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Crop Science 42:2092-2099 (2002)
© 2002 Crop Science Society of America

CELL BIOLOGY & MOLECULAR GENETICS

Design and Application of Microsatellite Marker Panels for Semiautomated Genotyping of Rice (Oryza sativa L.)

J. R. Coburna, S. V. Temnykha, E. M. Paulb and S. R. McCouch*,c

a Dep. of Plant Breeding, 252 Emerson Hall, Cornell Univ., Ithaca, NY 14853-1901
b GeneFlow Inc., 503 Mt. Vernon Ave., Alexandria, VA 22301
c Dep. of Plant Breeding, 240 Emerson Hall, Cornell Univ., Ithaca, NY 14853-1901

* Corresponding author (SRM4{at}cornell.edu)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The objective of this study was to develop a systematic and flexible method for assembling multiplex simple sequence repeat (SSR) marker panels for high throughput genome analysis in rice, Oryza sativa, and to test these panels on a set of cultivated rice germplasm. To do this, 159 microsatellite markers were fluorescently labeled and assembled into 21 multiplex panels for semiautomated genotyping, providing genome-wide coverage of the 12 rice chromosomes. Panels are comprised of an average of eight markers each, occurring at approximately 11-centimorgan (cM) intervals throughout the genome. On a standard set of 13 genetically diverse cultivars of Oryza, these markers detected an average of five alleles per locus and had a mean polymorphism information content (P.I.C. value) of 0.67. Polymerase chain reactions (PCR) were optimized on a per marker basis to generate a uniform amount of PCR product and each primer pair was assessed in replicated trials for reliability of allele size estimates. T4 DNA polymerase was used to treat PCR products where the standard deviation of allele molecular weight was greater than 0.5 base pairs (bp). This treatment minimized the variance so that, in the multiplex set reported here, the average std. dev./marker was 0.24 bp, allowing accurate discrimination of alleles that differed by a single nucleotide. The resulting data on allele sizes were then entered into GeneFlow analysis software for the evaluation of polymorphism patterns among diverse rice cultivars. The use of an automated software tool for designing multiplex panels on the basis of both highly polymorphic and more conservative SSR markers resulted in the development of a highly informative semiautomated genotyping system for applications in rice genetics and breeding.

Abbreviations: bp, base pair • cM, centimorgan • RCF, relative centrifugal force, P.C.A., principle component analysis • P.I.C., polymorphism information content • PCR, polymerase chain reaction • RFLP, restriction fragment length polymorphism • RM, rice microsatellite • SSR, simple sequence repeat • WTR, well-to-read


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
THE USE of fluorescently labeled microsatellite markers for genotyping on automated sequencers offers many advantages over analysis using traditional autoradiographic or silver-stained detection techniques. One such advantage is the large increase in throughput made possible by the multiplexing of many PCR products into a single lane. A second benefit is the significant increase in accuracy of allele sizing achieved by the use of an internal size standard in each lane and the availability of automated allele-calling algorithms. Overall, the automation increases the speed and accuracy of data collection and processing. The high sensitivity of detection also reduces the necessary volume (and therefore the cost) of the PCR reaction and allows detection of loci that are difficult to amplify.

Use of fluorescence-based semiautomated analysis of restriction fragments was first reported by Carrano et al. (1989). This method was then adapted and improved upon for microsatellite analysis (Edwards et al., 1991; Ziegle et al., 1992). Semiautomated methods of SSR genotyping are gradually replacing manual systems in plant breeding and genetics research. These methods facilitate the efficient application of microsatellite markers for high-throughput mapping (Rhodes et al., 1998; Ponce et al., 1999), pedigree analysis (Lexer et al., 1999), fingerprinting of accessions (Carrano et al., 1989), and assaying genetic diversity (Diwan and Cregan, 1997; Macaulay et al., 2001). The technology can also improve the efficiency of managing a germplasm collection, help deliver purity-proven seed stocks to growers, and provide the basis of intellectual property protection (Mitchell et al., 1997).

In rice, Blair et al. (2002) recently reported the use of 27 fluorescent labeled SSR markers organized into four panels for diversity analysis of Oryza species. However, to date no comprehensive, semiautomated SSR genotyping system providing whole genome coverage has been reported for Oryza, despite the fact that approximately 500 SSR markers are now publicly available for rice (Temnykh et al., 2001; http://www.gramene.org; verified June 12, 2002).

