scholarly journals Morphological diversity in growth characteristics of Jatropha curcas l. accessions from South- West Nigeria

2021 ◽  
Vol 38 (1) ◽  
pp. 109-119
Author(s):  
G.A. Adebusuyi ◽  
O.F. Oyedeji ◽  
V.I. Alaje ◽  
I.L. Sowunmi ◽  
Y.A. Dunmade
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Total Variation ◽  
Jatropha Curcas ◽  
Principal Component ◽  
Morphological Diversity ◽  
Component Analysis ◽  
Southwestern Nigeria ◽  
Breeding Programmes ◽  
Cluster 2

Jatropha curcas is a multi-purpose tree with significant economic importance that has not been fully exploited due to lack of adequate breeding programme in Nigeria. Consequently upon this, 31 accessions collected from 4 states in Southwestern Nigeria were assessed for their morphological diversity in order to establish this as a bed rock for further breeding programmes. Data were collected on plant height, numbers of leaves and collar diameter; these were subjected to analysis of variance, principal component analysis and cluster analysis using Minitab version 17. The results showed significant differences (p≤0.05) among the 31 accessions assessed. Principal component analysis indicated that the first three axes contributed 97.8% of the total variation observed. The first axis accounted for 68% of the total variation while the second and third axes accounted for 24.7% and 5.1%, respectively, of the total variation recorded. Cluster analysis as well as the dendrogram revealed three distinct clusters of genetic similarities and differences. High genetic similarities were observed among accessions collected from the different states whereas some accessions collected from similar regions had low genetic similarities. Cluster 1 consisted of 21 genotypes with their characters falli ng below the grand mean. Cluster 2 had nine genotypes, they produced the highest values for all the characters assessed. Cluster 3 with only one genotype has its values below the ground mean. Members of cluster 2 have proven to be superior. The existence of morphological diversity offers potential for selection among the accessions in the breeding of J. curcas from southwestern Nigeria.

scholarly journals Grouping and clustering of maize Lancaster germplasm inbreds according to the results of SNP-analysis

2017 ◽  
Vol 8 (3) ◽  
pp. 343-348 ◽  
Author(s):  
K. V. Derkach ◽  
T. M. Satarova ◽  
V. V. Borysova ◽  
V. Y. Cherchel ◽  
B. V. Dzyubeckiy
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Total Variation ◽  
Inbred Line ◽  
Gene Diversity ◽  
Principal Component ◽  
Component Analysis ◽  
Inbred Lines ◽  
Snp Markers ◽  
Snp Analysis

The objective of this article is grouping and clustering of maize inbred lines based on the results of SNP-genotyping for the verification of a separate cluster of Lancaster germplasm inbred lines. As material for the study, we used 91 maize (Zea mays L.) inbred lines, including 31 Lancaster germplasm lines and 60 inbred lines of other germplasms (23 Iodent inbreds, 15 Reid inbreds, 7 Lacon inbreds, 12 Mix inbreds and 3 exotic inbreds). The majority of the given inbred lines are included in the Dnipro breeding programme. The SNP-genotyping of these inbred lines was conducted using BDI-III panel of 384 SNP-markers developed by BioDiagnostics, Inc. (USA) on the base of Illumina VeraCode Bead Plate. The SNP-markers of this panel are biallelic and are located on all 10 maize chromosomes. Their range of conductivity equals >0.6. The SNP-analysis was made completely in automated regime on Illumina BeadStation equipment at BioDiagnostics, Inc. (USA). The grouping of the studied set of 91 inbred lines according to allelic state of SNP-markers and identifying cluster of Lancaster germplasm inbred lines in general selection of inbreeds used principal component analysis. The clustering and determining hierarchy in 31 Lancaster germplasm inbreds used quantitative cluster analysis. The share of monomorphic markers in the studied set of 91 inbred lines equaled 0.7%, and the share of dimorphic markers equaled 99.3%. Minor allele frequency (MAF) > 0.2 was observed for 80.6% of dimorphic markers, the average indicator of shift of gene diversity equaled 0.2984, PIC on average reached 0.3144. The index of gene diversity of markers varied from 0.1701 to 0.1901, pairwise genetic distances between inbred lines ranged from 0.0316–0.8000, the frequencies of major alleles of SNP-markers were within 0.5085–0.9821, and the frequencies of minor alleles were within 0.0179–0.4915. The average homozygosity of inbred lines was 98.8%. The principal component analysis of SNP-distances confirmed the isolation of the Lancaster group within the general set of analyzed inbred lines. Two-dimensional component analysis showed that the first principal component (PCA1) accounted for 36.0% of total variation and divided the investigated set of 91 inbred lines into two fractions, while all the inbred lines which are considered Lancaster based on pedigree information were included in one of the fractions. The second principal component (PCA2), which accounted for 12.1% of total variation, separated most of the Lancaster germplasm inbred lines from the others in this fraction, although the overlapping of the locations of Lancaster and non-Lancaster inbred lines was observed. Qualitative cluster analysis of 31 Lancaster germplasm inbred lines allowed us to identify two clusters: the first one includes 23 inbred lines of Ukrainian selection and the well known Mo17 (77.4% of total number of analysed lines) inbred line, and the second cluster included 6 inbred lines of Ukrainian selection and the well known Oh43 (22.6% of total number of analysed lines) inbred line. The isolation of two clusters within Lancaster germplasm indicates the genetic diversity in this plasm. The evaluation of genome similarities through allelic states of SNP-markers can successfully be used as a data source for classification and systematization of the gene pool of maize inbred lines.

