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Article

Combined Analysis of SRAP and SSR Markers Reveals Genetic Diversity and Phylogenetic Relationships in Raspberry (Rubus idaeus L.)

by
Zhifeng Guo
1,2,3,
Zhenzhu Fan
1,2,3,
Xueyi Li
1,2,3,
Haoqi Du
1,2,3,
Zhuolong Wu
1,2,3,
Tiemei Li
1,2,3,* and
Guohui Yang
1,2,3,*
1
College of Horticulture & Landscape Architecture, Northeast Agricultural University, Harbin 150030, China
2
Key Laboratory of Biology and Genetic Improvement of Horticultural Crops (Northeast Region), Ministry of Agriculture and Rural Affairs, Northeast Agricultural University, Harbin 150030, China
3
National-Local Joint Engineering Research Center for Development and Utilization of Small Fruits in Cold Regions, Northeast Agricultural University, Harbin 150030, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(6), 1492; https://doi.org/10.3390/agronomy15061492
Submission received: 16 May 2025 / Revised: 16 June 2025 / Accepted: 18 June 2025 / Published: 19 June 2025
(This article belongs to the Special Issue Conventional vs. Modern Techniques in Horticultural Crop Breeding)
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Abstract

:
Raspberry (Rubus idaeus L.) is a high-value horticultural crop recognized for its significant economic importance and exceptional nutritional profile. We analyzed 76 raspberry accessions (wild and cultivar) using simple sequence repeat (SSR) and sequence-related amplified polymorphism (SRAP) markers, and we established a standardized SRAP system for this species. Genetic similarity differed markedly between markers: SSR values spanned 0.47–0.98 (mean = 0.73), compared to the narrower range of 0.52–0.97 (mean = 0.75) for SRAP. Cultivar accessions exhibited higher intra-group homogeneity than wild accessions, and northeastern wild accessions showed more stable similarity metrics than Guizhou wild accessions. In hierarchical clustering, the resolution varied depending on the labeling marker. The cluster analysis by SSR markers identified two main clusters and further partitioned them into three clusters. In contrast, the SRAP system revealed two primary clusters, which subsequently diverged into five subclusters. SSR markers effectively captured population-level differentiation, whereas SRAP markers enabled precise discrimination of cultivars and ecotypes through non-coding region polymorphisms. Phylogenetic analyses confirmed closer genetic affinity between northeastern wild and cultivated accessions, which diverged significantly from Guizhou. This dual-marker approach revealed complementary information: SSR markers were used to survey genome-wide diversity, while SRAP markers were used to detect structural variations. Their integrated application enhances germplasm characterization efficiency and provides practical strategies for raspberry conservation and molecular breeding.