The purpose of this project was to develop and apply multiplex panels of fluorescently labeled microsatellite markers for semiautomated genotyping of O. sativa at the whole genome level. Combinations of primer pairs were assembled to accommodate the analysis of genetically diverse cultivars, providing a robust and flexible approach for detection of intra- and interspecific variability. The design and application of the multiplex panels was greatly facilitated by the use of the GeneFlow computer program (http://www.geneflowinc.com/; verified June 12, 2002). The software allowed us the assembly of panels in a semiautomated manner and facilitated the analysis of the data on SSR polymorphism among accessions representing a wide spectrum of cultivated rice germplasm.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Plant Material and DNA Extraction
Marker panels were tested on a standard set of 13 rice cultivars representing O. sativa spp. indica (IR36, IR64, Kasalath, N22, Teqing, Zhai-Ye-Qing-8, BS125), O. sativa spp. japonica (Azucena, Gihobyeo, Jing-Xi 17, Lemont, Nipponbare), and an intermediate Korean (Tongil) cultivar, Milyang 23. The set has been previously used to evaluate the polymorphism potential of the SSR markers developed in this lab as described by Cho et al. (2000). DNA was extracted by a modification of the chloroform microprep method as published by Fulton et al. (1995). Approximately 4 cm of young leaf tissue was harvested into 1.5-mL Eppendorf tubes held in racks suspended above liquid nitrogen. The frozen tissue was then crushed with glass rods before addition of extraction buffer. DNA extraction buffer contained: 100 mM Tris-HCl pH 8, 50 mM EDTA pH 8, 500 mM NaCl, 1.25% SDS (w/v), 8.3 mM NaOH, 0.38 g sodium bisulfite per 100 mL of buffer (added just before use). Two hundred microliters of extraction buffer was added to the frozen tissue (causing the buffer to freeze), and the racks containing the tubes were placed at room temperature until the extraction buffer thawed. Samples were then ground for about 5 s each with a power drill fixed with a plastic bit, rinsing the bit between samples. After grinding, an additional 550 µL of extraction buffer was added, samples were mixed, and then placed in a 65°C water bath for 20 to 60 min. Tubes were removed from the water bath, mixed, filled with a 24:1 mixture of chloroform and isoamyl alcohol (550–600 µL) and then placed on a shaker for 5 min.

After mixing, tubes were centrifuged at 13 000 rpm (RCF = 14926 g) for 10 min and the supernatant was removed with a pipette and placed into a new Eppendorf tube, where it was mixed with 2/3 times the volume of cold isopropanol. The tubes were inverted repeatedly to precipitate the DNA, followed by another centrifugation at 13 000 rpm (RCF = 14926 g) for 12 min to pellet the DNA. The supernatant was discarded, and the pellet was washed with 800 µL of cold 70% (v/v) ethanol, precipitated by centrifugation and dried. The pellet was resuspended in 50 µL of TE buffer in a 65°C water bath.

PCR Conditions
PCR was performed with a PTC-225 thermocycler (MJ Research Inc., Watertown, MA) as described by Chen et al. (1997) except that 13 µL of reaction mixture was used and 12 ng of template DNA, 0.3 units of polymerase and 1.5 to 3.0 pmol of each primer were added per reaction. Primers, labeled with hexachloro-6-carboxyfluorescein (HEX), tetrachloro-6-carboxyfluorescein (TET), or 6-carboxyfluorescein (FAM) dye phosphoramidites, were synthesized on an Applied Biosystem 392 (Applied Biosystem, Foster City, CA) by the Cornell BioResource Center. Unlabeled reverse primers were synthesized at Research Genetics (advertised as "RicePairs" at http://www.resgen.com; verified June 12, 2002). Markers were amplified individually or in groups of two because of the different PCR profiles required by markers in a panel. A table providing information about markers, panels, primer pairs, and annealing temperatures for PCR is available as a downloadable file from (http://ricelab.plbr.cornell.edu/publications/2002/coburn/; verified June 12, 2002).

Pooling of PCR products, T4 DNA Polymerase Treatment and Electrophoresis
PCR products were pooled by combining 2.0 µL of each amplified product and adding water if necessary to bring the products to a uniform dilution of 1:12. Equal amounts of each PCR product could be pooled because the PCR profiles of individual markers were adjusted so that product concentrations of each were similar.

