Euphytica (2010) 173:63–75 DOI 10.1007/s10681-009-0112-4
Implementation of simple sequence repeat markers to genotype Florida strawberry varieties Asha M. Brunings • Catalina Moyer Natalia Peres • Kevin M. Folta
•
Received: 2 July 2009 / Accepted: 22 December 2009 / Published online: 12 January 2010 Ó Springer Science+Business Media B.V. 2010
Abstract Simple sequence repeats (SSRs or microsatellites) are frequently used as a robust, rapid, and relatively inexpensive means of genotyping. Recent reports in cultivated strawberry (Fragaria 9 ananassa) have identified a widely applicable set of SSRs that permits discrimination of closely-related genotypes. In the present study this same set of SSRs is analyzed in cultivars commonly grown in the state of Florida, as well as advanced breeding selections from the University of Florida program. Nine primer pairs have been used to produce discrete SSR patterns for all lines tested, including diagnostic sets that distinguish between closely-related cultivars and/or breeding selections. A comparison of markers to known pedigrees is presented, along with analysis of relative
Electronic supplementary material The online version of this article (doi:10.1007/s10681-009-0112-4) contains supplementary material, which is available to authorized users. A. M. Brunings K. M. Folta (&) Horticultural Sciences Department, University of Florida, 1301 Fifield Hall, Gainesville, FL 32611, USA e-mail:
[email protected] A. M. Brunings K. M. Folta Graduate Program in Plant Molecular and Cellular Biology, University of Florida, 1301 Fifield Hall, Gainesville, FL 32611, USA C. Moyer N. Peres Gulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USA
relatedness between lines studied. We also detail important technical considerations for transferability and limitations of these technologies between laboratories. The resulting genotype data for cultivars commonly used in Florida are accessible as supplemental data in graphic format and allow comparison with other cultivars. Users can thus select the most diagnostic SSRs to test the lines in question. These resources provide new tools for breeders and nurseries to authenticate genotypes, follow inheritance patterns and enforce patent protection. As important, this report underscores the strengths and weaknesses of applying the original methodologies to a different plant population in independent laboratories. Keywords Microsatellites SSR markers Fragaria Strawberry Genotyping Fingerprinting
Introduction The cultivated strawberry, Fragaria 9 ananassa (2n = 8x = 56), is a high-value crop grown in many temperate and sub-tropical regions of the world. Strawberries for fresh-fruit production are typically grown as annuals in Florida. New plants are clonally propagated in nurseries by establishment of stolonborne daughter plants. The genetic integrity of the resulting stock is critical because large commercial growers purchase specific varieties for attributes that
123
64
match production desires and constraints. Traits related to flowering time, yield, fruit quality, fruit size, disease susceptibility, and other metrics directly related to production value frame a grower’s decision to adopt a given cultivar. The possibility of error accompanies high-volume vegetative production. It was reported that in 2007 a nursery error led to the delivery of plants identified incorrectly, leading to substantial losses for growers in the Southeast U.S. It is therefore extremely important to develop a method for genotyping strawberry cultivars with great precision. Cultivated strawberry arises from a narrow genetic base of approximately 50 founding clones (Dale and Sjulin 1990), and even the breeder’s trained eye may find it difficult to differentiate one strawberry genotype from another. Cultivars bear remarkably similar phenotypes, especially in the vegetative stage, and strawberry production materials are vegetatively propagated annually for most regions. Moreover, discerning phenotypic descriptors may vary greatly with environmental conditions and cultural practices. An objective set of DNA-based identifiers would have great utility in differentiating materials in a given population. Several different molecular genotyping tools have been devised to permit rapid and reliable identification of various strawberry cultivars. AFLP, RAPD, and ISSR markers have been employed to characterize strawberries as well as to enforce patent protection (Arnau et al. 2003; Congiu et al. 2000; Dangl et al. 2007; Degani et al. 1998; Garcia et al. 2002; Graham et al. 1996; Hancock et al. 1994; Milella et al. 2006; Tyrka et al. 2002). All of these genotyping vehicles have well-described limitations in reproducibility or ease of implementation that make them less attractive for cultivar identification. While microsatellites exhibit co-dominant inheritance, RAPDs and AFLP markers are usually dominant. Reproducibility of RAPDs is inconsistent and depends greatly on reaction conditions used (Ellsworth et al. 1993; MacPherson et al. 1993; Perez et al. 1998; Rabouam et al. 1999). Kuras et al. (2004) found that RAPDs and ISSR techniques need to be used in conjunction to overcome technical limitations of each individual technique. On the other hand, microsatellites, or Simple Sequence Repeats (SSRs), represent reliable, robust and consistent genotypic identifiers (Gupta et al.
