Crop Science Journal of Natural Resources and Life Sciences Education
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF) Free
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (19)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Narvel, J. M.
Right arrow Articles by Shoemaker, R. C.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Narvel, J. M.
Right arrow Articles by Shoemaker, R. C.
Agricola
Right arrow Articles by Narvel, J. M.
Right arrow Articles by Shoemaker, R. C.
Crop Science 40:1452-1458 (2000)
© 2000 Crop Science Society of America

CELL BIOLOGY & MOLECULAR GENETICS

Simple Sequence Repeat Diversity among Soybean Plant Introductions and Elite Genotypes

James M. Narvela, Walter R. Fehra, Wen-Chy Chub, David Grantc and Randy C. Shoemakerc

a Dep. of Agronomy, Ames, IA USA
b DNA Sequencing and Synthesis Facility, Ames, IA USA
c USDA-ARS-CICG, Dep. of Agronomy, Iowa State University, Ames, IA 50011 USA

wfehr{at}iastate.edu


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 Materials and methods
 Results and discussion
 REFERENCES
 
The use of molecular markers to facilitate the introgression of plant introduction (PI) germplasm into elite soybean [Glycine max (L.) Merr.] cultivars will depend on the amount of polymorphism that exists between elite genotypes and PIs. The objective of this study was to assess the simple sequence repeat (SSR) diversity of 39 elite soybean genotypes (Elites) and 40 PIs that were selected for high yield potential. A total of 397 alleles were detected among the 79 genotypes at 74 SSR marker loci. The number of alleles detected among the PIs was 30% greater than that detected among the Elites. There were 138 alleles specific to the PIs that occurred across 60 SSR loci and 32 alleles specific to the Elites that occurred across 27 SSR loci. Average marker diversity among the PIs was 0.56 and ranged from 0.0 to 0.84. Average marker diversity among the Elites was 0.50 and ranged from 0.0 to 0.79. Genetic similarity estimates based on simple matching coefficients revealed more genetic diversity among the PIs than among the Elites. The greatest genetic diversity was between the PIs and Elites. The ability of SSRs to distinguish among elite soybean genotypes and PIs with agronomic merit may assist with the transfer of favorable alleles from PIs into elite soybean cultivars.

Abbreviations: AFLP, amplified fragment length polymorphism • bp, base pair • cM, centimorgan • LG, linkage group • MG, maturity group • RAPD, random amplified polymorphic DNA • QTL, quantitative trait loci • SMC, simple matching coefficient • SSR, simple sequence repeat


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
Materials and methods
 Results and discussion
 REFERENCES
 
THE LIMITED GENETIC BASE of North American soybean cultivars is due to the contribution of fewer than 20 plant introductions (PIs) to the primary gene pool and to the repeated use of related parents in breeding programs (Gizlice et al., 1994). Expanding the genetic base of soybean may introduce unique favorable alleles for polygenic traits. It is not possible at present to evaluate directly alleles for polygenic traits in soybean; therefore, incorporation of PIs with agronomic merit into breeding programs has been used as an alternative strategy (Thorne and Fehr, 1970; Vello et al., 1984; Thompson and Nelson, 1998). It is not known if selection of PIs for agronomic potential affects their diversity relative to elite germplasm. Because most PIs have no known pedigree, the genetic diversity among PIs or between PIs and elite genotypes (Elites) cannot be estimated by a coefficient of parentage analysis.

DNA marker analysis is an alternative method of estimating the diversity of PIs that are candidates as parents in a breeding program. The hypothesis is that the more genetically diverse the PIs are from the elite parents, the more likely they are to possess unique alleles for traits of interest. Several studies have measured the diversity of PIs and Elites with restriction fragment length polymorphism (RFLP) markers. Greater diversity has been detected in PIs than in Elites, but the level of polymorphism has been low (Keim et al., 1989; Keim et al., 1992). Amplified fragment length polymorphic (AFLP) and random amplified polymorhpic DNA (RAPD) markers have been shown to be more polymorphic in soybean than RFLPs (Powell et al., 1996). Maughan et al. (1996) used 15 primer pairs for AFLP analysis of a broad sample of 23 soybean accessions including G. max and wild (Glycine soja Sieb. and Zucc.) genotypes. Of the 759 AFLP fragments detected in their study, 36% were polymorphic across all genotypes. Within the group of G. soja genotypes, 31% were polymorphic. Only 17% were polymorphic within the G. max group that included four PIs and 12 elite genotypes. Thompson et al. (1998) used 125 primers for RAPD analysis of 18 soybean ancestral lines and 17 PIs of Maturity Group I to III that were selected for their seed yield. They reported that 34% of the amplified fragments detected were polymorphic across the 35 genotypes and indicated that this marker system may be useful for introgressing favorable alleles from PIs into elite breeding populations.

