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Article

Genetic Diversity Analysis and Core Collection Construction of the Actinidia chinensis Complex (Kiwifruit) Based on SSR Markers

by
Guangming Hu
1,2,
Quan Jiang
1,
Zhi Wang
3,
Zuozhou Li
1,
Wenyue Liao
4,
Dandan Shen
4 and
Caihong Zhong
1,*
1
CAS Key Laboratory of Plant Germplasm Enhancement and Speciality Agriculture, Wuhan Botanical Garden, The Chinese Academy of Sciences, Wuhan 430074, China
2
College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
3
Institute of Fruit and Tea, Hubei Academy of Agricultural Sciences, Wuhan 430064, China
4
Yichang Academy of Agricultural Science, Yichang 443000, China
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(12), 3078; https://doi.org/10.3390/agronomy12123078
Submission received: 4 November 2022 / Revised: 26 November 2022 / Accepted: 30 November 2022 / Published: 5 December 2022
(This article belongs to the Section Crop Breeding and Genetics)
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Abstract

:
Kiwifruit belonging to the Actinidiaceae family is a perennial, dioecious vine called ‘the king of fruits’ due to its considerably nutritious and sweet characteristics. A. chinensis complex, including two main groups, A. chinensis var. chinensis and A. chinensis var. deliciosa, is a major component of Actinidia due to their huge economic value and the high degree of development and utilization. Wild resources are widely distributed in China, but are under serious threat due to extreme environments and destroyed habitats. Thus, it is of great significance for the conservation of kiwifruit resources and the sustainable development of the kiwifruit industry to evaluate the genetic diversity of existing genetic resources and to systematically construct a core collection of the A. chinensis complex. In this study, 40 high polymorphism microsatellites markers were used to investigate all accessions from the A. chinensis complex. A total of 888 alleles were marked with 22.2 alleles in each locus. The expected heterozygosity was 0.846, the observed heterozygosity was 0.622, the polymorphism information content was 0.835, and the Shannon information index was 2.369. Among these loci, the observed heterozygosity of 38 loci was lower than expected. The inbreeding coefficient was 0.257, which indicates that frequent hybridization occurred between close relatives. Analyses of molecular variance showed that genetic variations mainly came from the population. Finally, a core collection containing 93 accessions was constructed. The bank not only perfectly represented the genetic diversity of the original population, but also had excellent potential for development and utilization. Our research provides a crucial reference for the future conservation, germplasm identification, and genetic breeding of kiwifruit.

1. Introduction

Kiwifruit is a nutritious and sweet fruit accompanied by a high level of ascorbic acid [1]. It is considered a medicinal material due to its chemical composition and elevated antioxidant and anti-inflammatory properties [2]. It has gradually gained worldwide attention with increasing commercial and nutrition values [2,3]. Kiwifruit originated from Hubei province in China and was introduced to New Zealand in the early 20th century. Although it was domesticated for only a short time, some excellent cultivars, such as ‘Hayward’ and ‘Bruno’, were selected to be planted in more than twenty countries, with total international trade gradually increasing as well [3]. The development of the kiwifruit industry in China has been concentrated on in the last forty years, and great progress has been made in scientific research and industries. For example, more than 150 new varieties/lines have been bred so far, and some have been widely cultivated [4]. The common kiwifruit in the industry belongs to A. chinensis var. chinensis (e.g., ‘Donghong’, ‘Hort16A’, and ‘Zesy002’) and A. chinensis var. deliciosa (e.g., ‘Hayward’, ‘Xuxiang’, and ‘Qinmei’). They are related species in the A. chinensis complex [5,6], and are now the group with the highest degree of development and utilization and the greatest economic value. However, wild A. chinensis is only found in China [7].
Genetic resources are the basis of breeding progress and sustainable industrial development [8]. The National Actinidia Germplasm Nursery in China is the richest genetic repository of kiwifruit genotypes in the world and aims to protect the resources of the A. chinensis complex, which are mainly found in domesticated varieties, wild resources, and hybrid offspring. These germplasm resources are rich in genetic variations, providing a good opportunity for quality improvement [9]. Nonetheless, the global climate has been changing rapidly in recent years, and the original distributions of wild kiwifruit have been fragmented, which has largely influenced the genetic diversity of kiwifruit [10]. Thus, an effective assessment of the fruit’s genetic diversity would help to efficiently develop and utilize germplasm resources of the A. chinensis complex and would be of great significance to the global kiwifruit industry’s development.
Resource collection and protection are not only concentrated on quantity; on the contrary, blind collection wastes manpower, money, and time and is not appropriate for long-term protection. Core collection—selecting resources from a huge primary population to represent the genetic diversity of the whole population (>70%)—solves this problem [11]. The representative germplasm has research and application value in that core collection can easily be used to discover good traits and fully understand and effectively use t genes, thus improving the management and utilization of germplasm resources [12]. The core germplasm collections of many plants have been built: grain crops including rice [13], potato [14], and mung bean [15]; horticultural plants, such as fruits [16] and vegetables [17]; fiber crops, such as cotton [18]; sugar crops, such as sugarcane [19]; and beverage crops, such as tea tree [20]. However, research on kiwifruit is scarce. Therefore, constructing the core collection of the A. chinensis complex is an urgent problem to be solved, and it will play a significant role in the scientific conservation, genetic domestication, and utilization of the A. chinensis complex.
Genetic variation analysis is the premise of obtaining a core collection. At present, many types of markers are used for genetic diversity research and core collection construction, such as morphological markers [21,22], physiological and biochemical markers [23,24,25], and DNA markers [26,27]. Nevertheless, genetic variation analysis is not reliable for all data. For example, phenotypes, such as morphological characteristics and nutritional quality, are sensitive to various environments. Thus, genetic variation analysis will result in incorrect data that cannot represent genetic diversity in the original population [12]. DNA markers are less influenced by environment and plant development, so they gradually became the main path for genetic diversity research accompanied by rich variations and stable results [28]. To obtain better results, increasing numbers of research studies have combined DNA molecular markers with morphology, phenology, and biochemical markers. Simple sequence repeat (SSR, also called microsatellite) markers have been employed intensively in genetic diversity analysis due to their codominance, high polymorphism, high reliability, high genome coverage, and versatile platforms for genotyping [29]. Therefore, much research on core collection, especially in fruits based on SSR markers, has been performed. Kim et al. [30] used 19 SSR markers to construct a core collection of 166 special samples from 1344 apple accessions. Equally, Zhong et al. [31] employed 28 markers to estimate the genetic diversity and structures of 955 Akebia trifoliata samples and extracted 164 represented accessions as the core collection. In the present study, we used 40 high-polymorphism SSR markers to genotype 294 A. chinensis complex samples and evaluated their genetic diversity and structure. Finally, we constructed a core collection that could represent the genetic diversity of all the resources and has high utility value.

