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Published online 27 March 2006
Published in Crop Sci 46:1032-1038 (2006)
© 2006 Crop Science Society of America
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CROP BREEDING & GENETICS

Genetic Diversity of Chinese Cultivated Soybean Revealed by SSR Markers

Lixia Wang, Rongxia Guan, Liu Zhangxiong, Ruzhen Chang and Lijuan Qiu*

National Key Facility of Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081 Beijing, P.R. China

* Corresponding author (Qiu_lijuan{at}263.net)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
China is the center of origin of soybean [Glycine max (L.) Merr.] and is therefore expected to represent a primary source of germplasm for this crop. Genetic diversity assessments among Chinese soybean accessions should provide useful information for local and international soybean researchers to more effectively utilize this material. A sample of 129 accessions were selected to represent phenotypic variability for 14 agronomic and morphological traits in the Chinese soybean collection. These accessions were analyzed with 60 mapped simple sequence repeats (SSRs) to determine the genetic diversity represented. In total, 732 alleles were detected (12.2 alleles per locus) and the polymorphic information content (PIC) among accessions varied from 0.5 to 0.92 with a mean of 0.78. Pairwise coefficients of genetic distance among all accessions ranged from 0.05 to 0.91 (mean 0.23). Unweighted pair-group method arithmetic average (UPGMA) analysis showed that the accessions formed five major clusters; two contained primarily Northern ecotypes, one contained primarily Yellow River Valley ecotypes, and one contained Southern ecotypes. The fifth cluster contained a mixture of Northern and Yellow River Valley ecotypes. Accessions from the lower regions of the Yellow River Valley possessed the greatest allelic richness, had the lowest pair-wise genetic diversity estimates, and were dispersed throughout the five clusters, suggesting that the Yellow River Valley may be center of diversity for Chinese cultivated soybean. The results indicated that stratified sampling based on seven primary ecotypes may represent an optimal strategy for assembling a representative core collection of Chinese soybean.

Abbreviations: Hsp, Huanghuai Valley Spring soybean • Hsu, Huanghuai Valley Summer soybean • NA, number of alleles present • NEsp, Northeastern Spring soybean • Nsp, Northern Spring soybean • PCR, polymerase chain reaction • Sau, Southern Autumn soybean • Ssp, Southern Spring soybean • SSR, simple sequence repeat • Ssu, Southern Summer soybean • UPGMA, unweighted pair-group method average


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
SOYBEAN provides a major source of protein for human consumption and is one of the most important crops in the world. More than 20 000 accessions of soybean have been collected in situ in China. As crop germplasm collections grow larger, genetic diversity studies become of increasing importance because they provide information on genetic relationships among germplasm accessions (Hoisington et al., 1999; Liu et al., 2003). Such knowledge can provide insight into crop origins and evolution, and may be useful for designing strategies to establish core collections (Matus and Hayes, 2002; Roussel et al., 2004). Collectively, this information is helpful for designing future breeding efforts to improve soybean yield, quality, production, and pest management.

Chinese soybean production is generally subdivided into Northern Spring, Huanghuai Valley Summer (Hsu), and Southern production regions. Within these three primary regions, soybean production can be further divided either into 10 sub-regions according to the mode of production and the geographic region of origin (Bu and Pan, 1982), or as seven ecotypes according to ecogeographic regions of origin and planting system. The seven primary Chinese ecotypes include Northeastern Spring soybean (NEsp); Northern Spring soybean (Nsp); Huanghuai Valley Summer soybean (Hsp); Hsu; Southern Spring soybean (Ssp); Southern Summer soybean (Ssu), and Southern Autumn soybean (Sau) (Fig. 1 ). Planting systems consist of single cropping in Northern regions, double cropping in the Huanghuai region, and multiple cropping in the Southern regions. Since most soybean breeding programs have focused on variation within ecotypes, this factor is of major importance in Chinese soybean classification systems. Comparative genetic studies have mainly concentrated on accessions within ecotypes, but some have used province of origin and/or latitude of cultivation.


