1. Introduction
Strawberry (Fragaria ananassa Duch.), often referred to as the ‘queen of fruits’, is a perennial herb belonging to the genus Fragaria within the Rosaceae family. Its widespread cultivation globally can be attributed to its rich nutritional value and significant economic benefits [1,2]. The red-flowered strawberry represents a novel addition to the strawberry family, resulting from the hybridization of F. ananassa (2n = 8x = 56) and Potentilla palustris (2n = 6x = 42), and serves as a new type of ornamental flower [3]. Since the birth of the world’s first red-flowered strawberry variety, ‘Pink Panda’, in 1989 [4], researchers from various countries have focused on enhancing ornamental traits—such as flower size, petal color, number of petals, and petal shape—alongside enhancing resistance and fruit quality, including large size and high quality in varieties such as ‘Roman’, ‘Summer Breeze-Rose’, ‘Summer Breeze-Cherry’, and so on [5]. In addition to red-flowered strawberries, certain white-flowered varieties, such as ‘Tokun’, exhibit remarkable ornamental qualities [6]. This cultivar is distinguished by its prolific flowering, large flower diameter, deep double petals, and flowers above the leaf surface, making it a valuable parental source for the development of new ornamental strawberry varieties. Now, numerous new varieties of red-flowered strawberries have been bred globally [5]. However, there are notable differences in ornamental properties among germplasms. Therefore, understanding the specific traits, genetic diversity, and genetic relationships of these germplasm resources is crucial for effective breeding.
Genetic diversity is the foundation of biological diversity and serves as a driving force behind the stability and ongoing evolution of species. Various methods for studying genetic diversity include morphological, cytological, and molecular marker techniques [7]. Among these methods, morphological markers are the simplest and most convenient, capable of revealing the extent of genetic variation and, to some degree, uncovering valuable germplasms. However, morphological traits are easily influenced by ecological conditions [8]. In contrast, molecular markers are unaffected by ecological factors and can directly identify inter-individual differences at the DNA level. This approach offers several advantages, including high specificity, high accuracy, small sample volume requirements, and rapid analyses [9]. In recent years, molecular markers have been extensively applied in studies of plant genetic diversity, assessments of population structure, analyses of genetic relationships, and the construction of core collections [10]. Molecular markers serve as an effective complement to morphological markers. The combination of molecular and morphological markers enables a more direct and accurate identification of the genetic basis underlying plant genetic variation, thereby providing a technical foundation for the evaluation of germplasm resources and multi-target selection [11]. Research methodologies that combine phenotypic and molecular markers have been successfully applied to various species, including Rosa damascena [12], Camellia japonica [13], Clematis macropetala [14,15], and Chrysanthemum morifolium [16].
Inter-simple sequence repeats (ISSRs) can amplify inter-microsatellite sequences at multiple loci across the genome, and are capable of detecting both microsatellite and inter-microsatellite polymorphisms without prior knowledge of the DNA sequences. This method is rapid, stable, highly reproducible, and cost-effective [17]. ISSR has been widely utilized to analyze the genetic diversity and relationships among various plant species, including F. ananassa [18,19], Cerasus spp. [20], and Ficus carica L. [21]. A conserved DNA-derived polymorphism (CDDP) is a molecular marker technique for target genes, wherein primers are specifically designed for a gene or a characteristic of a gene family in plants [22]. The resulting amplification products are typically more closely associated with the target gene. The CDDP technique is characterized by its ease of operation, low cost, and abundance of polymorphisms, and it has been extensively employed to investigate the genetic diversity of various plants, including Paeonia suffuticosa [23], Camellia japonica [13], Rosa rugosa [24], Pistacia vera L. [25], and Musa L. [26]. However, its application in red-flowered strawberry research has not yet been reported. ISSR molecular marker technology aims to amplify the intronic regions of the genome, which is stochastic. In contrast, CDDP molecular marker technology serves as a purposive marker, capable of amplifying bands associated with functional genes, thereby revealing the polymorphisms within these genes. The combination of ISSR and CDDP markers, which amplify distinct regions of the genome, facilitates the establishment of more reliable genetic relationships among germplasms. This approach has been effectively applied to the analysis of genetic diversity, both among and within populations of plants, such as Iris aucheri [27] and Astragalus L [28]. These studies have significant implications for the development and utilization of plant resources, the conservation of valuable germplasms, and the advancement of breeding programs.
In this study, we integrated phenotypic traits with ISSR and CDDP molecular markers to analyze the genetic diversity and relationships among 18 strawberry germplasms. This approach not only facilitates the identification of superior ornamental traits within the strawberry germplasm, but also offers significant references into its evolutionary development. Furthermore, it provides a foundational basis for the innovation of ornamental strawberry germplasm and the selection of specific resources for practical applications.
2. Materials and Methods
2.1. Test Materials
The test materials comprised 18 strawberry germplasms, including 17 red-flowered varieties (lines) and 1 white-flowered variety (Table 1). Among these, eight were sourced from China, one from the United Kingdom, eight from the Netherlands, and the white-flowered variety was sourced from Japan. On 8 October 2023, the stolon seedlings were planted in gallon pots with an upper caliber of 15 cm, a lower caliber of 11 cm, and a height of 15 cm. A total of 30 plants were planted for each germplasm, all of which were placed in the horticultural facility greenhouse of Fujian Agriculture and Forestry University in China for cultivation. Fertilizer, water, and pest control were managed according to conventional methods.
2.2. Test Methods
2.2.1. Measurement of Phenotypic Traits
In accordance with the “Descriptors and Data Standard for Strawberry (Fragaria spp.)” [29], a total of 15 plants were randomly selected from each germplasm between late November 2023 and late February 2024. Observations of flower and plant correlation traits were conducted during the flower blooming date, while leaf correlation traits were observed during the late flower blooming date. Seventeen quantitative traits were assessed, including leaf length, leaf width, leaf thickness, petiole length, petiole diameter, plant height, size of plant, inflorescence stalk length, inflorescence stalk thickness, pedicel length, pedicel diameter, flower petal number, stamen number, calyx size, flower petal length, and flower petal width. Additionally, sixteen quality traits were observed through naked-eye observation, and values were assigned to these quality traits for subsequent analysis (Table 2).
