Genetic Assessment in the Andean Tropical Fruits Solanum quitoense Lam. and S. betaceum Cav.: Efforts Towards a Molecular Breeding Approach
by
,
Eduardo Morillo
1
,
Johanna Buitron
1,
Denisse Yanez
1,2,
Pierre Mournet
3,
Wilson Vásquez-Castillo
2,* and
Pablo Viteri
1 1
Estación Experimental Santa Catalina, Instituto Nacional de Investigaciones Agropecuarias (INIAP), Quito 170518, Ecuador
2
Ingeniería Agroindustrial y Alimentos, Universidad de Las Américas (UDLA), Quito 170125, Ecuador
3
Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD-BIOS), UMR 1334 AGAP, 34398 Montpellier, France
*
Author to whom correspondence should be addressed.
Plants 2025, 14(6), 874; https://doi.org/10.3390/plants14060874
Submission received: 31 December 2024
/
Revised: 19 February 2025
/
Accepted: 27 February 2025
/
Published: 11 March 2025
(This article belongs to the Special Issue Trends in Research on Plant Genetic Resources: Insects, Plant Diseases, and Genetic Diversity)
Abstract
:Solanum quitoense and S. betaceum called, respectively, naranjilla and tomate de arbol, are both tropical Andean fruits of growing interest in the region. Microsatellite primers (SSRs) identified by NGS technology in both species were screened for the development of SSR marker technology. In S. quitoense, it was found that 41 primers were successfully transferred to six Lasiocarpa closely related species. Using multiplex primer combinations with the M13-Tailing technology in the DNA analyzer LI-COR 4300s, the variability of these primers in seven S. quitoense landraces was characterized. This SSR survey confirmed the narrow genetic base of S. quitoense cultivars with the polymorphism of 14 SSR markers. Moreover, transferability rates and genetic diversity analysis revealed a closer genetic relationship between the species S. candidum and S. hirtum among the Lasiocarpa germplasm screened. On the other hand, 110 SSR primers were screened in four cultivars, segregating plants and wild-related accessions of S. betaceum. Polymorphisms for only eight SSR primers were found but including the wild relative S. unilobum; in S. betaceum, no SSR showed polymorphism confirming the high genetic homogeneity of the cultivars. The results of this study are potentially useful for S.quitoense and S. betaceum genomics, providing an initial set of SSR markers for molecular characterization in S. quitoense germplasm and perspectives for S. betaceum.
1. Introduction
The species Solanum quitoense Lam. (naranjilla) and S. betaceum Cav. (tomate de árbol or tree tomato) are both important commercial fruit crops for small- and medium-sized farmers in Ecuador and other countries of the region. Both plants are of great potential but require the development of new biotechnology tools to accelerate the processes of germplasm characterization and molecular breeding. S. quitoense is a native fruit crop of growing interest in Ecuador and the Andean region (Figure S1A). Taxonomically, it belongs to the section Lasiocarpa, which includes between 11 and 13 species within the Solanaceae family [1]. This fruit is known as “naranjilla” in Ecuador and “lulo” in Colombia. S. quitoense represents a crop of high economic potential for the Amazon region in Ecuador, where up to 93% of the national production is located [2]. Nevertheless, in recent years, an appreciable reduction in the crop’s productivity has been noted due to its susceptibility to several diseases and pests, such as fusariosis (Fusarium oxysporum species complex), nematodes (Meloidogine spp.), and fruit worm (Neoleucinodes elegantalis, Lepidoptera: Crambidae) [3,4]. The identification of sources of genetic resistance and the development of new varieties are critical points to be considered for the development of the crop.
Given these circumstances, it is important to characterize and explore the diversity of related species that are sources of genes for S. quitoense breeding. The origin of the taxon’s genetic diversity is located in Ecuador, Colombia, and Peru; however, it is estimated that there is low genetic variability in its cultivated form [5,6]. To increase the genetic base of S. quitoense, researchers have focused on resistance to pests, which are the main problem for crop production [7]. Many efforts have been reported for the development of interspecific hybrids in order to confer resistance to biotic problems, such as nematodes, but evidence has pointed to a loss of quality in the fruits [2]. Breeding efforts have been supported by morphological and physiological observation; however, there are limitations in the development of breeding programs due to the absence of genetic information. The estimation of the level of polymorphism and the development of specific molecular tools for S. quitoense are important scientific requests pointed out by previous studies because only heterologous primers have been tested and polymorphism has rarely been reported [6,8].
