Next Article in Journal
Multi-Scale Computational Enzymology: Enhancing Our Understanding of Enzymatic Catalysis
Previous Article in Journal
Chemokine Receptors in Epithelial Ovarian Cancer
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 15
Issue 1
10.3390/ijms15010377
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Diversity and Population Structure of Siberian apricot (Prunus sibirica L.) in China

by
Ming Li
,
Zhong Zhao
*,
Xingjun Miao
and
Jingjing Zhou
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2014, 15(1), 377-400; https://doi.org/10.3390/ijms15010377
Submission received: 27 September 2013 / Revised: 5 December 2013 / Accepted: 5 December 2013 / Published: 31 December 2013
(This article belongs to the Section Biochemistry)
Browse Figures
Versions Notes

Abstract

:
The genetic diversity and population genetic structure of 252 accessions from 21 Prunus sibirica L. populations were investigated using 10 ISSR, SSR, and SRAP markers. The results suggest that the entire population has a relatively high level of genetic diversity, with populations HR and MY showing very high diversity. A low level of inter-population genetic differentiation and a high level of intra-population genetic differentiation was found, which is supported by a moderate level of gene flow, and largely attributable to the cross-pollination and self-incompatibility reproductive system. A STRUCTURE (model-based program) analysis revealed that the 21 populations can be divided into two main groups, mainly based on geographic differences and genetic exchanges. The entire wild Siberia apricot population in China could be divided into two subgroups, including 107 accessions in subgroup (SG) 1 and 147 accessions in SG 2. A Mantel test revealed a significant positive correlation between genetic and geographic distance matrices, and there was a very significant positive correlation among three marker datasets. Overall, we recommend a combination of conservation measures, with ex situ and in situ conservation that includes the construction of a core germplasm repository and the implement of in situ conservation for populations HR, MY, and ZY.

1. Introduction

Evaluation of the level and structure of genetic diversity in natural populations of a species is important for plant breeding and genetic resource conservation programs [1]. Indeed, wild species play crucial roles in breeding programs because of their wide variability in terms of phenological, morphological, abiotic, biotic, and quality traits [2]. However, it has been widely reported that a large amount of genetic diversity has been lost in major crops due to drift and selection in comparison to the wild forms, thereby reducing the potential for crop improvement in modern agricultural systems [3]. Genetic variation must exist to maintain natural populations as evolutionarily viable units capable of adapting to changing environmental conditions in the long term [4]. Thus, a genetic resource management strategy should involve an investigation of the genetic diversity and the extent of genetic differentiation within and between populations and an understanding of the processes that maintain these variations.
In this work, we present a case study of Siberian apricot (Prunus sibirica L.) in China. Siberian apricot, a member of the genus Prunus belonging to the family Rosaceae, is an important ecological and economic tree species endemic to Asia. P. sibirica is distributed in regions of eastern Siberia, including the maritime territory of Russia, eastern regions of Mongolia, and northern regions of China [5]. In China, Siberian apricot is distributed throughout a total area of approximately 2 million hectare, with an annual seed production of nearly 1 million tons. It is able to grow in environments with a low temperature, strong wind, low rainfall, and poor soil. Considering its ecological benefits, such as water and soil conservation, windbreak, sand fixation, and environmental protection and greening, Siberian apricot is often used for afforestation in north China [6]. The seeds of P. sibirica also have important economic values: the seed shell can be pyrolyzed to activated carbon and pyroligneous liquor [7]; the seed kernel can be processed for protein drinks [8]; the seed kernel oil can be used for edible oils, cosmetics, biodiesel, and in the prevention of cardiovascular diseases and lowering of plasma cholesterol levels [9,10]. Traditional Chinese medicine uses the seed kernels in different preparations for treating asthma, coughs, and infant virus pneumonia [11].
Furthermore, Siberian apricot is an important breeding resource for apricot production. According to molecular studies, many apricot cultivars grown for fruit, particularly in China, belong to the species P. armeniaca obtained through introgression with P. sibirica [12]. For adaptation to the arid and cold environment in China and Russia, some cultivated apricots have been bred from P. sibirica or obtained by grafting with P. sibirica [13]. In addition, Chinese apricots and/or P. sibirica have been important for introgressing resistance to Plum pox virus (PPV) in North American germplasm [12]. The increased attention to the industrial value of Siberian apricot in recent years has highlighted the often low fruit set and tree productivity due to late-season frosts and a lack of variety breeding, with an insufficient fruit supply for industrial development. Therefore, the use of diverse wild resources to broaden the genetic base and increase the potential for the ecological adaptation of Siberian apricot cultivars allows important objectives to be pursued in breeding. Unfortunately, due to human interference, the area originally covered by wild resources has been greatly reduced, with the remaining area mostly found on mountains and cliffs. Thus, the genetic diversity and population structure of Siberian apricot in China should be immediately investigated for resource conservation and breeding.
DNA markers allow the direct assessment of relatedness at the DNA level by estimating the proportion of alleles in individuals and populations. Different molecular markers can be used for genomic analysis, and simple sequence repeats (SSR), inter-simple sequence repeats (ISSR), and sequence-related amplified polymorphism (SRAP) markers have been recognized as useful molecular markers for diversity studies, population genetic analysis, and other purposes in various species [14–16]. SSR markers have been well applied in apricot genetics due to the advantages of abundance in the apricot genome, co-dominance, and a high polymorphism rate [17–20]. ISSR markers are based on amplification of the specific regions between two SSR sequences, with the advantages of deep genome coverage, high effectiveness (time-effective and cost-effective manner), and wide use in apricot [21–23]. Recently used in apricot, SRAP markers target open reading frames (ORFs), combining simplicity, reliability, a moderate throughput ratio, and the disclosure of co-dominant markers [24,25]. However, as there are few different and combined analyses of these three markers, it is important for researchers to compare these markers to identify the approaches that best address the research concerns.
To our knowledge, there is no research to date focusing on the genetic diversity and population structure of Siberian apricot in China. Thus, the objectives of this study were to (i) characterize the genetic diversity and genetic differentiation of 21 Siberian apricot populations using SSR, ISSR, and SRAP markers; (ii) characterize the genetic structure in the populations; (iii) assess the correspondence among the three markers; and (iv) offer an effective conservation strategy for wild Siberian apricot.

2. Results and Discussion

2.1. Genetic Diversity Analysis

Ten respective primers for SSR, ISSR, and SRAP markers amplified 45, 103, and 120 putative genetic loci, with a total of 268 loci detected. For the SSR markers, we detected 4.50 observed number of alleles and 3.77 effective number of alleles; for the ISSR and SRAP markers, 91.3% and 82.5% bands, respectively, were polymorphic (Tables S1 and S2). Nei’s gene diversity (h), Shannon’s information index (I), and the percentage of polymorphic bands (PPB) were used to assess the genetic diversity (Table 1). The ISSR markers revealed the highest genetic diversity in population HC (h = 0.208, I = 0.310, PPB = 59.2%), followed by populations HR and AS, with a high level of diversity at the species level (h = 0.248, I = 0.387, PPB = 91.3%). According to the SRAP markers, population HR had the highest genetic diversity (h = 0.213, I = 0.326, PPB = 69.2), followed by populations ZY and MY, with a weaker level of diversity at the species level (h = 0.218, I = 0.344, PPB = 82.5%) than the ISSR markers. Based on the SSR markers, the highest genetic diversity was present in population AS (h = 1.231, I = 0.662), followed by populations MY and HC, with a high level of diversity at the species level (h = 1.639, I = 0.782). Populations KK and LX showed the lowest genetic diversity index using these three markers.
Compared to previous research in apricot species, the Siberia apricot in China showed a relatively high level of genetic diversity, higher than cultivated apricot (P. armeniaca) in Turkey revealed by ISSR markers (PPB = 88%) but lower than wild apricot (P. armeniaca) in the Ili Valley (PPB = 94.84%) [23,26]. The expected heterozygosity (0.713) revealed by the SSR markers was also higher than the apricot accessions (P. armeniaca) in the Maghreb region (0.593), but similar to the core collection apricot germplasm in China (0.731) [27,28]. As the same wild apricot resource, Siberia apricot has a slightly weaker genetic diversity index than wild apricot in the Ili Valley (PPB = 91.3% vs. 94.84%) but a larger population size and distribution area. As wild apricot in the Ili Valley has been accepted as the oldest and most diverse natural population, we suggest that the natural Siberia apricot populations in China retain a relatively high level of genetic diversity [29].
The HR and MY populations showed much higher diversity parameters and are close in geographical distance. The two populations are located at the western end of the Yanshan Mountains, a traditional Siberia apricot growing region. Because of their economic value, the Siberia apricot seeds are widely collected by villagers in north China, and most populations will be affected by human disturbance [10,30]. The areas in which the HR and MY population are located have an advanced economy, with most of the apricot resources being distributed on rocky cliff and humans rarely collecting the seeds. We suggest that the reduced impact of human disturbance is the reason for the higher diversity in populations HR and MY. With the exception of population ZD, the populations (ZY, LY, AS, HC, and HX) in northwest China all showed higher diversity parameters than the mean. This region has a warmer climate for Siberia apricot growth and overlaps with the P. armeniaca distribution. In recent years, apricots grown for the kernels have been cultivated in this region, including hybrid varieties of P. armeniaca and P. sibirica [31]. We believe that a genetic exchange with P. armeniaca occurred with P. sibirica in this region, affecting the genetic diversity of P. sibirica.

