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Crop Science 42:338-343 (2002)
© 2002 Crop Science Society of America

CROP BREEDING, GENETICS & CYTOLOGY

Genetic Diversity Patterns among Phytophthora Resistant Soybean Plant Introductions Based on SSR Markers

K. D. Burnham*,a, D. M. Francisa, A. E. Dorranceb, R. J. Fiorittoa and S. K. St. Martina

a Dep. of Hortic. and Crop Sci., Ohio State Univ., Wooster, OH 44691-4096
b Dep. of Plant Pathology, Ohio State Univ., Wooster, OH 44691-4096

* Corresponding author (burnham.14{at}osu.edu)


   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
Genetic diversity is low among elite Northern American soybean [Glycine max (L.) Merr.] breeding populations. Fewer than 20 soybean cultivars are responsible for 80% of the genes in public soybean cultivars released in recent years. Diversifying the soybean germplasm base could introduce new genes for agronomic diversity as well. Recently, soybean plant introductions (PIs) have been identified as additional sources of both partial and specific resistance to Phytophthora sojae M.J. Kaufmann & J.W. Gerdemann (syn. P. megasperma Drechs. f. sp. glycinea T. Kuan & D.C. Erwin). The objective of this study was to compare the genetic diversity present among soybean PIs resistant to P. sojae in relation to cultivars and breeding lines that represent U.S. breeding germplasm, and to develop guidelines for the genetic study and practical use of these resistant resources in applied breeding. Ninety-three accessions from South Korea, China, and Japan and 15 genotypes from the USA were evaluated for 52 simple sequence repeat (SSR) primer pairs. The South Korean material included 32 P. sojae resistant, 49 partially resistant, and 7 susceptible accessions. The SSR data were used to compute Nei's distance estimates. Clustering and multivariate analysis of Nei's distance estimates demonstrated that accessions from South Korea are genetically different from U.S. germplasm. These results indicate that the South Korean germplasm may contain alleles not present in U.S. cultivars, such as new alleles for P. sojae resistance. Additionally, this study identifies SSR markers that can be used to begin mapping P. sojae resistance alleles in South Korean germplasm.

Abbreviations: MDS, multidimensional scaling • PCA, principal components analysis • PCR, polymerase chain reaction • PI, plant introduction • SSR, simple sequence repeat • UPGMA, unweighted pair-group method arithmetic average


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
MORE THAN 500 public soybean cultivars are available for use in breeding programs throughout North America. However, studies have shown that the genetic diversity among these soybean cultivars is low (Gizlice et al., 1994; Delanney et al., 1983). Gizlice et al. (1994) showed that only 13 ancestral lines provided 80% of the genes in public soybean cultivars released from 1947 to 1988. Two distinct gene pools exist in the U.S., a southern pool and a northern pool; but within these pools the genetic diversity is low (Gizlice et al., 1993; Kisha et al., 1998).

Genetic diversity in soybean has been measured using both pedigree and molecular marker analyses (Gizlice et al., 1994; Sneller, 1994). Three types of molecular markers have been used to assess diversity, restriction fragment length polymorphisms, random amplified polymorphic DNA markers (Skorupska et al., 1993; Lorenzen et al., 1995; Kisha et al., 1998; Thompson and Nelson, 1998), and SSRs (Brown-Guedira et al., 2000). The development of a composite genetic linkage map for soybean that contains over 800 SSR markers (Cregan et al., 1999) facilitates the selection of polymerase chain reaction (PCR) based markers to survey genetic diversity in populations for which little data are available. Single sequence repeat markers are advantageous because of single locus inheritance and the ability to detect multiple alleles. Consequently, SSR markers were used in this study to determine genetic relatedness among soybean lines.

Resistance to the soybean pathogen P. sojae is controlled by two mechanisms. The first is partial resistance, which involves multiple genes and limited damage to the plant (Schmitthenner, 1985). The second is single gene resistance, in which P. sojae interacts with Rps genes in a gene for gene system (as defined by Flor, 1955) preventing disease development in the plant. Thus far, seven Rps genes have been identified in soybean, some of which appear to have more than one resistance allele (Diers et al., 1992; Anderson and Buzzell, 1991). Phytophthora sojae populations have been identified in Ohio and other Midwestern states which have a compatible interaction, leading to susceptibility, of the host plant with all of the commonly used Rps genes (Abney et al., 1997). Consequently, new sources of Rps genes are needed (Schmitthenner et al., 1994).

