Journal of Genetics and Genomics
(Formerly Acta Genetica Sinica)
August 2007, 34(8): 691
697
Reseach Report
Genetic Diversity and Interrelationship Among Mulberry
Genotypes
‰
Rita Banerjee , Sukhen Roychowdhuri, Haradhan Sau, Bimal Kumar Das, Pannalal Ghosh,
Beera Saratchandra
Central Sericultural Research and Training Institute, Berhampore 742101, India
Abstract: Fourteen morphometric traits were used to examine the genetic divergence of 25 mulberry (Morus spp.) genotypes from
varied agroclimatic conditions of India. Wide variation was observed for all the traits. The genotypes irrespective of their place of
collection were grouped into 10 different clusters. Seven accessions, that is, Baragura-2, Gorabandha-2, Kalimpong, Herbertpur,
Kollegal, Resham majri-7, and UP-14 each a cluster of unique entries will be of useful for genetic resources. Nevertheless, the correlation and path analysis suggest the direct selection of lamina length, fresh leaf weight, leaf area, and single leaf weight will be
rewarding for mulberry leaf yield improvement.
Keywords: mulberry; morphometric traits; genetic divergence; correlation; path analysis
It is widely accepted that genetic variation is of
fundamental importance for species’ conservation
[1−6]
.
The plant breeder is interested to know the genetic
divergence among the varieties or strains available
because crossing involving distantly related parents
provide a broad spectrum of variability to ensure the
efficiency of selection toward better types. The use of
diverse germplasm as a significant factor contributing
to high yield has been stressed by many workers using crops like rice, wheat, maize, and pepper [7−10].
Mulberry (Morus spp.), the host plant of silkworm (Bombyx mori) is perennial in nature. The plant
is widely distributed in India, easily adapted to different ecological conditions, and easily hybridized
both naturally and artificially which creates a wide
range of variability in the existing gene pool [11].
Characterization and evaluation of diverse genotypes
are important for long-term improvement in yield,
quality, and resistance to diseases [12, 13]. Morphomet-
ric characterization can be used as a tool to analyze
the genetic relationship among different genotypes of
mulberry and the information is utilized in mulberry
improvement program [14−17]. Since mulberry is highly
heterogeneous and heterozygous, vegetative propagation of selected clones has been practiced to produce
true to type planting materials so as to increase
production and productivity [18,19]. Besides,
conventional breeding programme also depends upon
the nature and magnitude of genetic variance of the
traits under consideration and interrelationship among
them before taking up a hybridization program.
Furthermore, the constitution of different clusters
helps in gaining clarity about the origin and
evolutionary trends. In a country like India,
abounding in divergent habitats for cultivated and
other Morus species, such elucidation of evolutionary
trends may prove to be a significant value in mulberry
breeding. In this work, the objectives were to
establish the genetic relationship of 25 well-identified
Received: 2006
11
21; Accepted: 2007
03
01
‰ Corresponding author. E-mail: [email protected]; Tel: 0091-03482-252 3962-64; Fax: 0091-03482-25 1046
www.jgenetgenomics.org
692
Journal of Genetics and Genomics
well-identified indigenous mulberry accessions using
different morphometric traits and to provide the theoretical base for pyramidization of genes for obtaining
superior genotypes.
Vol. 34 No. 8 2007
fall : ~ 1,200 mm ) with six-month old saplings under
lattice design with three replicates in irrigated condition during September, 2001 following recommended
package and practices. The plot size was maintained
as 14.4 mD14.4 m (12.6 m rows 0.9 m apart). A year
1
after establishment, observations were recorded on
Materials and Methods
different parameters for two consecutive years conTwenty four well-identified accessions of mul-
sidering five commercial silkworm rearing seasons,
berry from varied agroclimatic conditions prevailing
that is, April, June, September, November, and Feb-
in North India, South India, and Jammu & Kashmir
ruary in a year. A random sampling of five plants
and one-check cultivar were included in this study
from each accession was used to compute means for
(Table 1). The experiment was conducted at the Cen-
14 traits, such as total shoot length, number of
tral Sericultural Research and Training Institute, Ber-
branches/plant, length of longest shoot, number of
hampore, West Bengal (latitude: 24º 6hN; longitude:
nodes/shoot, lamina length, lamina width, petiole
88º 15hE; altitude: 19 m above MSL and annual rain
length, petiole width, single leaf weight, lamina weight,
Table 1
Mulberry genotypes used for cluster analysis
Sl. No.
Acc. No.
IC No.
