Genetic Variability and Trait Association Analysis for Agro-Morphological Markers in Mulberry Genetic Resources from Kashmir, India

Abstract

Genetic variability is the pre-requisite for the initiation of any improvement programme
for the identification and selection of superior entries over the existing cultivars. The
investigation was conducted in 47 genotypes maintained at mulberry germplasm block at
CSB-Central Sericultural Research and Training Institute (CSRandTI), Pampore for 13
agro-morphological traits to understand the available genetic variability for future
improvement in mulberry. Analysis of variance (ANOVA) showed highly significant
differences (P=0.01) between the genotypes for the agro-morphological traits studied.
High level of phenotypic and genotypic coefficient of variation (>20%) observed for
petiole length, petiole weight, leaf length, leaf width, number of leaf attached on main
shoot, number of nodes on main shoot, total shoot length, leaf weight of main shoot, main
shoot weight, total shoot weight per plant and total leaf weight or leaf yield indicated that
these traits are governed by genetic factors. High heritability estimates coupled with high
genetic advance as per cent of mean (GAM) for indicated additive gene action and
improvement can be made through selection. Correlation coefficient association analysis
revealed significant and positive correlation of leaf yield with yield components. The study
revealed importance of direct selection for the improvement of agro-morphological traits.

Full text

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Original Research Article https://doi.org/10.20546/ijcmas.2018.704.204
Genetic Variability and Trait Association Analysis for Agro-Morphological
Markers in Mulberry Genetic Resources from Kashmir, India
Pawan Saini1*, S.S. Chauhan1, Aftab A. Shabnam1,
Lal Chand2 and Narender Negi3
1CSB-Central Sericultural Research and Training Institute, Pampore–192121,
Jammu and Kashmir, India
2ICAR-Central Agro-forestry Research Institute, Jhansi-284003, Uttar Pradesh, India
3ICAR-Regional Station, National Bureau of Plant Genetic Resources, Shimla,
Himachal Pradesh, India
*Corresponding author
A B S T R A C T
Introduction
Mulberry is a fast growing deciduous,
perennial and highly heterozygous plant which
exhibit sexual polymorphism. It is believed to
have originated in the Northern hemisphere,
particularly in the Himalayan foothills and
spreads to the tropics of Southern hemisphere
(Benavidas et al., 1994). It can be grown in
diverse edapho-climatic conditions which
require more productive hybrids for
acclimatization in particular area (Tikader et
al., 2004). It is the primary food plant of
silkworm (Bombyx mori L.); hence,
availability of good quality leaf has great
impact on the sustainability and profitability
International Journal of Current Microbiology and Applied Sciences
ISSN: 2319-7706 Volume 7 Number 04 (2018)
Journal homepage: http://www.ijcmas.com
Genetic variability is the pre-requisite for the initiation of any improvement programme
for the identification and selection of superior entries over the existing cultivars. The
investigation was conducted in 47 genotypes maintained at mulberry germplasm block at
CSB-Central Sericultural Research and Training Institute (CSRandTI), Pampore for 13
agro-morphological traits to understand the available genetic variability for future
improvement in mulberry. Analysis of variance (ANOVA) showed highly significant
differences (P=0.01) between the genotypes for the agro-morphological traits studied.
High level of phenotypic and genotypic coefficient of variation (>20%) observed for
petiole length, petiole weight, leaf length, leaf width, number of leaf attached on main
shoot, number of nodes on main shoot, total shoot length, leaf weight of main shoot, main
shoot weight, total shoot weight per plant and total leaf weight or leaf yield indicated that
these traits are governed by genetic factors. High heritability estimates coupled with high
genetic advance as per cent of mean (GAM) for indicated additive gene action and
improvement can be made through selection. Correlation coefficient association analysis
revealed significant and positive correlation of leaf yield with yield components. The study
revealed importance of direct selection for the improvement of agro-morphological traits.
Ke ywords
Character association
analysis, Genetic
resources, Heritability,
Mulberry, Selection
Accepted:
16 March 2018
Available Online:
10 April 2018
Article Info

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of sericulture industry (Vijayan et al., 2010).
