Multivariate Analysis of Genetic Diversity among some Rice Genotypes Using Morpho-agronomic Traits
The availability of access to diverse genetic material is important to be successful
in any plant breeding effort. An investigation was undertaken to assess genetic
diversity of 24 known rice genotypes based on17 morpho-agronomic traits by using
multivariate analyses: hierarchical cluster and Principal Component (PC) analyses.
Cluster analysis separated the genotypes into two groups which are in contrast
for flowering, plant types, biomass, grain yield, seed width, seed length and
seed weight. The first three PCs explained 73.5% of the total variability. Days
to flowering, plant height, culm length, panicle length, biomass, seed length
and shape were the important traitsin differentiating the genotypes. The hierarchical
cluster and PC analyses were in agreement in grouping the genotypes. Parental
combinations from the two clusters with higher value of Euclidean distance could
be used for genetic improvement. Generally KOH1 is the most diverged genotype
from others. Thus crossing of this genotype with Azucena, CH1, KDML105 and SPR1
may result in heterotic expression in the F1 and substantial variability
in the subsequent segregating population.
Received: August 06, 2013;
Accepted: January 06, 2014;
Published: February 27, 2014
More than half of the population of the world depends on rice as the main source
of food. Rice is one of the worlds most important food crops in terms
of its production and area coverage. About 11% of the worlds cultivated
land is devoted for its production. Among the continents of the world, Asia
is the first both in production and consumption of rice. Most of the Asians
get 35-75% of their calories from rice (Khush, 2005).
In the year 2011 about 164.1million ha of land was used for rice production
worldwide. In the same year nearly equal amount of land was allocated for rice
in the content of Africa and in Thailand (FAO, 2013).
Genetic diversity has a multitude of importance. It is considered as a means
of survival and adaptation to changing environments (Gao,
2003; Rao and Hodgkin, 2002); it has a role in
collection and conservation of germplasm and crop improvement (Duran
et al., 2009). Moreover, the information helps to study heterosis
as genetic diversity between parents is generally related to the magnitude of
heterosis (Virk et al., 2003). This shows the
necessity of divergent parents in any crossing program in order to be benefited
from the hybrid vigor in the F1 and from the variability created
in the subsequent segregating population. Whether the objective is hybrid or
pure-line variety development, the availability of and access to diverse genetic
material is important to be successful in any plant breeding effort (Hoisington
et al., 1999; Naik et al., 2006; Maxted
et al., 2002; Rohman et al., 2004).
Evaluation of genetic resources for various agronomic traits and the assessment
of the amount of genetic variation within them is useful to allow more efficient
genetic improvement (Haussmann et al., 2004).
In order to further improve the already adapted crop varieties for traits of
agronomic importance, crossing with other parents with proved agronomic superiority
is necessary. This calls for the assessment of genetic diversity and identification
of parental lines for hybridization or crossing. The present investigation was
undertaken with the objective to assess genetic diversity of some known rice
genotypes by using morpho-agronomic traits and to identify the most important
character in differentiating the genotypes.
MATERIALS AND METHODS
Experimental materials and place of study: In the genetic diversity
study, a total of 24 rice genotypes were used. The materials include 13 indica,
four japonica, four New Plant Types, one tropical japonica and two NERICA
genotypes (Table 1). NERICA signifies New Rice for Africa;
genotypes developed by interspecific hybridization of O. glaberrima
and O. sativa (Samado et al., 2008).
The trial was conducted at Kasetsart University, Bangkhen Campus, Bangkok, Thailand
during 2012 rainy season.
Experimental design and data collection: The treatments were arranged
in a randomized complete block design with three replications. A spacing of
20 cm between genotypes and between plants was left.
|| Description of the 24 rice genotypes used for the diversity
analysis using 17 morpho-agronomic traits
Fertilization and insecticide application were done uniformly to all plots.
