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Assessment of genetic diversity of Czech sweet cherry cultivars using microsatellite markers
Biochemical Systematics and Ecology 63 (2015) 6e12
Contents lists available at ScienceDirect
Biochemical Systematics and Ecology journal homepage: www.elsevier.com/locate/biochemsyseco
Assessment of genetic diversity of Czech sweet cherry cultivars using microsatellite markers k a Kamal Sharma a, *, Haibo Xuan b, Petr Sedla a
Department of Genetics and Breeding, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences, Prague, Czech Republic b Kompetenzzentrum Obstbau-Bodensee, Schuhmacherhof 6, Ravensburg, Germany
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 June 2015 Received in revised form 10 September 2015 Accepted 12 September 2015 Available online 26 September 2015
We analyzed 24 sweet and wild cherry genotypes collected in Czech Republic to determine genetic variation, using previously described 16 SSR primers to adapt a fast, reliable method for preliminary screening and comparison of sweet cherry germplasm collections. All SSRs were polymorphic and they were able all together to distinguish unambiguously the genotypes. These SSR primers generated 70 alleles; the number of alleles per primer ranged from 2 to 7, with a mean of 4.4 putative alleles per primer combination. The primer UDP-98-412 gave the highest number of polymorphic bands (totally 7), while Empa2 and Empa3 gave the lowest number (2). The allele frequency varied from 2.1% to 87.5%. We observed 10% of unique alleles at different loci. The observed heterozygosity value ranged from 0.25 to 0.96 with an average of 0.72 while expected heterozygosity value varied from 0.22 to 0.75 with an average of 0.59. The PIC value ranged from 0.21 to 0.71 with a mean value of 0.523. Cluster analysis separated the investigated cultivars in two groups. High level of genetic diversity obtained in the collection and proved to be sufficiently genetically diverse and therefore these genotypes would be useful to breeders for the development of new cherry cultivars. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Prunus avium Sweet cherry Germplasm evaluation Microsatellites
1. Introduction The sweet cherry (Prunus avium L.) is a perennial plant having high level of heterozygosity originated in north eastern part of Turkey, near the Black Sea region (Zohary and Hopf, 2000). At present sweet cherry is cultivated across the Europe and in western Asian areas (Webster, 1996). The excellent favorable climatic conditions potentially increased domestication of wild cherry across the Europe (Wunsch and Hormaza, 2002). The annual production of sweet cherry is continuously increasing due to favorable climatic conditions and continuous demand for export marketing. A number of different sweet cherry varieties are grown at different zones of Czech Republic and also maintained at Research and Breeding Institute of Pomology (RBIP) Holovousy. The accurate description of genetic diversity in natural and artificial populations and identification of concrete sweet cherry genotypes rely on molecular techniques since morphological traits are easily influenced by environmental and
* Corresponding author. E-mail address: [email protected] (K. Sharma). http://dx.doi.org/10.1016/j.bse.2015.09.013 0305-1978/© 2015 Elsevier Ltd. All rights reserved.
K. Sharma et al. / Biochemical Systematics and Ecology 63 (2015) 6e12
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agronomical factors (Struss et al., 2001) and also offer several advantages over conventional phenotypic markers and has impact also in breeding programs and orchard establishment (Xuan et al., 2009). The identity and distinction of cultivars entirely depends upon the easily accessible and reliable genetic markers used for efficient breeding methods such as genetic identification, removal of duplications, fruit quality, tree growth characteristics and other traits (Galli et al., 2005). The genotype grouping and identification has been successfully applied jointly using phenotypic data and molecular markers (Ganopoulos et al., 2011). Isozymes were the first genetic markers applied for sweet cherry characterization and unique genetic profiles were studied by Granger et al. (1993) and Beaver et al. (1995). Later, cultivars were also differentiated based on RAPD markers by Gerlach and Stosser (1997). Apart from isozyme and RAPD, chloroplast and nuclear markers were also developed to study genetic diversity and phylogenetic analysis on cherries (Turkec et al., 2006). Among various molecular markers, SSR are highly polymorphic and abundant in eukaryotic genomes. Therefore, microsatellite markers are the best choice which are codominant and have advantages over other molecular markers due to their robustness and reproducibility. SSR analysis provides useful information for genotyping individual plants or cultivars and exploring genetic relatedness between accessions. Microsatellite markers have been used extensively for finger-printing purposes due to their high polymorphism and reproducibility (Wunsch and Hormaza, 2002; Fajardo et al., 2013). The most commonly used SSR primers in Prunus were derived from peach (Dirlewanger et al., 2002), sweet cherry (Sosinski et al., 2000; Dirlewanger et al., 2002) and sour cherry (Lacis et al., 2009). Several SSRs have been developed to determine the genetic relatedness in all Prunus species (Lacis et al., 2009; Antonius et al., 2012). The SSR are transferable among Prunus therefore same SSR primers are used for the detection of intra-species variation in related species (Dirlewanger et al., 2002; Wunsch and Hormaza, 2002). In sweet cherries, microsatellites have been used for germplasm characterization (Lacis et al., 2009), determination of genetic diversity (Dirlewanger et al., 2002; Wunsch and Hormaza, 2004), germplasm management (Wunsch and Hormaza, 2002), parentage analysis (Schueler et al., 2003), cultivar identification (Xuan et al., 2009) and mapping genetic linkage (Olmstead et al., 2008). The objective of this study is comparison of genetic diversity value in modern cultivars, old cultivars and wild germplasm originated from Czech collection at RBIP with the aim to interpret this in connection to present time status of sweet cherry germplasm.
2. Materials and methods 2.1. Plant material and DNA extraction Twenty two sweet cherry genotypes from breeding collection of the Research and Breeding Institute of Pomology (RBIP) Holovousy (Czech Republic) and two wild cherry genotypes collected from Prague west region were studied. All of the ac’ which is of German origin but in the past extensively grown cessions originated from Czech Republic except ‘Hedelfingenska in Czech. Genomic DNA was extracted from fresh young leaves following the protocol described by Hormaza (2002). DNA samples were diluted to concentration 1 ng/ml and stored at 20 C (Table 1). Table 1 Studied cultivars of Czech Republic with their pedigree. Sl.No.
