Effect of DNA methylation on molecular diversityof watermelon heirlooms and stability of methylationspecific polymorphisms across the genealogies

Padma Nimmakayala • Gopinath Vajja •

Renee A. Gist • Yan R. Tomason • Amnon Levi •

Umesh K. Reddy

Received: 6 May 2010 / Accepted: 14 September 2010 / Published online: 24 September 2010

� Springer Science+Business Media B.V. 2010

Abstract American watermelon heirlooms are phe-

notypically diverse in terms of their growth habits, fruit

traits and responses to biotic and abiotic stress. Wide

ranging DNA marker tools resolved narrow molecular

diversity among these collections. The current research

explored additional insights such as extent of diversity at

the methylation level among the watermelon cultivars.

DNA profiles were generated using Methylation-sensi-

tive AFLP assay for 47 watermelon heirlooms. Results

indicated that methylation specific diversity (43%) in

US watermelon heirlooms is higher than the diversity

(19.8%) estimated by several investigators using con-

ventional DNA markers. In tree topologies of Neighbor-

Joining (NJ) phenograms, the clustering pattern of

principal component analyses of separate data sets

obtained from the methylation specific isoschizomers

MspI and HpaII resolved the diversity associated with

methylation. Methylation-induced clustering was fur-

ther verified using model-based population structure

analysis. Our study clearly revealed the extent of

methylations that are shared between parental heirlooms

and progeny heirlooms, when tracked in known gene-

alogies and breeding histories of heirlooms. Methyla-

tion sites that were not carried over and de novo

methylations in the progeny heirlooms were fewer,

when compared to the methylations that are stable.

Keywords mAFLP � Watermelon heirlooms �Methylation � Epigenetic diversity �Linkage disequilibrium

Introduction

Watermelon is the fifth most economically important

vegetable crop and is grown in 44 states in the United

States. There is wide phenotypic diversity in terms of

growth habits, fruit traits and resistance to biotic and

abiotic stress (Levi et al. 2004). After using wide

ranging DNA marker tools, Levi et al. (2001, 2004),

concluded that there is a very narrow genetic

diversity at the DNA sequence level. In order to

exploit the cultivar phenotypic potential to the fullest

extent, insights into other sources of variation such as

epigenetic and functional diversity might be needed

to understand the interplay between wide phenotypic

diversity and narrow DNA sequence diversity.

Knowledge of DNA methylation profiles in cultivated

watermelon will contribute to our current understand-

ing of molecular divergence of watermelons.

Umesh K. Reddy contributed equally to this work.

P. Nimmakayala � G. Vajja � R. A. Gist �Y. R. Tomason � U. K. Reddy (&)

Gus R. Douglass Institute and Department of Biology,

West Virginia State University, Institute,

WV 25112-1000, USA

e-mail: ureddy@wvstateu.edu

A. Levi

US Vegetable Laboratory, USDA, ARS, 2875 Savannah

Highway, Charleston, SC 29414, USA

123

Euphytica (2011) 177:79–89

DOI 10.1007/s10681-010-0259-z

Methylation is a major epigenetic process that has

great impact on genetic variation in plant species

(Grant 1971; Wendel 2000). DNA methylation is a

common phenomenon in plants and is very important

in gene regulation in a number of biological

processes, and together with histone modifications

represent a self-propagating epigenetic network (He-

slop-Harrison 2000; Attwood et al. 2002). In plant

genomes, a large amount of CpG sequence was found

in regions upstream of the start codon and compar-

atively less in non-coding regions (Shimizu et al.

1997; Messeguer et al. 1991). Moreover, in Arabid-

opsis, the genome-wide high resolution mapping of

DNA methylation has revealed that over one-third of

the expressed genes contain methylations within

transcribed regions (Zhang et al. 2006).

Measuring sequence variations by methods such as

RAPD, RFLP, AFLP and variable number of tandem

repeats (VNTR) amplification (Karp et al. 1996) will

only give an estimate of variation in DNA sequences.

The amount of epigenetic variation that is stored by

the genomes cannot be adequately described by

conventional DNA techniques, and a detailed study

of epigenetic variation would be a major step forward

in understanding the dynamics of genetic diversity.

