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Article

Intergeneric Transfer of Simple Sequence Repeat Molecular Markers for the Study of Chaenomeles as Fruit Crop Breeding Material

by
Gunārs Lācis
*,
Katrīna Kārkliņa
,
Toms Bartulsons
and
Edīte Kaufmane
Institute of Horticulture, LV-3701 Dobele, Latvia
*
Author to whom correspondence should be addressed.
Horticulturae 2024, 10(11), 1233; https://doi.org/10.3390/horticulturae10111233
Submission received: 8 October 2024 / Revised: 11 November 2024 / Accepted: 19 November 2024 / Published: 20 November 2024
(This article belongs to the Special Issue Genetic Improvements and Germplasm Resources for Fruit and Vegetable Plants)
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Abstract

:
The genus Chaenomeles, part of the subfamily Maloideae within the Rosaceae family, comprises five recognized species and has long been valued for its ornamental qualities. However, the use of Chaenomeles japonica as a fruit crop is relatively recent, with its introduction into targeted breeding activities only occurring in the 1950s. Due to this, genetic information on the genus remains limited, and the application of molecular markers in crop breeding and further development have been narrow, relying primarily on non-specific marker applications in germplasm analysis. One potential solution is the transfer of molecular markers between genera, specifically from the related Maloideae genera. This study aimed to test the transferability of SSR markers developed for Malus to Chaenomeles, and to analyze the structure of available Chaenomeles germplasm. By including 74 Chaenomeles genotypes, 95 SSR markers originally developed for Malus were tested, with 25 proving effective for characterizing Chaenomeles germplasm. These adapted SSR markers successfully differentiated among Chaenomeles species, including Chaenomeles japonica, Chaenomeles speciosa, Chaenomeles cathayensis, and hybrids like Chaenomeles × superba and Chaenomeles × californica. The markers demonstrated high stability and repeatability, indicating their suitability for large-scale genetic research, species composition assessment, and breeding material evaluation. Given the limited studies on SSR markers in Chaenomeles, this research lays the foundation for further exploration, potentially expanding into the genetic diversity assessment and trait screening for breeding. As genetic research on Chaenomeles is still in its early stages, the development of additional markers will be crucial for advancing this crop.

1. Introduction

The genus Chaenomeles belongs to the subfamily Maloideae in the Rosaceae family with five recognized species: C. japonica (Thunb.) Lindl. (Japanese quince), C. cathayensis (Hemsl.) Schneider (Chinese quince), C. speciosa (Sweet) Nakai (flowering quince), C. thibetica Yü (Tibetan quince) and C. sinensis (Thouin) Koehne (Chinese quince) [1]. The species C. cathayensis, C. japonica and C. speciosa have been used to create interspecific hybrids, named C. × superba (C. speciosa × C. japonica), C. × vilmoriniana (C. speciosa × C. cathayensis), C. × clarkiana (C. japonica × C. cathayensis) and C. × californica (C. × superba × C. cathayensis), resulting in several hundreds of ornamental cultivars. All five Chaenomeles species are native to central and east Asia; however, they are also cultivated in northern Europe [2,3,4]. While the genus is known for its ornamental value, Chaenomeles domestication in the Baltics was focused on breeding cultivars of which the fruit could be used in food processing [5].
The fruits of C. japonica are valuable for their high organic acid, C vitamin, phenolic compound content and antioxidative properties [6,7,8,9,10]. Furthermore, their seeds are used in oil production [11] as a source of unsaturated and saturated fatty acids, as well as α-tocopherol, phytosterols and β-carotene [12]. To ensure that Chaenomeles plants yield frequently and with high-quality fruit, it is necessary to develop efficient breeding programs to develop new cultivars. Given that Chaenomeles breeding, like all fruit crop breeding, is a long-term process, molecular methods are valuable for both germplasm research and the selection of breeding material.
In Latvia, the breeding of Chaenomeles japonica as a fruit crop began in the 1950s. The first large-scale plantations of Japanese quince were established in the 1970s, reaching approximately 300 hectares by the 1980s. All these commercial plantations were started from seedlings, resulting in highly diverse plant material. At the Institute of Horticulture (LatHort), Japanese quince breeding efforts started in the 1990s intending to develop cultivars adapted to the Latvian climate. Only Chaenomeles japonica was used in breeding, as other species lack the winter hardiness for northern Europe. Between 1998 and 2002, LatHort collaborated with Swedish and Lithuanian scientists to evaluate a wide range of Chaenomeles germplasms as part of the EU Project “Japanese quince (Chaenomeles japonica)—a New European Fruit Crop for Produce of Juice, Flavour and Fibre” (EUCHA) [13]. This project aimed to identify and evaluate desirable biological and agronomic traits necessary for the development of promising cultivars, including the strong winter hardiness of both plants and flower buds, thornless plants, high productivity with consistent yields, strong resistance of plants and fruits to diseases, high fruit quality with a rich biochemical content, early ripening, and erect or semi-erect shrub structure. Following this evaluation, three cultivars were selected and registered in Latvia: ‘Rasa’ from Latvian breeding material, and ‘Darius’ and ‘Rondo’ from the EUCHA program. These cultivars are thornless and productive (yielding 4–8 kg per bush), with uniform fruits (40–60 g) that ripen in early to mid-September [4]. Among these, ‘Rondo’ is best suited for commercial cultivation due to its upright, easy-to-prune structure and high yield of quality fruit. It can also be grown using methods where plant strips are cultivated rather than mulched. The ‘Darius’ cultivar is spreading and high-yielding, but on poorer soils or in less-maintained plantations, fruit size may decrease, impacting product quality. ‘Rasa’ is recommended for cultivation with an agro-textile cover over plant strips, as branches tend to bend under high yield in later years, complicating care. In 2023, two new cultivars, ‘Jānis’ (SR 1-6) and ‘Silvija’ (SR 1-3), were submitted for registration in Latvia, noted for their high-quality fruits and high yield.
Japanese quince has become an important commercial fruit crop in Latvia, with the planting area expanding rapidly in recent years, reaching 723 hectares by 2022 [14]. Japanese quince breeding, including the pre-breeding and testing of new cultivars under production conditions, is now part of the LatHort Horticultural Plant Breeding Program, supported by the Ministry of Agriculture. This program’s primary goal is to develop and select cultivars that are well-suited to the conditions in Latvia and Northern Europe.
Molecular markers are used in plant breeding to assess genetic diversity, DNA fingerprinting, genetic mapping, hybrid identification and marker-assisted selection [15], thus speeding up the traditional breeding process. There have been studies on Chaenomeles using RAPD [3,16,17,18] and AFLP [19,20] markers. Based on RAPDs, Bartish and co-authors [16] concluded that C. cathayensis and C. japonica are the most distantly related, whereas C. speciosa might have arisen due to hybridization between the two other species. Further studies have proven interspecies hybridization within the Chaenomeles genus [17] and analyzed the relationship between the known Chaenomeles species, further establishing C. japonica and C. cathayensis as distant relatives [3,21]. Whereas RAPD markers are used to determine relationships between Chaenomeles species, AFLP markers are utilized to characterize different cultivars, mainly those cultivated in East Asia [18,19,20]. However, these studies are rather incomplete, often relying on plant material from just one breeding activity.
Given the limited or even non-existent information on the Chaenomeles genome, previous research on Chaenomeles focused on utilizing RAPD and AFLP markers. However, in recent years, SSR markers have become a more favored choice in experiments due to their high reproducibility and polymorphism, as well as the relative ease of use compared to the other two marker types [22]. To date, one study has been performed to develop SSR makers, resulting in 10 markers that can distinguish between different species of Chaenomeles [23]. Since de novo marker assembly is costly and time-consuming, and the sequences obtained by RNA-seq represent only the coding part of the genome, while SSR markers are mostly found in the non-coding part, thus only a small part of the genome is represented, molecular marker transfer between related species has been used as an alternative approach; a study on the chloroplast genome of Chaenomeles species has revealed highly polymorphic regions, repeat sequences and SSRs that are potential targets for molecular markers [1]. Several researchers have reported successful Malus SSR marker transfer to Pyrus [24], Cydonia [25,26] and Chaenomeles [27,28]. The currently available information reveals some gaps in the knowledge of Chaenomeles cultivars; there have been no robust studies on the genetic diversity and relationships of Chaenomeles developed in the Baltics, as well as more information could be gained on the transferability of Malus SSR markers on Chaenomeles, as only a relatively small number have been successfully transferred. The SSR molecular markers developed for the genus Malus are a promising potential source of suitable markers for Chaenomeles species. The genetic relatedness between Malus and Chaenomeles is supported by studies comparing both pollen morphology [29] and sequences from the nuclear DNA ITS region [30], as well as chloroplast DNA [1,31]. In all of these studies, Malus (apple) is identified as the closest relative among well-characterized plant genera, and it offers a broad selection of SSR markers for potential use.
Thus, this study aimed to test the transfer of SSR markers developed for Malus to Chaenomeles, and to analyze the structure of Chaenomeles sp. germplasm: cultivars developed at the Institute of Horticulture and hybrids grown at the National Botanical Garden of Latvia, as well as seedlings of C. cathayensis to assess their genetic diversity and internal relationship to increase knowledge on local germplasm and promote its use in the further breeding process.