Genotype
Pedigree
Origin
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Gran at Tim Amid Sandra Vilma Fabiola (Hedelfinger) Hedelfingenska Falesna Vanda Marta pozdní Chlumecka lka Ade Halka Justyna Lívia Elza Felicita Irena Christiana Debora Sylvana Horka Tamara 159 (wild) 161 (Wild)
Random seedling Krupnoplodnaja x Van Kordia x Vic Kordia x Seedling 13 Kordia x Vic Van x Kordia Random seedling Van x Kordia nka (Early Rivers) Kordia x Kasta Random seedling Knauffs Schwarze x Gran at Van x Stella Kordia x Starking Hardy Giant chlovicka I (Ziklova) Open pollination of Te Kordia x Starking Hardy Giant Krupnoplodnaja x Stella Kordia x Merton Reward Van x Kordia Kordia x Merton Reward Kordia x Van Open pollination of Van Krupnoplodnaja x Van Random seedling Random seedling
Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Germany Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic
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K. Sharma et al. / Biochemical Systematics and Ecology 63 (2015) 6e12
2.2. SSR markers The selection of SSR markers (Table 2) were as described by Aranzana et al. (2003), Schueler et al. (2003) and Vaughan and Russell (2004). The primers were selected based on amplification intensity, level of polymorphism with minimum noise of unspecific peaks and mutual compatibility for multiplex PCR. 2.3. PCR amplification and microsatellites analysis PCR reactions were performed according to the protocols in the genetic lab of Kompetenzzentrum Obstbau-Bodensee (KOB), Ravensburg, Germany as described by Xuan et al. (2009). PCRs were performed in a volume of 8 ml containing 1 ng of genomic DNA, Golden Taq 10, 1.5 mM of MgCl2 (Applied Biosystems), 0.1 mm of each primer pair (Eurofins) and 0.1 U of Taq DNA polymerase (GE healthcare). PCR amplification were carried out in a Eppendorf mastercycler (Eppendorf) with following PCR conditions: 94 C for 3 min, followed by 37 cycles of 1 min at 94 C, 1 min at 56 C, 2 min at 72 C and a final extension cycle for 7 min at 72 C. Multiplex PCR were set up using 3 to 4 primers in one PCR reaction. The forward primer for each pair was labelled either with Cy5 or Cy5.5. Microsatellite fragments were detected and analyzed by capillary electrophoresis using the Beckman CEQ 8000 (Beckman Coulter, Inc.) by comparison with internal size standard of 400. Cluster analyses were performed with the BioSci-software, to obtain the comparative dendrogram. Heterozygosity and homozygosity was calculated according to the method of Weir (1990). Polymorphic information content (PIC) or gene diversity was calculated using online software (Nagy et al., 2012). 3. Results and discussions In this study, 16 microsatellite primer pairs were applied to access the genetic diversity of 24 sweet cherry cultivars of Czech origin. One cultivar ‘Hedelfingensk a’ of German origin was also considered along with two wild cherry genotypes for comparative analysis. Compatibility of these primers were checked as described by Xuan et al. (2009) and screened for multiplex PCR. All of the primer pairs used generated amplification products (Table 2). The 16 primers generated a total of 70 alleles. The number of alleles per locus ranged from 2 (Empa2) to 7 (UDP-98-412) with an average of 4.4 alleles per locus (Table 3). The lowest alleles were obtained by Empa2 and Empa3 locus, whereas the highest alleles were obtained by UDP-98412 locus. The primer pairs EmpaS12, EmpaS01, Empa2, UDP-98-412, and Empa17 were monomorphic for most of the samples evaluated. Thirteen of the primers (94.28%) were reproducible and produced clear polymorphic bands for all loci. Most of the observed band sizes were in agreement with the expected size ranges (Table 2). The results of the genetic structure in the microsatellite loci revealed important genetic diversity. The allele frequency (pi) varied from 2.1% to 87.5% among the cultivars used in this study (Table 3). All the polymorphic alleles identified were common to cultivars of both Czech and German origin ‘Hedelfingensk a’. The similar range of allelic frequency has been reported in Lithuanian cultivars by Stanys et al. (2012). The alleles with frequency values of pi 10% were established as rare alleles. Among a total of 70 alleles, 7 were identified as rare. The unique alleles were found at the locus Cppct22, UDP98-412, EmpaS10, Empa026, Bppct37, EmpaS06 and UDP98-410. The presence of unique alleles also defines genetic individuality of plant populations which implies the existence of the genetic variation required to adapt ecological conditions. However unique alleles cultivars did not grouped into one clade except Tim (Fig. 1). The probable explanation is that number of unique alleles are lower and on only few loci. The allelic size ranges are lower than with those reported by
Table 2 Genetic diversity parameters calculated for 16 SSR markers in 24 Prunus accessions. Marker
Size range
All. No.
Ho
He
PIC
EMPA002 EMPA003 EMPA017 EMPA026 UDP98411 UDP98412 UDP98410 EMPaS01 EMPaS02 EMPaS06 Bppct37 Cppct6 Cppct22 EMPaS10 EMPaS12 EMPaS14 Total average
104e106 169e173 234e242 203e234 151e163 99e125 121e129 228e238 138e146 202e222 134e152 183e203 245e257 152e182 121e145 196e210
2 2 3 4 3 7 4 5 5 6 6 6 4 5 5 3 70 4.38
0.75 0.54 0.25 0.79 0.58 0.79 0.58 0.79 0.92 0.79 0.96 0.54 0.92 0.79 0.67 0.88
0.50 0.49 0.22 0.55 0.57 0.64 0.57 0.59 0.69 0.74 0.75 0.61 0.59 0.62 0.69 0.60
0.38 0.37 0.21 0.45 0.51 0.58 0.49 0.51 0.64 0.71 0.71 0.58 0.5 0.55 0.64 0.53
K. Sharma et al. / Biochemical Systematics and Ecology 63 (2015) 6e12
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Table 3 Alleles and their frequencies in the Czech Republic cultivars. EmpaS12
EmpaS14
EmpaS01
Empa2
EmpaS02
Cppct6
Cppct22
UDP-98-412
Size (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency %
121 135 137 143 145
4.1 10.4 33.3 41.7 10.4
54.1 27.1 18.8
39.6 2.1 50.0 4.2 4.2
50.0 50.0
45.8 18.8 4.2 6.3 25.0
10.4 58.3 8.3 2.1 16.7 4.2
50.0 39.6 8.3 2.1
6.3 4.2 2.1 29.2 52.1 4.2 2.1
EmpaS10
196 198 210
Empa026
28 230 232 234 238
Bppct37
104 106
Empa3
138 140 142 144 146
EmpaS06
183 185 197 199 201 203
UDP98-410
245 253 255 257
UDP98-411
99 111 115 117 119 121 125
Empa17
Size (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency %
152 154 162 166 182
47.9 2.1 6.3 37.5 6.3
39.6 2.1 54.2 4.2
8.3 18.8 20.8 39.6 10.4 2.1
43.7 56.3
16.7 10.4 41.7 12.5 16.7 2.1
35.4 2.1 54.2 8.3
22.9 58.3 18.8
2.1 87.5 10.4
203 205 218 234
134 138 140 144 150 152
169 173
202 204 206 216 218 222
121 125 127 129
151 159 163