1
MI-0029
IC-313977
2
MI-0080
IC-313814
3
MI-0154
IC-313775
4
MI-0252
IC-313796
5
MI-0290
IC-313671
6
MI-0296
IC-314005
7
MI-0301
8
9
Acc. name
Collection source (India)
Species
Kollegal
Karnataka
Morus indica L.
BC259
West Bengal
M. latifolia Poir.
UP-14
Uttaranchal
M. indica L.
Kalimpong
West Bengal
M. laevigata Wall.
Morus lombong
Karnataka
M. lambong
Acc.-16
Karnataka
M. indica L.
IC-314010
Acc.-1
Karnataka
M. indica L.
MI-0310
IC-314155
Chek majra
Uttaranchal
M. indica L.
MI-0312
IC-314023
Gulikadava
Kerala
M. indica L.
10
MI-0313
IC-314024
Seekupari
Tamil Nadu
M. indica L.
11
MI-0324
IC-313939
ERRC-101
Kerala
M. indica L.
12
MI-0326
IC-313941
ERRC-71
Kerala
M. indica L.
13
MI-0346
IC-314116
Tingari local
Assam
M. indica L.
14
MI-0349
IC-314119
Gorabandha-2
Meghalaya
M. indica L.
15
MI-0369
IC-314159
Resham majri-6
Uttaranchal
M. indica L.
16
MI-0370
IC-314160
Resham majri-7
Uttaranchal
M. indica L.
17
MI-0376
IC-314166
Kunjagao-2
Uttaranchal
M. indica L.
18
MI-0388
IC-314170
Herbertpur
Uttaranchal
M. alba L.
19
MI-0400
IC-314233
Krishnaswamy-2
Karnataka
M. indica L.
20
MI-0415
IC-314046
Guhanathpuram
Karnataka
M. indica L.
21
MI-0416
IC-314047
Keeraithodu
Karnataka
M. indica L.
22
MI-0431
IC-314182
Saharanpur Road
Uttaranchal
M. indica L.
23
MI-0437
IC-314185
Baragura-2
Uttaranchal
M. indica L.
24
MI-0439
IC-314187
RSRS, Sahaspur
Uttaranchal
M. latifolia Poir.
25
MI-0173
IC-313836
S-1635
West Bengal
M. indica L.
ZZZMJHQHWJHQRPLFVRUJ
Rita Banerjee et al.: Genetic Diversity and Interrelationship Among Mulberry Genotypes
693
single leaf area, fresh leaf weight, oven dry leaf
expressed as percentage of mean (ΔG %) was high
weight and leaf yield/plant.
(ƒ0.5) for fresh leaf weight, lamina weight, leaf area,
Genetic analysis, correlation and path analysis,
and single leaf weight (Table 2).
and cluster analysis on the basis of component traits
Leaf yield per plant showed significant positive
were carried out using Genetic Model of INDOSTAT
correlation with most of the parameters while fresh
software. The relationship among 25 accessions was
leaf weight was positively correlated with lamina
portrayed graphically in the form of Dendrogram.
length and width, petiole length and width, single leaf
weight, lamina weight, and leaf area in this respective
2
Results
order (Table 3).
The analysis of variance showed a wide range of
The path coefficient analysis provides direct and
variation and significant differences for all the traits.
indirect contribution of component traits toward leaf
The co-efficient of variation of phenotypic (PCV) and
yield/plant and residuals which cannot be described
genotypic (GCV) level were high (> 0.3) for oven dry
by such dimension characters. From the data (Table 4),
leaf weight, fresh leaf weight, lamina weight, single
it was observed that direct contribution of yield at-
leaf area, single leaf weight, and lead yield/plant and
tributes toward leaf yield/plant varied from the high-
medium to low for other parameters. The difference in
est value of 0.93 for lamina weight to the lowest value
the magnitude of PCV and GCV were more for peti-
of −0.04 for petiole width. The second highest direct
ole width. Heritability (h 2) was high (> 0.6) for lam-
effect on leaf yield/plant was exhibited by the number
ina weight, lamina width, lamina length, fresh leaf
of nodes/shoot followed by number of branches/plant,
weight, and length of the longest shoot, single leaf
respectively. Direct effect of single leaf area toward
area and petiole length; moderate for nodes/shoot,
leaf yield/plant was low; however, the trait may be
total shoot length, number of branches/plant, leaf
likely to contribute toward leaf yield via lamina
yield/plant and single leaf weight. Genetic advance
weight, lamina width, and fresh leaf weight. Besides,
Table 2
Genetic variation of morphological traits
Range
Parameter
PCV
ΔG%
169.40
22.09
16.75
26.18
1.43
18.74
13.89
21.16
0.74
23.93
14.06
12.14
21.59
23.17
0.64
3.7
12.05
9.68
16.00
21.43
15.99
0.78
4.55
17.62
15.6
15.63
11.87
0.79
4.16
21.44
19.1
35.03
4.38
3.09
0.66
.94
22.42
18.36
30.51
h2
Min.