2.2.2. DNA Extraction and Its Quality Testing
The unfolded young leaves of 18 strawberry germplasms were weighed, and total DNA was extracted from these varieties using the Biospin Omni Plant Genomic DNA Extraction Kit (Hangzhou Bioer Technology Co., Ltd., Hangzhou, China). The concentration and optical density (OD) values of the extracted DNA were measured using a UV spectrophotometer at wavelengths of 260 nm and 280 nm, respectively. The purity of the DNA was further verified through agarose gel electrophoresis. Subsequently, DNA samples of acceptable quality were uniformly diluted to a concentration of 20 ng/μL for subsequent PCR amplification assays.
2.2.3. ISSR and CDDP Primer Screening
The study utilized 100 ISSR universal primers published by the University of British Columbia (UBC) and 35 CDDP primers [22,30]. These primers were synthesized by Fuzhou Shangya Biotechnology Co. in Fuzhou, China. DNA from two strawberry varieties, ‘245’ and ‘Tokun’, which display notable phenotypic differences, served as templates. Primers that exhibited strong amplification, a clear background, and high polymorphism number were selected for the PCR amplification of 18 strawberry varieties (lines) (Table 3).
2.2.4. PCR Amplification and Product Detection
PCR amplification was conducted in a total reaction volume of 25 μL, comprising 2.5 μL of DNA template, 1.25 μL of primers, 12.5 μL of 2× SanTaq Fast PCR Mix premix (Vazyme Biotech Co., Ltd., Nanjing, China), and 8.75 μL of double-distilled water (ddH2O). The PCR amplification program consisted of the following steps: initial denaturation at 95 °C for 5 min; denaturation at 95 °C for 15 s; annealing at the annealing temperature (Tm) for 15 s; elongation at 72 °C for 30 s; followed by 35 cycles; a final extension at 72 °C for 6 min; and concluding the reaction at 12 °C for storage. To evaluate the amplification product, 10 μL was detected using electrophoresis on a 1.5% gel (110 V for 30–40 min), which was subsequently imaged with a gel imager and photographed for documentation.
2.3. Statistical Analysis
The variance, mean, standard deviation, and coefficient of variation were calculated for each quantitative trait using IBM SPSS Statistics 26. The leaves were scanned using an HP Scanjet G4050 scanner (China Hewlett-Packard Co., Ltd, Beijing, China) and flowers were photographed with a Canon EOS M5 camera (Canon China Co., Ltd., Beijing, China). The isometric classification method was applied to categorize the quantitative traits into ten levels based on the mean (X) and standard deviation (S). Specifically, the first level was defined as Xi < X − 2S, with each subsequent level increasing by 0.5S, and the tenth level was defined as Xi ≥ X + 2S. The relative frequency of each level (Pi) was then calculated, followed by the determination of the genetic diversity index using the Shannon–Wiener diversity index (H′) according to the following formula:
(1)
‘Pi’ represents the proportion of materials within the level of a trait relative to the total number of materials, while ‘In’ denotes the natural logarithm. Additionally, the distribution frequencies of the 18 quality traits and the Shannon–Wiener information index were calculated [13,31]. Correlation heatmaps for quantitative traits, percentage stacked histograms for quality traits, and clustering maps for phenotypic traits were generated using Origin 2024.The bands were counted based on the PCR amplification results of ISSR and CDDP markers. Clear bands at the same site were recorded as ‘1’, while the absence of bands was recorded as ‘0’, resulting in the establishment of a ‘0–1’ data matrix. The number of amplified bands, the number of polymorphic bands, and the percentage of polymorphic loci for each primer were calculated using Excel. Genetic similarity coefficients, the number of observed alleles, the number of effective alleles, Nei’s diversity index (H), and Shannon’s diversity index (I) for the 18 strawberry varieties (lines) were computed using PopGene 1.32 software [26]. Unweighted Pair Group Method Using Arithmetic Average (UPGMA) cluster analysis was performed based on DICE similarity coefficients using NTSYSpc 2.10e software [26,32,33]. Using the ISSR + CDDP data, the K value was determined with Structure 2.3.4 software. Subsequently, the corresponding Q value for each strawberry germplasm was calculated to analyze the population genetic structure of the strawberry germplasm resources and ascertain the genetic composition of each germplasm resource. The layout of the resulting image was performed using Adobe Photoshop 2023.
3. Results
3.1. Phenotypic and Genetic Diversity Analysis of 18 Strawberry Germplasms
3.1.1. Diversity Analysis of Quantitative Traits
The observations and statistical analyses of the quantitative traits of 18 strawberry germplasms revealed that all 17 quantitative traits exhibited rich diversity across the 18 strawberry germplasms (Table 4, Figure 1). The coefficients of variation ranged from 16% to 52%, with the highest coefficients observed for inflorescence stalk length and the lowest for flower petal width. Notably, 14 of the traits demonstrated coefficients of variation exceeding 20%, indicating substantial differences among the cultivars. The degree of variation for flower diameter, flower petal length, and flower petal width was moderate, with coefficients of variation of 19%, 17%, and 16%, respectively, suggesting relatively stable performance among these varieties. The Shannon–Wiener diversity index (H′) for the 17 quantitative traits ranged from 1.498 to 2.080, highlighting significant genetic differences and rich diversity among the strawberry germplasm resources under investigation. The Shannon–Wiener diversity index (H′) for flower diameter was the highest (2.080), followed closely by pedicel diameter and plant height, which had diversity indices of 2.070 and 2.068, respectively. Conversely, the flower petal number exhibited the lowest diversity index (1.498), followed by stamen numbers and leaf length, which had diversity indices of 1.911 and 1.918, respectively. It is noteworthy that ‘Summer Breeze-Rose’ and ‘Summer Breeze-Cherry’ exhibited deeply double flowers, characterized by petal numbers ranging from 7 to 15, and 10 to 17 petals, respectively. The mean petal numbers for ‘Summer Breeze-Rose’ and ‘Summer Breeze-Cherry’ were 11.8 and 13.73, respectively, both of which were significantly greater than those observed in other germplasms.