Simple sequence repeat (SSR) DNA markers have been shown to be highly polymorphic in soybean (Akkaya et al., 1992; Diwan and Cregan, 1997). SSRs are composed of a 1- to 6-base pair (bp) DNA sequence that is repeated a variable number of times. SSRs are amplified by PCR with primers that are complementary to the conserved sequences that flank an SSR locus. Polymorphic fragments (alleles) resulting from variations in SSR repeat length are separated electrophoretically to display genetic profiles of individuals. SSR alleles typically show monogenic-codominant inheritance that enables classification of homozygotes and heterozygotes in a segregating population.

Akkaya et al. (1992) used several types of SSRs to analyze the diversity of 43 soybean genotypes including ancestral and domestic cultivars representing the northern and southern U.S. gene pools. They determined that SSRs with (AT) and (ATT) repeat motifs were highly polymorphic in soybean and identified up to eight alleles at a single locus. Rongwen et al. (1995) identified 11 to 26 alleles at each of seven SSR loci in a diverse sample of soybean genotypes that included U.S. cultivars, G. max and G. soja PIs, and Chinese landraces. Maughan et al. (1995) detected 79 alleles across five SSR loci in a sample of 94 soybean accessions of G. max and G. soja genotypes. With 20 SSR markers, Diwan and Cregan (1997) were able to distinguish the 35 soybean genotypes that accounted for about 95% of the alleles present in North American soybean. They detected an average of 10.1 alleles per locus and an average marker diversity of 0.80.

There are no reports on the SSR diversity between Elites and PIs selected for their seed yield. The polymorphic nature of SSRs combined with their ease of analysis makes them a candidate marker system to assist with the introgression of favorable alleles from PIs into soybean cultivars. The objective of this study was to use 74 SSR markers to measure the genetic diversity of 39 Elites and 40 PIs from seven different countries that were selected for their seed yield.


   Materials and methods
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
Results and discussion
 REFERENCES
 
Germplasm and DNA Procedures
The 40 PIs and the 39 Elites included in the study were used for the development of populations AP10 to AP14 (Fehr and Cianzio, 1981) (Table 1) . The PIs were selected for their seed yield from the evaluation of over 200 PIs grown in a replicated test in Iowa in 1973. The Elites were selected for their seed yield from the Iowa Soybean Yield Test and the Uniform Regional Test, Northern States, in 1973. The 79 genotypes ranged from Maturity Group (MG) I to IV.


View this table:
[in this window]
[in a new window]
  		Table 1 Genetic similarity for 74 SSR markers among 40 soybean plant introductions (PIs), among 39 elite soybean genotypes (Elites), and between the PIs and Elites, as determined by average simple matching coefficients (SMC)

 
A 15 to 20 g sample of leaf material was collected from at least 10 different plants of each genotype. The leaf samples were kept on ice until they were frozen in liquid nitrogen and dried in a vacuum for {approx}3 d. The dried leaf samples were stored at -20°C until DNA extraction. The dried leaves were crushed within a plastic bag to obtain a homogenous sample of all plants within a genotype. Approximately 1 g of crushed leaf material was placed into a 50-mL screw-cap tube containing {approx}4 g of 3-mm glass beads. The leaf material was ground into a powder by agitation on a paint shaker. DNA was extracted from each sample by the CTAB protocol (Keim et al., 1988).

SSR Marker Selection
Eleven multiplex sets comprising 74 SSR markers developed by Narvel et al. (2000) were used for genotyping (Table 2) . Most of the SSR markers had ATT repeats because of their ease in allele scoring, but were otherwise randomly chosen by Narvel et al. (2000). Each SSR marker used was previously mapped in soybean (Cregan et al., 1999). The 74 SSR markers covered the 20 linkage groups (LGs) of soybean with an average of four markers analyzed per LG. The distribution of the 74 SSR markers across the 20 LGs was described by Narvel et al. (2000).


