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

Abstract

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.