2. Materials and Methods

2.1. Plant Material and Isolation of Genomic DNA

This study is based on 56 C. japonica genotypes maintained in genetic resources and breeding collections at the Institute of Horticulture (LatHort), Dobele, Latvia (56°37′0″ N, 23°16′0″ E), representing different stages of breeding of Chaenomeles as a fruit crop, 10 genotypes of C. cathayense received as seeds from the University Botanical Garden of Strasbourg (http://jardin-botanique.unistra.fr/, accessed on 10 September 2024), germinated and grown for leaf sampling, and 8 genotypes of Chaenomeles species and interspecific hybrids (C. speciosa, C. × superba, C. × californica) collected as leaf samples at the National Botanic Garden (NBD), Salaspils, Latvia (56°51′48″ N, 23°21′4″ E) (Table 1). Total DNA from young leaves of 74 Chaenomeles genotypes was isolated using the Genomic DNA Purification Kit (Thermo Scientific™, Vilnius, Lithuania) according to the manufacturer’s methodology. Quantification and quality evaluation by NanoDrop™ 1000 Spectrophotometer (ThermoFisher Scientific™, Waltham, MA, USA) was performed, followed by the standardization of DNA sample concentrations.

2.2. SSR Genotyping

A genomic DNA sample of 50 ng was used for PCR amplification in a 25 µL reaction containing 1× PCR reaction buffer, 2.5 mM MgCl2, 0.2 mM dNTPs, 2.5 pmol of dye marked forward and reverse primer, and 0.6 U Taq DNA polymerase (Thermo Scientific™, Lithuania). The SSR markers (Table S1) selected for application in Chaenomeles were adapted according to the published protocols [32,33,34], and further adaptation of the annealing temperature and PCR protocol according to the researched plant material and reagent kit. The final PCR conditions for the experimentally selected 25 SSR marker amplification were as follows: denaturation for 3 min at 95 °C, followed by 35 cycles of 30 s at 95 °C, 30 s at 54 to 58 °C (annealing temperatures of each SSR marker are compiled in Table 2), 1 min at 72 °C and a final extension step at 72 °C for 10 min. PCR reactions were run in the Mastercycler epgradient thermal cycler (Eppendorf, Hamburg, Germany). The PCR amplification product presence and quality were checked on a 1% agarose gel in 1× TAE buffer and visualized ethidium bromide dye. The same PCR products were then analyzed on an ABI PRISM® 3100 Genetic Analyzer (Applied Biosystems, Waltham, MA, USA) and genotyped using GeneMapper® Software v4.0 (Applied Biosystems, Waltham, MA, USA).

2.3. Statistical Analysis

SSR amplification fragments were represented in bp and the parameters characterizing the markers were calculated by the software GenAlEx 6.5 [35]. Principal coordinate analysis (PCoA) based on genetic distance was applied to identify the genetic structure of Chaenomeles germplasms and evaluate relationships among genotypes. The significance of the mutual similarity among the identified Chaenomeles genotype groups was evaluated and characterized using an analysis of molecular variance (AMOVA).
The model-based clustering method of STRUCTURE 2.3.3 [36,37] was applied to discover the possible genetic structure of the 74 Chaenomeles samples and define the most likely number of clusters (K value). Twenty independent runs of STRUCTURE were performed for each K value: (i) from 1 to 10, (ii) from 2 to 11, and (iii) from 3 to 12. Each run consisted of a burn-in period of 100,000 steps, followed by 100,000 Monte Carlo Markov chain replicates, assuming an admixture model and independent allele frequencies. No prior information was used for cluster definition. The most likely K was chosen to compare the average estimates of the likelihood of the data, ln(Pr(X|K)), for each value of K (Pritchard et al., 2000), as well as calculating the ad hoc statistics, ∆K, based on the rate of change in the ln-probability of the data between successive K values [38]. The proportion of membership (q) of each individual in each gene pool was estimated using Structure Harvester Web v0.6.94. The cluster matching and permutation were calculated by the software CLUMPP 1.1 [39] and visualized by Structure Plot V2.0 [40].
The AmaCAID R script [41] was employed to determine the minimal number of SSR molecular markers necessary for Chaenomeles germplasm discrimination.

3. Results

3.1. Transfer and Application of Malus-Developed SSR Markers in Chaenomeles

To test cross-species transfer to Chaenomeles, an initial set of 95 SSR markers from Malus, was selected (Table S1). Out of them, 67 had a successful amplification at least in some of the tested Chaenomeles samples. A further selection of markers was made by choosing markers that provided stable amplification in all samples, shortening the list of markers to 39. The selection results are demonstrated in the Supplementary Materials, Figures S1–S3. Out of the remaining markers, 14 were excluded for the following reasons: monomorphic (CH01f09, CH02d12, CH03a09), amplified more than one locus (CH01f12, CH03g07), monomorphic or not amplified in at least two sample groups (CH02b11, CH03d02, CH01f03, CH03g12, CH04d02, CH04e02, CH05g01, CH05g03, CH05d04). The complete list of all SSR markers used in the study is compiled in Table S1, and the final set of markers is summarized in Table 2.
Among the 25 selected SSR markers, the average number of alleles was 9.72, with the minimum being 4 (CH02a03) and the maximum being 16: Na—number of alleles, Ne—number of effective alleles, I—information index, Ho—observed heterozygosity, He—expected heterozygosity, F—fixation index (CH-Vf1 and CH05g07). The average information index was 1.627, ranging from 0.754 (CH01d03) to 2.254 (CH-Vf1). The observed heterozygosity (Ho) had an average of 0.563, varying from 0.369 (CH01d03) to 0.973 (CH05g11). For all markers, the expected heterozygosity (He) was higher than the observed heterozygosity (Ho), except for markers CH03b10 and CH05g11, where Ho was higher than He. The average marker fixation index (F) was 0.224, with values ranging from −0.533 (CH05g11) to 0.741 (CH01h02).
Principal coordinate analysis (PCoA) was conducted on the genotyping data to evaluate the effectiveness of the amplified SSR markers in characterizing Chaenomeles germplasm. The analysis divided the Chaenomeles genotypes into three distinct groups (Figure 1). Group 1 consisted of C. japonica genotypes. Group 2, the closest to Group 1, included genotypes of Chaenomeles species like C. speciosa, C. × californica and C. × superba, whereas Group 3 was composed solely of C. cathayensis and was the most distant from the other groups. There was no overlap between these three groups. The 25 selected SSR markers offered a high resolution, distinguishing nearly all 74 samples with unique genotypes, except for two C. japonica genotypes, ‘Darius’ and R. Indrāna 3, which were genetically identical. Additionally, all C. cathayensis genotypes were divided into two groups of identical genotypes: one including Samples #1 to #4, and the other Samples #5 to #10.
Overall, Group 1 had the highest mean values for both the total number of alleles and unique alleles (total number of alleles 6.52, 5.24, 1.16, unique alleles 3.68, 2.48, 0.52 for C. japonica, Chaenomeles spp. and C. cathayensis, respectively, Figure 1). In contrast, Group 2 exhibited the highest mean number of effective alleles (effective alleles 3.264, 3.801, 1.118 for Groups 1, 2 and 3, respectively) and higher values for genetic diversity parameters such as the observed heterozygosity and information index (Figure 2). Thirteen of the tested SSR markers (CH01a09, CH01d03, CH01d09, CH01h02, CH02c02b, CH02g01, CH03b10, CH03d01, CH03g06, CH04f06, CH05a02_a, CH05d03, CH05e03) exhibited a higher observed heterozygosity in Chaenomeles species (Group 2). Conversely, for the remaining markers, this indicator was higher for Group 1. The information index was also higher in Group 2 for markers CH01a09, CH01d03, CH01f03b, CH02c02b, CH02g01, CH03g06, CH04f06, CH05d03, CH05e03, CH05g07, CH05g11 and CH-Vf1. For the remaining twelve markers, it was higher in Group 1. All diversity parameter values were the lowest in Group 3 (C. cathayensis). According to the analysis of molecular variance (AMOVA), 78% of the variance was attributed to differences among individuals, while 9% was due to differences among populations.