View this table:
[in this window]
[in a new window]
  		Table 2 SSR markers included in the 11 multiplex sets

 
PCR Conditions
All PCR reagents were obtained from PE Biosystems (PE/ABI, Foster City, CA). The 10-µL reactions contained 1.0 µL of 30-ng genomic DNA, 5.0 {rho}mol total primer, 2.0 mM magnesium chloride, 200 µM of each dNTP, 1.0 unit of AmpliTaq Gold DNA polymerase, and 1.0-µL of GeneAmp 10x PCR Buffer II. PCR runs were conducted on a GeneAmp model 9600 or 9700 thermocycler (PE/ABI). The PCR protocol was 95°C for 10 min followed by 35 cycles of 95°C for 25 s, 58°C for 25 s, and 72°C for 25 s, followed by a final extension at 72°C for 60 min. The final extension was used to correct for nontemplate addition by Taq polymerase of a nucleotide, primarily adenosine, to the 3' end of amplification products by conversion to plus A alleles (Smith et al., 1995).

Multiplexing
A description of all multiplex sets including PCR and pooling conditions, primer sequences, and examples of electropherogram displays are provided in Soybase, the USDA-ARS sponsored genome database (http://129.186.26.94/publication_data/Narvel/multiplex.html; verified April 26, 2000). The primer sequences of SSR markers in multiplex sets nine through 11 are the same as those previously reported (Cregan et al., 1999).

An example of the multiplexing procedure is provided in Table 3 for Set 6. The volume of product pooled from each PCR for multiplex set six is indicated in Table 3. A 1.5-µL aliquot of the pooled sample was mixed with 2.4 µL of formamide, 0.5 µL of blue dextran/EDTA loading dye, and 0.6 µL of internal size standard GS-350 (PE/ABI). The size standard contained DNA fragments labeled with the fluorescent dye TAMRA (red) ranging in size from 35 to 350 bp. The sample was heated for 2 min at 95°C followed by immediate cooling on ice. A 1.3-µL volume was loaded in one lane of a 4.25% (w/v) polyacrylamide gel mounted on a PE/ABI model 377 automated DNA sequencer. Electrophoresis was carried out at 3000 V for 2 h. Data were collected with DNA Sequencing Collection software version 2.5 (PE/ABI) and analyzed with GENESCAN Prism software version 2.1 (PE/ABI). SSR allele sizes were estimated with GENOTYPER software version 2.0 (PE/ABI) and were rounded to the nearest whole number using the local Southern sizing algorithm (Elder and Southern, 1987).


View this table:
[in this window]
[in a new window]
  		Table 3 Description of eight SSR markers in multiplex set six including their dye label, linkage group designation, allele size range, and multiplex PCR and pooling conditions

 
Diversity Analysis
Genetic diversity was analyzed across the 79 genotypes, the 39 Elites, and the 40 PIs. The total number of alleles and the average number of alleles per locus were computed empirically from the allele size data. Each SSR locus was considered polymorphic if the frequency of the most common allele was less than 0.95 (Hartl, 1988, p. 11–13). Marker diversity (D) at a single locus (j) was estimated according to Nei (1987)(p. 178–179) as , where n was the number of individuals sampled, and xij was the observed frequency of the ith SSR allele at the jth SSR locus. For genotypes heterozygous at an SSR locus, each allele was considered to contribute one-half. Marker diversity values range from (0 <= D < 1.0), where a value of zero indicates a monomorphic locus. Average marker diversity () was estimated as , where r was the number of SSR loci sampled, and Dj was the observed value for the jth SSR locus. The standard error of was calculated according to Zhang and Allard (1986).

Genetic similarity among genotypes was estimated from the allele size data with simple matching coefficients , where m was the number of matches and n was the number of mismatches. For estimation of SMC, both alleles at a locus were considered by counting each locus twice. The SMC between two genotypes at a single locus may equal zero (no alleles in common), 0.5 (one similar allele and one dissimilar allele), or one (two similar alleles). Because most of the 79 soybean genotypes (lines) were homozygous and homogeneous, only a single allele was detected at a locus in most cases. For those infrequent cases when two different alleles were detected, manual adjustments were made to the SMC as needed. SMC were calculated using NTSYS-pc version 2.01 software (Rohlf, 1992). Average SMC were compared by a t-test (Steele and Torrie, 1980).