Xu et al. (2000) reported a step wise protocol and the

results pertaining to methylation-specific AFLP

(mAFLP). Keyte et al. (2006) carried out mAFLP

on 20 diverse hirsutum lines and two barbadense

cotton cultivars, and showed extensive methylation

specific polymorphism (MP) among the cultivars.

Keyte et al. (2006) also noted that their MP data

distinctly correlated with functional genes, which

provided additional clues as to how methylation

might have further expanded existing genetic diver-

sity. Madlung et al. (2002) assayed selfed synthetic

allotetraploids using methylation-sensitive amplified

polymorphism (MSAP) and showed that there was

variation in methylation patterns.

At the level of DNA sequence, alterations such as

deletions, point mutations and chromosomal rear-

rangements have been the main foci for decades in

explaining how genomes and populations evolved

and further diversified. There is no dispute that such

alterations are fundamental to generate variation,

both at neutral and non-neutral levels, upon which

selection intensified. Interestingly, there is evidence

that the mutational frequency of methyl-cytosine is

substantially greater than unmodified cytosines

(Rideout et al. 1990). The objective of this research

is to understand the dynamics of genetic diversity

among American watermelon [Citrullus lanatus

(Thunb.) Matsum. & Nakai] cultivars at the methyl-

ation level, and to estimate epigenetic diversity based

on methylation profiles to compare with previously

published DNA level diversity (Levi et al. 2004).

Materials and methods

Forty-seven heirloom watermelon cultivars (Table 1)

and a wild species accession Citrullus colocynthis

were grown under uniform conditions in a growth

chamber. DNA was extracted from the leaf tissues of

three 21 days old plants using the method described

in the QIAGEN DNeasy Plant Mini Kit.

Methylation-sensitive AFLP assays on all the DNAs

were carried out using standard protocols (Xu et al. 2000;

Keyte et al. 2006 and Liu et al. 2001). Adapter sequences

of HpaII and MspI were used from the protocol of Xu

et al. (2000). DNA samples from each cultivar were

digested with EcoRI then with HpaII, and for another set,

all the samples were digested with EcoRI then with MspI.

The ligation reaction containing 10 pmol of EcoRI

adaptors, 100 pmol of HpaII/MspI adaptors with 0.5

units of T4 DNA ligase and 1 ll of T4 DNA ligase buffer

was added to 20 ll of digested reaction. The adaptors

were ligated overnight at room temperature. The adaptor

ligated reaction was diluted 1:10 using 19 TE buffer.

Pre-selective amplification was performed using a single

selective base at the 30 end of each of the HpaII and

EcoRI primers. Selective amplification was carried out

using EcoRI labeled primers using a 4300 LI-COR DNA

analyzer. Adapter sequences of HpaII and MspI were

adopted from the protocol of Xu et al. (2000). The primer

combinations were EcoRI ? AAC/HpaII/MspI ? AC

(EAAC/HAC), EcoRI ? ACT/HpaII/MspI ? CT

(EACT/HCT), EcoRI ? ACC/HpaII/MspI ? CT

(EACC/HCT), EcoRI ? ACA/HpaII/MspI ? CA

(EACA/HCA), EcoRI ? ACA/HpaII/MspI ? AC

(EACA/HAC), EcoRI ? ACG/HpaII/MspI ? AC

(EACG/HAC), EcoRI ? AGC/HpaII/MspI ? AC

(EAGC/HAC), EcoRI ? AGC/HpaII/MspI ? CA

(EAGC/HCA), EcoRI ? ACA/HpaII/MspI ? CC

(EACA/HCC), EcoRI ? AAG/HpaII/MspI ? GC

(EAAG/HGC), EcoRI ? AAC/HpaII/MspI ? GC

(EAAC/HGC) and EcoRI ? ACG/HpaII/MspI ? CA

(EACG/HCA).