2. Materials and Methods

2.1. Plant Materials and DNA Extraction

A total of 294 accessions from the National Actinidia Germplasm Nursery (located in Hubei Province, China), including 240 individuals from A. chinensis var. chinensis and 54 samples from A. chinensis var. deliciosa were used for analysis. The 240 A. chinensis var. chinensis samples contained 111 cultivars (lines) and 129 wild resources, and the 54 A. chinensis var. deliciosa samples contained 37 cultivars (lines) and 17 wild resources. All the material information can be viewed in the Supplementary Materials (Table S1). The fresh young leaves were collected in 2020 and frozen in liquid nitrogen for DNA extraction. All total-genome DNA was extracted by a Plant Genomic DNA Kit (TSINGKE, China) according to the manufacturer’s instructions. A NanoDrop 8000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and 1% agarose gels were used to detect the concentration and quality of the DNA extracted.

2.2. SSR Primer Screening and Genotyping

We selected forty pairs of SSR markers distributed on twenty-nine chromosomes and validated by eight germplasm resources to genotype all the samples and ensured that each chromosome contained at least one locus at the same time [32]. For detailed information (e.g., code, base sequence, and position on chromosome, etc.) on primers, refer to Table S2 and Figure S1. The amplified method was modified from Liu et al. [33]. The amplified volume of PCR was 20 μL and the components were listed as follows: 1 ulgenome DNA template (20~50 ng), 10 μL 2 × Taq PCR MasterMix (including 3.0 mM MgCl2, 500 μM dNTPs, 0.1 U Taq DNA polymerase, 100 mM KCl, 20 mM Tris-HCl, Tiangen, Beijing, China), 0.15 μL forward primer(10 μM), 1.2 μL reverse primer(10 μM), 1.2 μL fluorescently labeled M13 primer, and 6.45 μL ddH2O. The procedure was as follows: initial denaturation at 94 °C for 5 min, then 35 cycles (94 °C, 30 s; 60 °C, 30 s; 72 °C, 30 s), and then 72 °C for 5 min. All primers used in this study were labeled by FAM, HEX, ROX, or TAMRA fluorescent dyes for convenient isolation of PCR products with ABI 3730XL automatic sequencer (Applied Biosystems Inc., Foster City, CA, USA) and read using Gene mapper v4.1.

2.3. Analysis of Genetic Diversity and Genetic Structure

Because the materials supplied for the experiment involved polyploid and the ploidy composition was complex, we could not determine the doses of alleles associated with high ploidy materials. We used the ‘allelic phenotypes’ dataset, which is a binary matrix created by recording the presence (1) or absence (0) of alleles for each microsatellite locus per accession [10,34]. The software POLYGENE v1.2 [35] was used to estimate several parameters for each locus, including the observed number of alleles (Na), effective number of alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), Shannon’s Information Index (I), polymorphism information content (PIC), and inbreeding coefficient (Fis). APE 5.0 in R was used for cluster analysis with neighbor-joining (NJ) methods [36] and embellished by iTOL v5 [37]. In order to show the genetic structure of the A. chinensis complex, we used STRUCTURE 2.3.4 [10,38] to analyze the genetic structure of different populations or groups, amd the potential number of genetic clusters (K) varied from 1 to 10. Ten independent simulations were run for each value of K with 100,000 burn-in steps followed by 1,000,000 Markov chain Monte Carlo (MCMC) steps, and we used online tools STRUCTURE HARVEST (http://taylor0.biology.ucla.edu/structureHarvester/, accessed on 1 March 2022) [39] to calculate the best K value for determining the optimal subgroup number. Repeat sampling was conducted by CLUMPP v1.1.2 [40], and the results were visualized via Distruct [41]. Additionally, analyses of molecular variance (AMOVA) and principal coordinate analysis (PCoA) were performed using GENALEX 6.5 [42].

