Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

2021 ◽  
Author(s):  
S.R. Singh ◽  
S. Rajan ◽  
Dinesh Kumar ◽  
V.K. Soni
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Single Linkage ◽  
Pod Yield ◽  
Diversity Assessment ◽  
Single Linkage Cluster ◽  
Linkage Cluster

Background: Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement.Methods: Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity.Result: Principal component analysis revealed that first five principal components possessed Eigen value greater than 1, cumulatively contributed greater than 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.

↵Genetic Diversity and Principal Component Analysis in Mungbean [Vigna radiata (L.) Wilczek] under Rainfed Condition

2021 ◽  
Author(s):  
Deepak Gupta ◽  
Suresh Muralia ◽  
N.K. Gupta ◽  
Sunita Gupta ◽  
M.L. Jakhar ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Seed Yield ◽  
Agricultural Research ◽  
Principal Component ◽  
Component Analysis ◽  
Grain Legume ◽  
Research Station ◽  
Rainfed Condition

Background: Mungbean is a short duration grain legume widely grown in south and Southeast Asia. The extent of variability through Principal Component Analysis (PCA) and cluster analysis in promising mungbean genotypes should be known for possible yield improvement. A study was undertaken to work out the extent of variability among twenty four mungbean genotypes through cluster analysis and Principal Component Analysis (PCA). Methods: The experiment was laid out in a randomized block design with three replications during kharif 2018 and 2019 at the experimental field of Agricultural Research Station, Navgaon (Alwar) under rainfed condition. Result: Principal component analysis revealed that the first three main PCAs amounted 78.80% of the total variation among genotypes for different traits. Out of total principal components, PC1 accounts for maximum variability in the data with respect to succeeding components. Number of branches per plant (28.62%), number of clusters per plant (23.55%) and seed yield (15.58%) showed maximum per cent contribution towards total genetic divergence on pooled basis. Cluster analysis showed that genotypes fall into seven different clusters and their inter and intra cluster distance showed genetic diversity between different genotypes. The maximum number of genotypes i.e., 8 was found in cluster II followed by cluster III comprising of 6 genotypes. Genotypes RMG-1138 and IPM-02-03 representing the mono genotypic cluster signifies that it can be the most diverse variety and it would be the appropriate genotype for hybridization with ones present in other clusters to tailor the agriculturally important traits and ultimately to boost the seed yield in mungbean under rainfed conditions.

Genetic variability determination in garden pea (Pisum sativum L sub sp. hortense Asch. and Graebn.) by using the multivariate analysis

2017 ◽  
Author(s):  
S.R. Singh ◽  
N. Ahmed ◽  
D. B. Singh ◽  
K. K. Srivastva ◽  
R. K. Singhand Abid Mir
Keyword(s):  
Genetic Variability ◽  
Plant Height ◽  
Principal Component ◽  
Single Linkage ◽  
Pod Yield ◽  
Yield And Quality ◽  
Single Linkage Cluster ◽  
Positive Traits ◽  
Linkage Cluster ◽  
Pod Length

A total of 32 genotypes collected from different geographical areas evaluated at one site to determine the genetic variability. Considerable diversity was found in different traits of horticultural importance. Principal component analysis showed more than 84 % of total multivariate variation for important traits in different genotypes. Pod yield, pod length,10 pods weight and pods per plant were found to be major traits contributing towards principal component-I. Similarly, seeds per pod, secondary branches/plant, pod length were main positive contributing traits towards second component. Ten pod weight, pod width were positively contributing component towards principal component third. Pods per plant and shelling percentage were main traits contributing to principal component-IV, where as plant height, pod length and pod width were major positively contributing traits towards principal component -V. PS-1100, Meethi Phali, PB-87 and FC-1 were most divergent genotypes. On the basis of cluster mean of single linkage cluster analysis, Custer-I was best for number of primary branches and shelling percentage, Cluster -II for plant height,10 pods weight and pod yield per plant, cluster-III for number of pods /plants and cluster IV for pod length. Selection of genotypes from divergent clusters and components having more than one positive traits for hybridization programme may lead to improvement in yield and quality of pea.

scholarly journals Assessment of genetic variability for micronutrient content and agro-morphological traits in rice (Oryza sativa L.)

2020 ◽  
Vol 80 (02) ◽  
Author(s):  
P. Madhubabu ◽  
R. Surendra ◽  
K. Suman ◽  
M. Chiranjeevi ◽  
R. Abdul Fiyaz ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Oryza Sativa L ◽  
Development Program ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Polished Rice ◽  
Iron And Zinc

Assessment of rice genetic diversity is critical step for trait specific varietal development program. In the present study, a collection of 281 Indian germplasm accessions were evaluated for genetic diversity study using 30 agro-morphological characters and grain iron and zinc contents in brown and polished rice. To identify the pattern of relatedness and associations, cluster analysis and principal component analysis coupled with correlation were used. Cluster analysis grouped 281 accessions into six main groups. Cluster 4 is the largest and had accessions with higher yield, zinc and iron content. Six components of principal component analysis indicated 76.4% of the total variation. The Principal Component (PC)1 showed 19.05%, while, PC2, PC3, PC4, PC5 and PC6 exhibited 14.23%, 13.61%, 11.58%, 7.59%, and 6.71% variability, respectively. Among the germplasm, three accessions IC145407, IC145357 and IC248034 have shown significant iron and zinc content in polished rice along with desirable grain yield. The information presented here will be useful in the development of rice varieties with high yield and micronutrient content.