2.2. Genetic Differentiation Analysis

Assuming Hardy-Weinberg equilibrium, the genetic differentiation coefficient was calculated from the allele frequencies estimated according to the square root method using POPGENE. As revealed by the ISSR markers, the relative magnitude of genetic differentiation among the populations (GST) was 0.18. An AMOVA analysis showed that 25.01% of the total gene diversity was found among the natural populations, whereas the remaining 74.99% of the total variation occurred within the populations (Table 2). The SRAP markers showed that the GST among the populations was 0.28, indicating that 76.16% of the total variance occurred within the populations. According to the SSR markers, the genetic differentiation among the populations (FST) was 0.15, indicating that 83.35% of the total variance occurred within the populations. The three markers all indicated a low level of inter-population genetic differentiation and high level of intra-population genetic differentiation in wild Siberia apricot. This result was further confirmed by the moderate level of gene flow (Nm) between the populations (1.58 for ISSR, 1.28 for SRAP, 1.37 for SSR).
Based on the values Fst (0.15) and Gst (0.18 and 0.28), a large amount of genetic variation in Siberia apricot was found within the populations, equivalent to the differentiation among the natural populations observed in wild apricot of the Ili valley (Fst = 0.137; Gst = 0.164) but lower than the North Africa apricot populations (Fst = 0.04) according to SSR markers [27,29]. Due to the cross-pollination reproductive system and self-incompatibility, we determined that Siberia apricot in China is an outcrossing species. A value of Nm > 1 indicates no significant genetic differentiation among populations [32]. In the present study, the gene flow was moderate and revealed a high level of genetic diversity maintained within the populations that was not susceptible to genetic drift. The mode of pollen and seed dispersal, which determines gene flow among populations, may partly account for this moderate differentiation. Although the pollen of Siberia apricot can be spread over a long distance by the combination of insects and wind, the large distribution area and largely discontinuous distribution negate this possibility. As a long-lived perennial woody plant, P. sibirica is widely distributed in regions of eastern Siberia, with large population sizes. To adapt to ecologically diverse habitats, it has probably accumulated considerable genetic variation within species. The populations in China may be characterized by shared ancestral polymorphisms, and have maintained large effective population sizes so that the shared variation has not been lost by drift. Considering the partly overlapping distribution with P. armeniaca and P. mandshurica, the variation found among the P. sibirica populations was possibly due to drift or crossing with other Prunus species. In addition, P. sibirica uses an animal-ingested seed dispersal system; its efficiency largely depends on the migration habits and activity of animals. Obligate fruit-eaters that feed mainly or solely on the fruit of P. sibirica during the maturation period, when fruit-ripening asynchrony occurs, may confine their activities within the areas where ripe fruits are available. As a result, the seed exchange among P. sibirica populations will be minimal. In addition, factors such as the low seed germination rate and breeding system may also partly contribute to the present population genetic differentiation in P. sibirica.

2.3. Population Structure and Cluster Analysis

To further elucidate the relationships among the populations, Nei’s unbiased measure of genetic distance was applied to calculate the genetic distances between them (Tables S3–S5); based on the results, a Cluster analysis of the distance matrices based on an UPGMA algorithm was used to generate a dendrogram. The population structure was analyzed using a Bayesian approach on 252 accessions implemented in the STRUCTURE software (version 2.3.4; Pritchard J.K., Stanford, CA, USA. http://pritchardlab.stanford.edu/structure.html) [33]. Following the method of Evanno [34], the ΔK values were plotted against the K numbers of the sub-groups. The maximum ΔK occurred at K = 2 for the three markers (Figure S1). We divided the accessions into different sub-groups considering membership probabilities of ≥0.50, and we incorporated the populations to two main groups according to the sub-group of the major accessions.
Considering the ISSR markers, the dendrogram divided the 21 populations into two main clusters, a large cluster with 13 populations and a small with 8 populations (Figure 1a). The STRUCTURE analysis indicated that the entire population could be divided into two groups: group I consisted of 9 populations, LY, ZD, AS, HC, HX, ZY, YG, GL, and HY; the other populations were clustered into group II (Figure 2a). Similar results were obtained with the STRUCTURE analysis and Cluster analysis, except for population HY. Five accessions in population HY had a similar genetic structure as group II. The Cluster analysis based on the SRAP data generated a unique dendrogram that divided the 21 populations into two main clusters, similar to the subgroups of the STRUCTURE analysis (Figure 1b). Group I consisted of populations LY, ZD, AS, HC, HX, ZY, YG, GL, and HY, with the other populations clustered into group II, similar to the results using the ISSR markers. The dendrogram of the Cluster analysis based on the SSR data divided the 21 populations into two main clusters, in agreement with the dendrogram based on the ISSR data (Figure 1c). When considering K = 2, the populations were split into two groups: populations LY, ZD, AS, HC, HX, ZY, YG, GL, and HY in group I and the remaining populations in group II.
The population genetic structure reflects interactions among species with regard to their long-term evolutionary history, mutation and recombination, genetic drift, reproductive system, gene flow, and natural selection [35,36]. Thus, an understanding of the level and structure of the genetic diversity of a crop is a prerequisite for the conservation and efficient use of the germplasm available for breeding [2]. In the present study, we analyzed the information obtained using three markers, and all of them indicated two groups according to dendrogram and STRUCTURE analyses, though a slight difference was found regarding population HY. Group I included populations LY, ZD, AS, HC, HX, ZY, YG, GL, and HY, and group II included populations YQ, HR, MY, CY, KZ, LiY, WC, LH, PQ, NC, LX and KK. It is noteworthy that the two groups appear to be divided by geographic distribution, with group I distributed in west of 115 °E and group II distributed in east of 115 °E. The dendrogram topology was generally consistent with the geographic distribution of these populations, indicating a possible correlation, and the Mantel test of correlation between the genetic and geographic distance matrices revealed a significant positive correlation (ISSR, r = 0.7379; SRAP, r = 0.6160; SSR, r = 0.5490; p ≤ 0.001). Most of the area of group I is located in the warm-temperate zone in northwest China, and group II is located in the mid-temperate zone in northeast and north China, and there is a large difference in light, temperature, and other climate conditions in these two regions [37]. For example, late-season frosts are a serious issue for apricot growth, and the duration and timing of frosts are different in these two regions, thus affecting the survival and blossoming time of Siberia apricot [38]. Plant growth and development are sensitive to climate [39]. In our field investigations, we found that the height of mature Siberia apricot trees was approximately 3–6 m in group I and 1.5–5 m in group II; thus, we hypothesized that environmental differences would affected the genetic structure of P. sibirica populations in the long term. In northern China, the distribution of P. sibirica overlaps with wild P. armeniaca and P. mandshurica [11]; The P. mandshurica is completely distributed in northeastern China, the region in which most of the group II populations are found. We hypothesized that genetic exchange of group I and group II with different species of Prunus would also affect the difference in genetic structure between the groups. In general, geographic difference and genetic exchange should be mainly responsible for the current genetic structure of populations.