S. betaceum is a native plant of South America (Figure S1B). Studies show that its most probable center of origin is the jungles and forests of the Tucumano-Bolivian reserve in northwestern Argentina and southern Bolivia. It is considered that the center of domestication of this fruit was in northern Peru and southern Ecuador [9]. The main tree-tomato-producing countries are Ecuador and Colombia. In the former, tree tomato cultivation is carried out mainly by small and medium producers in the provinces of Carchi, Imbabura, Pichincha, Cotopaxi, Tungurahua, Chimborazo, Bolívar, Cañar, Azuay, and Loja [9,10]. In these areas, the tree tomato cultivation area increased by 70% from 2015 to 2017, going from 4500 to 7600 ha [9,11].
The need to improve this fruit tree by breeding programs is essential. A drawback is that breeding periods take longer due to the productive cycle of this fruit crop [10]. Therefore, the use of modern techniques is undoubtedly favorable. Molecular techniques allow increasing knowledge of the distribution of the genetic diversity of different crops, and the selection of potential parents for the breeding program with desirable agronomic characteristics is required [12,13]. Molecular markers are one of the main techniques used to study variability and the traits of interest in crops of agricultural importance [14]. Genetic variability studies have been carried out specifically into tree tomatoes using arbitrary markers, for instance, AFLPs or heterologous SSRs transferred from other species of the genus Solanum [6,15,16,17].
In this study, first, we screened SSR primers from a DNA sequence dataset obtained from Illumina shotgun libraries in S. quitoense and S. betaceum. We then reported an analysis of SSR diversity in S. quitoense and wild close relatives that are currently being used for genetic improvement in Ecuador.
2. Materials and Methods
2.1. DNA Library and NGS Sequencing
Genomic DNA was isolated from dried leaf samples using a procedure reported by Souza et al. [18]. The biological material used for S. quitoense was the local variety “Palora” and “Gigante” for S. betaceum.
The DNA library was prepared using the NexteraTM DNA Sample Kit (Illumina, San Diego, CA, USA; Ref. GAO9115). DNA fragmentation started with 50 ng of purified genomic DNA, followed by end-polishing and sequencing adaptor ligation to prepare di-tagged DNA fragment libraries. The quality of the libraries was assessed using a 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA), and the concentration was quantified using a KAPA LibraryqPCR kit (KR0390; Kapa Biosystems, Woburn, MA, USA). Sequencing was performed in an MiSeq Sequencer (Illumina) using the 2 × 300 bp read mode.
2.2. Microsatellite Search and Primer Screening
To reduce the raw dataset and maximize the length of the sequences, an assembly of the sequences was performed using the ABySS (Assembly By Short Sequences) assembler [19]. The MIcroSAtellite identification tool (MISA) was used in order to search for microsatellite motifs among the assembled contigs [20]. The search used specific criteria for the design of primers; these included motifs 2–6 bp in size, with a minimum repeat number of four repetitions, and with a maximum difference between two SSRs of 100 bp. The primers would, thus, amplify one or more repeats, effectively encompassing them as the same repeat motif if these were 100 bp or less from each other. For the screening of SSR primers, a set of 100 primer pairs were selected for each species. Sequences containing di- and tri-SSR motifs with a minimum of ten repetitions were retained for the primer survey. SSR contigs were verified beforehand using BLAST v2.7.1. to ensure that they did not match any sequence. For S. quitoense, we screened 53 primer pairs corresponding to di-SSR motifs and 47 tri-SSR motifs; for S. betaceum, we screened 90 di-SSR motifs and 20 tri-motifs (see Supplementary Materials). The PCR amplification of the expected SSR fragments was first verified on agarose gels. The primers that did not produce a single PCR product or poor amplification were excluded from polymorphism screening.
2.3. Polymorphism Screening and DNA Genotyping
For S. quitoense, 60 DNA samples of seven landraces were used: 10 plants each from the following varieties: espinosa, agria, bolona, morada, baeza, and baeza roja. We also included 60 DNA samples from six Lasiocarpa species that are being used for interspecific experimental crosses in INIAP: S. candidum, S. hirtum, S. pectinatum, S. pseudolulo, S. sessiliflorum, and S. stramonifolium (see Table 1). For S. betaceum, 40 DNA samples were used: 10 plants each from Gigante anaranjado, Gigante morado, Puntón anaranjado, and Puntón morado. We also included five segregating plants from a cross between S. betaceum and S.unilobum, and five accessions from the wild relative S. unilobum from the INIAP genebank (Table 1).