234 240 242
Guarino et al. (2009) using Empa002, Empa003 primer pairs. This could be due to the fact that the allelic size ranges are dependent on the genotypes used in the study. Locus EmpaS06 as reported in previous study (Stanys et al., 2012) was 2nd most informative among our cultivars and so it really seems to be very suitable for sweet cherry genotype identification. In number of cultivars only one PCR fragment was identified. In such loci the cultivars were presumed to be homozygous. Cultivars used in the study were identified to be homozygous in 2e8 loci. The least homozygous were ‘Hedelfingensk a’, pozdní’ and ‘Debora’ (homozygous in 2 loci). The most homozygous was ‘Tamara’ (homozygous ‘Falesna Vanda’, ‘Chlumecka in 8 loci). The observed heterozygosity (Ho) value among the cultivars varied from 0.25 to 0.96 (0.72 on average). The expected heterozygosity (He) value varied from 0.22 to 0.75 (0.59 on average). Our results are in accordance with other studies across Prunus. The average He values obtained in other studies on sweet cherry were 0.46 (Clarke and Tobutt, 2003), 0.60 (Vaughan and Russell, 2004), 0.68 (Ganopoulos et al., 2011) and 0.65 (Stanys et al., 2012). The Ho was higher than He for the majority (14) of loci (Table 2) evaluated in Czech cultivars. We assume that higher heterozygosity can be required in natural selection and that in artificial breeding process He is higher more likely due to using of geographically and thus genetically distant genotypes. The observed heterozygosity value was lower than the expected heterozygosity value for the EMPAS12 and Cppct6 loci. In studied cultivars, only the EmpaS14, Empa2, Cppct22, Empa3 and Empa17 loci were scored as reasonably informative, whereas the remaining loci were highly informative. Meanwhile, the UDP-98-412 locus was the most informative. Despite few loci cultivars resulted in higher number of alleles per locus, the study demonstrated the higher genetic diversity and heterozygosity. The Polymorphic Information Content (PIC) value ranged from 0.21 to 0.71 with a mean value of 0.523. The highest PIC value was identified for Empa17 locus among the cultivars. The PIC also validates the higher gene diversity in accordance with Xuan et al. (2009). The genotype under work name ‘Falesna Vanda’ of presumptive pedigree Van x Kordia was analyzed for correct identification (there was unclear identity; in morphology it is very close to variety ‘Vanda’ (S1S6), but we detected S-genotype S4S6). We compared its SSR profile with ‘Vanda’ SSR profiles available at KOB, Germany database (Xuan et al., 2009). The SSR profiles distinctly varied from ‘Vanda’. The hypothesis of identity and potential mutation in S locus was rejected and now we have to identify the genotype by comparison of ‘Falesna Vanda’ SSR profile to SSR profiles of S4S6 varieties in collections of RBIP. 3.1. Genetic diversity Based on the 16 polymorphic loci, dendrogram was created which separated the genotypes into two main clusters. The first cluster consisted of 16 genotypes labelled as group 1 and the second cluster consisted of eight genotypes labelled as group 2 (Fig. 1). Both main groups contain new varieties, but according analysis of pedigrees there is some geographical trend in genetic variability explaining partially the structure of dendrograms. Czech breeding in last 50 years extensively using cultivar ‘Kordia’ and this ancestor is typical equal for both of groups. Group I is more affected by west side germplasms, mainly originated in Canada (Van, Stella, Vic), USA (Starking Hardy Giant) and UK (Merton Reward), but in group II is perceptible higher effect of European germplasms. Representatives of northern and central Europe are ‘Early Rivers’ (UK), ‘Knauffs t’ and ‘Ziklova’ (both Czech) and eastern Europe germplasm representing Krupnoplodnaja Schwarze’ (Germany), ‘Grana (Ukraine). ’ and In group 1, there is present one specific clade containing wild germplasm and old cultivars ‘Hedelfingenska ’, both known as random seedlings, originated probably in 19th century and representing almost the oldest ‘Chlumecka valuable culture germplasm in the region. We assume that these old varieties were originated probably very close to wild
10 K. Sharma et al. / Biochemical Systematics and Ecology 63 (2015) 6e12
Fig. 1. Dendrogram of 24 P. avium cultivars constructed using BioSci-software.
K. Sharma et al. / Biochemical Systematics and Ecology 63 (2015) 6e12
11
t’ population as random seedlings with very good productiveness and quality. On the other side next random seedling ‘Grana placed to main group II was selected as random seedling in second half of 20th century and its genetic value can be affected by many different already existing grown cultivars. Distinctness of cultivars is very important character and here SSR profiles and dendrogram created were used to detect if any cultivars are genetically identical. In group II the cultivar ‘Fabiola’ and ‘Justyna’ are half-sibs grouped into one cluster likely cultivars ‘Tamara’ and ‘Felicita’. Conversely, ‘Tamara’ and ‘Tim’ mentioned as full sibs from pedigree ‘Krupnoplodnaja’ x ‘Van’ have been evaluated as highly distinctive and placed to different clusters (‘Tim’ into group I and ‘Tamara’ into group II). This indicated some problem in cultivars pedigree. So, we analyzed S locus of these individuals to confirm expected pedigree. Whilst ‘Tamara’ (S1S9) expressed alleles of both expected parents (S6S9 x S1S3), Tim (S4S5) did not share any allele with anyone. This was strong result corresponding to SSR results commented in previous paragraphs and Tim's pedigree should be revised as random seedling. In past decade, SSR markers have been extensively used for molecular characterizations of sweet cherries. The studied accession revealed high polymorphism levels and high level of intra group variation which is in accord to previous reports (Dirlewanger et al., 2002; Wunsch and Hormaza, 2002). The high complexity and variation in the development of P. avium may be attributed due to continuous seed propagation by birds, natural hybridization between indigenous and introduced plants and human selection. The number of different SSR alleles identified in the Czech Republic has an enough amount of genetic variation which supports the effectiveness of microsatellite markers for the assessment of genetic diversity. Results indicate significant differences in geographically distinctive populations and also differences between cultivated and wild populations. The work also