynolds, M. P., Dhungana, P. & Eskridge, K. M. (2007). Structural equation modelling for studying genotype×environment interactions of physiological traits affecting yield in wheat. Journal of Agricultural Science, Cambridge 145, 151–161.CrossRefGoogle Scholar
Vaz Patto, M. C., Skiba, B., Pang, E. C. K., Ochatt, S. J., Lambein, F. & Rubiales, D. (2006). Lathyrus improvement for resistance against biotic and abiotic stresses: from classical breeding to marker assisted selection. Euphytica 147, 133–147.CrossRefGoogle Scholar
Waghmare, V. N. & Mehra, R. B. (2000). Induced mutations in grasspea (Lathyrus saticus L.). Lathyrus Lathyrism Newsletter 1, 21–24.Google Scholar
Wright, S. (1921). Correlation and causation. Journal of Agricultural Research 20, 557–585.Google Scholar