For DNA extractions, we used the protocols described by Souza et al. [18] for leaf tissues and Kang [21] for seeds. DNA was quantified by spectrophotometry and normalized to 5 ng/µL to carry out the PCR assays. Genotyping was performed in an LI-COR 4300s DNA analyzer (Lincoln, NE, USA) using the M13 Tailing methodology according to [22]. PCR amplifications were multiplexed in a 7 µL reaction volume with 10 ng DNA per reaction, 0.01 µM forward primer with M13 tail, 0.16 µM of reverse primer, 0.16 µM of M13 primer with IRDye700 or IRDye 800 fluorescence, 2.5 mM of MgCl2, 0.2 mM of dNTP, and 0.25 U of Gotaq DNA polymerase. SSR visualization was carried out using the IRDye fluorescence scanning system and allele size assignment was calculated in SAGA-GT™ v3.3.0 software (LI-COR, Lincoln, NE, USA) with a 50–350 bp molecular size marker IRDye (reference Cat. No. LI-COR 829-05343/44). SSR matrix data were exported to Excel for statistical analysis.
2.4. Statistical Analysis
Statistical parameters, such as the number of alleles per locus, observed (Ho) and expected heterozygosities (He), polymorphism information content (PIC) of each locus, and the presence of linkage disequilibrium (LD) were calculated with POWER MARKER 3.25 [23]. Multivariate analyses (PCO) were performed based on the Jaccard similarity coefficient and NJ method using the NTSYS-pc v2.02 program [24]. Unrooted majority rule consensus tree and bootstrap analysis were performed using 1000 replicates with the Phylip package’s Consensus tree program, version 3.6a3 [25].
3. Results
3.1. NGS Sequencing Analysis and In Silico SSR Identification
For S. quitoense, a total number of 1,400,090 sequences were examined, of which 31,759 assembled contigs were identified as containing SSR motifs. For S. betaceum, a total number of 1,732,580 sequences were examined, of which 68,685 assembled contigs contained SSR motifs. The distribution of the different repeat type classes for both crops is detailed in Table 2.
3.2. SSR Variability
An amount of 100 SSR primers were tested in S. quitoense (see Supplementary Materials), 96 had validated PCR amplification and 41 were polymorphic in the screened Lasiocarpa species (Table 3). As detailed in Table 3, primer SSR transferability was total for 10 primers to the six Lasiocarpa screened species, and for 9 primers, it resulted in less than 50%. Among the wild species, the highest transferability rate was observed with the species S. hirtum and S. candidum (78.0% and 87.8%, respectively), while for S. sessiliflorum, the PCR rate was only 48.8%. Sequences of the 41 SSR primers are available in Supplementary Materials.
In the case of the cultivated, 14 primers showed polymorphism in S. quitoense cultivars. The primers detailed in Table 4 are, thus, useful SSR markers for S. quitoense genotyping. Among these, primers mSq012, mSq018, and mSq059 revealed three SSR alleles while the others only two.
Table 5 shows the diversity statistics of the 14 SSRs in S. quitoense and the Lasiocarpa species. Allele number was higher with primer mSq066 with 10 alleles while primer mSq041 only revealed four alleles. For four primers, the observed heterozygosity was higher in the cultivated S. quitoense, and the PIC values were in all cases lower than those obtained in wild species. The PIC mean value for the cultivated decreased from 0.64 to a value of 0.178.
Regarding S. betaceum, an amount of 110 primers were tested (Supplementary Materials). The amplification of the expected fragment in 75 primers was validated. Eight of these primers (mSb005, mSb082, mSb089, mSb09, mSb09, mSb102, mSb104, and mSb106) were potentially informative when the wild relative S. unilobum was screened. We observed a high homozygosity and no SSR variability among the screened varieties, even though a significant number of primers were tested.
3.3. Genetic Diversity Analysis
The diversity analysis of Lasiocarpas and S. quitoense varieties based on polymorphic SSR primers evidenced an average of 6.4 alleles per locus. The Msq066 primer was the most informative with a PIC value of 0.76, the average PIC of the analysis was 0.64, and the genetic diversity was 0.69 (see Table 5). In the diversity analysis of S. quitoense landraces, we observed up to four alleles per primer. The Msq012 primer was the most informative with a PIC value of 0.511, the average PIC was 0.178, and the genetic diversity was 0.219.
The consensus tree showed clades differentiated by species for each of the Lasiocarpas analyzed (Figure 1). The species S. pseudolulo appears to be the most divergent out of the remaining Lasiocarpa species. The remaining analyzed species are clearly distributed into three differentiated groups: S. stramonifolium forms a group separate from the rest; S. sessiliflorum and S. pectinatum form a second group; while S. hitum, S. quitoense, and S. candidum form a third group. The species S candidum is shown as being more closely related to S. quitoense. The S. quitoense cluster is clearly differentiated (supported by a 100 bootstrap value), distributing the cultivars into four subgroups: Baeza roja and Espinosa were distinguished from the Baeza/Morada and Bolona/Agria subgroups.
