Botanical Studies (2007) 48: 243-253.

a

Equal contributors.

2

Present address: Department of Biotechnology, Fooyin

University, Kaohsiung 831, Taiwan.

3

Present address: Institute of Medical Biotechnology, Central

Taiwan University of Science and Technology, Taichung

40601, Taiwan.

4

Present address: Department of Biotechnology and

Bioinformatics, Asia University, Taichung 41354, Taiwan.

*

Corresponding author: E-mail: bohsing@gate.sinica.edu.

tw; Tel: 886-2-7892496; Fax: 886-2-7827954.

INTRODUCTION

Rice is the most important staple food for half the

world¡¦s population. The increase in global rice production

in recent years is no longer keeping pace with the growth

in consumption. Rice production in the next few decades

will face even greater challenges with a larger and more

affluent population, with greater demands for higher

production and better-quality rice. However, future

enhancement of global rice production faces the difficulties

of reduced arable land, water, and labor while maintaining

a sustainable agriculture system. Thus, great demands are

put on biotechnology to improve rice production.

Better understanding of the rice genome will facilitate

research on rice, which in turn speeds up the development

of rice biotechnology methods. The highly accurate

map-based genomic sequence of a japonica cultivar,

Nipponbare, was decoded in 2005 by the International

Rice Genome Sequencing Project (IRGSP) (IRGSP,

2005). Shortly before this sequence became available to

the public, a whole genome shotgun sequence of the rice

indica cultivar, 93-11, a parent of super hybrid rice, was

released (Yu et al., 2002). Polymorphisms between the

japonica and indica cultivars over the whole genomic

region were analyzed with use of the above two genomic

sequences (Feltus et al., 2004; Shen et al., 2004).

Single-nucleotide polymorphisms (SNPs) are the most

abundant sequence variations among closely related

genomes. They may be used as genetic markers because

they are detectable on a large scale, and they exist in high

density throughout the genome. Besides, they are generally

more stable than microsatellite markers. Many studies

have investigated the SNP distribution in human, mouse,

Arabidopsis, maize, and other model organisms. For

instance, previous research found a rate of one SNP per

242 or 348 sites from human expressed sequence tag (EST)

data (Cargill et al., 1999; Halushka et al., 1999). When

the International Human Genome Sequencing Consortium

announced the available draft sequence of the human

genome, the International SNP Map Working Group also

reported that SNPs occur every 1,000-2,000 bases, on

average, in a comparison of chromosomes from several

human beings (Sachidanandam et al., 2001). With the

Detection of SNPs between Tainung 67 and Nipponbare

rice cultivars

Ai-Ling HOUR

1,a

, Yao-Cheng LIN

1,a

, Pei-Fang LI

1,2

, Teh-Yuan CHOW

1,3

, Wei-Fu LU

1,4

, Fu-Jin

WEI

1

, and Yue-Ie C. HSING

1,

*

1

Institute of Plant and Microbial Biology, Academia Sinica, Taipei 11529, Taiwan

(Received September 28, 2006; Accepted December 18, 2006)

ABSTRACT.

Single nucleotide polymorphisms (SNPs) are known as the most detectable variations among

related genomes. We estimated the SNPs between Tainung 67 (TNG67), an elite cultivar of rice (Oryza sativa)

in Taiwan, and Nipponbare, the cultivar used for rice genome sequencing by the international consortium.

More than 6,000 expressed sequence tag (EST) sequences from developing panicles of TNG67 were compared

with the annotated gene sequences of Nipponbare. The estimated SNP rate is about 0.3% to 0.4% between the

two cultivars, with most of the insertions or deletions (indels) occurring on the 5

¡¦

or 3

¡¦

untranslated regions

(UTRs). The rate of transition substitutions on the 3

¡¦

UTR and the third codon positions is higher than that

of transversions but lower on 5

¡¦

UTR and first codon positions. The synonymous (Ks) and non-synonymous

(Ka) substitution distances are also calculated, and most of the Ka/Ks ratios are less than 1. Because the

SNP density is higher than that of other traditional markers, detection of SNPs in this report with subsequent

development of markers will allow genetic mapping and positional cloning between TNG67 and Nipponbare.

Keywords: Expressed sequence tag (EST); Nipponbare; RAP-DB; Rice; Single-nucleotide polymorphisms

(SNP); TNG67.

mOleCUlaR BIOlOgy

244

Botanical Studies, Vol. 48, 2007

progress of the International HapMap Project, more than

10 million SNPs have been characterized, many of them

associated with diseases, although the allele frequencies

are low (Kruglyak and Nickerson, 2001; Carlson et al.,

2003; The International Hap Map Consortium, 2003;

Thorisson and Stein, 2003).

The differences between japonica and indica rice have

been assessed from their relatively distinct rice genomic

sequences. About 1.7 to 3.7 SNPs and 0.1 indels per kb

were found in a comparison of Nipponbare and 93-11

(Feltus et al., 2004; Shen et al., 2004). Using EST or PCR-

sequencing methods, the SNP frequencies of Kasalath

(an indica land race), Koshihikari (a japonica cultivar in

Japan), W1943 (a wild accession of Oryza rufipongon),

Guang-Lu-Ai 4 (an indica cultivar from China) and

Senshou (an upland rice in Taiwan) were also analyzed for

SNPs (Nasu et al., 2002; Monna et al., 2006). Recently,

an Oryza SNP project initiated by the international rice

community plans to compare 21 rice genomes with each

other using a high throughput strategy (McNally et al.,

2006). The discovery of genome-wide SNPs will provide

a useful tool for selection in rice breeding and new genetic

knowledge of evolution and domestication.

