Next Article in Journal
Optimising Soilless Culture Systems and Alternative Growing Media to Current Used Materials
Previous Article in Journal
Willingness-to-Pay for Produce: A Meta-Regression Analysis Comparing the Stated Preferences of Producers and Consumers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Horticulturae
Volume 8
Issue 4
10.3390/horticulturae8040291
horticulturae-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Molecular Characterization of Prunus Cultivars from Romania by Microsatellite Markers

by
Anca Butiuc-Keul
1,2,
Ana Coste
3,*,
Dragoș Postolache
4,
Vasile Laslo
5,
Adela Halmagyi
3,
Victoria Cristea
6 and
Anca Farkas
1,2
1
Department of Molecular Biology and Biotechnology, Faculty of Biology and Geology, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania
2
Laboratory of Plant and Microbe Biotechnologies and Environmental Interactions, Center of Systems Biology, Biodiversity and Bioresources, Babeş-Bolyai University, 400006 Cluj-Napoca, Romania
3
NIRDBS, Institute of Biological Research Cluj-Napoca, Republicii St., 400015 Cluj-Napoca, Romania
4
National Institute for Research and Development in Forestry Marin Dracea, Boulevard Eroilor, 077190 Voluntari, Romania
5
Department of Environmental Engineering, Faculty of Environmental Protection, 410048 Oradea, Romania
6
Alexandru Borza Botanical Garden, Babeş-Bolyai University, 400015 Cluj-Napoca, Romania
*
Author to whom correspondence should be addressed.
Horticulturae 2022, 8(4), 291; https://doi.org/10.3390/horticulturae8040291
Submission received: 1 March 2022 / Revised: 22 March 2022 / Accepted: 26 March 2022 / Published: 30 March 2022
(This article belongs to the Topic Plant Breeding, Genetics and Genomics)
Browse Figures
Versions Notes

Abstract

:
In Romania, Prunus species have great economic and social importance. With the introduction of new cultivars arises the need to preserve and characterize the local Prunus germplasm. Thus, a set of 24 polymorphic SSRs were selected for the overall characterization, including 10 peach, 11 apricot and 5 nectarine cultivars. The average number of alleles per locus (Na = 1.958), in addition to overall observed (Ho = 0.299) and expected heterozygosity (He = 0.286) were lower or comparable to those reported in similar studies, probably explained by the smaller number of analyzed cultivars restricted to a smaller geographic area. Among 26 genotypes a total of 101 alleles were identified, of which 46 alleles were in peach, 55 in apricot and 40 in nectarine, respectively. Six alleles from six loci (CPPCT-030, Pchgms-003, Pchgms-004, Pchgms-010, UDP97-401, UDP98-405) were common to all taxonomic groups. The most informative loci were BPPCT-025, Pchgms-021 and UDP96-001 in peach; BPPCT-025, BPPCT-001 and UDP96-001 in nectarine; and BPPCT-002, BPPCT-025, Pchgms-004, Pchgms-020 and Pchgms-021 in apricot. Clustering and genetic similarity analysis indicated that the degree of interspecific divergence in peach and nectarine cultivars was less than that in peach and apricot. These results will be useful to prevent confusion between cultivars, to improve breeding strategies and to benefit the management of Prunus cultivars bred in Romania.

1. Introduction

Peaches (Prunus persica (L.) Batsch var. persica), apricots (Prunus armeniaca L.), and nectarines (Prunus persica (L.) Batsch var. nucipersica (Suckow) C. K. Schneid) are among the most important stone fruit crops in temperate climate zones. The centers of domestication of apricot cultivars are Central Asia, China and the Near East [1]. There are hypotheses assuming that several cultivars originated directly from the primary centers and others arose from the hybridization of genotypes from the secondary centers [2]. According to Kostina [3], apricot cultivars are classified into four major eco-geographical groups: Central Asian, Irano-Caucasian (with the richest variability), European and Dzhungar-Zailing (the Tien-Shan area on the border of Kazakhstan and China). The cultivars belonging to the European group show low diversity [4] and similar characteristics that make difficult the discrimination between cultivars [5] by traditional methods based on morphological descriptors. Thus, molecular markers could provide valuable tools for cultivar discrimination and selection of new genotypes. A different situation can be observed in the case of the Siberian apricot from China, where a relatively high level of genetic diversity was observed among populations, but at the same time, heterozygosity was reduced, most probably as a result of low-level inbreeding [6]. A high genetic diversity among cultivars was also found in Turkey, considered a rich source of diverse apricot germplasm [7], and also in Iran [8].
There is a similar situation in the cases of peaches and nectarines, with a low number of genotypes having been used for the selection of new varieties that have been cultivated around the world [9,10]. The diversity of these crops has been reduced by the use of varieties having parents from the same gene pool [11]. The United States peach cultivars are considered especially limited in their diversity [9], because most commercial cultivars originated from a few parental cultivars used in United States breeding programs in the early twentieth century. The result of these breeding practices was the erosion of the genetic variability of peaches [12,13,14]. Low level heterozygosity of peaches was also observed in Spain, where traditional varieties have been replaced by new ones from North America [15].
Considering the high genetic similarities among cultivars, priority should be given to find the most effective method of cultivar identification and discrimination. Hence, to avoid misidentification, efficient DNA fingerprinting tools are needed. SSRs (Simple Sequence Repeats) are one of the most powerful marker systems available for such applications because of characteristics such as high levels of polymorphism, codominant inheritance and high discrimination power [16]. Many SSR markers have been developed for different Prunus species [17], including peach and nectarine [18,19,20,21,22,23,24], and apricot [25,26]. Besides allowing DNA fingerprinting and phylogenetic relationship evaluation in Prunus germplasm [27,28,29], SSR markers have also proved to be useful for the identification of different peach and nectarine hybrids [30], the differentiation of closely related genotypes [31] and the identification of important agronomic traits through QTL mapping [32,33,34].
Over recent years, most of the Romanian publicly held Prunus collections were reorganized and the number of Prunus accessions increased, mainly due to the introduction of new selections and hybrids [35,36,37]. Despite their great importance, most stone fruit cultivars currently present in different Romanian collections have not been characterized by molecular methods. For this reason, we considered it appropriate and essential to perform a comprehensive screening of several apricot, peach and nectarine cultivars using SSR primers typically used for germplasm characterization in other countries, to further support breeding programs and good management practices in local collections.

