Euphytica 131: 53–63, 2003.
© 2003 Kluwer Academic Publishers. Printed in the Netherlands. 53
ISSR proﬁling of Indian cultivars of mulberry (Morus spp.) and its
relevance to breeding programs
K. Vijayan & S.N. Chatterjee∗
SeriBiotech Laboratory, CSB campus, Carmelram (P.O.), Sarjapur Road, Kodathi, Bangalore – 560 035, India;
(∗author for correspondence: e-mail: email@example.com)
Received 22 April 2002; accepted 27 December 2002
Key words: discriminant function analysis, genetic variability, ISSR markers, mulberry
Mulberry, Morus spp. has a wide range of use, the chief among them is to feed the silk producing caterpillar
Bombyx mori L. (Bombycidae; Bombycoidea). As a homeland of mulberry, India has a number of indigenous
mulberry species, of which a few are widely cultivated. In the present investigation genetic distance among such
eleven mulberry cultivars originated from six different states of India covering a wide geographic area extending
◦N latitude and 72◦Eto89
◦E longitude was studied using inter-simple sequence repeat primers.
Out of the 20 primers tested, 13 primers, viz, nine di-nucleotide, three tri-nucleotide and one penta-nucleotide
repeats, gave clear and reproducible band proﬁles. While the (AT)nrich primers could not amplify the DNA,
the (GA)n, (AC)n and (AG)n rich primers gave excellent ampliﬁcation proﬁles. The genetic distance among the
cultivars varied from a minimum of 0.053, between Punjab local and Bombay local, to a maximum of 0.431,
between Almora local and Sujanpur-5. Clustering of the cultivars according to nearest neighbor method created
three groups. The north-Indian cultivars made a separate and distinct group while the cultivars originated from
eastern and southern India occupied a distinct position. Almora local was found quite different from others. The
ﬁrst two canonical functions identiﬁed through discriminant function analysis accounted for 91.2% of the total
variability. Distribution of cultivars belonging to six different zones on canonical matrix realized from Discriminant
Function Analysis (DFA) revealed wider variability for West Bengal, Karnataka and Punjab which reaches the
group centroids of Uttar Pradesh and Himachal Pradesh. This attests to the past contribution of West Bengal in
east and Karnataka in south towards development of mulberry cultivars in different parts of India. Step-wise linear
regression analysis, further, identiﬁed two markers (825.1400 and 835.750) associated with leaf yield, which also
satisﬁed the Beta estimation, thereby testifying strong association of these two markers with leaf yield. This ﬁnding
along with the classiﬁcation of the eleven cultivars bear strong relevance to mulberry breeding for different agro
Mulberry (Morus L.), a tree species in the family Mor-
aceae, is widely used for its foliage to feed the silk-
worm, Bombyx mori L. (Bombycidae; Bombycoidea)
in China, Japan, India and other sericulturally im-
portant countries. The genus is believed to have
originated at the foothills of the Himalaya and is
distributed into tropical, subtropical and temperate
zones dispersed over centuries through human en-
deavor. Vavilov (1951) while reviewing the centers of
origin of cultivated plants divided phytography into
8 gene centers and placed Morus L. in China-Japan.
Presently, mulberry grows in warm and moist climatic
zones between 50◦N Lat. and 10◦S Lat. (Koidzumi,
1917), which includes South Eastern tip of Asia and
Japan; Java and Sumatra Islands; Oman, districts of
South Eastern end of Arabia; Cacasia, Persia and
West Asia; West Africa and South America includ-
ing Mexico. Taxonomically, the genus Morus L. was
classiﬁed in to ﬁve species- Morus alba L., M. nigra,
M. rubra L., M. tartarica L. and M. indica L. (Lin-
neaus, 1753). Later, many have classiﬁed the genus
based on ﬂoral characters emphasizing on the nature,
shape, length of style and stigma of female inﬂor-
escence (Koidzumi, 1917; Hotta, 1954; Katsumata,
1972). However, very high seed setting percentage has
been obtained in inter-speciﬁc hybridizations (Das &
Krishnaswami, 1965; Dandin et al., 1987; Vijayan et
al., 1994). This clearly brings out the possibility that
mulberry could harbor abundant genetic variations in
wilderness through out-crossing among the species.