Significant correlations were observed among 11 of the 17 quantitative traits, including highly significant positive correlations between leaf length and leaf width, petiole length, and size of plant, with correlation coefficients of 0.8720, 0.6347, and 0.7317, respectively (Figure 2). Furthermore, there were highly significant positive correlations between leaf width and petiole diameter, leaf thickness and pedicel diameter, petiole length and plant height, petiole diameter and pedicel diameter, stamen number and calyx size, as well as flower petal length and flower petal width, with correlation coefficients of 0.7544, 0.6698, 0.8493, 0.7523, 0.6161, and 0.6409.
3.1.2. Diversity Analysis of Quality Traits
We identified a total of 16 quality traits in 18 strawberry germplasms, each exhibiting varying degrees of variability. The Shannon–Wiener diversity index (H′) for these 16 quality traits ranged from 0.215 to 1.480, with the highest diversity index observed for flower color (1.480) and the lowest for petiole hair (0.215). Notably, the Shannon–Wiener diversity index (H′) for flower color, leaf shape, pedicel hair, and leaf texture exceeded 1, indicating a high level of genetic diversity among the 18 strawberry varieties (lines) (Figure 3). Notably, ‘140’, ‘243’, ‘245’, ‘Frisan’, ‘Roman’, ‘Gasana’, ‘Summer Breeze-Rose’, and ‘Summer Breeze-Cherry’ exhibit compact plants and attractive flower shapes. The flowers are vividly colored, with over two-thirds positioned above the foliage, enhancing their visibility.
3.2. Genetic Diversity Analysis of 18 Strawberry Germplasms Based on ISSR and CDDP Markers
3.2.1. DNA Extraction and Primer Screening Results
Genomic DNA from 18 strawberry varieties (lines) was extracted using a plant genomic DNA extraction kit. Electrophoresis revealed that the bands were clear and free from dispersion. The DNA concentrations of the 18 strawberry varieties ranged from 78 to 114 ng/μL, with OD260/OD280 ratios between 1.7 and 2.0. These quality tests confirmed that the DNA met the necessary standards for use in subsequent experiments. The genomic DNA of red-flowered (‘245’) and white-flowered (‘Tokun’) strawberry germplasms was utilized to screen 25 ISSR and 17 CDDP primers, which exhibited good repetitiveness and polymorphisms for the current experiment. Part of the amplification electropherogram results of the primer are shown in Figure 4.
3.2.2. Amplification Results and Polymorphism Analysis of ISSR and CDDP Molecular Markers
The PCR amplification of 18 strawberry germplasms using 25 ISSR primers resulted in a total of 188 clear bands, of which 145 were polymorphic, representing 75.91% of the total loci. The percentage of polymorphic loci exceeded 66.67% for all primers, with the exception of primers UBC 823 (40.00%) and UBC 864 (42.86%), which displayed a lower percentage of polymorphic loci (Table 5). Notably, the percentage of polymorphic loci for primers UBC 807, UBC 808, UBC 811, UBC 824, UBC 827, UBC 834, UBC 835, UBC 853, UBC 855, and UBC 880 exceeded 80%. Similarly, the PCR amplification of the same 18 strawberry germplasms using 17 CDDP primers resulted in a total of 159 clear bands, with 130 being polymorphic, accounting for 81.02% of the total loci. The percentage of polymorphic loci exceeded 80% for all primers except for CHI1 (57.14%), F3H2 (66.67%), ANS1 (78.57%), KNOX-2 (75.00%), and MADS-4 (66.67%) (Table 5). Additionally, a total of 347 bands were amplified by both ISSR and CDDP primers, with 275 of these being polymorphic, yielding an average percentage of polymorphic loci of 77.98%. These results indicate that CDDP markers amplified a greater number of polymorphic bands with fewer primers, suggesting that CDDP markers exhibit stronger polymorphism in the 18 strawberry germplasms.
The parameters of the number of alleles (Na), the number of effective alleles (Ne), Nei’s diversity index (H), and Shannon’s diversity index (I) for strawberry germplasms were calculated using PopGen32. The results from the ISSR molecular marker analysis indicated that the average Na among the 18 strawberry germplasm samples was 1.7539, the average Ne was 1.4084, the average H was 0.2443, and the average I was 0.3717 (Table 5). Additionally, the results from the CDDP molecular marker analysis indicated that the mean number of alleles (Na) among the 18 strawberry germplasms was 1.8175, with the mean number of effective alleles (Ne) at 1.4536, the mean Nei’s diversity index (H) at 0.2701, and the mean Shannon’s diversity index (I) at 0.4093 (Table 5. The ISSR + CDDP molecular marker analysis revealed that the average number of alleles (Na) among the 18 strawberry germplasm samples was 1.7796. Additionally, the average number of effective alleles (Ne) was 1.4267, the average Nei’s diversity index (H) was 0.2547, and the average Shannon’s diversity index (I) was 0.3869. These results indicate a relatively high level of genetic diversity among the 18 strawberry materials tested.
3.3. Cluster Analysis of 18 Strawberry Germplasms Based on Phenotypic Traits, ISSR, CDDP, and ISSR + CDDP Markers
The cluster analysis of 18 strawberry germplasms based on 33 phenotypic traits categorized these germplasms into three major clusters (Figure 5A). Cluster I comprises five varieties (lines), namely ‘Pink Panda’, ‘Merlan’, ‘Roman’, ‘Toscana’, and ‘Fenyun’. Cluster II includes nine varieties (lines), namely ‘140’, ‘243’, ‘Tarpan’, ‘Bubby Ann’, ‘SummerBreeze-Rose’, ‘Summer Breeze-Cherry’, ‘245’, ‘Gasana’, and ‘Frisan’. Cluster III contains four varieties (lines), namely ‘246’, ‘Zijinhong’, ‘Zijindaiyu’, and ‘Tokun’. Both Cluster I and Cluster III germplasms exhibit pink or white petal colors, while Cluster II displays a diverse range of petal colors, including light pink, pink, red, and dark red.