Figure 1
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  		Fig. 1. Geographic distribution of seven primary Chinese soybean ecotypes. NEsp = Northeastern Spring, Nsp = Northern Spring, Hsp = Huanghuai Valley Spring, Hsu = Huanghuai Valley Summer, Ssp = Southern Spring, Ssu = Southern Summer, Sau = Southern Autumn.

 
Evaluation of agronomic traits, pedigrees, geographic origins, isozymes, and DNA markers have been used for the assessments of soybean genetic diversity (Perry and McIntosh, 1991; Griffin and Palmer, 1995; Gizlice et al., 1996; Bernard et al., 1998; Dong et al., 2004). On the basis of isozyme data, Gorman (1984) reported that cultivated soybean accessions in Northeastern China were more diverse than those from Korea. However, wild soybean (G. soja Sieb. & Zucc.) accessions were more diverse in Korea than in China. Cui et al. (2000) analyzed Chinese soybean cultivars based on coefficients of parentage, and deduced a high level of genetic diversity among soybean breeding lines. On the basis of agronomic trait variation, Dong et al. (2001) defined three genetic diversity centers of Chinese wild soybean: the northeast, the Yellow River, and the coastal region of Southeastern China. Dong et al. (2004) also analyzed the phenotypic diversity of cultivated soybean resources based on agronomic traits, and pointed out that the phenotypic diversity center of cultivated soybean was the Yellow River Valley.

Because of (i) the limited data provided by isozymes, (ii) the influence of growing environment on agronomic trait evaluation, and (iii) possible errors or incomplete information in the documentation of pedigrees and origins of accession collections, these methods of assessing genetic diversity have largely been replaced by DNA marker analysis. DNA markers are stable and have proven to be genetically informative and useful for genotype discrimination (Keim et al., 1992; Skorupska et al., 1993; Nelson and Li, 1998). The SSRs (microsatellites) are codominant polymorphic markers, with a high information content per locus (Powell et al., 1996; Diwan and Cregan, 1997; Abe et al., 2003), and are suited to high throughput fingerprintings of large numbers of accessions. A number of diversity studies have employed DNA markers, but the samples surveyed have been limited (Liu et al., 2000; Hai et al., 2002).

Because it is widely planted in different ecogeographic regions in China, soybean has evolved into various ecotypes following long-term natural and artificial selection. An overview of the genetic diversity of Chinese soybean using DNA molecular markers should therefore be useful for soybean breeding and genetic studies. In this paper, we report a diversity analysis based on allelic variation at 60 SSR loci among 129 Chinese soybean genotypes. These accessions were selected based on their ecotypes and phenotypic variations for morphological and agronomic traits from the >20 000 Chinese accessions held ex situ. We also sought to dissect the genetic structure of the Chinese soybean collection, and to apply this information to develop an optimal grouping system suitable for a stratified sampling strategy to generate a representative core collection of Chinese soybean.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
One hundred and twenty-nine accessions, consisting of 122 landraces and seven cultivars, were selected from the entire Chinese ex situ soybean germplasm collection to represent the range in phenotypic variability for 14 agronomic and morphological traits (Table 1). These materials included 17 NEsp, 25 Nsp, 20 Hsp, 18 Hsu, 16 Ssp, 20 Ssu, and 13 Sau ecotype accessions from 21 provinces that spanned a latitude from 21°35' N to 49°10' N. Of the seven cultivars, three and two cultivars were from NEsp and Hsu respectively, and one each from Nsp and Ssp. All seed was obtained from the Chinese National Crop Germplasm Conservation Center (http://icgr.caas.net.cn, verified 6 Jan. 2006).


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  		Table 1. Catagorical descriptors for 14 agronomic and morphological traits in soybean.