In a separate experiment, markers that exhibited an average standard deviation higher than 0.5 bp were subjected to a post-PCR T4 DNA polymerase treatment, as recommended by Ginot et al. (1996). T4 DNA polymerase (0.5 U) was added to 24 µL of pooled PCR product. The treated PCR product pool was then incubated at 37°C for 30 min.

Before loading of gels, samples were prepared by combining 1 µL of the pooled PCR product with 1.75 µL deionized formamide, 0.5 µL loading dye, and 0.3 µL GENESCAN 500-TAMRA size standard (Perkin Elmer/Applied Biosystems). After denaturing at 85°C for 2 min, 0.8 µL of the sample was loaded into each lane. Electrophoresis was performed with 5% (w/v) Long Ranger (FMC Bioproducts, Rockland, ME), 6 M urea, 1x TBE 24-cm WTR gels on an ABI model 373A automatic sequencer (Perkin Elmer/Applied Biosystems). Gels were run at 30 W for a minimum of 3 h.

SSR Fragment Analysis
SSR fragment sizing was performed by the "Local Southern Method" and default analysis settings of the GeneScan version 3.1 (Perkin Elmer/Applied Biosystems). Size standard peaks were defined by the user. Allele calling was performed with Genotyper software, version 2.5 (Perkin Elmer/Applied Biosystems) and the two highest peaks were labeled using the size setting of 1/100th of a base. The GeneFlow software (http://www.geneflowinc.com/) was used for panel design allele binning and data analysis. Principle component analysis (P.C.A.) was performed by the NTSYS pc software package (Rohlf, 1998).


   RESULTS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Microsatellite Marker Characteristics and Panel Design
To develop microsatellite panels for whole-genome analysis, 159 SSR markers were selected from a collection of more than 500 currently published SSR markers [summarized by Temnykh et al. (2001)] on the basis of their map position, informativeness, and suitability for multiplexing. The markers were selected at approximately 11-cM intervals throughout the 12 chromosomes and were assembled into 21 panels containing from six to 11 markers each, with an average of eight markers per panel (for detailed summary of marker information, see URL http://ricelab.plbr.cornell.edu/publications/2002/coburn/). Nineteen of these panels are chromosome specific, facilitating ease of data collection and management. The remaining two panels encompass markers from several chromosomes that do not fit well into the other panels. Of the 159 markers, 52 (with RM numbers greater than 400) were derived from end sequences of large insert clones and therefore provide a direct link between the rice genetic and physical maps.

In each panel, fluorescent labels were assigned so that no two markers with the same dye label had overlapping allele size ranges (Fig. 1) . To ensure maximum usefulness of these panels with a broad range of germplasm, a 20-bp buffer zone was maintained separating the allele molecular weight ranges of SSR loci sharing the same fluorophore. This strategy was implemented to prevent overlap of alleles from untested rice cultivars that might fall outside the allele size range detected among the 13 test cultivars. Markers with allele sizes between 56 and 387 bp (as tested on the panel of 13 cultivars) were selected. Markers with allele sizes larger than 400 bp were eliminated for reasons of gel runtime and sizing accuracy on a 5% (w/v) polyacrylamide gel. The multiplex marker set included 84 dinucleotide, 36 trinucleotide, 6 tetranucleotide, and 33 complex repeats with a PCR product size range for individual marker loci varying from 3 to 128 bp. When evaluated on our standard panel of 13 cultivars, the set of 159 SSR markers detected from two to 10 alleles/locus, with an average of five alleles per marker, and had PIC values ranging from 0.26 to 0.89, with an average PIC value of 0.67.



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  		Fig. 1. Electropherogram of a typical multiplex panel, with shaded regions indicating the range of allele size variation among 13 diverse rice cultivars for a given locus. A 20-bp buffer zone is required between markers labeled with the same fluorophore to separate the zones corresponding to different loci.

 
PCR Optimization and Multiplexing Strategy
PCR reactions were optimized so that a uniform amount of amplified product would be produced for each microsatellite marker. To accomplish this, primers were initially tested on a subset of DNA test samples at three different primer concentrations (1.5, 2.25, and 3.0 pmol/reaction) and the originally published PCR conditions for each marker (referenced in Temnykh et al., 2001). The average peak heights per primer concentration of each SSR were graphed and for markers exhibiting average peak heights greater than 2000 fluorescence units, the number of PCR cycles was decreased from 30 to 25, while for those that had peak heights <500 fluorescence units, PCR cycles were increased from 30 to 35.