UPGMA clustering analysis was conducted on 18 strawberry varieties, resulting in the construction of a dendrogram. Based on the ISSR molecular marker data, the 18 strawberry germplasms were categorized into four primary clusters at a similarity coefficient of 0.77 (Figure 5B). Cluster I comprises three varieties: ‘Pink Panda’, ‘Toscana’, and ‘Fenyun’. Cluster II includes twelve varieties: ‘140’, ‘243’, ‘245’, ‘Bubby Ann’, ‘246’, ‘Frisan’, ‘Roman’, ‘Gasana’, ‘Tarpan’, ‘Summer Breeze-Rose’, ‘Summer Breeze-Cherry’, and ‘Merlan’. Cluster III consists of two varieties: ‘Zijinhong’ and ‘Zijindaiyu’, while Cluster IV contains only one variety, ‘Tokun’.
At a similarity coefficient of 0.77, the 18 strawberry germplasms were categorized into three major clusters based on CDDP molecular marker data (Figure 5C). Cluster I includes three species (lines) aligning with Cluster I in the ISSR molecular marker-based clustering results. Cluster II comprises ‘Zijinhong’, ‘Zijindaiyu’, and ‘Tokun’. Cluster III includes 12 species (lines), which corresponds to Class II of the ISSR molecular marker-based clustering results. Notably, the phenotype-based clustering results differed significantly from those based on molecular markers. However, the clustering results obtained from ISSR and CDDP were largely consistent across the three major clusters, with discrepancies noted in the clustering outcomes of the minor clusters.
To enhance the understanding of the relationships among the test materials, this study employed a combination of ISSR and CDDP marker data to construct a clustering tree (Figure 5D). At a genetic similarity coefficient of 0.77, the 18 strawberry germplasms were classified into four major clusters based on the ISSR + CDDP molecular marker data. Cluster I comprises ‘Pink Panda’, ‘Toscana’, and ‘Fenyun’; Cluster II includes ‘140’, ‘243’, ‘Gasana’, ‘245’, ‘Bubby Ann’, ‘Tarpan’, ‘246’, ‘Frisan’, ‘Roman’, ‘Summer Breeze-Rose’, ‘Summer Breeze-Cherry’, and ‘Merlan’. Cluster III consists of ‘Zijinhong’ and ‘Zijindaiyu’, while Cluster IV is represented by ‘Tokun’. The genetic similarity coefficients among the 18 strawberry germplasms, based on ISSR + CDDP markers, ranged from 0.6138 to 0.9856, with a mean value of 0.7241. Notably, ‘Toscana’ and ‘Fenyun’ exhibited the highest genetic similarity coefficient, indicating that these two varieties are the most closely related. Conversely, the lowest genetic similarity coefficients were observed between ‘Tokun’ and ‘Bubby Ann’, suggesting that these two varieties are the most distantly related.
3.4. Population Genetic Structure Analysis of 18 Strawberry Germplasms Based on ISSR + CDDP Markers
To further clarify the genetic composition of the test materials, 18 strawberry germplasm samples were analyzed for population structure using Structure software (version 2.3.4) based on ISSR + CDDP marker data. Following the method outlined by Evanno et al. [34], the change curve of ΔK was plotted against increasing K values. The analysis revealed that ΔK reached its maximum when K = 3, indicating that it is most appropriate to classify the 18 strawberry germplasms into three distinct groups (Figure 6A). The results of the population structure analysis classified the 18 strawberry germplasms into three distinct groups (Figure 6B). The first group included two germplasms, ‘Toscana’ and ‘Fenyun’, which had Q values of 0.996 and 0.991, respectively. The second group comprised nine germplasms: ‘140’, ‘243’, ‘245’, ‘246’, ‘Gasana’, ‘Tarpan’, ‘Roman’, ‘Frisan’, and ‘Bubby Ann’, exhibiting Q values that ranged from 0.688 to 0.989. The third group contained seven germplasms: ‘Pink Panda’, ‘Zijinhong’, ‘Zijindaiyu’, ‘Summer Breeze-Rose’, ‘Summer Breeze-Cherry’, ‘Merlan’, and ‘Tokun’, with Q values ranging from 0.510 to 0.991. Germplasms with a Q value of ≥ 0.6 are considered to have a relatively homogeneous genetic background, whereas germplasms with a Q value of <0.6 are classified as having a mixed origin [35]. Among the 18 strawberry germplasm samples analyzed, 12 exhibited a Q value greater than 0.9, representing 66.67% of the total. Additionally, 16 germplasm samples had a Q value of ≥0.6, accounting for 88.89%, while ‘Summer Breeze-Rose’ and ‘Summer Breeze-Cherry’ had a Q value of <0.6, corresponding to 11.11%. These findings suggest that the majority of the 18 strawberry germplasm samples possess a relatively homogeneous genetic background.
4. Discussion
Germplasm resources serve as the foundation for breeding [36,37]. A thorough understanding of kinship distance and variation among these resources is essential for the purposeful selection of parent plants and the development of superior varieties. Consequently, analyzing the genetic diversity of germplasm resources plays a crucial role in the breeding process. Plant phenotypic diversity arises from a combination of genetic and environmental factors. Understanding the variation and diversity within red-flowered strawberry germplasm resources is a crucial step in the identification, evaluation, conservation, and utilization of these resources. When the coefficient of variation exceeds 10%, it indicates that the trait exhibits significant variability among germplasms [38,39,40]. Likewise, a diversity index greater than 1 signifies a high level of phenotypic trait diversity [41]. In this study, the coefficient of variation and genetic diversity of the 33 phenotypic traits investigated showed considerable differences. The coefficients of variation for the 17 quantitative traits ranged from 16% to 52%. The Shannon–Wiener diversity index for these traits ranged from 1.498 to 2.080, with a mean value of 1.981. In contrast, the coefficients of variation for the 16 quality traits ranged from 12% to 69%. The Shannon–Wiener diversity index for the quality traits ranged from 0.215 to 1.480, with a mean value of 0.761. The results indicated that the coefficients of variation for the 33 phenotypic traits exceeded 10%, with 28 of these traits exhibiting coefficients of variation greater than 20%. Furthermore, the Shannon–Wiener diversity index for 21 traits was found to be greater than 1. These findings suggest that the phenotypic traits of the test materials exhibit substantial variability and genetic diversity. Notably, the Shannon–Wiener diversity index for quantitative traits was higher than that for quality traits, a result that aligns with previous studies conducted on Medicago ruthenica L. [42] and Agropyron [43]. This observation may be attributed to the differing genetic regularity associated with quality and quantitative traits.
