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DNA Research Advance Access originally published online on March 29, 2006
DNA Research 2006 13(2):43-51; doi:10.1093/dnares/dsi030
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© The Author 2006. Kazusa DNA Research Institute
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Genome-wide Searching of Single-nucleotide Polymorphisms among Eight Distantly and Closely Related Rice Cultivars (Oryza sativa L.) and a Wild Accession (Oryza rufipogon Griff.)

Lisa Monna*, Rieko Ohta, Haruka Masuda, Akiko Koike and Yuzo Minobe

Plant Genome Center 1-25-2 Kan-nondai, Tsukuba, Ibaraki 305-0856, Japan

Received 1 December 2005; revised 21 February 2006


   Abstract
Top
 Abstract
1. Introduction
 2. Materials and Methods
 3. Results and discussion
 Acknowledgements
 References
 
We searched the genomes of eight rice cultivars (Oryza sativa L. ssp. japonica and ssp. indica) and a wild rice accession (Oryza rufipogon Griffith) for nucleotide polymorphisms, and identified 7805 polymorphic loci, including single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels), in predicted intergenic regions. Polymorphisms are useful as DNA markers for genetic analysis or positional cloning with segregating populations of crosses. Pairwise comparison between cultivars and a neighbor-joining tree calculated from SNPs agreed very well with relationships between rice strains predicted from pedigree data or calculated with other DNA markers such as p-SINE1 and simple sequence repeats (SSRs), suggesting that whole-genome SNP information can be used for analysis of evolutionary relationships. Using multiple SNPs to identify alleles, we drew a map to illustrate the alleles shared among the eight cultivars and the accession. The map revealed that most of the genome is mono- or di-allelic among japonica cultivars, whereas alleles well conserved among modern japonica paddy rice cultivars were often shared with indica cultivars or wild rice, suggesting that the genome structure of modern cultivars is composed of chromosomal segments from various genetic backgrounds. Use of allele-sharing analysis and association analysis were also tested and are discussed.

Key words: sequence; variety-specific; allele; DNA marker; wild rice


   1. Introduction
Top
 Abstract
 1. Introduction
2. Materials and Methods
 3. Results and discussion
 Acknowledgements
 References
 
The genome sequence of the rice (Oryza sativa L. ssp. japonica) cultivar ‘Nipponbare’ was completely decoded in 2005 by the International Rice Genome Sequencing Project (http://rgp.dna.affrc.go.jp/IRGSP/index.html) through the use of clone-by-clone shotgun sequencing.1Go Before this announcement, a draft sequence of the ‘Nipponbare’ genome was released in 2002 and was extensively used by rice researchers.2Go,3Go The genome sequence of indica cultivar 93-11 was analyzed by whole-genome shotgun sequencing,4Go and sequence information on chromosome 4 of the ‘Guang-lu-ai 4’ cultivar (‘GLA4’) is publicly available.5Go

Making full use of this information, Feltus et al.6Go and Shen et al.7Go constructed and published genome-wide DNA polymorphism databases. Single-nucleotide polymorphisms (SNPs) and insertions and deletions (InDels) between japonica and indica cultivars over the whole genomic region are recorded in these databases.

Before the development of SNP typing technologies, cleaved amplified polymorphic sequence (CAPS) markers, which indirectly utilize SNPs, were popularly used for positional cloning of many agronomically important genes.8Go–12Go Nasu et al.13Go converted 213 SNPs throughout the whole genome into markers by the AcycloPrime FP method,14Go showing the effectiveness of SNPs as genetic markers in rice. The allele-specific PCR method has been used for SNP typing around the rice blast resistance gene Piz.15Go Information on SNP differences between japonica and indica promises to contribute DNA markers needed for positional cloning and marker-assisted breeding of new cultivars.

Previously, our group searched for SNPs in five cultivars, including ‘Nipponbare’, ‘Kasalath’ (indica cultivar) and a wild accession of Oryza rufipogon Griff., ‘W1943’, among 417 amplicons amplified from intergenic regions.13Go In the present study, we added more cultivars and more loci (amplicons) to search for DNA polymorphisms in six japonica and two indica cultivars and ‘W1943’ among 1117 amplicons. To understand genome structure variation among modern Japanese rice cultivars and to prepare molecular markers for marker-assisted breeding, we selected five japonica Japanese paddy rice cultivars. A Taiwanese upland rice, ‘Senshou’, was used as a representative of putative middle-distance japonica cultivars. Two indica cultivars were selected from different indica groups for effective discovery of variation. Among wild rice, one accession of O. rufipogon, considered to be a candidate ancestor of O. sativa, was selected for reference of allele origins.