3.2. Determining the Genetic Structure of Chaenomeles Germplasm

The Structure model approach [37] was used to analyze the Chaenomeles plant material internal structure. The ΔK method was employed to calculate the most likely number of clusters. To determine the optimal germplasm structure, three modeling variants were tested: K = 1 to 10, K = 2 to 11, and K = 3 to 12 (Figure 3 and Figure 4). When analyzing K = 1 to 10, two groups of samples were identified: the samples of C. japonica and all other Chaenomeles samples. There was a slight overlap between other Chaenomeles species and C. japonica, but no overlap with C. cathayensis, which aligns with other studies on interspecies relationships. When analyzing K = 2 to 11, four groups were identified: C. cathayensis, other Chaenomeles species, C. japonica Group 1 and C. japonica Group 2. This grouping is best supported by the PCoA. For K = 3 to 12, the same four groups were identified, but C. japonica Group 1 and Group 2 appeared in a different order, with consistency within the groups. All structures confirm the markers’ ability to distinguish between species and indicate the genetic differences among them, proving that all Chaenomeles cultivated as fruit plants are C. japonica without admixture from other species.
The most relevant for a given germplasm was recognized as K = 4 (Figure 3b), with each cluster having 10, 31, 25 and 8 genotypes, respectively. This differed from the initial subjective grouping of the genotypes (Figure 1), which had three groups; the structure-based model split the C. japonica group into two sub-groups. The new groups were as follows: Group 1 (Chaenomeles spp.), Group 2 (C. japonica), Group 3 (C. japonica) and Group 4 (C. cathayensis). The sole distinction was the split of the C. japonica group into two segments: 25 and 31 genotypes, respectively. This division shows relatedness to the origin of the plant material. The AMOVA analysis revealed a total variance of 31% among all four sample groups. Group 2 of C. japonica exhibited a higher average number of alleles compared to Group 1, with values of 5.92 and 4.48, respectively, and a greater number of effective alleles, with 3.278 in Group 2 versus 2.472 in Group 1. Additionally, Group 2 showed higher observed heterozygosity, with values of 0.661 compared to 0.604 in Group 1. The primary distinctions between these groups of C. japonica are evident in the allele composition. These differences are observed in the length (bp) range of amplification fragments and the unique fragments specific to each group (Figure 2).
Figure 4 shows the proportions of membership (q) of each Chaenomeles genotype. The admixture was very low, with 85% of genotypes having a membership value above 95%. The lowest level of admixture was in the C. cathayensis group (Group 4): 0.3%. Similarly, Group 1 (Chaenomeles spp.) also had a low level of admixture: no higher than 1.8%.
Some overlap was observed between Groups 2 and 3, including C. japonica genotypes. The admixture rate in group 2 ranged from 12.9% (7–25) to 38.6% (C27), with genotypes primarily having admixture from group 3. Overall, eight genotypes (9–44, 7–25, ‘Abava’, ‘Ada’, Brūvelis B, C10, C27, ‘Rasa’ (cuttings)) had an admixture of over 10%. In contrast, Group 3 had only three genotypes with a significant admixture from Group 2—C26 (11.8%), ‘Darius’ (old) (12.5%) and SR1-3 (48.1%). As per Structure analysis, Groups 1 (Chaeonemeles spp.) and 4 (C. cathayensis) had the same members and genetic parameter values as in the initial grouping; thus, only the data of the C. japonica group were re-evaluated, which is presented in Table S4.

3.3. Selection of the Lowest Number of Primers Needed for the Discrimination of All Chaenomeles Genotypes

The AmaCAID R script was employed to determine that the germplasm used in this study consisted of 64 unique haplotypes, and the lowest number of markers to discriminate between all haplotypes was six: CH01a09, CH03d01, CH05e03, CH01d09, CH03b10, CH04g07. The markers were then used to assess the genetic diversity markers of the germplasm based on the four groups devised by Structure analysis (Figure 4). Overall, using 6 primers provided nearly the same resolution as using 25 primers, as it was still unable to distinguish between the two C. japonica genotypes, Darius and R. Indrāna 3. Additionally, genotypes No. 19–94 and No. 17–20 also become indistinguishable. Similar to the results with 25 markers, the 6 markers grouped all C. cathayensis into the same two genotype groups: Group 1 included C. cathayensis samples #1, #2, #3 and #4; while Group 2 comprised C. cathayensis samples #5 through #10. The use of the minimal six-primer set maintained the relationship structure among Chaenomeles species, similar to Figure 1, including C. japonica genotypes and other Chaenomeles species (C. speciose, C. × superba and C. × californica), and C. cathayensis. However, there are minor shifts in the relative positioning of the C. japonica groups. The PCoA based on six SSR marker data shows four groups; however, the overlap between the two C. japonica groups is more pronounced, and the Chaenomeles spp. group is closer to the C. japonica group (Figure 5).