   Results and discussion
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results and discussion
REFERENCES
 
There was polymorphism among the 79 genotypes at 72 (97%) of the 74 SSR marker loci, where the frequency of the most common allele was 0.95 or less (Hartl, 1988, p. 11-13). The two monomorphic loci were Satt170 on linkage group (LG) C2 and Satt395 (F). Among the 40 PIs, 70 loci (95%) were polymorphic; the four monomorphic loci were Satt170 (C2), Satt227 (C2), Satt395 (F), and Satt182 (L). Among the 39 Elites, 67 loci (91%) were polymorphic; the seven monomorphic loci were Satt390 (A2), Satt170 (C2), Satt189 (D1b), Satt135 (D2), Satt384 (E), Satt395 (F), and Satt006 (L). The high percentage of polymorphic SSR loci detected in this study was consistent with previous studies (Rongwen et al., 1995; Maughan et al., 1995; Diwan and Cregan, 1997), and was much higher than that reported for RFLPs, AFLPs, and RAPDs (Keim et al., 1992; Maughan et al., 1996; Thompson et al., 1998).

A total of 397 alleles were detected across all genotypes. There were 365 alleles in the PIs and 259 alleles in the Elites (Table 4) . The number of alleles per locus across all genotypes ranged from 2 to 11 with an average of 5.4, from 1 to 10 in the PIs with an average of 4.9, and from 1 to 8 in the Elites with an average of 3.5. There were 71% of the alleles in the PIs and 53% of the alleles in the Elites at a frequency of 0.25 or less. Only 4% of the alleles in the PIs and 7% of the alleles in the Elites occurred at a frequency of 0.75 or higher. Comparisons were made between the PIs and Elites to identify alleles specific to either group or common to both. Of the total number of alleles detected in each group, 229 alleles were common, 138 were specific to the PIs, and 32 were specific to the Elites. More than 85% of the alleles specific to either group occurred at a frequency of 0.25 or less. A total of 63 loci discriminated between the PIs and the Elites, where at least one allele detected at each of the loci was specific to either group (Table 4). The PIs had at least one allele at 60 loci that was not detected in the Elites. The Elites had at least one allele at 27 loci that was not detected in the PIs. The ability of SSR markers to identify unique alleles in Elite and PI soybean germplasm is much greater than the ability of other marker systems. Thompson et al. (1998) reported that none of the RAPD fragments from the PIs and ancestral genotypes they analyzed were specific to one particular group.


View this table:
[in this window]
[in a new window]
  		Table 4 Number of alleles and marker diversity of 40 plant introductions (PIs) and 39 elite soybean genotypes (Elites) across 74 SSR marker loci

 
Marker diversity ranged from 0.02 to 0.84 for all genotypes, from 0 to 0.84 for the PIs, and from 0 to 0.79 for the Elites (Table 4). The average marker diversity was 0.56 ± 0.02 for all genotypes, 0.56 ± 0.02 for the PIs, and 0.50 ± 0.02 for the Elites. Marker diversity was greater in the PIs than in the Elites at 46 SSR loci. The Elites had greater diversity than the PIs at 27 loci. The 10 loci with the greatest diversity in the PIs or in the Elites occurred on nine separate LGs, indicating that molecular polymorphism was spread throughout the genome in both groups. The high level of SSR diversity detected among the PIs reflected their diverse geographic regions of origin. The relatively high level of diversity detected among the Elites was consistent with previous reports on SSR diversity in elite soybean (Diwan and Cregan, 1997). The presence of unique alleles in the Elites at low frequencies may reflect the mutation rate of SSR loci in soybean, estimated at 10-5 to 10-4 per generation (Diwan and Cregan, 1997), which is similar to the rates that have been reported in humans (Edwards et al., 1992).

The genetic similarity between genotypes, as estimated by SMC, ranged from 0.18 to 0.94 for the PI x PI comparisons, from 0.26 to 0.93 for the Elite x Elite comparisons, and from 0.23 to 0.66 for the PI x Elite comparisons. For each pair-wise comparison, 75% of the SMC between PIs, 53% between Elites, and 94% between the PIs and Elites were <0.50. The average SMC of a PI with all other PIs ranged from 0.34 to 0.51 with a group average of 0.44 (Table 1). The average SMC of an Elite with all other Elites ranged from 0.46 to 0.54 with a group average of 0.50, which was significantly higher (P < 0.01) than the PI group average. The average SMC of a PI with all other Elites ranged from 0.33 to 0.47 and averaged 0.40 across all comparisons, which was significantly lower than the average SMC within the PI or Elite groups. All the Elites had a lower average SMC across PIs than across Elites. There were 28 of the 40 PIs that had a lower average SMC with all Elites than with all other PIs. These results indicated that there was greater genetic diversity among the PIs than there was among the Elites and that the greatest amount of diversity was between the PIs and Elites.

