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Evaluation of SSR and SNP markers in Rubus glaucus Benth progenitors selection/ Avaliacao dos marcadores SSR e SNP na selecao de progenitores em Rubus glaucus Benth.


Rosaceae family comprises nearly 90 genera and 300 species, among them fruit trees with economic importance are included such as apples (Malus pumila Mill.) and pears (Pyrus spp.); stone fruits or drupes like peaches (Prunus persica); several ornamental species including the rose (Rosa spp.), and soft fruits as strawberries, raspberries, blackberries, among others. Different taxonomic classifications of the family has been proposed based upon morphology, whilst Schulze-Menz (1964) suggested a new family classification into subfamilies: Maloideae, Amygdaloideae, Rosoideae y Spiraeoideae based on chromosome number and fruit type (LONGHI, et al. 2014).

Genetic variability of Rubus genus is known over the world and has been widely studied over the phenotypical, morphological, chromosomal and molecular aspects (DOSSETT, et al. 2012; ALICE, et al. 1999; GRAHAM AND MCNICOL, 1995; GRAHAM et al., 1997). One of the most interesting features of the genus is the variability in the number of chromosomes, polyploidy and hybridization; in contrast, only Idaeobatus, Dalibarda, and Anoplobatus subgenera are predominantly diploid, whilst Dalibardastrum, Malachobatus, and Orobatus are exclusively polyploid (THOMPSON, 1995, 1997). Hybridization in Rubus occurs mainly between closely related species (NARUHASHI, N., 1990; KRAFT, 1995) and, in some cases, between subgenera (JENNINGS, 1979; WEBER, 1996; ALICE, et al. 1997), thus, some intersubgeneric hybrids possess commercial importance (WAUGH, et al. 1990).

Rubus glaucus or Andean blackberry is distributed over the main Colombian mountain and combines Idaeobatus and Rubus features. This specie is a fertile amphidiploid or allotetraploid, probably originated by genome fusion of two species (JENNINGS, 1988). (DELGADO, et al. 2010) found 28 chromosomes in R. glaucus cultivars, assuming a basic number n=7 for Rubus genus, it confirms its tetraploidy (4x).

Despite its well-known importance in the income generation for small producers, this cultivar has received little technological development, as a result, cultivar quality and productivity shown high variability, mainly due to the lack of formal varieties and the scarcity of planting material with good genetic and phytosanitary quality. Nowadays, planting of this specie is still done by the usage of local cultivars asexually propagated by growers (LOBO et al., 2002). This specie shows low yielding rates, mainly caused by anthracnose caused by por Glomerella cingulata (Stoneman) Spauld & H. Schrenk (teleomorph state of Colletotrichum gloeosporioides). This disease is considered the most devastating affecting R. glaucus, creating losses above the 50%. In addition, chemical treatment of this agent increases production costs (SALDARRIAGA-CARDONA, et al. 2008).

Associated Colombian blackberry producers, has highlighted the necessity to formalize the offer of planting material, starting by plant breeding schemes that allow the obtention of more productive varieties with morphological features that facilitates cultural activities and certain tolerance to fungal attack, especially those related to anthracnose. It is well known that the first step in plant breeding programs is the selection and characterization of promising cultivars.

In this regards, (BERTRAND, et al. 2008) has stated that with help of polymerase chain reaction (PCR), public institutions and commercial organizations in charge of plant breeding programs has implemented molecular markers, including SSR and progenitor genotyping to make more efficient those processes. In addition, the evolution of molecular techniques developed the polymorphisms of a single nucleotide (SNP). Bertrand et al. (2008), classified selection schemes assisted by markers onto 5 areas: 1) Development of parental population for its selection and hybridization, 2) construction of ligation maps for its evaluation over phenotypical features, 3) QTL (Quantitative trait loci) validation, confirming the position and effects of QTL, 4) Selection assisted by markers, and 5) marker validation (BERTRAND, et al. 2008).

In Colombia, some studies regarding genetic diversity of Rubus genus has been carried out: Zamorano et al. (2004) conducted a molecular and morphological characterization of species belonging this genus using Random Amplified Microsatellite (RAMS). Duarte et al. (2011) evaluated genetic relations of elite Colombian Rubus glaucus cultivars through AFLP analysis obtained by the employment of three primer combinations. (MARULANDA, et al. 2007) assessed genetic diversity of wild and cultivated species of R. robustus, R. urticifolius, R. glaucus and R. rosifolius through AFLP and SSR markers developed for R. alceifolius (heterologous markers, when applied to R. glaucus). Marulanda and Lopez (2009), performed molecular (SSR markers) and morpho-agricultural charcaterization for cultivated and wild varieties of Rubus glaucus with and without thorn, paying special attention to fruit size. (MARULANDA, et al. 2012) developed especific SSR markers for Rubus glaucus, aiming to obtain higher discrimination power. They concluded the necessity to develop more discriminatory molecular markers associated to morphological desired features. (LOPEZ-VASQUEZ, et al. 2013), found differential responses in blackberry cultivars against anthracnose attack.