Tainung 67 (TNG67), an elite japonica cultivar in

Taiwan, has been a leading commercial variety for several

decades (Huang, 1979). It is photoperiod insensitive, and

possesses many good agronomic characteristics. Currently,

it is a popular genetic stock in breeding programs and

scientific research in Taiwan. In the present work, we used

EST sequences of TNG67 to explore the polymorphism

between this cultivar adapted in Taiwan and another

japonica cultivar (Nipponbare) adapted in Japan.

maTeRIalS aND meTHODS

Construction of cDNa libraries and the sub-

sequent processing

The 0.5 mm and 6-8 cm developing panicles of rice

(Oryza sativa L. cv Tainung No. 67) were used to construct

a 5MPR and 6CPR cDNA library, respectively. Tissues

were harvested, immediately frozen in liquid nitrogen, and

stored at -80¢XC. Total RNA was extracted for cDNA library

construction using Lambda ZAPII (Stratagene). About

4,000 clones from each library were randomly selected

for sequencing. Raw sequencing data were evaluated by

the base calling program PHRED (Ewing and Green,

1998; Ewing et al., 1998). Vector-derived sequences were

screened by CROSS_MATCH (P. Green, http://www.

phrap.org/phredphrapconsed.html) according the vector

database. The EST reads were quality trimmed by the

PHRED quality score where Phred score < 20 within 10

consecutive bases. Reads that comprised over 100 bp

of contiguous satisfying quality were kept as successful

sequence for later analysis and were submitted to the

GenBank dbEST database with the accession numbers

EG709278 - EG713055 and EG713056-EG715450 for

5MPR and 6CPR, respectively.

assembly of eST contigs and data download

Since ESTs generally represent only partial cDNA

sequences, the assembly of overlapping

ESTs into putative

unique transcript contigs constitutes the first step for all

EST analyses. Two cDNA libraries were constructed

from developing panicles of 5 mm and 6 cm long, which

resulted in 3,787 and 2,354 EST sequences or a total

length of 1,473,449 and 871,401 bps for the two libraries,

respectively. The 6,112 sequences in these two EST

libraries were clustered by the TGICL program (Pertea et

al., 2003), which resulted in 659 assembled contigs and

3834 singletons (Figure 1).

The japonica variety Nipponbare has been sequenced

completely (IRGSP, 2005). The most updated annotations

of cDNA and genomic sequences from The Rice

Annotation Project Database (RAP-DB) (Ohyanagi et al.,

2006) were downloaded for similarity searches.

The EST sequences of other rice varieties reported

previously (Feltus et al., 2004; Katagiri et al., 2004; Shen

et al., 2004; Monna et al., 2006) were also downloaded

from GeneBank for comparison study.

Polymorphism level estimation

Similarity searches were performed with BLASTN

program (Altschul et al., 1997). Both of the assembled

contigs and singletons described above were aligned

against RAP-DB, with a cut-off E-value of 1E-50. The

resulting high-scoring segment pairs (HSPs) with a

minimum similarity of 95% and minimum coverage of

50% were then screened (Table 1).

The divergence levels based on the HSPs between

TNG67 and Nipponbare were estimated on ESTs, contigs,

and singletons. The polymorphisms were divided into

substitutions and indels. The polymorphisms on the 5¡¦ and

3¡¦ UTRs and three codon sites were calculated separately.

Synonymous and non-synonymous sub-

stitutions ratio

The synonymous (Ka) and non-synonymous (Ks)

substitution distances between contig sequence pairs were

estimated simultaneously by use of nucleotide and amino

acid sequences. The estimation was implemented by use

of the Diverge program of the GCG package (Wisconsin

Package). The methods and parameters used were Li¡¦s

model (Li, 1993) and Kimura¡¦s two-parameter substitution

model (Kimura, 1980). The Ka/Ks ratios were calculated

for sequence pairs for which both Ka and Ks values were

larger than zero.

ReSUlTS

We estimated the polymorphism proportions between

TNG67 and Nipponbare cultivars by using panicle ESTs

and annotated cDNA sequences. The distributions of

SNPs or indels on different gene coding regions were also

assessed. Furthermore, the Ka/Ks ratios were calculated.

HOUR et al. ¡X SNP detection of TNG67 rice

245

length and hit frequencies of eST sequences

Redundant ESTs were assembled into putative unique

transcript contigs before further analysis. These contig

sequences are longer than single-pass EST sequences,

which allows for construction of nearly full-length

cDNAs. As calculated from data shown in Figure 1, the

mean length of 6,112 EST sequences was 381 bps before

clustering. After clustering, the mean length for the 659

clustered contigs and 3,834 singletons were 548 and 348

bps, respectively. Both the EST and singleton sequences

showed a platykurtic distribution and were left skewed

while the clustered contigs were leptokurtic distributed

and right skewed.

Table 1 lists the percentages of sequences and lengths of

TNG67 EST sequences aligned with RAP-DB sequences

by use of the BLASTN program. Both the original and the

screened HSPs were used. The homologs of assembled

contigs found a match for 95% of the sequences in RAP-

DB, with about 85% of lengths aligned. This ratio was

much higher than with the other two kinds of unassembled

sequences, ESTs or singletons, and indicates increased

sequence quality after clustering.

The HSPs with more than 50% length alignment and

higher than 95% identity were screened. Table 1 shows

the similar proportions of HSPs screened from ESTs,

assembled contigs and other singletons. Only the screened

contig sequences were used for further analysis.