4. Discussion

A common feature of new plant crops, recently introduced or used as a food plant, has limited genomic information, which in turn limits the use of modern molecular methods in the study of genetic resources, breeding and the study of the heredity of traits. Japanese quince is one example of such a crop. The first activities for the introduction of the species of this genus as a fruit crop started only at the beginning of the 20th century [42], but targeted breeding attempts started only in the 1950s in Latvia [4,43], later continuing in Lithuania and Sweden. Researchers from these countries have provided most of the genetic research for this crop, including the use of DNA molecular markers [3,17]. In later years, similar studies were also carried out elsewhere, mainly for species systematics and chloroplast genome sequencing [1], but crop genome information is still insufficient, and the application of molecular markers in breeding and plant material studies are sporadic. Therefore, the set of SSR markers selected in this study is a significant contribution to the study of Chaenomeles and also allows for the application of the accumulated knowledge of systematically related crops.
Chaenomeles, similar to Malus, has a basic chromosome number of 17 and is diploid with 2n = 34 [44] and according to genetic studies, is more closely related to the Malus and Pyrus than to other Maloideae genera [30]. Therefore, the initial set of SSR markers created in Malus and mapped in Malus/Pyrus was selected for the study. Examining the linkage groups (LGs) covered by our 25 primers demonstrated that our set offers superior genome coverage compared to others. Therefore, selecting specific markers was crucial, as existing sets are insufficient. This comprehensive coverage is particularly important for analyzing breeding material and closely distinguishing related genotypes. Given the relatively low transferability of SSR markers from Malus to Chaenomeles observed in previous studies [27], a larger number of markers representing all linkage groups was selected for this study. Out of the 95 tested SSR markers developed in Malus and selected for this study, 25 markers, or 26% of all tested markers, provided stable and polymorphic amplification in Chaenomeles (Table S2), while another study on marker transferability from Malus to Chaenomeles resulted in a 58% transferability rate, which is also relatively small [27]. Despite the percentage of successful transfers in the current study being lower, it is important to note that Vanwynsberghe and co-authors (2009) looked at a smaller pool of markers (only 31) and a limited number of plant samples (8 genotypes). However, several markers that were discarded or successfully transferred from Malus to Chaenomeles were the same in both studies; for example, both studies concluded that markers CH01f02, CH01f12 and CH01h10 can be successfully transferred, and the current study also revealed 22 more markers that can be used for the assessment of genetic diversity in Chaenomeles, as well as identified markers that were not transferable, thus creating a framework for future studies to increase the pool of SSR markers developed for other Maloideae species which could be transferred to Chaenomeles. The study data support the argument that SSR markers first developed in Malus can be successfully transferred to Chaenomeles and utilized for genetic structure and diversity studies (Vanwynsberghe et al., 2009).
Out of all the tested Chaenomeles samples, representatives of species C. cathayensis had the highest number of loci (15 loci), of which the allele range matched up with the allele range originally described in Malus by Liebhard and co-authors [33] (Table S2), with C. japonica having the second highest number of loci (13 loci) (Tables S3 and S4). In many cases, the allele range exceeded both the lower and upper limits of the marker’s original range. The dissimilarities of allele length between the study and literature call into question the reliability of the markers, as well as the biological properties of the studied plant material. However, their overall performance is further demonstrated to be sufficient in assessing the genetic diversity of the chosen germplasm.
The 25 SSR markers successfully discriminated all the Chaenomeles species used in the study (Figure 1) and obtained unique genotypes for all samples; the set allows us to identify the samples and validate previous knowledge on the relationship between Chaenomeles species [3,17,21]. C. cathayensis was the most genetically distant of the three groups, whereas the Chaenomeles spp. group, which included C. speciosa and interspecific hybrids C. × superba (C. speciosa × C. japonica) and C. × californica (C. × superba × C. cathayensis) were closer to C. japonica genetically. The differences between the groups were also validated by Structure analysis, as the Chaenomeles spp. and C. cathayensis groups had very low admixture, no more than 2%. However, the genetic structure analysis also revealed that the C. japonica group consisted of two sub-groups with some overlap (Figure 4), and admixture ranged from over 10%, even up to almost 50% between the sub-groups, which calls for further analysis of the genetic makeup and origin of the C. japonica genotypes used in the study. This is also confirmed to some extent by the plant material origin: the C. japonica samples of both groups come from joint breeding activities, albeit at different stages of their development. The small admixture of Chaenomeles samples indicates relatively pure C. japonica material in the existing varieties and breeding material, which should still be verified by genome-wide sequencing. Another important thing to note is that C. cathayensis scored very low in all genetic diversity parameters, which leads to a belief that the seeds were gathered from the same plant or even the same fruit. Thus, the seedlings were genetically homogenous; however, the group also had the lowest admixture as per Structure analysis, indicating that the group was the “purest” and practically had no genetic overlap with the other Chaenomeles species. Overall, the genetic diversity parameters (Na, Ne, I, Ho) of the Chaenomeles germplasm could be considered higher than average (Table S3); however, studies on Malus with the same markers as used in the present study produced higher heterozygosity values [45,46,47,48], as well as a higher polymorphic information content score [49]. The discrepancy between the genetic diversity scores of Malus cultivars and the C. japonica and Chaenomeles spp. groups could be explained by the studies on apples having a much larger number of accessions analyzed, ranging from 273 [45] to 484 [48]; however, the sample size may not always indicate higher genetic diversity. In the present study, the C. japonica groups were the biggest, with 31 and 25 genotypes when split into two sub-groups. Nevertheless, the Chaeonomeles spp. group had the highest values in all genetic diversity parameters, despite consisting of 8 genotypes, whereas the C. cathayensis group had 10 genotypes but scored very low due to the group’s homogeneity (Table S3). Thus, it could be argued that genetic diversity is affected by the individuals’ uniqueness; the analysis of molecular variance validated this. According to the four Structure groups, the analysis of molecular variance indicated that most of the variance was explained by molecular variance within individuals. This means that most of the differences in genetic diversity were due to the variance of the loci of each genotype; thus, the overall diversity of the materials is dictated by the nature of the individuals within the group, not by the group itself. In the present study, the groups themselves are represented by the Chaenomeles species, which dominates in the genetic makeup; however, it does not necessarily describe the diversity of the individuals within the group, as was revealed with the C. japonica genotypes having some of the highest admixture percentages.
The number of private or unique alleles can also characterize genetic diversity. It is a trait that describes alleles that are unique to each of the groups in a set of genotypes and is not necessarily tied to the number of individuals in a group. Comparing a study performed on apples with a similar sample size to the groups in the present study [46] revealed that Chaenomeles had either a similar (C. japonica) or a higher (Chaenomeles spp.) number of private alleles. Similarly, in a study with a low sample size [50], all groups of apple genotypes had a lower number of private alleles than the Chaenomeles spp. group in the current study. This could be explained by the Chaenomeles spp. high taxonomic diversity of the group, which includes both intraspecific and interspecific hybrids. The utility in identifying private alleles in a certain group of genotypes lies in discovering new, unique traits that could be used in diversifying breeding material, which is particularly important in the case of Chaenomeles, as commercial breeding as a fruit crop has only been around since the 1950s [4], making it a relatively new crop compared to apples. Therefore, understanding the genetic structure of Chaenomeles genotypes and cultivars is important, for example, discovering the relatively high admixture of certain C. japonica genotypes creates an incentive to introduce genotypes from the C. cathayensis group or Chaenomeles spp. group to diversify the breeding material; however, this would mean that further studies would be necessary to discover valuable traits that could be carried over from one species to another. As such, it is important to identify molecular markers that can efficiently screen any given group of samples.
To extract the minimum number of representative SSR markers, the AMaCAID script was applied and markers CH01a09, CH01d09, CH03b10, CH03d01, CH04g07 and CH05e03 were identified as the most relevant. An assessment of the Chaenomeles genotypes using 6 SSR markers produced comparable results to those using 25 SSR markers; however, several C. cathayensis and C. japonica genotypes could not be discriminated with only six SSR markers, and the overlap between C. japonica genotypes was more pronounced (Figure 5). The application of these six markers showed that, in the case of the Chaenomeles spp., C. japonica (Group 2), and C. cathayensis groups, all the genetic diversity parameter values were lower than when using 25 markers (Table S4). However, the mean values of the information index and expected heterozygosity, as well as the polymorphic information content and the number of private alleles were higher in Group 3 (C. japonica). Combining these two findings, a small number of SSR markers could be used for the initial germplasm screening, followed by increasing the number of markers, to ensure that the germplasm’s genetic structure and genetic diversity are accurately depicted. Similar results have been obtained in other studies with SSR molecular markers [51].
Molecular markers have become essential tools in plant breeding, particularly for well-researched crops with extensive genomic data, where many marker systems exist for early trait identification and mapping. However, for minor and less-studied crops like Japanese quince, genomic information is extremely limited or non-existent, and marker systems for selecting desired traits are practically unavailable. Therefore, developing any molecular marker system is crucial. SSR markers, which bind to non-coding regions of the genome, are widely used to assess genetic diversity and population structure. For Chaenomeles breeding, this is especially important due to the heavy reliance on open-pollinated material in cultivar development, aiding in the selection of parent plants for further breeding stages. Genetic mapping, another key application of SSR markers, requires a substantial number of functional markers to achieve adequate map density. Reaching this goal would necessitate more comprehensive DNA sequence data of crops.
In the breeding of Chaenomeles, key traits have been identified for which introgression into new cultivars is essential, and for which future molecular marker development would be highly beneficial. Previous studies indicate that 80–90% of Japanese quince genotypes are self-incompatible, with yields heavily dependent on pollinator [52], which complicates large-scale cultivation. Understanding the degree of self-fertility and its genetic mechanisms has thus become a primary focus in the breeding program. Among the genotypes studied, only ‘Rasa’ and ‘Jānis’ showed partial self-fertility, while others, such as ‘Ada’ and ‘Alfa’ by A. Tīcs and hybrids SR1-1 and SR1-5, exhibited variable self-fertility across years [52,53], although the underlying mechanism remains unclear. For large-scale cultivation and mechanized management, an upright growth habit is crucial yet challenging to secure. ‘Rondo’ is a typical carrier of this trait, but it is less expressed in newer hybrids. Frost resistance in flower buds is another critical trait, as spring frosts increasingly impact yields, with genotype differences especially evident following harsh frosts, suggesting a genetic basis for this resilience. Limited information exists on the inheritance of these traits, and the lack of molecular markers for selection presents a significant challenge for advancing Japanese quince breeding.

