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 2

and 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: hilde.nybom@ltj.slu.se.

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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