confirms the microsatellites are very useful tool for the identification and characterization of sweet cherry plant material, especially in large, diverse collections, as well as compilation and comparative analysis of research data or study of relations between different gene pools. This study will act as resources for future P. avium diversity management. Acknowledgment This study was supported by the Ministry of Agriculture of Czech Republic Project NAZV No. QJ1210275, by project CIGA CULS No. 20144301 and by the European Social Fund in the Czech Republic and Ministry of Education Youth and Sports and OP Education for Competitiveness ESF/MEYS CZ.1.07/2.3.00/30.0040. References Antonius, K., Aaltonen, M., Uosukainen, M., Hurme, T., 2012. Genotypic and phenotypic diversity in finish cultivated sour cherry (Prunus cerasus L.). Genet. Resour. Crop Evol. 59, 375e388. Aranzana, M.J., Pineda, A., Cossaon, P., Dirlewanger, E., Ascasibar, J., Cipriani, G., Ryder, C.D., Testolin, R., Abbott, A., King, G.J., Iezzoni, A.F., Arus, P., 2003. A set of simple-sequence repeat (SSR) markers covering the Prunus genome. Theor. Appl. Genet. 106, 819e825. Beaver, J.A., Iezzoni, A.F., Ramm, C.W., 1995. Isozyme diversity in sour, sweet and ground cherry. Theor. Appl. Genet. 90, 847e852. Clarke, J.B., Tobutt, K.R., 2003. Development and characterization of polymorphic microsatellites from Prunus avium ‘Napoleon’. Mol. Ecol. Notes 3, 578e580. Dirlewanger, E., Cosson, P., Tavaud, M., Aranzana, J., Poizat, C., Zanetto, A., Arus, P., Laigret, F., 2002. Development of microsatellite markers in peach [Prunus persica (L.) Batsch] and their use in genetic diversity analysis in peach and sweet cherry (Prunus avium L). Theor. Appl. Genet. 105, 127e138. Fajardo, D., Morales, J., Zhu, H., Steffan, S., Harbut, R., Bassil, N., Hummer, K., Polashock, J., Vorsa, N., Zalapa, J., 2013. Discrimination of American cranberry cultivars and assessment of clonal heterogeneity using microsatellite markers. Plant Mol. Biol. Rep. 31, 264e271. Galli, Z., Halasz, G., Kiss, E., Heszky, L., Dobranszki, J., 2005. Molecular identification of commercial apple cultivars with microsatellite markers. Hort Sci. 40, 1974e1977. Ganopoulos, I.V., Kazantzis, K., Chatzicharisis, I., Karayiannis, I., Tsaftaris, A.S., 2011. Genetic diversity, structure and fruit trait associations in Greek sweet cherry cultivars using microsatellite based (SSR/ISSR) and morpho-physiological markers. Euphytica 181, 237e251. Gerlach, H.K., Stosser, R., 1997. Patterns of random amplified polymorphic DNAs for sweet cherry (Prunus avium L.) cultivar identification. J. Appl. Bot. Angew. Bot. 71, 212e218. Granger, A.R., Clarke, G.R., Jackson, J.F., 1993. Sweet cherry cultivar identification by leaf isozyme polymorphism. Theor. Appl. Genet. 86, 458e464. Guarino, C., Santoro, S., De, S.L., Cipriani, G., 2009. Prunus avium: nuclear DNA study in wild populations and sweet cherry cultivars. Genome 52, 320e337. Hormaza, J.I., 2002. Molecular characterization and similarity relationships among apricot (Prunus armeniaca L.) genotypes using simple sequence repeats. Theor. Appl. Genet. 104, 321e328. Lacis, G., Rashal, I., Ruisa, S., Trajkovski, V., Iezzoni, A.F., 2009. Assessment of genetic diversity of Latvian and Swedish sweet cherry (Prunus avium L.) genetic resources collections by using SSR (microsatellite) markers. Sci. Hortic. 121, 451e457. k, I., Gorji, A.M., Hegedu } s, G., Taller, J., 2012. PICcalc: an online program to calculate polymorphic information content for molecular Nagy, S., Poczai, P., Cerna genetic studies. Biochem. Genet. 50, 670e672. Olmstead, J.W., Sebolt, A.M., Cabrera, A., Sooriyapathirana, S.S., Hammar, S., Iriarte, G., Wang, D., Chen, C.Y., van der Knaap, E., Iezzoni, A.F., 2008. Construction of an intra-specific sweet cherry (Prunus avium L.) genetic linkage map and synteny analysis with the Prunus reference map. Tree Genet. Genomes 4, 897e910. Schueler, S., Tusch, A., Schuster, M., Ziegenhagen, B., 2003. Characterization of microsatellites in wild and sweet cherry (Prunus avium L) markers for individual identification and reproductive processes. Genome 46, 95e102. Sosinski, B., Gannavarapu, M., Hager, L.D., Beck, L.E., King, G.J., Ryder, C.D., Rajapakse, S., Baird, W.V., Ballard, R.E., Abbot, T.A., 2000. Characterization of microsatellite markers in peach (Prunus persica L.). Theor. Appl. Genet. 97, 1034e1041. Stanys, V., Baniulis, D., Morkunaite-Haimi, S., Siksnianiene, J.B., Frercks, B., Gelvonauskiene, D., Stepulaitiene, I., Staniene, G., Siksnianas, T., 2012. Characterising the genetic diversity of Lithuanian sweet cherry (Prunus avium L.) cultivars using SSR markers. Sci. Hortic. 142, 136e142. Struss, D., Boritzki, M., Glozer, K., Southwick, S.M., 2001. Detection of genetic diversity among populations of sweet cherry. J. Hortic. Sci. Biotech. 76, 362e367. Turkec, A., Sayar, M., Heinze, B., 2006. Identification of sweet cherry cultivars (Prunus avium L.) and analysis of their genetic relationships by chloroplast sequence characterised amplified regions (cpSCAR). Genet. Resour. Crop Evol. 53, 1635e1641. Vaughan, S.P., Russell, K., 2004. Characterization of novel microsatellites and development of multiplex PCR for large-scale population studies in wild cherry, Prunus avium. Mol. Ecol. Notes 4, 429e431.
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Webster, A.D., 1996. The taxonomic classification of sweet and sour cherries and a brief history of their cultivation. In: Webster, A.D., Looney, N.E. (Eds.), Cherries. Cab International, Wallingford, pp. 3e25. Weir, B.S., 1990. Genetic Data Analysis-Methods for Discrete Population Genetic Data. Sinauer Associates Inc. Publishers, Sunderland, MA, USA. Wunsch, A., Hormaza, J.I., 2002. Molecular characterisation of sweet cherry (Prunus avium L.) genotypes using peach [Prunus persica (L) Batsch] SSR sequences. Heredity 89, 56e63. Wunsch, A., Hormaza, J.I., 2004. Cloning and characterization of genomic DNA sequences of four self-incompatibility alleles in sweet cherry (Prunus avium L). Theor. Appl. Genet. 108, 299e305. Xuan, H., Wang, R., Buchele, M., Moller, O., Hartmann, W., 2009. Microsatellite markers (SSR) as a tool to assist in identification of sweet (Prunus avium) and sour cherry (Prunus cerasus). In: Proc. 1st international symposium on Biotechnol. of Fruit Species. Eds. Hanke MV et al. Acta Hort. 839, ISHS. Zohary, D., Hopf, M., 2000. Domestication of Plants in the Old World, third ed. Oxford University Press, New York, p. 182.