2. Materials and Methods

2.1. Plant Material

In this study, 10 peach (Prunus persica (L.) Batsch var. persica), 11 apricot (Prunus armeniaca L.), and 5 nectarine (Prunus persica (L.) Batsch var. nucipersica (Suckow) C. K. Schneid) cultivars [38,39] were analysed by SSR markers. The cultivars were maintained in the germplasm collection of the Agricultural Research and Development Station, Oradea. Cultivars were described according to specific descriptors (Table 1, Genres Project 61—for peach cultivars, IPGRI—for apricot cultivars).

2.2. SSR Analysis

Five different individuals were analyzed for each cultivar. Leaves were collected and mixed, and genomic DNA was isolated using the CTAB method described by Doyle and Doyle [40]. Allcultivars were initially screened with 34 SSR primers widely used for the molecular characterization of Prunus species [18,19,20,22]. Further analyses were performed with those SSR primers that generated amplicons in all Prunus species and which did not amplify unspecific fragments.
The final molecular screening included 24 selected SSR markers (Table 2). Seven microsatellite markers (BPPCT-001, BPPCT-002, BPPCT-014, BPPCT-025, Pchgms-020, Pchgms-021 and UDP96-001) were also screened on an Applied Biosystems 3500 Genetic Analyzer (Thermo Fisher Scientific, Waltham, MA, USA). Each forward primer analyzed on 3500 Genetic Analyzer contained a 5′-M13 (TGTAAAACGACGGCCAGT) tail for universal dye labelling [41] (Table 2). PCR amplifications were performed for 24 SSRs using PCR mix containing 12.5 μL 2× DreamTaq Green PCR master mix (Thermo Fisher Scientific, Waltham, MA, USA), 10.25 μL nuclease-free water (Lonza, Switzerland), 25 pmol of each primer (Eurogentec, Belgium) and 25 ng of genomic DNA in a final volume of 25 μL. DNA amplification was performed according the following program: 1. T = 94 °C, 4 min; 2. T = 94 °C, 30 s; 3. primer annealing at 50–58 °C (variable for different primers), 40 s; 4. elongation T = 72 °C, 40 s; steps 2–4 were repeated 35 times; 5. final elongation T = 72 °C, 5 min. Amplicons were separated on 1.5% agarose (Cleaver Scientific, Warwickshire, UK) gel in 1× TBE buffer (Lonza, Switzerland) and stained with 0.5 μg/mL ethidium bromide (Thermo Fisher Scientific, Waltham, MA, USA). At least 2 independent PCR amplifications were performed for each primer.
For the SSR markers screened on Applied Biosystems 3500 Genetic Analyzer (Thermo Fisher Scientific, Waltham, MA, USA) a 12.5 µL PCR reaction mixture was used, containing about 25 ng of template DNA, 200 nM of M13 5′-labeled with a fluorescent dye (FAM, VIC, NED, PET), 65 nM of M13-tailed locus-specific primer, 200 nM of untailed locus-specific primer, 1 U of Taq polymerase (Thermo Fisher Scientific, Waltham, MA, USA), 1× PCR buffer with 2 mM MgCl2, and 200 µM of each dNTP (Thermo Fisher Scientific, Waltham, MA, USA). Finally, 1 µL of PCR product from each of the four M13 dyes was pooled with 0.24 µL of LIZ500 standard ladder (Thermo Fisher Scientific, Waltham, MA, USA) and 12 µL of formamide to create three SSRs multiplexes in total. The pooled mixture was analyzed on an Applied Biosystems 3500 Genetic Analyzer (Thermo Fisher Scientific, Waltham, MA, USA), and fragment sizes were scored using GeneMarker V2.7.0 (SoftGenetics LLC, State College, PA, USA).
The Excel Macro AutoBin v0.9 [42] developed by Franc Salin was used to analyze the raw data set of amplified microsatellite markers and to determine the ‘bin’ size for each allele.
GenAlEx v6.5 (Peakall, Smouse, 2012) [43] was used to estimate at each locus, over all loci and for each Prunus accession, the observed (Na) and effective (Ne) number of alleles, the Shannon’s information index (I), and the observed (Ho) and expected (He) heterozygosity, weighted on the number of samples (UHE).
A two-dimensional plot of principal coordinates analysis (PCoA) of the studied Prunus cultivars was generated by the GenAlEx v6.5 [43].
The unweighted pair group with arithmetic mean (UPGMA) dendrogram was produced with Poppr v2.8.2 [44]. The polymorphic information content (PIC) was calculated using the on-line Gene-Calc Tool [45].
A genetic similarity matrix was generated using Jaccard’s similarity coefficient [46] in the NTSYSpc SIMQUAL module [47].
Table
Table 2. Number of observed alleles per locus among Prunus cultivars.
Table 2. Number of observed alleles per locus among Prunus cultivars.
LocusPrimer Sequence (5′→ 3′) Repeat Motif ReferencesSize-Range (bp)Peach Apricot NectarineNo. of Unique Alleles PIC
BPPCT-001* AAT TCC CAA AGG ATG TGT ATG AG
CAG GTG AAT GAG CCA AAG C		(GA)27[22]133–17642360.8076
BPPCT-002* TCG ACA GCT TGA TCT TGA CC
CAA TGC CTA CGG AGA TAA AAG AC		(AG)25 196–221392120.9102
BPPCT-004CTG AGT GAT CCA TTT GCA GG
AGG GCA TCT AGA CCT CAT TGT T		(CT)22 175–23013140.7005
BPPCT-010AAA GCA CAG CCC ATA ATG C
GTA CTG TTA CTG CTG GGA ATG C		AG)4 GG(AG)10 125–15012130.5907
BPPCT-014* TTG TCT GCC TCT CAT CTT AAC C
CAT CGC AGA GAA CTG AGA GC		(AG)23 204–23321130.5916
BPPCT-025* TCC TGC GTA GAA GAA GGT AGC
CGA CAT AAA GTC CAA ATG GC		(GA)29 168–21554490.8769
CPPCT-022CAATTAGCTAGAGAGAATTATTGGACAAGAAGCAAGTAGTTTG(CT)28CAA (CT)20[20]250–80022140.6363
CPPCT-029CCAAATTCCAAATCTCCTAACATGATCAACTTTGAGATTTGTTGAA (CT)24 80–20021130.5513
CPPCT-030TGAATATTGTTCCTCAATTCCTCTAGGCAAGAGATGAGA(CT)30 175–22022230.5897
Pchgms-001GGG TAA ATA TGC CCA TTG TGC AAT C
GGA TCA TTG AAC TAC GTC AAT CCT C		(AC)12(AT)6[18]160–20012130.5898
Pchgms-003GGA TCA TTG AAC TAC GTC AAT CCT C
CAA CCT GTG ATT GCT CCT ATT AAA C		(CT)14 200–22022130.592
Pchgms-004ATC TTC ACA ACC CTA ATG TC
GTT GAG GCA AAA GAC TTC AAT		(CT)21 150–20023240.7002
Pchgms-010GGTCACGCATCCTTTCATTT
GACACCTCCATTTGTATCAAAGC		T19A10 [48]180–20012120.3743
Pchgms-011AAGCAATAAAACCAGCAGCAA
TCAATCAATTGGCATGTTCG
TTGAGGCCCACTTATTAGCC
CCCCCATTATTCAAACTTCTG		(TA)11 250–30011130.5907
Pchgms-012CGACACTTAGCTAGAAGTTGCCTTA
TCAAGCTCAAGGTACCAGCA		(CT)9(TC)20(CA)9 200–45023270.8245
Pchgms-020* AATTGCATCACAGCAAGAGC
GGGGGTTTGGTTAAGATCG
CCCTTACCCCCTTACCACTT		(TA)15(TC)11 265–28024260.8101
Pchgms-021* ACCACCATTTTGGCTCTCTG
ACCACCACAACCAAACCATT		(TA)14 289–30631340.703
Pchgms-022ATAATCCGGCAGGGGTCTTA
TTGGGGTTTGTCAGTATTTTACA		(GA)14(AT)9 100–50011120.2392
Pchgms-023CTGCCGAAAGCATTTTGAAT
GAGCTCATGGCAACACAGAA		(TTC)5 300–50012130.5769
UDP96-001* AGTTTGATTTTCTGATGCATCC
TGCCATAAGGACCGGTATGT		(CA)17[19]123–14631360.8096
UDP97-401TAAGAGGATCATTTTTGCCTTG
CCCTGGAGGACTGAGGGT		(GA)19 100–15012120.3648
UDP97-402TCCCATAACCAAAAAAAACACC
TGGAGAAGGGTGGGTACTTG		(AG)17 125–17011230.5874
UDP97-403CTGGCTTACAACTCGCAAGC
CGTCGACCAACTGAGACTCA		(AG)22 100–11011120.3744
UDP98-405ACGTGATGAACTGACACCCA
GAGTCTTTGCTCTGCCATCC		(AG)9 100–15023240.6975
Total 465540101
Average 1.92.31.74.2
* The forward primer that contained a 5′-M13 (TGTAAAACGACGGCCAGT) tail.