Plant phenotypes are influenced by environmental factors to some extent, while molecular markers can reveal genetic variation in plants at the DNA level. This approach is a stable and reliable method for genetic analysis and serves as a crucial tool in studying the genetic diversity of germplasm resources [44]. In this study, 145 and 130 polymorphic bands were amplified using 25 ISSR and 17 CDDP primers, respectively. The average percentage of polymorphic loci for these two types of markers was 75.91% and 81.02%, respectively, both of which were higher than the percentages of polymorphic loci observed in strawberry germplasms using ISSR markers, as reported by Hua et al. [18] and Zhang et al. [19]. These findings indicate that both ISSR and CDDP markers can be effectively utilized in the genetic diversity studies of red-flowered strawberry. The Nei’s diversity index (H) and Shannon’s diversity index (I) values for 18 samples based on ISSR markers in this study were 0.2443 and 0.3717, respectively. These values were higher than those reported in a study on Cerasus spp. by Liu et al. [20] using ISSR molecular markers (H = 0.2240; I = 0.3623), but lower than those found by Yang et al. [45] in their research on Habenaria dentata (H = 0.2240; I = 0.3623). This discrepancy may be attributed to the differences in species and the primers used. Furthermore, the average H and I values for the 18 samples based on CDDP markers were 0.2701 and 0.4093, respectively. These values were higher than those obtained from the ISSR markers, indicating that CDDP markers more effectively reflect the genetic diversity of red-flowered strawberries compared to ISSR markers. The mean Nei’s diversity index (H) for the 18 strawberry germplasms, assessed using both ISSR and CDDP molecular markers, was higher than the average genetic diversity level of plants (0.22–0.23) reported by N. Hilde [46]. This finding indicates that both molecular markers effectively reflect the high level of genetic diversity present in the 18 strawberry germplasms.
In this study, 18 strawberry germplasms were clustered using phenotypic, ISSR, and CDDP markers. The results showed large differences between the phenotypic clustering and molecular marker clustering results. This difference may be due to several reasons. On the one hand, the disturbance of external factors resulted in unstable genetic expression, which led to large phenotypic differences, which is consistent with the findings of Yang et al. [47] and Zhai et al. [48]. On the other hand, it may be due to the insufficient number of morphological traits utilized in this study and the limited range of genetic differences probed by molecular markers to comprehensively reflect the genetic information of these germplasms, which resulted in the inconsistency between the results of phenotypic clustering and molecular marker clustering. In this study, 18 strawberry germplasms were classified into four and three classes based on ISSR and CDDP markers, respectively, at a genetic similarity coefficient of 0.77. The classification results of the two clustering methods were generally consistent, and only the clustering results of individual varieties differed. ISSR markers classified ‘Tokun’ as a separate cluster, while CDDP markers clustered ‘Tokun’ with ‘Zijinhong’ and ‘Zijindaiyu’. This clustering result is justified by the fact that ‘Tokun’, ‘Zijinhong’, and ‘Zijindaiyu’ are all hybrids of ‘Sweet Charlie’ [49,50,51]. Two primary factors contribute to the observed differences in the results of the two clustering methods. The first factor is the variation in the binding sites of the ISSR and CDDP primers, which leads to differences in both the number and location of amplified bands. This observation aligns with the findings reported by Nawroz Tahir et al. [52]. The second factor is that the two markers employed for cluster analysis in this study amplified differing numbers of polymorphic bands; specifically, the ISSR marker displayed a greater number of polymorphic bands compared to the CDDP marker. Consequently, the ISSR marker was able to cluster ‘Tokun’ separately from ‘Zijinhong’ and ‘Zijindaiyu’. To enhance the accuracy of distinguishing kinship among the germplasms, we used a combination of ISSR and CDDP markers for cluster analysis, and finally classified the 18 strawberry germplasms into four classes. However, even with the combination of the two markers, the results of the molecular markers still failed to differentiate between ‘Fenyun’ and ‘Tuscany’, suggesting that additional primers or alternative types of molecular markers may be necessary for their distinction.
Population structure analysis classified the 18 strawberry germplasms into three distinct classes, which was not entirely consistent with the UPGMA clustering results. This inconsistency arises because UPGMA clustering assumes a uniform evolutionary rate across all branches and constructs a clustering tree based on the genetic distance between pairs of germplasms. In contrast, population structure analysis utilizing the Structure software employs a Bayesian algorithm that infers population structure based on posterior probabilities by modeling the distribution of allele frequencies across various subgroups. Consequently, different algorithms may yield inconsistent results. Population structure analysis indicated that the genetic background of the 18 strawberry germplasm lines was relatively homogeneous, with 88.9% exhibiting a Q value greater than or equal to 0.6, while only 11.11% displayed a Q value less than 0.6. The two germplasms are ‘Summer Breeze-Rose’ and ‘Summer Breeze-Cherry’. Notably, the flower petal number for these germplasms range from 7 to 15 for ‘Summer Breeze-Rose’ and from 10 to 17 for ‘Summer Breeze-Cherry’, with an average flower petal number of 11.8 and 13.73, respectively. Moreover, the floral, foliar, and plant traits of both germplasms exhibit significant ornamental value and can serve as crucial resources for the development and utilization of ornamental strawberries, as well as for the selection and breeding of new varieties.
5. Conclusions
In this study, we analyzed the genetic diversity and population structure of 18 ornamental strawberry cultivars using phenotypic, ISSR, and CDDP markers. Our findings indicate that these 18 strawberry germplasms exhibit significant variation and genetic diversity at both phenotypic and molecular levels. The clustering results revealed notable discrepancies between phenotypic clustering and molecular marker clustering, while the ISSR and CDDP markers formed similar clusters. The average genetic similarity coefficient among the 18 strawberry germplasms was notably high, at 0.7241. Population structure analysis classified the 18 strawberry germplasms into three distinct groups, with a relatively high proportion (88.89%) exhibiting a Q value ≥ 0.6. This finding suggests that the genetic background of the 18 strawberry germplasms is relatively homogeneous. The floral, foliar, and plant traits of ‘Summer Breeze-Rose’ and ‘Summer Breeze-Cherry’ demonstrate significant ornamental value and can serve as crucial resources for the development and utilization of ornamental strawberries, as well as for the selection and breeding of new varieties.