2.4. Comparison of ISSR, SSR, and SRAP Markers

The three markers resulted in similar dendrogram and sub-group division results, revealing a possible correlation and uniformity among them. To obtain a more robust comparison, a Mantel matrix correspondence test was used in matrices of the genetic distance values of 252 accessions generated using the three markers. The test revealed a very significant positive correlation among them (ISSR with SRAP r = 0.766, ISSR with SSR r = 0.694, SRAP with SSR r = 0.631, p < 0.01). The results showed some differences with a report by Budak using ISSR, SSR, RAPD, and SRAP markers in buffalo grass (Buchloe dactyloides) [41], which indicated a significant positive correlation between ISSR and SSR (r = 0.66, p < 0.01), but non-significant correlation between SRAP and the other markers.
Despite the great and similar discriminating power of each marker system used, there were some differences detected. For instance, the number of total polymorphic and discriminant fragments was higher for the ISSR markers (94 polymorphic fragments), showing a higher capacity to reveal polymorphisms than SRAP. Previous research supports a higher capacity of ISSRs to reveal polymorphisms and to demonstrate a great potential to determine the intra- and inter-genomic diversity as compared to other arbitrary primers [42]. In this study, the SSR markers indicated a higher genetic structure difference among the populations. The SRAP markers showed the highest number of polymorphic and discriminating fragments (99 polymorphic fragments), demonstrating uniformity in the Cluster analysis and STRUCTURE analysis. In addition, as ISSRs and SRAPs are the dominant marker systems, the ancestral dissection of polyploids might be difficult for the comparison of SSR markers. Hence, the co-dominant nature of SSR markers would make them the marker of choice for segregation studies and genome mapping in apricot.

2.5. Combined Analysis

Few studies have compared the results obtained from individual vs. combined molecular marker datasets for the purpose of genetic diversity analysis [41,43]. In this study, the complete accession datasets were combined to reveal the true genetic structure in wild Siberia apricot populations. The SSR data were converted into a dominance data matrix to generate a dendrogram with combined ISSR and SRAP data. The software NTSYS 2.11 was applied to construct the neighbor-joining tree on the basis of the Jaccard similarity coefficient [44]. Following the method of Evanno [34], the obvious optimum ΔK occurred at K = 2 (Figure S1), which indicated that the entire population could be divided into two subgroups (i.e., SG 1 and SG 2) (Figure 2). With membership probabilities of ≥0.50, 107 wild Siberia apricot accessions were assigned to SG 1, and the other 147 accessions were assigned to SG 2. SG 1 and SG 2 could be further divided into four groups, with 94 and 128 accessions assigned to SG 1a and SG 2a (membership probabilities of ≥0.80), and 13 and 17 accessions assigned to SG 1b and SG 2b (0.50≤ membership probabilities of <0.80) [45]. SG 1b and SG 2b appear to have much stranger hybrids and a complicated genetic background. Furthermore, the neighbor-joining (NJ) tree showed 3 branches within the entire population, which was weakly consistent with the STRUCTURE analysis based on membership assignment (Figure 2). The PCoA analysis by NTSYS 2.11 graphically showed two distinct clusters for the entire population (Figure S2), a result that was highly related to the known germplasm information and the STRUCTURE subgroups.
The Cluster analysis or STRUCTURE analysis revealed that most accessions from the same population aggregated together. In population HY, 9 accessions were in placed into SG 1b, with the other 3 accessions being placed into SG 2b. The STRUCTURE analysis of some graft- and seed-propagated apricots in North African showing K = 2 and K = 4 were considered to best depict the genetic structure, reflecting a slight difference with our results [46]. Research on cultivated apricot in Spain revealed the best K value as 5 or 7, rather different from our results [47]. Differences in the species class, population size and distribution, and genetic background might lead to such results.

2.6. Conservation Considerations

The main objective in any plant genetic resource conservation program should be to maintain the highest possible level of genetic variability [48]. According to the results of our field survey, anthropogenic activities, such as fruit picking, seed collection, deforestation, and grazing, have undoubtedly influenced the natural habitats and reduced the area of wild Siberia apricot distribution. Seed collection might represent the highest threat for wild Siberia apricot at present and in the future due to the development of the Siberia apricot seed industry [5,10,28]. These activities will inevitably affect population regeneration, which would hinder resource conservation and economic development. This dilemma should be resolved through conservation measures.
To preserve the valuable wild Siberia apricot genetic resources and considering the large distribution area and dispersal situation, we recommend a combination of conservation measures that include ex situ and in situ conservation. Firstly, we recommend the construction of a core germplasm repository and the collection of germplasm resources in greater breadth and depth. This will allow seeds to be collected and exchanged between populations, increasing the genetic diversity in each area and conserving the scarce germplasm resources through natural regeneration. In fact, more attention should be given to ex situ efforts because it would be difficult to implement in situ conservation for all the populations due to the large distribution area and dispersal. Secondly, in situ conservation should be implemented immediately. Given their high level of genetic diversity and desirable growth patterns in their original habitat, populations HR and MY should be assigned a high priority. Additionally, the genetic diversity of the two subgroups should be protected, and a priority conservation measure should be instituted for population ZY. These measures should include the establishment of nature reserves and forest reservation and the banning of grazing. Thirdly, seed collection activity using wild resources should be controlled, and the development of cultivated Siberia apricot resources should be encouraged. For the sake of industrial development and local economies, the commercial planting of Siberia apricot should be established in suitable areas.

3. Experimental Section

3.1. Plant Materials

A total of 252 trees (12 trees per provenance) were collected from north China, a region with a continuous distribution of Siberian apricot in July and August 2012 (Figure 3). The collections covered a broad environmental span, with the longitudes ranging from N 34°35′ (LY, Shaanxi Province, China) to 44°01′ (LX, Inner Mongolia, China), latitudes ranging from 107°12′ (ZY, Gansu Province, China) to 120°03′ (CY, Liaoning Province, China), and elevations spanning from 228 to 1506 m. Every population consisted of 12 individuals, and more than 50 m separated each pair of individuals. The locations of the 21 populations are listed in Table 3. The seeds of every individual were collected, and the seed testas were used for DNA extraction.

3.2. DNA Extraction

The maternal tissues forming the testa were separated from the rest of the seed (embryo), and genomic DNA was extracted using the modified DNA extraction protocol of Martin and Li [26,47]. The DNA concentration was measured using an Epoch™ microplate spectrophotometer (BioTek, Winooski, VT, USA) and was diluted to a working concentration of 30 ng/μL.

3.3. DNA Amplification

The extracted apricot genomic DNA was separately amplified by PCR using 10 ISSR, SSR, and SRAP primers; the primers sequences and sources are listed in Tables S1 and S2 [16,24,49–54]. The amplifications were performed using a thermal gradient cycler (Applied Biosystems, Foster, CA, USA) with a total volume of 15 μL containing 30 ng genomic DNA, 0.25 mM primer, 3 mM MgCl2, 0.4 mM dNTPs, and 1 U Taq DNA Polymerase. The ISSR reactions were performed with an initial step of 3 min at 95 °C, followed by 35 cycles of denaturation at 94 °C for 60 s, annealing for 60 s, and extension at 72 °C for 120 s; a final extension was performed for 5 min at 72 °C. The SRAP cycling parameters included 3 min at 95 °C, 5 cycles of three steps of denaturing at 94 °C for 60 s, annealing at 35 °C for 60 s and extension at 72 °C for 60 s. In the following 35 cycles, the annealing temperature was increased to 50 °C, and one cycle of 5 min at 72 °C was used for extension. The ISSR and SRAP amplification products were analyzed by 1.8% agarose gel electrophoresis in 1× TBE buffer and stained with ethidium bromide; the products were photographed using a Gel Documentation System (Bio-Rad, Hercules, CA, USA). The amplifications for the SSR analysis were performed with an initial step of 3 min at 95 °C, followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 51 to 57 °C (depending on the primer) for 30 s, and extension at 72 °C for 60 s; a final extension was performed for 5 min at 72 °C. The PCR products were separated by electrophoresis through a 6% denaturing polyacrylamide gel, and the fragments were visualized by silver staining. The genotypes were assessed by eye on the fluorescent plate.