3. Results

The selected markers amplified a total of 101 alleles in the analysed Prunus cultivars, with a mean allelic richness of 55 alleles in apricot, 46 alleles in peach and 40 alleles in nectarine cultivars (Table 2). The percentage of polymorphic loci ranged from 66.67% in apricot cultivars to 45.85% in nectarines. The mean number of alleles per locus (Na) was higher in apricot cultivars (2.292), followed by peach (1.917) and nectarine cultivars (1.667). The most polymorphic loci were BPPCT-002 (12 alleles), BPPCT-025 (9 alleles), Pchgms-012 (7 alleles), BPPCT-001 (6 alleles), Pchgms-020 (6 alleles), and UDP96-001 (6 alleles) (Table 2). The electrophoretic patterns of several polymorphic markers are shown in the supplementary material (Figures S1–S8). Six common alleles were identified among all taxonomic groups as revealed by six loci (CPPCT-030, Pchgms-003, Pchgms-004, Pchgms-010, UDP97-401, UDP98-405) (Table S1). The estimated genetic diversity indices are shown in Table S2. The average value of unbiased heterozygosity (uHe) was highest in apricot cultivars (uHe = 0.369), followed by peach (uHe = 0.295) and nectarine (uHe = 0.253) (Table S2). Overall, 26 multilocus genotypes (MLGs) were observed in our data set and 67 private alleles identified. The highest number of private alleles was registered in apricot cultivars (43 private alleles/22 loci), while the lowest number of private alleles (6 private alleles/4 loci) was found in nectarine cultivars (Table S3). The analyzed SSR markers were highly polymorphic with PIC values ranging from 0.24 to 0.91 with an average of 0.63 (Table 2).
The UPGMA clustering analysis revealed two main groups in the analyzed Prunus germplasm (Figure 1). Cluster I was further divided into two specific clusters: Ia comprising peach cultivars and Ib with nectarine cultivars. Cluster II was also subdivided in two sub-groups comprising apricot cultivars with combined Romanian and North American origin (Figure 1). This clustering is justified by Jaccard’s similarity coefficient, with the highestvalues present between peach and nectarines, ranging from 0.26 to 0.39. The lowest genetic similarity was registered between apricot and peach cultivars, varying between 0.015 to 0.086 (Table S4).
This genetic clustering was also confirmed by principal coordinate analysis (PCoA) (Figure 2). The first and second principal coordinates accounted for 51.75% and 15.35% of the total variation, respectively, explaining the total genetic variation across 24 SSR loci in different Prunus cultivars.