Nonetheless, under cultivated conditions, especially in
tropical regions like India and Bangladesh, mulberry is
grown as clonal populations raised through stem cut-
tings, hence expected to have less genetic variability
within the population.
Like many other forage crops, breeding in mul-
berry mainly aimed at enhancing the foliage produc-
tion through heterosis breeding. Since, hybridization
between genetically distant parents yields higher het-
erosis than the one with closely related parents, identi-
ﬁcation of potential parents from genetically divergent
groups is a prerequisite for such breeding program.
Similarly, rooting ability in mulberry is a major factor
deciding the acceptability of a cultivar and is greatly
decided by the geographic origin of the cultivar. Stud-
ies with mulberry cultivars from both tropical and
temperate revealed that the cultivars from the former
regions are having higher rooting potential than those
from the latter regions (Devi & Agarwal, 1989; Sau
et al., 1995; Tikader et al., 1995). Thus, genetically
divergent indigenous mulberry parents are preferred
to exotic ones for breeding programs. Among the in-
digenous cultivars a few are very popular among the
farmers because of their wider adaptability to vary-
ing agro climatic conditions and comparatively better
yield potential. Since the genetic diversity among
these elite cultivars have not yet been worked out, it
was felt necessary to undertake such a study to estim-
ate the degree of genetic divergence present among
these cultivars so that suitable parents could be se-
lected for breeding experiments. Genetic divergent
studies with some of the phenotypic characters showed
existence of signiﬁcant genetic variability among a
number of indigenous cultivars (Mala et al., 1997;
Fotedar & Dandin, 1998; Vijayan et al., 1999). Since
most of the phenotypic characters, especially the met-
ric characters, are highly inﬂuenced by environmental
factors and the experiments were conducted with a
large number of exotic and indigenous cultivars to-
gether, selection of genetically divergent parents from
these indigenous cultivars, on the basis of these results,
often meets difﬁculties. Hence, an investigation on the
genetic variability of 11 cultivars, selected from a wide
range of agro climatic conditions, was undertaken us-
ing molecular markers to aid selection of better parents
The greater usefulness of ISSR markers has
already been established by plant breeders (Nagaoka
& Ogihara, 1997; Tsumura et al., 1996; Fang &
Roose, 1997; Fang et al., 1997; Ratnaparkhe et al.,
1998; Deshpande et al., 2001; Raina et al., 2001; Breto
et al., 2001) around the world. However, ISSR proﬁl-
ing used to be done earlier with radio-labelingor silver
staining on long polyacrylamide gel (Kantety et al.,
1995; Provost & Wilkinson, 1999) whereas the present
study was done with separation of ampliﬁed products
on agarose gel as done by Agaki et al. (1996),Tsumura
et al. (1996) and Nagaoka & Ogihara (1997). Com-
pared to other molecular tools like RAPD, the ISSR
proﬁling is more reliable, reproducible, and easy to
handle (Tsumura et al., 1996; Fang & Roose, 1997;
Fang et al., 1997; Ratnaparkhe et al., 1998). Thus,
ISSR primers were selected for the present study.
Materials and methods
Plant materials used
Eleven popular mulberry cultivars, originated from six
different agro climatic conditions were selected for the
study (Table 1). For each cultivar, leaf samples from 4
clones were selected en masse for DNA extraction.
DNA Extraction from leaf
Genomic DNA from the leaf samples was extrac-
ted using ‘nucleon’ phytopure plant DNA extraction
kit (RPN 8510) from Amersham Life science, Eng-
land following the instructions of the manufacturer.
