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Principal Component Analysis in Plant Breeding

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Shantanu Das
Shantanu Das
Debojit Sarma at Assam Agricultural University
Debojit Sarma
Indrani Chakraborty
Indrani Chakraborty
Indrani Chakraborty
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Nabarun Roy at Galgotias University
Nabarun Roy
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Abstract

One of the important approaches to plant breeding is hybridization followed by selection. Appropriate parents selection is essential to be used in crossing nurseries to enhance the genetic recombination for potential yield increase (Islam, 2004). Thus, study of many morphological characters in germplasm is important for assessment of the differences among populations as well as for assessment of their breeding potential. A large number of variables are often measured by plant breeders, some of which may not be of sufficient discriminatory power for germplasm evaluation, characterization and management (Maji and Shaibu, 2012). In such case, principal component analysis (PCA) may be used to reveal patterns and eliminate redundancy in data sets (Adams, 1995; Amy and Pritts, 1991) as morphological and physiological variations routinely occur in crop species. Hotelling (1933) indicated that PCA is an exploratory tool to identify unknown trends in a multidimensional data set. The PCA or canonical root analysis is a multivariate statistical technique attempt to simplify and analyze the inter relationship among a large set of variables in term of a relatively a small set of variables or components without losing any essential information of original data set. The PCA reduce relatively a large series of data into smaller number of components by looking for groups that have very strong inter-correlation in a set of variables and each component explained per cent (%) variation to the total variability. The first principal component is the largest contributor to the total variation in the population followed by subsequent components. The criteria used by Clifford and Stephenson (1975) and corroborated by Guei et al. (2005), suggested that the first three principal components are often the most important in reflecting the variation patterns among accessions, and the Biomolecule Reports
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Biomolecule Reports- An International eNewsletter BR/09/17/03
Principal Component Analysis in Plant Breeding
Shantanu Das1,*, Soumitra Sankar Das2, Indrani Chakraborty3, Nabarun Roy4,
Mallar Kanti Nath5 and Debojit Sarma6
1, 6Department of Plant Breeding and Genetics, Assam Agricultural University, Jorhat
2Department of Agricultural Statistics, Uttar Banga Krishi Viswavidyalaya, Cooch Behar
3Department of Plant Breeding and Genetics, Chaudhary Charan Singh Haryana Agricultural
University, Hisar
4,5Department of Agricultural Biotechnology, Assam Agricultural University, Jorhat
*E-mail: shanubrdr.oryza@gmail.com
One of the important approaches to plant breeding is hybridization followed
by selection. Appropriate parents selection is essential to be used in crossing nurseries to
enhance the genetic recombination for potential yield increase (Islam, 2004). Thus, study of
many morphological characters in germplasm is important for assessment of the differences
among populations as well as for assessment of their breeding potential. A large number of
variables are often measured by plant breeders, some of which may not be of sufficient
discriminatory power for germplasm evaluation, characterization and management (Maji and
Shaibu, 2012). In such case, principal component analysis (PCA) may be used to reveal
patterns and eliminate redundancy in data sets (Adams, 1995; Amy and Pritts, 1991) as
morphological and physiological variations routinely occur in crop species. Hotelling (1933)
indicated that PCA is an exploratory tool to identify unknown trends in a multidimensional
data set.
The PCA or canonical root analysis is a multivariate statistical technique
attempt to simplify and analyze the inter relationship among a large set of variables in term of
a relatively a small set of variables or components without losing any essential information of
original data set. The PCA reduce relatively a large series of data into smaller number of
components by looking for groups that have very strong inter-correlation in a set of variables
and each component explained per cent (%) variation to the total variability. The first
principal component is the largest contributor to the total variation in the population followed
by subsequent components. The criteria used by Clifford and Stephenson (1975) and
corroborated by Guei et al. (2005), suggested that the first three principal components are
often the most important in reflecting the variation patterns among accessions, and the
Biomolecule Reports ISSN:2456-8759
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Das et al., BR/09/17/03
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Biomolecule Reports- An International eNewsletter BR/09/17/03
characters associated with these are more useful in differentiating the accessions. Thus it is
useful for genetic improvement of important traits having larger contributions to the
variability rather than going for all the characters under study.
In PCA data reduction technique constitute (1) extract the most important
information from the data table, (2) compress the size of the data set by keeping only the
important information, (3) simplify the description of the data set; and (4) analyze the
structure of the observations and the variables. Often, only the important information needs to
be extracted from a data matrix. In this case, the problem is to figure out how many
components need to be considered. This problem can be overcome by using some guideline.
A first procedure is to plot the eigenvalues according to their size and to see if there is a point
in this graph (‘elbow’) such that the slope of the graph goes from ‘steep’ to ‘‘flat’’ and to
keep only the components which are before the elbow. This procedure is called the scree or
elbow test (Jolliffe, 2002 and Cattell, 1966). Another standard tradition is to keep only the
components whose eigenvalue is larger than the average. For a correlation PCA, the standard
advice to ‘keep only the eigenvalues larger than 1 (Kaiser, 1961). However, this procedure
can lead to ignoring important information (O’Toole et al., 1993). Another methodology
include the amount of total variance explained (i.e. >80%) by the principle components
(Johnson and Wichern, 1992). Likewise, most important components can be extracted for
interpreting the result.
Since PCA extract all the important components and highlight their
contribution toward the total variability, it can be the choice as an important tool to speed up
the breeding programme.
Reference:
Adams, M.W. (1995). An estimate of homogeneity in crop plants with special reference to
genetic vulnerability in dry season. Phseolus vulgaris. Ephytica 26: 665-679.
Amy, E.L. and Pritts, M.P. (1991). Application of principal component analysis to
horticultural research. Hort Sci. 26(4): 334-338.
Cattell, R.B. (1966). The scree test for the number of factors. Multivariate Behav. Res. 1:
245-276.
Clifford, H.T. and Stephenson, W. (1975). An Introduction to Numerical Classification.
Academic Press, London. p. 229.
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Biomolecule Reports- An International eNewsletter BR/09/17/03
Guei, R.G.; Sanni, K.A. and Fawole, A.F.J. (2005). Genetic diversity of rice (O. sativa L.).
Agron. Afr. 5: 17-28.
Hotelling, H., (1933). Analysis of a complex of statistical variable into principal components.
J. Educ. Psych., 24: 417-441.
Islam, M.R. (2004). Genetic diversity in irrigated rice. Pakistan J. Biol. Sci. 2: 226-229
Johnson, R.A. and Wichern, D.W. (1992). Applied multivariate statistical analysis.Prentice-
Hall, Inc.
Jolliffe, I.T. (2002). Principal Component Analysis. New York: Springer.
Kaiser, H.F. (1961). A note on Guttman’s lower bound for the number of common factors.
Br. J. Math. Stat. Psychol. 14:1-2.
Maji, A.T. and Shaibu, A.A. (2012). Application of principal component analysis for rice
germplasm characterization and evaluation. J. Plant Breed. Crop Sci. 4(6): 87-
93.
O’Toole, A.J., Abdi, H., Deffenbacher, K.A. and Valentin, D. (1993). A low dimensional
representation of faces in the higher dimensions of the space. J. Opt. Soc. Am.
[Ser A] 10:405-411.
... This analysis can help plant breeders in the selection process by dividing large datasets into smaller components by looking for groups that are strongly inter-correlated to a variable set. Each component is described as a percentage of variation of the total variability (Das et al., 2017). Hence, the objectives of this study were to assess the agronomic and yield traits of six tenera oil palm progenies collected in Southern Thailand, and to group the progenies based on these traits. ...
... The components making a large contribution may assist plant breeders in identifying limiting traits that can be utilized in hybridization and selection processes (Sapey et al., 2017). The first three PCs are the most important in reflecting the variation patterns among variables, and the characteristics associated with these are more useful in differentiating the variables (Das et al., 2017). In this study, the tenera oil palm progenies were classified into few groups according to their agronomic and yield traits. ...
Principal component analysis of agronomic and yield traits of six tenera oil palm progenies in Southern Thailand
Article
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Agronomic and yield traits of oil palm progenies are regularly analyzed by plant breeders and farmers. The objectives of this study were to evaluate the agronomic and yield traits of six tenera oil palm progenies collected in Southern Thailand, and to group the progenies based on their agronomic and yield traits. The experiment used a completely randomized design with 6 treatments (progenies), where each treatment consisted of 3 replications. The agronomic and yield traits were analyzed for variances and principal components. The results found show that most agronomic and yield traits exhibited significant differences among the oil palm progenies. In the biplot of agronomic traits between PC1 and PC2 with two groups of oil palms, the first group dominated in petiole diameter, leaf dry weight, leaf length, rachis length, petiole width, and leaf area, while the second group dominated in leaf number and leaf width. In the biplot of yield traits between PC1 and PC2 with two groups of oil palms, the first group dominated in bunch number, kernels per fruit and bunch, and average kernel weight, while the second group dominated in amount of oil per fruit and bunch, average fruit weight, and oil yield.
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... Their findings showed that PC1 explained 19.25% of the total variation and was related to a combination of sugar-acid ratio, titratable acidity, and magnesium content; PC2 explained 16.16% of the variation and was associated with variations in Mo and Ni levels; and PC3 explained 12.79% of the variation and was linked to Cu and Cr contents. These findings suggests that PCA is an efficient tool for capturing the patterns of variation among accessions, which could be used to accelerate breeding techniques, as noted by Das et al. (2017). ...
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... PCA emerges as a powerful tool for breeders facilitating the selection of optimal parental combinations for crosses, material for trait-targeted selection, and the identification of rare or distinctive chemotypes for inclusion in breeding pools. In addition, tracking changes in PCA trait diversity over breeding cycles optimizes of selection strategies (Das et al. 2017). ...
Molecular and morphological characterization of wild Mentha langifolia L. accessions from Zagros Basin: application for domestication and breeding
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  • GENET RESOUR CROP EV
Mentha longifolia L. is an aromatic medicinal plant species in the Lamiaceae family. This study aimed to assess the diversity of 20 M. longifolia accessions collected from the Zagros Mountain range in Iran and Iraq using morphological and biochemical traits, as well as start codon targeted (SCoT) molecular markers. The evaluation of 24 quantitative and qualitative phenotypic traits revealed considerable variation, particularly for leaf size, stem features and inflorescence morphology. Substantial variation was also found for essential oil content (1.54–5.49%), total phenol content (TPC) (128.59–333.28 mg GAE/g DW), and antioxidant activity (IC50 0.44–0.58 μg/mL). Pearson correlation analysis revealed associations between essential oil percentage and leaf width, leaf number, stem length, and flower number. Principal component analysis (PCA) identified key components of variation including essential oil content, leaf morphology, TPC, and floral traits. SCoT marker analysis generated 86 fragments, with 71.52% polymorphism and an average polymorphism information content (PIC) of 0.34. The lack of geographical clustering among the M. longifolia accessions may be attributed, to some extent, to gene flow or genetic material exchange among populations. The principal coordinate analysis (PCoA) biplot based on the first two PCs further confirmed higher genetic variability among Iranian accessions than the Iraqi counterparts. Notably, the Boukan accession represented a higher genetic distance from other accessions, suggesting that it is genetically unique. The present research revealed significant phenotypic, biochemical, and molecular diversity among M. longifolia accessions from Iran and Iraq. The observed diversity provides opportunities for selecting favorable genotypes. The selected individuals can be used as parents in breeding programs, where controlled crosses and selection combine advantageous alleles to develop new cultivars with improved traits. Accordingly, accessions like Khabat and Harir with high essential oil content and antioxidant potential can contribute novel alleles in breeding programs.
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... Correlation between traits may result from pleiotropy or high linkage between genes controlling these traits [84]. The results of the correlation analysis indicated a significant positive correlation between seed yield and pod length (0.45**), pod width (0.60**), weight of 20 seeds (0.67**), number of seeds in pods (0.65**), number it can be used to test for differences in complex traits among groups of individuals by utilizing PC scores in univariate statistical analyses [81]. The PCA results revealed that the first six components accounted for 97.8% of the available variation, with the first two components explaining 57.16% of the total variation. ...
Assessing the effects of 24-epibrassinolide and yeast extract at various levels on cowpea’s morphophysiological and biochemical responses under water deficit stress
Article
Full-text available
  • Nov 2023
  • BMC PLANT BIOL
Background Due to the factor of water deficit, which has placed human food security at risk by causing a 20% annual reduction in agricultural products, addressing this growing peril necessitates the adoption of inventive strategies aimed at enhancing plant tolerance. One such promising approach is employing elicitors such as 24-epibrassinolide (EBR) and yeast extract, which are potent agents capable of triggering robust defense responses in plants. By employing these elicitors, crops can develop enhanced adaptive mechanisms to combat water deficit and improve their ability to withstand drought condition. This study investigates the impact of different levels of EBR (0, 5, 10 µm) and yeast extract (0 and 12 g/l) on enhancing the tolerance of cowpea to water deficit stress over two growing seasons. Results The findings of this study demonstrate that, the combined application of EBR (especially 10 µm) and yeast extract (12 g/l) can increase seed yield (18%), 20-pod weight (16%), the number of pods per plant (18%), total chlorophyll content (90%), and decrease malondialdehyde content (45%) in cowpea, compared to plants grown under water deficit stress without these treatments. Upon implementing these treatments, impressive results were obtained, with the highest recorded values observed for the seed yield (1867.55 kg/ha), 20-pod weight (16.29 g), pods number per plant (9), and total chlorophyll content (19.88 mg g⁻¹ FW). The correlation analysis indicated a significant relationship between the seed yield, and total chlorophyll (0.74**), carotenoids (0.82**), weight of 20 seeds (0.67**), and number of pods (0.90**). These traits should be prioritized in cowpea breeding programs focusing on water deficit stress. Conclusions The comprehensive exploration of the effects of EBR and yeast extract across various levels on cowpea plants facing water deficit stress presents a pivotal contribution to the agricultural domain. This research illuminates a promising trajectory for future agricultural practices and users seeking sustainable solutions to enhance crops tolerance. Overall, the implications drawn from this study contribute significantly towards advancing our understanding of plant responses to water deficit stress while providing actionable recommendations for optimizing crop production under challenging environmental conditions.
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... Indeed, PCA effectively distills the essential factors and elucidates their contributions to total diversity, making it a useful instrument for accelerating the breeding programme (41,42). In this case, the major variability is caused by the traits such as number of filled grains, panicle length, panicle weight and plant height in the PC1. ...
Assessment of variability parameters and diversity of panicle architectural traits associated with yield in rice (Oryza sativa L.)
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... Rice cultivars with identical characteristics are identified using cluster analysis. Principal component analysis (PCA) as described by Diako et al. (2011) was used to define trends and investigate associations between different parameters and rice samples, PCA is a multivariate statistical analysis that uses a linear combination of variables to minimize data with a large number of associated variables into a much smaller collection of new variables that computes most of the variations contained in the original variables (Das et al. 2018). PCA aims to find the smallest number of components that can explain the most variance out of all the variables (Nachimuthu et al. 2014). ...
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... The principal component analysis (PCA) and cluster analysis are the two main approaches for grouping different plant population and genotypes (Ahmad and Noori, 2023;. If these analyzes are conducted based on high-heritability traits, they can reveal the genetic distance between genotypes (Das et al., 2017). In other words, these techniques are the multivariate statistical analysis attempt to simplify the relationship among a large population . ...
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Characterization of morpho-agronomic traits and powdery mildew resistance in mung bean (Vigna radiata)
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Background: Exploring genetic variation and screening for disease resistance is an important step in crop breeding initiatives but is lacking for many bean varieties including mung bean. The present study evaluated the diversity of 42 morpho-agronomic traits and screened mung bean genotypes for resistance to powdery mildew disease. A total of 132 mung bean and rice bean (R200) genotypes (as checks) were evaluated in an augmented incomplete block design across two cropping seasons. Pivot tables were used to analyse qualitative data, whereas the variation of 13 quantitative traits was examined using the generalized linear model (PROC GLM), agglomerative hierarchical clustering (AHC), and principal component analysis (PCA). Result: The genotypes displayed a wide variation for the majority of traits evaluated and significant differences were observed among genotypes, block effect, and between seasons. Similarly, the effects due to checks, genotypes, and genotypes and controls were significant. One mung bean (G32) genotype and one rice bean (R200) exhibited resistance to powdery mildew under field conditions. Principal component analysis revealed that the first four PCs explained 59.77% of the total variation among the genotypes studied. In addition, cluster analysis grouped all the genotypes into four major clusters. Conclusion: The trait variation recorded and resistance to powdery mildew disease provide valuable insight for developing breeding strategies especially with respect to reducing losses in mung bean and rice bean to powdery mildew.
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Evaluation of Rice Accessions for Anaerobic Germination, Yield and Attributing Parameters in Direct Seeded Rice condition
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Full-text available
  • Nov 2023
Improving the tolerance against anaerobic germination has been the leading target domain in lowland areas, especially in Direct Seeded Rice (DSR) conditions. Flash flooding is a major issue in lowland rice cultivation. Direct seeded rice is affected by anaerobic stress. Most higher yielding modern rice varieties die within a week of complete submergence, making them unsuitable alternatives of traditional rice landraces. The present study was carried out to identify the elite genotypes for tolerance against anaerobic germination and yield parameters among twenty-five diverse rice genotypes under DSR. Traits namely germination percentage, seedling height, days to 50% flowering, early seedling vigour, leaf length, leaf width, number of productive tillers, panicle length, panicle weight, number of filled grains, spikelet fertility, 1000 grain weight and single plant yield were recorded. Chitiraikar showed maximum germination percentage (100 %). The plant height was the highest in Garudan samba (124 cm). Chitiraikar showed the highest seedling vigour of 5185. Aanaikomban had 26 more productive tiller (26 Nos). Single plant yield was also greater in Aanaikomban (38.11 g). The genotypes Chitiraikar, Aanaikomban, Karunguruvai and CR1009 sub-1 had better performance when compared to other genotypes. The highest PCV% and GCV% were found in the early seedling vigour (92.56 % and 70.97%) and the lowest (moderate) in spikelet fertility (9.53% and 9.51 %). The traits viz., number of productive tillers (0.36**), panicle weight (0.64**), number of filled grains (0.211**), and 1000 grain weight (0.81**) exhibited a significant positive association with single plant yield. The results of the PCA exhibited that PC1, PC2 and PC3 accounted for 23.97 %, 18.47% and 14.14% variation respectively. Cluster analysis showed that cluster III had the highest mean and encompassed the major influencing traits. Six clusters were formed and the genotypes were grouped based according to their performance. Cluster I and II contains seven genotypes. Thus, choosing the genotypes with these important traits mentioned above can increase the tolerance for anaerobic germination in DSR condition.
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Evaluation of Rice Accessions for Anaerobic Germination, Yield and Attributing Parameters in Direct Seeded Rice condition
Article
Full-text available
  • Sep 2023
Improving the tolerance against anaerobic germination has been the leading target domain in lowland areas, especially in Direct Seeded Rice (DSR) conditions. Flash flooding is a major issue in lowland rice cultivation. Direct seeded rice is affected by anaerobic stress. Most higher yielding modern rice varieties die within a week of complete submergence, making them unsuitable alternatives of traditional rice landraces. The present study was carried out to identify the elite genotypes for tolerance against anaerobic germination and yield parameters among twenty-five diverse rice genotypes under DSR. Traits namely germination percentage, seedling height, days to 50% flowering, early seedling vigour, leaf length, leaf width, number of productive tillers, panicle length, panicle weight, number of filled grains, spikelet fertility, 1000 grain weight and single plant yield were recorded. Chitiraikar showed maximum germination percentage (100 %). The plant height was the highest in Garudan samba (124 cm). Chitiraikar showed the highest seedling vigour of 5185. Aanaikomban had 26 more productive tiller (26 Nos). Single plant yield was also greater in Aanaikomban (38.11 g). The genotypes Chitiraikar, Aanaikomban, Karunguruvai and CR1009 sub-1 had better performance when compared to other genotypes. The highest PCV% and GCV% were found in the early seedling vigour (92.56 % and 70.97%) and the lowest (moderate) in spikelet fertility (9.53% and 9.51 %). The traits viz., number of productive tillers (0.36**), panicle weight (0.64**), number of filled grains (0.211**), and 1000 grain weight (0.81**) exhibited a significant positive association with single plant yield. The results of the PCA exhibited that PC1, PC2 and PC3 accounted for 23.97 %, 18.47% and 14.14% variation respectively. Cluster analysis showed that cluster III had the highest mean and encompassed the major influencing traits. Six clusters were formed and the genotypes were grouped based according to their performance. Cluster I and II contains seven genotypes. Thus, choosing the genotypes with these important traits mentioned above can increase the tolerance for anaerobic germination in DSR condition.
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Low-dimensional representation of faces in higher dimensions of the face space
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Full-text available
Faces can be represented efficiently as a weighted linear combination of the eigenvectors of a covariance matrix of face images. It has also been shown [J. Opt. Soc. Am. 4, 519–524 (1987)] that identifiable faces can be made by using only a subset of the eigenvectors, i.e., those with the largest eigenvalues. This low-dimensional representation is optimal in that it minimizes the squared error between the representation of the face image and the original face image. The present study demonstrates that, whereas this low-dimensional representation is optimal for identifying the physical categories of face, like sex, it is not optimal for recognizing the faces (i.e., discriminating known from unknown faces). Various low-dimensional representations of the faces in the higher dimensions of the face space (i.e., the eigenvectors with smaller eigenvalues) provide better information for face recognition.
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Genetic Diversity in Irrigated Rice
Article
Full-text available
  • Feb 2004
Genetic diversity of 62 genotypes of irrigated rice originating from BRRI, IRRI and China were studied through Mahalanobis D<SUP>2</SUP>statistic. The genotypes were grouped into five clusters. The cluster II and IV contained the highest number of genotypes (16) and the cluster I contained the lowest (7). The highest intra cluster distance was noticed for the cluster I and the lowest for cluster III. The highest inter cluster distance was observed between cluster I and cluster IV followed by cluster I and cluster V, cluster I and cluster III, cluster III and cluster IV and lowest between cluster IV and cluster V. The highest cluster means for yield and other three yield contributing characters are obtained from cluster I, six highest and two second highest means for yield contributing characters are found in cluster III but the lowest cluster mean for yield. Therefore more emphasis should be given on cluster I for selecting genotypes as parents for crossing with the genotypes of cluster III, which may produce new recombinants with desired traits.
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Application of principal component analysis for rice germplasm characterization and evaluation
Article
  • Mar 2012
This study gave empirical evidence on sixteen agro-morphological data that were collected from one hundred and twenty three rice germplasm comprising of Oryza sativa and Oryza glaberrima lines including checks. The data was collected from thirteen villages in two States in Nigeria and were characterized using ANOVA model. Among the studied traits, high coefficients of variation were observed for number of unfilled grain per head (45.8%), grain weight (29.1%), 1000 grain weight (23.0%), tiller number at three weeks after planting (22.5%), and tiller number at maturity (20.9%). Seven out of the sixteen phenotypic traits measured were statistically significant at (P = 0.001 and P = 0.05), and 7 phenotypic variables also showed significant differences when subjected to univariate statistics at (P = 0.001 and P = 0.05). The association of all morphological traits was estimated by phenotypic correlation coefficient and showed that eight dependent variables were positively related. Cluster analysis using Ward's method classified the 123 populations into seven distinct groupings. A large number of genotypes was placed in cluster 5 (65 genotypes) followed by cluster 1 (20), cluster 4 (14) and cluster 3 (9), cluster 2 (8) and cluster 6 (7). Cluster 6 includes five checks with few sativa lines, cluster 5 with large grouping of sativa lines with only FARO 56 in that group. Cluster 1 consists of only the O. glaberrima. Clusters 2, 3 and 4 consisted of only O. sativa groups indicating no association between clustering pattern and eco-geographical distribution of genotypes. The maximum inter-cluster distance was observed between clusters indicating the possibility of high heterosis if individuals from these clusters are cross-bred. Principal component analysis resulted in the first two components with Eigen value greater than 1 accounting for 78% of the total variation. The results of Principal Component Analysis (PCA) were closely in line with those of the cluster analysis. These results can now be used by breeders to develop high yielding rice varieties and new breeding protocols for rice improvement.
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A note on Guttman's lower bound for the number of common factor
Article
  • Aug 2011
  • BRIT J MATH STAT PSY
Guttman's classic lower bound for the number of common factors is extended to the completely general case where communalities may lie in the closed interval from zero to one.
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Genetic diversity of rice (O. sativa L.)
  • Jan 2005
  • 17-28
  • R G Guei
  • K A Sanni
  • A F J Fawole
Guei, R.G.; Sanni, K.A. and Fawole, A.F.J. (2005). Genetic diversity of rice (O. sativa L.). Agron. Afr. 5: 17-28.