The multivariate analysis (Figure 2) corroborates Lasiocarpa species differentiation from S. quitoense varieties. Bolona and Baeza roja appeared more differentiated from the rest of the S. quitoense samples. In terms of the Lasiocarpa species, S. candidum, S. hirtum, and S. sessiliflorum are located closer to S. quitoense. The analysis revealed that S. stramonifolium, S.pseudolulo, and S. pectinatum were more divergent.
In S. betaceum varieties, no polymorphic SSR primer was confirmed; only eight SSR primers were revealed to be polymorphic when the wild-related S. unilobum samples were included. The absence of SSR variability in S. betaceum is certainly due to the fact that the genetic base in this crop is very limited and SSR screening should include a high number of primers.
4. Discussion
The availability of high-throughput sequencing with NGS technology allowed the possibility to identify a substantial number of microsatellite sequences in S. quitoense and S. betaceum at lower cost and effort than precedent approaches [26,27]. The development of SSR technology in undervalued crops is an appropriate tool for Andean research programs in order to detect easily genetic variability or polymorphisms in local germplasm or segregant materials. In this first assessment, we screened a set of SSR primers for both species, generating a first set of useful primers for DNA genotyping.
In the exploratory genetic survey with the most representative S. quitoense varieties and the most used wild Lasiocarpa species in INIAP, 14 polymorphic SSR primers were detected, they showed high inter- and intraspecific variability with a PIC average value of 0.64 which is a higher value compared to a previously reported in S. quitoense (0.40) using heterologous SSR primers from S. tuberosum genome [6]. According to the interpretation of Botstein et al. [28], given the polymorphism index, markers with PIC values greater than 0.5 are considered highly informative, which is the case for the developed markers reported here (i.e., the PIC values of the 13 SSR primers are higher than 0.5). The 14 markers were used only to analyze S. quitoense varieties, and the PIC decreased to 0.178, which showed a reduced genetic base in the samples analyzed. This result reveals a reduced genetic base present in the cultivars compared to the wild species screened, probably the result of selective breeding derived from the domestication process. However, these results cannot be extrapolated to the entire S. quitoense crop existent [29], indicating that in order to infer data towards a population, the analyzed sample must be representative; thus, these results only reflect the usefulness of these SSR markers.
Regarding SSR transferability in other Lasiocarpa species, 41 useful primers were obtained. The transferred primers indicated that wild species have higher allelic diversity than S. quitoense varieties. This could be explained by the domestication process that many Andean fruit trees have suffered, most of which went from being wild to cultivated in a short space of time and therefore still share multiple alleles [30,31]. The diversity analysis clearly differentiated each Lasiocarpa species, as previously reported by Torres et al. [6] in a preliminary assessment with a reduced number of heterologous SSR primers. Among the SSR markers evaluated in S. quitoense, 10 showed heterozygous patterns, revealing 20 alleles among the cultivars. According to Lobo et al. [5], this result can be due to the low degree of domestication that some Andean fruit have suffered, where is possible to find heterozygosity as a result of survival mechanisms developed towards changing environmental conditions. However, a low genetic differentiation was observed between S. quitoense varieties, this finding agrees with the low rates of morphological, physiological, and genetic variability previously described [1,6,7,32]. Autogamy and selection by domestication could bring a decrease in allelic richness [6,8]. Fragmentation in natural populations and the replacement of varieties due to susceptibilities to diseases could explain the low genetic base in S. quitoense [8]. Other authors [5] have mentioned that due to there being a low degree of domestication in some Andean fruit trees, it is possible to find heterozygosity.
Diversity genetic analysis corroborated Lasiocarpa species differentiation from the cultivated S. quitoense. Torres et al. [6] argued that specific Lasiocarpa SSR markers will provide a better genetic relationship, which is the case in this study. This screening clearly evidenced that the Lasiocarpa species currently being used in genetic crosses with S. quitoense are genetically closer and are more related than other wild species.
In contrast, for S. betaceum, no polymorphic SSR primer was detected even when a significant number of SSR primers were screened. Only eight SSR primers were revealed to be polymorphic when including DNA samples from the wild S.unilobum species. Contrary to what was expected, the wild materials of S. unilobum, a species used as parents in the genetic breeding program in Ecuador, did not differ significantly from S. betaceum. This result agrees with a recent report [33], while a greater genetic distance between both species was previously reported [34]. In the same context, our SSR screen confirms the narrow genetic basis of the cultivars of S. betaceum. This result agrees with preliminary surveys which concluded that S. betaceum has a narrow and homogeneous genetic pool and homozygosity due to its autogamy system [15,26]. Domestication practices have caused the genetic erosion of the crop due to the limited use of varieties.
It is important to mention that the SSR bank sequences generated by NGS technology should be further explored for additional screening in this fruit crop. Applying other molecular tools based on NGS technology should be explored for additional screening in S. betaceum.