3.4. Data Analysis

The ISSR and SRAP amplification fragments were scored according to a binary matrix where 0 and 1 coded for the absence and presence of a band, respectively. For each SSR locus, the allelic composition and number of total alleles were determined for each accession.
The genetic diversity was assessed using the program POPGENE 1.32 [55], as measured by the percentage of polymorphic bands (PPB), Nei’s gene diversity (h), and Shannon’s information index (I). The coefficient of gene differentiation (GST, FST) and gene flow (Nm) between the populations were also calculated using this program [35,40,56]. The AMOVA means obtained in Arlequin 3.11 were also used to calculate the genetic differentiation among the populations [57]. Mantel 2.0 was used to determine the correlation between the inter-population genetic distance and geographic distance matrices [58]. The SSR genotype banding patterns were converted into a “1” (present) and “0” (absence) matrix, and the tree topologies were constructed based on the neighbor-joining method using NTSYS 2.11 and MEGA 4 [44,59,60].
Genetic relationships among individuals were assessed by a multivariate principal component analysis (PCoA) performed with NTSYS 2.11 (Applied Biostatistics, Setauket, NY, USA) to identify the number of groups based on eigen vectors. Population structure was determined using the model-based program, STRUCTURE [33]. To identify the number of populations (K) capturing the major structure in the data, we used a burn-in period of 50,000 Markov Chain Monte Carlo iterations and 100,000 runs, with an admixture model following Hardy-Weinberg equilibrium, and correlated allele frequencies and independent loci for each run. Seven independent runs were performed for each simulated value of K, ranging from 2 to 20. The true K value was determined using both an estimate of the posterior probability of the data for a given K (as proposed by Pritchard et al.) and the Evanno ΔK [33,34].

4. Conclusions

In conclusion, our data initially confirm that all sets of ISSR, SSR, and SRAP markers provide an accurate picture of the population structure within wild Siberia apricot collections, information that is of critical importance for the design of genetic diversity and structure analyses. First, the results suggest that wild Siberia apricot in China has a relatively high level of genetic diversity; the populations HR and MY show very high diversity parameters, which was attributed to their being less affected by human disturbance. A low level of inter-population genetic differentiation and a high level of intra-population genetic differentiation were found, which was supported by a moderate level of gene flow. We believe that the predominant genetic variation found in wild Siberia apricot is attributable to the differences within populations, and is caused by the cross-pollination and self-incompatibility of this plant. Second, the STRUCTURE analysis indicated that the 21 populations can be divided into two main groups. The LY, ZD, AS, HC, HX, ZY, YG, GL, and HY populations were assigned to group I, and the other populations were assigned to group II. The geographic differences and genetic exchange should be mainly responsible for the observed genetic structure of the populations. The Mantel matrix correspondence test revealed a very significantly positive correlation among the datasets of the three markers. Third, the entire wild Siberia apricot population in Chian could be divided into two subgroups, with 107 accessions in SG 1 and 147 accessions in SG 2. Furthermore, the Mantel test revealed a significant positive correlation between the genetic and geographic distance matrices. Lastly, we recommend a combination of conservation measures, with ex situ and in situ conservation, such as the construction of a core germplasm repository, the collection of germplasm resources in greater breadth and depth, and the implementation of in situ conservation in populations HR, MY, and ZY. The information obtained from this collection of genotypes will be helpful for the development of good varieties for breeding programs and the conservation of the genetic resources of wild Siberia apricot in China.