Increased production of silk depends, to a
great extent on increased leaf yield of
mulberry plant (Sarkar et al., 1987). The
genetic resources could enable development of
cultivars not only with improved productivity
but also biotic and abiotic stress tolerance
(Tanksley and McCouch, 1997). Because of
wide behavioral variation and easily adapted
to varying ecological conditions, mulberry
easily hybridized both naturally as well as
artificially which creates a wide range of
variability in the existing gene pool (Zhao et
al., 2006). The extent of magnitude of genetic
variability in the mulberry germplasm helps in
the crop improvement through conventional
breeding. Genetic variability is the pre-
requisite for initiation of any crop
improvement programme including mulberry
and selection acts upon the variability which is
present in the genotypes. For making effective
selection basing upon the metric traits
estimation of genetic variability parameters
heritability and genetic advance as per percent
of mean (GAM) indicates the extent of trait
transmissibility generation to generation.
Trait association analysis draws a clear image
of inter-relationships and relative contribution
of independent variables on dependent
variables, which enables a plant breeder to
make selection procedures for crop
improvement (Dewey and Lu, 1959 and Bhat,
1973). Genetic variability and trait association
studies for various agro-morphological traits
in mulberry has been reported by various
researchers in India (Sarkar et al., 1987; Bari
et al., 1989; Susheelamma et al., 1998; Goel et
al., 1998; Vijayan et al., 1998; Masilamani et
al., 2000; Tikader and Roy, 2001; Tikader et
al., 2004; Tikader and Dandin, 2005; Rahman
et al., 2006; Doss et al., 2006; Banerjee et al.,
2007; Mallikarjunnappa et al., 2008; Vijayan
et al., 2010; Doss et al., 2011; Doss et al.,
2012; Biradar et al., 2015 and Suresh et al.,
2017). The North-Western Himalayan region
of India is gifted with very rich diversity of
mulberry with high morpho-genetic
variability. Characterization and evaluation of
diverse genotypes is a continuous process for
improvement in terms of yield, quality and
tolerance to biotic and abiotic stress to evolve
new varieties / hybrids for diverse agro-
climatic regions. Central Silk Board – Central
Sericultural Research and Training Institute,
Pampore has a collection of temperate
indigenous and exotic mulberry genotypes
representing five countries. Hence, the present
study was conducted with the objective of
characterization of mulberry genotypes and to
mine genetic variability among 47 mulberry
genotypes conserved in the field gene bank of
CSB-CSRandTI, Pampore to develop good
quality and high leaf yielding varieties /
hybrids suitable to highly variable agro-
climatic regions.
Materials and Methods
Experimental site and environment
The present study was conducted at the
Mulberry Germplasm Block, CSB-CSRandTI,
Pampore. The institute is situated at 34002’ N
latitude and 74093’ E longitude and at an
altitude of 1573 m above mean sea level. The
soil type is clay loam. The bio-climate of
Kashmir valley is dry temperate and humid.
The annual rainfall is 635 mm and the average
winter and summer temperature is 2.5 and
24.1°C.
Experimental material
The experimental material comprised of 47
mulberry genotypes (16 indigenous and 31
exotic) (Table 1). In April, 2015 the 47
genotypes were planted at spacing of 90 x 180
cm at mulberry germplasm block of the
institute and managed by following the
recommended agronomic package of practices
(Ahsan et al., 1990).

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Experimental data
Quantitative traits like petiole length (cm),
petiole weight (gm), leaf length (cm), leaf
width (cm), number of leaf attached on main
shoot, number of nodes on main shoot, total
number of shoots per plant, length of longest
shoot (cm), total shoot length (cm), leaf
weight of main shoot (kg), main shoot weight
(kg), total shoot weight per plant (kg) and total
leaf weight or leaf yield (kg) were recorded
from randomly sampled three replications.
The traits were recorded as per the DUS
descriptors developed by Central Sericultural
Research and Training Institute, Mysore
(Sivaprasad et al., 2016).
Statistical analysis and estimation of genetic
parameters
The mean data of the above mentioned traits
were statistically analyzed, using the standard
method suggested by Clewer and Scarisbrick
(2001), using the Windostat version 9.2
package program. Analysis of variance
(ANOVA) was done by the method suggested
by Panse and Sukhatme (1985). The
phenotypic coefficient of variation (PCV),
genotypic coefficient of variation (GCV) was
estimated as suggested by Burton and De vane
(1953). Heritability and genetic gain were
calculated by following Lush et al., (1945)
and Johnson et al., (1955) respectively. The
correlation coefficient analysis among all the
possible combination at phenotypic (rp) and
genotypic (rg) level were estimated employing
the formulae (Al-Jibourie et al., 1958).