5. Conclusions
An important application of NGS technology is the development of molecular markers for understudied crops. SSR markers are one of the most informative and versatile markers used in plant genetic research. This study allowed us to efficiently identify a large number of SSR loci for S. quitoense and S. betaceum at a low cost. The set of SSR markers reported here will be used for surveying diversity in a larger collection of S. quitoense and Lasiocarpa germplasm. The SSR screening suggested a loss of alleles in the crop caused by domestication and the fragmentation of their populations. The validated SSR primers could be used with other molecular markers for developing a genetic map as a tool for breeding this important Andean fruit crop.
Meanwhile, the SSR sequence libraries generated for S. betaceum can be further explored for the screening of markers; however, other technologies such as Genotyping by Sequencing (GBS) should also be assessed in this crop.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants14060874/s1, Figure S1. (A) S. quitoense plant and fruits. (B) S. betaceum plant and fruits; Table S1. SSR sequences Squitoense.
Author Contributions
Conceptualization: E.M.; methodology: E.M.; sequencing and bioinformatics: E.M. and P.M.; writing—original draft preparation: E.M. and J.B.; genotyping: D.Y. and J.B.; writing—review and editing: E.M. and W.V.-C.; genetic material: P.V. All authors have read and agreed to the published version of the manuscript.
Funding
This work was funded by INIAP and the project “Fortalecimiento de la investigación para mejorar la productividad y calidad de la naranjilla y tomate de árbol en el Ecuador” supported by AECID (Grant reference 2018/SPE/0000400192).
Data Availability Statement
SSR genotyping results can be found in the following drive: https://docs.google.com/spreadsheets/d/1mJM7VS2DkazMoC6hvb7kCtf8qeL59n8D/edit?usp=sharing&ouid=115434843326122486251&rtpof=true&sd=true (accessed on 27 February 2025).
Acknowledgments
The first author thanks the UMR AGAP (Genetic Improvement and Adaptation of Mediterranean and Tropical Plants Research Unit) from CIRAD in Montpellier, France, where DNA library preparation, sequencing, and bioinformatic analysis were performed. We also thank the National Department of Plant Genetic Resources, José Ochoa from INIAP for providing plant samples, and William Viera for reviewing the manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Unrooted majority rule consensus tree obtained with Phylip package and Consensus tree program showing bootstrap significant values with 1000 trees.
Figure 1.
Unrooted majority rule consensus tree obtained with Phylip package and Consensus tree program showing bootstrap significant values with 1000 trees.
Figure 2.
PCO analysis showing the dispersion of the 120 genotyped Lasiocarpa samples based on SSR polymorphism. S. quitoense genome. sq: S. quitoense; can: S. candidum; hir: S. hirtum; str: S. stramonifolium; pse: S.pseudolulo; pec: S. pectinatum.
Figure 2.
PCO analysis showing the dispersion of the 120 genotyped Lasiocarpa samples based on SSR polymorphism. S. quitoense genome. sq: S. quitoense; can: S. candidum; hir: S. hirtum; str: S. stramonifolium; pse: S.pseudolulo; pec: S. pectinatum.
Table 1.
Voucher information for biological material used in this research.
| Species | Type | Variety or Group | INIAP ID or Source | Origin | Lab. Code |
|---|---|---|---|---|---|
| S. quitoense | Cultivated | Espinosa | INIAP ECU-3567 | Pichincha-Ecuador | Sq1 |
| S. quitoense | Cultivated | Naranjilla agria | INIAP ECU-3817 | Bolívar-Ecuador | Sq2 |