Conceptualization, L.M., Q.C. and C.N.; methodology, L.M., Q.C., J.H. (Jiayi Hou) and C.N.; software, C.N., J.H. (Jiayi Hou) and J.H. (Jin He); validation, Y.Z., Q.X., W.W. and L.J.; formal analysis, C.N.; investigation, C.N. and S.Y.; resources, C.N.; data curation, C.N.; writing—original draft preparation, C.N.; writing—review and editing, C.N. and L.M.; visualization, C.N.; supervision, L.M., Q.C. and J.W.; project administration, L.M. and Q.C.; funding acquisition, L.M. and Q.C. All authors have read and agreed to the published version of the manuscript.
Data supporting the reported results can be requested by contacting the corresponding author. The data are not publicly available due to compliance with data protection regulations.
We gratefully acknowledge Yongchao Han (Institute of Industrial Crops, Hubei Academy of Agricultural Sciences, China) for his technical assistance.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Flowers and leaves of the 18 strawberry germplasm resources. Please refer to Table 1 for the code names of the 18 strawberry germplasms.
Enlarge this image.Figure 2. Correlation analysis was conducted among 17 quantitative traits of 18 strawberry germplasms. The asterisk (*) denotes significance at p ≤ 0.05, while the double asterisk (**) indicates significance at p ≤ 0.01. The analyzed traits include the following: LL (leaf length), LW (leaf width), LT (leaf thickness), PL (petiole length), PD (petiole diameter), PH (plant height), SP (size of plant), ISL (inflorescence stalk length), LIS (inflorescence stalk thickness), Pedicel L (pedicel length), Pedicel D (pedicel diameter), FD (flower diameter), FPN (flower petal number), SN (stamen number), CS (calyx size), FPL (flower petal length), and FPW (flower petal width).
Enlarge this image.Figure 3. Frequency distribution of variant types and the Shannon–Wiener diversity index of 16 quality traits in 18 strawberry germplasms. These red dots represent the Shannon–Wiener diversity index for each of the 16 quality traits. Numbers 1 to 5 represent the values assigned to various quality traits. Please refer to Table 1 for the code names of the 18 strawberry germplasms. The analyzed traits include the following: Foliar S (foliar state), LC (leaf color), L shape (leaf shape), SILM (shape of incisions of leaf margin), L text (leaf texture), HRSL (hair on the right side of the leaf), HBSL (hair on the back side of the leaf), Pet C (petiole color), Petiole H (petiole hair), PIRF (position of inflorescence relative to foliage), Inflo A (inflorescence architecture), Flower C (flower color), PS (petal shape), P Stamen (position of stamen), Pedicel H (pedicel hair), and PA (plant architecture).
Enlarge this image.Figure 4. PCR amplification electropherogram of primer UBC 851 (A) and primer F3H2 (B). Please refer to Table 1 for the code names of the 18 strawberry germplasms.
Enlarge this image.Figure 5. Cluster analysis plot of 18 strawberry germplasm resources based on phenotypic traits (A), and ISSR (B), CDDP (C), and ISSR + CDDP (D) markers. Please refer to Table 1 for the code names of the 18 strawberry germplasms.
Enlarge this image.Figure 6. Trend of the rational groups’ K number and estimated value ΔK based on Structure analysis (A). Population genetic structure analysis of 18 strawberry germplasms based on ISSR + CDDP markers (B). Please refer to Table 1 for the code names of the 18 strawberry germplasms.
Test materials and their sources.
| No. | Name | Code Name | Source | No. | Name | Code Name | Source |
|---|---|---|---|---|---|---|---|
| 1 | Fenyun | FYZ | China | 10 | Summer Breeze-Rose | MGH | Holland |
| 2 | 140 | SOZ | China | 11 | Summer Breeze-Cherry | YHH | Holland |
| 3 | 243 | SCZ | China | 12 | Gasana | JSH | Holland |
| 4 | 245 | SWZ | China | 13 | Tarpan | YMH | Holland |
| 5 | 246 | SLZ | China | 14 | Roman | LMH | Holland |
| 6 | Zijinhong | JHZ | China | 15 | Frisan | FLH | Holland |
| 7 | Zijindaiyu | DYZ | China | 16 | Bubby Ann | BSH | Holland |
| 8 | Toscana | TCS | China | 17 | Merlan | MLH | Holland |
| 9 | Pink Panda | PPY | England | 18 | Tokun | TXR | Japan |
Quality trait assignment table.
| Trait | Assignment of Each Character |
|---|---|
| Foliar state | Spoon = 1, Edging up = 2, Level = 3, Lever and top down = 4, Edging down = 5 |
| Leaf color | Yellow green = 1, Green = 2, Dark green = 3, Blue green = 4 |
| Leaf shape | Round = 1, Ellipse = 2, Rhombus = 3, Oval = 4, Upside-down oval = 5 |
| Shape of the incisions of the leaf margin | Serrate = 1, Crenate = 2 |
| Leaf texture | Supple = 1, Coriaceous and coarse = 2, Coriaceous and smooth = 3 |
| Hair on the right side of the leaf | Vertical = 1, Plagiotropic = 2, Level-stick = 3 |
| Hair on the back side of the leaf | Vertical = 1, Plagiotropic = 2, Level-stick = 3 |
| Petiole color | Yellow-green = 1, Purple-red = 2 |
| Petiole hair | Vertical = 1, Plagiotropic = 2, Level-stick = 3 |
| Position of inflorescence relative to foliage | Beneath = 1, Level width = 2, Above = 3 |
| Inflorescence architecture | Vertical = 1, Plagiotropic = 2 |
| Flower color | White = 1, Light pink = 2, Pink = 3, Red = 4, Dark red = 5 |
| Petal shape | Oblate = 1, Round = 2, Sector = 3, Oval = 4, Ellipse = 5 |
| Position of stamen | Low = 1, Intermediate = 2, High = 3 |
| Pedicel hair | Vertical = 1, Plagiotropic = 2, Level-stick = 3 |
| Plant architecture | Erect = 1, Intermediate = 2, Prostrate = 3 |
Twenty-five ISSR and seventeen CDDP primers in this study.