Significance of correlation coefficient at both
phenotypic and genotypic levels was tested by
comparing table ‘r’ value with the obtained
value. The Path coefficient is a standardized
partial regression coefficient and as such it is a
measure of direct and indirect effect of a set
variable (component characters) as a
dependent variable such as leaf yield. The
estimates of direct and indirect effect of
component characters on leaf yield were
computed using appropriate correlation
coefficient of different component characters
as suggested by Wright (1921) and elaborated
by Dewey and Lu (1959). Thus, the
correlation coefficient of any character/trait
with leaf yield was split into direct and
indirect effects adopting the standard formula.
Results and Discussion
Genetic variability, heritability and genetic
advance
For any genetic improvement programme in
crop plants, the availability of large genetic
stocks representing diverse genotypes is a pre
requisite. In addition to maintaining the pure
stocks of the entries, it is also essential to
make a systematic assessment of the extent of
variability present for various yield
components for effective selection of
genotypes to bring about improvement in the
desired direction.
The analysis of variance among forty seven
(47) genotypes of mulberry indicated highly
significant differences among them for
thirteen (13) agro-morphological traits
indicating presence of sufficient amount of
variability in respect of all the traits studied
(Table 2). The genotypic differences were
significant at P=0.01. Similar results are
reported by Sarkar et al., (1987), Tikader and
Roy (2001), Tikader et al., (2004), Doss et al.,
(2006), Banerjee et al., (2007),
Mallikarjunappa et al., (2008), Vijayashekara
(2009) and Biradar et al., (2015).
The estimates of range, population mean,
variance and genetic parameters viz.,
phenotypic, genotypic and environmental
coefficient of variation, heritability (broad
sense) and genetic advance for nut and kernel
traits are presented in Table 3 and Figure 1.

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The range in mean values does not reflect the
total variance in the traits studied amongst all
the genotypes. There are large differences
observed between the minimum and
maximum range. Hence, actual variance has to
be estimated for the characters to know the
extent of existing variability. The genotypic
variance measures the magnitude of genetic
variability present in the crop and phenotypic
variance indicates the amount of variation
which is due to the phenotypic values. The
estimated phenotypic variance for all the traits
was higher than genotypic variance. Similar
kinds of results were also reported by
Mallaikarjunappa et al., (2008) and Suresh et
al., (2017). The overall coefficient of variation
(CV) value ranges 9.79-23.71. The wide range
of variation obtained may be due to divergent
genotypes included in the study. The presence
of such wide variability in mulberry with
respect to all the traits indicating that
significant variation existed among the
genotypes.
The phenotypic coefficient of variation (PCV)
was also found to be higher than genotypic
coefficient of variation (GCV). Leaf yield is a
polygenic trait which is highly influenced by
the environmental factors. The phenotypic /
observable variation is the combined effect of
genetic factors as well as environmental
factors. High level of phenotypic and
genotypic coefficient of variation (>20%) was
observed for petiole length, petiole weight,
leaf length, leaf width, number of leaf attached
on main shoot, number of nodes on main
shoot, total shoot length, leaf weight of main
shoot, main shoot weight, total shoot weight
per plant and total leaf weight or leaf yield
indicated that these traits are governed by
genetic factors and existence greater
magnitude of genetic variability among the
genotypes and selection will be rewarded for
the improvement of these traits. While high
PCV (>20%) and moderate GCV (10.1-
20.0%) was recorded for total number of
shoots and length of longest shoot indicated
high influence of environment than genetic
factors and selection for these traits will be
less effective. These result are in agree with
the observation made by Goel et al., (1998),
Tikader et al., (2004), Banerjee et al., (2007),
Tikader and Kamble (2008a), Mallikarjunappa
et al., (2008), Doss et al., (2012), Biradar et
al., (2015) and Suresh et al., (2017).
The selection efficiency was higher when the
parameters had higher heritability. The
heritability estimates (broad sense) was ranged
from 47-93% and it is high for all the traits
studied (Tikader et al., 2004; Banerjee et al.,
2007; Biradar et al., 2015 and Suresh et al.,
2017) except total number of shoots exhibit
moderate level of heritability suggesting
additive gene effects and indicated high rate of
trait transmissibility into the future
generations. Hence, improvement can be made
by simple selection. High heritability
estimates suggested the major role of genetic
constitution in the expression of the characters
and such characters are considered to be
dependable from the breeding point of view.