1. Introduction

Raspberry (Rubus idaeus L.), a perennial shrub of the genus Rubus within the family Rosaceae, is recognized as a premium third-generation fruit crop. Raspberry is a diploid species with a chromosome number of 2n = 14. Its nutritional profile includes various vitamins, essential minerals, ellagic acid, and raspberry ketones [1,2]. The species holds significant economic value through fresh consumption and processing into value-added products such as jams and functional foods [3]. Globally, over 50 countries (notably Mexico, Serbia, and Chile) have developed comprehensive raspberry industries spanning cultivation, postharvest processing, and commercial distribution networks. Raspberry cultivation programs in China were established approximately a century ago by introducing elite cultivars (‘Tulameen’, ‘Heritage’, ‘Autumn Bliss’) and wild germplasm from Russia, Europe, and North America [4,5,6]. Currently, China maintains a diverse repository of raspberry genetic resources supporting its production systems.
Genetic diversity, a cornerstone of biodiversity, underpins species adaptation and survival and drives crop breeding and germplasm conservation efforts [7]. Raspberry cultivars possess substantial genetic variability shaped by artificial selection and inter-regional hybridization. However, phenotype-based classification in raspberry exhibits significant limitations [8]; genetic approaches are imperative for taxonomic resolution. Clarifying genetic diversity and evolutionary relationships is therefore essential for conservation planning [9]. Molecular markers provide high-resolution tools for assessing plant genetic diversity. In contrast, traditional morphological markers—vulnerable to environmental influences—often fail to reveal the genetic basis of traits. This limitation impedes precise germplasm identification, parental selection, and cultivar development in raspberry. Molecular approaches, however, provide high-resolution, stable, and quantifiable solutions for analyzing genetic diversity and phylogenetic relationships [10,11]. Molecular marker technologies are categorized into three generations based on technical principles and developmental stages. First-generation markers, such as restriction fragment length polymorphisms (RFLPs), showed limited practical utility due to technical constraints under experimental conditions [12]. Second-generation markers, incorporating PCR amplification, include two categories: random-primer markers (e.g., inter-simple sequence repeats, ISSRs) with low amplification efficiency [13] and specific-primer PCR-based markers. Despite the technical sophistication of third-generation sequencing-derived markers such as SNPs, their implementation necessitates substantial infrastructure investment, requires reference genome dependency, and lacks real-time monitoring capabilities during detection processes [14]. Consequently, among second-generation markers, simple sequence repeat (SSR) and sequence-related amplified polymorphism (SRAP) markers (both requiring specific primer designs) have emerged as mainstream technologies due to their operational simplicity, cost-effectiveness, and technical reliability [15].
SSR markers, which detect polymorphism in short tandemly repeated DNA sequences (1–6 bp), exhibit high polymorphism, multi-allelic variation, and codominant inheritance as well-established molecular tools [16]. However, their application incurs significant costs and is constrained by a limited number of available loci. In contrast, SRAP technology, targeting open reading frames (ORFs) through primers amplifying exonic and intronic regions, provides a cost-effective approach with broad de novo genomic coverage [17]. Nevertheless, SRAP analysis faces challenges in analytical complexity and reproducibility. Both systems demonstrate stability independent of environmental and epistatic interactions. Key applications in genetic studies include the following: SSR-based genotyping of 82 German raspberry accessions, which revealed constrained genetic diversity [18]; SRAP markers identifying ‘Deyangshi’ persimmon as a unique lineage within Ebenaceae [19]; and combined SSR–morphometric analyses that successfully differentiated eight Arctic raspberry cultivars [20].
Although molecular markers have been extensively utilized in crop genetics, raspberry genomic studies remain relatively underexplored. Current research predominantly focuses on phenotypic evaluations or single-marker systems, thereby failing to comprehensively resolve complex genomic architectures. Notably, inter-simple sequence repeat (ISSR) markers, despite demonstrating moderate polymorphism in berries, are constrained by dominant inheritance modes that limit their application in heterozygous loci analysis [21]. Importantly, the synergistic use of SSR and SRAP markers addresses these methodological limitations through complementary detection capabilities, enabling more robust genomic investigations.
Integrated application of SSR and SRAP marker systems offers significant advantages in raspberry breeding programs. SSR markers exhibit high codominance and stability, enabling precise assessment of genetic diversity and relatedness, thereby optimizing parental selection. SRAP markers are operationally simple, cost-effective, and efficiently screen for polymorphisms in functional genomic regions, making them highly suitable for initial screening of large populations. Both systems facilitate the localization of genes underlying key agronomic traits. This enables genotype-assisted selection at the seedling or even the seed stage, substantially accelerating the breeding cycle and improving selection efficiency. Furthermore, these markers synergistically enhance the accuracy of cultivar identification and the precision of genetic map construction. Collectively, they advance raspberry breeding towards a more precise and efficient paradigm, expediting the development of new cultivars.
This study developed an integrated SSR-SRAP genotyping framework to establish an optimized molecular profiling system for raspberry. We applied this dual-marker approach to characterize germplasm resources encompassing underutilized regional accessions from northeast China (Liaoning/Guizhou Provinces) and globally significant commercial cultivars. Through high-resolution genetic diversity and phylogenetic analyses, this work expands the genomic repository for Rubus breeding programs, enables precise marker-assisted selection for accelerated trait introgression, and establishes a standardized platform for future genomic studies in Rosaceae berries. The synergistic analytical power of combined SSR-SRAP markers overcomes limitations inherent to single-marker systems while revealing novel genetic architectures within under-characterized raspberry populations.

2. Materials and Methods

2.1. Plant Materials

This study analyzed 76 raspberry accessions representing four categories: 37 accessions from Northeast Agricultural University (Xiangyang Experimental Station, Harbin, China); 6 accessions maintained at Shenyang Agricultural University (Shenyang, China); 23 accessions provided by the Jilin Academy of Agricultural Sciences (Changchun, China); and 9 wild accessions collected from Fanjingshan Mountain (Tongren City, China). This integrated collection combines both in situ and ex situ conservation strategies, spanning major raspberry-producing regions across China from northeastern to southwestern geographical gradients (Table 1).

2.2. Genomic DNA Isolation and Quality Assessment

Mature plant leaf samples (0.1 g) were ground to powder in liquid nitrogen-cooled mortars, transferred to pre-chilled centrifuge tubes, and homogenized with 1–1.5 mL of carbohydrate-depleted buffer. After centrifugation at 5000× g for 10 min, supernatants were assessed for viscosity and color transition to white. Subsequent steps followed the modified cetyltrimethylammonium bromide (CTAB) protocol [22].

2.3. SSR-PCR Amplification Optimization in Raspberry

2.3.1. Primer Screening of SSR

Thirty pairs of SSR primers (BGI Genomics, Shenzhen, China) were preliminarily screened for their efficiency in amplifying raspberry using three randomly selected wild genotypes (Fanjingshan Mountain) and three cultivated accessions (Northeast Agricultural University Experimental Station). Ten pairs of primers (Table S1) showed consistent amplification efficiency across all specimens and produced distinct, well-separated bands in agarose gel electrophoresis (Figure S1). Therefore, these validated primers were utilized for subsequent analysis.