Panel Testing and Marker Quality Assessment
Fluorescent detection of alleles at all 159 loci in the 13 standard genotypes was tested for reliability on a minimum of three separate gels, with PCR products resulting from at least two different reactions. After calculating the average allele sizes and standard deviation statistics, a comparison among di-, tri-, tetranucleotide and complex repeat markers was performed. The average std. dev. (0.23 bp for the dinucleotide repeat SSRs, 0.32 bp for the trinucleotides, 0.18 for the tetranucleotide, and 0.26 bp for the complex repeat markers) indicated that there was no significant difference in marker quality among the different kinds of motifs and that an occasional single base pair polymorphism would be distinguishable for most of the markers using the system developed in this study.

T4 DNA Polymerase Treatment
When multiple PCR amplifications using the same markers and the same genotypes were sized on an ABI373, allele calling was more consistent with some markers than others. To achieve a std. dev. of <0.5 bp for each allele size estimate, 22 microsatellite markers for which the std. dev. of some or all of the allele molecular weights was consistently >0.5 bp (for details, see http://ricelab.plbr.cornell.edu/publications/2002/coburn/) were subjected to a post-PCR T4 DNA polymerase treatment. This was aimed at reducing plus-A modification of the PCR products by Taq DNA polymerase (Ginot et al., 1996). In our experiments, T4 polymerase treatments were associated with a mean reduction of 0.28 bp in the average std. dev. of allele molecular weight for the problematic markers. This represented a 50% reduction of average std. dev. and strongly suggests that the variation associated with allele size estimates at many microsatellite loci was due to the plus-A modification of PCR products by Taq DNA polymerase. After the T4 polymerase treatment, an average std. dev. of 0.24 was achieved for the 159 markers used in the panels.

Diversity Analysis
The data derived from these experiments were analyzed to evaluate the usefulness of these microsatellite panels for various applications in rice genetics and breeding. First, we determined to what extent our estimates of genetic variation obtained for the standard set of 13 rice cultivars was predictive for a larger set of germplasm. By comparing data derived from a common subset of 22 fluorescently labeled markers for the 13 cultivars with data obtained for 256 diverse rice cultivars (Y. Xu, Cornell Univ., 1998, unpublished), we found that the larger set of genotypes showed expanded allele size ranges, but in the majority of cases the differences did not exceed the 20-bp buffer zone established for the automated multiplex array. For only three markers (RM11, RM38, and RM212) did the size range exceed this buffer zone. Nonetheless, an average of only 44% of the specific alleles at any of the loci observed in the 256 cultivars were detected by the smaller set of germplasm.

The average P.I.C. value estimated for the 22 SSR markers based on the 13 cultivars (0.73) was very similar to that of the much larger collection of 256 cultivars (0.72). For 12 of the markers the P.I.C. values were almost identical for the two data sets, while for the other 10 markers, the estimates of P.I.C. differed significantly (Fig. 2) . Interestingly, there was no correlation between number of alleles detected by a marker and the degree of deviation in P.I.C. values, which suggests that the observed difference in polymorphism information content is largely due to variation in allele frequency between the two data sets. A similar trend was observed when we compared our data with the results reported by Ji et al. (1998), who examined a set of accessions comprised of 51 tropical and temperate japonicas. In this case, the average P.I.C. values estimated for japonica cultivars (51 japonicas in the Ji et al. (1998) study and five japonicas in the present study) using 30 SSR markers shared by the two studies were 0.47 and 0.45. These results demonstrate that the estimates derived from the standard set of 13 cultivars can be used as the basis for developing multiplex arrays for automated genotyping, because they provide a reasonable estimate of the polymorphism potential in O. sativa.



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  		Fig. 2. Pair-wise comparison of average P.I.C. values of SSR markers evaluated on 13 cultivars of Oryza sativa vs. 256 diverse O. sativa cultivars (Y. Xu, personal communication) (Comparison 1), and 5 vs. 51 O. sativa spp. Japonica cultivars (Ji et al., 1998) (Comparison 2). Differences between P.I.C. values for individual markers for each comparison are shown graphically.