Max.
400.67
845.03
647.12
0.57
5.18
9.17
6.79
0.54
Length of longest shoot (cm)
71.39
128.15
110.78
No. of nodes/shoot
17.71
27.43
Lamina length (cm)
11.32
Lamina width (cm)
7.61
Petiole length (cm)
2.06
Total shoot length (cm)
No. of branches/plant
Genetic
GCV
Mean
advance
28.47
Petiole width (cm)
0.43
1.06
0.74
0.17
0.10
37.01
19.11
13.61
Single leaf weight (g)
1.07
3.98
2.57
0.54
1.25
43.59
32.20
48.95
Lamina weight (g)
Single leaf area (cm2)
0.94
3.59
2.20
0.80
1.54
40.85
36.14
67.62
57.32
219.29
126.06
0.66
66.20
38.42
31.29
52.51
70.60
Fresh leaf weight (g)
1.49
5.74
3.49
0.78
2.47
43.83
38.76
Oven dry leaf weight (g)
0.27
0.89
0.52
0.08
.05
78.57
36.83
9.70
214.76
616.66
451.05
0.59
167.29
30.38
24.67
37.09
Leaf yield/plant (g)
h 2: heritability; GCV: genetic coefficient of variation; PCV: phenotypic coefficient of variation; ΔG%: genetic advance expressed as percentage of
mean.
ZZZMJHQHWJHQRPLFVRUJ
694
Journal of Genetics and Genomics
Table 3
Correlation coefficients of phenotypic levels
Traits
2
3
4
1
0.74**
0.69**
0.65**
0.18
0.33
2
3
0.75**
4
5
0.07
Vol. 34 No. 8 2007
6
7
8
9
10
11
12
13
14
0.009
−0.07
−0.01
0.001
−0.05
0.04
0.03
−0.12
0.57**
−0.22
−0.30
−0.34
−0.31
−0.30
−0.35
−0.26
−0.29
−0.16
0.18
0.35
0.34
0.31
0.30
0.25
0.27
0.30
0.33
−0.07
0.63**
−0.03
−0.03
0.09
0.12
−0.11
−0.11
−0.03
−0.02
−0.25
0.48*
0.72**
0.56**
0.76**
0.87**
0.81
0.80**
0.15
0.49
0.65**
0.55**
0.79**
0.91**
0.87**
0.83**
0.18
0.53**
0.51**
0.57**
0.68**
0.64**
0.65**
0.14
0.34
0.53
0.58**
0.51**
0.44*
0.11
0.31
0.86**
0.77**
0.72**
0.12
0.42*
0.91**
0.89**
0.16
0.51**
0.81**
0.06
0.54**
0.05
0.54**
5
0.85**
6
7
8
9
10
11
12
−0.11
13
* and ** represent significant difference at 1% and 5%, respectively.
Table 4
Path analysis taking leaf yield as dependent variable (direct effect in bold)
Traits
1
2
3
4
5
1
−0.071
−0. 057
−0.053
−0.007
0.0003
6
2
0.43
0.54
0.14
0.22
−0.14
−0.21
−0.24
−0.22
−0.18
−0.24
−0.22
−0.23
3
−0.14
−0.05
−0.19
−0.15
−0.08
−0.08
−0.08
−0.09
−0.06
−0.06
−0.07
−0.07
4
0.44
0.24
0.48
0.60
−0.02
−0.02
0.09
0.09
−0.07
−0.08
−0.04
−0.06
−0.21
5
−0.007
0.01
−0.03
0.003
−0.07
−0.06
−0.05
−0.06
−0.06
−0.06
−0.06
−0.06
−.01
6
−0.002
−0.021
0.22
−0.02
0.46
0.53
0.37
0.48
0.49
0.51
0.49
0.51
0.17
7
0.004
0.04
−0.04
−0.01
−0.07
−0.07
−0.09
−0.08
−0.06
−0.07
−0.07
−0.07
−0.004
8
−0.002
0.016
−0.02
−0.006
−0.03
−0.03
−0.03
−0.04
−0.03
−0.03
−0.03
−0.03
−0.003
9
0.02
0.32
−0.30
0.02
−0.85
−0.91
−0.66
−0.79
−0.98
−0.94
−0.88
−0.93
−0.17
10
−0.07
−0.41
0.29
−0.13
0.83
0.89
0.64
0.83
0.88
0.93
0.88
0.92
0.22
11
−0.003
−0.12
0.12
−0.02
0.27
0.29
0.22
0.27
0.27
0.29
0.31
0.29
0.05
12
−0.01
−0.16
0.13
−0.04
0.34
0.36
0.28
0.34
0.35
0.37
0.35
0.38
0.09
13
0.02
0.03
0.007
0.05
−0.03
−0.05
−0.006
−0.01
−0.03
−0.04
−0.02
−0.04
−0.15
0.003
7
−0.004
8
0.002
9
0.006
10
11
0.0008
0.0008
12
0.0027
13
0.019
−0.12
0.008
Residual effects (P) = 0.22.
