~ 4834 ~ Journal of Pharmacognosy and Phytochemistry 2019; 8(3): 4834-4839 E-ISSN: 2278-4136 P-ISSN: 2349-8234 JPP 2019; 8(3): 4834-4839 Received: 28-03-2019 Accepted: 30-04-2019 Pavan Devesh Research Scholar Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India PK Moitra Principal Scientist Department of Genetics and Plant Breeding, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, Madhya Pradesh, India RS Shukla Principal Scientist Department of Genetics and Plant Breeding, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, Madhya Pradesh, India Suneeta Pandey Assistant Professor Department of Genetics and Plant Breeding, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, Madhya Pradesh, India Correspondence Pavan Devesh Research Scholar Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India Genetic diversity and principal component analyses for yield, yield components and quality traits of advanced lines of wheat Pavan Devesh, PK Moitra, RS Shukla and Suneeta Pandey Abstract The present research work comprises sixty advanced lines of wheat, collected from Wheat Improvement Project, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur. These genotypes were evaluated in a randomized complete block design (RCBD) with three replications at Seed Breeding Farm, Department of Genetics and Plant Breeding, College of Agriculture, Jabalpur during Rabi 2016-17. The presence of genetic divergence among these lines was observed by Mahalanobis’s D 2 statistic. All the accesses were grouped into five distinct clusters. The highest number of genotypes appeared in cluster I (45) followed by cluster III (8) and cluster IV (5) while the lowest numbers of entries were reported in cluster II (1) & V (1). Principal component analysis (PCA) indicated that the seven principal components (PC1 to PC7) showed 66.22% of the total variability. The highest value of yield and its attributing traits were present in PC3, PC2, and PC1 respectively, whereas PC4 & PC7 were related to quality traits. Keywords: Cluster analysis, PCA, protein%, Hectolitre weight, yield, and wheat Introduction Wheat is the most important cereal crop for the majority of the world's populations. It contributes substantially to national food security by providing more than 50% calories to the people who mainly depend on wheat. The Food and Agriculture Organization of the United Nations (FAO) gracefully project the worldwide acclaim sticking with wheat as human food. Wheat is a rich source of carbohydrate and it provides about 20% of the food resources of the world (Farzi and Bigloo 2010) [5] . There are seventeen different species of wheat, out of which only three (i.e. Triticum aestivum, Triticum durum and Triticum dicoccum) are cultivated in the world. Triticum aestivum (bread wheat) is occupying more than 90% area followed by Triticum durum (9- 10%); however, very limited area of wheat is under Triticum dicoccum. India is the second largest wheat producer in the world and produced 86.50 million metric tonnes of wheat as per (FAO, 2016). Due to the high adaptation to varied environments, wheat is cultivated in almost all the states of India. The production of wheat has been increased manifold from 6.60 million tonnes at the time of independence to 97.44 million tonnes in 2016-17. The productivity has observed an increase of 473 per cent i.e. from 670 kg ha -1 to 3172 kg ha -1 during the above time period. Even with delayed sowing, the country recorded 30.71 million hectares acreage during rabi 2016-17 (Annual Report 2016-17, ICAR-IIWBR). Genetic diversity (D 2 statistic) developed by Mahalanobis (1928) [7] provides a measure of the magnitude of divergence between biological populations and the relative contribution of each component character to the total divergence (Nair and Mukherjee, 1960, Maurya and Singh, 1977) [10, 9] . Mahalanobis D 2 statistic is more reliable in the selection of potential parents for hybridization programme. The principal component analysis is intended to derive a small number of linear combinations (principal components) of a set of variables that retain many of the existing information in the original variables. Knowledge of Pattern of existing genetic variability, the trend of character association, identification of promising traits and extent of genetic divergence will definitely help the researcher to identify high yielding as well as quality attributing traits wheat lines. The objective of this study is to evaluate the potential genetic diversity among wheat genotypes by using cluster analysis and principal component analysis for selection of desired parents in hybridization programmes. Materials and methods Sixty advanced lines of wheat (10 Triticum durum and 50 Triticum aestivum) depicted in (Table 1) obtained from Wheat Improvement Project JNKVV, Jabalpur, were grown in