 
Indica–Japonica Differentiation
The panel of 13 cultivars contained seven accessions traditionally classified as subspecies indica (IR36, IR64, Kasalath, N22, Teqing, Zhai-ye-qing-8, and BS125), and five from the subspecies japonica (Azucena, Gihobyeo, Jing-Xi 17, Lemont, and Nipponbare). The allelic diversity in the two subspecies was compared by the Genotype module of the GeneFlow software (http://www.geneflowinc.com/). The results are presented in graphical form in Fig. 3 (figure also may be downloaded from http://ricelab.plbr.cornell.edu/publications/2002/coburn/). Comparison of SSR polymorphism within and between the groups representing the two subspecies in this study indicated that some regions of the genome contain SSR alleles that were observed only in the indica cultivars while others contained alleles that were specific to the japonica cultivars. Six of the markers (RM132, RM156, RM142, RM421, RM435, and RM477) clearly differentiated the two subspecies. Other regions showed allelic profiles that were more variable in indica than in japonica, or vice versa. There were also markers that were highly variable at both the inter- and intra-subspecific levels (RM481, RM464, RM171, RM333, RM206, and RM224), making them very useful for distinguishing closely related genotypes.



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  		Fig. 3. Comparison of Genotypes output of GeneFlow Genotype Module indicating distribution of fluorescently labeled microsatellite markers on the 12 chromosomes of rice. The marker positions are based on the map published by Temnykh et al. (2001). Accessions are numbered as: 1. IR36, 2. IR64, 3. Kasalath, 4. N22, 5. Teqing, 6. Zhai-Ye-Qing-8, 7. BS125, 8. Acucena, 9. Gihobyeo, 10. Jing-Xi 17, 11. Lemont, 12. Nipponbarre. Numbers to the right of the chromosomes indicate the Rice Microsatellite (RM) locus-ID. Different alleles are represented by different colors for any given marker. Marker names in black font indicate markers that were omitted during the GeneFlow analysis, but are part of the panels. Marker names written in underscored black font were mapped on different populations. Black rectangles located on the chromosome bars represent approximate centromere locations. Circles indicate markers with japonica- or indica-specific alleles for the accessions tested; diamonds and triangles denote markers that were hypervariable for the japonica or indica accessions tested, respectively.

 
It was observed that the level of SSR polymorphism is unevenly distributed along the chromosomes. This observation is consistent with earlier findings reported by Temnykh et al. (2000) and reflects a similar finding based on the sequencing of genes in maize (Zea mays L., Tenaillon et al., 2001). For this marker set, both subspecies were found to have relatively little genetic variation on chromosome 4 (especially in the long arm) and showed a higher level of variability on chromosomes 10 and 11. Differences between indica and japonica diversity were apparent on chromosomes 3 (japonica showed much less variation than indica), and the short arm of chromosome 12 (japonica showed much more variation than indica), results similar to those found by Mackill et al. (1996) using AFLP markers.

The genetic relationships among the 13 cultivars were examined by means of all 159 SSR markers and the results are presented in the P.C.A. diagram in Fig. 4 . The indicas all group in close proximity to each other, while the japonicas are found in two distinct clusters; tropical japonicas Lemont and Azucena in one cluster, and temperate japonicas Nipponbare, Gihobyeo, and Jing-Xi 17 in the other. These results agree with classical genetic subspecies classifications (Morishima and Oka, 1981) and with previous reports based on isozymes (Second, 1986; Glaszmann, 1987), restriction fragment length polymorphisms (RFLP) (Wang and Tanksley, 1989), intersimple sequence repeats (ISSR) (Blair et al., 1999) and SSRs (Blair et al., 2002; Ni et al., 2002). When the Tongil cultivar Milyang 23 (derived from an indica/japonica cross), was placed on this diagram to determine its relationship with the subspecies groups, it was found to cluster with the indica accessions.



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  		Fig. 4. NTSYS principle component analysis (P.C.A.) diagram illustrating the genetic relationships among the 13 Oryza sativa accessions evaluated with the set of 159 fluorescently labeled microsatellites.

