Genetic Diversity Analysis in Bread Wheat (Triticum aestivum L.) Using Mahalanobis D2 Statistics
A. G. Rajput *
Department of Genetics and Plant Breeding, College of Agriculture, Latur, (VNMKV, Parbhani), India.
A. M. Misal
VNMKV, Parbhani, India.
A. V. Jadhav
Department of Genetics and Plant Breeding, College of Agriculture, Latur, (VNMKV, Parbhani), India.
S. M. Umate
Wheat and Maize Research Unit, Parbhani. (VNMKV, Parbhani), India.
P. B. Wadikar
Department of Genetics and Plant Breeding, College of Agriculture, Latur, (VNMKV, Parbhani), India.
*Author to whom correspondence should be addressed.
Abstract
The present investigation was undertaken to assess the genetic diversity among 48 genotypes of bread wheat (Triticum aestivum L.) using Mahalanobis D² statistics, with the aim of identifying genetically diverse and high-performing lines suitable for future breeding programs. The genotypes were evaluated under a randomized block design with two replications for ten quantitative traits at the farm of Wheat and Maize research Unit, Parbhani during rabi season 2023-24. Based on D² values, the genotypes were grouped into 11 distinct clusters, indicating the presence of substantial genetic variability. Cluster II included the maximum number of genotypes (17), followed by Cluster I with 15 genotypes and Cluster IV with 8 genotypes. The remaining clusters, namely Cluster III, Cluster V, Cluster VI, Cluster VII, Cluster VIII, Cluster IX, Cluster X, and Cluster XI, each comprised a single genotype. The maximum inter-cluster distance was observed between Cluster V and Cluster VII, indicating the genotypes in these clusters are genetically most diverse and could serve as ideal parents for hybridization programs aimed at exploiting heterosis or generating transgressive segregants. Cluster mean analysis revealed that Cluster VII recorded superior mean values for key traits such as grain yield per plant, 1000 grain weight and number of grains per spike, while Cluster III and Cluster VI had moderate performance for most characters. The traits contributing most to genetic divergence were number of grains per spike (20.17%), harvest index (15.68%), spike length (12.90%), and plant height (12.67%). The results suggest that genotypes from diverse clusters, particularly those showing high inter-cluster distances and superior trait performance, can be effectively used in crossing programs to improve yield and genetic variability in wheat.
Keywords: Wheat germplasm, genetic divergence, multivariate analysis, genotype clustering, plant breeding