We discovered and typed 7805 DNA polymorphisms in the cultivars and a wild rice. Besides their use as DNA markers, SNPs can also be used for allele discrimination in the analysis of allele-sharing status among distant or related rice strains. Here, we propose an ‘allele-sharing map’ as an effective strategy to convert huge amounts of complicated SNP data into a compact but informative map for various study purposes. An allele-sharing map (i) discriminates alleles by a series of SNPs, not by individual SNPs; (ii) shows the allele-sharing status at each locus in multiple cultivars; and (iii) covers the whole genome. The allele-sharing map of multiple genetic resources, including wild relatives and modern cultivars, will help us to picture allele transmission in the crossing and breeding process, for the purpose of understanding the origins of modern cultivars, for breeding new cultivars or for association analyses of trait genes.


   2. Materials and Methods
Top
 Abstract
 1. Introduction
 2. Materials and Methods
3. Results and discussion
 Acknowledgements
 References
 
2.1. Plant materials
We searched eight cultivars and a wild accession of rice for DNA polymorphisms: O. sativa L. ssp. japonica paddy rice cultivars ‘Nipponbare’, ‘Koshihikari’, ‘Itadaki’, ‘Akihikari’ and ‘Kitaake’, and the Taiwanese upland rice ‘Senshou’; ssp. indica cultivars ‘Kasalath’ and ‘Guang-lu-ai 4’ (‘GLA4’); and O. rufipogon ‘W1943’.

Seeds of ‘Nipponbare’, ‘Kasalath’, ‘Koshihikari’, ‘Itadaki’, ‘Akihikari’ and ‘Senshou’ were provided by the National Institute of Crop Science, Tsukuba, Japan. Seeds of ‘Kitaake’ and ‘GLA4’ were provided by the Gene Bank of the National Institute of Agrobiological Science, Tsukuba. Seeds of ‘W1943’ were provided by the National Institute of Genetics, Mishima, Japan. Passport, trait and pedigree data of all cultivars except ‘W1943’ are publicly available from the Rice Research Database (http://www.pgcdna.co.jp/igs_system/top_e.html); those of ‘W1943’ are available from the Wild Rice Database (http://www.pgcdna.co.jp/cgi-bin/wrdb/content.cgi).

2.2. Searching for polymorphisms among rice cultivars and ‘W1943’
Genomic DNA was isolated from leaves by the cetyltrimethylammonium bromide method.16Go

PCR primers were designed to amplify 800–1000 bp genomic fragments from predicted intergenic regions of the publicly available ‘Nipponbare’ genome sequence [P1-derived artificial chromosome (PAC) or bacterial artificial chromosome (BAC) clone; http://rgp.dna.affrc.go.jp/IRGSP] and the RiceGAAS annotation system (http://ricegaas.dna.affrc.go.jp). Regions for analysis were selected evenly throughout each chromosome, at 1.8 cM intervals on average. In total, 1117 primer pairs successfully amplified single products from DNA of at least two cultivars, and served for sequence analysis with the DYE-namic ET Terminator reagent (Amersham Bioscience, Tokyo, Japan) in a MegaBACE 1000 DNA Sequencing System (Amersham Bioscience). Quadruple sequencing (two in each direction with independent PCR product as template) was performed on each cultivar to distinguish amplification errors or sequencing errors from real polymorphisms. Sequence data sets for each primer were then aligned using the DNASIS Pro software (Hitachi Software Engineering, Kanagawa, Japan) to detect polymorphisms.

2.3. Neighbor-joining tree based on SNPs
The neighbor-joining tree based on our SNP data were created using DNASIS Pro software from 4204 SNPs (excluding InDels >2 bp) found on 491 amplicons for which sequence data were obtained from all eight cultivars and ‘W1943’.

2.4. Linkage disequilibrium calculation
We used the computer program ‘interval’ to calculate values of D (coefficient of linkage disequilibrium) and D' (the normalized coefficient) and their confidence interval between two SNPs (or polymorphic trait loci). This program was released by the Statistical Genetics Group at Tokyo Women's Medical University (http://www.genstat.net/interval/index.html).

2.5. Publicly available database
The Rice SNP Database System constructed in this work is available on the Plant Genome Center website (http://www.pgcdna.co.jp/snps/).