2.4. Construction and Evaluation of the Core Collection

PowerCore v1.0 software [43] was used to independently determine the sampling ratio for core germplasm screening with the allelic maximization method. Differences in the genetic parameters between core germplasm and original germplasm were estimated by SPSS v26.0 [44] and the distribution range of core resources in the original germplasm was verified by PcoA to ensure the effectiveness of the core germplasm [45].

3. Results

3.1. Genetic Diversity and Variation of the A. chinensis Complex

A total of 888 alleles were amplified using 40 pairs of SSR primers, and the number of alleles (Na) of each locus ranged from 14 (A-Geo369) to 38 with an average of 22.2; the effective number of alleles (Ne) varied from 1.72 (A-Geo323) to 15.96 (A-Geo156), with an average value of 8.76. The observed heterozygosity (Ho) ranged from 0.32 (A-Geo083) to 0.79 (A-Geo158) and its mean value was 0.62; the expected heterozygosity (He) was between 0.42 (A-Geo323) and 0.94 (A-Geo156), with an average value of 0.85. The polymorphic information content (PIC) ranged from 0.41 (A-Geo323) to 0.93 (A-Geo156) with an average of 0.84. Moreover, Shannon’s Information Index (I) varied from 1.08 (A-Geo369) to 3.11 (A-Geo156), with a mean value of 2.37 (Table 1). Specifically, the observed heterozygosity (Ho) of all SSR loci, except for A-Geo167 and A-Geo323, was lower than the expected heterozygosity (He) of the corresponding site. The results suggest that the selected primers were suitable for genotyping the A. chinensis complex; moreover, the primers of loci A-Geo156, A-Geo229, and A-Geo054 showed higher results in the number of alleles (Na), the polymorphic information content (PIC), and Shannon’s Information Index (I) compared with other loci, therefore their genetic diversities were higher in the A. chinensis complex.
To further explore the difference between A. chinensis var. chinensis and A. chinensis var. deliciosa, we analyzed the genetic diversities of the two populations and found that the values of parameters included in Table 2 in A. chinensis var. chinensis were higher than those of A. chinensis var. deliciosa, which might be related to sample sizes, whereby more samples of A. chinensis var. chinensis provided more variations. Additionally, the inbreeding coefficient (Fis) of A. chinensis var. chinensis was 0.257 and the value of A. chinensis var. deliciosa was 0.225, which suggests that the close relatives interbred more frequently in A. chinensis var. chinensis. A hierarchical analysis of molecular variance (AMOVA) is presented in Table 3. A total of 97.10% of genetic variations occurred within populations and 2.90% was attributable to divergence among populations (p < 0.001), which shows that the genetic diversity within individuals is richer than that among populations.

3.2. Cluster Analysis of the A. chinensis Complex

Genotype data from 294 samples were used for cluster analysis. The rooted tree showed two main genetic clusters, A. chinensis var. chinensis and A. chinensis var. deliciosa (Figure 1a). However, some samples from the two populations were mixed, which revealed that the two variations are close. In addition, to validate our results, an unrooted tree was used, and we divided all samples into four clusters. Both clades 1 and 2 contained one A. chinensis var. deliciosa accession. Cluster 3 comprised four A. chinensis var. deliciosa accessions and fifty A. chinensis var. chinensis samples. Moreover, forty-eight A. chinensis var. deliciosa accessions and twenty-nine A. chinensis var. chinensis samples were involved in cluster 4 (Figure 1b). Additionally, the mixed pattern of clusters over the whole samples was further confirmed by PCo analysis (Figure 1c).

3.3. Genetic Structure of the A. chinensis Complex

To further identify the genetic structure of the A. chinensis complex, structuring was used to estimate the best K value. The posterior probability (lnP (D)) increased with the increase in the subgroup number, which suggested that it was highly undesirable to obtain the most appropriate K value with the methods using the logarithm of the probability of the data (ln P(D)) [46]. Thus, the method used to determine the K value was maximum-likelihood estimation based on ΔK, and the highest peak appeared at K = 2, indicating that the two genetic clusters were suitable (Figure 2a,b). Specifically, major A. chinensis var. deliciosa accessions showed one simple structure, but A. chinensis var. chinensis showed two distinctive clusters at K = 2 (Figure 2c). However, two distinctive clusters appeared in A. chinensis var. deliciosa and some parts were shared with A. chinensis var. chinensis at K = 3, which suggests that they were close. Moreover, the genetic structure of A. chinensis var. chinensis was mixed at K = 4 (Figure 2c). Furthermore, the four subgroups in cluster analysis showed different genetic structures especially at K = 4 (Figure 2d), and they corresponded with the results of cluster analysis in Section 3.2.

3.4. Core Collection of the A. chinensis Complex

3.4.1. Construction of the Core Collection

The core collection was determined by extraction with software and artificial supplementation. Core collection 1 contained 73 samples from 294 A. chinensis complex accessions using the allelic maximization method via PowerCore software. However, the automated processes in the software only considered the retention of the alleles and some excellent domesticated cultivars were not included. Thus, twenty accessions were added artificially and finally, we conducted core collection 2 comprised of 93 accessions (Table 4). The core collection contained 70 A. chinensis var. chinensis samples and 23 A. chinensis var. deliciosa samples, and the core collection contained 62 cultivars (lines) and 31 wild resources.