Polymorphisms were about 0.3% to 0.5%

With the HSPs received from BLASTN analysis

between TNG67 ESTs and RAP-DB sequences, the

proportion of polymorphisms, including substitutions and

gaps for ESTs, contigs and singletons, were calculated

separately. The proportion of total polymorphisms was

about 0.4% to 0.6% between aligned HSPs. The values

were reduced to 0.3% to 0.4% with screened alignments

(Table 1). The indel proportion was about 0.1% with or

without screening. However, the mismatched proportions

were reduced from 0.4% to 0.3% after screening. The

polymorphisms observed from the assembled contigs,

including 0.3% mismatch and 0.1% indels, with different

lengths from 1 bp to 11 bps (Table 2), were investigated in

Figure 1. Flow chart of the clustering of ESTs from panicles

of TNG67 and detection of SNPs. Total sequence numbers and

lengths are indicated.

Table 1. Summary of aligned and screened polymorphisms

from the BLAST results of TNG67 ESTs, contigs and

singletons against the RAP1 database. All data are percentages.

Both the original and screened data were used.

Aligned HSPs Screened HSPs*

EST

Sequence numbers

87.07

95.25

Length

80.56

91.29

Mismatch

0.42

0.31

InDel

0.10

0.10

Polymorphism

0.53

0.41

Contig

Sequence numbers

94.69

96.15

Length

85.54

90.40

Mismatch

0.31

0.22

InDel

0.10

0.10

Polymorphism

0.41

0.31

Singleton

Sequence numbers

82.37

94.14

Length

75.69

90.50

Mismatch

0.48

0.34

InDel

0.11

0.10

Polymorphism

0.58

0.44

*

The criterion for screening was at least 50% length alignment

and greater than 95% identity.

Table 2. Frequency distribution of continuous SNPs and indels

detected from assembled contigs of TNG67.

Length

SNP

Indel

1

523

181

2

44

20

3

2

7

4

1

3

5

0

4

6

0

1

6~10

0

6

Total

570

222

246

Botanical Studies, Vol. 48, 2007

detail to uncover the distribution on different positions and

types of substitution.

Distribution of indels and gaps in different

regions

The indels and transition/transversion substitutions

on different codon positions or 5

¡¦

and 3

¡¦

UTRs were

calculated for the screened contig sequences. Table 3 and

Figure 2 illustrate that the highest polymorphism portion

was found on the 3

¡¦

UTR (0.53%), with decreasing

polymorphisms on the 5

¡¦

UTR (0.41%) and the third codon

(0.30%). Of the three codon positions, the indels occurred

similarly, but the substitutions were distributed differently;

the transition proportions on the third codon were double

those of the other two codons. The indel and substitution

proportions of the first codon were slightly larger than

those of the second codon with the indel proportion in the

first codon being the highest among the three positions.

Only on the 5

¡¦

UTR was the proportion of indels higher

than that of substitutions and exactly the reverse on the 3

¡¦

UTR and coding regions.

most Ka/Ks ratios were less than 1

The nucleotide substitutions on protein coding sites are

defined as synonymous or non-synonymous substitutions

according to whether the corresponding amino acids were

changed. The ratio of Ka (number of non-synonymous

changes per nonsynonymous site) and Ks (number of

synonymous changes per synonymous site) reflects the

selective constraint on a gene.

The synonymous and non-synonymous substitution

distances were calculated with use of the selected

homologous sequence pairs. Of the 154 selected contigs,

133 had Ka/Ks ratios less than 1 (Figures 3 and 4), which

indicates that the selection constraint of these genes was

conserved. The annotations of the genes with Ka larger

than Ks are listed in Table 4. Interestingly, most of the Ka

and Ks values of these genes are quite low, which indicates

a high similarity between homologous pairs.

Table 3. Total length (base pairs) of T NG67 panic le EST

contigs and their polymorphism between homologous pairs on

comparison with Nipponbare genome annotation genes.

Total length Indel Transition Transversion

5¡¦ UTR

22,488 48 19

25

3¡¦ UTR

79,951 155 144

121

Codon 199,028 124 163

147

1st 66,318 48 32

46

2nd 66,338 37 38

37

3rd 66,372 39 93

64

Fi gu re 2. Polymorphism frequencies betwe en T NG67 and

Nipponbare sequences. Three types of differences, including

indels, transitions and t rans ve rs ions , on diffe rent genomic

regions are indicated.

Figure 3. Relation between Ka and Ks from 154 homologous

matches between TNG67 and Nipponbare sequences. The solid

diagonal line represents Ka=Ks. Most of the observed points are

Ka<Ks.

Figure 4. Fre quen cy dis tri buti on of Ka/ Ks ra tio for 1 54

homologous pairs between TNG67 and Nipponbare sequences.

The fitted normal distribution is shown as a curved line.

HOUR et al. ¡X SNP detection of TNG67 rice

247

DISCUSSION

The detection of SNPs between two rice cultivars may

be approached in several ways. Because the whole genome

sequence of Nipponbare and the EST sequences of TNG67

are available, in silico SNP detection by bioinformatics is

a relatively low-cost method.

Using the EST data generated in our laboratory, we

searched more than 6,000 ESTs from panicles and detected

a 0.3% polymorphism between TNG67 and Nipponbare.

The SNPs or indels were distributed over UTRs and

coding regions. The resulting data gave useful information

for future research involving the use of TNG67 as an

experiment material and Nipponbare genomic sequence as

a database resource.