| S. quitoense | Cultivated | Naranjilla bolona | INIAP ECU-6235 | Morona Santiago-Ecuador | Sq5 |
| S. quitoense | Cultivated | Morada | INIAP Breeding program | Morona Santiago-Ecuador | Sq8 |
| S. quitoense | Cultivated | Baeza | INIAP Breeding program | Morona Santiago-Ecuador | Sq9 |
| S. quitoense | Cultivated | Baeza roja | INIAP Breeding program | Morona Santiago-Ecuador | Sq14 |
| S. candidum | Wild | Lasiocarpa | INIAP ECU-13242 | Not available | Sc |
| S. hirtum | Wild | Lasiocarpa | INIAP ECU-6242 | Morona Santiago-Ecuador | Sh |
| S. pectinatum | Wild | Lasiocarpa | INIAP ECU-7875 | Pastaza-Ecuador | Sp |
| S. pseudolulo | Wild | Lasiocarpa | INIAP Breeding program | Not available | Sps |
| S. sessiliflorum | Wild | Lasiocarpa | INIAP ECU-5552 | Morona Santiago-Ecuador | Ss |
| S. stramonifolium | Wild | Lasiocarpa | INIAP Breeding program | Pichincha-Ecuador | St |
| S. betaceum | Cultivated | Puntón Anaranjado | INIAP Breeding program | Pichincha-Ecuador | PA |
| S. betaceum | Cultivated | Gigante Morado | INIAP Breeding program | Pichincha-Ecuador | GM |
| S. betaceum | Cultivated | Gigante Anaranjado | INIAP Breeding program | Pichincha-Ecuador | GA |
| S. betaceum | Cultivated | Puntón Morado | INIAP Breeding program | Pichincha-Ecuador | PM |
| S. betaceum × S. unilobum | Segregant | - | INIAP Breeding program | Pichincha-Ecuador | 12 GR |
| S. betaceum × S. unilobum | Segregant | - | INIAP Breeding program | Pichincha-Ecuador | GT10P8 |
| S. betaceum × S. unilobum | Segregant | - | INIAP Breeding program | Pichincha-Ecuador | GT13P25 |
| S. betaceum × S. unilobum | Segregant | - | INIAP Breeding program | Pichincha-Ecuador | GT33P7 |
| S. betaceum × S. unilobum | Segregant | - | INIAP Breeding program | Pichincha-Ecuador | Cruzam. 5 |
| S. unilobum | Wild | progenitor | INIAP Breeding program | Pichincha-Ecuador | TUP1 |
| S. unilobum | Wild | progenitor | INIAP Breeding program | Pichincha-Ecuador | 25AP1 |
| S. unilobum | Wild | progenitor | INIAP Breeding program | Pichincha-Ecuador | KUYP1 |
| S. unilobum | Wild | progenitor | INIAP Breeding program | Pichincha-Ecuador | M15P1 |
| S. unilobum | Wild | progenitor | INIAP Breeding program | Pichincha-Ecuador | SAN CARLOS |
Table 2.
Summary of sequencing and microsatellite (SSR) search in S. quitoense and S. betaceum.
| SSR Search Results | S. quitoense | S. betaceum |
|---|---|---|
| Total number of sequences examined | 1,400,090 | 1,732,580 |
| Total size of examined sequences (bp) | 274,369,691 | 296,038,400 |
| Total number of identified SSRs | 34,832 | 115,436 |
| Number of SSRs containing sequences | 31,759 | 68,685 |
| Number of sequences containing more than 1 SSR | 2370 | 30,063 |
| Number of SSRs present in compound formation | 2759 | 46,752 |
| Distribution of different repeat-type classes | ||
| Unit size | Number of SSRs | |
| 2 | 18,349 | 16,385 |
| 3 | 13,385 | 91,665 |
| 4 | 1841 | 4348 |
| 5 | 856 | 274 |
| 6 | 401 | 2.764 |
Table 3.
S. quitoense SSR primer transferability to six analyzed species of Lasiocarpa. The observed alleles for each species are indicated.
Table 3.
S. quitoense SSR primer transferability to six analyzed species of Lasiocarpa. The observed alleles for each species are indicated.
| Primer | S. sessiliflorim | S. stramonifolium | S. hirtum | S. candidum | S. pectinatum | S. pseudolulo | PCR Rate |
|---|---|---|---|---|---|---|---|
| mSq_03 | - | - | - | 236, 239 | - | - | 16.7% |
| mSq_04 | 125 | 125, 170 | 125, 170 | 125, 161 | 125, 146 | 125 | 100.0% |
| mSq_06 | 158 | 182 | 140, 158 | 170 | - | - | 66.7% |
| mSq_08 | - | 173 | 167, 176, 215 | 167 | 158 | 164 | 83.3% |