After extracting the DNA, the required dilution was
achieved through quantiﬁcation of the DNA with 0.8%
agarose gel electrophoresis, stained with ethidium
bromide, against the standard of λDNA (10ng/µl)
PCR ampliﬁcation of the DNA with ISSR primers
A total of 20 di, tri, and penta nucleotide primers
from the University of British Columbia Biotechno-
logy Laboratory primer set 9 were used for PCR
ampliﬁcation of the genomic DNA using 20 µlofre-
action mixture containing 2.0 µl of 10X PCR Buffer
of Genetaq, Genetix, Singapore (100 mM Tris-HCl
Table 1. Name, place of origin, Agro climatic region (as per the planning commission of India), mean longitude and latitude and the
mean morphological characters of the 11 cultivars of mulberry selected for the study
Cultivar Place of origin Agro-climatic region Longitude–Latitude Bran./ Height 100 leaf Leaf area Yield
(mean) plant (cm) wt. (gm) (cm2) (kg/plt/yr)
Almora local Utter Pradesh Upper Gangetic plain 78.31◦E – 28.89◦N 19 163 341 294 0.91
Punjab local Punjab Trans-Gangetic region 75.5◦E – 30.4◦N 35 188 264 304 1.63
Bombay local West Bengal Lower Gangetic region 89.35◦E – 23.0◦N 28 200 224 238 1.04
Mysore local Karnataka Southern plateau and hills 75.0◦E – 15.0◦N 26 163 233 199 1.23
Surat local Gujarat Gujarat plains and hills 72.0◦E – 23.0◦N 29 102 53 57 0.31
Himachal local Himachal P’desh Western Himalyan region 76.1◦E – 32.02◦N 32 215 314 259 0.63
Knava-2 Karnataka Southern plateau and hills 75.0◦E – 15.0◦N 34 135 341 283 1.77
Sujanpur-5 Punjab Trans-Gangetic region 75.5◦E – 30.4◦N 32 148 158 120 1.95
Sujanpur-1 Punjab Trans-Gangetic region 75.5◦E – 30.4◦N 27 150 358 163 1.50
Belidevalaya Karnataka Southern plateau and hills 75.0◦E – 15.0◦N 16 141 200 183 0.93
Tollygunj West Bengal Lower Gangetic region 89.35◦E – 23.0◦N 31 138 60 78 0.39
pH8.8; 500 mM KCl;15 mM MgCl2; 0.1% gelatin;
0.05% Tween 20 and 0.05% NP–40), 2 mM dNTP,
2; 1.0 mM Primer; 20 ng genomic DNA
and 1 unit of Taq polymerase enzyme (Gentaq, Ge-
netix, Singapore). The PCR schedule followed was
94 ◦C for 2 min followed by 35 cycles of 94 ◦Cfor
30 sec, 50 ◦C for 30 sec, 72 ◦Cfor2minandaﬁ-
nal extension of 10 min at 72 ◦C. The PCR product
was separated on 2.0% agarose gel in 1x Tris Boric
Acid buffer containing 5µl/100ml ethidium bromide
as stain. Photographs were taken under UV illumina-
tion with Nikon (FM2) camera using Kodak 400 ASA
ﬁlm. Scoring of bands was on the basis of presence
(=1) or absence (=0) of a particular band. Formamide
at 1.0%, 2.0%, 3.0% and 4% was added to the PCR
mixture to test its efﬁcacy to increase the band clarity,
as reported in some other crops (Fang et al., 1997).
Genetic distance among the genotype was calculated
following the method of Nei & Li (1979) as 1-
2Nij/(Ni+Nj), where Nij is the number of common
bands in i and j cultivars. Cluster analysis of the data
was carried out using nearest neighbor joining method
program in PHYLIP 3.5c (Felsenstein, 1993). The
SPSS/PC+ 10.0 (M.J. Norusis, SPSS Inc., Chicago)
was used for discriminant function analysis to work
out the spatial distribution of cultivars against the
geographic location of their origin. To represent the
states, the corresponding cultivars were provided with
a numerical value and these values were used for
discriminant function analysis (DFA) as shown in Fig-
ure 4. Stepwise linear regression analysis was used
for identifying markers associated with leaf yield. The
association of these two bands with leaf yield veriﬁed
through Beta curve estimation.
ISSR band proﬁles
The initial screening of the primers for clear and
repeatable band proﬁles showed that out of the 20
primers screened, only 13 primers (Table 2) yielded
ampliﬁcation products with clear bands. Of these, nine
primers annealed to di-nucleotide repeats, three an-
nealed to tri-nucleotide repeats and one annealed to
penta-nucleotide repeats. Out of the di-nucleotide re-
peat primers the (AT)n repeat primers did not produce
any ampliﬁcation products, while all the (AG)n, (TG)n
and (AC)n primers gave excellent ampliﬁcation. The
PCR ampliﬁcation products of most of the primers
yielded many deeply stained bands along with a few
weakly stained bands as shown in Figure 1. However,
the weakly stained bands were not considered for ana-
lysis though many of these bands showed variability
among the cultivars. Another important observation,
made in this study, was the effect of formamide on
the clarity of bands. Formamide at 1.0% reduced 2–
3 weakly stained bands in primers 861 and 862 but
at 2.0% it started inhibiting the PCR ampliﬁcation in
all most all the primers, which was evident from the
disappearance of many prominent bands, and at 3.0%
and above no PCR ampliﬁcation was observed.