2.3.2. SSR-PCR Amplification Protocol Optimization

The SSR-PCR amplification system was conducted following the protocol described by Graham [23]. SSR amplification was carried out in a 20 μL reaction volume containing 10× PCR buffer comprising Mg2+ (1.5 mmol/L), dNTPs (0.20 mmol/L), genomic DNA (30 ng), 1 U Taq polymerase, and SSR forward and reverse primers (0.4 mmol/L).
SSR-PCR amplifications were conducted following Graham [23] with thermal optimization via gradient testing (52–60 °C), empirically establishing 56 °C as the optimal annealing temperature (Figure S2). The DNAs were subjected to the following thermal reaction program: 5 min at 95 °C, 35 cycles of denaturation at 95 °C/1 min, annealing at 56 °C/1 min, extension at 72 °C/1 min, and final extension at 72 °C/4 min.

2.4. SRAP-PCR Amplification Optimization in Raspberry

2.4.1. Primer Screening of SRAP

A total of 25 primer pairs were screened across three randomly selected wild samples and three cultivated samples. Of these, 10 primer pairs (Me1/Em2, Me1/Em4, Me2/Em2, Me2/Em4, Me3/Em1, Me3/Em3, Me3/Em4, Me4/Em1, and Me4/Em4) consistently produced clear polymorphic bands in all tested accessions (Table 2) and were subsequently selected for downstream analyses (Table S2).

2.4.2. Optimization of SRAP-PCR Amplification System Reaction Parameters

The SRAP-PCR amplification protocol was conducted following the methodology established by Aneja [24]. Temperature gradient optimization was systematically performed, ultimately identifying 52 °C as the optimal annealing temperature (Figure S3). DNA was subjected to the following thermal reaction program: 5 min at 94 °C, 5 cycles of denaturation at 94 °C/1 min, annealing at 35 °C/1 min, extension at 72 °C/1 min, 30 cycles of denaturation at 94 °C/1 min, annealing at 52 °C/1 min, extension at 72 °C/1 min, and final extension at 72 °C/10 min.
The SRAP-PCR amplification system was optimized through orthogonal experimental design (Tables S3 and S4) and ultimately established in a total reaction volume of 25 μL containing 10× PCR buffer comprising Mg2+ (1.5 mmol/L), dNTPs (0.4 mmol/L), genomic DNA (60 ng), 0.6 U Taq polymerase, and SRAP forward and reverse primers (0.1 µmol/L).

2.5. Analysis of Amplification Products

The amplification products were separated on a 6% polyacrylamide gel using 0.5× TBE buffer as the electrophoresis medium, with electrophoresis performed at a constant current of 40 mA for 60–80 min, visualized by silver staining, and documented for band pattern analysis.

2.6. Data Statistics and Analysis

The bands were manually scored, with distinct bands recorded as ‘1’ and the absence of bands as ‘0’, to construct a binary (0/1) matrix. Genetic similarity coefficients among 76 raspberry germplasm resources were calculated using the software NTSYS-pc v2.1. Cluster analysis was performed with MEGA11, and a dendrogram was generated via the iTOL platform. Principal coordinate analysis (PCoA) was performed using ChiPlot (https://www.chiplot.online/ (accessed on 5 May 2025)). Additionally, POPGENE v32.0 was employed to compute genetic diversity parameters, including the number of polymorphic loci, percentage of polymorphic loci, observed number of alleles (Na), effective number of alleles (Ne), Nei’s gene diversity index (H), and Shannon’s information index (I).

3. Results

3.1. Amplification Efficiency and Polymorphism Analysis

Genetic diversity analysis using SSR markers demonstrated that Nei’s gene diversity index (H) ranged from 0.0539 to 0.4615, with a mean of 0.2570 per locus. Shannon’s information index (I) for SSRs ranged from 0.1550 to 0.6537 (mean = 0.4090). In contrast, SRAP markers exhibited narrower ranges for both indices: H spanned 0.1540–0.3603 (mean = 0.2697) and I spanned 0.2595–0.5371 (mean = 0.4031) (Table 2).
Comparative analysis revealed two distinct patterns. First, the range of H values for SRAP markers at 0.2063 was approximately half that of SSR markers at 0.4076, reflecting a more limited polymorphism distribution in SRAP data. Second, SRAP markers showed a slightly higher mean H value of 0.2697 compared to 0.2570 for SSR markers, potentially indicating moderately enhanced allelic diversity. Despite these differences, both marker systems demonstrated comparable Shannon index values, with a negligible mean I difference of 0.0059, indicating similar efficacy for genetic diversity characterization.