123
Euphytica (2010) 173:63–75
1996; Morgante and Olivieri 1993), and have been used for a number of crops, including polyploids (Alba et al. 2009; Arnau et al. 2003; Ashley et al. 2003; Buteler et al. 1999; Dangl et al. 2001, 2005, 2009; Guilford et al. 1997; Lian et al. 2001). These markers rely on the detection of rapidly evolving polynucleotide repeats that exhibit great variation, even between closely related accessions. SSRs have been employed extensively in strawberry because of their reproducibility and ability to sensitively detect subtle polymorphisms (Ashley et al. 2003; Bassil et al. 2006; Govan et al. 2008; Monfort et al. 2006; Lewers et al. 2005; Cipriani et al. 2006; Gil-Ariza et al. 2006; Sargent et al. 2003, 2006). In olive, a single SSR marker allowed differentiation between 96% of 118 cultivars, and with 3 SSRs almost all cultivars could be identified (Sarri et al. 2006). SSRs are especially useful in cultivated strawberry because the species has an octoploid genome, presenting as many as eight allelic variants for any given locus (Ashley et al. 2003). This number is even larger in instances where gene duplications provide a greater number of loci. This attribute makes the use of such markers especially compelling for genotyping in cultivated strawberry. With this motivation Govan et al. (2008) assayed 60 octoploid strawberry cultivars with over 100 published SSR primer pairs. The report defined 10 primer pairs that exhibited high reproducibility in SSR detection and showed complex patterns that varied greatly between cultivars. The markers spanned linkage groups allowing their use in establishing at least general linkage assignments. These tools allowed comparisons of relatedness among the 60 accessions and established a database for further comparisons. In this report the tools described in Govan et al. (2008) are employed to characterize important Florida cultivars and advanced breeding selections and to assess the general utility of the primer set in a different population. The suite of forensic identifiers is presented along with graphical representations for Florida cultivars that permits facile comparison between any two genotypes to aid the user in choosing the best SSR primer pairs to answer a given question. The information presented herein is designed to promote effective interaction between the breeder, the nurseryman or grower and molecular biology researchers to obtain high-confidence
Euphytica (2010) 173:63–75
authentication of germplasm relevant to the Florida strawberry industry.
Materials and methods Plant materials The genotypes used in this study are listed in Table 1. The germplasm chosen includes cultivars and advanced breeding selections from the University of Florida Strawberry Breeding Program. Leaves were harvested from field or greenhouse-grown plants and were chilled immediately on ice before DNA isolation or freezing at -20°C.
65
DNA isolation Genomic DNA was extracted from two independent sets of tissues, each at a different location, and from different plants. The first used a standard Qiagen DNeasy Plant Kit (Valencia, CA) isolation and was conducted at the UF Gulf Coast Research and Education Center in Wimauma, FL. A second, independent set was extracted using a modification of the CTAB protocol designed for pine cones (Chang et al. 1993) adapted for strawberry tissues (www.strawberrygenomics.com/proto cols/DNAprep.htm) at the Horticulture Department in Gainesville, FL, using 3 ml extraction buffer for approximately 0.5 g of leaf tissue (fresh or frozen). The nucleic acids in the sample were quantified by spectrophotometry and then diluted to 10 ng/ll with TE buffer.
Table 1 The Fragaria 9 ananassa accessions genotyped using the nine SSR sets and their parents are presented
PCR amplification
Genotype
The polymerase chain reaction (PCR) was performed with 5 ng of DNA per 25 ll reaction using the reaction parameters described (Sargent et al. 2003). For experimental consistency, master mixes were prepared that included enough template DNA to obtain nine aliquots, one for each of the primer pairs. The primers are those listed in Table 3 of Govan et al. (2008) using identical fluorophores. A summary of the amplicon peak size information and variability is provided in Table 2. The reactions were first analyzed with ethidium bromide staining on 2.2% agarose gels for normalization, and were then diluted to approximately 5 ng/ll for fragment analysis.
Parents
05-107
Radiance 9 Earlibrite
05-150
01-101 9 01-76
05-151
01-101 9 01-76
05-183
01-101 9 02-9
05-73
00-59 9 00-98
05-85 06-003
Strawberry Festival 9 Sugarbaby Seascape 9 Strawberry Festival
06-38
01-29 9 02-66
06-45
01-221 9 01-49
06-47
01-221 9 01-49
06-98
03-161 9 02-66
99-117
Strawberry Festival 9 95-322
Carmine
Rosa Linda 9 93-53
Delmarvel
Earliglow 9 Atlas
Dover
Florida Belle 9 71-189
Earlibrite
Rosa Linda 9 90-38
Elyana
96-114 9 95-200
Florida Belle
Sequoia 9 Earlibelle
LF-9
Strawberry Festival 9 Strawberry Festival
Radiance
Winter Dawn 9 99-35
Rosa Linda
87-418 9 87-200
Rubygem
Earlibrite 9 Carlsbad
Strawberry Festival
Rosa Linda 9 Oso Grande
Sweet Charlie
80-456 9 Pajaro
Treasure
A3 9 Oso Grande
Winter Dawn
93-103 9 95-316
SSR analysis Analysis of microsatellites was performed using two complementary methods. The first analyzed amplicon patterns with fluorescent detection using the ABI 3730xl DNA Analyzer (Foster City, CA) at the Interdisciplinary Center for Biotechnology Research (ICBR) of the University of Florida. The PCR products of an initial run using one template and all SSR primer pairs were diluted 10, 50, and 100-fold to determine the best dilution factor for each SSR. Generally, a 50-fold dilution produced high-quality results, but optimal loading was determined by inspection of PCR products on 2.2% agarose gels. Electropherograms were generated using the GeneMarker v. 1.70 software package (SoftGenetics, College Park, PA). The GeneScanTM
123
66 Table 2 The number of peaks and range of sizes detected in the Florida germplasm compared to the alleles detected by Govan et al. (2008)
a
EMFn111 did not perform in this study and was omitted from further analyses
Euphytica (2010) 173:63–75
SSR name
No. of informative Informative peak size No. of peaks peaks observed in range observed in this observed in this study study (bp) previous study
ARSFL11
8
258–278
14
257–321
ChFaM-023
7
144–169
16
161–200
EMFn111a
0
–
24
208–269
EMFn121
8
228–254
15
226–256
EMFn170
9
188–233
15
188–238
EMFn181
7
164–212
35
138–248
EMFn182
8
179–202
15
191–219
EMFv104
15
72–126
27
69–138
EMFvi136
8
132–160
18
111–188
EMFvi166
4
268–281
11
254–282