Supplementary Information

Table
Table S1. SSR and SRAP primers that produced repeatable polymorphic amplification patterns for the genotypes studied.
Table S1. SSR and SRAP primers that produced repeatable polymorphic amplification patterns for the genotypes studied.
PrimerAnnealing temp (°C)Total bandsPolymorphism bandsSequence (5′–3′)Reference
BC807501010(AG)8TUBC Primer Set #9 [61]
BC8185299(CA)8GUBC Primer Set #9 [61]
BC827511010(AC)8GUBC Primer Set #9 [61]
BC835541515(AG)8YCUBC Primer Set #9 [61]
BC8435087(CT)8GAUBC Primer Set #9 [61]
BC847541313(CA)8RCUBC Primer Set #9 [61]
BC8684886(GAA)6UBC Primer Set #9 [61]
BC87349119(GACA)4UBC Primer Set #9 [61]
BC8804887(GGAGA)3UBC Primer Set #9 [61]
BC88850118BDB(CA)7UBC Primer Set #9 [61]
Me1/Em1501514TGAGTCCAAACCGGAGC/GACTGCGTACGAATTTGCG. Li et al. [16]
Me1/Em45096TGAGTCCAAACCGGAGC/GACTGCGTACGAATTGAGG. Li et al. [16]
Me2/Em15096TGAGTCCAAACCGGACC/GACTGCGTACGAATTTGCG. Li et al. [16]
Me2/Em3501211TGAGTCCAAACCGGACC/GACTGCGTACGAATTAACG. Li et al. [16]
Me2/Em950106TGAGTCCAAACCGGACC/GACTGCGTACGAATTATTPF. Ai et al. [24]
Me4/Em750118TGAGTCCAAACCGGTCC/GACTGCGTACGAATTGCAPF. Ai et al. [24]
Me5/Em25098TGAGTCCAAACCGGTGC/GACTGCGTACGAATTTGAG. Li et al. [16]
Me8/Em95098TGAGTCCAAACCGGAAG/GACTGCGTACGAATTATTPF. Ai et al. [24]
Me1/Em6501917TGAGTCCAAACCGGAGC/GACTGCGTACGAATTCTTPF. Ai et al. [24]
Me8/Em8501715TGAGTCCAAACCGGAAG/GACTGCGTACGAATTGCCPF. Ai et al. [24]
Table
Table S2. List of SSR primers and Genetic diversity parameters for the genotypes studied.
Table S2. List of SSR primers and Genetic diversity parameters for the genotypes studied.
PrimerReferenceSSR motiveAnnealing temp (°C)Observed allelesEffective allelesShannon’s indexObserved heterozygosityExpected heterozygosityGenetic differentiation coefficientGene flow
AMPA101Hagen et al. [51](TC)11(AC)125654.78251.58820.51190.79250.12681.7221
AMPA119Hagen et al. [51](TA)95775.64331.82530.57140.82440.19641.0230
BPPCT039Dirlewanger et al. [50](GA)205554.46281.54880.44440.77750.12231.7946
pchgms3Sosinski et al. [54](CT)195753.56361.35220.49210.72080.17261.1986
pchgms5Sosinski et al. [54](CA)9(TA)85121.90270.66730.53570.47540.08722.6160
ssrPaCITA23Lopes et al. [52](AC)2(AG)185164.58831.63900.86510.78360.03576.7527
UDAp-414Messina et al. [53](AG)215643.33211.26520.19440.70130.33360.4995
UDAp-415Messina et al. [53](GA)215643.69561.34800.46830.73090.16581.2577
UDAp-420Messina et al. [53](CT)205653.90181.39080.64290.74520.11961.8403
UDP96-001Cipriani et al. [49](CA)175721.85290.65290.25000.46120.18711.0860
Mean---4.53.77261.32780.49760.70130.15431.3706
Table
Table S3. Nei’s (1978) unbiased genetic identity (above diagonal) and genetic distance (below diagonal) among Siberia apricot populations reveled by ISSR markers [62].
Table S3. Nei’s (1978) unbiased genetic identity (above diagonal) and genetic distance (below diagonal) among Siberia apricot populations reveled by ISSR markers [62].
POPLYZDASHCHXZYYGGLHYYQHRMYCYKZLiYWCLHPQNCLXKK
LY-0.92460.92480.93630.91120.91250.91920.91240.86730.84380.86950.89110.84250.85530.87750.86410.86450.85430.84370.83940.8399
ZD0.0784-0.95050.96160.95280.95090.94660.93090.91610.88390.89250.92050.87790.87580.88880.88070.88610.88070.86970.87070.8688
AS0.07820.0507-0.96370.94260.94060.94290.92390.90170.87970.89990.92820.89690.89030.90670.89480.88650.89490.87350.87990.8708
HC0.06580.03910.037-0.95190.94670.95890.93060.90890.88370.89420.93170.87840.8790.90680.89860.89970.89620.89140.87690.8717
HX0.0930.04830.05910.0492-0.9670.95920.93240.9390.87950.89920.90920.86130.86890.87490.87980.87860.87220.88150.8780.8831
ZY0.09160.05030.06120.05480.0336-0.95490.91390.91860.88720.89510.91670.89210.89220.90320.90930.89280.88060.87330.88120.884
YG0.08430.05490.05880.0420.04170.0461-0.94380.92930.87930.8960.91140.87260.86590.88790.90.89420.8850.87820.87620.878
GL0.09170.07160.07920.07190.070.09010.0578-0.94060.89890.89780.9110.87320.88750.88760.88980.89240.88680.86610.88050.8994
HY0.14240.08760.10350.09550.0630.08490.07330.0612-0.95110.91330.92480.89890.90540.90220.90920.91080.91780.90360.91230.9232
YQ0.16980.12340.12820.12370.12840.11970.12860.10660.0502-0.93970.94410.94520.9560.94240.94610.93410.94770.92440.93860.9471
HR0.13980.11370.10550.11190.10630.11080.10980.10780.09060.0622-0.95780.92160.92510.93760.9340.92040.9330.91690.93390.9183
MY0.11530.08290.07450.07080.09520.0870.09280.09320.07810.05750.0431-0.9390.93340.94610.93180.920.94110.9290.92830.9145
CY0.17140.13020.10880.12960.14930.11410.13630.13560.10660.05640.08170.0629-0.98370.96770.96650.92720.92620.8950.9260.9187
KZ0.15630.13260.11610.12890.14060.11410.1440.11940.09940.0450.07790.06890.0164-0.9680.96350.93350.93380.91120.92960.935
LiY0.13070.11780.09790.09780.13360.10180.11890.11920.10290.05930.06450.05540.03290.0325-0.97680.94270.94250.91280.93420.9258
WC0.1460.1270.11110.10690.1280.09510.10530.11680.09520.05540.06820.07060.03410.03720.0235-0.95840.94220.9010.92860.9377
LH0.14570.12090.12050.10570.12950.11340.11180.11390.09350.06820.0830.08340.07550.06880.0590.0425-0.95870.92820.94220.9448
PQ0.15750.1270.1110.10960.13680.12720.12210.12010.08580.05370.06930.06070.07670.06850.05920.05950.0421-0.96880.96620.9508
NC0.16990.13960.13520.11490.12610.13540.12990.14380.10130.07860.08670.07370.11090.09290.09120.10430.07450.0317-0.97210.9388
LX0.17510.13850.1280.13130.13010.12640.13220.12720.09180.06330.06840.07440.07690.0730.0680.07410.05950.03430.0283-0.9693
KK0.17440.14060.13830.13730.12430.12330.13010.1060.07990.05430.08520.08940.08480.06730.07710.06430.05680.05040.06320.0311-
The “-” means the boundary.
Table
Table S4. Nei’s (1978) unbiased genetic identity (above diagonal) and genetic distance (below diagonal) among Siberia apricot populations reveled by SRAP markers [62].
Table S4. Nei’s (1978) unbiased genetic identity (above diagonal) and genetic distance (below diagonal) among Siberia apricot populations reveled by SRAP markers [62].
POPLYZDASHCHXZYYGGLHYYQHRMYCYKZLiYWCLHPQNCLXKK
LY-0.96360.97570.97290.97440.97180.9610.94390.9470.90110.92720.94970.93580.92380.92580.91270.89830.90650.91860.90330.873
ZD0.0371-0.9740.97160.95360.96880.96060.92940.93740.90010.90460.92270.91680.89950.90440.89710.88380.89190.90440.89010.8613
AS0.02460.0263-0.9810.96920.96990.96220.9370.94870.91180.92740.94190.93370.90880.91230.9070.88650.89760.90790.8920.8553
HC0.02750.02880.0192-0.96840.97010.97260.94280.94770.91110.92420.93880.93030.91030.91320.90320.89550.90430.91880.88930.8664
HX0.02590.04750.03120.0321-0.97620.96530.95270.94220.91530.93630.94450.9320.91680.90830.90240.8920.89080.91220.89780.8708
ZY0.02860.03170.03060.03040.0241-0.97140.9510.94390.92130.92950.94150.92640.92120.91190.90120.90080.89780.9110.9010.8791
YG0.03980.04020.03850.02780.03540.029-0.94820.94910.9060.92950.94270.92010.90630.89940.88970.88830.89310.90060.8930.8594
GL0.05770.07320.0650.0590.04850.05020.0531-0.93610.91970.93230.94060.94160.92340.9050.90460.91340.89040.89580.88420.8719
HY0.05450.06460.05260.05370.05950.05780.05220.066-0.9460.93690.95470.9530.93230.91960.9280.94070.94310.92260.9120.8855
YQ0.10420.10520.09230.09310.08850.0820.09880.08370.0555-0.93650.93450.96480.96390.94060.94720.94370.92750.92290.9310.9281
HR0.07550.10030.07540.07890.06580.07310.07310.07010.06520.0656-0.98170.96090.95640.94690.9410.9380.93960.94980.93950.9112
MY0.05170.08040.05990.06320.05720.06020.0590.06120.04640.06780.0185-0.96530.95840.95410.94230.94490.96270.96160.94230.9099
CY0.06640.08690.06860.07220.07050.07640.08330.06020.04810.03590.03980.0353-0.98590.96490.96250.9580.95510.95210.94620.9235
KZ0.07920.10590.09570.0940.08680.08210.09840.07970.07010.03680.04460.04250.0142-0.97020.95990.94980.95150.94620.94710.9325
LiY0.07710.10050.09180.09080.09620.09220.10610.09980.08380.06130.05450.0470.03570.0302-0.98170.94820.95680.95280.95470.9272
WC0.09140.10860.09760.10180.10270.1040.11680.10020.07470.05430.06080.05950.03820.04090.0184-0.96110.9670.95730.95590.943
LH0.10720.12350.12050.11030.11430.10450.11850.09060.06110.0580.0640.05670.04290.05150.05320.0397-0.97010.94690.95470.9466
PQ0.09820.11440.1080.10060.11560.10790.1130.11610.05850.07520.06230.0380.04590.04970.04410.03360.0304-0.97180.96210.9281
NC0.08490.10050.09660.08470.09190.09320.10470.110.08060.08030.05150.03920.04910.05530.04840.04360.05450.0286-0.97770.9422
LX0.10170.11640.11430.11730.10780.10430.11320.12310.09210.07150.06240.05940.05530.05440.04640.04510.04630.03870.0225-0.9706
KK0.13590.14930.15630.14340.13830.12890.15150.13710.12160.07460.0930.09440.07960.06990.07560.05870.05490.07460.05950.0298-
The “-” means the boundary.
Table
Table S5. Nei’s (1978) unbiased genetic identity (above diagonal) and genetic distance (below diagonal) among Siberia apricot populations reveled by SSR markers [62].
Table S5. Nei’s (1978) unbiased genetic identity (above diagonal) and genetic distance (below diagonal) among Siberia apricot populations reveled by SSR markers [62].
POPLYZDASHCHXZYYGGLHYYQHRMYCYKZLiYWCLHPQNCLXKK
LY-0.80030.83110.78720.80990.86720.8090.7290.62430.68040.62180.67740.6770.60460.5460.5450.65040.77140.71010.75590.6019
ZD0.2228-0.94430.85460.80160.82440.86040.80240.70170.62870.66960.63990.72680.67820.68010.65080.6560.69620.59330.65350.617
AS0.1850.0573-0.84640.83790.84750.90350.830.74340.64380.64880.72160.81050.69750.69910.69340.71280.78460.7020.69940.6562
HC0.23920.15710.1668-0.77220.73410.77110.66640.65160.61270.70470.76770.76330.70420.73330.68020.60640.72780.67940.59610.5919
HX0.21080.22120.17690.2585-0.8270.89470.79890.7470.6820.72730.67510.74760.63680.59050.5770.7620.73160.62330.73290.6726
ZY0.14250.19310.16550.30910.1899-0.88510.85790.71430.71040.75480.75830.75730.67880.64870.68290.80210.76410.73840.8080.7918
YG0.21190.15040.10150.260.11120.122-0.82990.80990.66770.70730.72390.71220.58290.62310.57590.75190.74150.70340.69970.696
GL0.31610.22010.18630.40590.22450.15330.1864-0.69190.71720.78960.70920.70290.64910.6670.66410.80480.72570.62770.72910.7736
HY0.47120.35430.29650.42840.29170.33650.21090.3683-0.90120.75130.71890.7660.76340.69670.68360.78020.69880.60880.57350.7292
YQ0.38510.46410.44040.48980.38270.34190.4040.33240.1041-0.88940.78930.8070.85170.74480.76050.83240.78180.64130.62610.7811
HR0.47520.40110.43260.350.31840.28130.34630.23620.28590.1172-0.90010.8230.85580.82920.83720.84570.75070.63710.64120.7849
MY0.38950.44650.32630.26440.3930.27660.32310.34360.330.23660.1053-0.85360.82270.88580.85720.84540.84710.83460.73210.7997
CY0.39010.31910.21020.27020.29090.27810.33940.35250.26660.21440.19480.1583-0.88320.85150.84310.84010.81290.77620.74550.8575
KZ0.50330.38830.36030.35070.45140.38750.53970.43220.270.16050.15570.19520.1242-0.91430.92860.80440.7730.69440.6950.7668
LiY0.60510.38550.3580.31020.52680.43270.47310.4050.36150.29460.18730.12120.16070.0896-0.97340.83420.8280.77710.73360.7883
WC0.60690.42960.36610.38530.54980.38140.55190.40940.38040.27380.17770.15410.17060.07410.027-0.86010.79370.69970.68040.7524
LH0.43010.42150.33850.50010.27180.22050.28520.21720.24820.18350.16760.16790.17420.21760.18130.1507-0.87120.81010.87050.9197
PQ0.25950.36210.24260.31770.31250.2690.29910.32060.35850.24620.28680.1660.20710.25750.18880.23110.1378-0.87910.8820.8061
NC0.34230.5220.35390.38650.47270.30320.35190.46570.49630.44430.45080.18080.25340.36470.25220.35710.21060.1288-0.88570.8414
LX0.27990.42540.35750.51730.31080.21320.35710.3160.5560.46830.44440.31190.29370.36380.30970.38510.13870.12560.1213-0.8669
KK0.50760.48290.42140.52450.39670.23340.36240.25680.31580.24710.24220.22350.15380.26560.23790.28450.08370.21560.17260.1428-
The “-” means the boundary.
  • Figure S1.:

