Genetic Variability, Standardized Multiple Linear Regression and Principal Component Analysis to Determine Some Important Sesame Yield Components

Vina Eka Aristya, Taryono Taryono, Rani Agustina Wulandari


Sesame is an important commodity in supporting various industries such as low saturated fat oil producing and are often able to adapt under stressed grown conditions. Breeding sesame is undertaken to increase production and is possible by radiation induced polygenic characteristic changes with a gamma rays source. The study aims to identify the effectiveness of genetic variability, standardized multiple linear regression, and principal component analysis to determine some important sesame yield components for indirect selection. Eighteen sesame mutant lines (black and white types) were studied for eleven quantitative traits. Two sesame types were irradiated with eight doses (100-800 Gy) of gamma rays individually. Variability studies on seed yield and yield components are important raw material of high productivity for all studied traits. Standardized multiple linear regression analysis is the most effective way to provide information of relationship between seed yield and yield components in sesame mutant lines for indirect selection.


genetic variability; indirect selection; seed yield; sesame

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Akbar, F., Rabbani, M. A., Shinwari, Z. K., & Khan, S. J. (2011). Genetic divergence in sesame (Sesamum indicum L.) landraces based on qualitative and quantitative traits. Pakistan Journal of Botany, 43(6), 2737–2744. Retrieved from PDF

Ashri, A. (1994). Genetic resources of sesame: Present and future perspectives. In R. K. Arora & K. W. Riley (Eds.), Sesame biodiversity in Asia: Conservation, evaluation and improvement (pp. 25-39). New Delhi, IN: International Plant Genetic Resources Institute (IPGRI).

Begum, T., & Dasgupta, T. (2014). Induced genetic variability, heritability and genetic advance in sesame (Sesamum indicum L.). SABRAO Journal of Breeding and Genetics, 46(1), 21–33. Retrieved from J Breed Genet 46(1) 21-33 Begum & Dasgupta_r.pdf PDF

Biabani, A. R., & Pakniyat, H. (2008). Evaluation of seed yield-related characters in sesame (Sesamum indicum L.) using factor and path analysis. Pakistan Journal of Biological Sciences, 11(8), 1157–1160. Retrieved from PDF

Falconer, D. S. (1981). Introduction to quantitative genetics (2nd ed.). London, NY: Longmans Green.

Furat, S., & Uzun, B. (2010). The use of agro-morphological characters for the assessment of genetic diversity in sesame (Sesamum indicum L.). Plant Omic Journals, 3(3), 85–91. Retrieved from PDF

Gomez, K. A., & Gomez, A. A. (1984). Statistical procedures for agricultural research (2nd ed.). New York, USA: John Wiley & Sons, Inc.

IPGRI & NBPGR. (2004). Descriptors for Sesame (Sesamum spp.). Rome, IT: International Plant Genetic Resources Institute & New Delhi, IN: National Bureau of Plant Genetic Resources.

Jeffers, J. N. R. (1967). Two case studies in the application of principal component analysis. Journal of the Royal Statistical Society. Series C (Applied Statistics), 16(3), 225–236. crossref

Johnson, H. W., Robinson, H. F., & Comstock, R. E. (1955). Estimates of genetic and environmental variability in soybeans. Agronomy Journal, 47(7), 314-318. Retrieved from website

Kearsey, M.J., & Pooni, H. S. (1996). The genetical analysis of quantitative traits. Dordrecht, NL: Springer Science Business Media, B.V.

Knight, R. (1979). Quantitative genetics, statistics and plant breeding. In G. M. Halloran, R. Knight, K. S. McWhirter, & D. H. B. Sparrow (Eds.), Plant breeding (pp. 41-78). Brisbane, AU: Academy Press.

Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., & Schabenberger, O. (2006). SAS for mixed models (2nd ed.). Cary, NC: SAS Institute Inc.

Micke, A. (1996, June 1). 70 years induced mutations - To be reconsidered? Topic for discussion. Mutation Breeding Newsletter. No. 42, 22–25. Retrieved from PDF

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (5th ed.). New Jersey, USA: John Wiley & Sons, Inc. crossref

Narayanan, R., & Murugan, S. (2013). Studies on variability and heritability in sesame (Sesamum indicum L.). International Journal of Current Agricultural Research, 2(11), 52-55.

Parameshwarappa, S. G., Palakshappa, M. G., Salimath, P. M., & Parameshwarappa, K. G. (2009). Studies on genetic variability and character association in germplasm collection of sesame (Sesamum indicum L.). Karnataka Journal of Agricultural Sciences, 22(2), 252–254. Retrieved from website

Singh, R. K., & Chaudhary, B. D. (1979). Biometrical methods in quantitative genetic analysis. New Delhi, IN: Kalyani Publishers.

Siva, P. Y. V. N., Krishna, M. S. R., & Venkateswarlu, Y. (2013). Correlation, path analysis and genetic variability for economical characteristics in F2 and F3 generations of the cross AVT 3 × TC 25 in Sesamum (Sesamum indicum L.). Journal of Environmental and Applied Bioresearch, 1(2), 14–18. Retrieved from website

Steel, R. G. D., & Torrie, J. H. (1980). Principles and procedures of statistics: A biometrical approach (2nd ed.). Montréal, NY: McGraw-Hill Book Co.

Suja, K. P., Jayalekshmy, A., & Arumughan, C. (2004). Free radical scavenging behavior of antioxidant compounds of sesame (Sesamum indicum L.) in DPPH* system. Journal of Agricultural and Food Chemistry, 52(4), 912–915. crossref

Tripathi, A., Bisen, R., Ahirwal, R. P., Paroha, S., Sahu, R., & Ranganatha, A. R. G. (2014). Study on genetic in sesame (Sesamum indicum L.) germplasm based on morphological and quality traits. The Bioscan, 8(4), 1387–1391. Retrieved from Supplement/84Sup08 R BISEN_2467.pdf PDF

Weiss, E. A. (1971). Castor, sesame & safflower. London, UK: Leonard Hill Books.


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