600 LIZÒ size standard (Applied Biosystems, Foster City, CA) was used to accurately determine fragment peak sizes for all experiments. The standard also permitted calibration of the GeneMarker software to ensure that the base calling function was accurately identifying base sizes of the size standard and thus on alleles. The independent band patterns were compared directly to the data obtained from the ABI DNA Analyzer. The presence or absence of alleles was scored based upon comparison to the actual sequence obtained by samples that were sequenced. The software and subjective base calling were also tested by examination of patterns generated from highly homozygous diploid strawberry genotypes that presented less complex patterns. These trials presented the band patterns of a single allele, oftentimes consisting of more than one peak, and delineated the difference between actual PCR products and artefacts of the SSRgeneration process. A second independent set of amplicons was obtained from a subset of the primers and genotypes studied. The samples were diluted 10fold and analyzed using the LiCOR 4300 DNA Analyzer. Alleles were considered present only if both independent analyses resulted in detection of that particular allele no matter how high the score of an individual allele call made by the GeneMarker software. Cloning To calibrate the software, a subset of the PCR products obtained were sequenced prior to SSR analysis to verify a precise match of electropherogram peaks with
123
Peak size range observed in previous study (bp)
actual fragment sizes. PCR products were amplified using the aforementioned primers, the amplicons were cloned into the pGEM-T Easy vector (Promega, Madison, WI), and the presence of inserts was confirmed by restriction digestion with EcoRI (Promega, Madison, WI). Cloned PCR fragments were sequenced by the ICBR at the University of Florida. Pedigree analysis Known lineage relationships were obtained from C.K. Chandler of the University of Florida Gulf Coast Research and Education Center. Comparisons were made based on visual inspection of SSR patterns in parents and their presence/absence in associated progeny. Clustering Relationships between cultivars were resolved using the methods originally described (Marchese et al. 2005), based on their utility as demonstrated in Govan et al. (2008). All possible alleles detected across cultivars were converted to a binary system. For each genotype any given allele was scored as absent (0) or present (1). The information was used for pedigree analysis. Results were combined into a single matrix and analyzed with PAUP* using the unweighted pair group method with arithmetic mean (UPGMA). The analysis was performed online at the website created for this purpose at http://genomes.urv.cat/UPGMA (Garcia-Vallve et al. 1999).
Euphytica (2010) 173:63–75
Computational tools Alleles identified for each cultivar were tabulated. For graphical representation, the graphics capabilities of the R software environment (http://www.rproject.org/) were utilized to plot the alleles found with each SSR primer pair to create virtual gels displaying an approximation of the SSR pattern that allowed for visual comparison of alleles for each cultivar.
Results The study by Govan et al. (2008) provides a set of SSR primers with great utility across diverse strawberry accessions. The same resource should be evaluated in other laboratory environments, as transferability of the technology would impact its universal applicability. For broad and efficient implementation, the published protocols (Sargent et al. 2003; Govan et al. 2008) use a single set of touchdown PCR parameters for the entire group of 10 primer pairs, and implements multiplexing (uses several primers pairs in a single reaction) PCR amplifications. In contrast, we used a single primer pair in each reaction. Our results (Table 2) indicate that fewer amplicons were detected in the studied germplasm compared to that of the previous study (Govan et al. 2008). A total of 74 peaks over 25 genotypes were distinguished whereas Govan et al. (2008) resolved 166 independent peaks over 60 cultivars. The range of sizes is also significantly narrower (Table 2), as a result of obtaining a smaller number of peaks, and the inherent limit in diversity amongst the cultivars used in this study. The 74 allele peaks identified for nine SSRs in the tested Florida accessions are listed in Table 3 and vary between 4 and 15 alleles per SSR. The numerical information was used to prepare graphical genotype displays by plotting allele sizes for each SSR. One such fragment analysis for the ‘Strawberry Festival’ cultivar is presented in Fig. 1. A complete set for important Florida cultivars is available as supplemental data. We obtained varying band-intensities and amplified occasional non-specific bands that could lead to inaccurately designated alleles, detailed below. In
67
particular, neither of the two individual, independent laboratories conducting the present study could reliably amplify fragments using the EMFn111 primer pair. Subsequent investigation revealed that EMFn111 did not amplify because of an error in the published sequence that prohibited amplification (D. Sargent, pers. comm.). Generally, our results agreed well with the allele peaks reported in Govan et al. (2008). However, when individual alleles for a given cultivar are compared between studies, single base pair differences were observed. Such small insertions or deletions (indels) are at least theoretically possible and would constitute genuine separate alleles, thus their identification would be important. To investigate the source of the observed 1–2 bp variation in allele sizes, the PCR products of two alleles representing primer pair ChFaM-023 were cloned and sequenced to determine the actual allele size, and then compared to the apparent size identified using the base-designating software. Manual counting of bases from sequencing confirmed that the 1-bp deviation detected was not due to the presence of separate alleles (data not shown). To train our manual inspection of electropherograms, the same nine SSRs were used to amplify alleles from highly homozygous diploid strawberry genotypes (e.g. 5AF7; Slovin et al. 2009), confirming that the major peak of a single allele is oftentimes accompanied by leading or trailing minor peaks that can be attributed to PCR artefacts (data not shown). To investigate the consistency of results between laboratories, we compared the fingerprints obtained using all nine SSRs of the genotypes ‘Sweet Charlie’ and ‘Strawberry Festival’, and five SSRs on ‘Delmarvel’ with the published peaks (D. Sargent, pers. comm.; Govan et al. 2008). We found discrepancies such as the 1-bp shifts described above and missing and/or additional alleles compared to the published data. An example of ‘Delmarvel’ using EMFn182 is illustrated in Fig. 2. The alleles reported by Govan et al. (2008) for ‘Delmarvel’ using EMFn182 were 174, 179, 183, 186, 189, 192, and 198. Major differences are the absence of alleles 192 and 198 in our analysis, and the presence of allele 196, not reported by Govan et al. (2008). Moreover, it appears that most of the electropherogram is shifted 1-bp smaller, since we identified peaks of sizes 173, 179, 182, 185, 188, and 196.
