Hereditas 147: 142–153 (2010)
Genetic diversity in watermelon (Citrullus lanatus) landraces from Zimbabwe revealed by RAPD and SSR markers
C. MUJAJU 1 , J. SEHIC 1 , G. WERLEMARK 1 , L. GARKAVA-GUSTAVSSON 1 , M. FATIH 2and H. NYBOM 1
1Department of Plant Breeding and Biotechnology, Swedish University of Agricultural Sciences, Balsg å rd, Kristianstad, Sweden 2 CBM The Swedish Biodiversity Centre, Alnarp, Sweden
Mujaju, C., Sehic, J., Werlemark, G., Garkava-Gustavsson, L., Fatih, M. and Nybom, H. 2010. Genetic diversity in watermelon
( Citrullus lanatus ) landraces from Zimbabwe revealed by RAPD and SSR markers. – Hereditas 147: 142–153. Lund, Sweden.
eISSN 1601-5223. Received December 1, 2009. Accepted May 19, 2010.
Low polymorphism in cultivated watermelon has been reported in previous studies, based mainly on US Plant Introductions and
watermelon cultivars, most of which were linked to breeding programmes associated with disease resistance. Since germplasm
sampled in a putative centre of origin in southern Africa may harbour considerably higher variability, DNA marker-based diversity
was estimated among 81 seedlings from eight accessions of watermelon collected in Zimbabwe; fi ve accessions of cow-melons
( Citrullus lanatus var. citroides) and three of sweet watermelons (C . lanatus var. lanatus) . Two molecular marker methods were
used, random amplifi ed polymorphic DNA (RAPD) and simple sequence repeats (SSR) also known as microsatellite DNA. Ten
RAPD primers produced 138 markers of which 122 were polymorphic. Nine SSR primer pairs detected a total of 43 alleles with an
average of 4.8 alleles per locus. The polymorphic information content (PIC) ranged from 0.47 to 0.77 for the RAPD primers and
from 0.39 to 0.97 for the SSR loci. Similarity matrices obtained with SSR and RAPD, respectively, were highly correlated but only
RAPD was able to provide each sample with an individual-specifi c DNA profi le. Dendrograms and multidimensional scaling (MDS)
produced two major clusters; one with the fi ve cow-melon accessions and the other with the three sweet watermelon accessions. One
of the most variable cow-melon accessions took an intermediate position in the MDS analysis, indicating the occurrence of gene
fl ow between the two subspecies . Analysis of molecular variation (AMOVA) attributed most of the variability to within-accessions,
and contrary to previous reports, sweet watermelon accessions apparently contain diversity of the same magnitude as the
cow-melons.
Hilde Nybom, Balsg å rd, Department of Plant Breeding and Biotechnology, Swedish University of Agricultural Sciences, Fj ä lkestadsv ä gen 459, SE-291 94 Kristianstad, Sweden. E-mail: [email protected].
Citrullus lanatus , commonly known as watermelon and
belonging to Cucurbitaceae, is an important food crop in
many African countries. This annual diploid (2n � 2x � 22)
(S HIMOTSUMA 1963) species grows as a vine with a climb-
ing or sprawling growth habit, large green leaves with
three to fi ve deep lobes, medium-sized monoecious and
often bee-pollinated fl owers with short pedicels, medium
to large fruit with smooth skin and fl esh with a high water
content, and oval to oblong seeds of a white, grey, red or
brown colour. Watermelon has a centre of diversity in the
southern part of the continent which could also be the area
of origination ( RUBATZKY 2001; DANE and LANG 2004).
Two major forms of watermelon occur: C. lanatus var.
lanatus , the sweet watermelons and C. lanatus var. citroi-des, the cow-melons (citron and tsamma types) which,
although non-bitter, are not sweet. Citrullus lanatus var.
citroides is often cultivated but a diversity of feral forms
also exist. By contrast, C. lanatus var. lanatus is only
known from cultivation and has emerged as a result of a
domestication process involving selection for reddish
colour and sweetness. Thus, less variation can probably
© 2010 The Authors. This is an Open Access article.
be expected among sweet watermelons compared to cow-
melons.
In Africa, watermelon cultivation is prevalent in
drought-prone, semi-arid areas with an annual rainfall
below 650 mm. In these areas, watermelon is grown as a
staple food (edible seeds), a dessert (edible fl esh), and for
animal feed. The fruit can be eaten fresh or cooked. The
rind can be pickled or candied, while the seeds are baked
or roasted for consumption. Cultivation is based on seed-
propagated landraces and farmer varieties that have been
integrated with the indigenous knowledge, agricultural
practices, food habits and cultural dynamics of the rural
communities. Traditionally grown sweet watermelons and
cow-melons can be white-, yellow-, orange- or red-fl eshed
and have different fruit shapes and seed coat patterns
including colour variation of both fruit rinds and seeds.
Two main types of sweet watermelon are recognized:
vulgaris and mucosospermus . The vulgaris types are the
most widely cultivated forms and have red-fl eshed and
sweet fruits whereas the mucosospermus types belong to
the egusi watermelon, grown in west Africa, where the
DOI: 10.1111/j.1601-5223.2010.02165.x
Hereditas 147 (2010) Genetic diversity in watermelon 143
soft seeds are used as a source of edible oil ( JEFFREY 2001).