   3. Results and discussion
Top
 Abstract
 1. Introduction
 2. Materials and Methods
 3. Results and discussion
Acknowledgements
 References
 
3.1. Detection of SNPs in eight cultivars and ‘W1943’
We targeted intergenic DNA regions rather than coding regions in our search for SNPs because intergenic regions contained significantly more polymorphisms than coding regions, according to our test survey for five known genes (data not shown). Target cultivars included both closely and distantly related ones. We successfully analyzed 1117 amplicons of 600–1000 bp, distributed almost evenly across the whole genome. The numbers of amplicons on each chromosome are summarized in Table 1. We detected 7805 polymorphisms (including InDels) among eight cultivars and ‘W1943’. The rate of fragments containing at least one polymorphism among these eight cultivars and ‘W1943’ ranged from 75.7% (chr. 2) to 86.4% (chr. 3), and averaged 81.6% (Table 1). The total analyzed length was 888 015 bp, and the average length of amplicons was 795 bp.


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  		Table 1. Numbers of primer pairs (amplicons) analyzed in this study

 
3.2. Relations of the eight rice cultivars and ‘W1943’
We performed pairwise comparison between cultivars using polymorphisms on 491 amplicons for which nucleotide sequence data were obtained from all eight cultivars and ‘W1943’. As shown in Table 2, ‘W1943’ versus ‘Kasalath’ showed the most polymorphisms (3072 polymorphisms; polymorphic rate, 0.795%), and ‘Kitaake’ versus ‘Kasalath’ was second (2877; 0.745%). All combinations of japonica paddy rice cultivars showed low polymorphism rates; the lowest was in the ‘Koshihikari’ versus ‘Itadaki’ comparison (137; 0.035%).


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  		Table 2. Pairwise comparison between cultivars

 
Polymorphisms detected among the eight cultivars and ‘W1943’ on the 491 amplicons (which contained 4204 polymorphisms, both SNPs and single-nucleotide InDels) were used to create a neighbor-joining tree (Fig. 1). The tree demonstrates that japonica paddy rice cultivars, including the Taiwanese upland rice ‘Senshou’, have closer relations among themselves than is the case between the indica cultivars ‘Kasalath’ and ‘GLA4’ (Fig. 1). Our results are consistent with those of Garris et al.17Go, who analyzed 234 rice accessions with 169 simple sequence repeats (SSRs) and showed that the indica group had a very high rate of polymorphisms (99% of analyzed loci), whereas the temperate japonica group had a slightly lower rate (91%).17Go


Figure 1
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  		Figure 1. Neighbor-joining tree based on SNPs between nine cultivars. 4204 SNPs (excluding InDels >2 bp) found on 491 amplicons analyzed in nine cultivars were used to create a neighbor-joining tree. DNASIS Pro software was used for calculation. ‘GLA4’ = ‘Guang-lu-ai 4’. ‘W1943’ = O. rufipogon. ‘Senshou’ is a japonica upland rice cultivar. ‘Kasalath’ and ‘GLA4’ are indica cultivars. Others are japonica paddy rice cultivars.

 
Both pairwise comparison between the cultivars (Table 2) and the neighbor-joining tree calculated from the DNA polymorphism data (Fig. 1) revealed O. rufipogon ‘W1943’ to be much closer to ssp. japonica than to ssp. indica. Ohtsubo et al.18Go analyzed 101 strains of O. rufipogon with 24 loci of p-SINE1, and discovered that ‘W1943’ was the closest among analyzed rufipogon accessions to modern Japanese cultivars, sharing 21 of the 24 p-SINE1 loci with ‘Nipponbare’, ‘Koshihikari’, ‘Akihikari’ and ‘Sasanishiki’. These results suggest that ‘W1943’ might be an ancestor of Japanese cultivars.

These results show that genome-wide SNP information, as well as SSRs and p-SINE1, can be used to speculate about evolutionary processes or gene transmission in the development of cultivars from wild varieties.

3.3. Frequency of polymorphisms on each chromosome
The distributions of DNA polymorphisms detected among the eight cultivars and ‘W1943’ are plotted along a genetic map of each chromosome (Fig. 2). The polymorphisms are not distributed evenly along each chromosome, and densities are lower in regions adjacent to the centromeres of chromosomes 5 and 10. This observation is consistent with the reports by Nasu et al.13Go and Shen et al.7Go, and with an SNP map of ‘Nipponbare’ versus ‘9311’ (indica) produced by the Beijing Genomics Institute (http://rise.genomics.org.cn/rice/index2.jsp). Regions of lower polymorphism density (in other words, relatively well-conserved regions) were additionally detected at, for example, ~60 and ~110 cM on chr. 1 (Fig. 2). Chr. 2 was well conserved among the eight cultivars and ‘W1943’ (Fig. 2). Polymorphism-rich regions were found at, for example, ~85 and ~110 cM on chr. 11 (Fig. 2). The regions with low polymorphisms even in predicted non-coding nucleotide sequences can be speculated to have biological importance of some sort, for example, functional non-coding RNAs.19Go The relationship between SNP frequencies and gene densities or phenotypes, however, has not been investigated in this study. In addition, our observations are limited, and the polymorphism distribution will change when different combinations of rice strains are used.