Contents lists available at ScienceDirect
Biochemical Systematics and Ecology journal homepage: www.elsevier.com/locate/biochemsyseco
Assessment of genetic diversity of Czech sweet cherry cultivars using microsatellite markers k a Kamal Sharma a, *, Haibo Xuan b, Petr Sedla a
Department of Genetics and Breeding, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences, Prague, Czech Republic b Kompetenzzentrum Obstbau-Bodensee, Schuhmacherhof 6, Ravensburg, Germany
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 June 2015 Received in revised form 10 September 2015 Accepted 12 September 2015 Available online 26 September 2015
We analyzed 24 sweet and wild cherry genotypes collected in Czech Republic to determine genetic variation, using previously described 16 SSR primers to adapt a fast, reliable method for preliminary screening and comparison of sweet cherry germplasm collections. All SSRs were polymorphic and they were able all together to distinguish unambiguously the genotypes. These SSR primers generated 70 alleles; the number of alleles per primer ranged from 2 to 7, with a mean of 4.4 putative alleles per primer combination. The primer UDP-98-412 gave the highest number of polymorphic bands (totally 7), while Empa2 and Empa3 gave the lowest number (2). The allele frequency varied from 2.1% to 87.5%. We observed 10% of unique alleles at different loci. The observed heterozygosity value ranged from 0.25 to 0.96 with an average of 0.72 while expected heterozygosity value varied from 0.22 to 0.75 with an average of 0.59. The PIC value ranged from 0.21 to 0.71 with a mean value of 0.523. Cluster analysis separated the investigated cultivars in two groups. High level of genetic diversity obtained in the collection and proved to be sufficiently genetically diverse and therefore these genotypes would be useful to breeders for the development of new cherry cultivars. © 2015 Elsevier Ltd. All rights reserved.
Keywords: Prunus avium Sweet cherry Germplasm evaluation Microsatellites
1. Introduction The sweet cherry (Prunus avium L.) is a perennial plant having high level of heterozygosity originated in north eastern part of Turkey, near the Black Sea region (Zohary and Hopf, 2000). At present sweet cherry is cultivated across the Europe and in western Asian areas (Webster, 1996). The excellent favorable climatic conditions potentially increased domestication of wild cherry across the Europe (Wunsch and Hormaza, 2002). The annual production of sweet cherry is continuously increasing due to favorable climatic conditions and continuous demand for export marketing. A number of different sweet cherry varieties are grown at different zones of Czech Republic and also maintained at Research and Breeding Institute of Pomology (RBIP) Holovousy. The accurate description of genetic diversity in natural and artificial populations and identification of concrete sweet cherry genotypes rely on molecular techniques since morphological traits are easily influenced by environmental and
* Corresponding author. E-mail address: [email protected] (K. Sharma). http://dx.doi.org/10.1016/j.bse.2015.09.013 0305-1978/© 2015 Elsevier Ltd. All rights reserved.
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agronomical factors (Struss et al., 2001) and also offer several advantages over conventional phenotypic markers and has impact also in breeding programs and orchard establishment (Xuan et al., 2009). The identity and distinction of cultivars entirely depends upon the easily accessible and reliable genetic markers used for efficient breeding methods such as genetic identification, removal of duplications, fruit quality, tree growth characteristics and other traits (Galli et al., 2005). The genotype grouping and identification has been successfully applied jointly using phenotypic data and molecular markers (Ganopoulos et al., 2011). Isozymes were the first genetic markers applied for sweet cherry characterization and unique genetic profiles were studied by Granger et al. (1993) and Beaver et al. (1995). Later, cultivars were also differentiated based on RAPD markers by Gerlach and Stosser (1997). Apart from isozyme and RAPD, chloroplast and nuclear markers were also developed to study genetic diversity and phylogenetic analysis on cherries (Turkec et al., 2006). Among various molecular markers, SSR are highly polymorphic and abundant in eukaryotic genomes. Therefore, microsatellite markers are the best choice which are codominant and have advantages over other molecular markers due to their robustness and reproducibility. SSR analysis provides useful information for genotyping individual plants or cultivars and exploring genetic relatedness between accessions. Microsatellite markers have been used extensively for finger-printing purposes due to their high polymorphism and reproducibility (Wunsch and Hormaza, 2002; Fajardo et al., 2013). The most commonly used SSR primers in Prunus were derived from peach (Dirlewanger et al., 2002), sweet cherry (Sosinski et al., 2000; Dirlewanger et al., 2002) and sour cherry (Lacis et al., 2009). Several SSRs have been developed to determine the genetic relatedness in all Prunus species (Lacis et al., 2009; Antonius et al., 2012). The SSR are transferable among Prunus therefore same SSR primers are used for the detection of intra-species variation in related species (Dirlewanger et al., 2002; Wunsch and Hormaza, 2002). In sweet cherries, microsatellites have been used for germplasm characterization (Lacis et al., 2009), determination of genetic diversity (Dirlewanger et al., 2002; Wunsch and Hormaza, 2004), germplasm management (Wunsch and Hormaza, 2002), parentage analysis (Schueler et al., 2003), cultivar identification (Xuan et al., 2009) and mapping genetic linkage (Olmstead et al., 2008). The objective of this study is comparison of genetic diversity value in modern cultivars, old cultivars and wild germplasm originated from Czech collection at RBIP with the aim to interpret this in connection to present time status of sweet cherry germplasm.
2. Materials and methods 2.1. Plant material and DNA extraction Twenty two sweet cherry genotypes from breeding collection of the Research and Breeding Institute of Pomology (RBIP) Holovousy (Czech Republic) and two wild cherry genotypes collected from Prague west region were studied. All of the ac’ which is of German origin but in the past extensively grown cessions originated from Czech Republic except ‘Hedelfingenska in Czech. Genomic DNA was extracted from fresh young leaves following the protocol described by Hormaza (2002). DNA samples were diluted to concentration 1 ng/ml and stored at 20 C (Table 1). Table 1 Studied cultivars of Czech Republic with their pedigree. Sl.No.
Genotype
Pedigree
Origin
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Gran at Tim Amid Sandra Vilma Fabiola (Hedelfinger) Hedelfingenska Falesna Vanda Marta pozdní Chlumecka lka Ade Halka Justyna Lívia Elza Felicita Irena Christiana Debora Sylvana Horka Tamara 159 (wild) 161 (Wild)
Random seedling Krupnoplodnaja x Van Kordia x Vic Kordia x Seedling 13 Kordia x Vic Van x Kordia Random seedling Van x Kordia nka (Early Rivers) Kordia x Kasta Random seedling Knauffs Schwarze x Gran at Van x Stella Kordia x Starking Hardy Giant chlovicka I (Ziklova) Open pollination of Te Kordia x Starking Hardy Giant Krupnoplodnaja x Stella Kordia x Merton Reward Van x Kordia Kordia x Merton Reward Kordia x Van Open pollination of Van Krupnoplodnaja x Van Random seedling Random seedling
Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Germany Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic Czech Republic
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2.2. SSR markers The selection of SSR markers (Table 2) were as described by Aranzana et al. (2003), Schueler et al. (2003) and Vaughan and Russell (2004). The primers were selected based on amplification intensity, level of polymorphism with minimum noise of unspecific peaks and mutual compatibility for multiplex PCR. 2.3. PCR amplification and microsatellites analysis PCR reactions were performed according to the protocols in the genetic lab of Kompetenzzentrum Obstbau-Bodensee (KOB), Ravensburg, Germany as described by Xuan et al. (2009). PCRs were performed in a volume of 8 ml containing 1 ng of genomic DNA, Golden Taq 10, 1.5 mM of MgCl2 (Applied Biosystems), 0.1 mm of each primer pair (Eurofins) and 0.1 U of Taq DNA polymerase (GE healthcare). PCR amplification were carried out in a Eppendorf mastercycler (Eppendorf) with following PCR conditions: 94 C for 3 min, followed by 37 cycles of 1 min at 94 C, 1 min at 56 C, 2 min at 72 C and a final extension cycle for 7 min at 72 C. Multiplex PCR were set up using 3 to 4 primers in one PCR reaction. The forward primer for each pair was labelled either with Cy5 or Cy5.5. Microsatellite fragments were detected and analyzed by capillary electrophoresis using the Beckman CEQ 8000 (Beckman Coulter, Inc.) by comparison with internal size standard of 400. Cluster analyses were performed with the BioSci-software, to obtain the comparative dendrogram. Heterozygosity and homozygosity was calculated according to the method of Weir (1990). Polymorphic information content (PIC) or gene diversity was calculated using online software (Nagy et al., 2012). 3. Results and discussions In this study, 16 microsatellite primer pairs were applied to access the genetic diversity of 24 sweet cherry cultivars of Czech origin. One cultivar ‘Hedelfingensk a’ of German origin was also considered along with two wild cherry genotypes for comparative analysis. Compatibility of these primers were checked as described by Xuan et al. (2009) and screened for multiplex PCR. All of the primer pairs used generated amplification products (Table 2). The 16 primers generated a total of 70 alleles. The number of alleles per locus ranged from 2 (Empa2) to 7 (UDP-98-412) with an average of 4.4 alleles per locus (Table 3). The lowest alleles were obtained by Empa2 and Empa3 locus, whereas the highest alleles were obtained by UDP-98412 locus. The primer pairs EmpaS12, EmpaS01, Empa2, UDP-98-412, and Empa17 were monomorphic for most of the samples evaluated. Thirteen of the primers (94.28%) were reproducible and produced clear polymorphic bands for all loci. Most of the observed band sizes were in agreement with the expected size ranges (Table 2). The results of the genetic structure in the microsatellite loci revealed important genetic diversity. The allele frequency (pi) varied from 2.1% to 87.5% among the cultivars used in this study (Table 3). All the polymorphic alleles identified were common to cultivars of both Czech and German origin ‘Hedelfingensk a’. The similar range of allelic frequency has been reported in Lithuanian cultivars by Stanys et al. (2012). The alleles with frequency values of pi 10% were established as rare alleles. Among a total of 70 alleles, 7 were identified as rare. The unique alleles were found at the locus Cppct22, UDP98-412, EmpaS10, Empa026, Bppct37, EmpaS06 and UDP98-410. The presence of unique alleles also defines genetic individuality of plant populations which implies the existence of the genetic variation required to adapt ecological conditions. However unique alleles cultivars did not grouped into one clade except Tim (Fig. 1). The probable explanation is that number of unique alleles are lower and on only few loci. The allelic size ranges are lower than with those reported by
Table 2 Genetic diversity parameters calculated for 16 SSR markers in 24 Prunus accessions. Marker
Size range
All. No.
Ho
He
PIC
EMPA002 EMPA003 EMPA017 EMPA026 UDP98411 UDP98412 UDP98410 EMPaS01 EMPaS02 EMPaS06 Bppct37 Cppct6 Cppct22 EMPaS10 EMPaS12 EMPaS14 Total average
104e106 169e173 234e242 203e234 151e163 99e125 121e129 228e238 138e146 202e222 134e152 183e203 245e257 152e182 121e145 196e210
2 2 3 4 3 7 4 5 5 6 6 6 4 5 5 3 70 4.38
0.75 0.54 0.25 0.79 0.58 0.79 0.58 0.79 0.92 0.79 0.96 0.54 0.92 0.79 0.67 0.88
0.50 0.49 0.22 0.55 0.57 0.64 0.57 0.59 0.69 0.74 0.75 0.61 0.59 0.62 0.69 0.60
0.38 0.37 0.21 0.45 0.51 0.58 0.49 0.51 0.64 0.71 0.71 0.58 0.5 0.55 0.64 0.53
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Table 3 Alleles and their frequencies in the Czech Republic cultivars. EmpaS12
EmpaS14
EmpaS01
Empa2
EmpaS02
Cppct6
Cppct22
UDP-98-412
Size (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency %
121 135 137 143 145
4.1 10.4 33.3 41.7 10.4
54.1 27.1 18.8
39.6 2.1 50.0 4.2 4.2
50.0 50.0
45.8 18.8 4.2 6.3 25.0
10.4 58.3 8.3 2.1 16.7 4.2
50.0 39.6 8.3 2.1
6.3 4.2 2.1 29.2 52.1 4.2 2.1
EmpaS10
196 198 210
Empa026
28 230 232 234 238
Bppct37
104 106
Empa3
138 140 142 144 146
EmpaS06
183 185 197 199 201 203
UDP98-410
245 253 255 257
UDP98-411
99 111 115 117 119 121 125
Empa17
Size (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency Size % (bp)
Frequency %
152 154 162 166 182
47.9 2.1 6.3 37.5 6.3
39.6 2.1 54.2 4.2
8.3 18.8 20.8 39.6 10.4 2.1
43.7 56.3
16.7 10.4 41.7 12.5 16.7 2.1
35.4 2.1 54.2 8.3
22.9 58.3 18.8
2.1 87.5 10.4
203 205 218 234
134 138 140 144 150 152
169 173
202 204 206 216 218 222
121 125 127 129
151 159 163
234 240 242
Guarino et al. (2009) using Empa002, Empa003 primer pairs. This could be due to the fact that the allelic size ranges are dependent on the genotypes used in the study. Locus EmpaS06 as reported in previous study (Stanys et al., 2012) was 2nd most informative among our cultivars and so it really seems to be very suitable for sweet cherry genotype identification. In number of cultivars only one PCR fragment was identified. In such loci the cultivars were presumed to be homozygous. Cultivars used in the study were identified to be homozygous in 2e8 loci. The least homozygous were ‘Hedelfingensk a’, pozdní’ and ‘Debora’ (homozygous in 2 loci). The most homozygous was ‘Tamara’ (homozygous ‘Falesna Vanda’, ‘Chlumecka in 8 loci). The observed heterozygosity (Ho) value among the cultivars varied from 0.25 to 0.96 (0.72 on average). The expected heterozygosity (He) value varied from 0.22 to 0.75 (0.59 on average). Our results are in accordance with other studies across Prunus. The average He values obtained in other studies on sweet cherry were 0.46 (Clarke and Tobutt, 2003), 0.60 (Vaughan and Russell, 2004), 0.68 (Ganopoulos et al., 2011) and 0.65 (Stanys et al., 2012). The Ho was higher than He for the majority (14) of loci (Table 2) evaluated in Czech cultivars. We assume that higher heterozygosity can be required in natural selection and that in artificial breeding process He is higher more likely due to using of geographically and thus genetically distant genotypes. The observed heterozygosity value was lower than the expected heterozygosity value for the EMPAS12 and Cppct6 loci. In studied cultivars, only the EmpaS14, Empa2, Cppct22, Empa3 and Empa17 loci were scored as reasonably informative, whereas the remaining loci were highly informative. Meanwhile, the UDP-98-412 locus was the most informative. Despite few loci cultivars resulted in higher number of alleles per locus, the study demonstrated the higher genetic diversity and heterozygosity. The Polymorphic Information Content (PIC) value ranged from 0.21 to 0.71 with a mean value of 0.523. The highest PIC value was identified for Empa17 locus among the cultivars. The PIC also validates the higher gene diversity in accordance with Xuan et al. (2009). The genotype under work name ‘Falesna Vanda’ of presumptive pedigree Van x Kordia was analyzed for correct identification (there was unclear identity; in morphology it is very close to variety ‘Vanda’ (S1S6), but we detected S-genotype S4S6). We compared its SSR profile with ‘Vanda’ SSR profiles available at KOB, Germany database (Xuan et al., 2009). The SSR profiles distinctly varied from ‘Vanda’. The hypothesis of identity and potential mutation in S locus was rejected and now we have to identify the genotype by comparison of ‘Falesna Vanda’ SSR profile to SSR profiles of S4S6 varieties in collections of RBIP. 3.1. Genetic diversity Based on the 16 polymorphic loci, dendrogram was created which separated the genotypes into two main clusters. The first cluster consisted of 16 genotypes labelled as group 1 and the second cluster consisted of eight genotypes labelled as group 2 (Fig. 1). Both main groups contain new varieties, but according analysis of pedigrees there is some geographical trend in genetic variability explaining partially the structure of dendrograms. Czech breeding in last 50 years extensively using cultivar ‘Kordia’ and this ancestor is typical equal for both of groups. Group I is more affected by west side germplasms, mainly originated in Canada (Van, Stella, Vic), USA (Starking Hardy Giant) and UK (Merton Reward), but in group II is perceptible higher effect of European germplasms. Representatives of northern and central Europe are ‘Early Rivers’ (UK), ‘Knauffs t’ and ‘Ziklova’ (both Czech) and eastern Europe germplasm representing Krupnoplodnaja Schwarze’ (Germany), ‘Grana (Ukraine). ’ and In group 1, there is present one specific clade containing wild germplasm and old cultivars ‘Hedelfingenska ’, both known as random seedlings, originated probably in 19th century and representing almost the oldest ‘Chlumecka valuable culture germplasm in the region. We assume that these old varieties were originated probably very close to wild
10 K. Sharma et al. / Biochemical Systematics and Ecology 63 (2015) 6e12
Fig. 1. Dendrogram of 24 P. avium cultivars constructed using BioSci-software.
K. Sharma et al. / Biochemical Systematics and Ecology 63 (2015) 6e12
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t’ population as random seedlings with very good productiveness and quality. On the other side next random seedling ‘Grana placed to main group II was selected as random seedling in second half of 20th century and its genetic value can be affected by many different already existing grown cultivars. Distinctness of cultivars is very important character and here SSR profiles and dendrogram created were used to detect if any cultivars are genetically identical. In group II the cultivar ‘Fabiola’ and ‘Justyna’ are half-sibs grouped into one cluster likely cultivars ‘Tamara’ and ‘Felicita’. Conversely, ‘Tamara’ and ‘Tim’ mentioned as full sibs from pedigree ‘Krupnoplodnaja’ x ‘Van’ have been evaluated as highly distinctive and placed to different clusters (‘Tim’ into group I and ‘Tamara’ into group II). This indicated some problem in cultivars pedigree. So, we analyzed S locus of these individuals to confirm expected pedigree. Whilst ‘Tamara’ (S1S9) expressed alleles of both expected parents (S6S9 x S1S3), Tim (S4S5) did not share any allele with anyone. This was strong result corresponding to SSR results commented in previous paragraphs and Tim's pedigree should be revised as random seedling. In past decade, SSR markers have been extensively used for molecular characterizations of sweet cherries. The studied accession revealed high polymorphism levels and high level of intra group variation which is in accord to previous reports (Dirlewanger et al., 2002; Wunsch and Hormaza, 2002). The high complexity and variation in the development of P. avium may be attributed due to continuous seed propagation by birds, natural hybridization between indigenous and introduced plants and human selection. The number of different SSR alleles identified in the Czech Republic has an enough amount of genetic variation which supports the effectiveness of microsatellite markers for the assessment of genetic diversity. Results indicate significant differences in geographically distinctive populations and also differences between cultivated and wild populations. The work also confirms the microsatellites are very useful tool for the