scholarly journals GENETIC DIVERSITY AND PARENTS SELECTION FROM EXOTIC RICE (Oryza sativa L.) GERMPLASM

2012 ◽  
Vol 25 (1) ◽  
pp. 11-16
Author(s):  
A. A. Mamun ◽  
N. A. Ivy ◽  
M. G. Rasul ◽  
M. M. Hossain
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Oryza Sativa ◽  
Oryza Sativa L ◽  
Principal Component ◽  
Component Analysis ◽  
Rice Genotypes ◽  
Total Divergence ◽  
Five Axes

Genetic divergence among fifty exotic rice genotypes along with two check varieties were estimated using D2 and principal component analysis. The study was undertaken to select suitable donor parents for use in improved breeding program of Bangabandhu Sheikh Mujibur Rahman Agricultural University in 2009. Principal component analysis (PCA) revealed that the first five axes accounted for 58.10% of the total variation. As per cluster analysis, the genotypes were grouped into seven clusters consisting 11, 16, 7, 11, 1, 2 and 4 genotypes which revealed that there exist considerable diversity among the genotypes. Considering the magnitude of genetic distance, contribution of different characters towards the total divergence and magnitude of cluster means for different characters, the genotypes RG-BU-08-057, 61, 65, 67, 69, 71, 85, 86, 88, 94, 96, 98 and 99 might be selected as a suitable parent for future hybridization program.DOI: http://dx.doi.org/10.3329/bjpbg.v25i1.17007

scholarly journals Assessment of Genetic Divergence in Long Day Onion (Allium cepa L.) through Principal Component and Single Linkage Cluster Analysis

2020 ◽  
Vol 15 (1) ◽  
pp. 17-26
Author(s):  
Singh S R ◽  
Ahamed N ◽  
Srivastava K K ◽  
Kumar D ◽  
Yousuf S
Keyword(s):  
Cluster Analysis ◽  
Principal Component ◽  
Single Linkage ◽  
Long Day ◽  
Wide Range ◽  
Positive Loading ◽  
High Positive Loading ◽  
Single Linkage Cluster ◽  
Linkage Cluster ◽  
Bulb Yield

To assess the nature and magnitude of genetic diversity in long day onion germplasm by using the principal component analysis and single linkage cluster analysis an experiment was carried out with 34 onion genotypes. High coefficient of variation with wide range in traits indicated an appreciable variability in germplasm. Genotypes were classified into seven principal components having Eigen value > 1, cumulatively accounted for 83.87% of total variability. Principal Component - I contributed for 24.73% of total variation for followed by principal component-II (15.27%). PC-I had high positive loading for bulb weight (0.401), marketable yield (0.338), total bulb yield (0.401) and PC-II had high positive loading for plant height (0.412), PC-III for high T.S.S. (0.276) PC -IV for A grade bulbs (0.436), PC-V for polar diameter of bulbs (0.514), PC-IV negatively loaded with purple blotch (-0.461) and PC-VII for narrow neck thickness (-0 .515). Plotting PC-I aganist PC-II differenciated CITH-O-13, CITH-O-4, CITH-O-22, CITH-O-19, CITH-O-9, CITH-O-6 and CITH-O2 as most divergent genotype.On the basis of single linkage cluster means cluster-I was most importent for average bulb weight, minimum bolters, high marketble bulb percentage high marketable and total bulb yield whereas cluster -II was important for maximum nuber of leaves/plant and minimum neck thicknes. Highest inter-cluster distance was observed between cluter II and Cluster-I(873.5% ).Most divergent genotypes with high inter cluter distance could be the most appropriate parents for crop impovement in onion.

scholarly journals Characterization and Evaluation of Mungbean [Vigna radiata (L.) Wilczek] Germplasm through Morphological and Agronomic Characters

2020 ◽  
Author(s):  
K. S. Win ◽  
S. Win ◽  
T. M. Htun ◽  
N. K.K. Win
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Plant Height ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Flowering Plant ◽  
Agronomic Characters ◽  
Improvement Program

The study was conducted to assess the genetic diversity based on morphological and agronomic characters among 185 mungbean accessions by multivariate analysis such as cluster analysis and principal component analysis. The results exhibited that hierarchical cluster analysis divided into 7 clusters among the germplasm. The maximum number of accessions was observed in Cluster I with 60 accessions followed by Cluster II and Cluster III consists of 42 and 38 accessions, respectively. Cluster IV and VI comprised 18 accessions each whereas each of Cluster V and VII involved 5 accessions. Principal component analysis provided that the first three principal components accounted for 78.06 % of the total variability of agronomic characters. Among the study of agronomic characters, days to 50% flowering, days to maturity, plant height at flowering, plant height at maturity, number of pod bearing branches per plant and 100 seed weight were contributed with the first principal component (PC1) whereas were the number of cluster per plant, pod per plant and yield per plant with PC2 and seed per pod and pod length with PC3, respectively. According to the findings of this research, the significant presence of genetic diversity was presented among the tested mungbean accessions and provides a good chance for the selection of parents for the improvement program.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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