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.

STUDY ON PLANKTON OF DIFFERENT CATEGORIES OF LAKES IN SUMMER BY MEANS OF PRINCIPAL COMPONENT ANALYSIS, FACTOR ANALYSIS AND CLUSTER ANALYSIS

2010 ◽  
Vol 36 (1) ◽  
pp. 43-50
Author(s):  
Luo-Jun GONG ◽  
Shi-Ping ZHANG ◽  
Bang-Xi XIONG ◽  
Ding-Zhu LIU ◽  
Jin-Zhong LI ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
And Cluster Analysis

Identification of COVID-19 Clinical Phenotypes by Principal Component Analysis-Based Cluster Analysis

2020 ◽  
Author(s):  
Wenjing Ye ◽  
Lu Weiwei ◽  
Yanping Tang ◽  
Chen Guoxi ◽  
Li Xiaopan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Clinical Phenotypes

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.

scholarly journals Magnetic susceptibility to identify landscape segments on a detailed scale in the region of Jaboticabal, São Paulo, Brazil

2012 ◽  
Vol 36 (4) ◽  
pp. 1073-1082 ◽  
Author(s):  
Mariana dos Reis Barrios ◽  
José Marques Junior ◽  
Alan Rodrigo Panosso ◽  
Diego Silva Siqueira ◽  
Newton La Scala Junior
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Magnetic Susceptibility ◽  
Clay Fraction ◽  
Chemical Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Sao Paulo ◽  
São Paulo ◽  
Agricultural Potential

The agricultural potential is generally assessed and managed based on a one-dimensional vision of the soil profile, however, the increased appreciation of sustainable production has stimulated studies on faster and more accurate evaluation techniques and methods of the agricultural potential on detailed scales. The objective of this study was to investigate the possibility of using soil magnetic susceptibility for the identification of landscape segments on a detailed scale in the region of Jaboticabal, São Paulo State. The studied area has two slope curvatures: linear and concave, subdivided into three landscape segments: upper slope (US, concave), middle slope (MS, linear) and lower slope (LS, linear). In each of these segments, 20 points were randomly sampled from a database with 207 samples forming a regular grid installed in each landscape segment. The soil physical and chemical properties, CO2 emissions (FCO2) and magnetic susceptibility (MS) of the samples were evaluated represented by: magnetic susceptibility of air-dried fine earth (MS ADFE), magnetic susceptibility of the total sand fraction (MS TS) and magnetic susceptibility of the clay fraction (MS Cl) in the 0.00 - 0.15 m layer. The principal component analysis showed that MS is an important property that can be used to identify landscape segments, because the correlation of this property within the first principal component was high. The hierarchical cluster analysis method identified two groups based on the variables selected by principal component analysis; of the six selected variables, three were related to magnetic susceptibility. The landscape segments were differentiated similarly by the principal component analysis and by the cluster analysis using only the properties with higher discriminatory power. The cluster analysis of MS ADFE, MS TS and MS Cl allowed the formation of three groups that agree with the segment division established in the field. The grouping by cluster analysis indicated MS as a tool that could facilitate the identification of landscape segments and enable the mapping of more homogeneous areas at similar locations.

scholarly journals A phenetic study of the genus Rasahus Amyot & Serville (Heteroptera, Reduviidae)

1990 ◽  
Vol 1 (3) ◽  
pp. 131-144
Author(s):  
María Coscarón
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Morphological Characters ◽  
Basal Plate ◽  
Using Data ◽  
Diagnostic Importance

Cluster analysis by four methods and a principal component analysis were performed using data on 24 morphological characters of 27 species of the genus Rasahus (Peiratinae). The results obtained by the different techniques show general agreement. They confirm the present number of taxa and reveal the existence within the genus of three groups of species: scutellaris , hamatus and vittatus. The scutellaris group is constituted by R. aeneus (Walker), R. maculipennis (Lepelletier and Serville), R. bifurcatas Champion, R. castaneus Coscarón, R. guttatipennis (Stål), R. flavovittarus Stål, R. costarricensis Coscarón, R. scutellaris (Fabricius), R. atratus Coscarón, R. peruensis Coscarón, R. paraguayensis Coscarón, R. surinamensis Coscarón, R. albomaculatus Mayr, R. brasiliensis Coscarón and R. sulcicollis (Serville).The hamatus group contains R. rufiventris (Walker), R. hamatus (Fabricius), R. amapaensis Coscarón, R. arcitenens Stål, R. limai Pinto, R. angulatus coscarón, R. thoracicus Stål, R. biguttatus (Say), R. arcuiger (Stål), R. argentinensis Coscarón and R. grandis Fallou. The vittatus group contains R. vittatus Coscarón. The characters used to separate the groups of species are: shape of the pygophore, shape of the parameres, basal plate complexity, shape of the postocular region and hemelytra pattern. Illustrations of the structures of major diagnostic importance are included.

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