More genetic diversity based on SMC was detected in this study with SSRs compared with previous reports on other marker systems. Sneller et al. (1997) detected less genetic diversity from the analysis of 31 southern (MG V–VI) PIs, 15 southern (MG IV–VI) elite genotypes, and five northern (MG I–III) cultivars at 60 RFLP loci. They found that the greatest amount of genetic diversity was between the southern elite genotypes and the southern PIs. The lowest average SMC within a germplasm group was 0.72 and the average SMC between the southern elite lines and southern PIs was 0.65. They reported that only 5% of the average SMC among the southern elite genotypes and only 21% of the average SMC between the PIs and the southern elite genotypes were 0.60 or lower. By comparison, the average SMC for all comparisons in our study were lower than 0.60. With 53 RFLP markers, Kisha et al. (1998) evaluated the diversity between 53 elite northern (MG I–III) soybean genotypes and 14 northern (MG I–II) PIs that were selected for their agronomic value. They reported an average SMC of 0.61 between the two groups, which was higher than that detected in our study. Thompson et al. (1998) found that genetic similarity between ancestral soybean lines and northern PIs was 0.69, as determined on the basis of RAPD marker analysis.

The effectiveness of SSRs in distinguishing among PIs with agronomic merit and elite soybean genotypes may facilitate the introgression of PI germplasm through SSR marker-assisted selection strategies. This could be carried out in a backcross program for the simultaneous transfer of favorable alleles from PIs and recovery of the elite genetic background. This type of approach, termed advance backcross QTL analysis, has been used with success in tomato (Lycopersicon ssp.) for identifying and transferring alleles from unadapted germplasm into elite inbred lines (Tanksley et al., 1996; Fulton et al., 1997; Bernacchi et al., 1998a,b).

The differences in SSR diversity within and among the PIs and Elites were in agreement with genetic variance estimates for seed yield in populations developed from these genotypes. Vello et al. (1984) reported larger genetic variance estimates for seed yield in the Cycle 0 (C0) population of AP10 that was developed with the 40 PIs analyzed in our study compared with AP14 that was developed with the Elites. The population with the greatest genetic variability was AP12, which was developed with 50% PI and 50% Elite parentage. AP11 (25% PI parentage) and AP13 (75% PI parentage) also had greater genetic variability for seed yield than did AP14. The relationship between SSR diversity and genetic variability for seed yield in the C0 populations could not be directly estimated because four generations of random intermating were used to synthesize the C0 populations. To associate molecular marker diversity and genetic variance requires the assumption of equal contribution of parents to progeny in a similar genetic background, such as in biparental populations. This approach has been used to correlate quantitative genetic variance in soybean with other types of markers, such as RFLPs and RAPDs. The degree of correlation with RFLPs has been variable (Kisha et al., 1997; Manjarrez-Sandoval et al., 1997) and almost zero with RAPDs (Helms et al., 1997). Sneller et al. (1997) evaluated the association between RFLP diversity at 60 loci and the agronomic value of 31 PIs per se that represented a range of seed yield. They found that the two variables were not correlated. There are no reports on the relationship between SSR diversity and agronomic performances in soybean. Bohn et al. (1999) measured the correlation between diversity at 21 SSR loci among 11 winter wheat cultivars and progeny variance of the crosses derived from them for agronomic and quality traits. They did not detect any significant correlations between SSR parent diversity and progeny variance in any of the populations for any trait.

An important consideration in the use of SSR markers for parent selection is the extent to which SSR diversity reflects variability of expressed sequences or genomic regions that influence gene expression. An assessment of diversity with molecular markers is based on the comparison of alleles identical in state between two genotypes, but may not be predictive of genetic variance unless some unique marker alleles are linked to QTL (Moser and Lee, 1994). Relationships between marker diversity and progeny variance may only exist between related genotypes that have been developed over several years of breeding because of a greater presence of linkage disequilibrium between marker loci and QTL. The current USDA collection contains {approx}15 000 G. max PIs. The performances of many of these PIs has been determined for yield and other quantitative traits (Thompson et al., 1998). Designing crosses between Elites and desirable PIs that are diverse from the Elites based on a random set of SSR markers, or other types of markers, may not increase genetic diversity for useful phenotypes.

