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TURFGRASS SCIENCE

Genetic Diversity in Seven Perennial Ryegrass (Lolium perenne L.) Cultivars Based on SSR Markers

^a Dep. of Plant Sciences, Rutgers Univ., New Brunswick, NJ 08901
^b Dep. of Ecology and Evolutionary Biology, Univ. of California, 321 Steinhaus Hall, Irvine, CA 92697

* Corresponding author (bgaut{at}uci.edu)

	ABSTRACT

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An essential prerequisite to cultivar identification is to determine whether cultivars are differentiated genetically. We investigated genetic diversity among and within seven perennial ryegrass (Lolium perenne L.) cultivars (Loretta, Linn, Manhattan II, Affinity, Jet, Pennfine, and Palmer III) using simple sequence repeat (SSR) markers, with the goal of determining whether cultivars could be differentiated on the basis of genetic data. In each cultivar we genotyped 30 individuals with 22 SSR markers, 18 of which had not been reported previously. Our results indicated that each of the seven cultivars contained high but similar levels of genetic diversity. Within cultivar heterozygosity ranged from 0.589 to 0.643. The cultivars could be distinguished by a number of statistical criteria, including: (i) a small but significant proportion (14.6%) of among-cultivar genetic variation, based on analysis of molecular variance (AMOVA); (ii) significant between cultivar F_ST values that ranged from 0.065 to 0.197; (iii) separation of individuals in principal component analysis (PCA); and (iv) correct identification of individuals by the genotype assignment test, which is related to discriminant analysis. The genotype assignment test worked particularly well; it correctly assigned all 210 individuals to their cultivar of origin. Sampling analysis indicated that the genotype assignment requires data from at least 15 SSRs to be >99% accurate, suggesting future studies of genetic diversity in perennial ryegrass cultivars should use at least 15 SSR markers. Overall, the SSRs reported in this paper were highly effective for differentiating among cultivars.

Abbreviations: RFLP, restriction fragment length polymorphism • RAPD, random amplified polymorphic DNA • AFLP, amplified fragment length polymorphism • SSR, simple sequence repeat • AMOVA, analysis of molecular variance • HWE, Hardy-Weinberg equilibrium • PCA, principle components analysis

	INTRODUCTION

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PERENNIAL RYEGRASS (Lolium perenne L.) is a cool-season grass used extensively for both forage and turf. Since the advent of turf-type cultivars in the 1960s, the development and release of perennial ryegrass cultivars has increased steadily (Huff, 1997), making cultivar identification and property right protection difficult. Two features of perennial ryegrass breeding contribute to these difficulties. First, cultivar improvement has been based on limited germplasm (Funk et al., 1993), and thus many cultivars are both phenotypically and genetically similar. Second, perennial ryegrass is a diploid (2n = 2x = 14), self-incompatible species, and therefore each cultivar is a heterogeneous population of genotypes (Golembiewski et al., 1997).

Historically, cultivar identification and property right protection has been based on morphological characteristics such as plant height, spike length, leaf width, flag leaf length, and flower color, but molecular markers will also be valuable tools for cultivar identification. Recent papers have focused on using DNA based markers, particularly random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) markers, to measure genetic diversity in perennial ryegrass (Huff, 1997; Roldan-Ruiz et al., 2000; Sweeney and Danneberger, 1994, 1997, 2000). Both RAPDs and AFLPs detect substantial genetic variation within perennial ryegrass cultivars and generally demonstrate that cultivars can be discriminated on the basis of genetic characteristics. However, it appears that some closely related cultivars lack distinct genetic boundaries (Huff, 1997), and this can complicate cultivar identification. The comingling of genetic boundaries has been examined in detail only with RAPD data, and the extent of this problem may vary among marker systems.

An additional promising marker system is simple sequence repeats (SSRs). SSRs are short stretches of tandemly repeated di-, tri-, or tetranucleotide motifs (Weber, 1990) that have become the marker of choice for many genetic analyses. SSRs are broadly used for four reasons. First, each SSR locus is genetically well defined and codominant, making SSRs ideal for marker assisted breeding, genetic mapping and diversity measurement (Hearne et al., 1992; Powell et al., 1996). Second, SSRs are highly variable and therefore able to distinguish among closely related plant cultivars (Olufowote et al., 1997; Davila et al., 1998; Udupa et al., 1999). Third, because they are codominant, SSR data have become powerful tools for genotype assignment, whereby individuals are assigned to the population (or, in this case, cultivar) in which it has the highest probability of occurrence (Paetkau et al., 1995; Paetkau et al., 1997; Waser and Strobeck, 1998). Finally, SSR polymorphism is easily assayed by PCR. One disadvantage to SSRs is that their isolation and characterization is expensive and time consuming.