In comparison to the sweet watermelons, cow-melons
have a longer shelf life and can be stored for more than a
year under shade. The cow-melons are consumed mainly
after cooking to produce a meal called Nhopi in the Shona
language, and are also used as livestock feed.
In a recent study, DAVIS et al. (2007) screened world-
wide watermelon germplasm for resistance to powdery
mildew, and found that 36% and 15% of the 93 most resis-
tant accessions originated from Zimbabwe and Zambia,
respectively. This diversity is essential as it offers the
opportunity for production diversifi cation and the devel-
opment of new farming systems and new quality products
( BRUSH 2000). Although still very valuable in traditional
agrosystems, C. lanatus is presently, however, regarded
as a neglected and marginalized crop species in Africa,
and therefore treated as a mandate species for conserva-
tion by the Southern African Development Community
(SADC) Plant Genetic Resources Centre and the National
Plant Genetic Resources Centre Regional Network.
For effective conservation of watermelon, it is impor-
tant to obtain information about genetic diversity within
and between accessions. Few such studies have however
been conducted in southern Africa, except for an investi-
gation of morphological diversity in landraces of Citrullus
in Namibia ( MAGGS-KOLLING et al. 2000).
Other diversity studies have been conducted on a global
scale, based mainly on modern cultivars and United States
plant introductions (PIs) of selected accessions from Afri-
can countries. Thus, LEE et al. (1996) used RAPD markers
to estimate genetic diversity among watermelon cultivars,
and to construct an initial genetic linkage map for water-
melon. J ARRET et al. (1997) used SSR markers to deter-
mine genetic variation among PI accessions of C . lanatus
var. lanatus , C . lanatus var. citroides and the wild species
C . colocynthis , and delineated 4 groups at the 25% level
of genetic similarity. The largest group contained C. lana-tus var. lanatus accessions including the egusi watermel-
ons from Nigeria, the second only wild and cultivated
‘ citron ’ -type or C. lanatus var. citroides accessions, the
third an accession tentatively identifi ed as C. lanatus var.
lanatus , presumably a hybrid between C. lanatus var.
lanatus and C. lanatus var. citroides , and the fourth group
consisted of a single accession identifi ed as C. colocyn-this . In a RAPD-based study, LEVI et al. (2001a) found
low genetic diversity among 46 heirloom cultivars of
watermelon and concluded that cultivated watermelon has
a narrow genetic base. Furthermore, LEVI et al. (2001b)
assessed RAPD diversity in PIs, and found three groups
consisting of C . lanatus var. lanatus , C . lanatus var. cit-roides and C . colocynthi s, respectively.
Although to a considerable extent based on United
States plant introductions originally stemming from south-
ern Africa, the analysed material in the above-mentioned
studies is unlikely to represent the actual variation pres-
ently nurtured on traditional African farms where land-
races consist of variable but identifi able populations that
lack ‘ formal ’ improvement. Therefore, objectives of the
present investigation were to (1) to assess levels of intra-
and inter-accession diversity in some C. lanatus samples
collected in Zimbabwe and to estimate relatedness among
these accessions, and (2) to investigate the level of con-
gruence between RAPD and SSR fi ndings.
MATERIAL AND METHODS
Plant material and DNA extraction
Seeds from 10 watermelon accessions were obtained
from the National Plant Genetic Resources Center of
Zimbabwe (Fig. 1). Each accession consists of a batch
of seed obtained from a local farmer who allegedly has
harvested this seed from a single plant grown on his
farm. The accessions were collected in areas inhabited
by two distinct groups of people, the Shona (provinces
Mashonaland and Masvingo, cow-melons) and the
Ndebele (provinces Matabeleland and Midlands, sweet
watermelons). The seeds were germinated at 25 ° C in a
greenhouse at Balsg å rd in Sweden but two of the sweet
watermelon accessions never produced any seedlings. A
total of 81 plants were chosen for this study (Table 1).
DNA was extracted from young leaf tissue using the
E.Z.N.A. TM SP Plant DNA Mini Kit (Omega Bio-Tek,
Norcross, GA, USA). DNA concentration was estimated
visually using DNA low mass ladder (Invitrogen TM Life
Technologies, Carlsbad, CA, USA) and electrophoresis
in a 2% agarose gel.
RAPD analysis
The PCR protocol for RAPD primers used a total volume
of 25 μ l, containing 0.2 μ l of 5 U μ l �1 Taq DNA poly-
merase (Amersham Biosciences, Uppsala, Sweden), 3 μ l
of DNA template (10 ng μ l �1 ), 1.0 μ l of each primer (5 μ M)
(Eurofi ns MWG Operon, Ebersberg, Germany), 16.2 μ l
dH 2 O, 0.5 μ l of 10 mM dNTPs, 1.6 μ l of 25 μ M MgCl 2
and 2.5 μ l of reaction buffer (Thermo Fisher Scientifi c,
Surrey, UK). PCR was performed with a P x 2 Thermocy-
cler (Thermo Hybaid, Ulm, Germany) programmed for 45
cycles of 94 ° C for 15 s, 36 ° C for 45 s (with a ramp rate of
0.4 ° C s -1 ), and a fi nal extension of 72 ° C for 1.5 min. The
amplifi ed products were separated by electrophoresis in a
1.8% agarose gel, stained with ethidium bromide and pho-
tographed under UV illumination. Only clearly visible
DNA fragments with a length between 150 and 2200 bp
were used as markers. Scoring for the presence or absence
of DNA fragments was aided by the use of a 1 kb DNA
ladder and a control sample, which was run in triplicate to
check for reproducibility. Initially, a total of 27 RAPD
144 C. Mujaju et al. Hereditas 147 (2010)
Tab
le 1
. W
ithi
n-ac
cess
ion
gene
tic
vari
atio
n of
wat
erm
elon
(C
M c
ow-m
elon
, SW
M s
wee
t w
ater
mel
on)
coll
ecte
d in
Zim
babw
e, e
stim
ated
as
mea
n va
lue
for
Jacc
ard ’
s si
mil
arit
y co
effi c
ient
(%
JSC
), p
erce
ntag
e po
lym
orph
ic b
ands
/all
eles
, exp
ecte
d he
tero
zygo
sity
( H
E ), o
bser
ved
hete
rozy
gosi
ty (
H O )
and
Sha
nnon
’ s in
dex
(I).