The plants were protected from the possible damage of birds and rats by wire
mesh. Data for 17 morphological and agronomic traits were collected from each
plot using the Standard Evaluation System for rice (IRRI,
2002). Paddy-rice seed width and length were measured as averages of 15
seeds by using digital Rice Grain Analyzer. The traits studied include days
to flowering, No. of total tillers, number of productive tillers, plant height
(cm), culm length (cm), flag-leaf length (cm), panicle length (cm), number of
total spikelets, number of fertile spikelets, 100-seed weight (g), biomass (g),
yield per plant, harvest index (%), seed width (mm), seed length (mm), seed
shape (length, width ratio) and spikelet fertility (%).
Data analyses: Analysis of Variance (ANOVA) was computed by using CropStat7.2
and the mean values of traits were used for further analysis. The mean values
were then standardized to a mean of zero and variance of unity before cluster
analysis to remove the biases due to differences in the scale of measurement.
PAST 1.93 (Palaeontological Statistics; Hammer et al.,
2001) computer software was used for phenotypic correlation, cluster and
principal component analyses.
Analysis of variance: The univariate ANOVA showed significant (p<0.01)
variation among the rice genotypes for all the morpho-agronomic traits considered
(Table 2). The significance signifies the possibility of using
all traits for further analysis. From the result it was observed that CO39 was
the earliest (75.3 days) while KDML105 was the latest (149.67 days) genotypes
to flower. Similarly, CO39 and Azucena were the shortest (68.69 cm) and the
tallest (167 cm) genotypes, respectively.
||Minimum and maximum mean values and overall means, standard
errors and coefficients of variation of 17 morpho-agronomic traits of 24
|**: Significant at 0.01 probability level
The analysis also showed that KOH1 was the lowest (5.04 g) whereas, CH1 was
the highest (29.06 g) in yield among the genotypes. Also, NPT18 and NI were
the least (57.24%) and the most (95.18%) fertile genotypes identified, respectively.
Phenotypic correlation: In order to assess traits association, phenotypic
correlation analysis was done and the result is depicted in Table
3. Generally the result showed high order of correlation between most of
the traits under study. Days to flowering was highly and significantly correlated
with plant height (0.883), culm length (0.893), biomass (0.772) and harvest
index (-0.732). Similarly, yield per plant was highly and significantly correlated
with total tillers (0.541), plant height (0.525), panicle length (0.649), total
spikelets (0.585), filled spikelets (0.539), biomass (0.716), seed length (0.606)
and seed shape (0.622) towards the positive direction. Also, strong and significant
association of harvest index was detected with days to flowering (-0.732), plant
height (-0.603), culm length (-0.601), total spikelets (-0.455), biomass (-0.705)
and spikelet fertility (0.710). Spikelet fertility was negatively correlated
to all traits under study except harvest index.
Euclidean distance: Euclidean distance matrix was produced by assuming
276 total possible pairwise combinations of the 24 rice genotypes (Table
4). The distance coefficients ranged from 1.142 for NERICA3-NERICA4 to 10.267
for KOH1-Azucena pairwise cultivar combinations with a mean of 5.172. Also TW1-TW2
(1.743), CH2-CH3 (1.848) and NPT4-NPT18 (1.976) were the next smaller pairwise
Euclidean distances. Similarly, the next higher distance values were that of
KOH1 with CH1 (10.183), KDML105 (9.788) and SPR1 (9.368). KOH1 is generally
the most diverged genotype from others with higher mean Euclidean distance of
7.426 while RD31 was the least with mean Euclidean distance of 4.41.