    ΔK values for different numbers of populations assumed (K) in the STRUCURE analysis.

  • Figure S2.:

    Principal component analysis on combined three markers data sets of the entire population.

    • Acknowledgments

    We acknowledge the financial support of the Special Research Program for Public-Welfare Forestry of the Chinese State Forestry Administration (No. 200,904,020) and the Research Achievement Transformation Program of the Chinese Ministry of Science and Technology (No. 2009GB23,600,515).

    Conflicts of Interest

    The authors declare no conflict of interest.

    References

    1. Cohen, J.I.; Williams, J.T.; Plucknett, D.L.; Shands, H. Ex situ conservation of plant genetic resources: Global development and environmental concerns. Science 1991, 253, 866–872. [Google Scholar]
    2. Laidò, G.; Mangini, G.; Taranto, F.; Gadaleta, A.; Blanco, A.; Cattivelli, L.; Marone, D.; Mastrangelo, A.M.; Papa, R.; de Vita, P. Genetic diversity and population structure of tetraploid Wheats (Triticum turgidum L.) estimated by SSR, DArT and pedigree data. PLoS One 2013, 8, e67280. [Google Scholar]
    3. Evans, L.T. Crop Evolution, Adaptation and Yield; Cambridge University Press: London, UK, 1996. [Google Scholar]
    4. Sreekanth, P.M.; Balasundaran, M.; Nazeem, P.A.; Suma, T.B. Genetic diversity of nine natural Tectona grandis L.f. populations of the Western Ghats in Southern India. Conserv. Genet 2012, 13, 1409–1419. [Google Scholar]
    5. Wang, L.; Yu, H. Biodiesel from Siberian apricot (Prunus. sibirica L.) seed kernel oil. Bioresour. Technol 2012, 112, 355–358. [Google Scholar]
    6. Zhang, J.Y.; Fu, D.; Wei, Z.Z.; Zhao, H.L.; Zhang, T.H. Determination of the ability of several tree and shrub species to endure and survive extreme aridity with methods of limited areas under field condition in Horqin Sandy Land. Acta Ecol. Sin 2006. [Google Scholar] [CrossRef]
    7. Wei, Q.; Ma, X.; Dong, J. Preparation, chemical constituents and antimicrobial activity of pyroligneous acids from walnut tree branches. J. Anal. Appl. Pyrolysis 2010, 87, 24–28. [Google Scholar]
    8. Sze-Tao, K.; Sathe, S. Functional properties and in vitro digestibility of almond (Prunus. dulcis L.) protein isolate. Food Chem 2000, 69, 153–160. [Google Scholar]
    9. Kris-Etherton, P.M.; Zhao, G.; Binkoski, A.E.; Coval, S.M.; Etherton, T.D. The effects of nuts on coronary heart disease risk. Nutr. Rev 2001, 59, 103–111. [Google Scholar]
    10. Wang, L. Evaluation of Siberian apricot (Prunus. sibirica L.) germplasm variability for biodiesel properties. J. Am. Oil Chem. Soc 2012, 89, 1743–1747. [Google Scholar]
    11. Zhebentyayeva, T.; Ledbetter, C.; Burgos, L.; Llácer, G. Apricot. In Fruit Breeding; Springer: Berlin, Germany, 2012; pp. 415–458. [Google Scholar]
    12. Zhebentyayeva, T.N.; Reighard, G.L.; Lalli, D.; Gorina, V.M.; Krška, B.; Abbott, A.G. Origin of resistance to plum pox virus in apricot: What new AFLP and targeted SSR data analyses tell. Tree Genet. Genomes 2008, 4, 403–417. [Google Scholar]
    13. Michurin, I.V. Production of New Cultivated Varieties of Fruit Trees and Shrubs from Seed; Foreign Languages Publishing House: Moscow, Soviet Union, 1952. [Google Scholar]
    14. Arcade, A.; Anselin, F.; Rampant, P.F.; Lesage, M.C.; Paques, L.E.; Prat, D. Application of AFLP, RAPD and ISSR markers to genetic mapping of European and Japanese larch. Theor. Appl. Genet 2000, 100, 299–307. [Google Scholar]
    15. McGregor, C.; Lambert, C.; Greyling, M.; Louw, J.; Warnich, L. A comparative assessment of DNA fingerprinting techniques (RAPD, ISSR, AFLP and SSR) in tetraploid potato (Solanum tuberosum L.) germplasm. Euphytica 2000, 113, 135–144. [Google Scholar]
    16. Li, G.; Quiros, C.F. Sequence-related amplified polymorphism (SRAP), a new marker system based on a simple PCR reaction: Its application to mapping and gene tagging in Brassica. Theor. Appl. Genet 2001, 103, 455–461. [Google Scholar]
    17. Dondini, L.; Lain, O.; Geuna, F.; Banfi, R.; Gaiotti, F.; Tartarini, S.; Bassi, D.; Testolin, R. Development of a new SSR-based linkage map in apricot and analysis of synteny with existing Prunus maps. Tree Genet. Genomes 2006, 3, 239–249. [Google Scholar]
    18. Pedryc, A.; Ruthner, S.; Hermán, R.; Krska, B.; Hegedűs, A.; Halász, J. Genetic diversity of apricot revealed by a set of SSR markers from linkage group G1. Sci. Horticult 2009, 121, 19–26. [Google Scholar]
    19. Soriano, J.M.; Domingo, M.L.; Zuriaga, E.; Romero, C.; Zhebentyayeva, T.; Abbott, A.G.; Badenes, M.L. Identification of simple sequence repeat markers tightly linked to plum pox virus resistance in apricot. Mol. Breed 2012, 30, 1017–1026. [Google Scholar]
    20. Zhebentyayeva, T.; Reighard, G.; Gorina, V.; Abbott, A. Simple sequence repeat (SSR) analysis for assessment of genetic variability in apricot germplasm. Theor. Appl. Genet 2003, 106, 435–444. [Google Scholar]
    21. Ganopoulos, I.V.; Kazantzis, K.; Chatzicharisis, I.; Karayiannis, I.; Tsaftaris, A.S. Genetic diversity, structure and fruit trait associations in Greek sweet cherry cultivars using microsatellite based (SSR/ISSR) and morpho-physiological markers. Euphytica 2011, 181, 237–251. [Google Scholar]
    22. Li, M.-M.; Cai, Y.-L.; Qian, Z.-Q.; Zhao, G.-F. Genetic diversity and differentiation in Chinese sour cherry Prunus pseudocerasus Lindl., and its implications for conservation. Genet. Resour. Crop Evol 2009, 56, 455–464. [Google Scholar]
    23. Yilmaz, K.U.; Paydas-Kargi, S.; Dogan, Y.; Kafkas, S. Genetic diversity analysis based on ISSR, RAPD and SSR among Turkish Apricot Germplasms in Iran Caucasian eco-geographical group. Sci. Horticult 2012, 138, 138–143. [Google Scholar]
    24. Ai, P.-F.; Zhen, Z.-J.; Jin, Z.-Z. Genetic diversity and relationships within sweet kernel apricot and related Armeniaca species based on sequence-related amplified polymorphism markers. Biochem. Syst. Ecol 2011, 39, 694–699. [Google Scholar]
    25. Uzun, A.; Gulsen, O.; Seday, U.; Bircan, M.; Yilmaz, K.U. SRAP based genetic analysis of some apricot cultivars. Romanian Biotechnol. Lett 2010, 15, 5396–5404. [Google Scholar]
    26. Li, M.; Zhao, Z.; Miao, X.J. Genetic variability of wild apricot (Prunus. armeniac a L.) populations in the Ili Valley as revealed by ISSR markers. Genet. Resour. Crop Evol 2013. [Google Scholar] [CrossRef]
    27. Bourguiba, H.; Audergon, J.-M.; Krichen, L.; Trifi-Farah, N.; Mamouni, A.; Trabelsi, S.; Khadari, B. Genetic diversity and differentiation of grafted and seed propagated apricot (Prunus. armeniaca L.) in the Maghreb region. Sci. Horticult 2012, 142, 7–13. [Google Scholar]
    28. Wang, Y.; Zhang, J.; Sun, H.; Ning, N.; Yang, L. Construction and evaluation of a primary core collection of apricot germplasm in China. Sci. Horticult 2011, 128, 311–319. [Google Scholar]
    29. Tian-Ming, H.; Xue-Sen, C.; Zheng, X.; Jiang-Sheng, G.; Pei-Jun, L.; Wen, L.; Qing, L.; Yan, W. Using SSR markers to determine the population genetic structure of wild apricot (Prunus. armeniaca L.) in the Ily Valley of West China. Genet. Resour. Crop Evol 2006, 54, 563–572. [Google Scholar]
    30. Wang, L.; Chu, J. Optimization of biodiesel production from Siberian apricot (Prunus. sibirica L.) oil using response surface methodology. Asian J. Chem 2013, 25, 2577–2582. [Google Scholar]
    31. Liu, W.; Liu, N.; Zhang, Y.; Yu, X.; Sun, M.; Xu, M.; Zhang, Q.; Liu, S. Kernel-Using Apricot Resources and its Utilization. In XV International Symposium on Apricot Breeding and Culture; Avagyan, A., Ed.; Int. Soc. Horticultural Science: Leuven, Belgium, 2012; Volume 966, pp. 189–191. [Google Scholar]
    32. Slatkin, M. Gene flow in natural populations. Ann. Rev. Ecol. Syst 1985, 16, 393–430. [Google Scholar]
    33. Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 2000, 155, 945–959. [Google Scholar]
    34. 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]
    35. Slatkin, M. Gene flow and the geographic structure of natural. Science 1987, 236, 787–792. [Google Scholar]
    36. Schaal, B.; Hayworth, D.; Olsen, K.; Rauscher, J.; Smith, W. Phylogeographic studies in plants: Problems and prospects. Mol. Ecol 1998, 7, 465–474. [Google Scholar]
    37. Zhai, P.; Sun, A.; Ren, F.; Liu, X.; Gao, B.; Zhang, Q. Changes of Climate Extremes in China. Climatic Change 1999, 42, 203–218. [Google Scholar]
    38. Olukolu, B.A.; Trainin, T.; Fan, S.; Kole, C.; Bielenberg, D.G.; Reighard, G.L.; Abbott, A.G.; Holland, D. Genetic linkage mapping for molecular dissection of chilling requirement and budbreak in apricot (Prunus. armeniaca L.). Genome 2009, 52, 819–828. [Google Scholar]
    39. Walther, G.-R.; Post, E.; Convey, P.; Menzel, A.; Parmesan, C.; Beebee, T.J.; Fromentin, J.-M.; Hoegh-Guldberg, O.; Bairlein, F. Ecological responses to recent climate change. Nature 2002, 416, 389–395. [Google Scholar]
    40. Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl. Acad. Sci. USA 1973, 70, 3321–3323. [Google Scholar]