3.2. Genetic Diversity Analysis of Raspberry Germplasm

Building on the genetic diversity patterns observed between marker systems, population clustering further elucidated variations among the 76 raspberry accessions. Population clustering analysis of 76 raspberry accessions revealed distinct genetic patterns across three groups: A1 originating from Guizhou Province, China; A2 representing common cultivated varieties; and A3 collected from northeastern China. Population A2 demonstrated the highest genetic diversity using SSR markers, with the greatest effective allele count, Nei’s gene diversity index, and Shannon’s information index. All three metrics were significantly higher than those in A1 and A3 (Table 3).
SRAP marker analysis yielded consistent population classifications, showing polymorphic locus ratios of 53.42% for A1, 94.52% for A2, and 67.12% for A3. Consistent with SSR findings, A2 exhibited peak diversity indices (Ne = 1.3951, H = 0.2332, I = 0.3568), surpassing those of A1 and A3 (Table 3). Spearman’s rank correlation analysis confirmed strong concordance between marker systems, confirming the diagnostic consistency of SSR and SRAP markers for raspberry germplasm characterization.
Phylogenetic analysis revealed closer genetic relationships between wild accessions (Group A3) and cultivated varieties (Group A2), whereas Group A3 showed a distant relationship with wild Group A1 (originating from Guizhou Province, China) (Table 4). These results demonstrate the effectiveness of SSR markers in genetic screening of raspberry species.
Similarly, genetic distance analysis revealed an affinity between Group A3 and Group A2 that was stronger than the divergence between cultivated Group A2 and wild Group A1 (Table 4), supporting SRAP markers’ reliability for intra-genus genetic differentiation in raspberry.
In addition, principal coordinate analysis revealed that groups A2 and A3 clustered together in both SSR (Figure 1A) and SRAP (Figure 1B) analyses, while the majority of individuals in group A1 remained distinct.

3.3. Genetic Similarity Analysis of Raspberry Germplasm

Building on population genetic structure patterns, analysis of genetic similarity matrices further elucidated intra-group and inter-group relationships. For SSR markers, genetic similarity coefficients among raspberry accessions ranged from 0.473 to 0.982 (mean = 0.7275). Notably, the cultivated group A2 exhibited a higher mean similarity coefficient than the wild groups A1 and A3. Additionally, wild group A3 displayed greater intra-group genetic homogeneity compared to A1, with minimal variation observed among A3 accessions (Table S5).
Similar trends were observed in SRAP marker analysis, with similarity coefficients ranging from 0.520 to 0.972 (mean = 0.746). Conversely, wild group A1 exhibited greater intra-group similarity than both cultivated group A2 and wild group A3. Mirroring SSR data, A3 accessions maintained stable genetic similarity (Table S6).
These contrasting patterns demonstrate that SSR and SRAP markers target polymorphisms in distinct genomic regions. This highlights the importance of combining SSR and SRAP markers to comprehensively assess raspberry genetic diversity.

3.4. SSR and SRAP Cluster Analysis

In the SSR-based cluster analysis (Figure 2A), a vertical line at a genetic distance of 10 divided the 76 accessions into two major clusters (red vs. other colors). Within the second cluster, a cutoff distance of nine further separated the accessions into three subclusters. Similarly, the SRAP-based dendrogram (Figure 2B) showed two primary clusters (red vs. other colors) when a threshold genetic distance of 15 was applied, with the second cluster being subdivided into five subclusters at a distance of 10.
These results indicate that SSR markers offer lower resolution for fine-scale classification, making them preferable for species- or population-level differentiation. Conversely, SRAP markers demonstrated higher discriminatory power at the subcluster level, attributable to their capacity to target open reading frame (ORF)-associated polymorphisms, which facilitates precise cultivar identification and ecotype characterization.