In a previous study, we isolated SSR loci from perennial ryegrass and demonstrated both that the perennial ryegrass genome contains thousands of SSR loci and that SSR variation can differentiate closely related individuals (Kubik et al., 1999). Here we report 18 additional polymorphic SSR loci, and we used a total of 22 SSRs to genotype 30 individuals from each of seven perennial ryegrass cultivars. The genotypic data were analyzed with three objectives in mind. The first was to better characterize the distribution of genetic diversity within and among perennial ryegrass cultivars. The objective was to determine whether cultivars—not just individuals—can be differentiated on the basis of SSR polymorphism; such differentiation is an important prerequisite to property right protection and cultivar identification. The final objective was to explore the effects of sampling, particularly the number of loci used for genotyping and the number of individuals genotyped per cultivar, to determine guidelines for cultivar identification.

	MATERIALS AND METHODS

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Isolation and Characterization of SSRs
A size-fractionated perennial ryegrass genomic DNA library (Genetic Information Services Inc., Chatsworth, CA) was constructed with DNA from SNM56, an endophyte-free experimental clone from the Rutgers University turfgrass breeding program, and was enriched for GA and GT dinucleotide repeats. The library was plated on blue-gal/IPTG/ampicillin LB agar plates, and colonies were chosen for plasmid mini-preps (Qiagen Spin Miniprep Kit, Qiagen, Santa Clarita, CA). Plasmid DNA was sequenced using the Cy5 Auto Read Sequencing Kit (Pharmacia Biotech, Piscataway, NJ), using universal forward and reverse primers. DNA sequence reactions were read on the ALF express DNA sequencer (Pharmacia Biotech, Piscataway, NJ). The library was 90 to 100% enriched for GT repeats and 50% enriched for GA repeats, and thus there was no need for colony hybridization with GA- and GT-repeat probes.

Clones containing SSRs were analyzed for primer selection. Primer design was based on several criteria, including GC content >50% when possible, minimal repetitive DNA within a primer, no extensive palindromes within a primer, and no obvious pairing between primers.

To optimize PCR conditions, the SSRs were first amplified from SNM56 genomic DNA. PCR reactions contained 10 mM Tris-HCl pH 8.3, 50 mM KCl, 0.25 mM of each dNTP, 12.5 pmol of each primer, 0.5 units of Amplitaq DNA Polymerase (Perkin Elmer, Norwalk, CT), (6, 13) 20 ng of template DNA and between 1 to 4 mM MgCl₂, depending on the primer combination, in a total volume of 25 µl. PCR consisted of 30 cycles, each cycle with 1 min denaturation step at 94°C; a 1 minute annealing step at either 55, 60 or 65°C, depending on the optimum annealing temperature for a primer pair; and 2 min at 72°C. The PCR concluded with a 10 min elongation at 72°C.

Determining Genotypes
After PCR conditions were optimized for SNM56 genomic DNA, we initially ascertained whether an SSR was polymorphic by amplifying the SSR in a panel of seven individuals representing seven perennial ryegrass cultivars (Table 1). The amplified products were visualized on a 4% agarose gel. SSR loci that were visibly polymorphic were used to genotype a larger sample of individuals, whereas apparently monomorphic loci were excluded from further analysis.

Certified seed was obtained for each cultivar, and DNA was extracted from thirty randomly sampled seeds (Sweeney and Danneberger, 1997). For genotyping, PCR of SSR loci was performed with Cy5 end-labeled PCR primers (IDT Inc. Coralville, IA). PCR products were first visualized on a 4% agarose gel and then run on a 6% polyacrylamide gel for size estimation. The acrylamide gel was run on the ALFexpress DNA sequencer (Pharmacia Biotech, Piscataway, NJ). Allele sizes were estimated relative to 50–500 base pair size standards using Fragment Analyzer v. 2.0 (Pharmacia Biotech, Piscataway, NJ).