123
68
Euphytica (2010) 173:63–75
Table 3 Alleles observed for each SSR used in this study Allele no.
SSR no. ARSFL11
ChFaM-023
EMFn121
EMFn170
EMFn181
EMFn182
1
258
144
228
188
164
173
2
260
146
231
190
170
179
3
262
148
237
194
184
4
264
154
239
198
187
5
266
160
244
202
6
268
167
246
7
272
169
252
8
278
254
222
9
EMFvi136
EMFvi166
72
120
268
85
132
270
181
89
140
277
185
93
150
281
198
187
96
152
206
210
195
99
154
220
212
197
102
158
202
104
160
233
EMFv104
107
10
109
11
111
12
113
13
117
14
123
15
126
Fig. 1 Fragment analysis from ‘Strawberry Festival’. Allele fragment sizes from ‘Strawberry Festival’ plotted for each SSR. A set of graphical displays for many cultivars is available in the supplemental data
123
Euphytica (2010) 173:63–75
69
Fig. 2 Electropherogram obtained using the strawberry genotype ‘Delmarvel’ as a template for SSR EMFn182. The peaks for ‘Delmarvel’ are dark grey, and the peak calls are indicated in rectangular boxes below the graph. The light grey peaks are derived from the size standard, and indicate the positions for sizes 160, 180, and 200
To allow comparisons between accessions and test known genetic relationships, a cluster analysis was performed using methods previously described (Garcia-Vallve et al. 1999; Marchese et al. 2005). A dendrogram based on the 70 informative alleles was produced and is presented in Fig. 3. The results are difficult to decipher because of the paucity of parental lines utilized in the study. While sibling relationships were frequently observed (such as the close relatedness of ‘05-107’ and its parent ‘Radiance’), in other instances, the expected relationships were less conspicuous. For instance, ‘Strawberry Festival’ and ‘Treasure’ share a common parent, ‘05-150’ and ‘05151’ are full siblings, as are ‘06-45’ and ‘06-47’, and yet they share only a scant subset of alleles. Some known relationships are clearly reflected in the clustering. ‘Strawberry Festival’ was selfed to produce the laboratory accession ‘LF9’, and ‘Strawberry Festival’ was crossed with ‘Sugarbaby’ to produce the ‘05-85’ advanced breeding selection. In these cases the SSR amplicon patterns reflect the parentage extremely well. Analysis of ‘LF9’ produces fewer amplicons than its parent, ‘Strawberry Festival’. In Table 4 the allele peaks are listed for these genotypes. Although ‘LF9’ has 16 of the 33 alleles found in ‘Strawberry Festival’, ‘LF9’ has two additional peak sizes that were not found in ‘Strawberry Festival,’ 187 from EMFn182, and 132 from EMFvi136. A number of cultivars can be easily identified from those tested in this study because of the presence of a unique allele (Table 5). Although a comprehensive accounting of several SSR-based amplicons is more authoritative than the presence or absence of a single
allele, this information can be useful to specifically include or exclude a certain cultivar from a study. Table 6 lists the number of accessions amongst the
Fig. 3 Unrooted dendrogram displaying the relatedness of accessions analyzed based on 81 informative SSR alleles. The diagram shows that SSR patterns are useful for determining relationships between strawberry accessions. Two sets of full siblings are marked with the same symbols (* and °)
123
70
Euphytica (2010) 173:63–75
Table 4 Comparison of alleles found in ‘Strawberry Festival’ and ‘LF9’, a genotype derived from self-fertilization of ‘Strawberry Festival’ SSR
LF9
Strawberry Festival
ARSFL11
260
258
260
266
ChFaM-023
154
144
154
167
EMFn121
231
246
231
246
254
EMFn170
194
198
222
194
198
EMFn181
164
170
184
210
164
170
EMFn182
173
181
185
187
197
173
EMFv104
109
EMFvi136
120
132
150
154
158
EMFvi166
268
277
268
277
Table 5 SSR alleles that uniquely identify strawberry cultivars among the cultivars analyzed
Discussion
Accession
SSR
Allele size (bp)
05-85
EMFn121
237
06-98
EMFn121
244
99-117
ChFaM-023
160
Dover
EMFv104
99
Florida Belle
ARSFL11
262
Treasure
EMFn170
233
Whereas phenotypic descriptors may vary with climate, geography or cultural practices, forensic tools based on genetic material allow cultivar authentication with great certainty. DNA markers provide a robust, rapid, and relatively inexpensive means to differentiate closely related plant materials. In this study, the group of 10 primer sets that bracket well-conserved and readily amplifiable regions of strawberry genomic DNA were used to assay the germplasm typically grown by the Florida strawberry industry and the advanced breeding selections present in the University of Florida Strawberry Breeding Program. It is critical to characterize cultivars and advanced breeding selections for several reasons. The most important is maintenance of the genetic integrity of plant inventories. Strawberries are vegetatively propagated. Daughter plants emerge at the end of ranging stolons that freely meander in search of an appropriate place to root and commence vegetative growth. Adjacent pots are outstanding candidates for propagation by runners, leading to a potential source of genotype mixing. Careful management of runners and establishment of daughter plants are necessary to ensure that genotypes do not intermingle. If lapses in management allow mixing of genotypes, a method should be in place to identify genotypes before errors are perpetuated. Govan et al. (2008) defined a set of 10 primer pairs that amplify complex products among a wide range of strawberry accessions. These resources are not only useful for genotyping plants in a given