Sta
ndar
d er
rors
are
indi
cate
d in
par
enth
esis
. NP
L is
num
ber
of p
lant
s sa
mpl
ed.
Acc
essi
on n
o. (N
PG
RC
)N
PL
RA
PD
SS
R
%JS
C%
PL
H E
* *
I%
JSC
%P
LH
E * *
H O
I
CM
-2643
11
81.4
150.0
00.1
7 (
0.0
6)
0.2
5 (
0.0
9)
84.7
166.6
70.3
0 (
0.0
5)
0.4
6 (
0.0
8)
0.4
6 (
0.0
7)
CM
-2645
11
73.4
963.9
30.2
2 (
0.0
6)
0.3
3 (
0.0
9)
79.6
688.8
90.3
9 (
0.0
4)
0.5
3 (
0.0
7)
0.6
0 (
0.0
6)
CM
-2650
12
81.5
140.9
80.1
5 (
0.0
6)
0.2
2 (
0.0
8)
78.6
677.7
80.3
7 (
0.0
6)
0.5
6 (
0.0
9)
0.6
0 (
0.1
0)
CM
-2746
15
82.4
644.2
60.1
3 (
0.0
5)
0.2
0 (
0.0
7)
90.2
666.6
70.2
4 (
0.0
4)
0.3
6 (
0.0
8)
0.3
8 (
0.0
6)
CM
-2768
13
82.1
140.9
80.1
4 (
0.0
5)
0.2
1 (
0.0
8)
94.9
955.5
60.2
1 (
0.0
5)
0.3
9 (
0.0
9)
0.3
1 (
0.0
7)
* S
WM
-2839
1–
––
––
––
––
SW
M-2
854
880.1
939.3
40.1
4 (
0.0
7)
0.2
1 (
0.1
0)
73.5
077.7
80.3
7 (
0.0
6)
0.3
6 (
0.1
0)
0.6
0 (
0.1
0)
SW
M-2
879
10
72.6
050.8
20.1
9(0
.06)
0.2
8 (
0.0
9)
81.6
277.7
80.3
0 (
0.0
6)
0.3
9 (
0.1
1)
0.4
8 (
0.0
9)
Cow
-mel
on (
CM
) G
roup
62
69.4
648.0
30.2
4 (
0.0
2)
0.3
8 (
0.0
3)
71.6
088.8
90.4
1 (
0.0
2)
0.4
5 (
0.0
3)
0.7
6 (
0.0
4)
Sw
eet
wat
erm
elon (
SW
M)
G
roup
18
68.8
745.0
80.1
9 (
0.0
5)
0.2
9 (
0.0
7)
71.5
1100
0.3
7 (
0.0
4)
0.3
8 (
0.0
7)
0.6
3 (
0.0
7)
NB
. * A
cces
sion 2
839 w
ith a
sin
gle
pla
nt
was
incl
uded
in c
lust
er a
nal
ysi
s an
d o
rdin
atio
n, * * N
EI ’
s ex
pec
ted h
eter
ozy
gosi
ty.
primers were screened using four DNA samples. Ten
primers were used on the entire material (Table 2).
SSR analysis
The PCR protocol for SSR primers used a total volume of
10 μ l, containing 0.1 μ l of 5 U μ l -1 Taq DNA polymerase
(Amersham Biosciences, Uppsala, Sweden), 0.1 μ l of each
primer (100 μ M) (Eurofi ns MWG Operon, Ebersberg,
Germany), 7.1 μ l dH 2 O, 1 μ l of DNA template (10 ng
μ l �1 ), 0.2 μ l of 10 mM dNTPs, 0.4 μ l of 25 mM MgCl 2
and 1 μ l of reaction buffer (Thermo Fisher Scientifi c,
Surrey, UK). PCR was performed with a VWR Unocycler
(VWR, Stockholm, Sweden), programmed as: 94 ° C for
4 min, 34 cycles of 30 s at 94 ° C, 30 s at the appropriate
annealing temperature (Table 2), 30 s at 72 ° C, and a fi nal
extension of 7 min at 72 ° C. 4.0 μ l of the reaction volume
from ten randomly selected samples was checked for suc-
cessful amplifi cation on 2% agarose gels with subsequent
visualization of fragments using UV illumination.
