Dissecting the Genetic Variability in Wheat (Triticum aestivum L.) Genotypes Using Univariate and Multivariate Statistical Approaches

The univariate and multivariate statistical approaches were used to investigate the genotypic and phenotypic variability in ten wheat accessions. Eleven traits assessed during investigation were days to 50% flowering (DTF), area of flag leaf, plant height (PH), spike length, number of spikelets/spike (NSPS), number of grains/spike, days to maturity, 1000-grains weight (TSW), economic yield/plant (EY), biological yield/plant (BY), and harvest index (HI). The univariate analyses revealed significant variation among the evaluated genotypes for all the estimated traits. A positive correlation and significant associations of yield with DTF, BY, NSPS, and HI were observed, highlighting these traits as crucial for selection. High genetic coefficients of variation (GCV) were observed for EY, HI, and TSW, whereas other traits showed moderate-to-low GCV. The phenotypic coefficient of variation exceeded the GCV for each trait under study, indicating substantial environmental influences. Traits reflecting high heritability (h2) and genetic advances, namely PH, TSW, BY, EY, and HI, suggested substantial additive gene effects, making early-generation selection effective. Studies on multivariate analyses association through principal component analysis (PCA) and hierarchical cluster analyses categorized wheat accessions into distinct groups, revealing trait associations and genetic diversity. PCA-biplot analysis identified genotypes 1 (HTWYT/2020-21/48), 5 (ESWYT/2020-21/107), 10 (HTWYT/2020-21/39), and 4 (CWYT/2020-21/612) as the most diverse. These statistical approaches provide a comprehensive understanding of wheat genetic and phenotypic variability, offering valuable insights for yield improvement in breeding programs. The study emphasizes integrating multiple statistical techniques to enhance genotype selection precision and wheat breeding efficiency.. KEYWORDS :Cluster analysis, Correlation coefficient, Genotypes, Multivariate analyses, Univariate analyses, Wheat