4. Discussion

The primary aim of this study was to test and select a set of SSR markers suitable for analyzing genetic diversity among 26 Prunus genotypes pertaining to three different species (peach, apricot and nectarine) from a local Romanian collection, towards their conservation and utilization in future breeding programs. In this regard, 24 SSR primer pairs generating amplicons in all analyzed cultivars were tested, some of the band patterns being polymorphic.
These markers allowed the discrimination of 26 multilocus genotypes (MLGs) among analyzed Prunus cultivars. Altogether a total of 101 alleles were identified, of which 67 were private alleles and 34 were shared among the three Prunus species, with only 6 alleles common to all taxonomic groups.
The allele size in our study ranged from 133 to 176 bp for marker BPPCT-001, 196 to 221 bp for BPPCT-002, and 168 to 215 bp for BPPCT-025 (Tables S1 and S3). Other studies reported values between 128–168 bp [22], 166–229 bp [49] and 226–238 bp for BPPCT-002, and 178–202 bp for BPPCT-025 [22].
Our collection exhibited variable levels of overall genetic diversity compared to previous studies. The mean values for overall expected heterozygosity (He) in our collection ranged from 0.22 in nectarine to 0.28 in peach and 0.35 in apricot (Table S2). These data are comparable with those reported by Dettori et al., 2015 [17] (0.31 for peach and 0.27 for apricot), where 90 Prunus accessions belonging to five species were analyzed with 26 SSRs. Overall, the expected heterozygosity in Prunus accessions cultivated in Romania is weaker compared to that registered in Chilean (0.46 in nectarine and 0.57 in peach) [50] or Chinese (0.79 in apricot) [51] collections. Moreover, allelic richness is lower (Na = 1.6 to 2.3) than had earlier been reported (Na = 2.4 to 15.14) [4,51,52,53]. This might be explained by the lower number of genotypes, restricted to a smaller geographic area, analyzed in our study.
The most informative primers in our study were those targeting BPPCT-025, BPPCT-001, BPPCT-002, Pchgms-021, and UDP96-001 loci in peach cultivars, BPPCT-025, BPPCT-001 and UDP96-001 in nectarines, and BPPCT-002, BPPCT-025, Pchgms-004, Pchgms-020 and BPPCT-004 in apricot cultivars. Some of these primers generated either monomorphic or polymorphic patterns, as shown in previous studies [18,22,54,55,56].
Based on these results, we may conclude that the genetic diversity parameters reported here are lower or comparable with those previously described in different Prunus germplasms, showing a wide range of variation depending on the set of SSR markers used, the number of analyzed genotypes and the origin of analyzed accessions. These variations may be also influenced by the method of detection used [17].
Clustering analysis indicated a lower degree of interspecific divergence between peach and nectarine cultivars than between peach and apricot cultivars, also shown by Jaccard’s similarity coefficient. However, there was a separation of peach cultivars (subcluster Ia) from nectarines (subcluster Ib). Similar clustering results were reported by Rojas et al. (2008) [50]. The UPGMA cluster analysis shows the existence of two subclusters in apricot cultivars, with mixed Romanian and North American cultivars (Figure 1 and Figure 2). Subcluster IIb is composed mainly of Romanian validated apricot cultivars (Saturn, Sirena, Comandor and Rares) that were phenotypically selected for fruit quality within the improvement program performed during the period 1983–2006 at the Băneasa Research and Development Station for Fruit Tree Growing [16,57]. Subcluster IIa is composed of Romanian apricot cultivars (Carmela, Viorica) that were selected mainly for resistance to disease by hybridizing with North American varieties obtained from the Rutgers Fruit Research and Extension Center, Rutgers University (CR-2-63-1, CR-2-63-2, CR-24-12-1, CR-24-12-2, NJA13 and NJA20) [37,58]. In recent years, most apricot breeding programs in Europe used PPV-resistant cultivars from North America to introduce this trait into local germplasms [59]. While some associations between analyzed Prunus genotypes might be explained by complex crosses between local cultivars and American germplasm, for several genotypes this information is unknown.

5. Conclusions

In this study we used a set of 24 previously published SSRs markers for Prunus species and assessed their versatility in DNA fingerprinting 10 peach, 11 apricot and 5 nectarine cultivars from Romania. The average number of alleles per locus, and the overall observed and expected heterozygosity were lower or comparable to those reported in similar studies. Clustering and genetic similarity analysis showed that all genotypes are clustered in their respective taxonomic groups, outlining a lower interspecific divergence between peach and nectarine than between peach and apricot cultivars.
To our knowledge, this is the first study assessing the usefulness of microsatellite markers in the characterization of local Prunus collections comprising apricot, peach and nectarine cultivars. The results will support breeding programs to improve the management practices of local collections.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae8040291/s1. Table S1. Multilocus genotype summary inferred from 24 SSRs in 26 accessions belonging to three different Prunus species (10 peach, 11 apricot and 5 nectarine cultivars). Table S2. The main parameters over 24 SSR loci analyzed in peach, apricot and nectarine cultivars. Table S3. Summary of private alleles per locus for peach, apricot and nectarine cultivars. Table S4. Jaccard’s similarity coefficients among Prunus genotypes. Figure S1. Prunus electropherograms of polymorphic microsatellite marker BPPCT-001). Figure S2. Prunus electropherograms of polymorphic microsatelite marker BPPCT-002. Figure S3. Prunus electropherograms of polymorphic microsatelite marker BPPCT-014. Figure S4. Prunus electropherograms of polymorphic microsatelite marker BPPCT-025. Figure S5. Prunus electropherograms of polymorphic microsatelite marker Pchgms-020. Figure S6. Prunus electropherograms of polymorphic microsatelite marker Pchgms-021. Figure S7. Prunus electropherograms of polymorphic microsatelite marker UDP96-001. Figure S8. SSR markers in peach (a-BPPCT038; b-PMS067), apricot (c-pchcm3, d-pchcm10) and nectarine cultivars (e-CPPCT022, CPPCT029, CPPCT030; f-pchcm1, pchcm3, pchcm4); separation on 1.5% agarose gel stained with 0.5 μg/mL ethidium bromide. The order of the samples is shown in Table 1.

Author Contributions

Conceptualization, A.B.-K. and A.C.; methodology, A.B.-K., A.C. and D.P.; software, D.P.; validation, A.C. and D.P.; formal analysis, A.B.-K., A.C. and D.P.; investigation, A.B.-K., A.C., D.P. and V.L.; resources, V.L., A.B.-K., A.C. and D.P.; data curation, A.B.-K.; writing–original draft preparation, A.B.-K. and A.C.; writing–review and editing, A.B.-K., A.C., A.H., A.F. and V.C.; visualization, A.B.-K.; supervision, A.H. and A.F.; project administration, A.B.-K.; funding acquisition, A.C. and A.H. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported partially by the following projects: BIODIVERS 3-25N/2019 and BIOSERV PN19070304 of the core program PN2019-2022 and 22PFE/2018.

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article [and/or its Supplementary Materials].