4. Discussion

SSR markers, characterized by their codominant inheritance, are highly effective for elucidating genetic diversity in raspberry populations. These markers have been widely applied for cultivar identification within the Rosaceae species, particularly in raspberry germplasm characterization [25]. In raspberry research, 21 SSR markers enabled precise discrimination of 148 wild and cultivated black raspberry accessions, while two comprehensive fingerprinting databases incorporating 55 SSR markers were established for red raspberry genotyping [26,27]. Notably, SSR markers exhibit exceptional cross-species transferability within the raspberry genus. The primers originally designed for red raspberries by Fernández [27] demonstrated utility in genotyping black raspberries and five additional raspberries, with Marulanda [28] further confirming SSR marker portability between raspberries and blackberries. Conversely, SRAP markers are primarily employed in quantitative trait locus (QTL) mapping for advanced hybrids through targeted amplification of gene-coding regions, providing insights into agronomic trait architectures and kinship relationships. Although SRAP applications in raspberry remain unreported, their efficacy has been validated across multiple horticultural systems. In grape, SRAP markers resolved germplasm variation among interspecific hybrids and commercially significant groups [29]. In cabbage, researchers identified a male sterility-associated SRAP marker, while a fertility restorer (Rf)-linked SRAP marker was screened in rapeseed. Concurrently, a viral resistance gene-linked SRAP marker was obtained in celery [30]. Furthermore, genetic diversity analysis of 19 coconut cultivars using SRAP markers enabled the construction of a comprehensive genetic relationship map [31]. These findings collectively highlight the versatility of SRAP in resolving trait–gene associations and population structures across taxonomically distinct species.
Our comparative analysis revealed distinct characteristics of SSR and SRAP markers despite their shared applications in genetic studies. While Shannon’s information index (I) and genetic diversity assessments revealed consistent patterns across both marker types, SRAP markers demonstrated enhanced effectiveness in the other analytical components of this study. Notably, SRAP primers generated substantially more polymorphic bands (average 18.3 per locus) than SSR markers (average 6.2 per locus), consistent with findings from previous studies [32]. These results demonstrate that SSR markers predominantly reflect neutral genomic variation, whereas SRAP markers exhibit functional bias toward coding regions, likely due to their targeted amplification of gene sequences. Comparative efficacy evaluations confirmed SRAP makers’ superior performance in genetic discrimination, corroborating observations from citrus studies [33]. Our proposed SSR-SRAP integrated system effectively addresses the limitations of single-marker approaches, enabling comprehensive assessment of germplasm diversity and kinship relationships. This synergistic strategy has been validated in pear [34], grape [35], and citrus [36] research. The complementary “neutral–functional” dual-marker framework overcomes methodological constraints in population evolution analysis and trait inheritance dissection, establishing a novel paradigm for multidimensional genetic investigations.
Compared with traditional molecular markers, the SRAP system demonstrates superior efficiency in gene mapping, genetic diversity assessment, and DNA fingerprinting [37]. This technique specifically targets conserved coding regions flanking hypervariable introns, promoters, and spacers, enabling the design of primer combinations with enhanced specificity and cost-effectiveness [38]. Technical advantages of SRAP have driven its successful implementation in fruit crop genetics, including hawthorn [39], fig [40], and persimmon [41]. In Tetradium ruticarpum (A. Juss.) Hartley, a comparative study revealed SRAP’s analytical superiority over AFLP: despite generating fewer scorable fragments (188 vs. 353), SRAP achieved higher polymorphism rates (77.1% vs. 64.6%), increased cultivar-specific bands, and greater population differentiation capacity [42]. For hybridization analysis, SRAP effectively resolved genetic affinities between herbaceous peony and tree peony, demonstrating 89.3% similarity across 29 cultivars and confirming strong maternal inheritance in primary hybrids [43]. For interspecific variation analysis, Uzun employed SRAP markers to profile 83 citrus taxa comprising both cultivated species and hybrids [44]. Phylogenetic clustering revealed that intergeneric hybrids occupied intermediate positions but exhibited a predominant maternal lineage.
This study reports the first successful development of an SRAP-PCR protocol for raspberry, overcoming technical barriers in marker development caused by high genome heterozygosity in berry crops. Through orthogonal design optimization (Tables S3 and S4) accounting for interactive effects among multiple variables, we established a standardized SRAP reaction system, thereby addressing the absence of SRAP protocols in raspberry research. Among five critical factors tested, Taq DNA polymerase concentration showed the strongest influence on amplification efficiency. Optimal results were achieved at 0.6 U per 25 µL reaction system in raspberry. Comparatively, distinct optimal concentrations were observed across species: lingzhi mushroom required 2.5 U [45], persimmon utilized 1 U [46], and fig [40], grape [35], and citrus [36] each demonstrated species-specific optima. These findings confirm substantial interspecies variation in SRAP-PCR parameter requirements, necessitating systematic optimization for novel experimental systems.
The genetic architecture of raspberry exhibits significant complexity due to widespread interspecific introgression among cultivars. This phenomenon results in abundant heterozygous molecular markers within populations. While high interspecific cross ability frequently blurs distinctions between varieties, geographic isolation effectively maintains allelic diversity. Such conserved genetic resources provide critical advantages for germplasm enhancement and precision breeding in cultivated raspberries [47].
In this study, 76 raspberry germplasm accessions were categorized into three distinct groups: Group A1, Group A2, and Group A3. Genetic diversity analysis using SSR and SRAP markers revealed that Group A2 consistently exhibited superior genetic diversity parameters, compared to Groups A1 and A3. These findings indicate that cultivated varieties exhibit greater genetic diversity than wild populations, with advantageous alleles being effectively conserved through sustained domestication and selective breeding. Importantly, despite the limited sample sizes of wild populations (Group A1: 9 accessions; Group A3: 5 accessions), their polymorphic loci counts demonstrated richness comparable to Group A2. This suggests that even small wild populations retain substantial genetic variability. During our germplasm collection efforts, certain cultivars from Guizhou Province demonstrated enhanced cold tolerance by achieving typical growth performance in open-field conditions in Northeast China without protective measures. Consequently, for breeding programs aiming to develop cultivars with enhanced environmental adaptability (e.g., cold tolerance), we propose a dual-strategy approach: prioritizing elite cultivated varieties with superior agronomic traits while strategically integrating locally adapted wild germplasm to introgress ecologically resilient alleles [48].
In the detailed cluster analysis, we observed distinct patterns: the majority of Guizhou cultivars coclustered with a few common cultivars (e.g., ‘Polka’) in the SSR dataset (Figure 2A, yellow group), whereas SRAP analysis revealed complete segregation of Guizhou cultivars into an independent cluster (Figure 2B green group). This divergence highlights geography as a critical factor in clustering outcomes. Notably, ‘Heritage’ and ‘Heritage Seeding’, along with ‘Fertod Zamatos’ and ‘Fertod Zamatos Seeding’, failed to cluster together under either method, indicating pronounced trait segregation in raspberry progeny. Simultaneously, ‘Autumn Miss’ consistently clustered with ‘Autumn Bliss’, and ‘Yellow Raspberry’ with ‘Yellow Raspberry2’ across both techniques, confirming these nominally distinct accessions represent identical genotypes. Furthermore, despite including numerous Russian cultivars cultivated in Heilongjiang Province, we detected no significant clustering distinction between Russian and local Heilongjiang germplasm—strongly suggesting the historical introduction of northeastern China’s raspberries from Russia.
The phenomenon of nomenclatural redundancy is prevalent in fruit crops, causing significant confusion in both production systems and breeding programs. In China’s raspberry industry, which predominantly relies on introduced foreign cultivars, this issue is exacerbated by inconsistent naming practices during regional adaptation. For instance, cluster analysis of the autumn-fruiting cultivar ‘Heritage’, the summer-fruiting cultivar ‘Fertod Zamatos’, and their progenies (‘Heritage Seeding’ and ‘Fertod Zamatos Seeding’) revealed genetic divergence between parents and offspring. Genetic similarity coefficients between parental cultivars (‘Heritage’, ‘Fertod’) and their progenies were consistently below 0.85. Field evaluations demonstrated that ‘Fertod Zamatos Seeding’ lines exhibited superior phenotypic traits, including increased average berry weight (4.26 g vs. 3.89 g in ‘Fertod’) and ripening 10–14 days earlier. Notably, while the parental ‘Heritage’ is an autumn-fruiting type, its progenies predominantly produced summer fruits. These findings confirm measurable genetic and phenotypic divergence in seedling progenies relative to parental cultivars, necessitating systematic characterization of their agronomic performance for targeted utilization in breeding pipelines.