positive correlations and direct negative effects were
found in lamina length, petiole length, petiole width,
single leaf weight, and oven dry leaf weight, but their
indirect effect via number of branches/plant was positive. On the other hand, total shoot length and length
of the longest shoot recorded direct negative effect
toward leaf yield/plant, but their indirect effect via
oven dry leaf weight is positive.
A dendrogram was obtained by INDOSTAT using a total of 14 traits. The dendrogram showed
ZZZMJHQHWJHQRPLFVRUJ
Rita Banerjee et al.: Genetic Diversity and Interrelationship Among Mulberry Genotypes
Fig. 1
695
A dendrogram obtained by INDOSTAT for 25 mulberry genotypes based on morphometric traits
Baragura-2 (23), Gorabandha-2 (14), Kalimpong (4),
Herbertpur (18), Kollegal (1), Resham majri-7 (16),
and UP-14 (3) each of which is a cluster of unique
entries. Ten accessions, irrespective of the source
were grouped in a single large cluster. Three accessions Check majra (8), RSRS, Sahaspur (24) and Saharanpur Road (22) from Uttaranchal, two accessions
S-1635 (25) and BC259 (2) from West Bengal and
Keeraithodu (21) from Kerala were quite distinct
from other accessions (Fig. 1).
High heritability along with high genetic advance of
lamina weight, single leaf area, and fresh leaf weight
are probably due to the influence of the additive gene
effect. It was found that all the traits were under the
influence of both additive and nonadditive gene action which suggests that simple selection alone will
not be effective, hence hybridization followed by selection would be a better choice for mulberry improvement. Earlier studies reported similar observations for growth and yield traits in mulberry and emphasized targeted breeding in mulberry [14−17].
3
Discussion
Significant positive correlations among different
parameters would be useful in selecting diverse plant
There was less difference in the magnitude of
GCV and PCV for oven dry leaf weight, fresh leaf
weight, lamina weight, single leaf area, and single
leaf weight indicating consistency in the expression of
these traits irrespective of the growing condition
while the maximum for the other eight parameters
indicated the influence of environment in their governance. Relationship of heritability and genetic advance offer an idea regarding the type of gene action.
ZZZMJHQHWJHQRPLFVRUJ
types. However, the values one obtains from the correlation analysis sometimes do not reflect the results
true to type and selection based on these values give
poor or no response for selection. A character showing positive correlation may not have a direct effect
on the leaf yield but may contribute to yield via other
characters. In the present material, the direct effects of
the number of branches/plant, number of nodes/shoot,
lamina width, lamina weight, leaf area, and fresh leaf
696
Journal of Genetics and Genomics
Vol. 34 No. 8 2007
weight reflect the impact of these traits on leaf yield
high heritability. Thus cautious selection of genotypes
as certain parameters with negative direct effect had
from a diverse gene pool will be of great help toward
shown significantly positive correlation with leaf
mulberry projects and production.
yield. It also indicated that in many crops, positive
correlation between two characters could be a result
of high indirect effect via other characters [20]. For
instance, positive correlation of leaf yield/plant with
weight of single leaf was mainly contributed via
number of branches/ plant and number of nodes/shoot.
Acknowledgements: The authors are thankful to Dr.
Sinha MK, Principal Scientist and Dr. Goswami KK,
Principal Scientist, Central Research Institute for Jute
and Allied Fibres, Barrackpore, West Bengal for their
help in using the Genetic Model of INDOSTAT
package for analyzing the data.
Similarly, among the various correlations, the number
of branches/plant character showed no correlation
with leaf yield but exhibited positive direct effect via
total shoot length, number of nodes/plant, and length
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Rita Banerjee, Sukhen Roychowdhuri, Haradhan Sau, Bimal Kumar Das, Pannalal Ghosh,
Beera Saratchandra
Central Sericultural Research and Training Institute, Berhampore 742101, India
: 25 (Morus spp.) 14 , !"#$%
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Baragura-2, Gorabandha-2, Kalimpong, Herbertpur, Kollegal, Resham majri-7 > UP-14 "#?@ABC*D
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: aa"#$%aEFabc&G
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