Nine SSR primer pairs originally published by JOOBEUR
et al. (2006) were chosen based on recommendations by
Brita Dahl Jensen of the Dept of Agricultural Sciences,
Univ. of Copenhagen (Table 4). For proper separation of
fragments and their size determination, primers were fl uo-
rescently labeled at the 5 ́ -end with either FAM (MCP1-07,
MCP1-13, MCP1-21, MCP1-32, MCP1-37) or HEX
(MCP1-03, MCP1-12, MCP1-14, MCP1-28). The PCR
products were separated and analysed with capillary elec-
trophoresis on a 3730 DNA Analyser (Applied Biosys-
tems, Carlsbad, CA, USA). The size of the amplifi ed
products was calculated based on an internal standard
Fig. 1. A map of Zimbabwe showing collection sites. Provinces
indicated are: MSV – Masvingo, MTS – Matabeleland South,
MDL – Midlands, MSC – Mashonaland Central and MSW –
Mashonaland West.
Hereditas 147 (2010) Genetic diversity in watermelon 145
PrimerNucleotide sequence (5′ — � 3 ′ ) PM MM PIC RMI *
OPT-01 GGGCCACTCA 16 0 0.47 7.52
OPE-04 GTGACATGCC 6 2 0.53 3.18
OPK-14 CCCGCTACAC 16 1 0.65 10.40
OPD-20 ACCCGGTCAC 14 3 0.65 9.10
OPK-20 GTGTCGCGAG 15 0 0.68 10.20
OPC-05 GATGACCGCC 11 6 0.70 7.70
OPB-11 GTAGACCCGT 10 0 0.71 7.10
OPJ-13 CCACACTACC 12 1 0.72 8.64
OPT-05 GGGTTTGGCA 11 2 0.73 8.03
OPJ-06 TCGTTCCGCA 11 1 0.77 8.47
Total 122 16
Source of variation RAPD SSR
(a) Partitioning all accessions
G ST 0.48 0.12
Φ ST 0.47 * 0.11 *
(b) Partitioning with two major forms of cow-melons and sweet watermelons
Between group diversity (AMOVA) 43.70% * 0.83% *
Between accessions within groups (AMOVA)
17.22% * 10.02% *
Within accession diversity (AMOVA)
39.08% * 89.16% *
(c) Partitioning per each major form Cow-melons
G ST 0.34 0.11
Φ ST 0.34 * 0.10 *
Sweet watermelons
G ST 0.14 0.11
Φ ST 0.12 * 0.10 *
(500 ROXTM Size Standard) with GeneMapper ® Soft-
ware ver. 3.0 (Applied Biosystems, Carlsbad, CA, USA).
A manual binning step was included to assign all detected
alleles to repeat unit equivalents.
Data analysis
RAPD data
Each RAPD band was considered as an independent locus,
and polymorphic bands were scored as absent (0) or pres-
ent (1) for all the 81 plants. Polymorphic bands were then
used in the subsequent analyses. To evaluate the informa-
tiveness of each RAPD primer, a polymorphic index con-
tent (PIC) was calculated for each band according to SMITH
et al. (1997), as follows: PIC � 1 �∑Pi 2 , where P i is the
band frequency of the i-th allele. A marker index for each
of the RAPD primers was obtained by multiplying PIC-
value by number of polymorphic loci. A pairwise genetic
similarity matrix was generated using Jaccard similarity
coeffi cient ( WEISING et al. 2005). Variation within
accessions was estimated with four different parame-
ters: (1) mean percentage polymorphic bands, (2) mean
Jaccard similarity, (3) the expected heterozygosity which is
equivalent to Nei’s unbiased gene diversity H S ( NEI 1978)
when calculations are based on polymorphic and biallelic
loci, and when sample sizes are equal among populations,
and the Shannon diversity index ( WEISING et al. 2005).
Variation among accessions was calculated as the coef-
fi cient of genetic differentiation G ST (equivalent to the
fi xation index F ST for biallelic loci) according to the for-
mula G ST � (H T – H S )/H T where H T is the total genetic
diversity and H S is the mean within-accession diversity
( NEI 1977). Gene diversity parameters were obtained
using POPGENE ver. 1.32 ( YEH et al. 1997), assuming
Hardy-Weinberg equilibrium since watermelon plants
have mainly unisexual fl owers and are expected to be out-
crossing to a high degree. Analysis of molecular variance
(AMOVA) using Arlequin ver. 3.0 ( EXCOFFIER et al. 2005)
was calculated to partition genetic variation at different
levels; between sweet watermelons and cow-melons, and
between and within accessions. Levels of similarity (relat-
edness) among and within accessions were quantifi ed with
an UPGMA (unweighted pair-group method using arith-
metic averages) cluster analysis using NTSYS-pc, ver.
1.80 ( ROHLF 1993). Distortion was estimated with a
cophenetic correlation analysis between the Jaccard simi-
larity matrix and a similarity matrix generated from the
dendrogram.
SSR data
For single-locus evaluation of the SSR data, alleles at
each locus were assigned letter codes. PIC values for
all loci were calculated based on the allele frequencies.
POPGENE was used to calculate percentage polymor-
phic alleles within accessions, expected heterozygosity
H E , observed heterozygosity H O , and the Shannon
index. G ST values (weighted average of F ST for all
alleles) were calculated for differentiation among
accessions. Several AMOVAs were calculated to esti-
mate the partitioning of genetic variation at different
levels. Alternate homozygotes were assigned as 1 or 0,
and heterozygotes were given a value of 0.5 following
Table 2. Nucleotide sequences of RAPD primers used in the present study, number of polymorphic (PM) and monomorphic (MM) bands produced by each primer, PIC values and marker index values .