Frequency of polymorphisms in TNg67 panicle

eSTs

Agronomic traits and physiological characteristics

differ genetically among rice subspecies or cultivars, and

molecular polymorphisms have been reported recently

(McNally et al., 2006). This integrated knowledge enables

analysis of traits undergoing selection in the course of

domestication. In the present work, we compared TNG67

and Nipponbare sequences and found about three SNPs

per kb, or 0.3%. This rate is intermediate of two previously

published rates for Nipponbare compared with 93-11, a

japonica and an indica variety: 0.17% from Feltus et al.

(2004) and 0.71% from Shen et al. (2004). However, the

rate is higher than that found among japonica cultivars

by PCR analysis (Nasu et al., 2002; Monna et al., 2006).

We downloaded EST sequences of other rice cultivars

for analysis as described in Materials and Methods, and

the results are listed in Supplementary Table 1; previous

results of different approaches are listed in Supplementary

Table 2. The estimation of SNP frequency may differ

because of the calculation criteria of each method or the

biased bases of ratios. For example, Monna et al. (2006)

selected only 490 amplicons from 1,117 sites for which

effective data were obtained in all cultivars used. The

frequency of SNPs detected among japonica cultivars by

PCR was 0.03% to 0.05%, much lower than our result by

EST sequencing and in silico estimation.

Table 4. Genes with Ka/Ks >1, annotated by homologous RAP-DB sequences.

EST Contig # Locus name Sequence name Ks Ka

Annotation descriptions

CL8Contig1 AK100159

-

1.481 1.584 Glyceraldehyde-3-phosphate dehydrogenase (Gpc) protein

CL386Contig1 AK103374 Os07g0466300 0.758 0.762 Rurm1 protein

CL340Contig1 AK102983 Os09g0484300 0.127 0.166 HECT domain containing protein

CL641Contig1 Os03g0750300 Os03g0750300 0.064 0.103 Hypothetical protein

CL574Contig1 AY224520 Os06g0643300 0.046 0.092 Superal1

CL639Contig1 AK103440 Os07g0191000 0.039 0.051 Inositol monophosphatase family protein

CL340Contig1 AK102983 Os09g0484300 0.027 0.032 HECT domain containing protein

CL194Contig1 AK065975 Os05g0498700 0.029 0.03 Gda-1 protein

CL487Contig1 AK074018 Os05g0113900 0.025 0.029 Histone H2A

CL315Contig1 AK103501 Os03g0192700 0.017 0.025 Myo-inositol-1-phosphate synthase

CL343Contig1 AK061681 Os05g0553000 0.011 0.017 ATP synthase beta chain, mitochondrial precursor (EC

3.6.3.14)

CL484Contig1 AB117994 Os09g0326900 0.008 0.017 Translation initiation factor IF5 domain containing protein

CL507Contig1 AK059049 Os08g0242700 0.009 0.015 Hypothetical protein.

CL512Contig1 AK120568 Os03g0836500 0.008 0.014 Conserved hypothetical protein

CL391Contig1 AY332470 Os03g0794500 0.021 0.012 Glutamate dehydrogenase (EC 1.4.1.3) (GDH)

CL391Contig1 AY332470 Os03g0794500 0.011 0.012 Glutamate dehydrogenase (EC 1.4.1.3) (GDH)

CL410Contig1 Os06g0723900 Os06g0723900 0.009 0.011 (not hit)

CL288Contig1 AK106095 Os01g0502400 0.005 0.011 2OG-Fe(II) oxygenase domain containing protein

CL255Contig1 AK061254 Os02g0317400 0.005 0.009 Clathrin adaptor complex, small chain family protein

CL55Contig1 X81691

Os11g0220800 0.004 0.006 60S ribosomal protein L10 (QM protein homolog)

CL290Contig1 AK065538 Os01g0703600 0.002 0.006 Mu1 adaptin

248

Botanical Studies, Vol. 48, 2007

Information on the pedigree of TNG67 varieties was

retrieved from the Taiwan Rice Information System

constructed by the Taiwan Agricultural Research Institute

(http://tris.tari.gov.tw:8080) and redrawn as Figure 5. Since

this variety includes many indica varieties in pedigree,

the TNG67 genome may diverge from that of traditional

japonica cultivars from Japan such as Nipponbare.

Breeders¡¦ selection preferences may also affect the genetic

distances between cultivars. The rice breeding program

in Taiwan usually involves TNG67 in the pedigree, but

the commonly available database and information are

constructed with Nipponbare. Therefore, SNP analysis is

essential for the study of genetic mapping and phenotype-

genotype association.

Figure 5. The pedigree of TNG67. Black indicates japonica varieties, green indica varieties, and red javanica varieties.

HOUR et al. ¡X SNP detection of TNG67 rice

249

For example, TNG67 was used as research material

in serial analysis of gene expression (SAGE) (Su et al.,

2005), mutant generation involving sodium azide (Jeng et

al. 2003), T-DNA insertion mutants (Hsing et al., 2007),

and AFFYMETRIX array analysis. If the annotation

of flanking sequences or expression genes should be

based on the genomic sequences and cDNA databases

of Nipponbare, the polymorphism between TNG67 and

Nipponbare must be considered. Thus, for SAGE, the

probability of sequence polymorphisms is approximately

1% for the 4-bp restriction sites and approximately 3% for

the 10-bp tag sequences. An annotation system allowing

mismatches in such scales should be implemented.