Table 6 Number of individual accessions from the ones tested in this study distinguished by each SSR SSR
Number of accessions distinguished
ARSFL11
15
ChFaM-023
9
EMFn121
5
EMFn170
10
EMFn181
13
EMFn182
11
EMFv104
24
EMFvi136
12
EMFvi166
6
pool used in this study that can be discriminated by each SSR. Although we found that SSRs EMFv104 and EMFvi136 were both capable of distinguishing all accessions in combination with one of five other SSRs, the data gathered using EMFvi136 was more reproducible, robust, and therefore more useful. All accessions in this study could be distinguished from each other using one of fourteen SSR pairs (Table 7).
123
272
222
202
206
222
184
210
181
185
197
89
93
102
109
120
140
150
154
117
126
Euphytica (2010) 173:63–75
71
Table 7 Pairs of SSRs that can distinguish all accessions tested in this study EMFn181
EMFn182
ARSFL11 ChFaM-023 EMFn121 EMFn170 EMFn181
X
EMFv104
EMFvi136
X
X
X
X
EMFvi166
X X X
EMFn182 EMFv104
collection, they also potentially allow for comparisons to be made between accessions including those from other laboratories or breeding programs. In this way, adoption of this highly applicable set will provide a new standardized resource that permits any given cultivar to be discerned from others. General inferences on pedigree or perhaps even associations with traits of interest may be by-products of this research. The present work takes the study by Govan et al. (2008) and tests its transferability to two other laboratories. The results indicate that the markers are generally robust and agree well with published results with some caveats. For some alleles, we identified a consistent 1–2 bp deviation in allele size, which cannot be explained away by the addition of an additional non-template nucleotide (Clark 1988). This difference at the onset is hard to reconcile because SSR peaks that are the result of 2 or 3 bp repeats for a given primer set are not expected to be 1 bp apart in size. However, cloning and sequencing of allelic bands indicated the actual number of bases in the amplicon, allowing calibration of the software that directed accurate base calls. The small deviation can be attributed to experimental resolution limits of the assay and software, as rounding errors may contribute to this deviation. In addition, the same primers were tested against highly homozygous diploid strawberry genotypes to train our manual allele calls, providing great confidence in distinguishing authentic amplicon detection from spurious products of the PCR reaction. Technical differences between research laboratories could be assessed through analysis of common cultivars. Analysis of ‘Sweet Charlie’ and ‘Delmarvel’ indicates some minor differences between the results obtained here, published (Govan et al. 2008)
X
X
X
X X
X
and unpublished data (D. Sargent, pers. comm.). The basis for the differences may be attributed to technical variations between laboratories such as differences in thermal cycler ramping times, accuracy of temperature settings, polymerase enzymes, sample dilution, multiplexing, fragment analysis modality and the software used to analyze the output. Amplification of non-specific peaks (those that were not reproducible), was detected in comparisons of experimental replicates, even those performed in the same laboratory. These findings underscore the necessity for careful reproduction of experiments and training of manual scoring by analysis of simple genotypes or standards. In this case, the accuracy of the peak calls was verified by analysis of amplicons derived from the same primer pairs generated from highly homozygous diploid templates (data not shown). These genotypes produced conspicuous products of low complexity, allowing further calibration of software and training for manual peak detection. The small variations between and within laboratories suggest that these techniques be implemented on two levels when addressing an unknown genotype. First, a comparison of major amplicons from replicated data should define a discrete set of genotypes that match the experimental unknown. Genomic DNA from those candidates should be obtained and tested side-by-side with the unknown, using identical reagents and conditions. Such direct comparisons would allow faithful identification of unknown materials using an SSR approach. The SSRs used in this study varied in their ability to discriminate between genotypes, and as few as two SSRs could be used to distinguish any given genotype from the others. However, considering the potential differences described above, it would be good practice to use multiple SSRs with high discriminating ability,