Analysis of Genotypic Data
Observed heterozygosities (H_o), tests for Hardy-Weinberg Equilibrium (HWE), F_ST estimates and molecular analysis of variance (AMOVA) (Excoffier et al., 1992) were performed by Arlequin v. 2.000 (Schneider et al., 2000). R_ST, which is analogous to F_ST but assumes the stepwise mutation model, was also estimated with Arlequin v. 2.000. Significance tests for AMOVA, R_ST, F_ST, and HWE were based on at least 1000 permutations. Nei's (1978) distance was calculated in the TFPGA software program (Miller, 1994). Principal component analyses (PCA) were performed with Statistica v. 5.1 (StatSoft, Inc.) and were based on the correlation matrix derived from allele length data. The assignment calculator at http://www.biology.ualberta.ca/jbrzusto/alpha/Doh.html was used for genotype assignments; zero frequencies were adjusted to 1/2N, where N was the number of sampled individuals.

	RESULTS

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SSR Isolation and Genetic Variation Within Populations
We sequenced a total of 148 pUC clones. Primers were not designed for 55 clones because either the clones lacked SSR repeats, the SSR repeats were interrupted by cloning sites or the sequence data were poor. We designed PCR primers for the remaining 93 clones. Of the 93 primer sets, 11 gave no amplification product, 45 produced monomorphic products in our initial screening population of seven individuals, and 19 gave complex, multibanded patterns. The remaining 18 loci were polymorphic in the initial screening population and provided repeatable banding patterns. The primer sequences for the 18 loci are provided (Table 2). Altogether, 12% of the original 148 clones yielded useful markers.

Genetic Diversity Among Cultivars
We applied AMOVA to the full data set to investigate the distribution of genetic variation among cultivars. V_w, the total within-cultivar component of genetic variation, was 85.35%, and V_a, the among-population component of genetic variation, was 14.65%. Thus, most genetic variation resided within rather than between cultivars. Nonetheless, V_a was significantly different from zero (P < 0.000), which is indicative of significant genetic variation among cultivars.

Genetic differentiation among cultivars was confirmed by pairwise comparisons between cultivars. Between-cultivar F_ST varied from 0.065 to 0.197 (Table 4) and was significantly different from 0.0 in all cases, even after Bonferroni correction for multiple corrections (data not shown). R_ST was also significantly greater than 0.0 between all populations (Table 4).

View larger version (11K): [in this window] [in a new window]   Fig. 1. The NJ tree based on FST values. Scale bar indicates length of branches in FST units.

View larger version (14K): [in this window] [in a new window]   Fig. 2. The first three components of PCA for two contrasts between cultivars.

Genotype Assignment and Sampling Effects
The genotype assignment test makes full use of the data, including codominance of SSR markers. The assignment test assigns an individual genotype to the cultivar (or population) in which it has the greatest probability of occurrence (Paetkau et al., 1995, 1997; Waser and Strobeck, 1998) and is related to cross-validation in discriminant analysis (Waser and Strobeck, 1998). In essence, the test determines whether a perennial ryegrass individual has a genotype that is typical of its cultivar or whether it better represents the genetic characteristics of a different cultivar. It is worth noting that the genotype of the query individual is removed from its population of origin before test application, to avoid undue bias toward the population of origin (Waser and Strobeck, 1998). We applied the assignment test to the perennial ryegrass SSR data, and the test correctly assigned all 210 individuals to their cultivar of origin (Fig. 3). Thus, our data set of 22 loci per individual and 30 individuals per cultivar resulted in 100% accurate assignments.

View larger version (48K): [in this window] [in a new window]   Fig. 3. The effect of sampling on the genotype assignment test. Each bar represents the percentage of individuals that were correctly assigned for a given individual x locus combination. For each combination, ten data sets were chosen at random, and the mean and standard deviation of the ten data sets are shown. Dotted line delimits 99% accuracy.

Figure 3 reports the average percentage of individuals correctly assigned to their cultivar for a given locus x individual combination. It should be remembered that the results are specific to the seven cultivars in this study and also that the estimated standard deviations are inexact. Nonetheless, the analyses provided general insights into sampling considerations that can be summarized in three points. First, it was clear that the accuracy of the assignment test improves with the number of loci sampled. When 30 individuals per cultivar were sampled at 5 loci, the test works reasonably well, correctly assigning 88.3% of individuals on average. However, the test worked far better with 15 loci, when 99.2% of individuals are correctly assigned on average to cultivar. Second, if one assumes that an average accuracy of 99% is a reasonable performance criterion, the analyses clearly indicated that at least 15 loci need to be used for genotyping. We were unable to assign >99% of individuals correctly with fewer loci. Finally, it was difficult to determine how many individuals should be sampled per cultivar, because there is a trade off between the number of individuals and the number of loci. For example, with 22 loci only five individuals per cultivar were sufficient to reach an accuracy of 99%. In contrast, 30 individuals per cultivar are needed to reach 99% accuracy with 15 rather than 22 loci.