80 Euphytica (2011) 177:79–89

123

Table 1 List of the U.S.

watermelon heirloom

collections

Cultivar Source of

seeds

Year

released

Breeding parentage

All Sweet Sunseeds 1972 [Miles 9 Peacock] 9 Charleston gray

Astrakansk – – –

AU-Golden Producer Hollar 1993 A selection from AU-Producer

AU-Jubliant Hollar 1985 Jubliee 9 PI271778

AU-Producer Hollar 1985 Crimson Sweet 9 PI189225

Black Diamond Sunseeds – –

Black Stone Hollar – Black Diamond, Fairfax

Calhoun Gray Sunseeds – –

Calhoun Sweet Sunseeds – –

Charleston Gray NSL-5267 1954 Africa 8, Iowa Belle, Garrison, Hawkesbury

Coles Early Sunseeds 1982 –

Congo Syngenta 1949 [African 9 Iowa Belle] 9 Garrison

Crimson Sweet Hollar 1963 [Miles 9 Peacock] 9 Charleston Gray

Dixilee Hollar 1979 Texas W5, Wilt resistant Paecock, Fairfax

Dixi Queen Sunseeds 1890 F1 Hybrid

Dunbarton NSL-6637 1953 [African 9 Iowa Belle] 9 Garrison

Fairfax Sunseeds 1952 Garrison, African, Iwoa Belle, Hawkesbury

Family Fun Syngenta 1973 F1 Hybrid

Garrison NSL-2053 – –

Garrisonian Willhite 1957 Africa 8, Iowa Belle, Garrison, Hawkesbury

Georgia Rattlesnake Seed Savers – –

Golden Honey Hollar 1954 Mixed strain from Japan seed, Co.

Golden Midget NSL-5288 1959 New Hampshire Midget 9 Pumpkin Rind

Hawkesbury Syngenta 1936 –

Iopride Syngenta – –

Ironsides NSL-7369 1950 [Leesburg 9 Hawkesbury] 9 Garrison

Jubliee Hollar 1963 Africa 8, Iowa Belle, Garrison, Hawkesbury

King and Queen Hollar – –

Kleckely’s Sweet Seed Savers – –

Klondike Syngenta 1959 –

Klondike Sweet – – –

Leesburg NSL-7368 1936 Selection From Kleckley Sweet

Melitoplesky – – –

Mickylee Hollar 1986 Texas W5, Fairfax, Summit, Graybelle

Miles NSL-6688 1948 Dixi Queen 9 Klondike R-7

Minilee Hollar 1986 Texas W5, Fairfax, Summit, Graybelle

New Hamshire Midget Syngenta 1951 –

Northern Sweet Syngenta 1938 –

Parker Willhite – F1 Hybrid

Peacock Hollar 1939 –

Picnic Syngenta 1972 Peacock type

Prince Charles Hollar 1978 F1 Hybrid ….

Sangria – – –

Stone Mountain Hollar 1924 –

Sugar Baby Sunseeds 1955 Tough sweets selection

Summit – – –

Sweet Princess – – –

Euphytica (2011) 177:79–89 81

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Data scoring and analysis

Typically, AFLP bands are grouped into three types.

Type 1 are those in which bands are present (or

absent) in both the HpaII and MspI gels. Presumably,

no methylation events occurred in these bands. The

isoschizomers HpaII and MspI recognize the same

tetranucleotide sequence (50-CCGG-30), but display

different sensitivity to DNA methylation. Type 2 are

those in which bands are present in MspI based

amplification and absent in HpaII based amplification.

These bands are due to 50-CmCGG-30 cytosine meth-

ylation. Type 3 are those in which bands are present in

HpaII and absent in MspI based amplification. These

bands indicate the presence of hemi-methylated

50-CmCGG-30 or 50-mCCGG-30 sites, which can be

digested by HpaII but not by MspI (Walder et al.