5. Conclusions

This study established the first standardized SRAP marker system for raspberry by integrating the complementary strengths of SSR and SRAP techniques, overcoming critical challenges in developing gene-associated markers. SSR markers demonstrated neutral characteristics suitable for population genetic analyses, whereas SRAP markers effectively captured genotype–phenotype correlations through coding region targeting, thereby establishing a species-specific framework combining neutral and functional markers. The integration of genetic diversity profiling with kinship analysis significantly improved cultivar discrimination accuracy and enhanced the efficiency of breeding selection for trait pyramiding objectives. These methodological advances extend the analytical framework for multi-marker integration in non-model species (exemplified by raspberry) and provide scalable genomics-driven strategies. This research delivers precision breeding tools to improve raspberry fruit quality and stress resilience, while offering conceptual foundations to optimize germplasm conservation and utilization practices. In the future, our group will expand germplasm collections of raspberries and establish correlations between phenotypic traits and molecular markers across raspberry cultivars, thereby developing actionable strategies for germplasm conservation and molecular breeding.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15061492/s1, Figure S1: Amplification results of 30 raspberry germplasm accessions with primer Rubus9; Figure S2: Effects of different annealing temperatures on SSR reaction; Figure S3: Effects of different annealing temperatures on SRAP reaction; Figure S4: Amplification results of SRAP-PCR reaction system optimized by orthogonal design; Table S1: Sequences of 10 optimal SSR primer pairs versus 20 suboptimal primer pairs; Table S2: Primer sequence used in SRAP analysis; Table S3: Gradient test for SRAP-PCR reaction factor; Table S4: Orthogonal design of SRAP-PCR system; Table S5: Matrix of genetic similarity among 76 raspberry germplasm accessions based on SSR markers; Table S6: Matrix of genetic similarity among 76 raspberry germplasm accessions based on SRAP markers.

Author Contributions

Conceptualization, T.L., G.Y., and Z.G.; methodology, Z.G.; software, Z.G.; validation, Z.G.; formal analysis, Z.F.; investigation, Z.G. and T.L.; data curation, Z.G.; writing—original draft preparation, Z.G.; writing—review and editing, X.L. and T.L.; supervision, H.D. and Z.W.; project administration, G.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Key R&D Program of China (2022YFD1600500).