High heritability coupled with high genetic
gain was observed for leaf weight/plant (Goel
et al., 1998); lamina weight, 100 leaf weight,
number of shoots, petiole weight, total shoot
length and leaf yield per plant (Das and
Krishnaswamy, 1969; Tikader, 1997 and
Tikader et al., 2004); nodal distance, total
shoot length, leaf/twig ratio, weight of 100
fresh and dry leaves, single leaf area and leaf
yield (Doss et al., 2006), lamina weight, single
leaf area and fresh leaf weight (Banerjee et al.,
2007); fresh weight of 100 leaves
(Mallikarjunappa et al., 2008); number of
branches per plant, leaf yield per plant, leaf-
shoot ratio, hundred leaf weight and total
shoot length (Keshava Murthy et al., 2010);
total shoot length, number of leaves per plant,
single leaf area, leaf yield per plant, plant
height and weight of 100 fresh leaves (Biradar
et al., 2015) and total chlorophyll, specific leaf
area, single leaf area, petiole weight, single
lamina weight, total shoot length, plant height,

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shoots per plant, average shoot length, leaf
yield, shoot yield, intermodal distance and
harvest index (Suresh et al., 2017).
A character with high heritability and high
genetic gain may be due to additive gene
action (Panse, 1957) in the expression of these
traits and effective progress in improvement
through selection could be achieved for leaf
yield. The parameters without such
combination may appear because of non-
additive gene action, including dominance and
epistasis (Liang and Walter, 1968). It would
be worthwhile to resort to breeding
methodologies other than conventional
pedigree or backcross techniques as these
would leave the non-fixable component
unexploited. Hence, improvement of agro-
morphological traits would be effective
through phenotypic selection.
Trait-association studies
Correlation analysis
Correlation among the 13 yield attributing
characters revealed substantial differences
between phenotypic and genotypic
correlations (table 4). Significant correlation
of characters suggested that there is much
scope for direct and indirect selection for
further improvement. Genotypic correlation
coefficient provides measures of genetic
association between traits and helps to identify
the more important as well as less important
traits to be considered in breeding program
(Tiwari and Upadhyay, 2011). The magnitude
of genotypic correlations was higher than their
corresponding phenotypic correlations. This
can be interpreted as a strong inherent
genotypic relationship between characters
studied, though their phenotypic expression
was impeded by environmental factors. The
present findings are in conformity with Harer
et al., (2003), Kumar et al., (2003), Golani et
al., (2007), Chikkalingaiah et al., (2009),
Islam et al., (2010), Dar et al., (2011), Al-
Ayesh et al., (2012), Souza et al., (2012) and
Tasisa et al., (2012).
In the present investigation, leaf yield is
positively and significantly correlated with
petiole length, number of leaf attached, total
number of shoots, length of longest shoot,
total shoot length, leaf weight, shoot weight
and total shoot weight at both phenotypic and
genotypic level. Similarly, Sarkar et al.,
(1987), Bari et al., (1989), Vijayan et al.,
(1997b and 1998), Tikader and Roy (1999 and
2001), Tikader and Dandin (2005), Rahman et
al., (2006), Banerjee et al., (2007),
Mallikarjunnappa et al., (2008), Doss et al.,
(2012), Biradar et al., (2015) and Suresh et al.,
(2017) reported leaf yield association with
other quantitative traits in mulberry.
Length of longest shoot shows positive and
significant correlation with total shoot length,
leaf weight, shoot weight, total shoot weight
and leaf yield. Similar observation were also
made by Sarkar et al., (1987), Vijayan et al.,
(1997), Tikader and Roy (2001), Tikader and
Dandin (2005), Banerjee et al., (2007) and
Birader et al., (2015). Since, in sericulture
mulberry leaf productivity is a multifactorial
trait which depends upon a number of
quantitative traits like plant height, number of
shoots, length of shoot, leaf size and weight,
moisture retention capacity, total biomass, the
association between these traits appears to be
reasonable that improvement in these traits
through selection will enhance the leaf
productivity which will have great impact on
sericulture industry.
Path coefficient analysis
The relationship between growth parameters
may be negative or positive but it is the net
result of that particular trait and indirect effect
via other traits.
Table.1 List of temperate mulberry genotypes present at CSRandTI, Pampore

Int.J.Curr.Microbiol.App.Sci (2018) 7(4): 1799-1812
1804
Sl. No.
Variety
Species
Indigenous
Exotics
1.