 

   DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The panels of microsatellite markers presented here should greatly expedite genotyping involving whole genome scans. However, markers selected for these 21 panels represent only a fraction of the available SSR markers in rice. For many specific studies, combinations of markers not represented by these panels will be desirable. In our experience, panels of 10 to 12 SSR markers could be easily assembled for use in diversity studies of O. sativa using existing, publicly available microsatellite information. Many more markers (i.e., up to 20) can be reliably combined per lane (data not shown) when multiplex panels are assembled for use in evaluating segregation in a mapping population derived from a single pair of parents. For some applications such as mutant screening or targeted gene discovery, a high-resolution array of SSRs from a single region of the genome may be of interest. In these cases, the ability to assemble multiplex combinations readily for semiautomated genotyping will greatly improve the efficiency of genetic analysis. The required flexibility of panel design is available in the recently developed Multiplex Module of the GeneFlow program (http://www.geneflowinc.com/). Much of the underlying data and tools required for constructing new multiplex panels of SSR markers are available in the Gramene database (http://www.gramene.org).

The optimization of PCR reactions to ensure consistent marker concentration improved the efficiency and accuracy of gel scoring because it minimized the number of false calls by the Genotyper software (Perkin Elmer/Applied Biosystems). False allele calls are often due to "pull-up" peaks that result from excessive product concentrations or weak peaks that get lost in the background signal. Scoring markers with short amplification products (<100 bp) was improved by reducing the amount of unincorporated primer due to better utilization of the smaller peaks of the internal size standard as they were less often obscured by the dye front.

Several methods exist for increasing the throughput of semiautomated genotyping. One method involves pooling primer pairs for multiplex PCR reactions (Henegariu et al., 1997). Using the same SSRs described above, we have experimented with a duplex PCR system where marker combinations were selected by determining an average melting temperature (Tm) for the forward and reverse primers of each SSR, and pooling those that have a similar average Tm. Trials in our lab have found this simple method to be about 80% successful in amplifying two SSR markers in a single PCR reaction. If the same PCR profile is optimal for some or all markers in a panel, the time required for setting up the reactions and for pooling the PCR products prior to gel loading can be significantly reduced when multiplex PCR is employed.

To increase the accuracy of allele calling, we addressed the problem of plus-A modification using the T4 DNA polymerase treatment. This approach was advantageous because it required neither a redesign of primers, nor a significant change of our established PCR protocol. However, other studies report modifications of reverse primer sequences (Brownstein et al., 1996) or PCR protocols (Smith et al., 1995). In some cases, redesign of a primer may be helpful in adjusting allele sizes to fit in a multiplex scheme better, and/or modification of PCR conditions may enhance the ability to multiplex during the PCR amplification phase. Therefore, the advantages of the various options for minimizing plus-A modification of PCR products should be evaluated in light of the system as a whole.

One of the important aspects in any large-scale genotyping experiment is reliability and informativeness of the resulting data. We have performed a detailed analysis of the microsatellite data collected in this study in the conjunction with unpublished data kindly provided by Dr. Y. Xu and data from Ji et al. (1998) to demonstrate that the multiplex panels of microsatellite markers we designed and evaluated on a standard set of 13 rice cultivars can be reliably applied to a much wider range of rice cultivars. The employment of the "Compare Genotypes" function of the GeneFlow software helped to identify markers easily that appear to have indica or japonica specific alleles. These may prove useful for the identification of subspecific introgressions in progeny derived from indica x japonica crosses. The information can also be used to decide which markers are likely to be most informative for evaluating diversity in different gene pools. For instance, for distinguishing cultivars within the same subspecific group, SSR markers can be selected which detect a higher level of polymorphism for a given subspecies. On the contrary, markers with conserved subspecific alleles may be more informative in assigning a cultivar to a certain phylogenetic group. In our study, three distinct clusters of rice cultivars revealed by the P.C.A. were in good agreement with previous genetic subspecies classifications. As a tool for whole-genome scans, the multiplex panels based on microsatellite markers can be efficiently used for whole-genome mapping, evaluation of genetic diversity and estimation of genetic relationships between O. sativa cultivars of different origin.


   ACKNOWLEDGMENTS
 
We gratefully acknowledge financial support for this project from the Rockefeller Foundation (RF99001#726) and an unrestricted gift from RiceTec Inc. We thank Yunbi Xu for the use of his unpublished data for the purpose of comparison with our data set. In addition, we thank Sharon Mitchell for critical review of the manuscript and Lois Swales for assistance with formatting.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Joint contribution of Cornell University funded by a grant from the Rockefeller Foundation (RF99001#726) and an unrestricted gift from RiceTec Inc.

Received for publication June 25, 2001.


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
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 DISCUSSION
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