1983). Genetic similarities based on Jaccard’s coef-

ficients (Jaccard 1908) were calculated using the

SIMQUAL program of the numerical taxonomy

multivariate analysis system (NTSYS-pc) version

2.0 software package (Rohlf 1994). The resulting

genetic similarity indices were used to generate a tree

using the neighbor joining method (Saitou and Nei

1987). PAUP *4.0 was used to generate 1,000 boot-

strap replicates for testing the reliability of the dataset

and to draw a consensus tree. Principal component

analysis (PCA) based on the genetic similarity

matrices was performed using DCENTER and EIGEN

algorithms of NTSYS-pc. The program STRUC-

TURE (version 2.2) utilizes a model-based (Bayesian)

clustering algorithm (Pritchard et al. 2000). Assump-

tions were made to form 2–6 subpopulations (K). In

this method, individual genotypes were assigned to

unknown clusters with the assumption that Hardy–

Weinberg equilibrium or linkage equilibrium exists

within clusters, but is absent between clusters and all

clusters have distinct allele frequencies (Pritchard

et al. 2000). For each subpopulation, the program was

run three times with a Burn in period of 100,000 and

MCMC reps of 200,000.

Evaluation of linkage disequilibrium (LD)

among the methylated sites

LD among the methylated sites was estimated using

the software TASSEL. Pair wise LD (r2, P and D0)were estimated. The loci pairs were considered to be

in significant LD if P \ 0.01 or r2 [ 0.5. As the

methylation sites were not known for its chromo-

somal location, intra/inter-chromosomal LD could

not be estimated.

Results

Methylation profiles in watermelon heirlooms

DNA methylation profiles were generated using MSA

assays for 47 watermelon heirlooms and one single

accession of the species Citrullus colocynthis (Table 1).

A CCGG site for a particular heirloom was classified as

‘‘methylated’’ if a band was uniquely present in the MspI

or HpaII lane but not in both (Fig. 1). To facilitate

comparison, two lanes containing amplicons of both

isoschizomers (EcoRI/HpaII and EcoRI/MspI) of a

single heirloom were loaded next to each other in the

gels. A total of twelve primer combinations were used in

both isoschizomer orientations. The primer combina-

tions EAAC/HAC, EACT/HCT, EACC/HCT, EACA/

HCA, EACA/HAC, EACG/HAC, EAGC/HAC,

EAGC/HCA, EACA/HCC, EAAG/HGC, EAAC/

HGC, and EACG/HCA amplified a total number of

17, 33, 22, 21, 16, 32, 25, 29, 8, 21, 23, and 47 bands

respectively. Among the heirlooms, Peacock had 8

MspI-specific bands, whereas the heirlooms Sweet

Princess, Hawkesbury and Calhoun Gray each had 66

MspI-specific bands. For the HpaII, the heirlooms

Family Fun and Dunbarton had the least (35) and the

highest (69) number of bands. The heirloom Peacock

and the wild accession PI386016 of C.colocynthis had

the least number of total bands (HpaII ? MspI), 52 and

69 respectively. The heirloom Au-Golden Producer had

the highest number (125) of total bands. The heirlooms

Miles (120), All Sweet (121), Golden Honey (122),

Stone Mountain (124), and Northern Sweet (124) also

had a very high number of total bands. In the current

study, analysis was performed by separating the meth-

ylation datasets of HpaII and MspI, as the methylations

generated by MspI are from fully methylated sites and

the profiles of HpaII, are derivatives of hemi-

methylation.

Estimation of epigenetic diversity based

on methylation sites

The methylated bands that are polymorphic (MP) were

used to construct Neighbor-Joining (NJ) phenograms

82 Euphytica (2011) 177:79–89

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to explore whether the patterns of genetic relationships

were consistent with those determined previously

(Levi et al. 2004). The first and the second analyses

included specific MPs of MspI and HpaII, respec-

tively, to compare the tree topologies of complete

methylation (MspI) versus hemi-methylation (HpaII).

Both the trees were rooted using C.colocynthis as the

outgroup. When estimated based on the MspI-specific

MPs, the range of genetic diversity among various

groups was within 16–43%. In contrast, GDs between

the groups ranged between 17–36% when estimated

based on MPs specific to HpaII. Subsequently, the

major clusters of the combined NJ phenogram were

supported with Bootstrap Values (BVs) in the range of

52–94%. The NJ phenogram made from HpaII-

specific MPs resolved into two clear clusters (Fig. 2).