Confirmation of homologous alignment by

screening

The sequencing of ESTs was not confirmed by

duplicated processes; thus, errors in sequences cannot be

overlooked (Pertea et al., 2003). The sequence of panicle

ESTs have been estimated by autoSNP (Barker et al.,

2003) with the assembled contigs. Only 22 of 659 contigs

have SNPs, and most of them occurred on GC-rich or

simple-repeated regions. Since we are using consensus

sequences of contigs, the sequencing errors are very low

and should be neglected. The clustering of EST sequences

to longer and higher coverage contigs reduce the error

mentioned above. Genome sequencing and comparative

genomic studies have documented gene duplications in

the rice genome (Goff et al., 2002; Yu et al., 2002; IRGSP,

2005; Yu et al., 2005). In the current study, the screening

of HSPs with thresholds of 95% similarity and 50%

coverage was a strategy intended to reduce the possibility

of non-orthologous alignments.

From the results illustrated in Table 1, the total

polymorphism was reduced from 0.41% to 0.31% after

clustering, which implies that sequencing error may

account for approximately 0.1% of polymorphism.

Similar results can be estimated from other cultivars

(Supplementary Table 1). With the described screening

criteria, confirmation of homologous alignments can be

processed automatically and objectively.

The coverage rates of alignments were reduced by

10% when we screened the homologous alignments with

similarity higher than 95% and when more than 50% of the

length of sequences was aligned. The criteria of screening

are empirical and usually result in robust assessments.

Thus, in subsequent analyses, including SNP distribution

in different coding positions and Ka/Ks ratio, we used the

screened contigs as query sequences for correctness.

Distribution of polymorphism

The polymorphisms observed in the current study did

not just occur on one base pair; instead, about 10% of

polymorphisms occurred on more than 1-bp lengths (Table

2).Under the cut-off E-value of 1E-50 and screening

criteria, the total probability of indels was lower than that

of SNPs (Figure 2), but the probability of indels longer

than 1 bp was higher than that of SNPs. The mean total

polymorphism frequency was 0.5% for the 3¡¦ UTR region,

0.4% for the 5¡¦ UTR region, and 0.3% for the three codons

of the coding regions. The average polymorphism on

the third codon was higher than on the other two, which

is consistent with theoretical knowledge of selection

constraint (Nei, 2005). The indels occurred more

frequently in UTR regions than in coding regions because

of possible serious alternation consequences in the latter.

The frequency of transition and transversion substitutions

was almost the same in the first and second codons but

differed in the third codon.

Implication from evolutionary distances

The calculation of Ks and Ka substitution distances

gives aspects of functional constraint for corresponding

genes. In the current study, the divergence of most aligned

genes was low and Ka/Ks ratios were usually below 1;

thus, the gene function is conserved between these two

cultivars with high polymorphism. Table 4 lists 21 genes

with Ka>Ks. Most of them have small absolute values

of Ka and Ks because the sequences¡¦ coding regions are

consistent even under unconstrained function. A possible

cause was the bias introduced by low Ks (Xing and Lee,

2006).

Conclusion

SNPs, the basic units of genomic diversity, have high

density even among closely related genomes, which

makes them preferred markers for fine mapping and

understanding evolutionary dynamics. These SNP sites

can then be used in designing primers and marker-assisted

selection. SNPs detected from panicle EST libraries may

be especially helpful in investigating the development and

function of panicles for the elite local cultivar Tainung 67.

Acknowledgments.

This work was supported by the

Thematic Project, Academia Sinica, Taipei, Taiwan

.

The

authors would like to thank Anthony H.C. Huang for his

critical review of this manuscript, Dr. Ming-Hsing Lai

for his helpful comments and discussion, and Ms. Laura

Heraty for English editing.

lITeRaTURe CITeD

Altschul, S.F., T.L. Madden, A.A. Schaffer, J. Zhang, Z. Zhang,

W. Miller, and D.J. Lipman. 1997. Gapped BLAS T and

P SI-BLAST: a new generation of protein database search

programs. Nucleic Acids Res. 25: 3389-3402.

Barker, G., J . Batley, H. O¡¦Sullivan, K.J. Edwa rds, and D.

Edwards. 2003. Redundancy based detection of sequence

pol ymorphis ms in expres s ed se quence t ag data using

autoSNP. Bioinformatics 19: 421-422.

Cargill, M., D. Alts huler, J. Ireland, P. Sklar, K. Ardlie, N.

Patil, N. Shaw, C.R. Lane, E.P. Lim, N. Kalyanaraman, J.

Nemesh, L. Ziaugra, L. Friedland, A. Rolfe, J. Warrington,

250

Botanical Studies, Vol. 48, 2007

R . L i ps h ut z , G. Q. Da l e y, a n d E. S . L a nd e r. 1 99 9.

Characterization of s ingle-nucleotide polym orphisms in

coding regions of human genes. Nat. Genet. 22: 231-238.

Carlson, C.S., M.A. Eberle, M.J. Rieder, J.D. Smith, L. Kru-

glyak, and D.A. Nickerson. 2003. Additional S NPs and

linkage-disequilibrium analyses are necessary for whole-

genom e association studies in humans. Nat. Genet. 33:

518-521.

Feltus, F.A., J . Wan, S.R. Schulze, J .C. Estill, N. Jiang, and

A.H. Paterson. 2004. An SNP resource for rice genetics and

breeding based on subspecies indica and japonica genome

alignments. Genome Res. 14: 1812-1819.

Ewing, B., L. Hillier, M.C. Wendl, and P. Green. 1998. Base-

c alli ng of aut omate d seque nc er trace s us ing phre d. I.

Accuracy assessment. Genome Res. 8: 175-185.

Ewing, E. and P. Green. 1998. Bas e-cal ling of automa ted

s eque ncer t races us ing phred. II. Error probabi liti es.

Genome Res. 8: 186-194.

Goff, S.A., D. Ricke, T.H. Lan, G. Pres ting, R. Wang, M.