5. Conclusions

  • Overall, the study shows that the 25 adapted Malus SSR markers are suitable for the genetic characterization of Chaenomeles plant material, with the possibility of using a small set of 6 markers for initial screening and then increasing the number of markers to achieve more precise data on the genetic structure and diversity of the chosen germplasm.
  • The set of adapted SSR markers effectively distinguishes between Chaenomeles species and confirms the results previously obtained with other molecular markers that C. japonica is genetically closer to C. speciosa and interspecies crosses C. × superba and C. × californica than C. cathayensis. Compared to previously used ones, SSR markers provide high stability and repeatability and are suitable for large-scale genetic research of germplasm or breeding material and for assessing species composition.
  • There are few studies on using SSR markers on Chaenomeles cultivars and species; thus, the study sets up the groundwork for further work in the field, which could include not only assessing the genetic diversity of Chaenomeles, but also screening for valuable traits for breeding. Given the limited level of genetic research on Chaenomeles, an additional set of markers will undoubtedly be valuable for the further development of this crop.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/horticulturae10111233/s1, Table S1: List of SSR markers selected for transfer to the application in Chaenomeles germplasm analysis; Table S2: Characterization of a set of 25 SSR markers based on groups of Chaenomeles samples identified through principal Coordinates analysis (PCoA); Table S3: Characterization of a minimal set of 25 SSR markers based on groups of Chaenomeles samples identified through Structure analysis; Table S4: Characterization of a minimal set of six SSR markers based on groups of Chaenomeles samples identified through Structure analysis. Figure S1. PCR amplification to test Malus marker transfer to randomly selected C. japonica cultivars from the Institute of Horticulture genetic resource collection. Figure S2. PCR amplification of Chaenomeles material from the National Botanic Garden of Latvia, using SSR markers from Malus. 1—C. speciosa ‘Brilliant’, 2—C. speciosa ‘Scarlet’, 3—C. × californica, 4—C. × superba, 5—C. × superba ‘Pink Trail’, 6—C. × superba ‘Vermillion’, 7—C. × superba ‘Crimson and Gold’, 8—C. × superba ‘Stanford Red’. Figure S3. PCR amplification of C. cathayensis material received as a seed sample from the University Botanical Garden of Strasbourg, France, using SSR markers from Malus. 1—10: C. cathayensis genotypes #1 through #10.

Author Contributions

Conceptualization, G.L. and E.K.; methodology, G.L.; software, G.L.; validation, K.K. and T.B.; formal analysis, K.K.; investigation, K.K. and T.B.; resources, G.L.; data curation, K.K.; writing—original draft preparation, K.K. and G.L.; writing—review and editing, G.L. and E.K.; supervision, E.K.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by the Latvian Ministry of Agriculture, project no. 10.9.1-11/24/1543-e “Horticultural crop breeding program for the development of breeding material to support the conventional, integrated and organic agricultural crop production technologies”.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author.