However, the main split was not supported by BVs,

but internally the sub-clustering in cluster II showed

robust support with a BV range of 52–95%. The MspI-

specific MPs resolved into three different clusters, with

a BV range of 56–83% (Fig. 3). Interestingly, some of

the heirlooms that got clustered into two major groups

in the NJ tree constructed from HpaII-specific MPs

were seen hitchhiking into a third cluster in the NJ tree

of MspI.

To corroborate results of the diversity analysis, we

also carried out a PCA using the MPs of HpaII. The

first three eigenvectors (Vector I = 33.61, Vector

II = 9.34, Vector III = 6.21) that were used to

construct the PCA cumulatively absorbed variance

of 49.17, indicating good support of the analysis. This

PCA resolved into two clusters as in the NJ

phenogram of HpaII-specific MPs. The clustering

pattern of the heirlooms can be noted in Fig. 4a. The

PCA constructed using the first three eigenvectors

(Vector I = 22.31, Vector II = 12.29, Vector

III = 8.49) derived from similarity indices of MspI-

specific MPs resolved into three distinct clusters

(Fig. 4b). The first cluster included the heirlooms

Sugar Baby, Dixie Queen, Peacock, Jubilee, Garriso-

nian, Dixilee, Au-Producer, Crimson Sweet, Golden

Midget, Congo, New Hampshire Midget, and

Minilee. The second cluster contained heirlooms

Family Fun, Garrison, Coles Early, Parker, King, and

Queen, Dunbarton, Picnic, Summit, Ironsides, Klon-

dike Sweet, Melitoplesky, Prince Charles, and Geor-

gia Rattlesnake. The heirlooms in cluster I and cluster

III of MspI-specific PCA were similar of cluster I and

II, respectively, of HpaII-specific PCA. The most

intriguing result of the current study is that the second

(middle) cluster of MspI-specific PCA contains

heirlooms from the first and second cluster PCA of

HpaII, thus confirming the shifting of heirlooms into

a new cluster due to methylations of MspI sites. It

clearly showed that the HpaII-specific hemi-methyl-

ated products could not resolve into the third cluster,

which was newly formed by using the fully methyl-

ated MPs of MspI. To further support this finding, a

model based population structure program was used

assuming (K = 2 to K = 6) using MPs of MspI to

resolve the clustering pattern induced by DNA

methylation effects. The analysis of K = 3 had a

highest mean alpha value of 0.13, which supports the

formation of three subpopulations in the current

study. Further, the analysis K = 3 had three clusters

with FSTs (population differentiation estimates) of

0.44, 0.63, and 0.29 respectively, indicating the

extent of divergence within individual clusters. The

analysis that involved higher K assumptions (more

than K = 3) was not informative and had a smaller

mean alpha. Allele frequency divergence (AFD) was

1.44 between the first and second cluster, and was

Fig. 1 DNA methylation profiles in a set of US watermelon heirlooms by methylation sensitive AFLP technique (H;HpaII, M;MspI)

Euphytica (2011) 177:79–89 83

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0.84 between the first and third clusters. AFD

between the second and third clusters was also 1.44.

The first cluster of K = 3 analysis (Fig. 5) is in

total agreement with the first cluster of PCA of MspI

(Fig. 4b) except in the case of heirloom Peacock. The

cultivars that formed the second (middle) cluster in

K = 3 analysis were same as the middle cluster(II) in

the PCA of MspI with minor changes in the case of the

heirlooms Melitoplesky (#17)and Prince Charles

(#19) that showed shared ancestry between adjacent

clusters. In addition to population structure, LD is

another important feature that deals with the non-

random association of alleles at different loci within a

population. LD parameters r2 and D0 were estimated

among the methylation sites and significant LDs

(P \ 0.01) are presented in Fig. 6. Our results showed

that 71 MP pairs had strong LD that is r2 above 0.5.

Average r2 for all the genome-wide methylation

polymorphisms (6724 MP pairs) was 0.08 indicating

genome wide distribution of methylation.

Inheritance and stability of methylation specific

sites across generations

Based on breeding histories and pedigree records, we

selected four sets of heirlooms that had parental

heirlooms and derivative progenies. We compared

methylation sites present in parents and the extent of

methylation that transmitted across generations to

progeny and further impacted the varietal evolution.