Dunn, J. Glazebrook, A. Sessions, P. Oeller, H. Varma, D.

Hadley, D. Hutchison, C. Martin, F. Katagiri, B.M. Lange,

T. Moughamer, Y. Xia, P. Budworth, J. Zhong, T. Miguel,

U. Paszkowski, S. Zhang, M. Colbert, W. L. Sun, L. Chen,

B. Cooper, S. Park, T.C. Wood, L. Mao, P. Quail, R. Wing,

R. Dean, Y. Yu, A. Zharkikh, R. Shen, S. Sahasrabudhe,

A. Thomas , R. Cannings , A. Gutin, D. Prus s, J. Reid,

S . Tavtigian, J. Mitchell, G. Eldredge, T. S choll, R. M.

Miller, S. Bhatnagar, N. Adey, T. Rubano, N. Tusneem, R.

Robins on, J. F eldhaus, T. Macalma, A. Oliphant, and S.

Briggs. 2002. A draft sequence of the rice genome (Oryza

sativa L. ssp. japonica). Science 296: 92-100.

Halushka, M.K., J.B. F an, K. Bentley, L. Hsie, N. S hen, A.

Weder, R. Cooper, R. Lipshutz, and A. Chakravarti. 1999.

P atterns of single-nucleotide polymorphisms in candidate

genes for blood-pres sure homeos tasis. Nat. Genet. 22:

239-247.

Hsing, Y.I., C. G. Chern, M.J . F an, P.C. Lu, K.T. Chen, S.F.

Lo, P.K. Sun, S.L. Ho, K.W. Lee, Y.C. Wang, R. Ko, W.L.

Huang, J.L. Chen, C.I. Chung, Y.C. Lin, A.L. Hour, Y.W.

Wang, Y.C. Chang, M.W. Tsai, Y.S . Lin, Y.C. Che n, S .

Chen, H.M. Ten, C.P. Li, C.K. We y, C.S . Ts eng, M.H.

L ai, S .C. Huang, L.J. Chen, and S.M. Yu. 2007. A rice

gene activation/knockout mutant resource for functional

genomics. Plant Mol. Biol. 63: 351-364.

Huang, C.S. 1979. Development of rice variety Tainung 67. J.

Agri. Res. China 28: 57-66.

IRGSP. 2005. The map-based sequence of the rice genome. Na-

ture 436: 793-800.

Jeng, T.L., C.S. Wang, T.H. Tseng, C.L. Chen, and J.M. Sung.

2003. Starch biosynthesizing enzymes in developing grains

of rice cultivar Tainung 67 and its sodium azide-induced

rice mutant. Field Crops Res. 84: 261-269.

Katagiri, S., J. Wu, Y. Ito, W. Karasawa, M. Shibata, H.

Kanomori, Y. Katayose, N. Namiki, T. Matsumoto, and T.

S asaki. 2004. End sequencing and chromosomal in silico

mapping of BAC clone derived from an indica rice cultivar,

Kasalath. Breed. Sci. 54: 273-279.

Kimura, M. 1980. A simple method for estimating evolutionary

rates of base substitutions through comparative studies of

nucleotide sequences. J. Mol. Evol. 16: 111-120.

Kruglyak, L. and D.A. Nickerson. 2001. Variation is the spice of

life. Nat. Genet. 27: 234-236.

Li, W.H. 1993. Unbiased estimation of the rates of synonymous

and nonsynonymous substitution. J. Mol. Evol. 36: 96-99.

McNally, K.L., R. Bruskiewich, D. Mackill, C.R. Buell, J.E.

Lea ch, a nd H. Le ung. 2006. Sequenc ing multiple and

diverse rice varieties. Connecting whole-genome variation

with phenotypes. Plant Physiol. 141: 26-31.

Monna, L., R. Ohta, H. Masuda, A. Koike, and Y. Minobe.

2006 . Gen ome -wid e s e arc hin g of s i ngl e-nu cl eot ide

polymorphisms among eight distantly and closely related

rice cultivars (Oryza sativa L.) and a wild accession (Oryza

rufipogon Griff.). DNA Res. 13: 43-51.

Nasu, S., J. Suzuki, R. Ohta, K. Hasegawa, R. Yui, N. Kitazawa,

L. Monna, and Y. Minobe. 2002. Search for and analysis

of single nucleotide polymorphisms (SNPs) in rice (Oryza

sativa, Oryza rufipogon) and establishment of SNP markers.

DNA Res. 9: 163-171.

Nei , M. 2 005. Se le cti onis m a nd neut rali sm in m ole cula r

evolution. Mol. Biol. Evol. 22: 2318-2342.

Ohyanagi, H., T. Tanaka, H. Sakai, Y. Shigemoto, K. Yamaguchi,

T. Ha ba ra, Y. F uj ii, B . A. An toni o, Y. Naga m ura, T.

Imanishi, K. Ikeo, T. Itoh, T. Gojobori, and T. Sasaki. 2006.

The Rice Annotation Project Database (RAP-DB): hub for

Oryza sativa ssp. japonica genome information. Nucleic

Acids Res. 34: D741-744.

Pertea, G., X. Huang, F. Liang, V. Antonescu, R. S ultana, S .

Karamycheva, Y. Lee, J . White, F. Cheung, B. P arvizi,

J. Tsai, and J. Quackenbus h. 2003. TIGR Gene Indices

clus te ring tool s (T GIC L): a softwa re s yst em for fa st

clustering of large EST datasets. Bioinformatics 19:

651-652.

Sachidanandam, R., D. Weissman, S.C. S chmidt, J.M. Kakol,

L.D. S t ei n, G. Mart h, S . S he rry, J .C. Mu lli kin , B.J .