Recently, potential new sources of resistance to P. sojae have been described. A screen of 1015 USDA soybean PIs identified 32 accessions, largely from South Korea, that may contain new Rps genes, or effective Rps gene combinations. In addition, many of the South Korean PIs in that survey had very high levels of partial resistance compared with the standard U.S. cultivar ‘Conrad’, which has a score of 3.5. Scores from 3.1 to 4.0 indicate a very high level of partial resistance (Dorrance and Schmitthenner, 2000).

The objective of this study was to evaluate the genetic diversity among P. sojae resistant South Korean PIs, and to assess their relationships to U.S. germplasm. This study also sought to identify SSR markers that are unique to P. sojae resistant South Korean PIs to begin searching for the location of new alleles. The information on genetic relatedness gained from this study should provide a guide to further investigate the P. sojae resistant PIs and to employ them in cultivar development.


   MATERIALS AND METHODS
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS AND DISCUSSION
 REFERENCES
 
Plant Materials
There were 110 soybean lines selected for this study (Table 1). All of the plant material was obtained from the USDA Soybean Germplasm Collection in Urbana, IL, or from the Ohio Agricultural Research Development Center soybean breeding program. Accessions were chosen to establish samples representing geographical populations, as well as resistance to P. sojae. Lines from the USA were chosen to represent the Midwestern gene pool. Also included was a representative cultivar from the Southern USA, ‘Essex’. The 15 lines from the USA represent a sample of the genetic diversity in North America and serve to link the results of this study to previous work. The study included 32 lines identified as having resistance to eight races of P. sojae (Dorrance and Schmitthenner, 2000). Twenty-nine of the resistant lines were from South Korea, two were from Japan, and one was from Russia. Forty-nine PIs with partial resistance were also included in the study, 48 from South Korea and one from Japan (Dorrance and Schmitthenner, 2000). Additionally seven susceptible (no Rps genes and no partial resistance) South Korean lines (PI 407947, PI 408010-1, PI 424165, PI 424179A, PI 424189, PI 424219B, and PI 424543) were chosen as a random sample of susceptible germplasm to serve as an out-group. The Chinese germplasm was represented by five accessions, two ancestral cultivars used by Kisha et al. (1998) and three PIs that are resistant to P. sojae Races 7, 17, and 25. These three races have a susceptible interaction with all known Rps genes and many Rps gene combinations (Lohnes et al., 1996).


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  		Table 1. List of the lines used in the study, includes maturity group (MG), country of origin, province within South Korea, and the number code used in Fig. 2.

 


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  		Fig. 2. Plot of genetic distances among individual South Korean accessions and U.S. genotypes in two dimensions from multidimensional scaling analysis.

 
DNA Isolation
Leaf tissue was collected in the greenhouse from a single plant of each PI or cultivar and placed on ice for transport to the laboratory. Fresh tissue from each plant was placed in a zipper-type bag containing extraction buffer (0.35 M Sorbitol, 0.1 M Tris, 0.005 M EDTA, 0.02 M NaBisulfite). The tissue was ground and transferred to a microfuge tube. An equal volume of lysis buffer (0.2 M Tris, 0.05 M EDTA, 2.0 M NaCl, 2% hexadecyltrimethylammonium bromide) was added to the tube, followed by 5% sarcosine. The tubes were incubated at 65°C to completely lyse the cells. Lysis was followed by a chloroform:isoamyl alcohol extraction and the DNA precipitated with isopropanol.

Genetic Analysis
SSR primer pairs were chosen to survey the soybean genome using the composite soybean genetic map (Cregan et al., 1999). Two SSR markers were selected from each linkage group, and additional markers were selected near known Rps genes. A total of 52 SSR primer pairs (Research Genetics Inc., Huntsville, AL) were used in this study (Table 2). Polymerase chain reactions were performed as recommended by the manufacturers in a total of 25 mL containing 30 ng of genomic DNA. Amplified PCR products were resolved on 5% high-resolution agarose gels (Amresco, Solon, OH) and stained with ethidium bromide for visualization of the DNA products.


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  		Table 2. List of simple sequence repeat (SSR) markers used in the study.

