Next Article in Journal
Predicting Eye and Hair Color in a Turkish Population Using the HIrisPlex System
Next Article in Special Issue
Potential of In Vitro Culture of Scutellaria baicalensis in the Formation of Genetic Variation Confirmed by ScoT Markers
Previous Article in Journal
Insights of Improved Aroma under Additional Nitrogen Application at Booting Stage in Fragrant Rice
Previous Article in Special Issue
Molecular Characterization of Tinospora cordifolia (Willd.) Miers Using Novel g-SSR Markers and Their Comparison with EST-SSR and SCoT Markers for Genetic Diversity Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 13
Issue 11
10.3390/genes13112093
genes-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

Population Diversity Analysis Provide Insights into Provenance Identification of Dendrobium catenatum

by
Xin-Yi Wu
1,2,
Ting-Zhang Li
1,2,
Fang Zheng
1,
Jian-Bing Chen
1,
Yue-Hong Yan
1 and
Jiu-Xiang Huang
2,*
1
Key Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization, The Orchid Conservation and Research Center of Shenzhen and the National Orchid Conservation Center of China, Shenzhen Key Laboratory for Orchid Conservation and Utilization, Shenzhen 518114, China
2
South China Limestone Plants Research Center, College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Genes 2022, 13(11), 2093; https://doi.org/10.3390/genes13112093
Submission received: 11 October 2022 / Revised: 5 November 2022 / Accepted: 5 November 2022 / Published: 10 November 2022
(This article belongs to the Special Issue Phylogenetics, Genetics, and Breeding of Medicinal Plants)
Browse Figures
Versions Notes

Abstract

:
Dendrobium catenatum (Dendrobium officinale) is a valuable genuine herb. The source of this species is difficult to be identified by traditional methods including morphology, spectroscopy, and chromatography. We used the restriction site-associated DNA sequencing (RAD-seq) approach to perform the high-throughput sequencing of 24 D. catenatum provenances. In this study, 371.18 Gb clean data were obtained, and 655,057 high-quality SNPs were selected after their filtration. We used phylogenetic tree, genetic structure, and principal component analyses to examine the genetic diversities and genetic relationships of the 109 accessions. We found that D. catenatum could be divided into two groups, and each group was closely related to the distribution of the sampling sites. At the population level, the average nucleotide diversity (π) of the D. catenatum population mutation parameters was 0.1584 and the expected heterozygosity (HE) was 0.1575. The GXLPTP07 accessions showed the highest genetic diversity in terms of the private allele number, observed heterozygosity, and nucleotide diversity. The Mantel test showed a significant positive correlation between the genetic and geographic distances among the overall distribution. A genetic information database of D. catenatum was established, which confirmed that RAD-seq technology has the potential to be applied in the identification of medicinal Dendrobium of different origins.