	DISCUSSION

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It can be difficult and time consuming to distinguish perennial ryegrass cultivars on morphological characteristics alone, and for this reason it is important to develop molecular markers to aid cultivar identification. Several marker systems have been applied to perennial ryegrass, including AFLPs (Roldan-Ruiz et al., 2000; Sweeney and Danneberger, 2000), RAPDs (Huff, 1997; Stammers et al., 1995; Sweeney and Danneberger, 1994, 1997), RFLPs (Charmet et al., 1997), and SSRs (Kubik et al., 1999). We focused on SSRs because of their high level of polymorphism and because they are codominant, which makes them particularly useful for genetic mapping and property right protection.

In this study, we reported 18 new SSR loci and used the SSRs to estimate genetic diversity in seven perennial ryegrass cultivars. The SSR data revealed two major points about genetic diversity within cultivars. First, the total amount of genetic diversity, averaged over all 22 loci, was remarkably uniform across the seven perennial ryegrass cultivars. This result parallels Huff's (1997) observation that 9 of 11 perennial ryegrass cultivars in his study had high and relatively similar within-cultivar levels of RAPD diversity. Second, genotypes commonly deviate from HWE, with a bias toward excess homozygosity. On the one hand, this observation was not unexpected, because selection with small breeding populations favors excess homozygosity, as does the possibility of undetected null alleles in our data. On the other hand, a substantial proportion (30 of 154) of locus-by-cultivar comparisons deviated from HWE toward excess heterozygosity, including six locus x cultivar combinations with an H_o of 1.00 (Table 3). Observations of excess heterozygosity are not uncommon in plants (e.g., Eguiarte et al., 1993; Ledig, 1986), but it is not clear what promotes excess heterozygosity in perennial ryegrass cultivars. Possibilities include inbreeding depression at the loci in question or mutation rates so rapid that the homozygote condition is exceedingly rare.

Two graphical representations of the SSR data–the NJ tree and PCA (Fig. 1 and 2)—deserve further discussion. Several aspects of the relationships depicted by the NJ tree are incongruent with the breeding history of the cultivars (Table 1). For example, it seems unlikely that Linn and Pennfine are closely related because they represent early forage-type and turf-type cultivars, respectively, that were sampled from two different states (Oregon and Pennsylvania). Nonetheless, they cluster closely together in the NJ tree. In contrast, Affinity, Jet, and Palmer III do not cluster closely on the tree despite their similar breeding histories (Table 1). There were several potential sources of incongruency between breeding histories and the NJ tree. One possible explanation is that the metric used to build the tree (F_ST) is somehow biased. To examine this possibility, we built an NJ tree based on Nei's 1978 genetic distances and produced an identical topology. Thus, incongruencies between breeding histories and the NJ tree did not appear to be a function of F_ST per se. It is more likely that phylogenetic analyses, including cluster analyses like UPGMA, rely on assumptions that are fundamentally inappropriate for these data. For example, distance metrics commonly assume neutral evolution, whereas these cultivars have experienced cycles of phenotypic recurrent selection. Similarly, phylogenetic methods assume bifurcation, not reticulation, of taxa, but perennial ryegrass cultivars like Affinity, Jet, and Palmer III represent cultivars which are the result of cycles of recurrent phenotypic selection from a pool of similar germplasm and therefore do not represent bifurcating lineages. As a result of these concerns, the NJ tree should be interpreted as a graphical representation of F_ST distances among cultivars, but should not be considered an accurate representation of cultivar relationships.

The second graphical representation is based on PCA (Fig. 2). PCA discriminates among cultivars reasonably well but also suggests limited intermingling of individuals from different cultivars. It is important to remember, however, that the first three principal components of PCA represent a small proportion of the total variance, and thus the three components fail to capture much of the information from the data. For this reason, the PCA underestimates our ability to discriminate among cultivars.