Figure 2
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  		Figure 2. Distribution of DNA polymorphisms detected among the eight rice cultivars (O. sativa) and one wild relative (O. rufipogon, ‘W1943’) analyzed in this study along each chromosome. The horizontal scale indicates the genetic distance from the distal end of the short arm (on the International Rice Genome Sequencing Project marker-based physical map of ‘Nipponbare’; http://rgp.dna.affrc.go.jp/IRGSP/). The vertical scale indicates the number of polymorphisms of each amplicon. The red box shows the location of the centromere.

 
3.4. Allele-sharing map
As most of the amplicons analyzed in this study have multiple DNA polymorphisms, genotypes of series of SNPs or InDels on amplicons were displayed as patterns (‘haplotypes’) for precise allele discrimination (Table 3). Discriminated alleles are shown in Fig. 3 in different colors and are aligned by genetic distance from the distal end of the short arm of each chromosome. Allele-sharing status is thus illustrated for the whole genome. In Fig. 3, the 1117 amplicons analyzed in this study are aligned; 183 of them are mono-allelic (non-polymorphic) among the eight cultivars and ‘W1943’.


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  		Table 3. An example of how to discriminate independent alleles by SNPs, and the relationship with Pia (Magnaporthe grisea resistance-a) type

 

Figure 3
Figure 3
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  		Figure 3. Allele-sharing profile of all chromosomes of the eight cultivars and ‘W1943’ based on SNPs obtained in this study. Chromosomes of eight cultivars and ‘W1943’ are aligned from left according to the genetic relationship to the standard cultivar ‘Nipponbare’. A specific pattern discriminated with more than one SNP or InDel in the same amplicon was deemed to be an independent allele. Amplicons with at least one SNP among the nine cultivars are aligned with linkage position (cM). Linkage positions of amplicons are determined as the positions of rice PAC or BAC clones published by the International Rice Genome Sequencing Project (http://rgp.dna.affrc.go.jp/IRGSP/). A white box indicates that the cultivar shares an allele with ‘Nipponbare’. Boxes of different colors indicate different numbers of alleles discovered at the same amplicon. Secondary alleles from the left are colored red, third alleles yellow, fourth blue, fifth green and sixth pink. A gray box indicates no data, including ‘not analyzed’ and ‘unable to be amplified’. Numbers and ‘CEN’ on the right indicate position on linkage map (cM) and position of centromere, respectively.

 
Figure 3 clearly shows that most regions of japonica cultivars have two alleles (di-allelic) or a single allele (mono-allelic) in common, and only a minor proportion had three or more alleles. As ‘Kasalath’, ‘GLA4’ and ‘W1943’ have additional alleles in some of these regions, some amplicons have up to six alleles in the eight cultivars and ‘W1943’ (Fig. 3). Moreover, in some multi-allelic amplicons, japonica paddy rice cultivars share the same allele with indica cultivars or with the wild rice (Fig. 3).

Garris et al.17Go showed that the allele variation among 234 rice accessions (O. sativa) was 7.3 in indica, 4.9 in temperate japonica and 11.8 in all analyzed accessions, indicating that cultivated rice has high diversity in its genome structure.

In this study, we reaffirmed the existence of allele variation even among very limited modern cultivars with a new set of SNPs. Further, the wild rice ‘W1943’ was suggested to share alleles with modern Japanese cultivars in a considerable part of the genome. This result supports the assumption that the genome of cultivated rice consists of a mosaic of various chromosomal segments derived from a broad range of wild rice.

Further polymorphism analysis with SNPs and other DNA markers using various accessions, selected from the Rice Diversity Research Set of germplasm (RDRS)20Go and from wild rice collections, will be of great help for understanding the process of domestication and adaptation of each cultivar of O. sativa.

3.5. Potential of allele sharing analysis for association analysis
An allele-sharing map is a diagram that shows the allele-sharing status at each analyzed point on each chromosome. If trait information is available for analyzed strains, possible trait-associated genomic regions can be screened at a glance.