Figure 1. PCR ampliﬁcation products obtained from ISSR-810
and separated on 2.0% agarose gel. Arrows indicate presence of
A total of 100 bands were scored from the 11 cul-
tivars, of which 72 bands were polymorphic (Table 2).
The maximum number of bands observed in a single
primer was 11, for ISSR 835 [(AG)nYC]. However,
higher percentage of polymorphism was obtained in
both tri (ACC)nand penta (GGGGT)nnucleotide re-
peat primers. Similarly, the highest number of bands
was generated for Himachal local (68) while the least
number was observed for Sujanpur-1 (55) (Figure 2).
Genetic diversity among the 11 cultivars
The genetic distance calculated according to Nei &
Li (1979) revealed maximum genetic distance (0.431)
between Sujanpur-5 and Almora local (Table 3). Sim-
ilarly, the minimum genetic distance, observed among
the cultivars, was 0.053, between Bombay local and
Punjab local. The average genetic distance among the
cultivars was 0.238 ±0.080 (Mean±SD). Regard-
ing the genetic distance among cultivars of the same
states, the two cultivars from Karnataka, Bilidevalaya
and Kanva-2, showed maximum genetic diversity
(0.250), followed by West Bengal cultivars (Bom-
bay local and Tollugunj) (0.246) and the cultivars
from Punjab, Sujanpur-5 and Punjab local revealed a
genetic distance of 0.227.
Cluster analysis of the cultivars
The dendrogram realized through nearest neighbor
joining method grouped the 11 cultivars into three
broad clusters (Figure 3). The cluster ‘A’ has three cul-
tivars namely Mysore local, Kanva-2 and Sujanpur-5.
The average cluster distance for this group was 0.188
±0.038. The cluster ‘B’ comprised of Sujanpur-1,
Tollygunj and Bilidevalaya, all these cultivars were
originated in three different agro climatic conditions.
However, the mean genetic distance among these cul-
tivars (0.140 ±0.033) was less than that of the cluster
‘A’. The cluster ‘C’ was the biggest one and it was
composed of the 5 cultivars, Almora local, Pun-
jab local, Bombay local, Himachal local and Surat
local. These cultivars were mostly from the northern
and western parts of India. This group bore presum-
ably a high genetic diversity among the cultivars,
which was evident from the average genetic distance
(0.229±0.109) observed among them. It could also be
observed that the maximum genetic distance, from the
central node of the dendrogram (0.313) was found in
Almora local of the cluster ‘C’.
Discriminant function analysis
The discriminant function analysis of the cultivars
against the place of its origin revealed that the ﬁrst
two Eigen values accounted for 91.2%of the total vari-
ability (Table 4) and the canonical correlation for the
ﬁrst two functions were very high, 0.932 and 0.766.
The value of the unstandardized canonical functions
at group centroid revealed that the values for func-
tions three to ﬁve were mostly far low magnitude.
Between the ﬁrst two values the highest (3.27) was
Figure 2. Histogram showing total number of markers generated by 13 ISSR primers used for each cultivars.
Figure 3. Dendrogram realized from genetic dissimilarity matrix using nearest neighbor method.
Table 2. Polymorphism generated by the 12 selected ISSR primers in 11 mulberry cultivars
Primer code Sequence Total bands Polymorphic Plymorphism (%)
809 AGA GAG AG A GAG AG A GG 8 4 50. 00
810 GAG AGA GAG AGA GAG AT 8 6 75.00
811 GAG AGA GAG AGA GAG AC 9 8 88.89
812 GAG AGA GAG AGA GAG AA 10 7 70.00
825 ACA CAC AC A CAC ACA CC 6 4 66 .6 7
830 TGT GTG TGT GTG TGT GG 7 5 71.43
834 AGA GAG AG A GAG AG A GYT 8 4 50 .0 0
835 AGA GAG AG A GAG AG A GYC 11 6 54.55
850 GT GT GT GT GT GT GT GTYA 1 0 0.00
861 ACC ACC ACC ACC ACC ACC 8 8 100.00
862 AGC AGC AGC AGC AGC AGC 9 5 55.58
864 ATG ATG ATG ATG ATG ATG 4 3 75.00
881 GGGG TGG GGT GGG GTG 11 11 100.00
Total 100 72 72.00
Y = pyrimidine.
Table 3. Genetic distance among the 11 cultivars of mulberry according to the method of Nie & Li (1979)
Cultivars Almora Punjab Bombay Mysore Surat Himachal Kanva-2 Sujanpur-5 Suanpur-1 Belide
local local local local local local valaya
Punjab local 0.136
Bombay local 0.344 0.053
Mysore local 0.398 0.222 0.225
Surat local 0.377 0.216 0.188 0.220
Himachal local 0.380 0.167 0.170 0.277 0.256
Kanva-2 0.400 0.236 0.270 0.157 0.217 0.276
Sujanpur-5 0.431 0.227 0.262 0.231 0.224 0.220 0.175
Sujanpur-1 0.415 0.206 0.194 0.194 0.203 0.246 0.190 0.145
Belidevalaya 0.426 0.216 0.188 0.220 0.180 0.225 0.250 0.190 0.122
Tollygunj 0.403 0.260 0.246 0.216 0.210 0.237 0.213 0.220 0.120 0.177
observed for Gujarat and the minimum was observed
for Karnataka (–0.008). The discriminant function plot
realized from the ﬁrst two canonical functions (Fig-
ure 4) revealed that considerable genetic variability
was present among the cultivars included under West
Bengal, Karnataka and Punjab. This is evident from
the fact that the distribution of the cultivars from West
Bengal is extended from the ordinates of 1.8 to 1.0
X-axis by –2.5 to –0.3 Y-axis, overlapping with the
group centroid of Uttar Pradesh. Like wise, the variab-
ility among the cultivars of Karnataka is extended over
the area delineated by –3 to 1.0 X-axis by 0.9 to 1.2
Y-axis and the area included the group centroid of Pun-
jab. The orientation of the cultivars from Punjab was
covering a wide region extending beyond the centroid
of Utter Pradesh and nearly touching the centroid of
Himachal Pradesh. Gujarat stood far apart from all
others at the intersection of 1.8 in the Y-axis and 3.25
at the X-axis. This, is in agreement with the genetic
distance estimated according to the methods of Nie &
Linear regression analysis and curve estimation
Step-wise linear regression analysis of the ISSR mark-
ers against the dependent variable, leaf yield, had
identiﬁed two ISSR markers 825.1400 and 835.750 hav-
ing positively signiﬁcant correlation (R2= 0.720 and
0.678 respectively) with leaf yield. The curve estim-
ation of these bands against the leaf yield generated
linear, quadratic and exponential regression ﬁgures
Table 4. Eigenvalues for the ﬁrst 5 canonical functions identiﬁed through discriminant
function analysis of the ISSR markers against the place of origin of the cultivars
Function Eigenvalue % of Cumulative Canonical
1 6.585 73.2 73.2 0.932
2 1.619 18.0 91.2 0.786
3 0.463 5.1 96.3 0.563
4 0.263 2.9 99.2 0.456
5 0.070 0.8 100.0 0.256
Unstandardized canonical functions at group centroids
Utter Pradesh 1.256 –0.536 0.331 0.811 0.312
Punjab 0.103 0.337 0.666 –0.188 –0.005
West Bengal 1.480 –1.401 –0.285 –0.284 –0.002
Karnataka –2.243 –0.008 –0.247 0.195 –0.108
Gujrat 3.271 1.586 –0.582 0.214 –0.179
Himachal Pradesh –1.065 1.001 –0.437 0.425 0.417
Figure 4. A scatter plot showing the distribution of states, on the basis of the genetic variability revealed by the cultivars through ISSR
markers, against the ﬁrst two canonical discriminant functions. Squares denote the group centroid while respective markings are used to denote
the distribution of members belonging to each group.
Table 5. Regression coefﬁcient of the identiﬁed ISSR markers with leaf yield in
DNA marker Regression Regression d.f. F-value Signiﬁcance
analysis coefﬁcient (R2)
825.1400 Linear 0.518 9 9.69 0.012
Exponential 0.518 9 9.69 0.012
Quadratic 0.425 9 6.65 0.030
835.750 Linear 0.460 9 7.68 0.022
Quadratic 0.460 9 7.68 0.022
Exponential 0.556 9 11.26 0.008
Figure 5. Curve estimation of the ISSR markers, 825.1400 and
835.750, against leaf yield in the regression plot.
(Figure 5), where in the linear quadratic and expo-
nential regression coefﬁcient values [R2]fortheband
825.1400 were 0.450, 0.450 and 0. 550 respectively
(Table 5). The same for the band 835.750 were 0.518,
0.158 and 0.425 respectively.
The genetic distance within a crop species provides
a measure of the average genetic divergence among
cultivars of the species. Relationships derived from
agronomic traits proved to be useful for the analysis
of variability (Smith, 1984), selection of parents for
hybridization (Frei et al., 1986) and for prediction of
progeny performance (Graﬁus, 1956). However, with
recent advances in molecular biology, analysis of ge-
netic variability with molecular markers is preferred to
that with agronomic traits as it is not inﬂuenced by the
environmental factors. Among the various molecular
markers, ISSR proﬁling is one of the most reliable
tools extensively used in many crop plants to work
out the genetic relation ships (Rafalski et al., 1991;
Devos & Gale, 1992; Fang & Roose, 1997; Fang et
al., 1997; Nagaoka & Ogihara, 1997), to solve issues
related with genetics and structures of speciﬁc popu-
lations (Culley & Wolfe, 2001) and also to identify
genes associated with many economically important
characters like disease resistance (Ratnaprakhe et al.,
Genetic variations in mulberry were studied
mainly on the basis of morpho-biochemical characters
(Mala et al., 1997; Fotedar & Dandin, 1998; Vijayan
et al., 1999). Most of these studies restricted their aim
to work out the genetic diversity among different spe-
cies, clubbing both exotic and indigenous accessions
together. Vijayan et al. (1999) made attempts to es-
timate the genetic diversity among the 62 mulberry
cultivars indigenous to India, irrespective of their spe-
cies and ploidy status. Signiﬁcant genetic divergence
was observed among these indigenous mulberry cul-
tivars on the basis of the leaf yield traits. Later,
isozyme patterns were used for identifying genetic-
ally divergent plants among the mulberry (Hirano &
Naganuma, 1979; Kitaurak, 1983; Ventkataswarlu et
al., 1989). In the meanwhile, attempts were also made
to use RAPD primers for estimating the genetic vari-
ation in parents as well as hybrids (Lou et al., 1998;
Weiguo et al., 2000). However, no work has been
reported yet using more stable and reliable markers
like ISSR and RFLP. In this study, for the ﬁrst time,
attempts were made to estimate genetic divergence
among closely related cultivars, which are very popu-
lar among the farmers due to their higher adaptability
to various agro climatic conditions and better yield po-
tential. The use of ISSR markers in the present study
has brought out certain interesting facts as quite con-
trary to the popular perception. Wang et al. (1994)
commented that the most abundant di-nucleotide re-
peat sequence in plant nuclear DNA is (AT)n, followed
by (A)n/(T)nand (AG)n/(CT)n. Common tri- and
tetra nucleotide repeat motifs include (AAT)n/(ATT)n,
(GT)nsequences. However, in the present study
it could be seen that none of the (AT)nprimers
could amplify the genomic DNA. However, excel-
lent result was obtained from (AG)nand (GA)nrepeat
primers. Similarly the tri-nucleotide repeat (ACC)n
has given clear band proﬁles with 100% polymorph-
ism. Tsumura et al. (1996), also found (AG)n/(CT)n
primers as suitable for Pseudotsuga menziesii and
Crptomeria japonica genomes. Likewise, Fang et al.
(1997) got excellent results with (CA)nprimers in
The ineffectiveness of formamide in increasing the
clarity of bands is another point of importance. In
many of the earlier studies with other taxa, it was
observed that formamide helps to increase the clar-
ity of the bands by eliminating weaker bands and
streak formations (Tsumura, 1996; Fang et al., 1997).
However, in the present investigation addition of form-
amide did not show much effect on the clarity of
the bands, though it eliminated a few weakly stained
bands at 1% level in two of the primers (UBC-861 and
UBC-862). Thus, use of formamide in mulberry for
ISSR primers was not found necessary as it interfered
with PCR ampliﬁcation of the DNA.
The cluster analysis has grouped the 11 cultivars,
which are being widely cultivated at different parts
of India and were collected for the study from tradi-
tional sericulture zones, into three distinct groups. The
grouping has highlighted some interesting pattern as
some of the cultivars having close geographic origin
got themselves grouped in one cluster as in the case
of the Northern and Western Indian cultivars. While
the South-Indian and East-Indian cultivars along with
two of the Punjab cultivars were grouped into two
separate clusters. Among the south Indian cultivars,
Bilidevalaya, which is similar to Tollygunj in leaf mor-
phology, especially in leaf lobation, has come very
closer to it. In an earlier study, Vijayan et al. (1999) ap-
plied Mahalanobis D2analysis to group 62 accessions
on the basis of agronomic traits and the same resul-
ted to the formation of seven clusters, one of which
included 43 accessions. This big cluster included cul-
tivars like Almora local, Punjab local, Sujanpur-5,
Tollygunj. However, these cultivars could be differen-
tiated into separate groups on the basis of molecular
markers, in the present study. Similarly, in the earlier
study the Mysore local and Kanva-2 had been found
in separate groups but in this study these two from
Karnataka were grouped in one cluster. Thus, the
present study, using molecular marker, could bring
out more rational status of the genetic relationship
among these most popular cultivars. In this regard, it
is better to note that the ﬁndings of Fang & Roose
(1997), Nagaoka & Ogihara (1997) and Raina et al.
(2001) using both RAPD and ISSR along with RFLP
methods have clearly demonstrated the usefulness of
ISSR primers in delineating the genetic relationships
among closely related cultivars. Breto et al. (2001)
could even work out the variability present among the
clones of a vegetatively propagated crop such as Citrus
clementina. Thus, the genetic diversity obtained in this
study could be of much use to the breeders of mulberry
in India as well as other tropical countries.
Discriminant function analysis is principally done
to test the veracity of linear relationship between in-
dependent variables and the groups (Press & Wilson,
1978) and in this case the idea is to check whether mo-
lecular proﬁling substantiates the geographic group-
ings on the basis of origin of the cultivars. The high
contribution of the ﬁrst two canonical functions, their
signiﬁcant correlation, and the distribution of the loca-
tions on the canonical discriminant plot on the basis of
the genetic variability of the cultivars conﬁrm presence
of signiﬁcant genetic variability among the cultivars.
The overlapping of the members of the groups of West
Bengal, Punjab and Karnataka revealed by DFA is ad-
ditional information not revealed by cluster analysis.
In this context it is to be noted that prior to nineteenth
century sericulture was primarily conﬁned to the then
Bengal Presidency and genetic materials along with
sericulture technology moved from there to other parts
of India (Chatterjee, 1993; Ravindran et al., 1997).
In twentieth century, Karnataka became very active
in collecting genetic resource materials from different
parts of India. The overlapping of genetic variability
for West Bengal, Karnataka and Punjab is an import-
ant information supporting this genetic dispersal of
mulberry through human endeavor (Chatterjee et al.,
Genetic markers with high ﬁdelity is very useful
for any breeding programs. In mulberry, so far no such
genetic markers were identiﬁed, which could be used
for ‘marker assisted selection’ (MAS) in parents and
hybrids. In this study, for the ﬁrst time, two genetic
markers namely 825.1400 and 835.750, were identiﬁed
through stepwise linear regression of the DNA makers
with leaf yield. Later, the signiﬁcance of these mark-
ers was further tested through estimation of regression
curve with leaf yield. The high correlation and regres-
sion coefﬁcient obtained indicated that these markers
could be used to identify high yielding hybrids in mul-
berry breeding. The identiﬁcation of DNA markers
associated with yield components through regression
statistics has been attempted not only in this lab (Se-
thuraman et al., 2002) but also by others working
on plant (He et al., 2002) as also in animal system
(Yonash et al., 2000) However, inheritance of these
markers is to be investigated in detail for better ge-
netic understanding of these markers. Attempt in this
direction is under way and it may take some time,
considering the long juvenile period of mulberry.
Authors express their gratitude to Dr. B. Saratchandra,
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