Phenotypic correlation network analysis of garlic variables

Anderson Rodrigo da Silva, Paulo Roberto Cecon, Mário Puiatti

Abstract


In this paper we applied weighted correlation networks in order to discover correlation structures and link patterns of sixteen garlic variables related to leaf, bulb and other vegetative and growth variables. By using the Fruchterman-Reingold algorithm, correlation clusters and other structures could be easily identified. Overall, we detected a link between clusters of leaf and bulb variables. The harvest index was negatively associated with vegetative variables, as expected. In addition, bulb growth rate was positively associated with leaf area rate, root growth rate and plant liquid assimilation rate.


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Epskamp, A., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D. & Borsboom, D. (2012). qgraph: Network Visualizations of Relationships in Psychometric Data. Journal of Statistical Software, 48(4), 1-18.

Evans, G. C. (1982). The quantitative analysis of plant growth. London: Blackwell Scientific Publications.

Fruchterman, T. & Reingold, E. (1991). Graph drawing by force-directed placement. Software – Practice & Experience, 21(11), 1129-1164.

Honorato, A. R. F. (2012). Avaliação de cultivares de alho na região de Mossoró-RN. (Dissertação de mestrado). Universidade Federal Rural do Semi-Árido, Brasil.

Kassahun, T., Akhilesh, T. & Kebede, W. (2010). Genetic variability, correlation and path coefficient among bulb yield and yield traits in Ethiopian garlic germoplasm. Indian Journal of Horticulture, 64(4), 489-499.

Langfelder, P. & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9(559), 1-13.

Mario, P. C., Viviana, B. V. & Marya, I. A. (2008). Low genetic diversity among garlic accessions detected using RAPD. Chilean Journal of Agricultural Research, 68(1), 3-12.

Olawuyi, O. et al. (2014). Accession × Treatment Interaction, Variability and Correlation Studies of Pepper (Capsicum spp.) under the Influence of Arbuscular Mycorrhiza Fungus (Glomus clarum) and Cow Dung. American Journal of Plant Sciences, 5(5), 683-690.

Puiatti, G. A. et al. (2013). Cluster analysis applied to nonlinear regression models selection for the description of dry matter accumulation of garlic plants. Revista Brasileira de Biometria, 31(3), 337-351.

R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Available from: . Accessed on 01/11/2014.

Reis, R. M. et al. (2014). Nonlinear regression models applied to clusters of garlic accessions. Horticultura Brasileira, 32(2), 178-183.

Singh, R. K., Dubey, B. K., Bhonde, S. R. & Gupta, R. P. (2011). Correlation and path coefficient studies in garlic (Allium sativum L.). Journal of Spices and Aromatic Crops, 20(2), 81–85.

Singh, S. R. et al. (2013). Character association and path analysis in garlic (Allium sativum L) for yield and its attributes. SAARC Journal of Agriculture, 11(1): 45-52.

Ursem, R., Tikunov, Y., Bovy, A., Berloo, R. & Eeuwijk, F. (2008). A correlation network approach to metabolic data analysis for tomato fruits. Euphytica, 161(1-2), 181–193.




DOI: http://dx.doi.org/10.33837/msj.v1i3.99

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Copyright (c) 2017 Anderson Rodrigo da Silva, Paulo Roberto Cecon, Mário Puiatti

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License: Creative Commons - Attribution 4.0 International.