Classification of a collection of sesame germplasm using multivariate analysis
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J. Crop Sci. Biotech. 2016 (JUNE) 19 (2) : 151~155 DOI NO. 10.1007/s12892-015-0099-4 RESEARCH ARTICLE
Classification of a Collection of Sesame Germplasm using Multivariate Analysis Kang Bo Shim*, Seong Hyu Shin, Ji Young Shon, Shin Gu Kang, Woon Ho Yang, Sung Gi Heu Crop Cultivation & Environment Research Division, NICS, Suwon 4411-707, Korea Received: October 1. 2015 / Revised: October 30. 2015 / Accepted: November 5. 2015 Ⓒ Korean Society of Crop Science and Springer 2016
Abstract Sesame (Sesamum indicum L.) is an important edible oil crop. Meteorological factors such as temperature, rainfall, and the amount of solar radiation determine the yield potential of sesame. To identify phenotypic diversity and to infer genotypic backgrounds in a collection of 250 sesame germplasm accessions, we classified the germplasm based on variation in morphological traits using principal component (PC) and cluster analysis. The sesame germplasm was grouped based on five PCs, which accounted for 82.3% of total variation. The first PC (PC1) was positively correlated with days to flowering, days to maturity, and number of capsules per plant, whereas the second PC (PC2) was negatively correlated with all characteristics except capsule-bearing stem length. The third component (PC3) was highly positively correlated with capsule length and plant height. We constructed a scatter diagram of the first two PCs (PC1 vs. PC2), revealing four distinct groups of eigenvectors. Most sesame germplasm was widely distributed among Groups I, II, III, and IV. Group III showed a wide range of distribution in the diagram. Otherwise, the distribution of the 250 germplasm accessions was more compact in a scatter diagram of PC1 vs. PC3 compared with PC1 vs. PC2. Groups I, II, III, and IV contained 142, 102, 2, and 3 sesame germplasm accessions, respectively. The two germplasm in Group III were collected from different regions, as were the three germplasm in Group IV. The results show that the distribution of sesame origin is wider than the regions examined in view of phenotypic diversity. Key words : cluster analysis, germplasm, principal component analysis, sesame
Introduction Sesame (Sesamum indicum L.) is one of the most important edible oil crops in Asia and Africa. Sesame originated in the African savanna. Meteorological factors such as temperature, rainfall, and the amount of solar radiation determine the yield potential of this crop. Various statistical techniques have been used to model crop yield, including correlation, regression, and cluster analysis. Principal component analysis (PCA) transforms a set of correlated variables into a new set of uncorrelated variables known as principal components (PCs), which are derived in decreasing order of importance. Thus, the first PC accounts for as much of the variance of the original data as possible, followed by the second, etc. (Richman 1986). Dr. Kangbo Shim (
) E-mail: [email protected] Tel: +82-31-695-4091; Fax: +82-31-695-4095
The Korean Society of Crop Science
Cluster analysis (CA), which is co
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