 
Sixty SSR loci were selected for use in this study. These loci were uniformly distributed across the 20 soybean genetic linkage groups (Cregan et al., 1999) and have been recently shown to be effective for the assessment of Chinese soybean diversity (Wang et al., 2003; Xie et al., 2003). Fifty-six of the SSRs had a core motif ATT(Satt*), and two each had AT(Sat*) or CT(Sct*) motifs. Genomic DNA of each accession was extracted from three or four seeds by the SDS method described by Xie et al. (2003). Polymerase chain reaction (PCR) was performed in a final volume of 20 µL, containing 10 mM Tris-HCl, 50 mM KCl, 2 mM MgCl2, 100 µM of each dNTP, 0.4 µM of each primer, 20 ng genomic DNA, and 1 U of Taq DNA polymerase. Each of the 35 PCR cycles consisted of 30 s at 94°C for template denaturation, 30s at 47°C for primer annealing, and 30s at 72°C for primer extension. The PCR reaction was completed with a 5-min incubation at 72°C. The PCR products were separated by 6% PAGE and visualized by silver staining. Each distinct allele of every SSR locus was reamplified with 5' fluorescent labeled primers to obtain accurate allele sizing, and separated on a Megabase 1000 system (Amersham Bioscience, USA). As the Megabase system estimates allele size to one decimal place, we rounded to the nearest integer as recommended by Matus and Hayes (2002). Alleles that differed in size by at least one nucleotide were independently assayed.

For the statistical analysis, the patterns at all SSR loci were scored for each polymorphic band as 1 for band presence and 0 for band absence. This allowed an estimate at each locus of the number of alleles present (NA) and the PIC value. The PIC value was calculated by the formula

Formula
where Pi represents the frequency of the ith allele. Similarity coefficients based on SSR profiles were calculated according to Nei and Li (1979), and a dendrogram based on the similarity matrix and UPGMA clustering was produced using the software package NTSYSpc (Rohlf, 1992). Genetic distances between populations were estimated by POPGEN32 (Yeh and Boyle, 1997). One thousand bootstrap replications were performed on the similarity matrix using WinBoot software developed by Yap and Nelson (1996).

The number of accessions representing each ecotype differed. Given that sample size influences NA and PIC values (Comps et al., 2001; Leberg, 2002), a permutation method of random repeated sampling based on a uniform sample size of n = 12 accessions for each ecotype was used to standardize the NA and PIC value. Within each ecotype, 1000 permutations were performed, and the average of the NA and PIC values were considered as the expected values for comparison among equal-sized samples drawn from the seven different ecotypes.


   RESULTS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Phenotypic Diversity
Levels of phenotypic variation for both morphological and agronomic traits among the 129 accessions were very similar to those present in the entire collection of over 20 000 accessions (Tables 2 and 3). This indicates that the 129 accessions provided a representative sample of the phenotypic diversity present in the entire Chinese soybean collection.


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  		Table 2. Ranges in phenotypic variation for nine morphological traits among 129 soybean accessions and the entire Chinese collection of >20 000 accessions.

 

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  		Table 3. Ranges in phenotypic variation for five agronomic traits among 129 soybean accessions and the entire Chinese collection of >20 000 accessions.

 
Allelic Diversity
In all, 732 alleles were detected among the 129 accessions at the 60 SSR loci. The NA at a locus varied from 4 (satt309) to 29 (satt281), with a mean of 12.2 alleles per locus. This indicates a high level of diversity within the collection. Approximately 23% of the alleles were unique and detected in only one accession. Sixty-two accessions possessed at least one null allele (veryifed by repeated PCR) at 32 of 60 SSR loci, as has been noted in other crop species (Matus and Hayes, 2002; Romero et al., 2003). Two alleles at a single locus were observed in one accession. In another accession we also detected three alleles at a locus. Such heterozygosity likely resulted from either natural cross-pollination, which occurs at a rate of about 0.5% in soybean (Bai and Gai, 2002), or from the use of three or four seeds as the source of template DNA. The PIC values ranged from 0.50 for satt230 to 0.92 for satt281, with a mean of 0.78 per locus.