In recent years, it has been carried out the differential expression of blackberry cultivars against to anthracnose (Colletotrichum gloeosporioides) through transcriptome analysis (RNA-Seq) where two cultivars (UTP-1, tolerant & UTP-4, susceptible) were inoculated with a highly pathogenic strain of C. gloeosporioides, together with a control treatment (cultivar inoculated with sterile water). Afterwards, RNA was extracted 72 hours later and the genetic material sequencing were compared between treatments (unpublished results). This study allowed the design of new molecular markers (SSR and SNPs) which were finally used in this project.

In order to start with plant breeding processes for Rubus glaucus, evaluations with SSR and SNPs were conducted over promissory cultivars that potentially could be used in future breeding schemes. Genetic distance and other features such as thorn presence/absence and fruit size were considered at the time of selecting cultivars.

Materials and methods

Plant material and DNA extraction--Fifteen Andean blackberry cultivars with agricultural interesting features coming from participative selections made with producers in different regions of the country were selected. These cultivars were previously characterized with heterologous (transferred from other Rubus specie) and homologous SSR markers (developed for R. glaucus) (MARULANDA, et al. 2012). Selected cultivars shown differential response against C. gloeosporioides attack (LOPEZ-VASQUEZ, et al. 2013). Table 1, gather all data related to the sampled material (name, place of collection, thorn presence/absence and response against C. gloeosporioides (MORALES, et al. 2010).

DNA extraction of healthy foliar tissue was accomplished using the commercial Plant DNeasy Mini Kit (QIAGEN) following manufacturer instructions.

Molecular marker development--The development of the SSR and SNPs markers from a previous RNA-Seq analysis of the R. glaucus interaction against C. gloeosporioides, and its further use in this study is described.

SSR molecular markers--Detection of the simple sequence repeats (SSR) from the transcriptome analysis was completed using the MIcroSAtellite (MISA) software. From these sequences, 22 primers were designed. Rubus glaucus genome possess several microsatellite with different repetitions and lengths, as well as the majority of plant genomes analyzed so far. Thus, it was decided to select sequences with longer repetition than tri- nucleotides given that they has demonstrated to be more polymorphic and reproducible than microsatellite with di-nucleotide repetitions (VUKOSAVLJEV, et al. 2015; FAN, et al. 2013). Primer design was limited to sequences with a high number of repetitions of the base unit (> 5 for tri- nucleotide and > 4 for repetitions bigger than tetra- nucleotides). Another criterion were to select primers with annealing temperatures between 58[degrees]C and 61[degrees]C and expected PCR product sizes between 100 and 200 base pairs (bp) (see Table 2). In addition, Table 3 shows homology of generated primer sequences with other Rosaceae family species.

Amplification reactions for this type of markers was accomplished following described conditions by (MARULANDA, et al. 2012). The "touchdown" amplification profile consisted of 32 denaturing cycles at 95[degrees]C by 1 minute; annealing for 1 minute with decrease of 1[degrees]C every two cycles from 63[degrees]C to 58[degrees]C; 10 cycles at 59[degrees]C and 10 cycles at 58[degrees]C; elongation at 72[degrees]C for 1 minute.

Afterwards, amplicon visualization was conducted over denaturing 6% polyacrylamide electrophoresis gels. Obtained results was analyzed through GenAlex v6.2 (PEAKALL AND SMOUSE, 2006) and PAST (Paleontological statistics software package for education and data analysis) (HAMMER, et al. 2001). HardyWeinberg Equilibrium (HWE) analysis was evaluated employing the Markov chain in GenAlex v6.2 (PEAKALL AND SMOUSE, 2006). In the SSR analysis, it was also incorporated another specie belonging to Rubus genus as external group.