Acknowledgments

The authors of the publication express their gratitude to colleagues from the University Botanical Garden of Strasbourg (http://jardin-botanique.unistra.fr/) and the National Botanic Garden (NBD), Latvia (https://nbd.gov.lv/en/) for sharing the plant material and participating in its collection.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Sun, J.; Wang, Y.; Liu, Y.; Xu, C.; Yuan, Q.; Guo, L.; Huang, L. Evolutionary and Phylogenetic Aspects of the Chloroplast Genome of Chaenomeles Species. Sci. Rep. 2020, 10, 11466. [Google Scholar] [CrossRef] [PubMed]
  2. Rumpunen, K.; Kviklys, D.; Kaufmane, E.; Garkava, L. Breeding Chaenomeles—A New Aromatic Fruit Crop. Acta Hortic. 1998, 484, 211–216. [Google Scholar] [CrossRef]
  3. Bartish, I.V.; Garkava, L.P.; Rumpunen, K.; Nybom, H. Phylogenetic Relationships and Differentiation among and within Populations of Chaenomeles Lindl. (Rosaceae) Estimated with RAPDs and Isozymes. Theor. Appl. Genet. 2000, 101, 554–563. [Google Scholar] [CrossRef]
  4. Kaufmane, E.; Ruisa, S. Breeding of New Cultivars of the Fruit Crop Japanese Quince (Chaenomeles japonica) in Latvia. Acta Hortic. 2020, 1281, 51–57. [Google Scholar] [CrossRef]
  5. Rumpunen, K.; Trajkovski, V.; Bartish, I.; Garkava, L.; Nybom, H.; Laencina, J.; Ros, J.M.; Jordan, M.J.; Hellin, P.; Tigerstedt, P.M.A.; et al. Domestication of Japanese Quince (Chaenomeles japonica). Acta Hortic. 2000, 538, 345–348. [Google Scholar] [CrossRef]
  6. Hellína, P.; Vilaa, R.; Jordána, M.J.; Laencinaa, J.; Rumpunen, K.; Rosa, J.M. Characteristics and Composition of Chaenomeles Fruit Juice. In Japanese Quince—Potential Fruit Crop for Northern Europe; Rumpunen, K., Ed.; Department of Crop Science, Swedish University of Agricultural Sciences: Uppsala, Sweden, 2003; pp. 141–148. ISBN 91-631-3765-8. [Google Scholar]
  7. Urbanavičiūte, I.; Liaudanskas, M.; Bobinas, Č.; Šarkinas, A.; Rezgiene, A.; Viskelis, P. Japanese Quince (Chaenomeles japonica) as a Potential Source of Phenols: Optimization of the Extraction Parameters and Assessment of Antiradical and Antimicrobial Activities. Foods 2020, 9, 1132. [Google Scholar] [CrossRef]
  8. Krasnova, I.; Ruisa, S.; Segliņa, D. Investigations of the Biochemical Composition of Chaenomeles japonica Fruits. Cheminė Technol. 2007, 4, 16–20. [Google Scholar]
  9. Rubinskienė, M.; Viškelis, P.; Viškelis, J.; Bobinaitė, R.; Shalkevich, M.; Pigul, M.; Urbonavičienė, D. Biochemical Composition and Antioxidant Activity of Japanese Quince (Chaenomeles japonica) Fruit, Their Syrup and Candied Fruit Slices. Sonininkysté Ir Daržininkysté 2014, 33, 45–52. [Google Scholar]
  10. Czubinski, J.; Ruško, J.; Górnaś, P. Japanese Quince Seeds as a Promising Rich Source of Proteins and Essential Amino Acids. Plant Foods Hum. Nutr. 2021, 76, 533–535. [Google Scholar] [CrossRef]
  11. Granados, M.V.; Vila, R.; Laencina, J.; Rumpunen, K.; Ros, J.M. Characteristics and Composition of Chaenomeles Seed Oil. In Japanese Quince—Potential Fruit Crop for Northern Europe; Rumpunen, K., Ed.; Department of Crop Science, Swedish University of Agricultural Sciences: Uppsala, Sweden, 2003; pp. 127–139. [Google Scholar]
  12. Górnaś, P.; Siger, A.; Segliņa, D. Physicochemical Characteristics of the Cold-Pressed Japanese Quince Seed Oil: New Promising Unconventional Bio-Oil from by-Products for the Pharmaceutical and Cosmetic Industry. Ind. Crops Prod. 2013, 48, 178–182. [Google Scholar] [CrossRef]
  13. Rumpunen, K.; Kviklys, D.; Kauppinen, S.; Ruisa, S.; Tigerstedt, P.M.A. Breeding Strategies for the Fruit Crop Japanese Quince (Chaenomeles japonica). In Japanese Quince—Potential Fruit Crop for Northern Europe; Rumpunen, K., Ed.; Department of Crop Science, Swedish University of Agricultural Sciences: Uppsala, Sweden, 2003; pp. 59–80. [Google Scholar]
  14. Kaufmane, E.; Segliņa, D.; Górnaś, P. Japanese Quince (Chaenomeles japonica)—From Field via Lab to Table: The Role of “Green Technologies”. In Latvian Academy of Science-Yearbook 2021; Spārītis, O., Ed.; Zinātne Ltd.: Rīga, Latvia, 2022; pp. 119–121. ISBN 9789934599132. [Google Scholar]
  15. Amiteye, S. Basic Concepts and Methodologies of DNA Marker Systems in Plant Molecular Breeding. Heliyon 2021, 7, e08093. [Google Scholar] [CrossRef] [PubMed]
  16. Bartish, I.V.; Rumpunen, K.; Nybom, H. Genetic Diversity in Chaenomeles (Rosaceae) Revealed by RAPD Analysis. Plant Syst. Evol. 1999, 214, 131–145. [Google Scholar] [CrossRef]
  17. Bartish, I.V.; Rumpunen, K.; Nybom, H. Combined Analyses of RAPDs, CpDNA and Morphology Demonstrate Spontaneous Hybridization in the Plant Genus Chaenomeles. Heredity 2000, 85, 383–392. [Google Scholar] [CrossRef] [PubMed]
  18. Kaneko, Y.; Nagaho, I.; Bang, S.W.; Matsuzawa, Y. Classification of Flowering Quince Cultivars (Genus Chaenomeles) Using Random Amplified Polymorphic DNA Markers. Breed Sci. 2000, 50, 139–142. [Google Scholar] [CrossRef]
  19. Zang, D.K.; Chen, H.; Zheng, L.; Guo, X.F. Analysis on the Phylogenetic Relationship of Elite Cultivars in Chaenomeles Using AFLP. Sci. Silvae Sin. 2009, 45, 39–43. [Google Scholar]
  20. He, J.; Fan, J.; Li, S.; Huang, B.; Ban, X.; Wang, Y. Genetic Variability of Cultivated Chaenomeles speciosa (Sweet) Nakai Based on AFLP Analysis. Biochem. Syst. Ecol. 2014, 57, 445–450. [Google Scholar] [CrossRef]
  21. Garkava, L.P.; Rumpunen, K.; Bartish, I.V. Genetic Relationships in Chaenomeles (Rosaceae) Revealed by Isozyme Analysis. Sci. Hortic. 2000, 85, 21–35. [Google Scholar] [CrossRef]
  22. Dar, A.A.; Mahajan, R.; Sharma, S. Molecular Markers for Characterization and Conservation of Plant Genetic Resources. Indian J. Agric. Sci. 2019, 89, 1755–1763. [Google Scholar] [CrossRef]
  23. Shao, W.; Huang, S.; Zhang, Y.; Jiang, J.; Li, H. Comparative Transcriptomics Provides a Strategy for Phylogenetic Analysis and SSR Marker Development in Chaenomeles. Sci. Rep. 2021, 11, 1–14. [Google Scholar] [CrossRef]
  24. Yamamoto, T.; Kimura, T.; Sawamura, Y.; Kotobuki, K.; Ban, Y.; Hayashi, T.; Matsuta, N. SSRs Isolated from Apple Can Identify Polymorphism and Genetic Diversity in Pear. Theor. Appl. Genet. 2001, 102, 865–870. [Google Scholar] [CrossRef]
  25. Yamamoto, T.; Kimura, T.; Soejima, J.; Sanada, T.; Ban, Y.; Hayashi, T. Identification of Quince Varieties Using SSR Markers Developed from Pear and Apple. Breed Sci. 2004, 54, 239–244. [Google Scholar] [CrossRef]
  26. Khoramdel Azad, M.; Nasiri, J.; Abdollahi, H. Genetic Diversity of Selected Iranian Quinces Using SSRs from Apples and Pears. Biochem. Genet. 2013, 51, 426–442. [Google Scholar] [CrossRef] [PubMed]
  27. Vanwynsberghe, L.; Decq, L.; Keulemans, J. Transferability of Malus × domestica Microsatellite Markers to Other Species and Genera of the Maloideae Subfamily. Acta Hortic. 2009, 839, 567–574. [Google Scholar] [CrossRef]
  28. Zhang, Y.Y.; Qi, H.; Guo, Q.M.; Sun, Z.Y.; Zhou, F.Q.; Li, S.B. Analysis of Genetic Diversity in Chaenomeles Using Apple EST-SSRs. Biotechnol. Bull. 2016, 32, 93–98. [Google Scholar]
  29. Wang, Y.; Huang, Z.; Ma, W.; Liu, J.; Tian, L.; Zhou, Y.; Shang, F.; Guo, P. Comparative Pollen Morphology of the Genus Chaenomeles Lindl. (Rosaceae): Diagnostic Features and Implications for Taxonomy. Diversity 2023, 15, 960. [Google Scholar] [CrossRef]