Data Availability Statement

The data from this study are presented in the manuscript and Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 15 01492 g001
Figure 1. Principal coordinate analysis of SSR and SRAP. A1: Wild accessions from Guizhou Province, China; A2: common cultivated varieties; A3: wild accessions from northeastern China. (A) Principal coordinate analysis of SSR. (B) Principal coordinate analysis of SRAP.
Figure 1. Principal coordinate analysis of SSR and SRAP. A1: Wild accessions from Guizhou Province, China; A2: common cultivated varieties; A3: wild accessions from northeastern China. (A) Principal coordinate analysis of SSR. (B) Principal coordinate analysis of SRAP.
Agronomy 15 01492 g001
Agronomy 15 01492 g002
Figure 2. SSR and SRAP cluster analysis of 76 raspberry germplasm resources. Different colors represent different ethnic groups. (A) SSR cluster analysis. (B) SRAP cluster analysis.
Figure 2. SSR and SRAP cluster analysis of 76 raspberry germplasm resources. Different colors represent different ethnic groups. (A) SSR cluster analysis. (B) SRAP cluster analysis.
Agronomy 15 01492 g002
Table 1. Names and sources of test samples.
Table 1. Names and sources of test samples.
No.SampleLatin NameNo.SampleLatin Name
1Boyne 1R. idaeus L.39Autumn Miss 2R. idaeus L.
2Big Red Raspberry 1R. idaeus L.40Yellow Raspberry2 2R. idaeus L.
3Seeding20 1R. idaeus L.41R. parvifolius L. 2R. parvifolius L.
4Beauty22 1R. idaeus L.42R. hirsutus Thunb. 2R. hirsutus Thunb.
5Meeker 1R. idaeus L.43R. feddei Levl. 2R. feddei Levl.
6Fertod Zamatos Seeding 1R. idaeus L.44R. komarovi Nakai. 2R. komarovi Nakai.
7Heritage Seeding 1R. idaeus L.45Shopskarena 2R. idaeus L.
8Tulameen 1R. idaeus L.46Black Red Berry 2R. idaeus L.
9Caroline 1R. idaeus L.47R. crataegifolius Bunge. 2R. crataegifolius Bunge.
10European Red 1R. idaeus L.48Double Season Raspberry 2R. idaeus L.
11Seeding32 1R. idaeus L.49Suiberry1 2R. idaeus L.
12Reveille 1R. idaeus L.50Canada1 2R. idaeus L.
13Heritage 1R. idaeus L.51Bulgaria1 2R. idaeus L.
14Erika 1R. idaeus L.52Thornless 2R. idaeus L.
15Him Top 1R. idaeus L.53Black Raspberry 2R. occidentalis L.
16Lyulin 1R. idaeus L.54Australian Red 2R. idaeus L.
17Sweet Raspberry 1R. idaeus L.55Aures 2R. idaeus L.
18Nova 1R. idaeus L.56Ruby 2R. idaeus L.
19Fertod Zamatos 1R. idaeus L.57Met 2R. idaeus L.
20Trust 1R. idaeus L.58Fall Gold 2R. idaeus L.
21Kirzhach 1R. idaeus L.59Feng Manhong 2R. idaeus L.
22Barnaulskaja 1R. idaeus L.60Qiu Feng 2R. idaeus L.
23Humility 1R. idaeus L.61Nootka 2R. idaeus L.
24Sun 1R. idaeus L.62Willamette 2R. idaeus L.
25Meteor 1R. idaeus L.63R. buergeri Miq. 3R. buergeri Miq.
26DNS20 1R. idaeus L.64R. pectinellus Maxim. 3R. pectinellus Maxim.
27Yellow Giant 1R. idaeus L.65R. xanthocarpus Bureau et Franch. 3R. xanthocarpus Bureau et Franch.
28DNS29 1R. idaeus L.66R. trianthus Focke 3R. trianthus Focke
29DNS33 1R. idaeus L.67Guizhou1 3–
30DNS34 1R. idaeus L.68Guizhou3 3–
31Ruby Necklace 1R. idaeus L.69R. eustephanus Focke ex Diels. 3R. eustephanus Focke ex Diels.
32DNS35 1R. idaeus L.70R. coreanus Miq. 3R. coreanus Miq.
33Taiberi 1R. idaeus L.71Mount Fanjing1 3–
34Reward 1R. idaeus L.72Qiu Ping 4R. idaeus L.
35Local Red Raspberry 2R. idaeus L.73Polka 4R. idaeus L.
36Ruby Jade 2R. idaeus L.74Autumn Bliss 4R. idaeus L.
37Yellow Raspberry 2R. idaeus L.75Ding Kang 4R. idaeus L.
38Xiao Nieman 2R. idaeus L.76Xin Xing 4R. idaeus L.
Note: The en dashes (–) denote accessions with a taxonomically unresolved status at both the cultivar and species levels, while the numerical codes correspond to geographic provenances, as follows: 1 Xiangyang Experimental Station, Northeast Agricultural University (Harbin, Heilongjiang, China; 45°48′ N 126°30′ E, 151 m a.s.l.); 2 Fruit Research Institute, Jilin Academy of Agricultural Sciences (Changchun, Jilin, China; 43°54′ N 125°18′ E, 215 m a.s.l.); 3 Fanjingshan Mountain (Tongren City, Guizhou, China; 27°54′ N 108°42′ E, 2570 m a.s.l.); 4 Germplasm Repository, Shenyang Agricultural University (Shenyang, Liaoning, China; 41°48′ N 123°24′ E, 43 m a.s.l.). Collectively, these sites form a geographic transect spanning 23.5° of latitude and 2527 m of elevation across China’s major raspberry distribution zones.