 
Statistical Analysis
To determine the genetic distance between any two given genotypes, Nei's genetic distance matrices were computed (Nei, 1972). Nei's distance is based on the average identities of randomly chosen genes within and between populations or samples. This method is appropriate for populations with multiple alleles per locus and for populations shaped by diverse evolutionary forces or mating systems. The formula used to compute Nei's distance (D) was:

where

pxi = frequency of allele x in population i, and pxj = the frequency of allele x in population j. The genetic distances were then clustered using unweighted pair-group method, arithmetic average (UPGMA) clustering. The NTSYSpc statistical package, version 2.00 (Exeter software, Setauket, NY) was used to calculate Nei's genetic distance matrices and perform UPGMA clustering (Rohlf, 1997). Given that some of the geographical populations were represented by small sample sizes and that UPGMA clustering is a visualization technique that does not provide a statistical test of the observed pattern, bootstrapping was used to ensure that the resultant clusters represented the true distribution of genetic distances. One hundred bootstrap samples for each cluster analysis were calculated using the PHYLIP 3.5c statistical package (Feldstein, 1993).

As a supplement to clustering based on Nei's distance and UPGMA, geographical samples were compared for allelic frequency for each marker. The matrix of gene frequencies was clustered by principal components analysis (PCA) using SAS statistical software (SAS Institute, 1988). Principal components analysis was based on a correlation matrix of the gene frequency data. Plots of the first three principal components were visualized and the eigenvector matrix was inspected to determine which markers were most informative for clustering.

NYSYSpc was also used to perform multidimensional scaling (MDS) analysis on the large genetic distance matrix generated when comparing individual genotypes. Principal components analysis is similar to PCA in that it computes eigenvectors to account for the sum of the variance. However, MDS allows the results to be fitted to a two dimensional plot that is useful for interpretation of the relationships between genotypes.

To determine whether any markers were associated with resistance, the South Korean PIs were divided into resistant and susceptible categories based on disease response to eight races of P. sojae, as described by Dorrance and Schmitthenner (2000). Chi-square tests were performed using PROC FREQ with SAS statistical software (SAS Institute, 1988) as a measure of independence between marker class and resistance response.


   RESULTS AND DISCUSSION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
REFERENCES
 
In this study, 37 of the 52 SSR markers were polymorphic with two alleles detected at 17 loci, three alleles detected at 18 loci, and four alleles detected at two loci. The remaining 15 markers were not polymorphic and excluded from the analysis. Nei's genetic distance was used to estimate genetic relationships, with values ranging from 0.08 to 0.50, the smaller values indicating a close genetic relationship.

Cluster analysis of genetic distances showed that the South Korean accessions were more closely related to the accessions from Japan than to accessions from the USA or China (Fig. 1) . The node between the South Korean/Japan branch and the USA/China branch was supported in 100 of 100 bootstraps. Bootstrapping resamples the data to create 100 data sets and then clusters each set to create 100 UPGMA clusters based on the genetic distances. Because the genetic distance between South Korea and the USA was supported in 100 bootstraps, it is likely that different alleles are found in these two populations. Because of the small number of Japanese and Chinese lines sampled, these results may not be representative of the entire germplasm collection from these sources. However, perhaps some unique alleles found in South Korea can now be incorporated into the U.S. germplasm.



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  		Fig. 1. Dendogram showing the genetic distances among populations from South Korea, Japan, China, and the USA. Bootstrap values from 100 replications are indicated at the nodes.

 
Principal components analysis was used as an alternative to UPGMA clustering as a means of visualizing relationships in the data. Principal components analysis is a technique by which the variables (allele frequencies) are correlated with one another to be combined into components, which are independent of other subsets of variables (components). In our data set of gene frequency values for 52 markers, 52 principal components were computed. Each principal component is a linear combination of the original variables, with coefficients equal to the eigenvectors of the correlation matrix. The principal components are sorted by descending order of the eigenvalues, which are equal to the variances of the components (SAS Institute, 1988). For this data, the eigenvalues indicate that the first three components provide an adequate summary of the data, accounting for 65% of the variation. Subsequent components contribute little, and 100% of the variation is explained in the first nine principal components. This result suggests that gene frequency data contains information useful for separating the geographical samples. Graphs of the first three principal components agreed with the UPGMA clustering, showing that accessions representing the South Korean population clustered closely with the sample from Japan and were distant from the U.S. and Chinese samples. An advantage of PCA is that the eigenvector matrix can be used to determine which markers contribute most to the variance and therefore are of most use in distinguishing the population samples. Not all markers provide equal information (they do not account for equal amounts of variance between samples). For example, monomorphic markers are not useful for distinguishing the population samples. Informative markers will have high positive and negative loadings. Inspection of the eigenvector matrix indicates that Satt142, Sat_131, Satt038, Satt157, Satt182, and Satt152 were the most informative markers. This information will be useful for researchers who seek to expand the sampling done here to include more lines or accessions.