* RAPD marker index.
Table 3. Partitioning of genetic variation using G ST and AMOVA on both RAPD and SSR data taking into account (a) no prior grouping of accessions, and (b, c) grouping into two major forms (cow-melons and sweet watermelons)
* Signifi cant at 1%, P � 0.01
146 C. Mujaju et al. Hereditas 147 (2010)
Designation in ref * forward/reverse 5 ’ to 3 ’ F- sequence3 ’ to 5 ’ R- sequence SSR motif AT( o C) AN Fragment size PIC
MCPI-07-M13FMCPI-07-R
GGTTATGGCCATCTCTCTGCGAGAGTGGGCGTAAGGTGAG
(AAG)9 55 3 236/253/255 0.39
MCPI-32-M13FMCPI-32-R
AAGGCTGCAGAGACCATGACAATGATGAAGAACGGGCAAG
(AAG)5(ATC)8 55 3 265/268/271 0.77
MCPI-28-M13FMCPI-28-R
AATGTTAAGCAGTAAGCACATGGACACCGGAGAAGGTGAATTG
(AAG)9 55 3 273/282/283 0.79
MCPI-03-M13FMCPI-03-R
GCATAAACCACCTGTGAGTGGATGGCTTTGCGTTTCATTTC
(TG)8 55 4 195/200/215/220 0.80
MCPI-12-M13FMCPI-12-R
GGAGTAGTGGTGGAGACATGGTCCTTTCTCTTTCGCAAACTTC
(AAG)7N69(AT)26 55 5 154/170/230/233 /247 0.80
MCPI-37-M13FMCPI-37-R
AATCTTCCCCATGCCAAAACGACTTCCAAACCCTCCCTTC
(AAT)9 55 5 123/165/177/192/220 0.87
MCPI-21-M13FMCPI-21-R
AAAGTTTTCATGCCAACGTATCTCAGCCAATATGGTCAAATAGC
(AG)11 55 5 181/184/192/195 /200 0.88
MCPI-13-M13FMCPI-13-R
TTCCTGTTTCATGATTCTCCACTCAGAATGGAGCCATTAACTTG
(AG)25 55 7 208/210/214/216 /218 /220/222
0.88
MCPI-14-M13F/2MCPI-14-R
TCAAATCCAACCAAATATTGCGAGAAGGAAACATCACCAACG
(AAT)15 55 8 227/242/255/257 /261 /274/282/285
0.97
STAUB et al. (2000). SSR fragments were also scored
phenotypically as multilocus profi les, and Jaccard sim-
ilarity was used to produce a similarity matrix from
which an UPGMA cluster analysis was constructed,
and the distortion effect estimated with a cophenetic
correlation analysis.
Both marker types
Correlation between the two separate Jaccard similarity
matrices with RAPD and SSR data, respectively, was
investigated with a Mantel test (MXCOMP in NTSYS-pc,
using 9999 permutations to compute the signifi cance of a
given correlation). In addition, a multidimensional scaling
analysis (MDS) was applied to another Jaccard similarity
matrix containing the combined RAPD and SSR data.
While clustering methods show a hierachical, categorical
structure which is inherently incapable of describing gra-
dients or multiple patterns in data ( CRISP and WESTON
1993), ordinations are designed to reveal multiple, con-
tinuous, and overlapping patterns of variation ( SNEATH
and SOKAL 1973) and are most appropriate under a nonhi-
erarchical model of infraspecifi c variation ( SWOFFORD and
BERLOCHER 1987).
RESULTS
RAPD analyses
The 10 RAPD primers used in this study produced 138
scorable RAPD markers of which 122 (88.4%) were
polymorphic (Table 2). These markers ranged in molecu-
lar weight from approx. 150 to approx. 2200 base pairs
(bp), and PIC values for RAPD primers ranged from 0.47
(OPT-01) to 0.77 (OPJ-06). RAPD marker index values
ranged from 3.18 (OPE-04) to 10.40 (OPK-14).
Four different estimators of within-accession variation
were calculated (Table 1), ranging from 39.3 to 63.9 for
percentage polymorphic bands, 72.6 to 82.5% for mean
Jaccard similarity, 0.13 to 0.22 for expected heterozygos-
ity, and 0.20 to 0.33 for Shannon’s index. The three most
diverse accessions according to all of these estimators
were CM2645, SWM2879 and CM2643, whereas the
order among the four less variable accessions varied
between the different estimators. When calculations were
performed across all cow-melon accessions, and all sweet
watermelon accessions, respectively, variability was only
slightly lower for sweet watermelon in spite of being rep-
resented by only two accessions as compared to fi ve for
cow-melon.
Analysis of molecular variance (AMOVA) within and
among seven accessions of watermelons divided into two
major groups of cow-melons and sweet watermelons
(Table 3) revealed that 43.7% of the total variation resides
between these two groups, 17.2% between accessions
within groups and 39.1% within accessions. The overall
G ST for estimating between-accession differentiation
regardless of major grouping was 0.48, i.e. very similar to
the AMOVA Φ ST value of 0.47. G ST and AMOVA Φ ST
values obtained in calculations carried out separately for
the two major groups, showed more differentiation among
Table 4. Description of SSR loci used in the study, and PIC values .