    41. Budak, H.; Shearman, R.C.; Parmaksiz, I.; Dweikat, I. Comparative analysis of seeded and vegetative biotype buffalograsses based on phylogenetic relationship using ISSRs, SSRs, RAPDs, and SRAPs. Theor. Appl. Genet 2004, 109, 280–288. [Google Scholar]
    42. Zietkiewicz, E.; Rafalski, A.; Labuda, D. Genome fingerprinting by simple sequence repeat (SSR)-anchored polymerase chain reaction amplification. Genomics 1994, 20, 176–183. [Google Scholar]
    43. Russell, J.; Fuller, J.; Macaulay, M.; Hatz, B.; Jahoor, A.; Powell, W.; Waugh, R. Direct comparison of levels of genetic variation among barley accessions detected by RFLPs, AFLPs, SSRs and RAPDs. Theor. Appl. Genet 1997, 95, 714–722. [Google Scholar]
    44. Rohlf, F. NTSYS-PC, Numerical Taxonomy System for the PC Exeter Software, Version 2.1; Applied Biostatistics Inc Setauket: E. Setauket, NY, USA, 2000. [Google Scholar]
    45. Zhang, P.; Li, J.; Li, X.; Liu, X.; Zhao, X.; Lu, Y. Population structure and genetic diversity in a rice core collection (Oryza sativa L.) investigated with SSR markers. PLoS One 2011, 6, e27565. [Google Scholar]
    46. Bourguiba, H.; Khadari, B.; Krichen, L.; Trifi-Farah, N.; Mamouni, A.; Trabelsi, S.; Audergon, J.-M. Genetic relationships between local north african apricot (Prunus armeniaca L.) germplasm and recently introduced varieties. Sci. Horticult 2013, 152, 61–69. [Google Scholar]
    47. Martín, C.; Herrero, M.; Hormaza, J.I. Molecular characterization of apricot germplasm from an old stone collection. PLoS One 2011, 6, e23979. [Google Scholar]
    48. Karp, A. Molecular Tools in Plant Genetic Resources Conservation: A Guide to the Technologies; Bioversity International: Rome, Italy, 1997. [Google Scholar]
    49. Cipriani, G.; Lot, G.; Huang, W.-G.; Marrazzo, M.; Peterlunger, E.; Testolin, R. AC/GT and AG/CT microsatellite repeats in peach [Prunus persica (L) Batsch]: Isolation, characterisation and cross-species amplification in. Prunus. Theor. Appl. Genet 1999, 99, 65–72. [Google Scholar]
    50. Dirlewanger, E.; Cosson, P.; Tavaud, M.; Aranzana, M.; Poizat, C.; Zanetto, A.; Arús, P.; Laigret, F. Development of microsatellite markers in peach [Prunus persica (L.) Batsch] and their use in genetic diversity analysis in peach and sweet cherry (Prunus avium L.). Theor. Appl. Genet 2002, 105, 127–138. [Google Scholar]
    51. Hagen, L.S.; Chaib, J.; Fady, B.; Decroocq, V.; Bouchet, J.P.; Lambert, P.; Audergon, J.M. Genomic and cDNA microsatellites from apricot (Prunus armeniaca L.). Mol. Ecol. Notes 2004, 4, 742–745. [Google Scholar]
    52. Lopes, M.; Sefc, K.; Laimer, M.; da Câmara Machado, A. Identification of microsatellite loci in apricot. Mol. Ecol. Notes 2002, 2, 24–26. [Google Scholar]
    53. Messina, R.; Lain, O.; Marrazzo, M.; Cipriani, G.; Testolin, R. New set of microsatellite loci isolated in Apricot. Mol. Ecol. Notes 2004, 4, 432–434. [Google Scholar]
    54. Sosinski, B.; Gannavarapu, M.; Hager, L.; Beck, L.; King, G.J.; Ryder, C.; Rajapakse, S.; Baird, W.; Ballard, R.; Abbott, A. Characterization of microsatellite markers in peach [Prunus persica (L.) Batsch]. Theor. Appl. Genet 2000, 101, 421–428. [Google Scholar]
    55. Yeh, F.; Yang, R.-C.; Boyle, T.; Ye, Z.; Mao, J.X. POPGENE, the User-Friendly Shareware for Population Genetic Analysis. In Molecular Biology and Biotechnology Centre; University of Alberta: Edmonton, AB, Canada, 1997. [Google Scholar]
    56. Shannon, C.E.; Weaver, W. The Mathematical Theory of Communication; Urbana, I.L., Ed.; University of Illinois Press: Champaign, IL, USA, 1949; Volume 19, p. 1. [Google Scholar]
    57. Excoffier, L.; Laval, G.; Schneider, S. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol. Bioinforma. Online 2005, 1, 47–50. [Google Scholar]
    58. Liedloff, A. Mantel (Version 2.0): Mantel Nonparametric Test Calculator; Queensland University of Technology: Brisbane, Australia, 1999. [Google Scholar]
    59. Cao, Q.; Lu, B.R.; Xia, H.; Rong, J.; Sala, F.; Spada, A.; Grassi, F. Genetic diversity and origin of weedy rice (Oryza sativa f. spontanea) populations found in North-eastern China revealed by simple sequence repeat (SSR) markers. Ann. Bot 2006, 98, 1241–1252. [Google Scholar]
    60. Tamura, K.; Dudley, J.; Nei, M.; Kumar, S. MEGA4: Molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol. Biol. Evol 2007, 24, 1596–1599. [Google Scholar]
    61. Isshiki, S.; Iwata, N.; Khan, M.M.R. Issr variations in eggplant (Solanum melongena L.) and related Solanum species. Scientia Hortic 2008, 117, 186–190. [Google Scholar]
    62. Nei, M. The theory of genetic distance and evolution of human races. J. Hum. Genet 1978, 23, 341–369. [Google Scholar]
    Ijms 15 00377f1a 1024Ijms 15 00377f1b 1024
    Figure 1. UPGMA dendrogram for the wild Siberia apricot populations based on Nei’s genetic distance [40], as revealed using (a) ISSR markers; (b) SRAP markers; and (c) SSR markers. The distance coefficients between populations obtained by Cluster analyses are marked on branches and the x-axis.
    Figure 1. UPGMA dendrogram for the wild Siberia apricot populations based on Nei’s genetic distance [40], as revealed using (a) ISSR markers; (b) SRAP markers; and (c) SSR markers. The distance coefficients between populations obtained by Cluster analyses are marked on branches and the x-axis.
    Ijms 15 00377f1aIjms 15 00377f1b
    Ijms 15 00377f2 1024
    Figure 2. Neighbor-joining trees of 252 wild Siberia apricot accessions.
    Figure 2. Neighbor-joining trees of 252 wild Siberia apricot accessions.
    Ijms 15 00377f2
    Ijms 15 00377f3 1024
    Figure 3. Geographic distribution of the 21 P. sibirica populations sampled in this study.
    Figure 3. Geographic distribution of the 21 P. sibirica populations sampled in this study.
    Ijms 15 00377f3
    Table
    Table 1. Genetic diversity parameters of the Siberian apricot populations analyzed.
    Table 1. Genetic diversity parameters of the Siberian apricot populations analyzed.
    PopulationISSRSRAPSSR
    hIPPB (%)hIPPB (%)hI
    LY0.1910.28554.40.1450.22043.31.0990.612
    ZD0.1560.23848.50.1410.21645.01.1200.632
    AS0.2000.30159.20.1650.25455.81.2310.662
    HC0.2080.31059.20.1760.27157.51.1570.631
    HX0.1970.29456.30.1780.27155.01.0970.591
    ZY0.1980.29353.40.1900.28859.21.0950.606
    YG0.1560.23042.70.1580.24149.21.1340.629
    GL0.1400.21245.60.1480.22848.31.0970.596
    HY0.1610.23944.70.1380.20942.51.0080.577
    YQ0.1660.24847.60.1650.24849.21.0270.579
    HR0.2040.30458.30.2130.32669.21.1060.602
    MY0.1970.29154.40.1860.28360.01.1410.633
    CY0.1540.23548.50.1380.21348.31.1000.612
    KZ0.1840.27550.50.1720.26254.20.9770.544
    LiY0.1600.24148.60.1660.25455.01.0250.573
    WC0.1610.24247.60.1490.23050.00.9450.541
    LH0.1740.25950.50.1370.21045.81.0810.592
    PQ0.1440.22147.60.1340.21350.01.1330.621
    NC0.1610.24348.50.1460.22652.50.9830.554
    LX0.1610.23843.70.1320.20040.80.8910.512
    KK0.1390.21141.80.1200.18336.70.9390.534
    Mean0.1720.25850.10.1570.24050.81.0660.592
    At the species level0.2480.38791.30.2180.34482.51.6390.782
    h, Nei’s gene diversity index; I, Shannon’s information index; PPB, the percentage of polymorphic bands.
    Table
    Table 2. Analysis of molecular variance (AMOVA) within/among Siberian apricot populations.
    Table 2. Analysis of molecular variance (AMOVA) within/among Siberian apricot populations.
    ISSRSRAPSSR
    Source of varianceVariance componentRatio (%)Variance componentRatio (%)Variance componentRatio (%)
    Among populations3.09825.013.56123.84%1.43716.65
    Within populations9.28974.9911.37876.16%7.19283.35
    A p value < 0.001 was considered significant.
    Table
    Table 3. Geographical locations of the different P. sibirica populations used in this study.
    Table 3. Geographical locations of the different P. sibirica populations used in this study.
    Population codeSeed collection sitesLatitude (°N)Longitude (°E)Elevation (m)
    LYLingyou, Shannxi Province34°35′107°46′1,292
    ZDZhidan, Shannxi Province36°48′108°45′1,238
    ASAnsai, Shannxi Province37°04′109°09′1,252
    HCHuachi, Gansu Province36°12′107°56′1,244
    HXHuanxian, Gansu Province36°31′107°17′1,190
    ZYZhenyuan, Gansu Province35°37′107°02′1,281
    YGYanggao, Shanxi Province40°07′113°54′1,097
    GLGuangling, Shanxi Province39°49′114°34′1,284
    HYHunyuan, Shanxi Province39°32′113°28′1,442
    YQYanqing, Beijing Province40°26′116°14′641
    HRHuairou, Beijing Province40°36′116°44′382
    MYMiyun, Beijing Province40°31′117°13′228
    CYChaoyang, Liaoning Province41°42′120°03′627
    KZKazuo, Liaoning Province41°01′119°44′405
    LiYLingyuan, Liaoning Province40°53′119°12′560
    WCWeichang, Hebei Province41°56′117°44′1,127
    LHLonghua, Hebei Province41°15′117°21′675
    PQPingquan, Hebei Province41°02′118°32′583
    NCNingcheng, Inner Mongolia41°45′119°01′1,134
    LXLinxi, Inner Mongolia44°01′118°20′1,207
    KKKeshiketeng, Inner Mongolia43°16′117°33′1,221