  30. Campbell, C.S.; Donoghue, M.J.; Baldwin, B.G.; Wojciechowski, M.F. Phylogenetic Relationships in Maloideae (Rosaceae): Evidence from Sequences of the Internal Transcribed Spacers of Nuclear Ribosomal DNA and Its Congruence with Morphology. Am. J. Bot. 1995, 82, 903–918. [Google Scholar] [CrossRef]
  31. Sun, J.; Zhao, D.; Qiao, P.; Wang, Y.; Wu, P.; Wang, K.; Guo, L.; Huang, L.; Zhou, S. Phylogeny of Genera in Maleae (Rosaceae) Based on Chloroplast Genome Analysis. Front. Plant Sci. 2024, 15, 1367645. [Google Scholar] [CrossRef]
  32. Hokanson, S.C.; Szewc-McFadden, A.K.; Lamboy, W.F.; McFerson, J.R. Microsatellite (SSR) Markers Reveal Genetic Identities, Genetic Diversity and Relationships in a Malus × domestica Borkh. Core Subset Collection. Theor. Appl. Genet. 1998, 97, 671–683. [Google Scholar] [CrossRef]
  33. Liebhard, R.; Gianfranceschi, L.; Koller, B.; Ryder, C.D.; Tarchini, R.; van de Weg, E.; Gessler, C. Development and Characterisation of 140 New Microsatellites in Apple (Malus × domestica Borkh.). Mol. Breed. 2002, 10, 217–241. [Google Scholar] [CrossRef]
  34. Vinatzer, B.A.; Patocchi, A.; Tartarini, S.; Gianfranceschi, L.; Sansavini, S.; Gessler, C. Isolation of Two Microsatellite Markers from BAC Clones of the Vf Scab Resistance Region and Molecular Characterization of Scab-Resistant Accessions in Malus Germplasm. Plant Breed. 2004, 123, 321–326. [Google Scholar] [CrossRef]
  35. Smouse, R.P.P.; Peakall, R. GenAlEx 6.5: Genetic Analysis in Excel. Population Genetic Software for Teaching and Research—An Update. Bioinformatics 2012, 28, 2537–2539. [Google Scholar]
  36. Falush, D.; Stephens, M.; Pritchard, J.K. Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies. Genetics 2003, 164, 1567–1587. [Google Scholar] [CrossRef] [PubMed]
  37. Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of Population Structure Using Multilocus Genotype Data. Genetics 2000, 155, 945–959. [Google Scholar] [CrossRef] [PubMed]
  38. Evanno, G.; Regnaut, S.; Goudet, J. Detecting the Number of Clusters of Individuals Using the Software STRUCTURE: A Simulation Study. Mol. Ecol. 2005, 14, 2611–2620. [Google Scholar] [CrossRef]
  39. Jakobsson, M.; Rosenberg, N.A. CLUMPP: A Cluster Matching and Permutation Program for Dealing with Label Switching and Multimodality in Analysis of Population Structure. Bioinformatics 2007, 23, 1801–1806. [Google Scholar] [CrossRef]
  40. Ramasamy, R.K.; Ramasamy, S.; Bindroo, B.B.; Naik, V.G. STRUCTURE PLOT: A Program for Drawing Elegant STRUCTURE Bar Plots in User Friendly Interface. Springerplus 2014, 3, 1–3. [Google Scholar] [CrossRef]
  41. Caroli, S.; Santoni, S.; Ronfort, J. AMaCAID: A Useful Tool for Accurate Marker Choice for Accession Identification and Discrimination. Mol. Ecol. Resour. 2011, 11, 733–738. [Google Scholar] [CrossRef]
  42. Moskalets, T.Z.; Moskalets, V.V.; Vovkohon, A.H.; Shevchuk, O.A.; Matviichuk, O.A. Modern Breeding and Cultivation of Unpopular Fruits and Berries in Ukraine. Ukr. J. Ecol. 2019, 3, 180–188. [Google Scholar]
  43. Ruisa, S. Breeding of Japanese Quince (Chaenomeles japonica) and Evaluation of the Promising Genotypes for Fruit Production. Proc. Latv. Acad. Sci. 1995, 110, 5–6. [Google Scholar]
  44. Kauppinen, S.; Kviklys, D.; Rumpunen, K.; Stanys, V.; Svensson, M. Propagation of Japanese Quince (Chaenomeles japonica) Plants; Department of Crop Science, Swedish University of Agricultural Sciences: Uppsala, Sweden, 2003; pp. 81–92. ISBN 91-631-3765-8. [Google Scholar]
  45. Patzak, J.; Paprštein, F.; Henychová, A.; Sedlák, J. Comparison of Genetic Diversity Structure Analyses of SSR Molecular Marker Data within Apple (Malus × domestica) Genetic Resources. Genome/Natl. Res. Counc. Can. = Génome/Cons. Natl. De Rech. Can. 2012, 55, 647–665. [Google Scholar] [CrossRef]
  46. Garkava-Gustavsson, L.; Mujaju, C.; Sehic, J.; Zborowska, A.; Backes, G.M.; Hietaranta, T.; Antonius, K. Genetic Diversity in Swedish and Finnish Heirloom Apple Cultivars Revealed with SSR Markers. Sci. Hortic. 2013, 162, 43–48. [Google Scholar] [CrossRef]
  47. Urrestarazu, J.; Miranda, C.; Santesteban, L.G.; Royo, J.B. Genetic Diversity and Structure of Local Apple Cultivars from Northeastern Spain Assessed by Microsatellite Markers. Tree Genet Genomes 2012, 8, 1163–1180. [Google Scholar] [CrossRef]
  48. Larsen, B.; Toldam-Andersen, T.B.; Pedersen, C.; Ørgaard, M. Unravelling Genetic Diversity and Cultivar Parentage in the Danish Apple Gene Bank Collection. Tree Genet Genomes 2017, 13, 1–12. [Google Scholar] [CrossRef]
  49. Potts, S.M.; Han, Y.; Khan, M.A.; Kushad, M.M.; Rayburn, A.L.; Korban, S.S. Genetic Diversity and Characterization of a Core Collection of Malus Germplasm Using Simple Sequence Repeats (SSRs). Plant Mol. Biol. Rep. 2012, 30, 827–837. [Google Scholar] [CrossRef]
  50. Khachtib, Y.; Zinelabidine, L.H.; Bouda, S.; Hamdali, H.; Hammada, S.; Haddioui, A. Genetic Characterization of Cultivated Apple (Malus × domestica Borkh.) in Morocco Using Microsatellite (SSR) Markers. Ecol. Genet. Genom. 2022, 23, 100122. [Google Scholar] [CrossRef]
  51. Miteca, H.; Castro, M.H.; Meneses, M.; Prat, L.; Muñoz, C.; Hinrichsen, P. Validation of a Minimal Panel of Microsatellite Markers for Blueberry Cultivar Identification and Frequency of Spontaneous Mutations. Chil. J. Agric. Res. 2024, 84, 28–42. [Google Scholar] [CrossRef]
  52. Kaufmane, E.; Rumpunen, K. Pollination, Pollen Tube Growth and Fertilization in Chaenomeles japonica (Japanese Quince). Sci. Hortic. 2002, 94, 257–271. [Google Scholar] [CrossRef]
  53. Kaufmane, E.; Ruisa, S.; Karklina, K. The Effects of Pollinizers, Pollen and Pistil Quality, and Fruit Set of Japanese Quince (Chaenomeles japonica) Cultivars and Perspective Hybrids. In Proceedings of the Acta Horticulturae; International Society for Horticultural Science: Leuven, Belgium, 2023; pp. 621–627. [Google Scholar]
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Figure 1. Principal coordinate analysis (PCoA) of 74 Chaenomeles genotypes based on 25 SSR markers. Group 1—C. japonica, Group 2—Chaenomeles spp., Group 3—C. cathayensis.
Figure 1. Principal coordinate analysis (PCoA) of 74 Chaenomeles genotypes based on 25 SSR markers. Group 1—C. japonica, Group 2—Chaenomeles spp., Group 3—C. cathayensis.
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Figure 2. Characterization of the Chaenomeles germplasm group discrimination. (a) Characteristics of alleles identified in Chaenomeles germplasm groups; (b) heterozygosity of the Chaenomeles germplasm groups.
Figure 2. Characterization of the Chaenomeles germplasm group discrimination. (a) Characteristics of alleles identified in Chaenomeles germplasm groups; (b) heterozygosity of the Chaenomeles germplasm groups.
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Figure 3. ΔK plot for (a) K = 2 (K 1 to 10), (b) K = 4 (K 2 to 11) and (c) K = 4 (K 3 to 12). calculated based on the 25 SSR marker data of the 74 Chaenomeles genotypes.
Figure 3. ΔK plot for (a) K = 2 (K 1 to 10), (b) K = 4 (K 2 to 11) and (c) K = 4 (K 3 to 12). calculated based on the 25 SSR marker data of the 74 Chaenomeles genotypes.