BC-259
M. alba
Indigenous
2.
Botatul
M. alba
Indigenous
3.
Brentul Kashmir
M. alba
Indigenous
4.
C-4
M. alba
Indigenous
5.
C-763
M. alba
Indigenous
6.
C-776
Indigenous
7.
C-1733
M. alba
Indigenous
8.
Chinese white
M. alba
China
9.
Enshatakosuke
M. bombycis
Japan
10.
French
M. alba
France
11.
Goshoerami
M. multicaulis
Japan
12.
Himachal local
M. indica
Indigenous
13.
Ichinose
M. alba
Japan
14.
Kasuga
M. multicaulis
Japan
15.
Kanva-2
Indigenous
16.
Kokusou-27
M. alba
Japan
17.
Lajward
Indigenous
18.
Mandalay (S-1)
M. alba
Burma
19.
Mysore local
M. indica
Indigenous
20.
Obawase
M. bombycis
Japan
21.
Punjab local
M. alba
Indigenous
22.
Rokokuyaso
M. multicaulis
Japan
23.
S-36
Indigenous
24.
S-41
M. alba
Indigenous
25.
S-54
Indigenous
26.
S-146
M. alba
Indigenous
27.
S-799
M. alba
Indigenous
28.
S-1301
M. alba
Indigenous
29.
S-1531
M. alba
Indigenous
30.
S-1635
M. alba
Indigenous
31.
S-1708
M. alba
Indigenous
32.
T-4
M. alba
Indigenous
33.
T-10
M. alba
Indigenous
34.
Tomeiso
M. alba
Japan
35.
Tr-10
M. alba
Indigenous
36.
Zagtul
M. alba
Indigenous
37.
AR-14
M. alba
Indigenous
38.
BR-2
M. alba
Indigenous
39.
Okinowa
Unknown
Japan
40.
V1
Indigenous
41.
English Black
France
42.
K2 x Kosen
Unknown
Indigenous
43.
ME-27
Exotic
44.
ME-53
Exotic
45.
ME-58
Exotic
46.
Almora Local
Indigenous
47.
S-140
Indigenous

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Table.2 Analysis of variance for yield attributing biometric traits in temperate mulberry germplasm accessions of Jammu and Kashmir
Source of
variation
Df
Petiole
Length
(cm)
Petiole
Weight
(gm)
Leaf
Length
(cm)
Leaf
Width
(cm)
Number of leaf
attached
Number of
nodes
Total
number
of shoots
Length of
longest shoot
(cm)
Total Shoot
length (cm)
Leaf
Weight
(kg)
Shoot
weight
(kg)
Total shoot
weight (kg)
Leaf
Yield
(Kg)
Replication
2
0.6854
0.2466
0.3141
5.9313
228.0976
60.3688
0.6436
66.1489
8419.2139
0.0020
0.0007
0.0181
1.1571
Treatment
46
3.4385**
5.8768**
32.8055**
23.0879**
36882.9570**
1137.9948**
3.4588**
1871.5785**
111964.4922**
0.3996**
0.9802**
9.6256**
6.8373**
Error
92
0.3857
0.4165
2.8909
3.1308
887.3910
28.7583
0.9407
140.7866
10919.3145
0.0153
0.0542
0.3771
0.5240
P ** =0.01
Table.3 Genetic parameters for yield attributing biometric traits in temperate mulberry germplasm accessions of Jammu and Kashmir
Sl.
No.