| mSq_12 | 165 | 165 | 164 | 161 | 164 | 164 | 100.0% |
| mSq_13 | 145, 247 | 145, 247 | 145, 247 | 145 | 142, 145 | 142, 154 | 100.0% |
| mSq_16 | 247 | - | 235, 238, 256 | 247 | 244 | 238 | 83.3% |
| mSq_18 | 128, 137, 140, 143 | 137, 140, 143 | 137, 140, 143 | 122,140, 143 | 140, 143 | - | 83.3% |
| mSq_19 | - | - | - | 224, 227 | 206 | 200, 209 | 66.7% |
| mSq_20 | 195 | - | 195 | 201, 210 | - | - | 50.0% |
| mSq_21 | - | - | 136, 154 | 133 | 136 | 163 | 66.7% |
| mSq_23 | - | - | - | 107, 134 | 140 | - | 33.3% |
| mSq_24 | 104, 158 | 104, 149 | 125, 152 | 131 | 152 | 104, 143, 149 | 100.0% |
| mSq_26 | 105 | 111 | 105, 108 | 108 | 108 | - | 83.3% |
| mSq_27 | 104 | 104 | 98, 104 | 95, 104 | 89, 95, 104 | 98, 104 | 100.0% |
| mSq_28 | 153 | 150 | 153, 168 | 153, 162 | 153, 174 | - | 83.3% |
| mSq_29 | - | - | 145 | - | - | - | 16.7% |
| mSq_31 | - | - | - | 190 | 190 | - | 33.3% |
| mSq_33 | - | - | - | 143 | 134 | 122 | 50.0% |
| mSq_35 | - | 239 | 230, 242, 251 | - | 248 | - | 50.0% |
| mSq_36 | 110, 112 | 121 | 118 | 118, 130 | 112 | 115 | 100.0% |
| mSq_37 | - | 237 | 237, 249 | 234 | 246 | - | 66.7% |
| mSq_38 | 113, 146 | 113, 128 | 113, 128 | 113, 140 | 113, 128 | 113, 128 | 100.0% |
| mSq_40 | 209 | 206, 221 | 206, 221, 230 | 206, 218 | 206 | 206 | 100.0% |
| mSq_43 | - | 133 | - | 133 | - | 133 | 50.0% |
| mSq_44 | 118 | 114 | 126, 134 | 118 | 134 | 130 | 100.0% |
| mSq_46 | - | - | 231 | - | - | 231 | 33.3% |
| mSq_49 | - | 89 | 89 | 89, 91 | 89, 91 | 89 | 83.3% |
| mSq_50 | 160 | 182 | 150 | 150, 172 | 150 | - | 83.3% |
| mSq_51 | - | - | 235 | 247 | - | - | 33.3% |
| mSq_56 | - | 112 | 112 | 130 | - | 112 | 66.7% |
| mSq_57 | - | 158 | 168, 172, 176, 190 | 162 | 172 | - | 66.7% |
| mSq_58 | - | 140 | 144 | 144 | 176 | 160 | 83.3% |
| mSq_59 | - | 183 | 189, 191,193 | 179 | 179 | 179 | 83.3% |
| mSq_63 | - | 147 | 135, 137, 143 | - | - | - | 33.3% |
| mSq_66 | 142 | 156 | 150, 172 | 148 | 154 | 178 | 100.0% |
| mSq_68 | 176 | - | - | 172, 188 | - | - | 33.3% |
| mSq_84 | 82 | 82 | - | - | 88 | 90 | 66.7% |
| mSq_87 | 142 | 142 | - | 130, 142 | 150, 180 | 142, 162 | 83.3% |
| mSq_91 | - | - | 121 | 121, 135, 147 | - | - | 33.3% |
| mSq_93 | - | 129, 159 | 117, 141 | 133, 143 | - | 119, 137 | 66.7% |
| % | 48.8% | 68.3% | 78.0% | 87.8% | 70.7% | 58.5% |
Table 4.
SSR markers useful for genotyping S. quitoense varieties.
| Primer | Forward Primer | Reverse Primer | Motif | Size (pb) | Alleles |
|---|---|---|---|---|---|
| mSq006 | TTACAGGGGAAGAGGGG | CGTATTTGTGTCTTATGTGGG | (AAT)11 | 170 | 170, 191 |
| mSq012 | TTCAAGTGTCAAGATTCAAG | AATTGTGTCAACTCTTACCC | (TTA)16 | 194 | 164, 194, 203 |
| mSq016 | CCATTATGCCTATCAATTCC | CTCGTCCCAAGAACAAAA | (AAT)12 | 256 | 244, 256 |
| mSq018 | TCTCCAAGATCCATGATT | AGGATGCTTCTTTTGATG | (ATT)13 | 143 | 140, 143, 247 |
| mSq036 | ACCAGCTTCAGAACATCAAA | GATTATTCTAGTAGCCGTCCCT | (AAT)9 | 118 | 118, 124 |
| mSq040 | AGTAAGTCACTCCAGTCTATTCA | CTAGTCCCCAAGCGAA | (ATT)11 | 221 | 209, 221 |
| mSq049 | ACAGGTATTACAAAGTCCACA | TTGGGAGCTTGTTTGTT | (AT)14 | 107 | 89, 107 |
| mSq050 | AATGCGAGGTGTGATAAATG | CATGTTGATGGTTTGGGA | (AT)15 | 172 | 172, 174 |
| mSq058 | AGATAGTCCTTCCCACCT | AAGAAAGTGATTTCGCC | (AT)14 | 162 | 156, 162 |
| mSq059 | TGAAGTCATAGCCACCAAC | CCACAAAGTTCCCTAATAAATC | (TA)15 | 195 | 179, 191, 195 |
| mSq063 | GCTTGAACAAACCAATTTCA | TTGCCACCAACTGAGGA | (TA)14 | 157 | 155, 157 |
| mSq066 | AGTCCCCTTGTATCTGGTG | GGAGAAAGGCAAGTGAGAG | (AT)15 | 162 | 162, 168 |
| mSq068 | TAAAATTAACACGACCCACA | AAGTGGCAAAGACGCA | (TA)17 | 188 | 186, 188 |
| mSq091 | CCGATTATGCAAGAAAGGT | GAGCTAGTTTAGCCTATTTTGGT | (AT)16 | 147 | 147, 165 |
Table 5.