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Vavilov, N.I. Phytogeographical basis of plant breeding. In: Chester KS (transl.), The origin, variation, immunity and breeding of cultivated plants. Chron. Bot. 1951, 13, 13–54. [Google Scholar]
  2. Rostova, N.S.; Sokolova, E.A. Variability of anatomical and morphological leaf characters in apricot (Armeniaca scop.) species and varieties. Bull. Appl. Bot. Genet. Plant Breed. 1992, 146, 74–86. (In Russian) [Google Scholar]
  3. Kostina, K.F. The use of varietal resources of apricots for breeding. Trud. Nikit. Bot. Sad. 1969, 40, 45–63. [Google Scholar]
  4. Martín, C.; Herrero, M.; Hormaza, J.I. Molecular Characterization of Apricot Germplasm from an Old Stone Collection. PLoS ONE 2011, 6, e23979. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Verde, I.; Abbott, A.G.; Scalabrin, S.; Jung, S.; Shu, S.; Marroni, F.; Zhebentyayeva, T.; Dettori, M.T.; Grimwood, J.; Cattonaro, F.; et al. The high-quality draft genome of peach (Prunus persica) identifies unique patterns of genetic diversity, domestication and genome evolution. Nat. Genet. 2013, 45, 487–494. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Wang, Z.; Kang, M.; Liu, H.; Gao, J.; Zhang, Z.; Li, Y.; Wu, R.; Pang, X. High-level genetic diversity and complex population structure of siberian apricot (Prunus sibirica L.) in China as revealed by nuclear SSR Markers. PLoS ONE 2014, 9, e87381. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Akpınar, A.E.; Koçal, H.; Ergül, A.; Kazan, K.; Şelli, M.E.; Bakır, M.; Aslantaş, S.; Kaymak, S.; Sarıbaş, R. SSR-based molecular analysis of economically important Turkish apricot cultivars. Genet. Mol. Res. 2010, 9, 324–332. [Google Scholar] [CrossRef]
  8. Khadivi-Khub, A.; Yarahmadi, M.; Jannatizadeh, A.; Ebrahimi, A. Genetic relationships and diversity of common apricot (Prunus armeniaca L.) based on simple sequence repeat (SSR) markers. Biochem. Syst. Ecol. 2015, 61, 366–371. [Google Scholar] [CrossRef]
  9. Scorza, R.; Sherman, W.B.; Lightner, G.W. Inbreeding and co-ancestry of low chill short fruit development period freestone peaches and nectarines produced by the University of Florida breeding program. Fruit Var. J. 1988, 43, 79–85. [Google Scholar]
  10. Llácer, G.; Alonso, M.; Rubio, M.J.; Batlle, I.; Iglesias, I.; Vargas, F.J.; García-Brunton, J.; Badenes, M.L. Situación del material vegetal del melocotonero utilizado en España. ITEA 2009, 105, 67–83. [Google Scholar]
  11. Aranzana, M.J.; Carbó, J.; Arús, P. Microsatellite variability in peach [Prunus persica (L.) Batsch] cultivar identification, marker mutation, pedigree inferences and population structure. Theor. Appl. Genet. 2003, 106, 1341–1352. [Google Scholar] [CrossRef] [PubMed]
  12. Aranzana, M.J.; Abbassi, E.-K.; Howad, W.; Arus, P. Genetic variation, population structure and linkage disequilibrium in peach commercial varieties. BMC Genet. 2010, 11, 69. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Cao, K.; Zheng, Z.; Wang, L.; Liu, X.; Zhu, G.; Fang, W.; Cheng, S.; Zeng, P.; Chen, C.; Wang, X.; et al. Comparative population genomics reveals the domestication history of the peach, Prunus persica, and human influences on perennial fruit crops. Genome Biol. 2014, 15, 415. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Abidi, W.; Moreno, M.A.; Gogorcena, Y. Genetic diversity among nectarine [Prunus persica (L.) Batsch] seedlings in agronomical and biochemical fruit quality traits Journal of new sciences. Agric. Biotechnol. 2017, 48, 2888–2896. [Google Scholar]
  15. Llácer, G.; Alonso, J.M.; Rubio-Cabetas, M.J.; Battle, I.; Iglesias, I.; Vargas, F.J.; García-Brunton, J.; Badenes, M.L. Peach industry in Spain. J. Am. Pomol. Soc. 2009, 63, 128–133. [Google Scholar]
  16. Morgante, M.; Olivieri, A. PCR-amplified microsatellites as markers in plant genetics. Plant J. 1993, 3, 175–182. [Google Scholar] [CrossRef]
  17. Dettori, M.T.; Micali, S.; Giovinazzi, J.; Scalabrin, S.; Verde, I.; Cipriani, G. Mining microsatellites in the peach genome: Development of new long-core SSR markers for genetic analyses in five Prunus species. SpringerPlus 2015, 4, 337. [Google Scholar] [CrossRef] [Green Version]
  18. Sosinski, B.; Gannavarapu, M.; Hager, L.D.; Beck, E.; King, G.J.; Ryder, C.D.; Rajapakse, S.; Baird, W.V.; Ballard, R.E.; Abbott, A.G. Characterization of microsatellite markers in peach [Prunus persica (L.) Batsch]. Theor. Appl. Genet. 2000, 101, 421–428. [Google Scholar] [CrossRef]
  19. Testolin, R.; Marrazo, T.; Cipriani, G.; Quarta, R.; Verde, I.; Dettori, T.; Pancaldi, M.; Sansavini, S. Microsatellite DNA in peach [Prunus persica (L.) Batsch] and its use in fingerprinting and testing the genetic origin of cultivars. Genome 2000, 43, 512–520. [Google Scholar] [CrossRef] [PubMed]
  20. Aranzana, M.J.; Garcia-Mas, J.; Carbó, J.; Arús, P. Development and variability of microsatellite markers in peach. Plant Breed. 2002, 121, 87–92. [Google Scholar] [CrossRef]
  21. Aranzana, M.J.; Pineda, A.; Cosson, P.; Dirlewanger, E.; Ascasibar, J.; Cipriani, G.; Ryder, C.D.; Testolin, R.; Abbott, A.; King, G.J.; et al. A set of simple-sequence repeat (SSR) markers covering the Prunus genome. Theor. Appl. Genet. 2003, 106, 819–825. [Google Scholar] [CrossRef] [PubMed]