As a test case of screening trait-associated regions by using the allele sharing status and trait information of each cultivar, we examined linkage disequilibrium (LD) between alleles at an amplicon in the neighborhood of the rice blast resistance gene Pia (Magnaporthe grisea resistance-a, 36 cM on chr. 11) and the resistance type of all cultivars. At the nearest amplicon, S0186 (27.8 cM on chr. 11), cultivars without Pia (Nipponbare, Koshihikari and Itadaki) share allele 1, while those with Pia (Akihikari and Kitaake) share allele 2 (Senshou has allele 1 but Pia type is unknown) (Table 3). To calculate the D' value, we sequenced the amplicons to determine the allele type of 28 cultivars for which Pia type information is available (Table 3). Calculation performed with ‘interval’ software showed significant LD between haplotypes on S0186 and Pia (D' = –0.8333, 99% CI [–1.0000, –0.1075]). The same calculation for S0161 (5.5 cM on chr. 10), not linked to Pia and with the same allele-sharing pattern among the five japonica paddy rice cultivars, showed no significant LD (data not shown).

An allele-sharing map made from cultivars and varieties that are closely related but with obviously different traits in principle makes it possible to predict genetic regions responsible for certain traits. Calculating D' for Pia and its nearest amplicon showed the effectiveness of this strategy. It is difficult to obtain a statistically significant result using only a small number of cultivars for SNP searching, but one can screen candidate regions, and additional testing with more cultivars and varieties with reliable trait information will identify regions coding traits of interest. Such strategy should be particularly effective in crop plants, such as rice, with abundant cultivars and for which reliable trait information and pedigree data are available. Especially in Japan, there are a number of closely related rice cultivars with very low allele variation that nevertheless clearly differ in important traits, and the trait information was accumulated during their long history of cultivation. Making full use of these materials and their allele-sharing profiles will make it possible to study traits that are difficult to analyze at the individual plant level. As analyses with too few cultivars or bias in the genetic background of a population will cause incorrect results, however, appropriate consideration must be given to the structure and scale of populations to be analyzed.

3.6. SNPs as molecular markers
About 90% of InDels detected between japonica and indica by computer analysis function as markers.7Go SNPs can be converted into markers by conventional methods such as CAPS, dCAPS and allele-specific PCR, or by various established typing systems such as AcycloPrime FP.16Go Using abundant SNPs and InDels on all chromosomes, one can easily establish molecular markers and draw graphical genotypes of pedigrees to select an individual with an ideal genetic background for the next crossing. This will not only accelerate the breeding of new cultivars but will also make it possible to introduce genomic fragments with comparable precision to that of genetic modification. Additionally, cultivars developed with this strategy can be discriminated easily by SNP typing, so they can be solidly protected as intellectual property.

The SNP information collected in this study can be freely obtained from our database (http://www.pgcdna.co.jp/snps/). Users can retrieve SNPs in any genetic region between any two or more cultivars, and browse the detailed information to design markers. Further, as SNP information can be obtained from aligned data (as shown in Table 3), allele-sharing analyses are facilitated. We believe it to be an effective tool for rice researchers.

Nucleotide polymorphism information on multiple cultivars opens a new dimension of genome sequence comparison. Versatile applications, gene identification, genome breeding and cultivar discrimination will be facilitated by the accumulation of such information.


Figure 4
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  		Figure 4. Pia (Magnaporthe grisea resistance-a, 36 cM on chr. 11) type and allele type at its nearest amplicon, S0186, of 28 japonica paddy rice cultivars. (a) Twenty-eight japonica paddy rice cultivars with reliable information about Pia type were selected. Allele-type at amplicon S0186 (chr. 11, 27.8 cM, see Table 3) was determined by direct sequencing. (b) Summary of (a) and the result of D' value calculation using the ‘interval’ software (see Materials and Methods).

 


   Acknowledgements
Top
 Abstract
 1. Introduction
 2. Materials and Methods
 3. Results and discussion
 Acknowledgements
References
 
This work was supported in part by the project ‘Development of DNA Marker-aided Selection Technology for Plants and Animals’ of the Ministry of Agriculture, Forestry and Fisheries (project No. 2201).


   Footnotes
 
*To whom correspondence should be addressed. Tel. +81-29-839-4823, Fax. +81-29-839-4824, E-mail: monna{at}pgcdna.co.jp

Communicated by Masahiro Yano


   References
Top
 Abstract
 1. Introduction
 2. Materials and Methods
 3. Results and discussion
 Acknowledgements
 References
 

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