Traits
Mean ± SE
Range
Variance
Coefficient of Variability
H2
(Broad
sense)
(%)
Genetic
Advance
(GA)
GA as per
cent of
means
Minimum
Maximum
PV
GV
EV
General
CV (%)
PCV
(%)
GCV
(%)
ECV
(%)
1
Petiole length (cm)
4.22±0.36
2.40
6.83
1.40
1.02
0.39
14.72
28.09
23.92
14.72
0.73
1.77
41.96
2
Petiole weight (gm)
2.96±0.37
0.77
6.50
2.24
1.82
0.42
21.82
50.57
45.62
21.82
0.81
2.51
84.77
3
Leaf length (cm)
14.50±0.98
8.77
24.33
12.86
9.97
2.89
11.73
24.74
21.78
11.73
0.78
5.73
39.51
4
Leaf width (cm)
11.11±1.02
6.20
19.70
9.78
6.65
3.13
15.92
28.14
23.21
15.92
0.68
4.38
39.42
5
Number of leaf attached
204.90±17.20
30.67
671.67
12885.91
11998.52
887.39
14.54
55.40
53.46
14.54
0.93
217.74
106.27
6
Number of nodes
28.27±3.10
1.67
72.00
398.50
369.75
28.76
18.97
70.62
68.02
18.97
0.93
38.16
134.97
7
Total number of shoots
5.13±0.56
2.67
7.67
1.78
0.84
0.94
18.89
25.98
17.84
18.89
0.47
1.30
25.24
8
Length of longest shoot
(cm)
121.23±6.85
74.33
167.33
717.72
576.93
140.79
9.79
22.10
19.81
9.79
0.80
44.36
36.59
9
Total shoot length (cm)
558.99±60.33
250.33
977.67
44601.04
33681.73
10919.31
18.69
37.78
32.83
18.69
0.76
328.54
58.77
10
Leaf weight (gm)
0.67±0.07
0.09
1.44
0.14
0.13
0.02
18.46
56.47
53.37
18.46
0.89
0.70
103.90
11
Shoot weight (kg)
1.06±0.13
0.17
2.31
0.36
0.31
0.05
21.88
56.60
52.20
21.88
0.85
1.06
99.17
12
Total shoot weight (kg)
3.40±0.35
0.37
7.61
3.46
3.08
0.38
18.08
54.75
51.68
18.08
0.89
3.41
100.49
13.
Leaf Yield (kg)
3.05±0.42
0.40
6.79
2.63
2.10
0.52
23.71
53.11
47.52
23.71
0.80
2.67
87.59

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1806
Table.4 Estimate of genotypic and phenotypic correlation among yield attributing biometric traits in temperate mulberry germplasm
accessions of Jammu and Kashmir
Character
Level
Petiole
Length
(cm)
Petiole
Weight
(gm)
Leaf
Length
(cm)
Leaf
Width
(cm)
Number
of leaf
attached
Number
of nodes
Total
number
of shoots
Length of
longest
shoot
(cm)
Total
Shoot
length
(cm)
Leaf
Weight
(kg)
Shoot
weight
(kg)
Total
shoot
weight
(kg)
Leaf
Yield
(Kg)
Petiole length
(cm)
G
P
1.0000
1.0000
0.5311**
0.4430**
0.5879**
0.5512**
0.4573**
0.4580**
-0.0113
0.0030
-0.0103
-0.0030
-0.0860
-0.0398
0.1602
0.1139
0.1385
0.1143
0.2831**
0.2207**
0.2144*
0.1729*
0.2132
0.1522
0.2461**
0.1696**
Petiole weight
(gm)
G
P
1.0000
1.0000
0.6241**
0.5226**
0.6236**
0.4441**
-0.1626
-0.1496
-0.0257
-0.0090
-0.3118*
-0.2041*
-0.0826
-0.1199
-0.1854*
-0.1662*
0.1303
0.1085
-0.0286
-0.0534
-0.0269
-0.0580
0.0506**
0.0182**
Leaf length
(cm)
G
P
1.0000
1.0000
0.9286**
0.8307**
-0.1407
-0.1121
-0.1370
-0.1195
-0.2670**
-0.2172**
-0.0147
-0.0361
-0.0777
-0.0937
0.1485
0.1166
-0.0464
0.0119
0.0471
0.0420
0.0497
0.0408
Leaf width
(cm)
G
P
1.0000
1.0000
-0.0702
-0.0487
-0.1247
-0.0901
-0.2160*
-0.1752*
-0.0523
-0.0209
-0.0743
-0.0741
0.2378*
0.1737*
0.0086
0.0453
0.1026
0.0885
0.1400
0.0855
Number of
leaf attached
G
P
1.0000
1.0000
-0.2468**
-0.2256**
0.0822
0.0354
0.5948**
0.5391**
0.3899**
0.3293**
0.6749**
0.6622**
0.7462**
0.6967**
0.5753**
0.5448**
0.6346**
0.5819**
Number of
nodes
G
P
1.0000
1.0000
0.1298
0.0810
0.1142
0.1091
0.1335
0.1135
0.1051
0.1051
-0.0052
0.0143
-0.1337
-0.1009
0.0074
0.0178
Total number
of shoots
G
P
1.0000
1.0000
0.4135
0.3520
0.7918
0.8008