NB: AT � annealing temperature and AN � allele number. * SSR markers described by JOOBEUR et al. (2006).
Hereditas 147 (2010) Genetic diversity in watermelon 147
cow-melon (G ST and Φ ST � 0.34) than sweet watermelon
accessions (G ST � 0.14 and Φ ST � 0.12).
Results of the cluster analysis were illustrated in a den-
drogram (Fig. 2). The cophenetic correlation between the
genetic similarity matrix and the dendrogram was 0.93,
suggesting a very high goodness of fi t ( ROHLF 1993). Two
major clusters were differentiated at 37% genetic similar-
ity: one larger cluster containing the fi ve cow-melon
accessions and one smaller cluster with the three sweet
watermelon accessions. Within the sweet watermelon
cluster, one larger subcluster contained all samples of
SWM2854 (Matabeleland South) together with some
samples of SWM2879 (Midlands) and the single sample
of SWM2839 (Midlands) while the smaller subcluster
contained the remaining samples of SWM2879. The
neighbouring provinces Midlands and Matebeleland
South have a similar climate and trading between these
areas is frequent.
The cow-melon cluster contained samples of fi ve different
accessions, two from Masvingo (CM2645 and CM2650),
two from Mashonaland Central (CM2643 and CM2746) and
one from Mashonaland West (CM2768). While Mashona land
Central and Mashonaland West are adjacent, they are both
more than 450 kilometers from Masvingo (Fig. 1). One of the
accessions from Masvingo, CM2645, formed a distinct sub-
cluster splitting off at 54% similarity from the remainder. The
other four accessions clustered together showing weak dif-
ferentiation except for accession CM2643 for which all but
one of the samples formed a single subcluster.
SSR analyses
A total of 43 SSR alleles were observed with an average
of 4.78 alleles per locus, and with a PIC index ranging
from 0.39 (MPCI-07) to 0.97 (MPCI-14) (Table 4). In
contrast to the RAPD analysis, several samples shared
band profi les at all investigated SSR loci, producing 10
inseparable groups with 2 to 7 samples each (Fig. 4).
Again, four different estimators of within-accession
variation were calculated (Table 1). Values for percentage
polymorphic alleles varied from 55.6 to 88.9 and for mean
Jaccard similarity from 73.5 to 95.0%. Values for expected
heterozygosity, 0.21 to 0.39, were lower than values for
observed heterozygosity (0.36 to 0.56) for each accession
except SWM2854. Finally, the Shannon index varied
from 0.31 to 0.60. There was less coherence between the
different estimators for SSR-based variation compared to
in the RAPD analysis. Still, two accessions were indicated
as more variable than the remainder, namely CM2645 and
CM2650. CM2645 had the highest variability also accord-
ing to RAPD analysis. Similarly, CM2746 instead had
very low variability according to both RAPD and SSR.
When the whole cow-melon group was compared to
the sweet watermelons, the latter showed slightly lower
variation, just as in the RAPD analysis, except for num-
ber of polymorphic alleles which was higher for sweet
watermelons.
Analysis of molecular variance (AMOVA) within
and among seven accessions of watermelons divided
into two major groups of cow-melons and sweet water-
melons (Table 3) revealed that only 0.8% of the total
variation resides between these two groups, 10%
between accessions within groups and 89.2% within
accessions. The overall G ST for between-accession
differentiation was 0.12, very similar to the AMOVA
Φ ST value of 0.11. Calculations carried out separately
for differentiation among cow-melon and among sweet
watermelon accessions, respectively, produced almost
identical values (G ST � 0.11 and Φ ST � 0.10) for the two
data sets.
The cophenetic correlation between the genetic similar-
ity matrix and the cluster analysis was 0.96, suggesting a
very high goodness of fi t. The same two groups as in the
RAPD-based analysis (Fig. 4) were retrieved; sweet water-
melons differentiated from cow-melons at 15% genetic
similarity. Within the cow-melons, CM2645 formed a dis-
tinct subcluster just as in the RAPD-based analysis (Fig. 3).
Similarly, CM2643 from Mashonaland Central was also
relatively well differentiated from the others. Contrary to
the RAPD analysis, two of the other three cow-melon
accessions were also well-delimited, with only one over-
lapping sample.
Both marker types
A Mantel test demonstrated a highly signifi cant correla-
tion between the RAPD and SSR datasets, r � 0.848 (P �
0.001). Multidimensional scaling, conducted on combined
RAPD and SSR data, produced the same two major clus-
ters as the dendrograms. The sweet watermelon acces-
sions were completely intermingled, as also all the
cow-melon accessions except for those belonging to
CM2645 which had consistently lower values on the fi rst
component and thus took an intermediate position between
the sweet watermelon samples at one side, and the remain-
ing cow-melon samples at the other side.
Morphological variation
Watermelon variation was also observed in the greenhouse
where three individuals per accession (only one in acces-
sion SWM2839) were allowed to grow to fruition (FigFig. 5).
In the cow-melon group, the three plants of accession
CM2643 showed great uniformity in fruit shape and colour,
consistent with the well-defi ned subcluster in both RAPD-
and SSR-based dendrograms. Accessions CM2746 and
CM2768 were each divided into two separate subclusters
in the RAPD dendrogram but the fruiting plants happened
148 C. Mujaju et al. Hereditas 147 (2010)
Fig. 2. UPGMA dendrogram of watermelon landraces from Zimbabwe using RAPD data, showing two major clusters, A cow-melons
(CM) and B sweet watermelon (SWM). C is a sub-cluster of plants from accession 2645.