The mean NA was the highest in Nsp, followed by Hsu > Ssp = Ssu > NEsp > Hsp, and lowest in Sau (Table 4). The mean PIC value followed similar trends with the exception of NEsp and Sau, which had higher PIC values compared with Ssu and Hsp. Random repeated subsampling based on a sample size of 12 accessions provided reasonably similar rankings for the mean PIC values, when compared with that obtained based on the 129 accessions (r = 0.879; P < 0.01). However, the NA values were not significantly correlated (r = 0.413; P > 0.05) between the two sampling methods. The data tentatively suggested that as the number of accessions per ecotype increased, the NA also appeared to increase (r = 0.64; P > 0.05), which is reasonable. Hence, comparison of NA among ecotypes is not useful unless subsample sizes within each ecotype are standardized. While PIC values appeared to be much less influenced by sample size, the overall results indicate that random repeated sampling is advisable to evaluate genetic diversity parameters among populations represented by differing numbers of accessions.


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  		Table 4. Number of accessions, mean number of alleles (NA), and polymorphic information content (PIC) values per locus based on random repeated sampling of n = 12 accessions and the total number of accessions for seven Chinese soybean ecotypes.

 
Cluster Analysis
Pairwise genetic similarity coefficients among individual accessions varied from 0.05 to 0.91, with a mean of 0.23. The dendrogram based on genetic similarities between accessions showed that the 129 accessions formed five major clusters, and that the clusters largely corresponded to ecotype (Table 5). Cluster I contained 20 accessions, of which 12 were NEsp types and the remaining eight were Nsp, Ssp, and Hsu types; the 12 accessions in cluster II were a mixture of NEsp, Nsp, Hsp, and Hsu types; Cluster III contained 21 accessions, of which 16 were Nsp types; Cluster IV contained 15 accessions made up primarily of 12 Hsp types; Cluster V was the largest group consisting of 61 accessions, of which 47 were Ssp, Ssu, and Sau types. This grouping indicates that soybean accessions originating from southern China are closely related and difficult to distinguish genetically. Since Hsu types were dispersed throughout the clusters (Table 5), and had the numerically highest repeated subsampling NA and PIC values (Table 4) and the lowest average pair-wise genetic diversity estimates (Table 6), we postulate that the Yellow River Valley is the center of diversity for cultivated soybean. This hypothesis based on molecular marker data is supported by reports of other researchers who evaluated both morphological and agronomic traits in Chinese soybean (Chang, 1994; Vavilov, 1987; Zhou et al., 1998). For example, Chang (1989) observed that the middle to downstream regions of the Yellow River Valley contained the greatest abundance of wild soybean germplasm, which also exhibited high variation for seed weight and color and plant architecture. In 1981, Hymowitz clearly put forward that the origin of cultivated soybean was the Yellow River Valley region.


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  		Table 5. Soybean accessions with their respective geographic origins and assigned clusters of genetic similarity based on 60 SSR markers subjected to unweighted pair-group method arithmetic average analysis.

 

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  		Table 6. Genetic diversity estimates between seven primary Chinese soybean ecotypes based on SSR data.{dagger}

 
On the basis of our estimates of genetic distance (Table 6 and Fig. 2 ), the seven ecotypes formed three major regional populations—the Northern region (NEsp and Nsp), the Yellow River Valley (Hsp and Hsu), and the Southern region (Ssp, Ssu, and Sau). Ssp and Ssu types were the most similar. Yellow River Valley ecotypes were somewhat more closely related to those in the Northern region than to those in the Southern region. However, the 1000 replications of bootstraping analysis suggested that the confidence for the cluster of Hsu and Hsp ecotypes was modest (20.4%), likely reflecting the scattered distribution of the Hsu accessions among all five dendrogram clusters.