From the MPs dataset, the proportion of methylations

that are not transmitted or lost in the progeny and the

Fig. 2 Phenogram obtained with HpaII-specific methylation polymorphisms. The numbers adjacent to some nodes indicate bootstrap

confidence values (1,000 bootstrap replicates)

84 Euphytica (2011) 177:79–89

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extent of unique methylations accumulated in the

progeny heirloom could be identified. Details of

stably inherited MPs, methylations that are lost or not

carried (nc), and de novo methylation sites or unique

sites (u) are presented in Fig. 7. Our genealogy

analysis of MPs clearly indicated the proportions of

shared MPs, unique or de novo methylations and the

methylations that are lost in varietal evolution or

history.

Heirloom Ironside is a derivative of a three way

cross [Leesburg (l) 9 Hawkesbury (h)] 9 Garrison

(Table 1). A majority of MPs (70%) were stably

inherited by the heirloom Ironside. A set of 30% of

MPs were not transmitted or lost in the process of

varietal evolution of Ironside. 0.7% of the total MPs

in Ironside were de novo in nature. In breeding

histories of the heirlooms Crimson Sweet and All

Sweet, which were selected from the recombinants

of a cross [Miles (m) 9 Peacock (p)] 9 Charleston

Gray, only 17% of MPs could not be traced when

compared to the MPs that were present in the

parental genomes. A set of 6% MPs were collec-

tively de novo or unique in the heirlooms Crimson

Sweet and All Sweet. In individual breeding histo-

ries of Blackstone and Miles, a majority of *90%

of MPs was stably inherited and only a small set of

MPs were de novo or unique (Fig. 7). Our results

clearly indicate that the proportions of unique or de

novo methylations and of methylations that are

present in parental heirlooms and not inherited or

lost are lower than the proportion of methylations

that are stably inherited.

Fig. 3 Phenogram obtained with MspI-specific methylation polymorphisms. The numbers adjacent to some nodes indicate bootstrap

confidence values (1,000 bootstrap replicates)

Euphytica (2011) 177:79–89 85

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Discussion

DNA methylation has been reported to result in

epigenetic effects, genomic imprinting, chromatin

remodeling, dosage compensation, and variation in

DNA replication timing, all of which can lead to

altered gene expression and phenotypes (Gonzalgo

and Jones 1997; Meyer et al. 1994; Rossi et al. 1997;

Tariq and Paszkowski 2004; Finnegan et al. 1996;

Pikaard 1999; Bartee et al. 2001; Richards and Elgin

2002; Meng et al. 2003; Chan et al. 2004; Holliday

and Pugh, 1975). Among the forms of methylation,

methylation of cytosine sites such as CG and CNG

is the most common (Gruenbaum et al. 1981;

McClelland 1983; Oakeley and Jost 1996; Keyte et al.

2006). Among the methods of detecting cytosine

methylation are either conversion of unmethylated

cytosines into thymines using bisulphate treatment

and subsequent DNA sequencing (Gonzalgo and

Jones 1997) or use of mAFLP using the isoschizo-

mers HpaII and MspI that show differential sensitiv-

ity to DNA methylation (Walder et al.1983; Xu et al.

2000; McClelland et al. 1994).

The main objective of the current study was to

resolve methylation-specific genetic diversity and to

compare with genetic diversity resolved by Levi et al.

(2004) on the same set of American heirloom cultivars

using DNA markers [Inter Simple Sequence Repeat

Fig. 4 Principal component analysis depicting DNA methylation effects on watermelon heirlooms clustering pattern A. HpaII-

specific MPs only and B. MspI-specific MPs only

Fig. 5 Population structure analysis using MspI-specific MPs resolving three (K = 3) subpopulations. Scale of Y axis represents

probability of log likelihood

86 Euphytica (2011) 177:79–89

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makers (ISSR) and AFLP markers]. In the current

study, epigenetic diversity was noted to be 16–43%

among the cultivated watermelons, in contrast to

3.2–19.8% of genetic diversity estimated using con-

ventional DNA markers by Levi et al. This study

revealed that the diversity at the methylation level is

three times higher than the genetic diversity revealed

by DNA markers on the same set of heirloom DNAs.

