Multivariate analysis for assessing the genetic diversity and association patterns of yield attributing traits in little millet (Panicum sumatrense)

Abstract

Little millet is an important small millet grown mostly in India. The availability of genetic variability is a critical requirement for crop improvement. Principal Component Analysis (PCA) and correlation study was undertaken in a set of 28 little millet genotypes to estimate genetic diversity, association pattern among seven quantitative traits and to select suitable genotypes for crop improvement. In cluster analysis, the genotypes were classified into three distinct clusters, each with one or two subgroups. Cluster I had the major genotypes with comparable ancestry, followed by Cluster II. The total variance was split into seven major principal components, with the top two PCs with eigenvalues greater than one accounting for 82.57% of the overall variation. PC1, which explained a larger part of the variance (58.05%), was strongly influenced by days to 50% flowering, days to maturity, number of productive tillers per plant, 1000-grain weight, and grain yield. PC2 was primarily influenced by plant height and fodder yield. PCA and association analysis revealed a significant positive association between grain yield and the number of productive tillers per plant, 1000-grain weight, plant height. These traits would be useful for direct selection for little millet crop improvement. In both cluster analysis and PCA, the genotypes DHLM 14-5, IIMR LM-8004, NDL LM1, TNPSu 242, VS 33, WV 168, DPLN 1 and VS 38 displayed diversity, implying that these lines may be used in crossing programme to generate further genetic variability to select suitable transgressive segregants.

Keywords

• little millet
• genetic diversity
• correlation
• principal component analysis
• biplot