Revelation of Genetic Diversity in Advanced Breeding Lines of Soybean using Principal Component Analysis

Abstract

The present investigation was carried out to examine the genetic variability and important yield-affecting characteristics of soybean (Glycine max (L.) Merrill) using Principal Component Analysis (PCA). The experiment was conducted at JNKVV, Jabalpur, during the Kharif season of 2022, with 30 advanced breeding lines and five national checks in a randomized block design with three replications. PCA helped us to simplify the data and identify the most essential traits contributing to yield. The analysis revealed that three principal components (PCs) with eigenvalues greater than 1.00 explained 86.3% of the total variability among the studied traits. PC1, which accounted for 67.7% of the variation, was linked to seed yield per plant, primary branches per plant, days to maturity, days to 50% flowering, number of seeds per plant, number of pods per plant, harvest index, and biological yield per plant. PC2 and PC3, contributing 10.0% and 8.6% of the variation, respectively, were associated with the number of clusters per plant, pods per plant, 100-seed weight, and days to 50% flowering. High PC scores in these components indicated genotypes with significant variability, notably JS 25-37, JS 25-31, JS 25-39 for PC1, JS 25-22, JS 25-47 for PC2, and JS 25-45, JS 25-33 for PC3. These findings underscore the importance of these genotypes and traits for targeted breeding programs to enhance soybean yield.

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