Figure 2
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  		Fig. 2. Unweighted pair-group method arithmetic average cluster analysis of genetic distances among seven Chinese soybean ecotypes using 60 SSR loci. Numbers at the branch points indicate support for groupings to the right of the number; values are a percentage of 1000 bootstrap datasets that exhibited the cluster. NEsp = Northeast Spring, Nsp = Northern Spring, Hsp = Huanghuai Valley Spring, Hsu = Huanghuai Valley Summer, Ssp = Southern Spring, Ssu = Southern Summer, Sau = Southern Autumn.

 

   DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Analysis of 14 agronomic and morphological traits indicated that the 129 accessions provided a representative sample of the phenotypic diversity present in the entire Chinese soybean collection. Thus, the SSR analysis conducted on these accessions should provide some insight into the genetic diversity of the entire collection of 20 000 accessions. Our estimate of >12 alleles per locus implies that a significant level of diversity is present in soybean. In comparable soybean studies, Narvel et al. (2000) calculated a mean of 10.2 alleles per locus among 39 elite genotypes and 40 plant introductions using 74 SSR loci. Burnham et al. (2002) detected less allelic richness using 52 SSR loci among 88 soybean accessions from South Korea. Unique SSR alleles are of particular interest, and 22.7% of such alleles were observed among the 129 accessions surveyed in our study. These alleles were distributed uniformly among the ecotype accessions. Thus, this collection of accessions should serve as a source of novel genetic variation for use in soybean breeding and genetic improvement.

The ranking of ecotypes for NA or PIC values by random repeated subsampling of 12 accessions, as compared with values obtained from all 129 accessions (i.e., variable accession number per ecotype), indicated that sample size strongly influenced NA. The PIC values were less influenced by sample size. Our results indicate that random repeated subsampling should be used in diversity studies conducted among populations with diverse sample sizes.

In our study, the Hsu ecotype possessed the numerically highest NA and PIC values and its representative accessions were distributed across all five major UPGMA clusters. This suggests that the lower region of the Yellow River Valley is the most probable center of diversity for the Chinese cultivated soybean. This is consistent with the report of Dong et al. (2004), who analyzed variations in agronomic traits among 20 000 soybean accessions.

Given that many germplasm collections involve a large number of accessions, the concept of a core collection was developed to emphasize the conservation and evaluation of important germplasm (Brown, 1989). Yonezawa et al. (1995) recommended the use of stratified sampling for forming a core collection. Li et al. (2000) pointed out the importance of first determining an optimal grouping system in which both the genetic identity of accessions within subpopulations and the genetic differentiation among accessions between subpopulations are maximized. Thus, the basis for stratification in a core collection is to divide the entire collection into groups which are genetically distinct as possible, followed by sampling within groups. The genetic structure present in a collection can be crop- or species-dependent, and for some crops, geographic origin and other criteria associated with genetic differentiation have all been used as a basis for grouping (Erskine and Muehlbauer, 1991; Li et al., 2000). As a photoperiod-sensitive species, soybean has a rather narrow region of adaptation. In our study, accessions tended to form general groups according to the latitude at which they were adapted, but also grouped according to their ecotypes. An explanation for this latter result is that in China, microclimate is strongly influenced by topography at a given latitude(Gai and Wang, 2001; Fu et al., 2002). Thus, ecogeographical region is an important indicator for the classification of Chinese soybean. We recommend that stratified sampling among Chinese soybean accessions should be based on ecotype and geographic criteria and diversity analysis results to construct a Chinese soybean core collection (Qiu et al., 2003) by adding the samples we characterized in this paper.


   ACKNOWLEDGMENTS
 
We greatly appreciate Dr. Reid G. Palmer at Iowa State University, Ames, IA, USA for professionally reviewing this manuscript. We also thank www.smartenglish.co.uk (verified 6 Jan. 2006) and three anonymous reviewers for linguistic correction of this manuscript.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Funded projects include National Basic Research (2004CB117203), National Five-Year Plan (2004BA525B06), and National High-Tech (2003AA207060 and 2004AA211111).

Received for publication January 15, 2005.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 




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