The genetic diversity within the South Korean population was measured by comparing soybean PIs that were collected in all eight provinces of South Korea. The genetic distances within South Korea were smaller than those observed in the larger geographical analysis. Each province clustered separately, indicating that genetic diversity does exist within South Korea, but it was not dependent on the geographical relationship of the provinces. This analysis suggests that South Korean germplasm may represent a distinct gene pool, supporting the conclusion of Abe et al. (1992). It is possible that Korea may be one of the centers for genetic diversity of soybean (Abe et al., 1992). Within South Korea, soybean genetic diversity does not seem to be related to geographical location along a latitudinal or longitudinal gradient based on the SSR markers and accessions used in this study.

Kisha et al. (1998) speculated that many of the genes or alleles that exist in ancestral lines have been lost in the extensive breeding that has taken place in the U.S. during the last 40 yr. They further suggested that diversity could be increased with crosses between lines in the northern and southern gene pools. Another strategy to diversify germplasm using lines included in this study is supported by the identification of resistance to P. sojae (Dorrance and Schmitthenner, 2000). Genetic differences between the South Korean PIs and U.S. lines may help guide crossing strategies. In order to visualize the diversity and provide a resource for selecting lines to use in breeding crosses, the matrix of genetic distances between all South Korean accessions and U.S. genotypes was subjected to MDS analysis, and the resultant two-dimensional plot is presented in Fig. 2 .

Eighty-four soybean lines from South Korea were used in this study. The majority of these lines (77 lines) were resistant or partially resistant to the pathogen P. sojae (Dorrance and Schmitthenner, 2000). However, a small group (7 lines) of susceptible lines were included in the study to determine if there was a significant genetic distance between lines with different disease reactions. Including these lines permitted the calculation of genetic distances as well as the identification of SSR markers unique to resistant or susceptible South Korean PIs. The genetic distance between the resistant South Korean material and the susceptible South Korean material was 0.11, a relatively small difference and one that is not supported by bootstrapping. The results of bootstrapping showed that the genetic distance between the resistant and susceptible lines was only supported in 21 of 100 clusters, indicating that resistant and susceptible germplasm do not represent distinct gene pools.

In order to determine if any SSR markers were associated with resistance to P. sojae in the South Korean lines, a chi-square test was performed using only the resistant and susceptible lines, and excluding the partially resistant lines. Six markers were found to be significantly associated with the resistant lines ({alpha} = 0.05); Satt216, Satt228, Satt409, Satt458, Satt534, and Satt589. Of these six, Satt228, Satt409, and Satt589 are located on linkage group A2; this may indicate the presence of a unique resistance gene on A2. Simple sequence repeat markers were used that were close to the known Rps genes, and none of these markers were significantly associated with the resistant South Korean lines. These six markers and the chi-square test will provide a starting point to identifying markers linked to resistance to P. sojae in the South Korean germplasm.

The South Korean accessions appear to represent a pool of germplasm that is distinct from the U.S. germplasm. These results and previous phenotypic data provide evidence that this germplasm may be a source of alleles not present in the U.S. germplasm. It will be important to evaluate the South Korean PIs in crosses and elucidate which alleles could be used to improve the genetic diversity and performance of U.S. cultivars. Identifying specific genes for resistance to P. sojae is a logical next step in exploiting this source of genetic diversity.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 
Salaries and research support provided by State and Federal Funds appropriated to the Ohio Agricultural Research and Development Center (OARDC), the Ohio State University. Funding for this research was provided in part by the Ohio Soybean Council.

Received for publication May 14, 2001.


   REFERENCES
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 NOTES
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS AND DISCUSSION
 REFERENCES
 




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