Hereditas 147 (2010) Genetic diversity in watermelon 149
to belong to the same subclusters and produced relatively
uniform fruits. By contrast, plants of CM2645 (well dif-
ferentiated according to the RAPD and SSR dendrograms
but containing high levels of intra-accession variability)
and CM2650 (heterogenous but with the three fruiting
plants belonging to the same subcluster) showed consider-
able intra-accession variation in rind colour and pattern.
For the sweet watermelon group, all fruiting plants belonged
to the larger of the two subclusters in both RAPD and SSR
dendrograms. Nevertheless, the three plants of SWM2854
Fig. 3. UPGMA dendrogram of watermelon landraces from Zimbabwe using SSR data, showing two major clusters, A cow-melons
(CM) and B sweet watermelon (SWM). C is a sub-cluster of plants from accession 2645.
150 C. Mujaju et al. Hereditas 147 (2010)
and the single plant of SWM2839 exhibited considerable
variation in fruit colour and rind pattern whereas the three
plants of SWM2879 were quite similar.
DISCUSSION
Evaluation of molecular marker methods
The number of markers produced by each RAPD primer in
this study is relatively high (an average of 14 marker bands
per primer) possibly due both to the fact that the primers
had a high GC content (60 – 80%) which has proven to yield
polymorphic band patterns ( LEVI et al. 2001a) and to the
pronounced differentiation between sweet watermelons
and cow-melons. LEVI et al. (2001a) reported an even
higher average of 22 markers per band per primer, possibly
because C . colocynthis was included in the analysis.
The use of RAPD in estimating genetic diversity has
been much debated due to potential problems with, e.g.
Fig. 4. Three-dimensional plot of watermelon accessions using multi-dimensional scaling on combined RAPD and SSR data. CM
refers to cow-melon and SWM to sweet watermelon.
Fig. 5. Watermelon fruits in the greenhouse at Balsg å rd in 2008. Accessions represented are 2643 ( A , one fruit), 2645 ( B , two fruits),
2650 ( C , two fruits), 2746 ( D , two fruits) and 2768 ( E , two fruits) from the cow-melon group, and 2839 ( F , one fruit), 2854 ( G , three
fruits) and 2879 ( H , two fruits) from the sweet watermelon group.
Hereditas 147 (2010) Genetic diversity in watermelon 151
reproducibility, primer competition and the inability to dis-
tinguish heterozygotes from homozygotes ( NYBOM 2004;
WEISING et al. 2005). Co-dominantly inherited SSR mark-
ers were therefore used as a complement to the RAPD
data. These SSR markers were only moderately polymor-
phic, with 3 to 8 alleles per locus, and low PIC-values indi-
cating that they may not have suffi cient discriminatory
power. Contrary to RAPD, SSR markers were not able
to provide each sample with an individual-specifi c DNA
profi le. In spite of spending approximately the same
amount of resources (time and money) on the RAPD and
SSR analyses, the RAPD-based data appear to yield more
information suggesting that this method can be suffi cient
for assessing genetic diversity, especially in laboratories
that have restricted access to technical facilities.
Levels of within-accession diversity estimated as Jac-
card similarity were rather similar between the two marker
types. For all other parameters, values were generally
higher for SSR-based data compared to RAPD. The dis-
crepancy was especially large when values for expected
heterozygosity and Shannon index were compared. Simi-
lar results have been noted in many other reports; SSR-
based estimates of expected heterozygosity were 1.7 to
4.6 times as high as estimates obtained with the dominant
marker methods RAPD and AFLP in the same plant
material (reviewed by NYBOM 2004).
The commonly used estimators of differentiation are
mathematically tied to estimates of expected heterozygos-
ity (these parameters are negatively correlated). Conse-
quently, G ST for differentiation among accessions was
much higher for the RAPD-based data compared to the
SSR-based data in our study. Similarly, the corresponding
AMOVA Φ ST estimate was much higher for RAPD com-
pared to SSR (except for among the two sweet water-
melon accessions). Previous studies have often reported
that values for differentiation are similar between domi-
nant and co-dominant markers when applied to the same
plant material (reviewed by NYBOM 2004). However,
according to JOST (2008), the interpretation of G ST and
Φ ST as measures of differentiation produces nonsensical
results when gene diversity is high. Therefore, the RAPD-
based estimations are likely to be more sound than the
SSR-based in our study.
Differentiation between the two major forms
Dendrograms derived from UPGMA cluster analysis and
multidimensional scaling indicated strong differentiation
between cow-melons and sweet watermelons. In addition,
partitioning of variation with AMOVA exhibited signifi -
cant variation (44% with RAPD and 0.8% with SSR, P �
0.01) between these forms. Considerable differentiation
between sweet watermelons and cow-melons has been
reported also by LEVI et al. (2000, 2001a, 2001b) using
RAPD, LEVI et al. (2005) using RAPD and ISSR, JARRET
et al. (1997) using SSR, and NAVOT and ZAMIR (1987)
using isozymes.