3.4.2. Verification of the Core Collection

The genetic parameters of the final core collection were estimated and compared with the primary collection to verify the effectiveness of the genetic diversity of the core germplasm (Table 5). Core collection 1 accounted for 24.83% of the all collection. The number of alleles (Na), the effective number of alleles (Ne), the observed heterozygosity (Ho), the expected heterozygosity (He), the polymorphic information content (PIC), and Shannon’s Information Index (I) account for 95.05%, 103.69%, 102.09%, 100.35%, 100.60%, and 101.60% of the original set, respectively. Compared with collection 1, core collection 2 accounted for 31.63% and, except for the number of alleles (Na) being slightly higher than that of core collection 1, the values of other genetic parameters were lower. Similarly, the parameters of both collections were higher than raw accessions, except for the number of alleles (Ne).
Both core collections effectively represented the genetic diversity of the original populations, but we chose collection 2 as the final core collection because it had a higher breeding value. There were no significant differences in the ratios of the retained alleles between the original population and the core collection, but it was higher than the remaining collection. In addition, the effective number of alleles (Ne) and Shannon’s Information Index (I) were significantly higher than the other two collections (p < 0.05), which indicated that the constructed core collection had fine representativeness (Figure 3).
PCoA was used to further understand the distribution of accessions from the core collection in the original collection, and the results show that core germplasm was well-distributed and could represent all germplasms for preservation and utilization (Figure 4). Moreover, 93 core resources could be completely distinguished when combined with 4 markers with higher PIC values in 93 fingerprint spectrums (Figure S2) obtained by SSR markers, which was beneficial to the management and accurate identification of the core collection.

4. Discussion

Molecular markers can reveal genetic variations at the DNA level. Therefore, SSR was used for a genetic diversity study of the germplasm, genetic map construction, kinship identification, and core germplasm collection due to its codominant inheritance and rich polymorphism [27]. The automatic genotyping greatly reduced the error rate of artificial reading as compared with traditional polyacrylamide gel electrophoresis. Meanwhile, capillary fluorescence electrophoresis has the characteristics of safety, low toxicity, and high accuracy [26]. In our study, 888 alleles were detected in 294 A. chinensis complex accessions using 40 SSR markers with 22.2 alleles in each locus, with a mean polymorphism information content of 0.835 and Shannon’s Information Index of 2.369, which indicates that these markers were highly polymorphic. This might be due to our large sample sizes and the rigorous screening of primers [47]. Moreover, our markers (He = 0.846, Ho = 0.622, PIC = 0.835, I = 2.369) showed stronger typing ability compared with similar studies, and the overall performance of the primer parameters was significantly better than similar studies of Actinidia species, e.g., A. eriantha [48] (He = 0.570, Ho = 0.540, I = 1.120), and A. arguta [49,50] (Ho = 0.591, PIC = 0.768, and He = 0.775, Ho = 0.684, PIC = 0.658). Thus, SSR primers with high polymorphism are suitable for revealing genetic diversity of the germplasm [51].
Genetic diversity analysis of A. chinensis var. chinensis and A. chinensis var. deliciosa showed that the A. chinensis complex had a high level of genetic diversity (Ho = 0.618, He = 0.818), but the level of genetic diversity of A. chinensis var. chinensis (Ho = 0.625, He = 0.849) was higher than that of A. chinensis var. deliciosa (Ho = 0.611, He = 0.786), which might be caused by different sample sizes. Moreover, the genetic diversity level in our research was higher than major fruit trees, such as pear (Na = 9.5, Ho = 0.53, He = 0.62, I = 1.187) [52], persimmon (Na = 2.000, Ne = 1.318, I = 0.344) [53], pomegranate (Na = 5.72, Ho = 0.41, He = 0.55) [54], peach (Na = 5.9, Ho = 0.20, He = 0.41) [55], and cherry (Na = 4.00, Ne = 1.97, Ho = 0.46, He = 0.41) [56], but lower than apples (Na = 23.06, Ne = 6.59, Ho = 0.81, He = 0.83) [57], which indicates that the growth characteristics of heterosis and the propagation pattern of extensive hybridization are beneficial to improving genetic diversity [58]. In addition, the observed genetic diversity level was higher than A. arguta and A. eriantha because the markers were evenly distributed throughout the genome and the samples were diverse [49,59]. The inbreeding coefficient of 0.241 revealed that the A. chinensis complex had obvious inbreeding. Therefore, except for natural hybridization materials, the samples used in this research also included the germplasm created by artificial selection or crossbreeding, which causes partial heterozygote loss and influences the maintenance of genetic diversity, common occurrences in fruit crops [60].
Liu et. al. [61] analyzed the genetic variations and gene flow among seven species of Actinidia based on SSR markers and found significant genetic differences between the species. Thereinto, similar genetic diversity appeared and common genetic introgression was detected in A. chinensis var. chinensis and A. chinensis var. deliciosa. In fact, both populations were close in morphology, and comparative genomics also proved that A. chinensis var. deliciosa originated from A. chinensis var. chinensis [5]. These results were identified in our research via cluster analysis, principal coordinate analysis, and genetic structure analysis. Specifically, 294 samples were divided into 4 groups by cluster analysis; groups one to three mainly contained A. chinensis var. chinensis, and group four comprised A. chinensis var. deliciosa, which corresponded with the genetic structure. Moreover, an analysis of molecular variance (AMOVA) revealed that 2.9% of total genetic variations resulted from differences between populations, and 97.10% were attributed to individual differences within the groups, consistent with previous studies on the A. chinensis complex [58]. The low level of genetic diversity between populations showed no limit in gene flow, which further explains the high genetic diversity of the A. chinensis complex [10]. The consequences, including significant genetic differences and shared haplotypes, which indicated genetic mixing between the two groups, were confirmed using chloroplast gene sequence fragments [62], and similar results were identified with other methods, such as AFLP [63] and RAPD [64].
Morphological data and molecular marker data are usually used for core collection construction. However, there are considerable limitations to constructing a core collection via morphological data because of the influences of developmental stages and the environment [12]. On the contrary, molecular markers dependent on plant genomes have abundant genetic variation and are independent of the developmental stage of plants. Moreover, they can be used to obtain a large amount of marker information quickly. Therefore, molecular markers have been proven to be an ideal data source for constructing a core collection [30]. PowerCore software is popular in core collection construction and can process phenotypic and DNA molecular data [12,25,65]. Although SSR markers are widely used in studies on the genetic diversity of Actinidia, core collection construction has remained unclear until now, especially for the A. chinensis complex [66]. This study selected 93 germplasm (31.63%) from 294 accessions for the core collection construction of the A. chinensis complex based on SSR markers. Among them, twenty accessions selected artificially were considered excellent practical varieties. Without considering artificial addition, seventy-three accessions (24.83%) were sufficient to represent the genetic diversity of the original germplasm. On the contrary, more domesticated varieties decreased the genetic diversity, which indicates that genetic domestication of crops reduces the genetic diversity of germplasm resources [60]. Compared with other core collections, including pomegranate (19.3%) [54], apple (12.4%/17%) [16,30], and Akebia trifoliata (17.2%) [31], the proportion of the core collection in this study was higher, but it was lower than partial trees, such as apricot (53.55%) [67]. Nonetheless, the core collections listed above still lack the replenishment of special and important germplasms because the construction of the core collection should not only be based on the conservation of genetic diversity and the effective utilization of germplasm resources, but also be suitable for practical application needs by adding germplasms with special statuses [68]. A significance test was performed on the retention of genetic diversity in the core collection in this study and the results show that the core collection basically represented the genetic diversity of the original resources. Additionally, PCoA shows that core resources were evenly distributed in the primitive population, which indicates that they could represent others effectively. Thus, it is vital for diversity conservation and genetic improvement that this core collection is effectively preserved and managed [69]. Increasing numbers of core collections have been constructed using a combination of traits and DNA molecular markers [12]. However, the traits are rarely obtained because kiwifruit is a perennial vine and wildly distributed [70], especially wild resources. Therefore, in the future, we will attempt to construct a core collection combined with molecular and morphological data based on the SSR data gleaned in this research.

5. Conclusions

In this study, we used SSR markers to investigate all individuals of the A. chinensis complex population from the National Actinidia Germplasm Nursery. A core collection containing 93 resources with genetic diversity and high application value was constructed via analyses of genetic diversity, structure, and kinship of 294 accessions. The results reveal that all SSR loci showed high polymorphism, and most loci showed heterozygous deletion, which indicates that there was obvious close hybridization in A. chinensis complex. Moreover, the inbreeding coefficient (Fis) indicates that there was obvious close hybridization between A. chinensis var. deliciosa originating from A. chinensis var. chinensis, which was related to the characteristics of the dioecism of kiwifruit. Additionally, the high genetic diversity of the A. chinensis complex might result from obvious genetic differentiation among varieties and crossover heredity of gene introgression. Our study provides a fundamental insight into the collection of germplasm resources, variety improvement, and protection of new variety rights.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12123078/s1, Figure S1: The physical location map of 40 SSR molecular markers; Figure S2: SSR fingerprints of core collection germplasms; Table S1: In formation on test materials; Table S2: Summary statistics for the 40 SSR markers.

Author Contributions

Conceptualization, C.Z. and G.H.; Software, Z.W. and G.H.; Resources Investigation and Collection, G.H., W.L. and D.S.; Data Curation, G.H. and Q.J.; Writing—Original Draft Preparation, G.H. and Q.J.; Writing—Review and Editing, C.Z. and Z.L.; Funding Acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The work was supported by grants from the National Key Research and Development Program of China (2019YFD1000200) and the Key Research and Development Program of Hubei Province (2021BBA100).

Data Availability Statement

Data are contained within the article.

Acknowledgments

We are grateful to all the people who have been helpful to the manuscript, including the editors and reviewers of the journal, as well as all the researchers in the Kiwifruit Research Center of Wuhan Botanical Garden, Chinese Academy of Sciences.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 12 03078 g001 550
Figure 1. Cluster analysis of the A. chinensis complex. (a) Rooted cluster map. (b) Unrooted cluster map. (c) Principal coordinate analysis.
Figure 1. Cluster analysis of the A. chinensis complex. (a) Rooted cluster map. (b) Unrooted cluster map. (c) Principal coordinate analysis.
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Agronomy 12 03078 g002 550
Figure 2. Population structure analysis of the A. chinensis complex. (a) Based on the average value of lnP(D), draw the subgroup grouping scatter diagram. (b) Based on ΔK value, draw subgroup grouping scatter diagram. (c) When K = 2~4, the genetic structure of the A. chinensis var. chinensis and A. chinensis var. deliciosa. (d) When K = 2~4, the genetic structure of the group1~group4 germplasm. Each color in the (c,d) represents a genetic cluster.
Figure 2. Population structure analysis of the A. chinensis complex. (a) Based on the average value of lnP(D), draw the subgroup grouping scatter diagram. (b) Based on ΔK value, draw subgroup grouping scatter diagram. (c) When K = 2~4, the genetic structure of the A. chinensis var. chinensis and A. chinensis var. deliciosa. (d) When K = 2~4, the genetic structure of the group1~group4 germplasm. Each color in the (c,d) represents a genetic cluster.
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Agronomy 12 03078 g003 550
Figure 3. Genetic diversity retention ratio of the all collection, core collection, and remaining collection. There were significant differences in different letters (p < 0.05).
Figure 3. Genetic diversity retention ratio of the all collection, core collection, and remaining collection. There were significant differences in different letters (p < 0.05).
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Agronomy 12 03078 g004 550
Figure 4. Distribution of principal coordinates of the core collection in all collection.
Figure 4. Distribution of principal coordinates of the core collection in all collection.
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Table
Table 1. Genetic diversity of 40 SSR loci in 294 accessions.
Table 1. Genetic diversity of 40 SSR loci in 294 accessions.
LocusNaNeHoHePICIFis
A-Geo1012512.3170.7120.9190.9132.6770.225
A-Geo114287.3920.6650.8650.8552.4850.23
A-Geo131224.5540.4990.780.7642.0780.36
A-Geo0142812.0120.70.9170.9112.710.236
A-Geo1422110.7920.7320.9070.92.60.194
A-Geo1563815.9640.5990.9370.9343.1060.361
A-Geo1582213.1530.7890.9240.9192.7170.146
A-Geo167162.4480.6120.5910.5711.461−0.034
A-Geo018188.3580.6060.880.872.3650.312
A-Geo1882612.6970.6870.9210.9162.7820.254
A-Geo198237.7880.6450.8720.862.380.259
A-Geo218195.2260.6280.8090.7932.0980.223
A-Geo2293214.5430.6730.9310.9272.9690.277
A-Geo249219.6540.670.8960.8882.5030.252
A-Geo2571910.170.6830.9020.8932.4960.243
A-Geo266204.450.610.7750.7582.0590.213
A-Geo275203.6780.4950.7280.7071.8340.32
A-Geo2902210.8770.6650.9080.9012.570.268
A-Geo2932110.7280.6170.9070.92.5870.319
A-Geo3022311.6470.6550.9140.9082.6340.284
A-Geo3072011.450.7310.9130.9062.5720.199
A-Geo316229.6920.6060.8970.8882.4630.324
A-Geo323151.7210.4970.4190.411.091−0.186
A-Geo3352111.1570.6830.910.9042.6170.249
A-Geo341207.3430.6260.8640.8522.3360.275
A-Geo350228.9950.6070.8890.8792.450.317
A-Geo365197.9450.6470.8740.8632.350.26
A-Geo369141.9010.3470.4740.4481.0760.269
A-Geo377187.3770.630.8640.8532.3220.272
A-Geo038297.1660.6590.860.8492.4690.235
A-Geo3932010.7110.5490.9070.8992.5550.395
A-Geo401217.0180.6440.8580.8422.220.249
A-Geo4072613.6830.6570.9270.9222.820.291
A-Geo415187.8640.6320.8730.8612.330.276
A-Geo042167.810.6060.8720.8592.2640.305
A-Geo4262411.9990.7160.9170.9112.6930.219
A-Geo427213.3010.4790.6970.6841.8730.312
A-Geo0543114.2690.7110.930.9262.8780.236
A-Geo068233.9740.5970.7480.7291.970.202
A-Geo083246.50.320.8460.8322.3180.622
Mean22.28.7580.6220.8460.8352.3690.257
Na: the number of alleles; Ne: the effective number of alleles; Ho: the observed heterozygosity; He: the expected heterozygosity; PIC: the polymorphic information content; I: Shannon’s Information Index; Fis: inbreeding coefficient.
Table
Table 2. Genetic diversity of 40 SSR loci in different variant populations.
Table 2. Genetic diversity of 40 SSR loci in different variant populations.
PopulationnNaNeHoHePICIFis
A. chinensis24021.7258.6890.6250.8490.8382.3670.257
A. deliciosa5417.1506.4420.6110.7860.7712.0860.225
Mean 19.4387.5660.6180.8180.8052.2270.241
n: the number of samples.
Table
Table 3. AMOVA analysis of different variant populations.
Table 3. AMOVA analysis of different variant populations.
Source of VariationDegrees of FreedomSum of SquareMean of SquareVariance ComponentPercentage of Variation (%)p
Among variants1259.56259.562.132.90<0.001
Within variants29220,86971.4771.4797.10<0.001
Total29321,128.56-73.60100-
p: p values based on 10,000 permutations, p < 0.001 indicates very significant variation.
Table
Table 4. Information on core germplasm resources of the A. chinensis Complex.
Table 4. Information on core germplasm resources of the A. chinensis Complex.
NumberNameCodeVariantCategorySelection Method
1CSXZSIII3-0-2-4A. chinensis var. chinensisCultivars (lines)PowerCore
2CXHY43-2-24A. chinensis var. chinensisCultivars (lines)PowerCore
3Taibao No. 24-1-1-2A. chinensis var. chinensisCultivars (lines)PowerCore
4Jinyang4-3-1-1A. chinensis var. chinensisCultivars (lines)PowerCore
5Jinyi4-3-2-1A. chinensis var. chinensisCultivars (lines)PowerCore
6Kuimi4-3-3-1A. chinensis var. chinensisCultivars (lines)PowerCore
7Huayou4-4-2-2A. chinensis var. chinensisCultivars (lines)PowerCore
8DH-14-6-5-2NA. chinensis var. chinensisCultivars (lines)PowerCore
9DD-1114-7-5-2A. chinensis var. chinensisCultivars (lines)PowerCore
10Hongyang4-8-4-2A. chinensis var. chinensisCultivars (lines)PowerCore
11Fengyue4-9-2-2A. chinensis var. chinensisCultivars (lines)PowerCore
12Wanjin5-10-1-3A. chinensis var. chinensisCultivars (lines)PowerCore
13Chuhong5-11-3-4A. chinensis var. chinensisCultivars (lines)PowerCore
14Jinxia5-2-2-1A. chinensis var. chinensisCultivars (lines)PowerCore
15Hort16A5-4-2-4A. chinensis var. chinensisCultivars (lines)PowerCore
16Cuiyu5-7-2-1A. chinensis var. chinensisCultivars (lines)PowerCore
17Taishanghuang5-8-1-1A. chinensis var. chinensisCultivars (lines)PowerCore
18ZH8792 ♂8-9-7-2A. chinensis var. chinensisCultivars (lines)PowerCore
19FC-1-1-4A-13-2-3A. chinensis var. chinensisCultivars (lines)PowerCore
20XB-48-2-10A-1-6-3A. chinensis var. chinensisCultivars (lines)PowerCore
21XB-49-5-9A-2-5-1A. chinensis var. chinensisCultivars (lines)PowerCore
22XB-51-1-3A-3-6-1A. chinensis var. chinensisCultivars (lines)PowerCore
23CD-19-1-28B-20-1-1A. chinensis var. chinensisCultivars (lines)PowerCore
24Wanmi5-8-2-2A. chinensis var. chinensisCultivars (lines)PowerCore
25P6107260705-8-4-1A. chinensis var. chinensisWild resourcesPowerCore
2617-SCLS-41A-10-2-1A. chinensis var. chinensisWild resourcesPowerCore
2717-SCLS-50A-11-5-2A. chinensis var. chinensisWild resourcesPowerCore
2818-ANXN-31A-5-2-3A. chinensis var. chinensisWild resourcesPowerCore
2918-ANXN-33A-5-3-4A. chinensis var. chinensisWild resourcesPowerCore
3017-SCLS-2A-5-5-3A. chinensis var. chinensisWild resourcesPowerCore
3117-SCLS-33A-9-2-1A. chinensis var. chinensisWild resourcesPowerCore
3218-HN-1B-22-1-2A. chinensis var. chinensisWild resourcesPowerCore
3318-HN-13B-23-1-2A. chinensis var. chinensisWild resourcesPowerCore
3418-ZHLS-1B-23-6-5A. chinensis var. chinensisWild resourcesPowerCore
3517-ZET-2B-25-7-3A. chinensis var. chinensisWild resourcesPowerCore
36JXRJB-27-5-2A. chinensis var. chinensisWild resourcesPowerCore
37YSZY44 ♂B-29-2-2A. chinensis var. chinensisWild resourcesPowerCore
3818-AHJX-02B-30-3-2A. chinensis var. chinensisWild resourcesPowerCore
3918-AHJX-04B-30-4-5A. chinensis var. chinensisWild resourcesPowerCore
4018-AHQM-12B-31-7-3A. chinensis var. chinensisWild resourcesPowerCore
4118-AHQM-21B-32-2-2A. chinensis var. chinensisWild resourcesPowerCore
4218-AHQM-25B-32-3-5A. chinensis var. chinensisWild resourcesPowerCore
4318-AHQM-28B-32-5-2A. chinensis var. chinensisWild resourcesPowerCore
4418-AHQS-07B-32-7-5A. chinensis var. chinensisWild resourcesPowerCore
4518-AHQS-08B-32-8-3A. chinensis var. chinensisWild resourcesPowerCore
4618-AHQS-06B-33-1-3A. chinensis var. chinensisWild resourcesPowerCore
4718-AHQS-10B-33-3-3A. chinensis var. chinensisWild resourcesPowerCore
4818-AHQS-22B-33-7-5A. chinensis var. chinensisWild resourcesPowerCore
4918-AHQY-19B-34-5-2A. chinensis var. chinensisWild resourcesPowerCore
5018-AHXN-11B-35-2-4A. chinensis var. chinensisWild resourcesPowerCore
5118-AHXN-12B-35-3-3A. chinensis var. chinensisWild resourcesPowerCore
5218-AHXN-13B-35-3-4A. chinensis var. chinensisWild resourcesPowerCore
5318-AHXN-16B-35-4-1A. chinensis var. chinensisWild resourcesPowerCore
5418-AHXN-20B-35-4-4A. chinensis var. chinensisWild resourcesPowerCore
55Donghong3-10-9A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
56Moshan No. 2 ♂3-7-4-2A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
57Lushanxiang4-1-4-2A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
58Tongshan 54-2-1-1SA. chinensis var. chinensisCultivars (lines)Supplementary cultivar
59Moshan No. 44-2-2-2NA. chinensis var. chinensisCultivars (lines)Supplementary cultivar
60Jinnong4-2-4-1A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
61Moshan No. 1 ♂4-3-3-2A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
62Zaoxian4-4-1-1NA. chinensis var. chinensisCultivars (lines)Supplementary cultivar
63Huabao No. 14-6-1-1A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
64Golden peach4-6-11-1A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
65Xiaya No. 14-6-4-1A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
66Guihai No. 44-8-1-1A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
67Moshan No. 7 ♂4-8-1-2A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
68Jinyuan2-2-3-3A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
69Jinyan4-8-10-2A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
70Jinmei7-2-3-1A. chinensis var. chinensisCultivars (lines)Supplementary cultivar
71Jinmei2-4-3-2A. chinensis var. deliciosaCultivars (lines)PowerCore
72Longshanhong4-12-5-1A. chinensis var. deliciosaCultivars (lines)PowerCore
73Xinguan No. 24-3-5-2EA. chinensis var. deliciosaCultivars (lines)PowerCore
74Shixuana No. 24-4-5-3A. chinensis var. deliciosaCultivars (lines)PowerCore
75Dongmei4-5-1-3A. chinensis var. deliciosaCultivars (lines)PowerCore
76Qinxiang4-5-2-1NA. chinensis var. deliciosaCultivars (lines)PowerCore
77Chang’an No. 14-6-3-1A. chinensis var. deliciosaCultivars (lines)PowerCore
78Xiangma No. 64-6-4-3A. chinensis var. deliciosaCultivars (lines)PowerCore
79Xuxiang4-7-3-1A. chinensis var. deliciosaCultivars (lines)PowerCore
80Chuanmi No. 14-7-4-2A. chinensis var. deliciosaCultivars (lines)PowerCore
81Xuxiang ♂4-8-4-3A. chinensis var. deliciosaCultivars (lines)PowerCore
82Qinmei5-10-4-3A. chinensis var. deliciosaCultivars (lines)PowerCore
83Guichang5-11-6-3A. chinensis var. deliciosaCultivars (lines)PowerCore
84Hayward5-9-1-4A. chinensis var. deliciosaCultivars (lines)PowerCore
85Huamei No. 15-9-2-1A. chinensis var. deliciosaCultivars (lines)PowerCore
8618-20161395B-26-5-1A. chinensis var. deliciosaCultivars (lines)PowerCore
87Shixuana No. 1B-28-2-2A. chinensis var. deliciosaCultivars (lines)PowerCore
88Miliang No. 1 B2831B-14-1-1A. chinensis var. deliciosaCultivars (lines)PowerCore
8918-AHHS-01B-29-5-1A. chinensis var. deliciosaWild resourcesPowerCore
90Moshan No. 3 ♂3-19-2A. chinensis var. deliciosaCultivars (lines)Supplementary cultivar
91Sanxia No. 14-2-5-2A. chinensis var. deliciosaCultivars (lines)Supplementary cultivar
92Xianglv4-5-4-2EA. chinensis var. deliciosaCultivars (lines)Supplementary cultivar
93Bruno4-7-5-3A. chinensis var. deliciosaCultivars (lines)Supplementary cultivar
♂: Male germplasm resources
Table
Table 5. Comparison of genetic diversity among all collections, core collection, and remaining collection.
Table 5. Comparison of genetic diversity among all collections, core collection, and remaining collection.
PopulationnNaNeHoHePICI
All collection294 (100%)22.28.7580.6220.8460.8352.369
Core collection 173 (24.83%)21.19.0810.6350.8490.842.407
Core collection 293 (31.63%)21.9759.0580.6270.8480.8382.401
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MDPI and ACS Style

Hu, G.; Jiang, Q.; Wang, Z.; Li, Z.; Liao, W.; Shen, D.; Zhong, C. Genetic Diversity Analysis and Core Collection Construction of the Actinidia chinensis Complex (Kiwifruit) Based on SSR Markers. Agronomy 2022, 12, 3078. https://doi.org/10.3390/agronomy12123078

AMA Style

Hu G, Jiang Q, Wang Z, Li Z, Liao W, Shen D, Zhong C. Genetic Diversity Analysis and Core Collection Construction of the Actinidia chinensis Complex (Kiwifruit) Based on SSR Markers. Agronomy. 2022; 12(12):3078. https://doi.org/10.3390/agronomy12123078

Chicago/Turabian Style

Hu, Guangming, Quan Jiang, Zhi Wang, Zuozhou Li, Wenyue Liao, Dandan Shen, and Caihong Zhong. 2022. "Genetic Diversity Analysis and Core Collection Construction of the Actinidia chinensis Complex (Kiwifruit) Based on SSR Markers" Agronomy 12, no. 12: 3078. https://doi.org/10.3390/agronomy12123078

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