  22. Dirlewanger, E.; Cosson, P.; Tavaud, M.; Aranzana, M.J.; Poizat, C.; Zanetto, A.; Arús, P.; Laigret, F. 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. 2002, 105, 127–138. [Google Scholar] [CrossRef] [PubMed]
  23. Ahmad, R.; Potter, D.; Southwick, S.M. Genotyping of peach and nectarine cultivars with SSR and SRAP molecular markers. J. Am. Soc. Hortic. Sci. 2004, 129, 204–210. [Google Scholar] [CrossRef] [Green Version]
  24. Wünsch, A.; Carrera, M.; Hormaza, J.I. Molecular characterization of local Spanish peach [Prunus persica (L.) Batsch] germplasm. Genet. Resour. Crop Evol. 2006, 53, 925–932. [Google Scholar] [CrossRef]
  25. Messina, R.; Lain, O.; Marrazzo, M.T.; Cipriani, G.; Testolin, R. New set of microsatellite loci isolated in apricot. Mol. Ecol. Notes 2004, 4, 432–434. [Google Scholar] [CrossRef]
  26. Maghuly, F.; Fernandez, E.B.; Ruthner, S.; Pedryc, A.; Laimer, M. Microsatellite variability in apricots (Prunus armeniaca L.) reflects their geographic origin and breeding history. Tree Genet. Genomes 2005, 1, 151–165. [Google Scholar] [CrossRef]
  27. Anderson, N.A. Diversity of Low Chill Peaches (Prunus persica) from Asia, Brazil, Europe and the USA. Master’s Thesis, Office of Graduate Studies of Texas A&M University, College Station, TX, USA, 2010. [Google Scholar]
  28. Fernández, I.; Martí, A.; Font i Forcada, C.; Kamali, K.; Rubio-Cabetas, M.J.; Wirthensohn, M.; Socias, I.; Company, R. Molecular analyses of evolution and population structure in a worldwide almond [Prunus dulcis (Mill.) D.A. Webb syn. P. amygdalus Batsch] pool assessed by microsatellite markers. Genet. Resour. Crop Evol. 2015, 62, 205–219. [Google Scholar] [CrossRef]
  29. Urrestarazu, J.; Errea, P.; Miranda, C.; Santesteban, L.G.; Pina, A. Genetic diversity of Spanish Prunus domestica L. germplasm reveals a complex genetic structure underlying. PLoS ONE 2018, 13, e0195591. [Google Scholar] [CrossRef] [Green Version]
  30. Comlekcioglu, S.; Kuden, A.B. Evaluation of peach and nectarine hybrids using SSR marker assisted selection approach. Acta Hortic. 2015, 1084, 105–111. [Google Scholar] [CrossRef]
  31. Minas, I.S.; Font i Forcada, C.; Dangl, G.S.; Gradziel, T.; Dandekar, A.M.; Crisosto, C.H. Discovery of non-climacteric and suppressed-climacteric bud sport mutations originating from a climacteric Japanese plum cultivar (Prunus salicina lindl.). Front. Plant Sci. 2015, 6, 316. [Google Scholar] [CrossRef] [Green Version]
  32. Forcada, C.; Velascom, L.; Socias, I.; Company, R.; Fernández i Martí, Á. Association mapping for kernel phytosterol content in almond. Front. Plant Sci. 2015, 6, 530. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Hernández Mora, J.R.; Micheletti, D.; Bink, M.; Van de Weg, E.; Cantín, C.; Nazzicari, N.; Caprera, A.; Dettori, M.T.; Micali, S.; Banchi, E.; et al. Integrated QTL detection for key breeding traits in multiple peach progenies. BMC Genom. 2017, 18, 404. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Cai, L.; Quero-García, J.; Barreneche, T.; Dirlewanger, E.; Saski, C.; Iezzoni, A. A fruit firmness QTL identified on linkage group 4 in sweet cherry (Prunus avium L.) is associated with domesticated and bred germplasm. Sci. Rep. 2019, 9, 5008. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Cociu, V. 50 years of apricot varieties breeding in Romania. Acta Hortic. 2001, 701, 355–358. [Google Scholar] [CrossRef]
  36. Botu, M. Status of the Prunus collections in Romania. In Report of a Working Group on Prunus; Maggioni, L., Lipman, E., Eds.; Biodiversity International: Rome, Italy, 2006; pp. 87–88. [Google Scholar]
  37. Balan, V.; Tudor, V.; Topor, E. Apricot genetics and breeding in Romania. Acta Hortic. 2007, 760, 483–489. [Google Scholar] [CrossRef]
  38. Catalogue of Cultivated Plants from Romania, București. 2003; pp. 69–75. (In Romanian)
  39. Germplasm colletions of fruit tree and strawberry from Romania. I.C.D.P. Pitești-Mărăcineni. 2006; pp. 163–205. (In Romanian)
  40. Doyle, J.; Doyle, J.L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 1987, 19, 11–15. [Google Scholar]
  41. Schuelke, M. An economic method for the fluorescent labeling of PCR fragments. Nat. Biotechnol. 2000, 18, 233–234. [Google Scholar] [CrossRef]
  42. Salin, F. Autobin. 2010. Available online: https://hal.inrae.fr/hal-02824452 (accessed on 21 March 2021).
  43. Peakall, R.; Smouse, P.E. GenAlEx 6.5:genetic analysis in Excel. Population genetic software for teaching and research—An update. Bioinformatics 2012, 28, 2537–2539. [Google Scholar] [CrossRef] [Green Version]
  44. Kamvar, Z.N.; Tabima, J.F.; Grünwald, N.J. Poppr: An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2014, 2, e281. [Google Scholar] [CrossRef] [Green Version]
  45. Miks, S.; Bińkowski, J. Gene Calc. Available online: https://gene-calc.pl/pic (accessed on 22 March 2022).
  46. Jaccard, P. Nouvelles recherches sur la distribution florale. Bull. Soc. Vaud. Des Sci. Nat. 1908, 44, 223–270. [Google Scholar]
  47. Rohlf, F.J. NTSyS-p.c. Numerical Taxonomy and Multivariate Analysis System (Version 2.0); Exeter Software Publishers Ltd.: Setauket, NY, USA, 1998. [Google Scholar]
  48. Wang, Y.; Georgi, L.L.; Zhebentyayeva, T.N.; Reighard, G.L.; Scorza, R.; Abbott, A.G. High-throughput targeted SSR marker development in peach [Prunus persica (L.) Batsch]. Genome 2002, 45, 319–328. [Google Scholar] [CrossRef] [PubMed]