| Primer Type | Primer Name | Primer Sequences 5′-3′ | Tm (°C) |
|---|---|---|---|
| ISSR | UBC 807 | AGAGAGAGAGAGAGAGT | 53.0 |
| UBC 808 | AGAGAGAGAGAGAGAGC | 55.0 | |
| UBC 810 | GAGAGAGAGAGAGAGAT | 53.0 | |
| UBC 811 | GAGAGAGAGAGAGAGAC | 55.0 | |
| UBC 812 | GAGAGAGAGAGAGAGAA | 53.0 | |
| UBC 816 | CACACACACACACACAT | 53.0 | |
| UBC 823 | TCTCTCTCTCTCTCTCC | 55.0 | |
| UBC 824 | TCTCTCTCTCTCTCTCG | 53.0 | |
| UBC 827 | ACACACACACACACACG | 53.0 | |
| UBC 834 | AGAGAGAGAGAGAGAGYT | 55.8 | |
| UBC 835 | AGAGAGAGAGAGAGAGYC | 55.0 | |
| UBC 836 | AGAGAGAGAGAGAGAGYA | 52.3 | |
| UBC 840 | GAGAGAGAGAGAGAGAYT | 53.0 | |
| UBC 844 | CTCTCTCTCTCTCTCTRC | 52.3 | |
| UBC 845 | CTCTCTCTCTCTCTCTRG | 55.0 | |
| UBC 847 | CACACACACACACACARC | 55.0 | |
| UBC 848 | CACACACACACACACARG | 55.0 | |
| UBC 851 | GTGTGTGTGTGTGTGTYG | 54.3 | |
| UBC 853 | TCTCTCTCTCTCTCTCRT | 52.0 | |
| UBC 855 | ACACACACACACACACYT | 57.0 | |
| UBC 857 | ACACACACACACACACYG | 55.0 | |
| UBC 864 | ATGATGATGATGATGATG | 51.0 | |
| UBC 868 | GAAGAAGAAGAAGAAGAA | 50.0 | |
| UBC 874 | CCCTCCCTCCCTCCCT | 60.8 | |
| UBC 880 | GGAGAGGAGAGGAGA | 55.0 | |
| CDDP | CHI1 | GCCGTTGGAGCTACAC | 50.0 |
| DFR1 | GATCCTGCCTGAGCAAGG | 54.3 | |
| ERF3 | TGGCTSGGCACSTTCGA | 53.0 | |
| F3H1 | GGGAGAAGCTGTGCG | 56.0 | |
| F3H2 | GGTGGCCGGATAAGCCGG | 52.3 | |
| F3H3 | CGGCTGCAACCCAGTAACG | 54.3 | |
| WRKY-R1 | GTGGTTGTGCTTGCC | 50.0 | |
| WRKY-R2 | GCCCTCGTASGTSGT | 50.0 | |
| WEKYR-2B | TGSTGSATGCTCCCG | 55.0 | |
| ANS1 | GGCCTGCAGCTCTTCT | 56.0 | |
| ANS2 | GCGTCCCCAACTCGATC | 59.0 | |
| MYB1 | GGCAAGGGCTGCCGC | 59.0 | |
| MYB2 | GGCAAGGGCTGCCGG | 59.0 | |
| KNOX-1 | AAGGGSAAGCTSCCSAAG | 53.0 | |
| KNOX-2 | CACTGGTTGGGAGCTSCAC | 59.0 | |
| MADS-1 | ATGGGCCGSGGCAAGGTGC | 64.0 | |
| MADS-4 | CTSTGCGACCGSGAGGTG | 59.0 |
Diversity analysis of the 17 quantitative traits.
| Quantitative Character | Range | Mean ± Standard Deviation | Variable Coefficient (%) | H′ |
|---|---|---|---|---|
| Leaf length/cm | 2.30~8.50 | 4.70 ± 1.22 | 25.96 | 1.918 |
| Leaf width/cm | 2.40~8.20 | 4.45 ± 1.12 | 25.17 | 1.972 |
| Leaf thickness/mm | 0.12~0.52 | 0.26 ± 0.06 | 23.08 | 2.013 |
| Petiole length/cm | 2.60~15.10 | 7.56 ± 2.46 | 32.54 | 2.049 |
| Petiole diameter/mm | 0.96~3.05 | 1.63 ± 0.37 | 22.30 | 2.009 |
| Plant height/cm | 5.30~19.60 | 11.26 ± 2.89 | 25.67 | 2.068 |
| Size of plant/cm | 6.98~27.90 | 14.78 ± 3.81 | 25.78 | 2.027 |
| Inflorescence stalk length/cm | 0.07~12.20 | 4.38 ± 2.29 | 52.28 | 2.016 |
| Inflorescence stalk thickness/mm | 0.58~2.63 | 1.75 ± 0.41 | 23.43 | 2.035 |
| Pedicel length/cm | 0.99~8.50 | 3.48 ± 1.60 | 45.98 | 1.974 |
| Pedicel diameter/mm | 0.46~1.69 | 1.12 ± 0.26 | 23.21 | 2.070 |
| Flower diameter/cm | 1.45~3.87 | 2.74 ± 0.51 | 18.61 | 2.080 |
| Flower petal number | 4.00~17.00 | 6.94 ± 2.73 | 39.33 | 1.498 |
| Stamen numbers | 12.00~62.00 | 26.16 ± 6.18 | 23.62 | 1.911 |
| Calyx size/cm | 0.93~3.93 | 2.16 ± 0.58 | 26.85 | 2.021 |
| Flower petal length/cm | 0.82~1.87 | 1.21 ± 0.20 | 16.53 | 1.964 |
| Flower petal width/cm | 0.89~2.00 | 1.31 ± 0.21 | 16.03 | 2.047 |
ISSR and CDDP of primer polymorphisms.