0.1038
0.0454
0.2634
0.1530
0.5883
0.4429
0.5271**
0.4457**
Length of
longest shoot
(cm)
G
P
1.0000
1.0000
0.8640**
0.7782**
0.6333**
0.5817**
0.8367**
0.7427**
0.8323**
0.7777**
0.8311**
0.7442**
Total shoot
length (cm)
G
P
1.0000
1.0000
0.4225**
0.3423**
0.6389**
0.5210**
0.8623**
0.7765**
0.8167**
0.7461**
Leaf weight
(gm)
G
P
1.0000
1.0000
0.8515**
0.8108**
0.6660**
0.6282**
0.8074**
0.7442**
Shoot weight
(kg)
G
P
1.0000
1.0000
0.8449**
0.7951**
0.8630**
0.7771**
Total shoot
weight (kg)
G
P
1.0000
1.0000
0.9404**
0.8975**

Int.J.Curr.Microbiol.App.Sci (2018) 7(4): 1799-1812
1807
Table.5 Direct (diagnol) and indirect effects of component characters contributing to total leaf weight in in temperate mulberry
germplasm accessions of Jammu and Kashmir
Character
Level
Petiole
Length
(cm)
Petiole
Weight
(gm)
Leaf
Length
(cm)
Leaf
Width
(cm)
Number
of leaf
attached
Number
of nodes
Total
number
of
shoots
Length
of
longest
shoot
(cm)
Total
Shoot
length
(cm)
Leaf
Weight
(kg)
Shoot
weight
(kg)
Total
shoot
weight
(kg)
Leaf
Yield
(Kg)
Petiole length (cm)
G
P
0.0016
-0.0369
0.0009
-0.0163
0.0009
-0.0203
0.0007
-0.0169
0.0000
-0.0001
0.0000
0.0001
-0.0001
0.0015
0.0003
-0.0042
0.0002
-0.0042
0.0005
-0.0081
0.0003
-0.0064
0.0003
-0.0056
0.2461
-0.0063
Petiole weight (gm)
G
P
0.0652
0.0319
0.1228
0.0721
0.0766
0.0377
0.0766
0.0320
-0.0200
-0.0108
-0.0032
-0.0006
-0.0383
-0.0147
-0.0101
-0.0086
-0.0228
-0.0120
0.0160
0.0078
-0.0035
-0.0038
-0.0033
-0.0042
0.0506
0.0013
Leaf length (cm)
G
P
-0.1488
0.0036
-0.1580
0.0034
-0.2532
0.0066
-0.2351
0.0055
0.0356
-0.0007
0.0347
-0.0008
0.0676
-0.0014
0.0037
-0.0002
0.0197
-0.0006
-0.0376
0.0008
0.0117
0.0001
-0.0119
0.0003
0.0498
0.0003
Leaf width (cm)
G
P
0.0674
-0.0110
0.0919
-0.0106
0.1369
-0.0199
0.1474
-0.0239
-0.0104
0.0012
-0.0184
0.0022
-0.0318
0.0042
-0.0077
0.0005
-0.0109
0.0018
0.0350
-0.0042
0.0013
-0.0011
0.0151
-0.0021
0.1400
-0.0020
Number of leaf attached
G
P
-0.0005 -
0.0002
-0.0067
-0.0077
-0.0058
-0.0057
-0.0029
-0.0025
0.0410
0.0512
-0.0101
-0.0116
0.0034
-0.0018
0.0244
0.0276
0.0160
0.0169
0.0277
0.0339
0.0306
0.0357
0.0236
0.0279
-0.6346
0.0298
Number of nodes
G
P
0.0004
-0.0001
0.0011
-0.0002
0.0058
-0.0020
0.0053
-0.0015
0.0104
-0.0039
-0.0422
0.0171
-0.0055
0.0014
-0.0048
0.0019
-0.0056
0.0019
-0.0044
0.0018
0.0002
0.0002
0.0056
-0.0017
0.0074
-0.0003
Total number of shoots
G
P
0.0106
0.0011
0.0386
0.0058
0.0330
0.0062
0.0267
0.0050
-0.0102
-0.0010
-0.0161
-0.0023
0.1238
-0.0286
-0.0512
-0.0101
-0.0980
-0.0229
-0.0128
-0.0013
-0.0326
-0.0044
-0.0728
-0.0127
0.5271
-0.0128
Length of longest shoot
(cm)
G
P
-0.0255
-0.0138
0.0132
0.0146
0.0023
0.0044
0.0083
0.0025
-0.0948
-0.0654
-0.0182
-0.0132
-0.0659
-0.0427
-0.1593
-0.1213
-0.1377
-0.0944
-0.1009
-0.0706
-0.1333
-0.0901
-0.1326
-0.0944
0.8311
-0.0903
Total shoot length (cm)
G
P
0.0836
0.0405
-0.1119
-0.0589
-0.0469
-0.0332
-0.0448
-0.0263
0.2354
0.1167
0.0806
0.0402
0.4781
0.2837
0.5217
0.2757
0.6038
0.3543
0.2551
0.1213
0.3858
0.1846
0.5207
0.2751
0.8167
0.2643
Leaf weight (gm)
G
P
0.1411
0.0867
0.0649
0.0426
0.0740
0.0458
0.1185
0.0682
0.3364
0.2601
0.0524
0.0413
0.0517
0.0178
0.3156
0.2285
0.2106
0.1344
0.4984
0.3928
0.4244
0.3184
0.3319
0.2467
0.8074
0.2923
Shoot weight (kg)