Mortimore, D.L. Willey, S.E. Hunt, C.G. Cole, P.C. Coggill,

C.M. Rice, Z. Ning, J. Rogers, D.R. Bentley, P.Y. Kwok,

E.R. Mardis, R.T. Yeh, B. Schultz, L. Cook, R. Davenport,

M. Da nte, L. F ulton, L. Hill ier, R.H. Wat erst on, J .D.

McPherson, B. Gilman, S . S chaffner, W.J. Van Etten, D.

Reich, J. Higgins, M.J. Daly, B. Blumenstiel, J. Baldwin,

N. Stange-Thomann, M.C. Zody, L. Linton, E.S. Lander,

and D. Altshuler. 2001. A map of human genome sequence

va ria tio n co nta in ing 1 .42 m il li on s i ngle nuc le ot ide

polymorphisms. Nature 409: 928-933.

Shen, Y.J., H. Jiang, J.P. Jin, Z.B. Zhang, B. Xi, Y.Y. He, G.

Wang, C. Wang, L. Qian, X. Li, Q.B. Yu, H.J. Liu, D.H.

Chen , J .H. Gao, H. Huang, T.L. S hi, a nd Z.N. Ya ng.

2004. Development of genome-wide DNA polymorphism

database for map-based cloning of rice genes. Plant Physiol.

135: 1198-1205.

HOUR et al. ¡X SNP detection of TNG67 rice

251

Su, C.L., C.I. Chung, Y.C. Lin, P.C. Lu, F.J. Wei, Y.I.C. Hsing,

and A.L. Hour. 2005. Statistics analysis of rice SAGE data.

J. Genet. Mol. Biol. 16: 248-260.

The International Hap Map Consortium. 2003. The International

HapMap Project. Nature 426: 789-796.

Thorisson, G.A. and L. D. Stein. 2003. The SNP Consortium

website: past, present and future. Nucleic Acids Res. 31:

124-127.

Wisconsin Package Version 10.3. Genetics Computer Group

(GCG), Madison, WI.

Xing, Y. and C. Lee. 2006. Can RNA selection pressure distort

the measurement of Ka/Ks. Gene 370: 1-5.

Yu, J., S. Hu, J. Wang, G. K. Wong, S. Li, B. Liu, Y. Deng, L.

Dai, Y. Zhou, X. Zhang, M. Cao, J. Liu, J. Sun, J. Tang, Y.

Chen, X. Huang, W. Lin, C. Ye, W. Tong, L. Cong, J. Geng,

Y. Han, L. Li, W. Li, G. Hu, X. Huang, W. Li, J. Li, Z. Liu,

L. Li, J. Liu, Q. Qi, J. Liu, L. Li, T. Li, X. Wang, H. Lu,

T. Wu, M. Zhu, P. Ni, H. Han, W. Dong, X. Ren, X. Feng,

P. Cui, X. Li, H. Wang, X. Xu, W. Zhai, Z. Xu, J. Zhang,

S. He, J. Zhang, J. Xu, K. Zhang, X. Zheng, J. Dong, W.

Zeng, L. Tao, J. Ye, J. Tan, X. Ren, X. Chen, J. He, D. Liu,

W. Tian, C. Tian, H. Xia, Q. Bao, G. Li, H. Gao, T. Cao, J.

Wang, W. Zhao, P. Li, W. Chen, X. Wang, Y. Zhang, J. Hu,

J. Wang, S. Liu, J. Yang, G. Zhang, Y. Xiong, Z. Li, L. Mao,

C. Zhou, Z. Zhu, R. Chen, B. Hao, W. Zheng, S. Chen, W.

Guo, G. Li, S. Liu, M. Tao, J. Wang, L. Zhu, L. Yuan, and

H. Yang. 2002. A draft sequence of the rice genome (Oryza

sativa L. ssp. indica). Science 296: 79-92.

Yu, J., J. Wang, W. Lin, S. Li, H. Li, J. Zhou, P. Ni, W. Dong,

S. Hu, C. Zeng, J. Zhang, Y. Zhang, R. Li, Z. Xu, S. Li, X.

Li, H. Zheng, L. Cong, L. Lin, J. Yin, J. Geng, G. Li, J. Shi,

J. Liu, H. Lv, J. Li, J. Wang, Y. Deng, L. Ran, X. Shi, X.

Wang, Q. Wu, C. Li, X. Ren, J. Wang, X. Wang, D. Li, D.

Liu, X. Zhang, Z. Ji, W. Zhao, Y. Sun, Z. Zhang, J. Bao, Y.

Han, L. Dong, J. Ji, P. Chen, S. Wu, J. Liu, Y. Xiao, D. Bu,

J. Tan, L. Yang, C. Ye, J. Zhang, J. Xu, Y. Zhou, Y. Yu, B.

Zhang, S. Zhuang, H. Wei, B. Liu, M. Lei, H. Yu, Y. Li, H.

Xu, S. Wei, X. He, L. Fang, Z. Zhang, Y. Zhang, X. Huang,

Z. Su, W. Tong, J. Li, Z. Tong, S. Li, J. Ye, L. Wang, L.

Fang, T. Lei, C. Chen, H. Chen, Z. Xu, H. Li, H. Huang, F.

Zhang, H. Xu, N. Li, C. Zhao, S. Li, L. Dong, Y. Huang, L.

Li, Y. Xi, Q. Qi, W. Li, B. Zhang, W. Hu, et al. 2005. The

genomes of Oryza sativa: a history of duplications. PLoS

Biol. 3: e38.