Our results showed that DNA methylation is wide-

spread within the cultivated watermelon germplasm

and the proportion of unique or de novo methylations

and the methylations that are not carried over or lost

are proportionately smaller, when compared to the

methylations that are stable and shared among the

parental heirlooms and their progeny.

Ellul et al. (2007), Wehner et al. (2008) and Parris

(1949) reported extensive phenotypic variation for

fruit characters such as shape, color, size, rind

thickness, flesh color, flesh texture, flesh flavor, sugar

content, size, shape and color of the seeds, as well as

plant characteristics such as vigor, earliness, prolif-

icacy, resistance to diseases, insects and/or mechan-

ical injury, stress tolerance, fruit setting tendency and

ecological adaptation. Systematic analyses involving

transmission genetics and QTL location will provide

direct evidence on how these methylation specific

polymorphisms impact phenotypic diversity.

Fig. 6 Significant LD among 426 MP pairs

Fig. 7 Pattern of inheritance and stability of methylated specific polymorphism across the genealogies and breeding histories

Euphytica (2011) 177:79–89 87

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There are comparable studies from other plants

such as in Arabidopsis (Cervera et al. 2002; Riddle

and Richards 2002), rice (Ashikawa 2001; Wang et al.

2004; Sakthivel et al. 2010), Pisum (Knox and Ellis

2001) and cotton (Keyte et al. 2006) suggesting that

the MPs are widespread among plants and can serve as

epigenetic markers for use in plant breeding. Data on

the influence of DNA methylation on QTLs and the

potential use of epigenetic markers for QTL analysis

is on hand for humans (Gibbs et al. 2010). Similar

mechanisms may be the secret for the high degree of

phenotypic variation known to be present among the

American heirloom watermelon cultivars despite their

derivation from a narrow genetic base. The explora-

tion of cultivated watermelon diversity is truly

dynamic and requires investigations at all levels of

the molecular spectrum, from sequence variations to

epigenetic to functional analyses. It seems likely that

additional insights will continue to emerge from the

interplay between structural, functional genomics, and

epigenetics, along with the phenotypic potential and

selection dynamics of these heirlooms in plant

breeding programs.

The information on epigenetic diversity in culti-

vated watermelon revealed in the current study will

further contribute to our understanding of phenotypic

plasticity of watermelon heirlooms across ecological

zones. Higher and significant LD among the meth-

ylation sites may indicate the existence of some

common methylation control mechanism among

these sites. This is the first study that undertook LD

analysis among methylation sites. When we used the

MP sites in the current study for LD-based associ-

ation mapping of various phenotypic trait variation in

these heirlooms, a set of MP sites were identified that

are significantly linked to the QTLs with wide

ranging effects of various fruit traits in watermelon

heirlooms (Data unpublished).

Results pertaining to identification of shared and

de novo methylations across genealogies helped to

understand their pace in varietal evolution, or the

level of stability of methylations within the same

generation and subsequent generations. These results

provide clear evidence that methylation patterns are

highly heritable across watermelon breeding histories

and hence will be of great interest to watermelon

breeders. More generally, our demonstration of stable

inheritance of numerous epi-alleles over many gen-

erations highlights the need to integrate epigenetic

information into genetic studies. In support of our

results, Peng and Zhang (2009) confirmed that plants

are known to accumulate and memorize the methyl-

ation modifications and further faithfully transmit

them to progeny. The findings of Johannes et al.

(2009) recently provided the first rationale to identify

epi-allelic variants that contributed to heritable

variation in complex traits.

Conclusions

A natural extension of our study would be to examine

the effects of methylation on gene expression.

Although PCA results presented here suggest patterns

of methylation diversity and hitchhiking, mapping of

these patterns could only identify how these methy-

lations alter phenotypic variation. Our immediate

endeavor is to further validation by isolating individ-

ual MPs through extensive gel elution and sequencing

for methylated PCR that uses bisulfite-treated DNAs.

Acknowledgments The authors are grateful to Drs. Vickie

Woolf, Clint Magill and Gerry Hankins for their critical

comments. Funding support is provided by USDA-CSREES

Research (Grant #2007-38814-18472).

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