Table 2. Analysis of the number of sites and gene polymorphisms.
Table 2. Analysis of the number of sites and gene polymorphisms.
TypePrimerTotal Number of BandsNumber of
Polymorphic
Bands		Percentage of
Polymorphic
Bands		Nei’s Gene
Diversity Index		Shannon’s
Information
Index
SSRRubus1181688.9%0.05390.1550
Rubus22121100.0%0.18980.4538
Rubus3252288.0%0.23920.3559
Rubus4252392.0%0.25060.3867
Rubus510880.0%0.16520.2692
Rubus6302893.3%0.41310.6018
Rubus7262596.1%0.35510.5140
Rubus81010100.0%0.20250.3312
Rubus9262492.3%0.23940.3691
Rubus10181794.4%0.46150.6537
Tol2091949.262.5704.090
Mean20.919.492.6%0.25700.4090
SRAPMe1/Em2201575.0%0.15400.2595
Me2/Em21515100%0.22850.3707
Me2/Em3322990.6%0.35440.4991
Me2/Em42323100%0.36030.5371
Me3/Em1393692.3%0.23870.3626
Me3/Em31919100%0.30040.4749
Me3/Em43030100%0.34050.4845
Me4/Em12222100%0.22410.3584
Me4/Em4646195.3%0.23110.3520
Me1/Em4262076.9%0.26470.3326
Tol2902709.302.6974.0314
Mean292793.0%0.26970.4031
Note: Primer nomenclature followed user-defined conventions, with full sequences provided in Supplementary Materials (Tables S1 and S2). Population genetic parameters—including percentage of polymorphic bands (PPB), Nei’s gene diversity (H), and Shannon’s information index (I)—were computed using POPGENE version 1.32. Higher values of Nei’s H indicate greater genetic divergence among cultivars, while elevated Shannon’s I values denote increased overall genetic diversity.
Table 3. Comparison of the genetic diversity between raspberry populations.
Table 3. Comparison of the genetic diversity between raspberry populations.
TypeNameNumber of
Samples		Number of
Polymorphic
Loci		Percentage
of
Polymorphic
Loci		Effective
Number of
Alleles		Nei’s Gene
Diversity Index		Shannon’s
Information
Index
SSRA193764.91%1.38730.22430.3356
A2625291.23%1.43320.24950.3769
A353154.39%1.32050.18850.2837
SRAPA193953.42%1.26510.16430.2647
A2626994.52%1.39510.23320.3568
A354967.12%1.24910.16430.2661
Note: A1: Wild accessions from Guizhou Province (the nine accessions labeled as 3 in Table 1), China; A2: common cultivated varieties (the five accessions labeled as 2 in Table 1—where samples directly correspond to their respective Latin names); A3: wild accessions from northeastern China.
Table 4. Genetic agreement and genetic distance of 3 populations.
Table 4. Genetic agreement and genetic distance of 3 populations.
TypeNameA1A2A3
SSRA1*****0.92600.8723
A20.0769*****0.9391
A30.13660.0628*****
SRAPA1*****0.92570.9321
A20.0772*****0.9434
A30.07030.0582*****
Note: The notation ***** denotes content that cannot be analyzed.
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Guo, Z.; Fan, Z.; Li, X.; Du, H.; Wu, Z.; Li, T.; Yang, G. Combined Analysis of SRAP and SSR Markers Reveals Genetic Diversity and Phylogenetic Relationships in Raspberry (Rubus idaeus L.). Agronomy 2025, 15, 1492. https://doi.org/10.3390/agronomy15061492

AMA Style

Guo Z, Fan Z, Li X, Du H, Wu Z, Li T, Yang G. Combined Analysis of SRAP and SSR Markers Reveals Genetic Diversity and Phylogenetic Relationships in Raspberry (Rubus idaeus L.). Agronomy. 2025; 15(6):1492. https://doi.org/10.3390/agronomy15061492

Chicago/Turabian Style

Guo, Zhifeng, Zhenzhu Fan, Xueyi Li, Haoqi Du, Zhuolong Wu, Tiemei Li, and Guohui Yang. 2025. "Combined Analysis of SRAP and SSR Markers Reveals Genetic Diversity and Phylogenetic Relationships in Raspberry (Rubus idaeus L.)" Agronomy 15, no. 6: 1492. https://doi.org/10.3390/agronomy15061492

APA Style

Guo, Z., Fan, Z., Li, X., Du, H., Wu, Z., Li, T., & Yang, G. (2025). Combined Analysis of SRAP and SSR Markers Reveals Genetic Diversity and Phylogenetic Relationships in Raspberry (Rubus idaeus L.). Agronomy, 15(6), 1492. https://doi.org/10.3390/agronomy15061492

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