| Primer Type | Primer Name | Band Number | Polymorphic Band Number | Percentage of Polymorphic Loci (%) | Number of Alleles (Na) | Number of Effective Alleles (Ne) | Nei’s Diversity Index (H) | Shannon’s Diversity Index (I) |
|---|---|---|---|---|---|---|---|---|
| ISSR | UBC 807 | 6 | 6 | 100.00 | 2.0000 | 1.5168 | 0.3066 | 0.4678 |
| UBC 808 | 8 | 7 | 87.50 | 1.8889 | 1.5180 | 0.3038 | 0.4573 | |
| UBC 810 | 11 | 8 | 72.73 | 1.7273 | 1.4489 | 0.2615 | 0.3891 | |
| UBC 811 | 6 | 5 | 83.33 | 1.8333 | 1.3753 | 0.2366 | 0.3718 | |
| UBC 812 | 8 | 6 | 75.00 | 1.7500 | 1.5302 | 0.3017 | 0.4416 | |
| UBC 816 | 6 | 4 | 66.67 | 1.5000 | 1.2116 | 0.1296 | 0.2053 | |
| UBC 823 | 5 | 2 | 40.00 | 1.6000 | 1.2420 | 0.1407 | 0.2232 | |
| UBC 824 | 9 | 8 | 88.89 | 1.7778 | 1.4388 | 0.2688 | 0.4074 | |
| UBC 827 | 7 | 6 | 85.71 | 2.0000 | 1.7139 | 0.3783 | 0.5475 | |
| UBC 834 | 10 | 9 | 90.00 | 1.9000 | 1.4593 | 0.2741 | 0.4206 | |
| UBC 835 | 5 | 4 | 80.00 | 1.6000 | 1.3166 | 0.2049 | 0.3142 | |
| UBC 836 | 9 | 7 | 77.78 | 1.7778 | 1.4381 | 0.2627 | 0.3989 | |
| UBC 840 | 6 | 4 | 66.67 | 1.8333 | 1.4536 | 0.2788 | 0.4258 | |
| UBC 844 | 10 | 7 | 70.00 | 1.7000 | 1.3740 | 0.2333 | 0.3575 | |
| UBC 845 | 4 | 3 | 75.00 | 1.5000 | 1.1968 | 0.1265 | 0.2014 | |
| UBC 847 | 3 | 2 | 66.67 | 1.6667 | 1.5013 | 0.2798 | 0.4056 | |
| UBC 848 | 9 | 7 | 77.78 | 1.7778 | 1.3838 | 0.2325 | 0.3580 | |
| UBC 851 | 9 | 6 | 66.67 | 1.7778 | 1.4165 | 0.2551 | 0.3902 | |
| UBC 853 | 7 | 6 | 85.71 | 1.8571 | 1.6127 | 0.3492 | 0.5098 | |
| UBC 855 | 12 | 12 | 100.00 | 2.0000 | 1.5425 | 0.3215 | 0.4876 | |
| UBC 857 | 8 | 6 | 75.00 | 1.6250 | 1.2861 | 0.1736 | 0.2705 | |
| UBC 864 | 7 | 3 | 42.86 | 1.4286 | 1.1879 | 0.1217 | 0.1907 | |
| UBC 868 | 9 | 6 | 66.67 | 1.7778 | 1.3725 | 0.2387 | 0.3727 | |
| UBC 874 | 7 | 5 | 71.43 | 1.7143 | 1.3033 | 0.1958 | 0.3129 | |
| UBC 880 | 7 | 6 | 85.71 | 1.8333 | 1.3683 | 0.2315 | 0.3642 | |
| Total | 188 | 145 | - | - | - | - | - | |
| Mean | 7.52 | 5.8 | 75.91 | 1.7539 | 1.4084 | 0.2443 | 0.3717 | |
| CDDP | CHI1 | 7 | 4 | 57.14 | 1.5714 | 1.4400 | 0.2451 | 0.3537 |
| DFR1 | 6 | 5 | 83.33 | 1.8333 | 1.5635 | 0.3251 | 0.4790 | |
| ERF3 | 13 | 13 | 100.00 | 2.0000 | 1.4899 | 0.2982 | 0.4606 | |
| F3H1 | 6 | 5 | 83.33 | 1.8333 | 1.4421 | 0.2685 | 0.4120 | |
| F3H2 | 12 | 8 | 66.67 | 1.6667 | 1.3955 | 0.2258 | 0.3389 | |
| F3H3 | 11 | 9 | 81.82 | 1.8182 | 1.5433 | 0.3126 | 0.4619 | |
| WRKY-R1 | 7 | 6 | 85.71 | 1.8571 | 1.5819 | 0.3342 | 0.4925 | |
| WRKY-R2 | 10 | 9 | 90.00 | 1.9000 | 1.5787 | 0.3364 | 0.4997 | |
| WEKYR-2B | 8 | 8 | 100.00 | 2.0000 | 1.3564 | 0.2423 | 0.3943 | |
| ANS1 | 14 | 11 | 78.57 | 1.7857 | 1.3550 | 0.2279 | 0.3588 | |
| ANS2 | 5 | 4 | 80.00 | 1.8000 | 1.1472 | 0.1185 | 0.2189 | |
| MYB1 | 14 | 12 | 85.71 | 1.8571 | 1.4304 | 0.2623 | 0.4043 | |
| MYB2 | 12 | 10 | 83.33 | 1.8333 | 1.3961 | 0.2449 | 0.3797 | |
| KNOX-1 | 10 | 8 | 80.00 | 1.8000 | 1.5513 | 0.3117 | 0.4559 | |
| KNOX-2 | 8 | 6 | 75.00 | 1.8750 | 1.4482 | 0.2600 | 0.3964 | |
| MADS-1 | 10 | 8 | 80.00 | 1.8000 | 1.5413 | 0.3142 | 0.4629 | |
| MADS-4 | 6 | 4 | 66.67 | 1.6667 | 1.4504 | 0.2644 | 0.3895 | |
| Total | 159 | 130 | - | - | - | - | - | |
| Mean | 9.35 | 7.65 | 81.02 | 1.8175 | 1.4536 | 0.2701 | 0.4093 | |
| ISSR + CDDP | Total | 347 | 275 | - | - | - | - | - |
| Mean | 8.26 | 6.55 | 77.98 | 1.7796 | 1.4267 | 0.2547 | 0.3869 |
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; Hou, Jiayi 1 ; He, Jin 1 ; Zheng, Yanju 1 ; Yang, Shugen 1 ; Lai, Jiang 1 ; Xie, Qian 1 ; Wang, Wei 1 ; Wu, Jinghua 1 ; Chen, Qingxi 1
; Miao, Lixiang 1 1 College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China;