Rendering Multivariate Statistical Models for Genetic Diversity Assessment in A-Genome Diploid Wheat Population

Abstract

Diversifying available natural resources to cope with abrupt climatic changes and the necessity to equalize rising agricultural production with improved ability to endure environmental influence is the dire need of the day. Inherent allelic variability regarding significant economic traits featuring both enhanced productivity and environmental adaptability is one such prominent need. To address this requirement, a series of analyses were conducted in this study for exploring natural diploid wheat germplasm resources. The current study involved 98 Recombinant Inbred Lines (RILs) populations developed by crossing two diploid ‘A’ sub-genome wheat species, Triticummonococcum and Triticum boeoticum, enriched with valuable alleles controlling, in particular, biotic and abiotic stresses tolerance. Their 12 phenotypic traits were explored to reveal germplasm value. All traits exhibited vast diversity among parents and RILs via multivariate analysis. Most of the investigated traits depicted significant (p < 0.05) positive correlations enlightening spikelet per spike, total biomass, seed weight per spike, number of seeds per spike, plant height, and days to heading as considerably focused traits for improving hexaploid wheat. Principal component analysis (PCA) exhibited 61.513% of total variation with three PCs for 12 traits. Clustering of genotypes happened in three clades, and the two parents were separated into two extreme clusters, validating their enrichment of diversity. This study provided beneficial aspects of parental resources rich in diverse alleles. They can be efficiently exploited in wheat improvement programs focusing on introgression breeding and the recovery of eroded genetic factors in currently available commercial wheat cultivars to sustain calamities of environmental fluctuations.

Keywords

• multivariate analyses
• principal component analysis
• cluster analysis
• genetic diversity
• RILs population
• yield attributes