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Figure 4. Structure analysis with 74 Chaenomeles genotypes, using 25 SSR markers, inferred at (a) K = 2 (K 1 to 10), (b) K = 4 (K 2 to 11) and (c) K = 4 (K 3 to 12). Each genotype is represented by a vertical line, with colors matching the K number.
Figure 4. Structure analysis with 74 Chaenomeles genotypes, using 25 SSR markers, inferred at (a) K = 2 (K 1 to 10), (b) K = 4 (K 2 to 11) and (c) K = 4 (K 3 to 12). Each genotype is represented by a vertical line, with colors matching the K number.
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Figure 5. Principal coordinate analysis (PCoA) of 74 Chaenomeles genotypes based on six SSR markers grouped according to Structure-discovered genetic structure: 1—Chaenomeles spp., 2—C. japonica, 3—C. japonica, 4—C. cathayensis.
Figure 5. Principal coordinate analysis (PCoA) of 74 Chaenomeles genotypes based on six SSR markers grouped according to Structure-discovered genetic structure: 1—Chaenomeles spp., 2—C. japonica, 3—C. japonica, 4—C. cathayensis.
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Table 1. Description of Chaenomeles sp. plant material utilized for the transfer of SSR molecular markers from the related genera.
Table 1. Description of Chaenomeles sp. plant material utilized for the transfer of SSR molecular markers from the related genera.
Sample No.Sample NameSpeciesSource
cd 087AbavaC. japonicaCultivar bred by A. Tīcs in Pūre, Latvia (started in 1951).
cd 067AdaC. japonica
cd 084AgraC. japonica
cd 068AlfaC. japonica
cd 085AntaC. japonica
cd 092ArtaC. japonica
cd 086Tīca 45C. japonica
cd 0724-6C. japonicaPlant material bred as open-pollinated hybrids by S. Ruisa at the LatHort in the 1980s, using plant material sourced from the best seedlings gathered from Latvian commercial orchards, as well as plant material developed by breeder P. Sukatnieks. Cultivar ‘Rasa’ is registered in Latvia.
cd 0704-22C. japonica
cd 0717-25C. japonica
cd 0788-139C. japonica
cd 0739-11C. japonica
cd 0759-44C. japonica
cd 07910-17C. japonica
cd 08111-45C. japonica
cd 07417-20C. japonica
cd 07619-94C. japonica
cd 07719-4C. japonica
cd 08019-94C. japonica
cd 093Dobeles 2-29C. japonica
cd 100Rasa (Clone 1)C. japonica
cd 103Rasa (Clone 2)C. japonica
cd 096Rasa (Clone 3)C. japonica
cd 088Rasa (Clone 4)C. japonica
cd 115C9C. japonicaPlant material bred during common Latvian–Swedish–Lithuanian breeding program (1992–2002). Cultivars ‘Darius’ and ‘Rondo’ are registered in Latvia.
cd 083C10C. japonica
cd 066C12C. japonica
cd 082C13C. japonica
cd 062C16C. japonica
cd 063C19C. japonica
cd 064C20C. japonica
cd 069C26C. japonica
cd 065C27C. japonica
cd 099Darius (Clone 1)C. japonica
cd 102Darius (Clone 2)C. japonica
cd 097Darius (Clone 3)C. japonica
cd 098Rondo (Clone 1)C. japonica
cd 101Rondo (Clone 2)C. japonica
cd 095Rondo (Clone 3)C. japonica
cd 114SR1-1C. japonicaPlant material was bred as open-pollinated hybrids by S. Ruisa at the LatHort and selected in 2017–2023, using plant material sourced from the best seedlings gathered from Latvian commercial orchards, as well as plant material developed during common Latvian–Swedish–Lithuanian breeding program.
cd 108SR1-1aC. japonica
cd 107SR1-2C. japonica
cd 104SR1-3C. japonica
cd 112SR1-4C. japonica
cd 105SR1-4aC. japonica
cd 110SR1-5C. japonica
cd 109SR1-5aC. japonica
cd 111SR1-6C. japonica
cd 106SR2-0C. japonica
cd 113SR2-9C. japonica
cd 116BrūvelisC. japonicaUnknown origin genotypes from A. Brūvelis farm.
cd 117Brūvelis BC. japonica
cd 089R.Indrāna 1C. japonicaUnknown origin genotypes from R. Indrāns farm.
cd 090R.Indrāna 2C. japonica
cd 094R.Indrāna 3C. japonica
cd 091R.Indrāna 4C. japonica
cd 119BrilliantC. speciosaCollected as a leaf sample from the National Botanic Garden (NBD), Latvia (https://nbd.gov.lv/en/, accessed on 10 September 2024).
cd 120ScarletC. speciosa
cd 122C. californicaC. × californica
cd 118Chaenomeles superbaC. × superba
cd 121Pink TrailC. × superba
cd 123VermillionC. × superba
cd 124Crimson and GoldC. × superba
cd 125Stanford RedC. × superba
cd126Sample #1C. cathayensisReceived as a seed sample from the University Botanical Garden of Strasbourg, France (http://jardin-botanique.unistra.fr/, accessed on 10 September 2024).
cd127Sample #2C. cathayensis
cd128Sample #3C. cathayensis
cd129Sample #4C. cathayensis
cd130Sample #5C. cathayensis
cd131Sample #6C. cathayensis
cd132Sample #7C. cathayensis
cd133Sample #8C. cathayensis
cd134Sample #9C. cathayensis
cd135Sample #10C. cathayensis
Table 2. Characterization of the selected SSR markers amplified in the 74 tested Chaenomeles genotypes.
Table 2. Characterization of the selected SSR markers amplified in the 74 tested Chaenomeles genotypes.
LocusAnnealing Temperature, °CLocus Characteristics *
NaNeIHoHeFAllele Range, bp
CH01a095493.9771.7120.5000.7490.332183–223
CH01d035471.5850.7540.1220.3690.670108–140
CH03d015473.5141.4850.5410.7150.244119–167
CH05e0354136.0582.0220.6760.8350.191161–202
CH-Vf154167.2752.2540.7260.8630.158170–218
CH01d0954123.6881.7330.5830.7290.200142–168
CH03d105672.9781.4200.5810.6640.125220–234
CH01f03b56114.3481.7020.6760.7700.122113–136
CH04f0656113.8731.7020.6350.7420.144107–158
CH01F0254114.7511.8470.5630.7900.286183–205
CH01h025663.2891.3680.1810.6960.74188–136
CH01g0558116.0882.0130.7840.8360.06284–106
CH02c02b54113.8851.6490.5540.7430.25496–108
CH02g0154103.8411.6120.5920.7400.200161–192
CH02a035442.1290.9150.4730.5300.108123–151
CH03b105461.8991.0120.4860.473−0.028168–196
CH03b0654125.0121.8680.7030.8000.122152–172
CH03g0654135.3942.0100.3380.8150.585106–118
CH04g0754104.8171.8070.2500.7920.68491–153
CH05h055463.3471.4630.6620.7010.05692–162
CH05c0654116.3011.9980.5920.8410.297135–174
CH05d035473.2581.3800.4370.6930.370156–192
CH05a02_a5453.8771.4420.6990.7420.059173–229
CH05g1154112.7381.2920.9730.635−0.533137–161
CH05g0754167.2742.2090.7390.8630.143125–157
Average: 9.724.2081.6270.5630.7250.224-
* Na—number of alleles, Ne—number of effective alleles, I—information index, Ho—observed heterozygosity, He—expected heterozygosity, F—fixation index.
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Lācis, G.; Kārkliņa, K.; Bartulsons, T.; Kaufmane, E. Intergeneric Transfer of Simple Sequence Repeat Molecular Markers for the Study of Chaenomeles as Fruit Crop Breeding Material. Horticulturae 2024, 10, 1233. https://doi.org/10.3390/horticulturae10111233

AMA Style

Lācis G, Kārkliņa K, Bartulsons T, Kaufmane E. Intergeneric Transfer of Simple Sequence Repeat Molecular Markers for the Study of Chaenomeles as Fruit Crop Breeding Material. Horticulturae. 2024; 10(11):1233. https://doi.org/10.3390/horticulturae10111233

Chicago/Turabian Style

Lācis, Gunārs, Katrīna Kārkliņa, Toms Bartulsons, and Edīte Kaufmane. 2024. "Intergeneric Transfer of Simple Sequence Repeat Molecular Markers for the Study of Chaenomeles as Fruit Crop Breeding Material" Horticulturae 10, no. 11: 1233. https://doi.org/10.3390/horticulturae10111233

APA Style

Lācis, G., Kārkliņa, K., Bartulsons, T., & Kaufmane, E. (2024). Intergeneric Transfer of Simple Sequence Repeat Molecular Markers for the Study of Chaenomeles as Fruit Crop Breeding Material. Horticulturae, 10(11), 1233. https://doi.org/10.3390/horticulturae10111233

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