  49. Wünsch, A. Cross-transferable polymorphic SSR loci in Prunus species. Sci. Hortic. 2009, 120, 348–352. [Google Scholar] [CrossRef]
  50. Rojas, G.; Méndezm, M.A.; Muñoz, C.; Lemus, G.; Hinrichsen, P. Identification of a minimal microsatellite marker panel for the fingerprinting of peach and nectarine cultivars. Electron. J. Biotechnol. 2008, 11, 4–5. [Google Scholar] [CrossRef]
  51. Zhang, Q.P.; Liu, D.C.; Liu, S.; Liu, N.; Wei, X.; Zhang, A.M.; Liu, W.S. Genetic diversity and relationships of common apricot (Prunus armeniaca L.) in China based on simple sequence repeat (SSR) markers. Genet. Resour. Crop Evol. 2014, 61, 357–368. [Google Scholar] [CrossRef]
  52. Romero, C.; Pedryc, A.; Muñoz, V.; Llácer, G.; Badenes, M.L. Genetic diversity of different apricot geographical groups determined by SSR markers. Genome 2003, 46, 244–252. [Google Scholar] [CrossRef] [PubMed]
  53. Sanchez-Pérez, R.; Ruiz, D.; Dicenta, F.; Egea, J.; Martinez-Gomez, P. Application of simple sequence repeat (SSR) markers in apricot breeding: Molecular characterisation, protection, and genetic relationships. Sci. Hortic. 2005, 103, 305–315. [Google Scholar] [CrossRef]
  54. Arismendi, M.J.; Almada, R.; Pimentel, P.; Bastias, A.; Salvatierra, A.; Rojas, P.; Hinrichsen, P.; Pinto, M.; Di Genova, A.; Travisany, D.; et al. Transcriptome sequencing of Prunus sp. rootstocks roots to identify candidate genes involved in the response to root hypoxia. Tree Genet. Genomes 2015, 11, 11. [Google Scholar] [CrossRef]
  55. Dondini, L.; Lain, O.; Geuna, F.; Banfi, R.; Gaiotti, F.; Tartarini, S.; Bassi, D.; Testolin, R. Development of a new SSR-based linkage map in apricot and analysis of synteny with existing Prunus maps. Tree Genet. Genomes 2007, 3, 239–249. [Google Scholar] [CrossRef]
  56. Köse, M.A.; Çetinsağ, N.; Gürcan, K. De novo transcriptome assembly and SSR marker development in apricot (Prunus armeniaca). Turk. J. Agric. For. 2017, 41, 305–315. [Google Scholar] [CrossRef]
  57. Balan, V.; Oprea, M.; Drosu, S.; Chireceanu, C.; Tudor, V.; Petrisor, C. Maintenance of biodiversity of apricot tree phenotypes in Romania. Acta Hortic. 2001, 701, 199–206. [Google Scholar] [CrossRef]
  58. Balan, V.; Tudor, V.; Dumitrescu, E.C.; Topor, E. Genetic particularities for the biology of early apricot phenotypes created in Romania. Acta Hortic. 2010, 862, 143–150. [Google Scholar] [CrossRef] [Green Version]
  59. Krška, B. Genetic Apricot Resources and their Utilisation in Breeding. In Breeding and Health Benefits of Fruit and Nut Crops; Soneji, J.R., Nageswara-Rao, M., Eds.; IntechOpen: Rijeka, Croatia, 2018. [Google Scholar]
Horticulturae 08 00291 g001 550
Figure 1. Unweighted pair group method with arithmetic mean (UPGMA) tree based on Nei’genetic distances calculated from 24SSRs. Generated with Poppr v2.8.2 [44].
Figure 1. Unweighted pair group method with arithmetic mean (UPGMA) tree based on Nei’genetic distances calculated from 24SSRs. Generated with Poppr v2.8.2 [44].
Horticulturae 08 00291 g001
Horticulturae 08 00291 g002 550
Figure 2. A two-dimensional plot of the Principal Coordinate Analysis (PCoA) of SSR data showing the clustering of 26 Prunus cultivars: 10 peach (cluster I, subcluster Ia), 5 nectarine cultivars (cluster I, subcluster Ib) and 11 apricot cultivars (cluster II with two subclusters: IIa and IIb). Generated with GENALEX 6.5 program [43].
Figure 2. A two-dimensional plot of the Principal Coordinate Analysis (PCoA) of SSR data showing the clustering of 26 Prunus cultivars: 10 peach (cluster I, subcluster Ia), 5 nectarine cultivars (cluster I, subcluster Ib) and 11 apricot cultivars (cluster II with two subclusters: IIa and IIb). Generated with GENALEX 6.5 program [43].
Horticulturae 08 00291 g002
Table
Table 1. Characterization of apricot, peach and nectarine cultivars according to descriptors used in the genus Prunus: 1. Genotype; 2. Country; 3. Vigor of the tree (1. very small; 3. small; 5. medium; 7. great; 9. very great); 4. Blooming (1. extremely early; 2. very early; 3. early; 5. intermediate; 7. late; 8. very slow; 9. extremely late); 5. Harvest time (1. extremely early; 2. very early; 3. early; 5. average season; 7. late; 8. very slow; 9. extremely late); 6. Size of the fruit (1. very small (<20 g); 3. small (21–30 g); 5. medium (31–40 g); 7 great (41–50 g); 8. very big (51–60 g); 9. extremely big (>60 g); 7. Shape of the fruit (Apricot: 1. spherical; 3. ovoid; 5. cordiforme; 6. ovo-conical; 7. oblong; 9. elliptical. Peach: 1. very flat; 2. slightly flattened; 3. ovoid; 4. cordiforme; 5. oblong, 6. elongated); 8. Color of the fruit skin (Apricot: 1. greenish yellowish; 2. open cream; 3. cream; 4. yellow; 5. open orange; 6. orange; 7. dark orange. Peach: 1. green; 2. yellowish green; 3. cream; 4. yellowish cream; 5. yellow; 6. open orange); 9. Extension color coverage (Apricot: 1. absence; 3. reduced; 5. medium expansion; 7. widely extended. Peach: 1. absent; 2. red trail; 3. red stripe; 5. partially red; 6. red extended; 7. red; 8. bright red); 10. Color of the pulp (Apricot: 1. yellowish green; 2. yellowish white; 3. cream; 4. cream; 5. yellow; 6. opened orange; 7. orange; 8. dark orange; 9. Red. Peach: 1. greenish white; 2. white; 3. white yellowish; 4. yellowish; 5. yellow; 6. yellow orange; 7. orange; 8. red); 11. Firmness of the pulp (1. extremely soft; 3. soft; 5. medium; 7. firm; 9. very firm); 12. Taste of the pulp (1. very poor; 3. lower; 5. mediocre; 7. good; 9. excellent); 13. Adhesion of the kernel to the pulp (1. nonadherent; 2. semiadherent; 3. adherent); 14. Origin; 15. Compatibility (SI–Self-incompatible; SC–Self-Compatible).