Despite concerns over graphical representation of SSR data, four criteria indicate that SSR variation was sufficient to differentiate among the perennial ryegrass cultivars in this study. First, the AMOVA over all data had a significant among-population component of variation. Second, all pairwise F_ST and R_ST values were significantly greater than zero, indicating that pairs of cultivars were distinguished on the basis of allelic frequencies. Third, locus-by-locus AMOVA suggests that all 22 loci had significant between-cultivar components of variation. Finally, the genotype assignment test correctly identified individuals by their cultivar of origin.

In some respects, our SSR results parallel previous studies based on AFLPs and RAPDs. For example, AFLP markers differentiated between cultivars Herbie and Merbo on the basis of correspondence analysis (Roldan-Ruiz et al., 2000). However, the general applicability of AFLP for cultivar identification is as yet uncertain because only two cultivars have been examined in detail with this method. In addition, a recent survey of restriction amplification fragment length polymorphism (RAFLP) in perennial ryegrass indicates that useful segregating markers are difficult to find (Sweeney and Danneberger, 2000). RAPD data analyzed with AMOVA differentiate among most of eleven perennial ryegrass cultivars (Huff, 1997). One difference between RAPD results and our SSR results is that the RAPD data were not able to differentiate between some of the cultivars in Huff's 1997 study, whereas SSRs differentiated among all cultivars in this study. It is not yet clear if this difference between RAPD and SSR results are due to the marker systems, the cultivars sampled, the number of individuals sampled per cultivar (10 for RAPDs and 30 for SSRs), or a combination of all three factors.

The genotype assignment test requires codominant information like that available from SSRs (Waser and Strobeck, 1998) and thus has yet to be applied to dominant marker systems like AFLPs and RAPDs. One interesting feature of the genotype assignment test is that it computes the probability of a genotype assuming HWE (Waser and Strobeck, 1998). We showed, however, that HWE is not maintained for the SSR data. As a result, the probabilities computed for each individual during an assignment test are probably inaccurate. Nonetheless, the relative rank of assignment probabilities across cultivars was accurate for our data, because we showed that all 210 individuals in our study have their highest probability of occurrence in their cultivar of origin. In short, each individual can be identified as typical of the cultivar from which it was sampled.

Because the genotype assignment test may be useful for property right protection and cultivar identification, we examined the sampling properties of the test. Although one must be careful not to generalize too broadly from this study of seven cultivars, our results suggest that genotypes should be based on 15 or more SSR loci in order for the assignment test to be accurate (Fig. 4). It is less clear how many individuals need to be sampled per cultivar, but it seems prudent to examine a relatively large sample—i.e., 20 or more individuals.

We presented several analyses of SSR data, but it remains to be seen which analytical method will be most helpful for cultivar identification and property right protection. Although this study suggests that the genotype assignment test will be useful for these purposes, there is still a need to determine if the test is useful over broader samples of germplasm. We chose cultivars that represent both closely and distantly related germplasm (Table 1), but additional studies of genetic diversity in closely related perennial ryegrass cultivars and different generations of perennial ryegrass cultivars will be helpful.

Furthermore, the perennial ryegrass community needs to compose standards to prove essential derivation at the genetic level before SSRs can be used for property right protection. Questions that need to be addressed by the community include: How should molecular markers be used to complement existing morphological standards? How many individuals should be genotyped per cultivar, with how many and what loci? What analytical methods should be used in conjunction with molecular data? Most importantly, what analytical standards must be met to prove essential derivation at the genetic level? For example, what proportion of individuals need to be accurately assigned with the assignment test before two cultivars are considered distinct—i.e., 50%, 75%, 95% or 100%? The same issue applies to other analytical methods–for example, what value of F_ST or R_ST (and based on what size sample?) sufficiently discriminates cultivars to the point that they can be considered essentially derived at the genetic level? Here we documented that SSR data differentiate among perennial ryegrass cultivars, including cultivars with similar breeding histories, using several analytical methods. We also explored sampling issues in the hope that answers to some of the aforementioned questions can begin to be formulated.

	ACKNOWLEDGMENTS

 
The authors thank an anonymous reviewer for helpful comments and also appreciate C.R. Funk for his support, insight, and discussion. This work was supported by the Rutgers University Center for Turfgrass Science, the New Jersey Agriculture Experiment Station, and the New Jersey Turfgrass Association.

Received for publication August 15, 2000.

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