SNP molecular markers--Bowtie2 v2.2.4 (LANGMEAD, et al. 2012) and samtools v0.1.19 (LI AND DURBIN, 2009) software were used in the SNP marker identification. Given that whole genome sequencing of Rubus glaucus had not been carried out so far, the Fragaria vesca genome was employed as reference genome, as well as the comparison between tolerant and susceptible samples. Finally, SNPs were identified in 200 genes from susceptible and tolerant R. glaucus against C. gloeosporioides, allowing the design of 78 primers. In addition, homology of generated primer sequences with Rosaceae family was evaluated. Table 4, present in detail primer sequences of the SNP markers (UNIGENE primers).

Amplification reactions for SNP markers was accomplished following described conditions by (MARULANDA, et al. 2012). The "touchdown" amplification profile consisted of 32 denaturing cycles at 95[degrees]C by 1 minute; annealing for 1 minute with decrease of 1[degrees]C every two cycles from 64[degrees]C to 59[degrees]C; 10 cycles at 58[degrees]C and 10 cycles at 57[degrees]C; elongation at 72[degrees]C for 1 minute.

SNP's fragment visualization was accomplished through gel electrophoresis and amplicons were sequenced by extension using the ABI PRISM[R] BigDyeTM Terminator Cycle Sequencing kit in a capillary ABI PRISM[R] 3730XL (96 capillary type) sequencer.

To analyze SNP sequences and to corroborate homology of obtained data in the sequence, BLAST (Basic Local Alignment Search Tool--NCBI) tool was employed using an E- cutoff value of 0.000001. Then, an individual analysis of each UNIGENE consisting of a multiple sequence alignment with Clustal Omega (EMBL - EBI), online version ( clustalo/). Finally, for the alignment of obtained sequences for all samples it was employed the MAFFT software, online version ( A dendogram was obtained through the clustering method Neighbor Joining with UPGMA (Unweighted Pair Group Method with Arithmetic Mean), the substitution model proposed by (JUKES AND CANTOR, 1969) and a replacing number of 100. Genetic diversity parameters were estimated for haploid data with GenAlex 6.5b4 software (PEAKALL AND SMOUSE, 2006).

Results and discussion

Microsatellite marker analysis--It was found 4799 simple sequence repeats consisting mainly of di-nucleotide repetitions, followed by tri- and tetra-nucleotide repetitions. From the 22 evaluated SSR markers, 15 yielded positive amplification generating 29 loci and 58 alleles. Thirteen of them amplified 2 loci and the allelic number was about 15. Informative alleles were approximately 3 (see Table 5). In that regards, (DOSSETT, et al. 2012) showed that when assessing genetic diversity in R. occidentalis cultivars using SSR markers, observed allelic diversity was low with 3 or least alleles in 15 of the 21 evaluated loci, similarly to the observed in this study where allelic number was set around 3.

Expected heterozygosity (He) ranged between 0,607 and 0,7575; whilst observed heterozygosity (Ho) varied among 0,5665 and 1. Consequently, (GRAHAM, et al. 2004) explains that Rubus genus comprises highly heterozygous species. In that study Rubus idaeus varieties with thorn (e.g. Latham) are compared with glabrous ones (e.g. Glen Moy) demonstrating that thorn-possessing varieties showed higher heterozygosity levels than thorn-absent varieties. These differences associated to a morphological feature could support obtained values for R. glaucus, values that would be corroborated once progenies are established.

(DOSSETT, et al. 2012) argues that R. occidentalis cultivars show a noticeable heterozygosity level. For this specie in every evaluated locus (SSR), Ho was higher than He. Parallel, for Rubus glaucus this behavior was the same for the majority of markers (higher Ho values), excepting the marker CL2322. Additionally, (DOSSETT, et al. 2012) explains that this phenomena could be attributed to selection process and clonal propagation, similar situation to R. glaucus in Colombia where local selections made by producers are asexually propagated. Respect to variability parameters, (CLARK, et al. 2013) detected for R. fruticosus, a diploid specie with polyploidy ancestors and invasive behavior in United States, very low allele numbers, ranging between zero and 2,56 alleles per locus. That reported values are lower than obtained in the present study where the average value for polymorphic alleles was 5,448. This behavior is supported considering that polyploidy species is expected to obtain higher values, such as R. glaucus.

In the HWE analysis, five markers were in equilibrium while the rest (10) showed significant or highly significant disequilibrium (Table 5). (FU, et al. 2016) reported that a loss of the HWE for the specie Ziziphus jujube is explained because there did not existed a random selection of the samples, similar to this case of study, where samples corresponded to selected and asexually propagated cultivars.

Genetic diversity estimation through Dice index allowed the construction of a dendogram, depicted in Figure 1. Detachment of cultivar UTP1 is explained considering its recognized tolerance to C. gloeosporioides attack in the RNA-Seq analysis. The presence of groups in the distance analysis evidence a geographical tendency, corroborating that the interchange of planting material in Colombia is apparent. The fact of thorn present/absent cultivar clustering contributes to the design of future breeding schemes.

(DOSSETT, et al. 2012) assessed the genetic diversity of cultivated and wild plants of R. occidentalis, a berry from temperate regions from North America and Europe, through the usage of 21 SSR markers aiming to stablish a plant breeding process over a germplasm bank that was thought to possess low diversity levels. This study raised the probability to perform the breeding process with higher levels of Ho in cultivated samples rather than wild ones, similar situation observed in the present study, where Ho in a general trend were higher than He. McCallum et al. (2016) carried out the construction of a ligation map for the auto-tetraploid specie, Vaccinium corymbosum, through SNPs and SSR markers obtained from a Genotyping by Sequencing (GBS) analysis, a technique that combines DNA fragmentation with restriction enzymes and its further sequencing with high performance tools. This work yielded 207 codominant primer pairs. Obtained SSR primers have made genetic characterizations more efficient by covering larger portions of the genome.

Previous works published by (MARULANDA, et al. 2007; 2012), have characterized R. glaucus cultivars transferring SSR markers from other Rubus species to R. glaucus, with positive polymorphic amplification for some markers and no amplification or monomorphic results for others, similarly to this study. With the use of the new SSR markers polymorphic amplification of the samples was achieved.

(LONGHI, et al. 2014; SALAZAR, et al. 2015) has reported that after the emergence of the Next Generation Sequencing (NGS) techniques, the Rosaceae specie Fragaria vesca has received the major SSR marker design derived from analysis using those techniques, with more than 4000 markers reported to the date. Other species including Malus spp, Prunus spp, Pyrus spp, Rosa spp. and Rubus spp., have also had significant developments (LONGHI, et al. 2014). The massive SSR development derived from high performance sequencing have triggered the use of these type of markers and have diminished costs associated to genetic characterizations at the time that new regions of the genome are covered.

SNP marker analysis--From the 78 evaluated SNP-containing DNA fragments, 36-yielded positive amplification. Obtained amplicon sequences showed high homology with Rosaceae species: Prunus spp. (29%); Fragaria vesca (23%); Pyrus spp. (5%); Malus spp. (2%) (Table 4). Other homologies were established with species of other families (9%) and another corresponded to sequences with non-reported homologies in public data bases (32%). Using sequence alignments (Figure 2), a dendogram was constructed with the clustering method of Figure 3. This dendogram showed 4 clusters with any clustering tendency by morphologic features (thorn presence/absence) nor geographical origin. The first group comprises cultivars UTP1, UTP11, UTP6, UTP2; the second consisted of the UTP16, UTP21, UTP4, UTP3 cultivars; the third clustered UTP15, UTP26, UTP5 and UTP27; and the fourth possessed the most distant cultivars, UTP7 and UTP28. Surprisingly, cultivars with desirables features (UTP1, UTP4 y UTP7) were located in different groups, an important annotation to guide the progenitor selection in breeding processes. Both SSR and SNP-derived dendograms allowed the progenitor selection with noticeable differences in their genomes.

Sequence homology result were consistent with other Rosaceae species. It is expected that R. glaucus shows high homology within its family to species that have complete or partially sequenced genomes. In addition, is important to state that the development of SNP markers from transcriptome analysis has been widely used in Prunus spp. (Rosaceae) to evaluate segregant populations of pears in Europe. The employment of SNP markers in the evaluation of germplasm banks of peaches and the construction of microarrays from transcriptome analysis-derived SNPs in apples, have been also reported. Based in that evidence, the development of such methodologies have improve the selection processes in breeding programs (YAMAMOTO AND TERAKAMI, 2016).

Genetic diversity parameters for haplotypical data are presented in Table 6. It was found 1162 SNP-containing fragments, corresponding to 1082 effective SNPs and a polymorphism of 12,49%. In regards to the specific nature of each SNP and SSR marker, biallelic nature of SNP markers makes their discrimination power lower than SSR. In that sense, the greater variability observed in SSR compared to SNPs allows better possibilities in the identification of cultivars and its genetic variability assessment (SANCHEZ-PEREZ, et al. 2006), making them leading markers for genotyping, fine mapping or to increasing QTL resolution.

Progenitor selection--Genetic diversity analysis between previously selected cultivars are used to recommend progenitors susceptible to be used in future breeding processes (Table 7). In order to make those recommendations, the morphologic features related to thorn presence/absence and C. gloeosporioides tolerance (very desirable features in new cultivars) were also considered; in that sense, tolerant or moderately tolerant without thorn material was privileged. Respect to cluster analysis, there were selected samples from different clustering groups. Moreover, cultivars UTP5, UTP20 and UTP28 possess, according to producers, fruits with greater size, making them very popular in Colombia, despite its thorn presence and significant susceptibility to C. gloeosporioides.

(HE, et al. 2014), states that plant breeding can be performed through two main strategies, classic and molecular approaches. The classic process employs closely related varieties that could interbreed, whilst the molecular breeding consist in the application of molecular biology and biotechnology approaches to accomplish the development of new cultivars through the Marker-assisted Selection (MAS) and the genetic transformation (MOOSE AND MUMM, 2008). This work employed SSR and SNP markers in the development of a progenitor population aiming to move towards hybridization processes that permit an increase in the genetic gain.


This work evidence the utility of molecular markers to assess the genetic diversity of possible progenitors susceptible to be employed in future breeding processes. This aspect, widely known as the development of a parental population, determines in great manner the success of breeding schemes. The SSR and SNP markers employed in this study, allowed the characterization of such population studying different genome regions. Morphological and fungal tolerance selection criteria, previously evaluated, were also considered in the selection of six progenitors.



Authors express their thanks to Universidad Tecnologica de Pereira (Colombia) and the General System of Royalties (National Planning Department) for financing the research program "Development of scientific and technologies in biotechnology applied to the sectors of health and agro-industry in the department of Risaralda "identified with BPIN code 2012000100050.

This project was completed in the frame of the genetic access contract 195 (2018-05-25).


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Ana Maria Lopez (1), Carlos Felipe Barrera (2), Marta Leonor Marulanda (3)

Corresponding author:

Received: July 19, 2018

Accepted: November 27, 2018

(1) PhD Fellow (Universidad de Caldas). Universidad Tecnologica de Pereira. Plant Biotechnology Laboratory. Pereira-Colombia. E-mail: (ORCID 0000-0002-5138-1806)

(2) PhD. Universidad Nacional de Colombia-Medellin. Agricultural Sciences Faculty. Arauca-Colombia E-mail: (ORCID 0000-0002-5015-2956)

(3) PhD. Universidad Tecnologica de Pereira. Research, Innovation and Extension Vice-chancellory. Pereira-Colombia. E-mail: (ORCID 0000-0002-5015-2956)

Caption: Figure 1. Dendogram obtained from SSR markers employing the Dice Index.

Caption: Figure 2. Segment of the sequence alignment with UNIGENE37334 with Clustal Omega software (EMBL-EBI), online version (

Caption: Figure 3. Dendogram obtained from SNP-containing DNA fragment alignments employing the Neighbor Joining clustering method.
Table 1. Description of promissory R. glaucus cultivars.

Code         Latitude (N)          Longitude (W)          Height
                                                       (m. a. s. l.)

UTP1      4[degrees]52'15.0"    75[degrees]37'32.4"        2000
UTP2       4[degrees] 39'7"     75[degrees] 35'26.3"       2014
UTP3**     4[degrees]38'36"     75[degrees]28'41,5"        2300
UTP4**    4[degrees]48'99.2"     75[degrees]41'86"         1950
UTP5       5[degrees] 2'2.7"    75[degrees] 27'10.5"       1800
UTP6      4[degrees] 44'45.1"   75[degrees] 36'39.6"       1850
UTP7      4[degrees]11'36.1"    75[degrees]48'14.6"        2000
UTP11      4[degrees]79'33"      74[degrees]42'68"         2288
UTP15      6[degrees]99'44"      72[degrees]98'80"         2157
UTP16     6[degrees]59'39".1     72[degrees]59'13"         2176
UTP20**   4[degrees]13'23.8"    76[degrees]25'35.9"        2380

UTP21     4[degrees]13'23.8"    76[degrees]25'35.9"        2380

UTP26     6[degrees]09'15.4"    75[degrees]23'00.1"        2000
UTP27     6[degrees]09'15.4"    75[degrees]23'00.1"        2000
UTP28**   6[degrees]09'15.4"    75[degrees]23'00.1"        2000

Code       Thorn        Collect      Response against Colletotrichum
          Presence/      Place           gloeosporioides* attack

UTP1       Absence     Risaralda                Tolerant
UTP2      Presence      Quindio                 Tolerant
UTP3**     Absence      Quindio            Moderately tolerant
UTP4**    Presence     Risaralda            Very susceptible
UTP5      Presence       Caldas             Very susceptible
UTP6       Absence     Risaralda           Moderately tolerant
UTP7       Absence      Quindio            Moderately tolerant
UTP11      Absence    Cundinamarca         Moderately tolerant
UTP15      Absence     Santander                Tolerant
UTP16     Presence     Santander            Very susceptible
UTP20**   Presence       Valle              Very susceptible
                       del Cauca
UTP21     Presence       Valle              Very susceptible
                       del Cauca
UTP26      Absence     Antioquia           Moderately tolerant
UTP27     Presence     Antioquia            Very susceptible
UTP28**   Presence     Antioquia            Very susceptible

* (Morales, Y. M., Marulanda, M. L.,; Isaza, L., 2010).
** Cultivars with outstanding fruit size

Table 2. SSR primer markers for Rubus glaucus.

Identification code               SSR motif

CL 1004. Contig2_A11_94_1       [(GAA).sub.7]
CLllOl.Contig1_11_110_1       [(TCACCC).sub.4]
CL 1366. Contig2_A11_134_1      [(GAA).sub.6]
CL1491.Contig11_A11_143_1       [(CTT).sub.6]
CL150.Contig2_A11_12_1          [(TTG).sub.6]
CL1916.Contig1_A11_167_1      [(AAAGTG).sub.4]
CL1891.Contig3_A11_166_1        [(GCA).sub.7]
CL2218.Contig3_A11_177_1        [(GAA).sub.6]
CL2322.Contig2_A11_181_1        [(CAT).sub.6]
CL2455.Contig1_A11_192_1        [(AGC).sub.6]
CL2455.Contig1_A11_193_1        [(AAG).sub.7]
CL2364.Contig3_A11_186_1      [(GTGGTA).sub.4]
CL274. Contig3_A11_22_1         [(TGT).sub.6]
CL2958.Contig1_A11_2191         [(TTC).sub.7]
CL2556.Contig1_A11_201_1      [(AGAGGG).sub.4]
CL3540.Contig2_A11_254_1        [(TCT).sub.7]
CL2787.Contig1_A11_2101        [(GGAAA).sub.4]
CL3301. Contig1_A11 237_1      [(AGAA).sub.5]
CL3840.Contig1_A11_265_1        [(GAA).sub.7]
CL4175.Contig2_A11_292_1        [(CTT).sub.7]

Identification code                 F Primer


Identification code                 R Primer

CL2455.Contig1_A11_192_1      CAACAATCACCAACCCAAGAC

Identification code          Annealing     Expected
                             Temperature   Product
                                           Size (Bp)

CL 1004. Contig2_A11_94_1       60,0          141
CLllOl.Contig1_11_110_1         59,9          113
CL 1366. Contig2_A11_134_1      60,3          129
CL1491.Contig11_A11_143_1       59,5          100
CL150.Contig2_A11_12_1          60,0          124
CL1916.Contig1_A11_167_1        59,8          129
CL1891.Contig3_A11_166_1        60,2          139
CL2218.Contig3_A11_177_1        60,0          149
CL2322.Contig2_A11_181_1        60,0          146
CL2455.Contig1_A11_192_1        60,3          138
CL2455.Contig1_A11_193_1        59,5          148
CL2364.Contig3_A11_186_1        59,9          159
CL274. Contig3_A11_22_1         60,6          160
CL2958.Contig1_A11_2191         60,6          112
CL2556.Contig1_A11_201_1        59,1          156
CL3540.Contig2_A11_254_1        60,2          151
CL2787.Contig1_A11_2101         59,8          116
CL3301. Contig1_A11 237_1       59,8          85
CL3840.Contig1_A11_265_1        62,1          156
CL4175.Contig2_A11_292_1        60,3          160

Table 3. Accession number and homologous sequences for developed
SSR markers.

  Identification code       Genbank Accession Number

CL1004.Contig2_A11_94_1             MH516338
CL1101.Contig1_A11_110_1            MH516339
CL1366.Contig2_A11_134_1            MH516340
CL1491.Contig11_A11_143_1           MH516341
CL150.Contig2_A11_12_1              MH516342
CL1916.Contig1_A11_167_1            MH516343
CL1891.Contig3_A11_166_1            MH516344
CL2218.Contig3_A11_177_1            MH516345
CL2322.Contig2_A11_181_1            MH516346
CL2455.Contig1_A11_192_1            MH516347
CL2455.Contig1_A11_193_1            MH516348
CL2364.Contig3_A11_186_1              ***
CL274.Contig3_A11_22_1               ***
CL2958.Contig1_A11_219_1              ***
CL2556.Contig1_A11_201_1              ***
CL3540.Contig2_A11_254_1              ***
CL2787.Contig1_A11_210_1              ***
CL3301.Contig1_A11_237_1              ***
CL3840.Contig1_A11_265_1              ***
CL4175.Contig2_A11_292_1              ***

                                   Homologous sequences in other
                                        rosaceae species

  Identification code       accession number           Specie
                                in other
                            Rosaceae species

 CL1004.Contig2_A11_94_1     XM_024331960.1        Rosa chinensis
CL1101.Contig1_A11_110_1     XM_008365700.2      Malus x domestica
CL1366.Contig2_A11_134_1     XM_024327521.1        Rosa chinensis
CL1491.Contig11_A11_143_1         ***
 CL150.Contig2_A11_12_1      XM_004293478.2    Fragaria vesca subsp.
CL1916.Contig1_A11_167_1     XM_024315090.1        Rosa chinensis
CL1891.Contig3_A11_166_1     XM_024315199.1        Rosa chinensis
CL2218.Contig3_A11_177_1     XM_021953392.1         Prunus avium
CL2322.Contig2_A11_181_1     XM_024325000.1        Rosa chinensis
CL2455.Contig1_A11_192_1     XM_020567229.1        Prunus persica
CL2455.Contig1_A11_193_1     XM_021972419.1         Prunus avium
CL2364.Contig3_A11_186_1     XM_024325669.1        Rosa chinensis
 CL274.Contig3_A11_22_1      XM_024304460.1        Rosa chinensis
CL2958.Contig1_A11_219_1     XM_009353829.2    Pyrus x bretschneideri
CL2556.Contig1_A11_201_1     XR_002271838.1        Prunus persica
CL3540.Contig2_A11_254_1     XM_024301335.1        Rosa chinensis
CL2787.Contig1_A11_210_1      XR_907125.1      Fragaria vesca subsp.
CL3301.Contig1_A11_237_1     XM_007217879.2        Prunus persica
CL3840.Contig1_A11_265_1     XM_021945224.1         Prunus avium
CL4175.Contig2_A11_292_1     XM_024301524.1        Rosa chinensis

Table 4. Primer sequences for employed SNP markers.


Sequence               Forward                  Reverse


                                  Homologous sequences in other
                                       Rosaceae species

Sequence         Genbank        Reported                Specie
identification   accession   accession number
                  number        in other
                             Rosaceae species

Unigene11151     MH479026    XM_008223370.1     Primus mume
Unigene11157     MH479027    XM 024322668.1     Rosa chinensis
Unigene11255     MH479028    XM 024331931.1     Rosa chinensis
Unigene12343     MH479029    XM 021960956.1     Primus avium
Unigene12924     MH479030    XM 004287281.2     Fragaria vesca subsp.
Unigene13090     MH479031    XM 024300962.1     Rosa chinensis
Unigene1465      MH479032    XM 024340632.1     Rosa chinensis
Unigene14681     MH479033    XM_024324523.1     Rosa chinensis
Unigene14822     MH479034    XM 021968382.1     Primus avium
Unigene14951     MH479035    XM_008377277.2     Mains x domestica
Unigene15095     MH479036    XM 021964662.1     Primus avium
Unigene15115     MH479037    XM 011465001.1     Fragaria vesca subsp.
Unigene15294     MH479038    XM 024315181.1     Rosa chinensis
Unigene15456     MH479039    XM 024302866.1     Rosa chinensis
Unigene15499     MH479040    XM 024307824.1     Rosa chinensis
Unigene15574     MH479041    XM 020565943.1     Primus persica
Unigene16239     MH479042    XM_007221893.2     Primus persica
Unigene16323     MH479043    XM 024317156.1     Rosa chinensis
Unigene16368     MH479044    XM 024304514.1     Rosa chinensis
Unigene16415     MH479045    XM_024307003.1     Rosa chinensis
Unigene16433     MH479046    XM_021957543.1     Primus avium
Unigene16551     MH479047    XM 024324543.1     Rosa chinensis
Unigene2247      MH479048    XM 008234124.2     Primus mume
Unigene2361      MH479049    XM_021957567.1     Primus avium
Unigene2373      MH479050    XM 021946702.1     Primus avium
Unigene242       MH479051    XM 024332998.1     Rosa chinensis
Unigene31842     MH479052    XM_004289533.2     Fragaria vesca subsp.
Unigene33255     MH479053    XM 024315336.1     Rosa chinensis
Unigene34996     MH479054    XM_004295163.2     Fragaria vesca subsp.
Unigene351       MH479055    XM_024307531.1     Rosa chinensis
Unigene42870     MH479056    XM_018645776.1     Pyrus x bretschneideri

Table 5. Genetic variability of SSR markers for R. glaucus.

SSR marker                   Number     N       Na        Ne
                             of Loci

CL150.Contig2_A11_12_1         2       14,5     5,5     3,6495
CL1916.Contigl_A11_167_1       2       15,5     5,5     2,6845
CL 1891 .Contig3_A11_1 661     2        16       6      3,886
CL1101.Contigl_A11_110_1       2        16      4,5      2,34
CL1366.Contig2_A11_134_1       2        15       6      4,3335
CL1491.Contigll_A11_143_1      2        16       8      4,168
CL2455.Contigl_A11_192_1       2        14      4,5     3,829
CL2556.Contigl_A11_201_1       2        16       6      3,1315
CL1004.Contig2_A11_94_1        1        15       4      2,542
CL2322.Contig2_A11_181_1       2        16      6,5     3,527
CL2455.Contigl_A11_193_1       2       15,5     6,5     4,063
CL3840.Contigl_A11_265_1       2        16       5      2,9505
CL2364.Contig3_A11_186_1       2        15      4,5     3,426
CL274.Contig3_A11_22_1         2        16       4      3,2315
CL2958.Contigl_A11_219_1       1        16      4,5     2,906
All SSR (Average)                     15,448   5,448    3,407
Standard Deviation                    0,127    0,283    0,182

SSR marker                     Ho       He      HWE

CL150.Contig2_A11_12_1         1      0,703      ns
CL1916.Contigl_A11_167_1       1      0,6275    ***
CL 1891 .Contig3_A11_1 661     1      0,735      ns
CL1101.Contigl_A11_110_1       1      0,6975    ***
CL1366.Contig2_A11_134_1       1      0,7245     ns
CL1491.Contigll_A11_143_1    0,7815   0,7575     *
CL2455.Contigl_A11_192_1     0,9645   0,7375     *
CL2556.Contigl_A11_201_1     0,844    0,681      *
CL1004.Contig2_A11_94_1      0,733    0,607      *
CL2322.Contig2_A11_181_1     0,5665    0,71      **
CL2455.Contigl_A11_193_1     0,969    0,7315     *
CL3840.Contigl_A11_265_1       1      0,658      *
CL2364.Contig3_A11_186_1       1      0,697      ns
CL274.Contig3_A11_22_1       0,7815   0,6525     ns
CL2958.Contigl_A11_219_1     0,938    0,638      **
All SSR (Average)            0,911    0,685
Standard Deviation           0,035    0,015

N: Allele number; Na: Polymorphic allele number; Ne: Informative
allele number; Ho: Observed heterozygosity; He: Expected
heterozygosity; HWE: Hardy-Weinberg equilibrium (Detection of
significant differences through Chi2 test); ns: Non-significant
differences, *P<0.05; **P<0.01, ***P<0.001.

Table 6. Genetic diversity parameters for haplotypical data obtained
with SNP markers in R. glaucus.

Parameter                                     Average value

Number of SNP-containing regions                   1162
Number of SNP-containing effective regions         1082
Percentage of polymorphic SNPs                    12.49%

Parameter                                     Standard deviation

Number of SNP-containing regions                    0.005
Number of SNP-containing effective regions          0.003
Percentage of polymorphic SNPs                        --

Table 7. Progenitor selection for future breeding schemes.

Selected cultivar   Thorn presence/ absence       Response against
                                              C. gloeosporioides attack

UTP1                     Absence (SE)                 Tolerant
UTP5                     Presence (CE)            Very susceptible
UTP7                     Absence (SE)            Moderately tolerant
UTP11                    Absence (SE)            Moderately tolerant
UTP20                    Presence (CE)            Very susceptible
UTP28                    Presence (CE)            Very susceptible

Selected cultivar   Cluster number   Cluster number
                      with SNPs         with SSR

UTP1                      1                4
UTP5                      3                1
UTP7                      4                2
UTP11                     1                2
UTP20                     2                3
UTP28                     4                3
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Title Annotation:Genetics and plant breeding
Author:Lopez, Ana Maria; Barrera, Carlos Felipe; Marulanda, Marta Leonor
Publication:Revista Brasileira de Fruticultura
Date:Mar 1, 2019
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