252

Botanical Studies, Vol. 48, 2007

Supplementary Table 1. Polymorphism percentages, including mismatches and gaps, between the Nipponbare annotated cDNA

sequences, RAP-DB sequences, and EST sequences of other cultivars. The similarity search was performed by using BLASTN with

E-value at 1E-50 and screened alignments of at least 95% identity and 50% coverage.

Original

Contig

Singleton

subsp. cultivar

coverage mm gap pol coverage mm gap pol

hybrid

LYP9

92.765 0.675 0.146 0.821 87.276 0.913 0.224 1.137

PA64s

89.982 0.540 0.124 0.663 84.265 0.833 0.223 1.056

indica

9311

92.736 0.733 0.164 0.898 85.295 1.036 0.205 1.241

IR36

88.448 0.766 0.067 0.833 83.883 1.173 0.086 1.259

IR64

80.875 1.719 0.162 1.881 64.812 2.224 0.234 2.458

Milyang23

90.899 1.660 0.387 2.047 76.821 2.595 0.752 3.347

Minghui63

73.666 1.216 0.305 1.522 67.576 1.342 0.471 1.813

Nagina22

83.904 0.965 0.210 1.174 68.110 3.104 0.787 3.891

Pokkali

81.958 1.241 0.193 1.434 73.218 1.666 0.239 1.905

japonoca Donganbyeo 79.565 0.809 0.309 1.118 79.565 0.809 0.309 1.118

Koshihikari

93.352 0.582 0.179 0.761 86.488 0.715 0.276 0.991

Nackdong

88.185 0.631 0.094 0.725 93.835 1.672 0.105 1.777

PI560247

94.405 0.543 0.046 0.589 92.722 0.756 0.050 0.806

Screened

Contig

Singleton

subsp. cultivar coverage % mm % gap % pol % coverage % mm % gap % pol %

hybrid

LYP9

94.858 0.478 0.143 0.621 93.868 0.597 0.205 0.802

PA64s

93.768 0.379 0.120 0.500 92.376 0.471 0.195 0.666

indica

9311

95.816 0.564 0.157 0.720 94.948 0.699 0.192 0.891

IR36

93.012 0.528 0.060 0.588 92.542 0.674 0.069 0.743

IR64

91.372 1.052 0.144 1.196 90.460 1.256 0.207 1.463

Milyang23

91.234 1.228 0.368 1.596 88.617 1.692 0.545 2.237

Minghui63

76.390 0.812 0.288 1.100 76.762 0.843 0.356 1.200

Nagina22

92.282 0.666 0.201 0.867 91.457 0.939 0.406 1.345

Pokkali

86.120 0.914 0.184 1.098 86.369 1.005 0.225 1.230

japonoca Donganbyeo

83.271 0.714 0.312 1.026 84.779 1.085 0.419 1.505

Koshihikari

95.272 0.218 0.170 0.388 94.969 0.328 0.218 0.546

Nackdong

91.828 0.274 0.089 0.363 93.031 0.305 0.104 0.409

PI560247

96.796 0.310 0.041 0.350 96.023 0.351 0.040 0.391

HOUR et al. ¡X SNP detection of TNG67 rice

253

Supplementary Table 2. Polymorphism between cultivars from the present work and previous studies.

Comparison type

Cultivars compared

SNP % Indel % Polymorphism % Data sources Ref.

hybrid/japonica

LYP9

vs. Nipponbare 0.478 0.143

0.621

EST / RAP-DB a

PA64s

vs. Nipponbare 0.379 0.120

0.500

EST / RAP-DB a

indica/japonica

9311

vs. Nipponbare 0.564 0.157

0.720

EST / RAP-DB a

9311

vs. Nipponbare 0.710 0.200

0.910

BGI / IRGSP 3

9311

vs. Nipponbare 0.170 0.011

0.181

BGI / IRGSP 1

GLA4

vs. Nipponbare 0.630

PCR amplicons 4

GLA4

vs. Koshihikari 0.637

PCR amplicons 4

IR36

vs. Nipponbare 0.528 0.060

0.588

EST / RAP-DB a

IR64

vs. Nipponbare 1.052 0.144

1.196

EST / RAP-DB a

Kasalath vs. Nipponbare 0.733

PCR amplicons 4

Kasalath vs. Koshihikari 0.744

PCR amplicons 4

Kasalath vs. Nipponbare 0.710 0.123

0.833

BES / IRGSP 2

Milyang23 vs. Nipponbare 1.228 0.368

1.596

EST / RAP-DB a

Minghui63 vs. Nipponbare 0.812 0.288

1.100

EST / RAP-DB a

Nagina22 vs. Nipponbare 0.666 0.201

0.867

EST / RAP-DB a

Pokkali vs. Nipponbare 0.914 0.184

1.098

EST / RAP-DB a

japonoca/japonoca Donganbyeo vs. Nipponbare 0.714 0.312

1.026

EST / RAP-DB a

Koshihikari vs. Nipponbare 0.218 0.170

0.388

EST / RAP-DB a

Koshihikari vs. Nipponbare 0.038

PCR amplicons 4

Nackdong vs. Nipponbare 0.274 0.089

0.363

EST / RAP-DB a

PI560247 vs. Nipponbare 0.310 0.041

0.350

EST / RAP-DB a

TNG67

vs. Nipponbare 0.216 0.095

0.311

EST / RAP-DB a

indica/indica

GLA4

vs. Kasalath 0.547

PCR amplicons 4

a

Present study.

1

Feltus et al., 2004;

2

Katagiri et. al., 2004;

3

Shen et. al., 2004;

4

Monna et. al., 2006.