    Share and Cite

    MDPI and ACS Style

    Li, M.; Zhao, Z.; Miao, X.; Zhou, J. Genetic Diversity and Population Structure of Siberian apricot (Prunus sibirica L.) in China. Int. J. Mol. Sci. 2014, 15, 377-400. https://doi.org/10.3390/ijms15010377

    AMA Style

    Li M, Zhao Z, Miao X, Zhou J. Genetic Diversity and Population Structure of Siberian apricot (Prunus sibirica L.) in China. International Journal of Molecular Sciences. 2014; 15(1):377-400. https://doi.org/10.3390/ijms15010377

    Chicago/Turabian Style

    Li, Ming, Zhong Zhao, Xingjun Miao, and Jingjing Zhou. 2014. "Genetic Diversity and Population Structure of Siberian apricot (Prunus sibirica L.) in China" International Journal of Molecular Sciences 15, no. 1: 377-400. https://doi.org/10.3390/ijms15010377

    APA Style

    Li, M., Zhao, Z., Miao, X., & Zhou, J. (2014). Genetic Diversity and Population Structure of Siberian apricot (Prunus sibirica L.) in China. International Journal of Molecular Sciences, 15(1), 377-400. https://doi.org/10.3390/ijms15010377

    Article Metrics

    Back to TopTop