G
P
-0.0335
-0.0133
0.0045
0.0041
0.0073
-0.0009
-0.0013
-0.0035
-0.1167
-0.0537
0.0008
-0.0011
-0.0412
-0.0018
-0.1308
-0.0573
-0.0999
-0.0402
-0.1332
-0.0625
-0.1564
-0.0771
-0.1321
-0.0613
0.8630
-0.0599
Total shoot weight (kg)
G
P
0.0844
0.0806
-0.0107
-0.0307
0.0187
0.0223
0.0406
0.0469
0.2278
0.2885
-0.0529
-0.0534
0.2329
0.2346
0.3295
0.4118
0.3414
0.4112
0.2637
0.3327
0.3345
0.4210
0.3960
0.5296
0.9404
0.4753

Int.J.Curr.Microbiol.App.Sci (2018) 7(4): 1799-1812
1808
Fig.1 Coefficient of variations (ECV, PCV and GCV), heritability (Broad Sense) and genetic advancement 5% for 47 mulberry
genotypes for agro-morphological markers

Int.J.Curr.Microbiol.App.Sci (2018) 7(4): 1799-1812
1809
So it is necessary to determine the path
coefficients which partition the observed
correlation into direct and indirect effects and
also ravels the cause and effect relationship
between yield and their related traits. By
partitioning the phenotypic and genotypic
correlations, the direct effect of a chosen trait
on leaf yield and its indirect effect through
other characters were computed (Table 5).
The path coefficient estimates indicated that
total shoot length had the highest positive
direct effect (0.6038) followed by leaf weight
(0.4984), total shoot weight (0.3960), leaf
width (0.1474), total number of shoots
(0.1238), petiole weight (0.1228), number of
leaf attached (0.0410) and petiole length
(0.0016) indicated that selection for these
traits would improve leaf yield. Leaf length (-
0.2532), length of longest shoot (-0.1593),
shoot weight (-0.1571) and number of nodes
(-0.0.422) showed negative direct effect on
leaf yield. Similar results were obtained by
Banerjee et al., (2007), Doss et al., (2012) and
Suresh et al., (2017).
The results of path analysis from germplasm
lines also indicated high positive indirect
effect of total shoot length (0.5207 and
0.4781) had highest positive indirect effect on
leaf yield via total shoot weight and total
number of shoots. Considering the overall
direct and indirect effects of various growth
parameters on leaf yield in mulberry, total
number of shoots, total shoot weight, total
shoot length may be the most valuable
characters of mulberry for the selection
programme.
The present study indicated that there is
adequate genetic variability present in the
genotypes studied. Based on the studies on
genetic variability parameters (broad sense
heritability, genetic advance) and trait
association (Correlation and Path analysis) it
is concluded that length of longest shoot, total
number of shoots, total shoot length, shoot
weight, leaf weight and total shoot weight
were the most important yield attributing
components. A wide spectrum of genetic
variability among the genotypes indicated the
possibilities of improvement in leaf yield
through successful breeding programmes.
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How to cite this article:
Pawan Saini, S.S. Chauhan, Aftab A. Shabnam, Lal Chand and Narender Negi . 2018. Genetic
Variability and Trait Association Analysis for Agro-Morphological Markers in Mulberry
Genetic Resources from Kashmir, India. Int.J.Curr.Microbiol.App.Sci. 7(04): 1799-1812.
doi: https://doi.org/10.20546/ijcmas.2018.704.204