The detection of yield QTL from the PIs analyzed in this study could be achieved by SSR marker analysis of AP10 to AP14. AP10 to AP14 have undergone five cycles of recurrent selection. By conducting a genome scan at {approx}10-cM intervals on the original parents and on the cycle parents, QTL could be detected by identifying linkage blocks from the PIs that have been maintained despite the number of selection cycles. Because of the variation in the percentage of PI parentage in the base populations of AP10 to AP14, linkage blocks that have been maintained across populations might be detected, which would support the presence of favorable yield QTL. Following the detection of QTL, they could be introgressed into elite genetic backgrounds through marker-assisted selection strategies to minimize any potential linkage drag. Regions in the genome at which PI-derived genetic material has been maintained in AP10 to AP14 may serve as candidate regions for further searches of the germplasm collection for superior yield QTL. This approach still would have the inherent limitation of genetic background specificity for QTL-marker allele relationships, but may aid in the selection of candidate PIs from the thousands of accessions available to soybean breeders.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 Materials and methods
 Results and discussion
 REFERENCES
 
Journal Paper No. 18637 of the Iowa Agric. and Home Econ. Exp. Stn., Ames, IA 50011. Project No. 3107, and supported by the Hatch Act, the State of Iowa, and the Iowa Soybean Promotion Board.

Received for publication October 13, 1999.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results and discussion
 REFERENCES
 




This article has been cited by other articles:


Home page
 Crop Sci.Home page
Y.-B. Fu, G. W. Peterson, and M. J. Morrison
Genetic Diversity of Canadian Soybean Cultivars and Exotic Germplasm Revealed by Simple Sequence Repeat Markers
Crop Sci., September 1, 2007; 47(5): 1947 - 1954.
[Abstract] [Full Text] [PDF]

Home page
 Crop Sci.Home page
L. Wang, R. Guan, L. Zhangxiong, R. Chang, and L. Qiu
Genetic Diversity of Chinese Cultivated Soybean Revealed by SSR Markers
Crop Sci., March 27, 2006; 46(3): 1032 - 1038.
[Abstract] [Full Text] [PDF]

Home page
 Crop Sci.Home page
M. D. Smalley, W. R. Fehr, S. R. Cianzio, F. Han, S. A. Sebastian, and L. G. Streit
Quantitative Trait Loci for Soybean Seed Yield in Elite and Plant Introduction Germplasm
Crop Sci., March 1, 2004; 44(2): 436 - 442.
[Abstract] [Full Text] [PDF]

Home page
 Crop Sci.Home page
G. N. Ude, W. J. Kenworthy, J. M. Costa, P. B. Cregan, and J. Alvernaz
Genetic Diversity of Soybean Cultivars from China, Japan, North America, and North American Ancestral Lines Determined by Amplified Fragment Length Polymorphism
Crop Sci., September 1, 2003; 43(5): 1858 - 1867.
[Abstract] [Full Text] [PDF]

Home page
 GeneticsHome page
D. A. Berry, J. D. Seltzer, C. Xie, D. L. Wright, E. S. Jones, S. Sebastian, and J. S. C. Smith
Assessing Probability of Ancestry Using Simple Sequence Repeat Profiles: Applications to Maize Inbred Lines and Soybean Varieties
Genetics, September 1, 2003; 165(1): 331 - 342.
[Abstract] [Full Text] [PDF]

Home page
 Crop Sci.Home page
J. A. Hoeck, W. R. Fehr, R. C. Shoemaker, G. A. Welke, S. L. Johnson, and S. R. Cianzio
Molecular Marker Analysis of Seed Size in Soybean
Crop Sci., January 1, 2003; 43(1): 68 - 74.
[Abstract] [Full Text] [PDF]

Home page
 Crop Sci.Home page
A. Demirbas, B. G. Rector, D. G. Lohnes, R. J. Fioritto, G. L. Graef, P. B. Cregan, R. C. Shoemaker, and J. E. Specht
Simple Sequence Repeat Markers Linked to the Soybean Rps Genes for Phytophthora Resistance
Crop Sci., July 1, 2001; 41(4): 1220 - 1227.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF) Free
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via ISI Web of Science (19)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Narvel, J. M.
Right arrow Articles by Shoemaker, R. C.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Narvel, J. M.
Right arrow Articles by Shoemaker, R. C.
Agricola
Right arrow Articles by Narvel, J. M.
Right arrow Articles by Shoemaker, R. C.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
The SCI Journals Agronomy Journal Vadose Zone Journal
Journal of Natural Resources
and Life Sciences Education		 Soil Science Society of America Journal
Journal of Plant Registrations Journal of
Environmental Quality		 The Plant Genome