identification and characterization of sweet cherry plant material, especially in large, diverse collections, as well as compilation and comparative analysis of research data or study of relations between different gene pools. This study will act as resources for future P. avium diversity management. Acknowledgment This study was supported by the Ministry of Agriculture of Czech Republic Project NAZV No. QJ1210275, by project CIGA CULS No. 20144301 and by the European Social Fund in the Czech Republic and Ministry of Education Youth and Sports and OP Education for Competitiveness ESF/MEYS CZ.1.07/2.3.00/30.0040. References Antonius, K., Aaltonen, M., Uosukainen, M., Hurme, T., 2012. Genotypic and phenotypic diversity in finish cultivated sour cherry (Prunus cerasus L.). Genet. Resour. Crop Evol. 59, 375e388. Aranzana, M.J., Pineda, A., Cossaon, P., Dirlewanger, E., Ascasibar, J., Cipriani, G., Ryder, C.D., Testolin, R., Abbott, A., King, G.J., Iezzoni, A.F., Arus, P., 2003. A set of simple-sequence repeat (SSR) markers covering the Prunus genome. Theor. Appl. Genet. 106, 819e825. Beaver, J.A., Iezzoni, A.F., Ramm, C.W., 1995. Isozyme diversity in sour, sweet and ground cherry. Theor. Appl. Genet. 90, 847e852. Clarke, J.B., Tobutt, K.R., 2003. Development and characterization of polymorphic microsatellites from Prunus avium ‘Napoleon’. Mol. Ecol. Notes 3, 578e580. Dirlewanger, E., Cosson, P., Tavaud, M., Aranzana, J., Poizat, C., Zanetto, A., Arus, P., Laigret, F., 2002. Development of microsatellite markers in peach [Prunus persica (L.) Batsch] and their use in genetic diversity analysis in peach and sweet cherry (Prunus avium L). Theor. Appl. Genet. 105, 127e138. Fajardo, D., Morales, J., Zhu, H., Steffan, S., Harbut, R., Bassil, N., Hummer, K., Polashock, J., Vorsa, N., Zalapa, J., 2013. Discrimination of American cranberry cultivars and assessment of clonal heterogeneity using microsatellite markers. Plant Mol. Biol. Rep. 31, 264e271. Galli, Z., Halasz, G., Kiss, E., Heszky, L., Dobranszki, J., 2005. Molecular identification of commercial apple cultivars with microsatellite markers. Hort Sci. 40, 1974e1977. Ganopoulos, I.V., Kazantzis, K., Chatzicharisis, I., Karayiannis, I., Tsaftaris, A.S., 2011. Genetic diversity, structure and fruit trait associations in Greek sweet cherry cultivars using microsatellite based (SSR/ISSR) and morpho-physiological markers. Euphytica 181, 237e251. Gerlach, H.K., Stosser, R., 1997. Patterns of random amplified polymorphic DNAs for sweet cherry (Prunus avium L.) cultivar identification. J. Appl. Bot. Angew. Bot. 71, 212e218. Granger, A.R., Clarke, G.R., Jackson, J.F., 1993. Sweet cherry cultivar identification by leaf isozyme polymorphism. Theor. Appl. Genet. 86, 458e464. Guarino, C., Santoro, S., De, S.L., Cipriani, G., 2009. Prunus avium: nuclear DNA study in wild populations and sweet cherry cultivars. Genome 52, 320e337. Hormaza, J.I., 2002. Molecular characterization and similarity relationships among apricot (Prunus armeniaca L.) genotypes using simple sequence repeats. Theor. Appl. Genet. 104, 321e328. Lacis, G., Rashal, I., Ruisa, S., Trajkovski, V., Iezzoni, A.F., 2009. Assessment of genetic diversity of Latvian and Swedish sweet cherry (Prunus avium L.) genetic resources collections by using SSR (microsatellite) markers. Sci. Hortic. 121, 451e457. k, I., Gorji, A.M., Hegedu } s, G., Taller, J., 2012. PICcalc: an online program to calculate polymorphic information content for molecular Nagy, S., Poczai, P., Cerna genetic studies. Biochem. Genet. 50, 670e672. Olmstead, J.W., Sebolt, A.M., Cabrera, A., Sooriyapathirana, S.S., Hammar, S., Iriarte, G., Wang, D., Chen, C.Y., van der Knaap, E., Iezzoni, A.F., 2008. Construction of an intra-specific sweet cherry (Prunus avium L.) genetic linkage map and synteny analysis with the Prunus reference map. Tree Genet. Genomes 4, 897e910. Schueler, S., Tusch, A., Schuster, M., Ziegenhagen, B., 2003. Characterization of microsatellites in wild and sweet cherry (Prunus avium L) markers for individual identification and reproductive processes. Genome 46, 95e102. Sosinski, B., Gannavarapu, M., Hager, L.D., Beck, L.E., King, G.J., Ryder, C.D., Rajapakse, S., Baird, W.V., Ballard, R.E., Abbot, T.A., 2000. Characterization of microsatellite markers in peach (Prunus persica L.). Theor. Appl. Genet. 97, 1034e1041. Stanys, V., Baniulis, D., Morkunaite-Haimi, S., Siksnianiene, J.B., Frercks, B., Gelvonauskiene, D., Stepulaitiene, I., Staniene, G., Siksnianas, T., 2012. Characterising the genetic diversity of Lithuanian sweet cherry (Prunus avium L.) cultivars using SSR markers. Sci. Hortic. 142, 136e142. Struss, D., Boritzki, M., Glozer, K., Southwick, S.M., 2001. Detection of genetic diversity among populations of sweet cherry. J. Hortic. Sci. Biotech. 76, 362e367. Turkec, A., Sayar, M., Heinze, B., 2006. Identification of sweet cherry cultivars (Prunus avium L.) and analysis of their genetic relationships by chloroplast sequence characterised amplified regions (cpSCAR). Genet. Resour. Crop Evol. 53, 1635e1641. Vaughan, S.P., Russell, K., 2004. Characterization of novel microsatellites and development of multiplex PCR for large-scale population studies in wild cherry, Prunus avium. Mol. Ecol. Notes 4, 429e431.
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K. Sharma et al. / Biochemical Systematics and Ecology 63 (2015) 6e12
Webster, A.D., 1996. The taxonomic classification of sweet and sour cherries and a brief history of their cultivation. In: Webster, A.D., Looney, N.E. (Eds.), Cherries. Cab International, Wallingford, pp. 3e25. Weir, B.S., 1990. Genetic Data Analysis-Methods for Discrete Population Genetic Data. Sinauer Associates Inc. Publishers, Sunderland, MA, USA. Wunsch, A., Hormaza, J.I., 2002. Molecular characterisation of sweet cherry (Prunus avium L.) genotypes using peach [Prunus persica (L) Batsch] SSR sequences. Heredity 89, 56e63. Wunsch, A., Hormaza, J.I., 2004. Cloning and characterization of genomic DNA sequences of four self-incompatibility alleles in sweet cherry (Prunus avium L). Theor. Appl. Genet. 108, 299e305. Xuan, H., Wang, R., Buchele, M., Moller, O., Hartmann, W., 2009. Microsatellite markers (SSR) as a tool to assist in identification of sweet (Prunus avium) and sour cherry (Prunus cerasus). In: Proc. 1st international symposium on Biotechnol. of Fruit Species. Eds. Hanke MV et al. Acta Hort. 839, ISHS. Zohary, D., Hopf, M., 2000. Domestication of Plants in the Old World, third ed. Oxford University Press, New York, p. 182.