Genetic parameters calculated in 120 individuals of S. quitoense and Lasiocarpa accessions. Values for S. quitoense are differentiated in italics.
Table 5.
Genetic parameters calculated in 120 individuals of S. quitoense and Lasiocarpa accessions. Values for S. quitoense are differentiated in italics.
| Primer | Genotypes | Availability Data (Na) | Alleles | Gene Diversity (He) | Heterozygosity (Ho) | PIC | |||
|---|---|---|---|---|---|---|---|---|---|
| mSq006 | 6 | 113 | 6 | 0.65 | 0.019 | 0.06 | 0.019 | 0.61 | 0.019 |
| mSq012 | 7 | 107 | 6 | 0.68 | 0.592 | 0.41 | 0.936 | 0.64 | 0.511 |
| mSq016 | 10 | 112 | 6 | 0.73 | 0.142 | 0.13 | 0.115 | 0.69 | 0.132 |
| mSq018 | 13 | 116 | 6 | 0.71 | 0.492 | 0.66 | 0.875 | 0.66 | 0.371 |
| mSq036 | 9 | 114 | 8 | 0.63 | 0.018 | 0.13 | 0.019 | 0.61 | 0.018 |
| mSq040 | 10 | 113 | 7 | 0.75 | 0.497 | 0.62 | 0.925 | 0.71 | 0.374 |
| mSq049 | 4 | 115 | 4 | 0.55 | 0.499 | 0.46 | 0.964 | 0.49 | 0.375 |
| mSq050 | 9 | 113 | 7 | 0.67 | 0.073 | 0.05 | 0.000 | 0.64 | 0.070 |
| mSq058 | 7 | 106 | 7 | 0.75 | 0.043 | 0.00 | 0.000 | 0.72 | 0.042 |
| mSq059 | 10 | 109 | 7 | 0.75 | 0.203 | 0.08 | 0.061 | 0.71 | 0.189 |
| mSq063 | 6 | 110 | 7 | 0.65 | 0.077 | 0.05 | 0.000 | 0.58 | 0.074 |
| mSq066 | 10 | 108 | 10 | 0.78 | 0.117 | 0.11 | 0.042 | 0.76 | 0.110 |
| mSq068 | 6 | 108 | 5 | 0.71 | 0.499 | 0.01 | 0.000 | 0.66 | 0.375 |
| mSq091 | 6 | 113 | 5 | 0.61 | 0.019 | 0.04 | 0.019 | 0.53 | 0.019 |
| Mean | 8 | 111.2 | 6.4 | 0.69 | 0.23 | 0.19 | 0.28 | 0.64 | 0.178 |
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Morillo, E.; Buitron, J.; Yanez, D.; Mournet, P.; Vásquez-Castillo, W.; Viteri, P. Genetic Assessment in the Andean Tropical Fruits Solanum quitoense Lam. and S. betaceum Cav.: Efforts Towards a Molecular Breeding Approach. Plants 2025, 14, 874. https://doi.org/10.3390/plants14060874
AMA Style
Morillo E, Buitron J, Yanez D, Mournet P, Vásquez-Castillo W, Viteri P. Genetic Assessment in the Andean Tropical Fruits Solanum quitoense Lam. and S. betaceum Cav.: Efforts Towards a Molecular Breeding Approach. Plants. 2025; 14(6):874. https://doi.org/10.3390/plants14060874
Chicago/Turabian StyleMorillo, Eduardo, Johanna Buitron, Denisse Yanez, Pierre Mournet, Wilson Vásquez-Castillo, and Pablo Viteri. 2025. "Genetic Assessment in the Andean Tropical Fruits Solanum quitoense Lam. and S. betaceum Cav.: Efforts Towards a Molecular Breeding Approach" Plants 14, no. 6: 874. https://doi.org/10.3390/plants14060874
APA StyleMorillo, E., Buitron, J., Yanez, D., Mournet, P., Vásquez-Castillo, W., & Viteri, P. (2025). Genetic Assessment in the Andean Tropical Fruits Solanum quitoense Lam. and S. betaceum Cav.: Efforts Towards a Molecular Breeding Approach. Plants, 14(6), 874. https://doi.org/10.3390/plants14060874
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