Table 1. Characterization of apricot, peach and nectarine cultivars according to descriptors used in the genus Prunus: 1. Genotype; 2. Country; 3. Vigor of the tree (1. very small; 3. small; 5. medium; 7. great; 9. very great); 4. Blooming (1. extremely early; 2. very early; 3. early; 5. intermediate; 7. late; 8. very slow; 9. extremely late); 5. Harvest time (1. extremely early; 2. very early; 3. early; 5. average season; 7. late; 8. very slow; 9. extremely late); 6. Size of the fruit (1. very small (<20 g); 3. small (21–30 g); 5. medium (31–40 g); 7 great (41–50 g); 8. very big (51–60 g); 9. extremely big (>60 g); 7. Shape of the fruit (Apricot: 1. spherical; 3. ovoid; 5. cordiforme; 6. ovo-conical; 7. oblong; 9. elliptical. Peach: 1. very flat; 2. slightly flattened; 3. ovoid; 4. cordiforme; 5. oblong, 6. elongated); 8. Color of the fruit skin (Apricot: 1. greenish yellowish; 2. open cream; 3. cream; 4. yellow; 5. open orange; 6. orange; 7. dark orange. Peach: 1. green; 2. yellowish green; 3. cream; 4. yellowish cream; 5. yellow; 6. open orange); 9. Extension color coverage (Apricot: 1. absence; 3. reduced; 5. medium expansion; 7. widely extended. Peach: 1. absent; 2. red trail; 3. red stripe; 5. partially red; 6. red extended; 7. red; 8. bright red); 10. Color of the pulp (Apricot: 1. yellowish green; 2. yellowish white; 3. cream; 4. cream; 5. yellow; 6. opened orange; 7. orange; 8. dark orange; 9. Red. Peach: 1. greenish white; 2. white; 3. white yellowish; 4. yellowish; 5. yellow; 6. yellow orange; 7. orange; 8. red); 11. Firmness of the pulp (1. extremely soft; 3. soft; 5. medium; 7. firm; 9. very firm); 12. Taste of the pulp (1. very poor; 3. lower; 5. mediocre; 7. good; 9. excellent); 13. Adhesion of the kernel to the pulp (1. nonadherent; 2. semiadherent; 3. adherent); 14. Origin; 15. Compatibility (SI–Self-incompatible; SC–Self-Compatible).
GenotypeCountryVigor of the TreeBlooming AgeHarvest MaturitySize of the FruitShape of the FruitColor of the Fruit SkinExtension Color CoverageColor of the PulpFirmness of the PulpTaste of the PulpAdhesion of the Kernel to the PulpOriginCompatibility
123456789101112131415
Peach cultivars
P1AlexiaRO53382432592Cross Flacara x Marygold
SCDP Baneasa		SC
P2CollinsUSA 55462465661Jerseyland x NJ188 (=Raritan Rose x NJ125). NJ125 = NJ66325 op (=J.H. Halle x Goldfinch)SC
P3AmaliaRO53573333581Roubidoux x FlacaraSC
P4JerseylandUSA75673585771NJ104325 op [= J.H. Halle x NJ41 (=Slappey x Dewey)] SC
P5SpringoldUSA 54242495773Cross [(Fireglow x Hiley) x Fireglow] x SpringtimeSC
P6CardinalUSA74461575763Self-fertilization
of the Redhaven variety		SC
P7AntoniaRO43473383582-SC
P8SouthlandUSA53673483581Self-fertilization
of the Halehaven variety		SC
P9RedhavenUSA55661576561Cross Halehaven x KalehavenSC
P10Superbă de toamnăRO54773392581Elberta x MayflowerSC
Apricot cultivars
C1RareşRO33163555571Cross B12/6 x NJA13SC
C2MamaiaRO58763656571Complex hybridization between Marculesti1 (Ananas x Ananas) x Marculesti 5 (Targii de Bucuresti x Ananas)SC
C3ComandorRO67683455571Cross B 17/52 x Mr 43/1SC
C4CR-2-63-1 (Cream Ridge 2–63)USA American selectionSI
C5CR-2-63-2 (Cream Ridge 2–63)USA64472637371American selectionSI
C6CR-24-12-1USA72281557791American selection
C7CR-24-12-2USA American selection
C8SaturnRO56562457952Hybridization of
Marculesti selection 40		SC
C9VioricaRO553816-7771Cross B 3/9 (P1) x NJA20SC
C10SirenaRO57874546671Cross Mr 37/1 x Mr 21/50SC
C11CarmelaRO56583578671Cross Farmigdale x NJA20SC
Nectarine cultivars
N1CrimsongoldUSA73453695572Nectarine selection x July ElbertaSC
N2RomamerRO75254634551Cross 624029148 x RR 48–153SC
N3DeltaRO55453575582Romania,
SCDP-Constanta		SC
N4CoraRO55673575672Romania,
SCDP-Constanta		SC
N5ARK 165USA 3 166 6 -
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Butiuc-Keul, A.; Coste, A.; Postolache, D.; Laslo, V.; Halmagyi, A.; Cristea, V.; Farkas, A. Molecular Characterization of Prunus Cultivars from Romania by Microsatellite Markers. Horticulturae 2022, 8, 291. https://doi.org/10.3390/horticulturae8040291

AMA Style

Butiuc-Keul A, Coste A, Postolache D, Laslo V, Halmagyi A, Cristea V, Farkas A. Molecular Characterization of Prunus Cultivars from Romania by Microsatellite Markers. Horticulturae. 2022; 8(4):291. https://doi.org/10.3390/horticulturae8040291

Chicago/Turabian Style

Butiuc-Keul, Anca, Ana Coste, Dragoș Postolache, Vasile Laslo, Adela Halmagyi, Victoria Cristea, and Anca Farkas. 2022. "Molecular Characterization of Prunus Cultivars from Romania by Microsatellite Markers" Horticulturae 8, no. 4: 291. https://doi.org/10.3390/horticulturae8040291

APA Style

Butiuc-Keul, A., Coste, A., Postolache, D., Laslo, V., Halmagyi, A., Cristea, V., & Farkas, A. (2022). Molecular Characterization of Prunus Cultivars from Romania by Microsatellite Markers. Horticulturae, 8(4), 291. https://doi.org/10.3390/horticulturae8040291

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop