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 (https://www.ebi.ac.uk/Tools/msa/ clustalo/). Finally, for the alignment of obtained sequences for all samples it was employed the MAFFT software, online version (www.ebi.ac.uk/Tools/mafft). 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).
ALICE, L.A.; ERIKSSON, T.; ERIKSEN, B.; CAMPBELL, C.S. Intersubgeneric hybridization between a diploid raspberry, Rubus idaeus, and a tetraploid blackberry, R. caesius (Rosaceae). American Journal of Botany, New York, v.84, p.171, 1997.
BERTRAND, C.; COLLARD, Y.; MACKILL, D. J. Marker-assisted selection: an approach for precision plant breeding in the twenty--firs century. Philosophical Transactions of the Royal Society of London Series B, Biological Sciences, New York, v.363, p.557-572, 2008.
CLARK, L.V.; EVANS, K.J.; JASIENIUK, M. Origins and distribution of invasive Rubus fruticosus L. agg. (Rosaceae) clones in the Western United States. Biological Invasions, Berlin, v.15, n.6, p.1331-1342, 2013.
DELGADO, L.; URIBE, M.; MARULANDA, M. L. Estandarizacion de la tecnica citogenetica "SQUASH". Scientia Et Technica, Pereira, v.17,n.46, p.74-79,2010.
DOSSETT, M.; BASSIL, N. V.; LEWERS, K.S.; FINN, C. E. Genetic diversity in wild and cultivated black raspberry (Rubus occidentalis L.) evaluated by simple sequence repeat markers. Genetic Resources and Crop Evolution, Dordrecht, v.59, n.8, p. 1849-1865, 2012.
DUARTE-DELGADO, D.; CHACON M. I.; NUNEZ, V.; BARRERO, L. S. Preliminary assessment of AFLP fingerprinting of Rubus glaucus Benth. elite genotypes. Agronomia Colombiana, Bogota, v.29, n.1, p.7-16, 2011.
FAN, L.; ZHANG, M.Y.; LIU, Q.Z.; LI, L.T.; SONG, Y.; WANG, L.F.; ZHANG, S.L.; WU, J. Transferability of newly developed pear SSR markers to other Rosaceae species. Plant Molecular Biology Reporter, Dordrecht, v.31, n.6, p.1271-1282, 2013.
FU, P. C.; ZHANG, Y. Z.; YA, H. Y.; GAO, Q. B. Characterization of SSR genomic abundance and identification of SSR markers for population genetics in Chinese jujube (Ziziphus jujuba Mill.). Peer J, United State, v.4, p.1735, 2016.
GRAHAM, J. AND MCNICOL R.J. An examination of the ability of RAPD markers to determine the relationships within and between Rubus species. Theoretical and Applied Genetics, Berlin, v.90, p.7/8, p.1128-1132, 1995.
GRAHAM, J.; SMITH, K.; MACKENZIE, K.; JORGENSON, L.; HACKETT, C.; POWELL, W. The construction of a genetic linkage map of red raspberry (Rubus idaeus subsp. idaeus) based on AFLPs, genomic-SSR and EST-SSR markers. Theoretical and Applied Genetics, Berlin, v.9, n.4, p.740-749, 2004.
GRAHAM, J.; SQUIRE, G. R.; MARSHALL, B.; HARRISON, R.E. Investigation of rubus breeding anomalies and taxonomy using RAPD analysis. Molecular Ecology, London, v.6, n.11, p.1001-1008, 1997.
HAMMER, O.; HARPER, D. A. T.; RYAN, P. D. PAST-Palaeontological statistics. 2001. Disponivel em: <www.uv.es/~ pardomv/pe/2001_1/past/pastprog/past.Pdf>. Acesso em: 25 jul. 2001.
HE, J.; ZHAO, X.; LAROCHE, A.; LU, Z. X.; LIU, H.; LI, Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Frontiers in Plant Science, Lausane, v.5, p.484, 2014.
JENNINGS, D. L. Raspberries and blackberries: their breeding, diseases and growth. London: Academic Press, 1988. p.230.
JENNINGS, D. L. Resistance to Leptosphaeria coniothyrium in the red raspberry and some related species. Annals of Applied Biology, London, v.93, n.3, p.319-326, 1979.
JUKES, T. H. AND CANTOR, C. R. Evolution of protein molecules. Mammalian protein Metabolism, New York, v.3, p.21-132, 1969.
KRAFT, T.; NYBOM, H.; WERLEMARK, G. Rubus vestervicensis (Rosaceae)--its hybrid origin revealed by DNA fingerprinting. Nordic Journal of Botany, Oxford, v.15, n.3, p.237-242. 1995.
LANGMEAD, B.; SALZBERG, S. L. Fast gapped-read alignment with Bowtie 2. Nature Methods, New York, v.9. n.4, p.357-359, 2012.
LI, H.; DURBIN, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, New York, v.25, n.14, p.1754-1760, 2009.
LOBO, M.; MEDINA, C.L.; DELGADO, OA.; ZULUAGA, M L.; CARDONA, M.; OSORIO, A. Recursos geneticos de frutales andinos en el sistema de bancos de germoplasma del estado colombiano. In: SEMINARIO NACIONAL DE FRUTALES DE CLIMA FRIO MODERADO, 4., 2002, Anais ... Medellin.
LONGHI, S.; GIONGO, L.; BUTI, M.; SURBANOVSKI, N.; VIOLA, R.; VELASCO, R.; & SARGENT, D. Molecular genetics and genomics of the Rosoideae: state ofthe art and future perspectives. Horticulture Research, London, v.1, p.1-18, 2014.
LOPEZ-VASQUEZ, J.; CASTANO-ZAPATA, J.; MARULANDA, M.; LOPEZ, A. Characterization of Anthracnose resistance caused by Glomerella cingulata and productivity of five Andean blackberry genotypes (Rubus glaucus Benth.). Acta Agronomica, Palmira, v.62, n.2, p.174-185, 2013.
MARULANDA, M L.; AND LOPEZ, A.M. Characterization of thornless Rubus glaucus in Colombia. Canadian Journal of Pure & Applied Sciences, Burnaby, v.3. n.3, p.875-885, 2009.
MARULANDA, M.L.; LOPEZ, A.M.; AGUILAR, S.B. Genetic diversity of wild and cultivated Rubus species in Colombia using AFLP and SSR markers. Crop Breeding and Applied Biotechnology, Londrina, v.7, n.3, p.242252, 2007.
MARULANDA, ML.; LOPEZ, A.M.; URIBE, M. Molecular characterization of the Andean blackberry, Rubus glaucus, using SSR markers. Genetics Molecular Research, Ribeirao Preto, v.11, p.322-331, 2012.
MCCALLUM, S.; GRAHAM, J.; JORGENSEN, L.; ROWLAND, L.J.; BASSIL, N.V.; HANCOCK, J.F.; WHEELER, E.J.; VINING, K.; POLAND, J.A.; OLMSTEAD, J.W.; BUCK, E.; WIEDOW, C.; JACKSON E.; BROWN, A.; HACKETT, C.A. Construction of a SNP and SSR linkage map in autotetraploid blueberry using genotyping by sequencing. Molecular Breeding, Dordrecht, v.36, p.1-24, 2016.
MOOSE, S.P.; MUMM, R.H. Molecular plant breeding as the foundation for 21 century crop improvement. Plant Physiology, Lancaster, v.147, p.969-977, 2008.
MORALES, Y. M.; MARULANDA, M. L.; ISAZA, L. Caracterizacion morfologica y patogenica de aislamientos del genero Colletotrichum spp. causantes de la antracnosis en mora de castilla (Rubus glaucus Benth.) provenientes de los departamentos de Caldas, Quindio y Risaralda (Colombia). 2010. 53 f. Thesis (Doctor)--Facultad de Ciencias Basicas y Tecnologias, Universidad del Quindio, Quindio, 2010.
NARUHASHI, N. Rubusx semi-nepalensis, a new natural hybrid from Nepal Himalaya . Journal of Japanese Botany, Tokyo, v.65, n.6, p.186-191, 1990.
PEAKALL, R. O. D.; SMOUSE, P. E. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Resources, Oxford, v.6, n.1, p.288-295, 2006.
SALAZAR, J. A.; RUBIO, M.; RUIZ, D.; TARTARINI, S.; MARTINEZ-GOMEZ, P.; DONDINI, L. SNP development for genetic diversity analysis in apricot. Tree Genetics & Genomes, Heidelberg, v.11, p.15, 2015.
SALDARRIAGA-CARDONA, A.; CASTANO-ZAPATA, J.; ARANGO-ISAZA, R. Caracterizacion del agente causante de la antracnosis en tomate de arbol, manzano y mora. Revista de la Academia Colombiana de Ciencias Exactas, Fisicas y Naturales, Bogota, v.32, n. 123, p.145-156, 2008.
SANCHEZ-PEREZ, R.; MARTINEZ-GOMEZ, P.; DICENTA, F.; EGEA, J.; RUIZ, D. Level and transmission of genetic heterozygosity in apricot (Prunus armeniaca L.) explored using simple sequence repeat markers. Genetic Resources and Crop Evolution, Dordrecht, v.53, n.4, p.763--770, 2006.
SCHULZE-MENZ, G. K. Rosaceae. In: MELCHIOR, H. Engler's syllabus der pflanzenfamilien. Berlin: Gerbruder Borntraeger, 1964. p.209-218.
THOMPSON, M. M. Chromosome numbers of Rubus species at the national clonal germplasm repository. HortScience, Alexandria, v.30, n.7, p.1447-1452, 1995.
THOMPSON, M. M. Survey of Chromosome Numbers in Rubus (Rosaceae: Rosoideae). Annals of the Missouri Botanical Garden, St Louis, v.84, p.128, 1997.
VUKOSAVLJEV, M.; ESSELINK, G.D.; VAN'TWESTENDE, W.P.; COX, P.; VISSER, R.G.; ARENS, P.; SMULDERS, M.J. Efficient development of highly polymorphic microsatellite markers based on polymorphic repeats in transcriptome sequences of multiple individuals. Molecular Ecology Resources, Oxford, v.15, n.1, p.17-27, 2015.
WAUGH, R.; VAN DE VEN, W.T.G.; PHILLIPS, M.S.; POWELL, W. Chloroplast DNA diversity in the genus Rubus (Rosaceae) revealed by Southern hybridization. Plant Systematics and Evolution, Wien, v.172, n.1/44, p.65-75, 1990.
WEBER, H. E. Former and modern taxonomic treatment of the apomicticrubus complex. Folia Geobotanica, Jena, v.31, n.3, p.373-380, 1996.
YAMAMOTO, T.; TERAKAMI, S. Genomics of pear and other Rosaceae fruit trees. Breeding Science, Tolyo, v.66, n.1, p.148-159, 2016.
ZAMORANO, A.; MORILLO, A.; MORILLO, Y.; MUNOZ, J. Caracterizacion Molecular con Microsatelites Aleatorios RAMs, de la Coleccion de mora Rubus spp. In: SEMINAR OF BIOTECHNOLOGY, 9, 2004. Anais... Palmira: Universidad Nacional de Colombia, 2004.
Ana Maria Lopez (1), Carlos Felipe Barrera (2), Marta Leonor Marulanda (3)
Corresponding author: email@example.com
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: firstname.lastname@example.org (ORCID 0000-0002-5138-1806)
(2) PhD. Universidad Nacional de Colombia-Medellin. Agricultural Sciences Faculty. Arauca-Colombia E-mail: email@example.com (ORCID 0000-0002-5015-2956)
(3) PhD. Universidad Tecnologica de Pereira. Research, Innovation and Extension Vice-chancellory. Pereira-Colombia. E-mail: firstname.lastname@example.org (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 (https://www.ebi.ac.uk/Tools/msa/clustalo/).
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 Absence 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 CL 1004. Contig2_A11_94_1 CAGATTTGAATTATGGTGGGTGT CLllOl.Contig1_11_110_1 GACCCAACTATGCTTGTCGTTAC CL 1366. Contig2_A11_134_1 AAGGATGATTGTCACGTATGAGG CL1491.Contig11_A11_143_1 CTTGGCTTTAGAAACTTGGGAGT CL150.Contig2_A11_12_1 CCATCAAGATTGAGTTTGCTTCT CL1916.Contig1_A11_167_1 ACAGCCAAGAATGACCTACAAAA CL1891.Contig3_A11_166_1 GAGGGAGAGATTTGGAGATGAAT CL2218.Contig3_A11_177_1 AAGCTTTCAAGTGCAACCTACTG CL2322.Contig2_A11_181_1 CTGTTTGCGAAGGATCTGTAAC CL2455.Contig1_A11_192_1 AGCTTGGACTGTGAACAAGGAT CL2455.Contig1_A11_193_1 CAGATTTCAGCCAAGAAGAGGTT CL2364.Contig3_A11_186_1 CCAAACATGAAATCAGTAGGGAA CL274. Contig3_A11_22_1 CTGTTGTTATCGCTGTTGTTGAT CL2958.Contig1_A11_2191 TTATTTCTCTCCAAAATGCAACG CL2556.Contig1_A11_201_1 AGAGGTGTGGTGTTGTTTGTTGT CL3540.Contig2_A11_254_1 CAACTCCAATCTCAGCTTTCTGT CL2787.Contig1_A11_2101 TAGATCTTAGGCCTCGTTTGGTT CL3301. Contig1_A11 237_1 TGTGTATGGATATAGGGAGGGTG CL3840.Contig1_A11_265_1 GAAGTCAAAGTCCTGGAGGAGAG CL4175.Contig2_A11_292_1 CTGTGATCATCTTCTTCCTGCTT Identification code R Primer CL 1004. Contig2_A11_94_1 TCCTTTCCTTCTCACCCTTTAAC CLllOl.Contig1_11_110_1 GATTGGAACACGAGACCTACAAC CL 1366. Contig2_A11_134_1 ACTCGGCAATCCATTCTCTATTT CL1491.Contig11_A11_143_1 CTTCAAAGAAGAAAGTTGTTGGC CL150.Contig2_A11_12_1 ATTGAAGAATGCAACGAGATGAT CL1916.Contig1_A11_167_1 ACGTGAAAACTGAGTTGGAAGAG CL1891.Contig3_A11_166_1 GTGCCATAAGCTTACAGGTTCAG CL2218.Contig3_A11_177_1 TTTGGGATTTTGGAATTTTTCTT CL2322.Contig2_A11_181_1 TGACGCAATGATATTACGATGAG CL2455.Contig1_A11_192_1 CAACAATCACCAACCCAAGAC CL2455.Contig1_A11_193_1 CGATCTCCTTCTTCTTCCTCTTT CL2364.Contig3_A11_186_1 TCATAAGAGGGCCATAAGAATGA CL274. Contig3_A11_22_1 AGAGACCTTGTGAAGGAGTGGTT CL2958.Contig1_A11_2191 AAAAAGGAACAAACACCTGAACC CL2556.Contig1_A11_201_1 AAAATGCCACTTTTCCTATTGAA CL3540.Contig2_A11_254_1 CGATATTGACGACTCTACCTTCG CL2787.Contig1_A11_2101 CCAAACACTTGAAAGGAAAGCTA CL3301. Contig1_A11 237_1 TGTTCCTTCTTCCTTCCTTCTTT CL3840.Contig1_A11_265_1 CTCACTCTCCGTAAACCCATCAC CL4175.Contig2_A11_292_1 ACCAAAGCTTTTACCTTGGTGTT 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 Reported 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. vesca 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. vesca 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. Primers Sequence Forward Reverse identification Unigene11151 TATGTGGGGGTGAAGAAAGC ACAGGACCCAATCATCCAAC Unigene11157 CCAAGGAAACTTGCTCCAAC AGCCTTAAACTTGCCAGCAC Unigene11255 TGATGGCGCAGATAAGAAGA AGACTCAACAGCGCCAACTT Unigene12343 TGGATCCAGATGAGTCCAGA CGGACGTTTTCCCAAATCTA Unigene12924 GGACCAATTCCTTGTGTGCT TGCCGTGACTGTATCCTTGA Unigene13090 GGCTCAGAACTGTGGGGTTA CACATTGTAGGCATCCCAGA Unigene1465 TCGTCTGTTTTGGCTCTTGA TACTCCCCTTGCTTGAGTCG Unigene14681 ATCAGGAATGGGCTGAGCTA AGCAGCCTTCAAACTCTCCA Unigene14822 TACTGGATCGCTCAGCTCCT TGTGTACACCAACCCGAATG Unigene14951 ATGGCAGTACCCAAATCAGC TGGGTAATTGATGGTGGTGA Unigene15095 TTCCTGCTGATGAATGCAGA GAACCTGTCCTTGGAGCTTG Unigene15115 CCATTCATGGGGTAATTTGC AAGCTTTCCCAATTGCCTCT Unigene15294 GCCAGGAGTTTGCTGAGTTC ATGGGCAAGTAGCTCTCCAA Unigene15456 ACAAGCTTCTGGTGGAAGGA AAGAAGAGCCCCGTCAAACT Unigene15499 TGCACCAACAGACCATAAGC ATAATTCCCACAGGCTGTCC Unigene15574 CCTGCTGAGGTGGAATCAGT CGATCCAACAAACATGCCTA Unigene16239 AGAGGGAGGATCAAGGAGGA GGGCATTGTATCATGTACGG Unigene16323 AAAGACGGTGGAGAGGAACC TTTATGTAGGAGGCCGCAAG Unigene16368 GGTTGCCAAGATCAAAGAGG CCGGTGTGCTTAGTTCCTTG Unigene16415 GCGGGTGCAGATAAGAAAAG TTCTTCTTGCGCTCCATAGC Unigene16433 AGCAGGAGAGGAAACTCCAA TGGCATAAAGCTCAAGATGC Unigene16551 CTTGTTTCCCCTTCATCCAA TTGCAGCATTTCCCTCTCTT Unigene2064 AGTACACGGATGCCTTGCTT GAGGCGCTACAGGGATGTTA Unigene2247 GTGTCCGGAGATCAAGCAAC CTTTGATAGCCTGCCCAATC Unigene2361 AGCAGTTGCTGCACTTTCAA AACGCCTGTTCACTTTTTGC Unigene2373 TGGCCTCACCAACACTTGTA GAGTCGCCACAGCGATAGAT Unigene242 GGAGAAGAAGCTGCTGGAGA TCGCTCTTGACCCTCTCAAT Unigene31842 CTGGCCAGAAGAAGGATTGA TCTCCAAGAAGAATGTTTGAAGG Unigene33255 TGATGGCTTGAAGCTTTTGA AGCGCTTGAAACAAATTTCC Unigene34846 GTCAGGACCTCAGTGCTGCT GGTGGGTGAGTACCAAATATGTC Unigene34996 ATCAGTGCTGGGGTGAATG CTCCCCTGATGCGATCTTAG Unigene351 TTGCTGATGACACGAATGGT TTGTTGGCAAATGTCCGATA Unigene36231 AAGGGAGATGTGGTGTTGGA TAAAACCAACACCCCCAAGA Unigene3673 GGGCTGCACCTCTTTGTATC ACATGCTCCCACAAACGAAT Unigene37334 GTCCACAAGGCTTCCTTCAG GATTCTGTTGCCCTTGCACT Unigene42870 TAGAGGGCTCGAAGAAGGTG AGGTCCATCTTGCTGGGTAG 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. vesca 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. Vesca 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 Unigene2064 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. Vesca Unigene33255 MH479053 XM 024315336.1 Rosa chinensis Unigene34846 Unigene34996 MH479054 XM_004295163.2 Fragaria vesca subsp. Vesca Unigene351 MH479055 XM_024307531.1 Rosa chinensis Unigene36231 Unigene3673 Unigene37334 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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