Hierarchical cluster analysis: Hierarchical clustering was attempted
by using paired group algorithm with different distance measures like Gower,
Euclidean, Mahalanobis and Manhattan. The result showed that Gower, Euclidean
and Manhattan distance measures yielded similar dendrogram topology and similar
cluster membership of the rice genotypes; however, Mahalanobis distance measure
yielded different dendrogram topology which was characterized by chaining of
genotypes. The dendrogram of the morpho-agronomic traits grouped the genotypes
into two clusters with additional subgroups in each groups. Group I was composed
of 17 genotypes and group II included 7 genotypes. Group I was made up of miscellaneous
type of genotypes as it was composed of 10 indica, four NPT, one tropical
japonica and two NERICA types. This group was further divided into four subgroups:
Subgroup1 contained the two NERICAs; subgroup 2 was made up of the four NPTs
and seven indica types; subgroup 3 contained a distinct cultivar CH1,
this variety was characterized by longer flag-leaf, higher number of spikeletes
and higher yield and subgroup 4 included KDML105, Azucena and TDK1. Members
of this subgroup were late maturing and tall plant types. However, group II
contained four japonica and three indica types. This group also
further divided in to three subgroups: Subgroup 1 contained TW1 and TW2 (japonica
types); subgroup 2 was made up of CH2, CH3 and CO39 (indica types) and
subgroup 3 was composed of NI and KOH1, japonica types (Fig.
1). ANOVA was run to declare whether any of the mean differences of the
traits between the two groups were significant. Accordingly, the result showed
significant (p<0.01) differences for most of the traits under study except
for traits related to tillers and spikeletes and harvest index.
||Phenotypic correlation coefficients between 17 morpho-agronomic
traits in 24 rice genotypes
|DF: Days to flowering, TNT: Total No. of tillers, PT: Productive tillers, PHT: Plant height, CL: Culm length, FLL: Flag-leaf length, PNL: Panicle length, TSP: Total spikelets, FSP:
Filled spikelets, HSW: Hundred-seed weight, BM: Biomass, YLD: Yield per plant, HI: Harvest index, SW: Seed width, SL: Seed length, SSH: Seed shape, SPF: Spikelet fertility
**,*: Significant at 0.01 and 0.05 probability levels, respectively
||Pairwise Euclidean distance coefficients for all possible
combinations of the 24 genotypes using 17 morpho-agronomic traits
||Dendrogram of 24 rice genotypes based on 17 morpho-agronomic
traits constructed by means of paired group algorithm and Euclidean distance
On average, the genotypes in cluster II were significantly earlier (91.8 vs.
113.2 days) and shorter (82.99 vs. 123.15 cm) than that of cluster I. Compared
with cluster I, the genotypes in cluster II had significantly lower biomass
(26.38 vs. 58.14 g) and grain yield (11 vs. 18.41 g) (Table 5).
Principal component analysis: The result of the principal component
analysis (PCA) is depicted in Fig. 2 and Table
6. The objective of principal component analysis is reduction of dimensionality
of a data set with a large number of correlated variables or traits (Jolliffe,
2002). PCA was carried out by using 24 genotypes and 17 traits. In the analysis
a total of 17 PCs, equals to the number of traits, were extracted. However,
the first five PCs with eigen values greater than 1 were retained. The result
showed that 89.68% of the variability was explained by the first five Principal
Component (PC) axes. Out of the five, the first and the second explained 44.52
and 16.64% of the variation, respectively. Days to flowering, plant height,
culm length, panicle length, biomass, seed length and shape were the important
traits contributing to the first PC. In the second PC, however, total and productive
tillers were important. Similarly, flag-leaf length, harvest index, seed width
and spikelet fertility were important in the third axis. While only 100-seed
weight was important in the fourth axis. Total and filled spikelets and yield
per plant were the important traits contributing to the fifth PC (Table
6). The first axis differentiated genotypes which were late flowering, tall,
higher in biomass and grain yield, with slender (narrow and tall) seeds from
genotypes which were early flowering, short, low biomass and grain yield with
round (wide and short) seeds. The second axis however differentiated genotypes
with higher total and productive tillers from those with low tillers. Generally
the PC analysis broadly grouped the genotypes based on zones of origin into
temperate and tropical categories.
|| Mean-squares and cluster means for 17 morpho-agronomic traits
of the 24 rice genotypes
|**,*: Significant at 0.01 and 0.05 probability levels
|| Eigenvalues, total variance, cumulative variance and eigenvectors
for 17 morpho-agronomic traits in the 24 rice genotypes
The ones located to left of the two dimensional plane being temperate with
the exception of CO39, while those to the right being tropical in zone of origin
||Scattered distribution of the 24 rice genotypes by using 17
morpho-agronomic traits on the first two principal component axes
The significant (p<0.01) variation among the rice genotypes for all the
morpho-agronomic traits and the range of values obtained for most of the traits
indicates a sizable variability in the genotypes studied for the 17 traits considered.
The correlation study showed that late flowering is important in attaining tall
plant types, higher biomass and lower harvest index. It also showed that more
number of tillers, tall plant types (increased height and panicle length), higher
number of spikelets, heavier above ground mass, seed length and shape were the
important traits in attaining higher yield per plant in rice. However, early
flowering, short plant types (lower height and culm length), lower number of
total spikelets, light above ground-mass but higher level of spikelet fertility
are important to improve harvest index. Significant correlations of yield and
yield-related traits were reported by other workers in inbred lines, hybrids
and landrace rice (Chakravorty et al., 2013;
Janwan et al., 2013; Seesang
et al., 2013).
The pairwise Euclidean distance coefficients estimated in this study ranged
from 1.142-10.267 with a mean of 5.172. However, Caldo et
al. (1996a) estimated a range of Euclidean distance varying between
2.23 and 16.71 with a mean of 7.55 for 78 improved rice genotypes by using 33
qualitative and quantitative traits. In a separate study of 81 ancestral lines
of Philippines modern rice genotypes, Caldo et al.
(1996b) computed Euclidean distance estimates ranging from 3.97-17.389 with
a mean of 8.80 using 41 traits.The output of hierarchical cluster analysis exhibited
similar dendrogram topology and cluster membership of the rice genotypes for
Gower, Euclidean and Manhattan distance measures. This confirms the stability
of the dendrogram constructed. Generally, the first group was characterized
by late flowering, tall plant types, higher biomass and grain yield, slender
(narrow and long) and heavier seeds. Unlike the first group, the second group
was characterized by early flowering, short plant types, lower biomass and grain
yield, round (wide and short) and light weighted-seeds.
Principal Components (PCs) are orthogonal and independent of each other (Mohammadi
and Prasanna, 2003); they explain the variability which was not explained
by the others. In this study, the total variability was explained by five PCs.
This may indicate the contribution of many traits with higher level of correlation
to explain the gross diversity. In a study of Caldo et
al. (1996b), 75% of the variability was accounted for by seven PCs.
In their work, Chakravorty et al. (2013) explained
the total variability by six PC axes; Caldo et al.
(1996a, b) explained 67% of the variation by 10
PCs. Jolliffe (2002) stated that PCs are ordered so that
the first few retain most of the variation present in all of the original variables.
In this experiment, the first and the second PCs explained 61.16% of the variability,
the first being the most important. Accordingly, the traits included in the
first PC especially those with comparatively high loadings are important in
separating the genotypes. Generally, the PC analysis showed high level of diversity
of the rice genotypes as the entire variation cannot be explained by few principal
components. High level of simple sequence repeat (SSR) DNA marker diversity
was also reported for the same set of genotypes investigated (Worede
et al., 2013).
The study showed the presence of considerable level of diversity in the genotypes
analyzed and the importance morpho-agronomic traits to study genetic diversity.
Days to flowering, plant height, culm length, panicle length, biomass, seed
length and shape were the important traits in differentiating the genotypes
under study. The hierarchical cluster analysis was in general agreement with
the PC analysis in grouping the genotypes into two clusters. Parental combinations
from the two clusters with higher value of Euclidean distance (dissimilarity)
coefficient could be used for genetic improvement by crossing. Generally KOH1
is the most diverged genotype from others. Thus crossing of this genotype with
the likes of Azucena, CH1, KDML105 and SPR1 may result in heterotic expression
in the F1 and substantial variability in the subsequent segregating
The study was supported by Rural Capacity Building and East Africa Agricultural
Productivity Projects of the Ethiopian Ministry of Agriculture.
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