In both cluster analyses, CM2645 from Masvingo was
distinct from the rest of the cow-melons. Moreover, this
accession occupied a position in between of the cow-
melons and the sweet watermelons in the MDS analysis,
indicating that gene fl ow may have taken place between
the two forms. Further evidence of putative hybridization
is obtained from the fact that CM2645 had the highest
amount of intra-accession variation according to most of
the RAPD- and SSR-based parameters. Sampling of mate-
rial for our study was carried out in such a way that this
particular accession happens to be the one growing closest
to the three sweet watermelon accessions. It should,
however, be pointed out that both forms occur in all of the
sampled provinces, and gene fl ow therefore could take
place anywhere in the watermelon-growing area.
Variation within and among accessions
Values for expected heterozygosity within watermelon
accessions ranged between 0.13 and 0.22 in the RAPD-
based data. If seeds had been collected at random in the
watermelon fields, even higher levels would probably
have been obtained. However, in our study, most plants
from the same accession are likely to be either full sib-
lings or half-sibs since the seed batches were collected
from fruits of a single plant. In spite of the resulting close
relatedness among samples within accessions, our RAPD-
based values for expected heterozygosity are rela-
tively high. Mean values for within-population expected
heterozygosity reported in a large compilation of studies
on wild plant species were, e.g. 0.13 for annuals and 0.20
for short-lived perennials, and 0.12 for selfi ng, 0.18 for
mixed breeding, and 0.27 for outcrossing species ( NYBOM
2004).
By contrast, the SSR-based values for expected
heterozygosity in watermelon, 0.21 – 0.39, are consider-
ably lower than those reported for wild species, e.g. 0.46
for annuals and 0.55 for short-lived perennials, and 0.41
for selfi ng, 0.60 for mixed breeding and 0.65 for outcross-
ing species ( NYBOM 2004). Levels of observed heterozy-
gosity in the watermelon accessions varied between 0.36
and 0.53, i.e. only slightly below the grand mean of 0.58
in the compilation of wild species ( NYBOM 2004). Con-
trary to NYBOM (2004) where SSR-based H O values gener-
ally were lower than H E , SSR-analysis in watermelon
revealed higher values for observed heterozygosity com-
pared to expected heterozygosity in all accessions except
SWM2854. Possibly this discrepancy is due to the fact
that the watermelon seedlings were not obtained after
random mating but instead from single mothers, resulting
in decreased values for expected heterozygosity.
152 C. Mujaju et al. Hereditas 147 (2010)
Both marker types indicated signifi cant differentiation
between accessions, both when counted across all acces-
sions and when calculated within each of the two major
groups, cow-melons and sweet watermelons. The RAPD-
derived estimates of among-accession differentiation
( Φ ST � 0.47, G ST � 0.48) were similar to those obtained for
wild annual ( Φ ST � 0.62, G ST � 0.47) or short-lived peren-
nial species ( Φ ST � 0.41, G ST � 0.32) reported by NYBOM
(2004) but higher than values obtained for, e.g. mixed
breeding ( Φ ST � 0.40, G ST � 0.20) and outcrossing species
( Φ ST � 0.27, G ST � 0.22). The much lower measures of dif-
ferentiation revealed by SSR compared to RAPD may be
artefactual; according to JOST (2008), G ST necessarily
approaches zero when gene diversity is high, even if sub-
populations are completely differentiated.
In previous studies, higher levels of genetic diversity
have been reported within C . lanatus var. citroides com-
pared to C . lanatus var. lanatus ( NAVOT and ZAMIR 1987;
JARRET et al. 1997) . By contrast, our study indicates that
sweet watermelon and cow-melon have similar levels of
genetic diversity in Zimbabwe. This variation refl ects the
heterogeneous nature of landraces compared to uniform
commercial varieties examined in previous studies. In
southern Africa, watermelon landraces are often grown in
the more marginal, risk-prone habitats and ethnological
niches for which the improved varieties are not suitable.
Here, genefl ow mostly depends on informal seed
exchanges and farmer practices. Local seed sources, other
than the farmers ’ own seed, have the advantage that the
variety or mixture is usually known to be adapted to the
agro-ecological and socio-economic conditions of a given
area. Also, farmers continuously select for better water-
melon features to mitigate against the effects of a harsh
climatic environment. This in turn affects the distinction
of particular accessions since the plants are open polli-
nated and there is rarely an isolation distance practiced
onfarm. Moreover, hybridization with non-cultivated
forms may also occur; edible watermelons often grow
together with weedy forms of watermelons resulting from
introgression between cultivated forms of both major
groups and wild forms of var. citroides . The existence of
watermelon weedy types was also reported in Namibia
( MAGGS-KOLLING et al. 2000).
Conclusion
Both molecular markers confi rmed signifi cant differentia-
tion between the two subspecies of Citrullus lanatus , and
revealed considerable variation among and within water-
melon accessions, for both cow-melon and sweet water-
melon types. In domesticated crops, landraces have been,
and still are, the primary source of genetic diversity for
plant breeding. It is therefore prudent to further explore
the organization of landrace diversity and the forces that
shape and maintain within- and among-landrace diversity.
Thus, more research should be undertaken to further
assess variability within each of the subspecies using more
accessions, and investigate possible associations with util-
ity values, geographical origin, and/or socio-economic
patterns.
Acknowledgements – We thank Å sa Gunnarson for technical help in the laboratory. Funding was received from Nordiska Ministerr å det.
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