123
72
and to include a parallel DNA sample from a known strawberry reference genotype when trying to genotype an unknown plant. The EMFn111 primer pair did not produce amplicons in our hands, even when new primers were synthesized and fluorophores swapped. It was determined that the primers used represented an incorrect sequence due to an error in the original report. The correct sequences were furnished and are printed here to correct the literature (EMFn111F 50 -GAAGCT CCTCTCACAAAGTTAAGG-30 and EMFn111R 50 TCAACAACAACATCAACAAAGG-30 ; D. Sargent, pers. comm.), although they were not included in our analyses. Currently there is a revitalized interest in breeders’ rights and physical control of materials arising from various public and private breeding programs and molecular tools have been used successfully in a court of law to ascertain cultivar identity (Congiu et al. 2000). The use of forensic molecular markers is considered a critical facet of these enforcements. The SSRs are useful for uniquely identifying each accession from a scientist’s or commercial grower’s point of view. The 74 alleles provided enough variability to distinguish even closely-related accessions. However, the common 1–2 bp deviation in allele size calls may complicate applications of this technology to patent enforcement or other trials that demand high-precision genotyping. To overcome these problems, the PCR conditions need to be optimized for each primer pair rather than relying on widely applicable PCR conditions. Even so, although the methodology as described herein would be appropriate to provide evidence that two cultivars have distinct haplotypes, it would not be adequate to provide irrefutable proof that two detected alleles are the same. Many accessions share common amplification products and therefore cannot be the sole basis for such decisions. For that purpose, cloning and sequencing the SSRbased alleles would provide a precise accounting of alleles and remove any doubt regarding allele size. Fragment analysis using the bioanalyzer could be used for initial screening of a plant in question, followed by sequencing to clear up any uncertainties. The data obtained from sequencing the SSR alleles can be submitted with a plant patent application as an identifying characteristic. In addition, it would be crucial that analyses are performed independently in
123
Euphytica (2010) 173:63–75
at least two laboratories and analyzed by experienced technicians. The validation of the published protocol and its application to the Florida strawberry germplasm provides some opportunities for assisting the breeding program. Amplicon patterns derived from small amounts of seedling tissue can verify successful crosses, detect instances of outcrossing and perhaps contribute to patterns of association among important traits. The genotyping protocol tested shows great resolution as it can even differentiate between a parental line and offspring arising from self-pollination. An example of this is an internal test in which the patterns of the accession ‘LF9’ and ‘Strawberry Festival’ were analyzed. The ‘LF9’ genotype is not a cultivar, instead it is a strawberry line developed exclusively due to its robust growth in culture and ease of transformation (Folta et al. 2006). Making all practical assumptions, ‘LF9’ is a product of a ‘Strawberry Festival’ self-fertilization, thus ‘LF9’ must contain all of the alleles present in ‘Strawberry Festival’ and no additional ones. In this study, most of the ‘LF9’ alleles appeared in ‘Strawberry Festival’, but several parental alleles were not present in the offspring (Table 4). This finding is the result of increasing homozygosity in subsequent generations, leading to a lower number of allelic variants. The results in Table 4 show this to generally be the case for the ‘Strawberry Festival’/‘LF9’ relationship but not for all alleles. Specifically, discrepancies were found where ‘LF9’ has two alleles that consistently do not occur in ‘Strawberry Festival’, the 187 allele derived from SSR EMFn182, and the 132 allele derived from SSR EMFvi136. All other alleles occurring in ‘LF9’ are present in ‘Strawberry Festival’. Such differences can only be attributed to possibly homogenization of SSR loci between subgenomes or generational changes in the expansion/ contraction of the polynucleotide repeat. These instances are highly unlikely but should be noted. It is more likely that the occurrence of alleles in the offspring that the parents do not have, is due to the suppression of alleles by the competition of allelespecific primers (Myakishev et al. 2001). These changes are again artefacts of the process and reinforce that multiple primer pairs with great variations in amplicon size are required for effective genotyping.
Euphytica (2010) 173:63–75
On a much larger scale the information can assist in the design of strategic crosses, ensuring that crosses do not significantly limit heterozygosity. It is also a way to monitor the introgression of unique or wild germplasm in long-term breeding objectives. With regard to the germplasm studied herein, comparison of the number of alleles detected in the Florida strawberry germplasm set against the number of alleles found in the study by Govan et al. (2008) permits some interesting interpretations. There are many fewer alleles present in the Florida germplasm, suggesting that the resource was constructed from a narrower set of genetics than that which comprises the bulk of the European accessions. This finding is somewhat anticipated as the European cultivars assayed represent plant materials arising from many different origins and breeding programs. Still, it may signal that future breeding efforts might benefit from a parallel introgression of additional diversity from ranging sources. SSR-based fingerprinting can guide the increase in genetic diversity by selection of genotypes with different alleles than those present in existing breeding programs. Cluster analysis (Fig. 3), which was performed to assess whether closely related strawberry accessions clustered closely together, also supports this conclusion as even closely related genotypes frequently cluster in different parts of the dendrogram when they would be expected to be quite close. The non-UF Florida cultivar ‘Treasure’ shares a parent with ‘Strawberry Festival’, yet it clearly clusters independent from the UF materials. These findings indicate that only a small amount of deviation can distort the clustering process as most banding patterns are consistent between related genotypes. The data in Fig. 1 depict a typical graphical display of a genotyping experiment, particularly useful for demonstration purposes. The graphical genotype displays for important Florida varieties is available as supplementary data. For comparison of a few genotypes at a time, it is useful to print the graph for one cultivar on a transparent sheet and overlay it with that of another. This provides a visual overview of the entire dataset for the cultivar and allows comparison of the overall pattern. Similarities become easily identifiable and small deviations are minimized. The absence or presence of a single PCR product or of a 1-bp deviation which would result in a less than perfect match if the matching was done by
73
comparing raw numbers, would not detract from the similarities of the overall pattern. This allows the user to best design a truly diagnostic experiment. In summary, this report echoes the conclusion by Govan et al. (2008) that their limited set of primers are a powerful means to genotype a specific strawberry cultivar. Further analysis indicated that the results were robust and could differentiate even between full siblings (i.e. 05-150 and 05-151, and 06-45 and 06-47). The technology is not without caveats as clear discrepancies in direct transferability have been noted. Taken together, the results show that the widely applicable primer set previously defined can be used to detect variability in a separate strawberry population, but true differentiation between genotypes must rely on large differences detected among several diagnostic primer pairs. Acknowledgements This research was funded by a grant from the University of Florida IFAS Dean for Research (KMF and NP). The authors thank Dr. Daniel Sargent for valuable discussions and Kayla Shea Childers for technical assistance. The authors also recognize Dr. Craig K. Chandler for helpful insights and starting materials.
References Alba V, Montemurro C, Sabetta W, Pasqualone A, Blanco A (2009) SSR-based identification key of cultivar of Olea europaea L. diffused in Southern-Italy. Sci Hortic 123:11–16 Arnau G, Lallemand J, Bourgoin M (2003) Fast and reliable strawberry cultivar identification using inter simple sequence repeat (ISSR) amplification. Euphytica 129:69– 79 Ashley MV, Wilk JA, Styan SMN, Craft KJ, Jones KL, Feldheim KA, Lewers KS, Ashman TL (2003) High variability and disomic segregation of microsatellites in the octoploid Fragaria virginiana Mill. (Rosaceae). Theor Appl Genet 107:1201–1207 Bassil NV, Gunn M, Folta K, Lewers K (2006) Microsatellite markers for Fragaria from ‘Strawberry Festival’ expressed sequence tags. Mol Ecol Notes 6:473–476 Buteler MI, Jarret RL, LaBonte DR (1999) Sequence characterization of microsatellites in diploid and polyploid Ipomoea. Theor Appl Genet 99:123–132 Chang S, Puryear J, Cairney J (1993) A simple and efficient method for isolating RNA from pine trees. Plant Mol Biol Rep 11:113–116 Cipriani G, Pinosa F, Bonoli M, Faedi W (2006) A new set of microsatellite markers for Fragaria species and their application in linkage analysis. J Hortic Sci Biotechnol 81:668–675
123
74 Clark JM (1988) Novel non-templated nucleotide addition reactions catalyzed by procaryotic and eucaryotic DNA polymerases. Nucleic Acids Res 16:9677–9686 Congiu L, Chicca M, Cella R, Rossi R, Bernacchia G (2000) The use of random amplified polymorphic DNA (RAPD) markers to identify strawberry varieties: a forensic application. Mol Ecol 9:229–232 Dale A, Sjulin TM (1990) Few cytoplasms contribute to NorthAmerican strawberry cultivars. HortScience 25:1341– 1342 Dangl GS, Mendum ML, Prins BH, Walker MA, Meredith CP, Simon CJ (2001) Simple sequence repeat analysis of a clonally propagated species: a tool for managing a grape germplasm collection. Genome 44:432–438 Dangl GS, Woeste K, Aradhya MK, Koehmstedt A, Simon C, Potter D, Leslie CA, McGranahan G (2005) Characterization of 14 microsatellite markers for genetic analysis and cultivar identification of walnut. J Am Soc Hortic Sci 130:348–354 Dangl GS, Lee EW, Sims ST, Golino DA (2007) A new system for strawberry cultivar identification developed at Foundation Plant Services (FPS), University of California, Davis, using simple sequence repeat (SR) primers. In: NASS/NASGA proceedings, pp 118–121 Dangl GS, Yang J, Golino D, Gradziel T (2009) A practical method for almond cultivar identification and parental analysis using simple sequence repeat markers. Euphytica 168:41–48 Degani C, Rowland L, Levi A, Hortynski J, Galletta G (1998) DNA fingerprinting of strawberry (Fragaria 9 ananassa) cultivars using randomly amplified polymorphic DNA (RAPD) markers. Euphytica 102:247–253 Ellsworth DL, Rittenhouse D, Honeycutt RL (1993) Artifactual variation in randomly amplified polymorphic DNA binding patterns. Biotechniques 14:214–217 Folta K, Dhingra A, Howard L, Stewart P, Chandler C (2006) Characterization of LF9, an octoploid strawberry genotype selected for rapid regeneration and transformation. Planta 224:1058–1067 Garcia MG, Ontivero M, Diaz Ricci JC, Castagnaro A (2002) Morphological traits and high resolution RAPD markers for the identification of the main strawberry varieties cultivated in Argentina. Plant Breed 121:76–80 Garcia-Vallve S, Palau J, Romeu A (1999) Horizontal gene transfer in glycosyl hydrolases inferred from codon usage in Escherichia coli and Bacillus subtilis. Mol Biol Evol 16:1125–1134 Gil-Ariza DJ, Amaya I, Botella MA, Munoz BJ, Caballero JL, Lopez-Aranda JM, Valpuesta V, Sanchez-Sevilla JF (2006) EST-derived polymorphic microsatellites from cultivated strawberry (Fragaria 9 ananassa) are useful for diversity studies and varietal identification among Fragaria species. Mol Ecol Notes 6:1195–1197 Govan C, Simpson DW, Johnson AW, Tobutt KR, Sargent DJ (2008) A reliable multiplexed microsatellite set for genotyping Fragaria and its use in a survey of 60 F. 9 ananassa cultivars. Mol Breeding 22:649–661 Graham J, McNicol RJ, McNicol JW (1996) A comparison of methods for the estimation of genetic diversity in strawberry cultivars. Theor Appl Genet 93:402–406
123
Euphytica (2010) 173:63–75 Guilford P, Prakash S, Zhu JM, Rikkerink E, Gardiner S, Bassett H, Forster R (1997) Microsatellites in Malus 9 domestica (apple): abundance, polymorphism and cultivar identification. Theor Appl Genet 94:249–254 Gupta PK, Balyan IS, Sharma PC, Ramesth B (1996) Microsatellites in plants—a new class of molecular markers. Curr Sci 70:45–54 Hancock JF, Callow PA, Shaw DV (1994) Randomly amplified polymorphic DNAs in the cultivated strawberry, Fragaria 9 ananassa. J Am Soc Hortic Sci 119:862–864 Kuras A, Korbin M, Zurawicz E (2004) Comparison of suitability of RAPD and ISSR techniques for determination of strawberry (Fragaria 9 ananassa Duch.) relationship. Plant Cell Tissue Organ Cult 79:189–193 Lewers KS, Styan SMN, Hokanson SC, Bassil NV (2005) Strawberry genbank-derived and genomic simple sequence repeat (SSR) markers and their utility with strawberry, blackberry, and red and black raspberry. J Am Soc Hortic Sci 130:102–115 Lian C, Nara K, Nakaya H, Zhou Z, Wu B, Miyashita N, Davis TM (2001) Development of microsatellite markers in polyploid Salix reinii. Mol Ecol Notes 1:160–161 MacPherson JM, Eckstein PE, Scoles GJ, Gajadhar AA (1993) Variability of the random amplified polymorphic DNA assay among thermal cyclers, and effects of primer and DNA concentration. Mol Cell Probes 7:293–299 Marchese A, Tobutt KR, Caruso T (2005) Molecular characterisation of Sicilian Prunus persica cultivars using microsatellites. J Hortic Sci Biotechnol 80:121–129 Milella L, Saluzzi D, Lapelosa M, Bertino G, Spada P, Greco I, Martelli G (2006) Relationships between an Italian strawberry ecotype and its ancestor using RAPD markers. Genet Resour Crop Evol 53:1715–1720 Monfort A, Vilanova S, Davis TM, Aru´s P (2006) A new set of polymorphic simple sequence repeat (SSR) markers from a wild strawberry (Fragaria vesca) are transferable to other diploid Fragaria species and to Fragaria 9 ananassa. Mol Ecol Notes 6:197–200 Morgante M, Olivieri AM (1993) PCR-amplified microsatellites as markers in plant genetics. Plant J 3:175–182 Myakishev MV, Khripin Y, Hu S, Hamer DH (2001) Highthroughput SNP genotyping by allele-specific PCR with universal energy-transfer-labeled primers. Genome Res 11:163–169 Perez T, Albornoz J, Dominguez A (1998) An evaluation of RAPD fragment reproducibility and nature. Mol Ecol 7:1347–1357 Rabouam C, Comes AM, Bretagnolle V, Humbert J, Perique G, Bigot Y (1999) Features of DNA fragments obtained by random amplified polymorphic DNA (RAPD) assays. Mol Ecol 8:493–503 Sargent DJ, Hadonou AM, Simpson DW (2003) Development and characterization of polymorphic microsatellite markers from Fragaria viridis, a wild diploid strawberry. Mol Ecol Notes 3:550–552 Sargent DJ, Clarke J, Simpson DW, Tobutt KR, Aru´s P, Monfort A, Vilanova S, Denoyes-Rothan B, Rousseau M, Folta KM, Bassil NV, Battey NH (2006) An enhanced microsatellite map of diploid Fragaria. Theor Appl Genet 112:1349–1359
Euphytica (2010) 173:63–75 Sarri V, Baldoni L, Porceddu A, Cultrera NGM, Frediani M, Belaj A, Trujillo I, Cionini PG (2006) Microsatellite markers are powerful tools for discriminating among olive cultivars and assigning them to geographically defined populations. Genome 49:1606–1615 Slovin JP, Schmitt K, Folta KM (2009) An inbred line of the diploid strawberry Fragaria vesca f. semperflorens for
75 genomic and molecular genetic studies in the Rosaceae. BMC Plant Methods 5:15 Tyrka M, Dziadczyk P, Hortyn´ski J (2002) Simplified AFLP procedure as a tool for identification of strawberry cultivars and advanced breeding lines. Euphytica 125:273–280
123