1. Introduction

The genus Dendrobium Swartz (1799) is one of the largest genera in Orchidaceae [1], and it consists of approximately 1450 species [2]. Dendrobium is highly valued for its medicinal, ornamental, scientific and economics uses, but its medicinal value has attracted the most attention. There are nearly 80 species of Dendrobium in China. Many Dendrobium species have been extensively used in Traditional Chinese Medicine (TCM), including D. catenatum Lindl (D. officinale Kimura and Migo) and D. huoshanense C.Z. Tang and S.J. Cheng [3,4]. Many studies have shown that Dendrobium polysaccharide has antitumor effects [5], immune enhancement [6,7], antioxidation [8,9], anti-inflammatory [10,11], and hypoglycemic effects [12,13]. There is also research showing that Dendrobium can improve learning and memory [14]. The medicinal Dendrobium industry has developed rapidly in recent years, and some growers have carried out cross-breeding for medicinal Dendrobium in pursuit of increasing their yield and profits, which has caused confusion in the market regarding the Dendrobium varieties. There are great differences in the pharmaceutical components between Dendrobium species [15], which affect the efficacy or curative effect of them. A large number of medicinal Dendrobium products on the market have lost their active ingredients, and variable quality and effective identification methods are lacking. Traditional morphological identification, microscopic identification, and the physical and chemical identification methods can only be used for the interspecies identification of medicinal Dendrobium [16,17,18]. Obviously, it is difficult for conventional techniques to identify the provenance of medicinal Dendrobium as they are similar in their morphological and anatomical characteristics and even in their chemical components. One of the most important factors for the modernization of Chinese medicines is the quality of the traditional Chinese medicines, and the key support for it is the study of its genuineness [19]. A variety of genuine medicinal materials had been confirmed that the effective components of different producing areas are very different [20,21,22]. D. catenatum is a geo-authentic Chinese medicinal material, and so its identification of provenance is very important.
For identification purposes, researchers have established a full-sequence database that is based on the rDNA ITS region of the Dendrobium variety “Fengdou” [23]. The phylogenetic tree constructed from matK and rbcL data could distinguish five types of medicinal Dendrobium [24]. For the purpose of the swift and precise identification of thirteen wild and cultivated Dendrobium species belonging to two sections Formosae and Chrysotoxae, the researchers designed the rDNA ITS region sequence analysis [25]. In recent years, the molecular identification of Dendrobium plants has also been studied. Inter-simple sequence repeats (ISSR) molecular fingerprinting markers had been employed to authenticate eight populations of D. officinale using 10 primers [26]. Two genuine population had been authenticated based on the SNPs of the rDNA ITS region [27]. However, microsatellite studies have typically used a low number of markers (<25), which increases the danger of underestimating the genetic structure due to a lack of polymorphic markers [28]. When one is using a low number of genetic markers, larger sample sizes are required to accurately estimate the allele frequencies and diversity [29], which might be difficult to achieve, especially for Dendrobium as it is an endangered species that has been severely damaged. The SNPs produce more precise estimates of the population-level diversity, and they use a higher power to identify the groups in clusters than the methods of using microsatellites do [30]. To evaluate and quantify the potential marker-specific biases, researchers have compared the microsatellite variation with genome-wide SNPs. They concluded that a few thousand random SNPs are sufficient to accurately estimate the genome-wide diversity and to distinguish between the populations with different levels of genetic variation [31].
Currently, with the development of high-throughput sequencing technology, SNPs are increasingly used in species identification and population genetic research [32]. Compared with traditional molecular markers, the SNPs that are obtained by high-throughput sequencing can provide a large amount of accurate and reliable information for a genetic and evolutionary analysis [33]. Reduced representation genome sequencing has been widely used in the field of population research in areas such as population genetic analysis [34], marker development [35], genetic map construction and whole-genome association analysis [36,37]. These methods, which consist of restriction site-associated DNA sequencing (RAD-seq), have been successfully applied to the study of the population structure, genetic distance, and genetic diversity of many species [38,39,40,41]. Currently, high-quality D. catenatum genome data have been obtained and published [42]. Therefore, the RAD-seq of this species of medicinal Dendrobium can provide a large number of reliable SNPs to distinguish the provenances of them.
To protect the wild population of D. catenatum and meet the increasing market demand, the following protective measures are recommended: (a) in situ conservation; (b) building an ex situ conservation base; (c) establishing a provenance database to strictly control the use of wild populations while ensuring the reliability and detectability of their provenance. For the creation of management and conservation strategies, information on the genetic diversity and population structure are crucial. To better understand the genetic diversity, genetic organization, and divergence of the wild populations of D. catenatum, we produced and analyzed the SNPs for these populations using RAD-seq. Accurately identifying the provenances of D. catenatum based on RAD-seq will help growers to choose high-quality provenances for artificial cultivation and propagation. This research not only has important significance for the evolution, molecular breeding and biogeography of Dendrobium, but it also provides important enlightenment for the identification methods of other medicinal plants.

2. Materials and Methods

2.1. Sample Collection and DNA Extraction

The plant materials that were sequenced in this study were collected from the National Orchid Conservation & Research Center of Shenzhen, and the specimens were deposited in the National Orchid Conservation Center herbarium (NOCC) (Table 1). All of the plant materials were collected with the permission of this institution, and all of the samples were identified by Prof. Zhong-Jian Liu. We complied with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna. Detailed information about localities and samples are given in Table 1 and Figure 1. Specifically, we collected 109 samples were from 24 wild populations of D. catenatum, and 10 samples were from 2 wild populations of D. huoshanense. We selected 3–5 individuals from each population and sampled their leaves. The leaves were dried in sealed plastic bags that were filled with silica gel until the DNA extraction was performed.
The total genomic DNA was extracted using a Plant Genomic DNA kit (Tiangen, Beijing, China) according to the manufacturer’s protocol. For all of the samples, the DNA was quantified using a Qubit spectrophotometer (Invitrogen, Carlsbad, CA, USA).

2.2. RAD Library Development and Sequencing

The RAD sequencing libraries were generated using the VAHTS Universal DNA Library Prep Kit for Illumina (Vazyme Biotech Co., Ltd., Nanjing, China, ND604) following the manufacturer’s recommendations. In brief, the RAD-seq reduced representation libraries were prepared following the digestion procedure using the Hae III (New England Biolabs, Ipswich, MA, USA) enzyme, which was followed by a barcode ligation, a DNA purification and a selective DNA amplification and a size selection. Pair-end sequencing with a read length of 150 bp was performed to produce approximately 3 Gb of raw data for each sample using the Illumina HiSeq 4000 platform (Illumina, San Diego, CA, USA) at Novogene (Beijing, China).

2.3. SNP Calling

The raw Illumina paired-end reads were filtered using a personally designed program “filter-G 30.0-adapter 1-poly_A 1-Q 20,0.5-mean 25-insert $ insertSize-Q20 90” to discard the reads with adapter sequences, poly_A tails, a poor base quality (Q < 20), and those that were less than 25 bp in length. The cleaned reads were aligned to the D. catenatum genome (NCBI accession number NC_037361.1) using BWA (version 0.7.12) [43] default parameters, and they were sorted using Samtools (version 1.9), and following this, they were de-duplicated using the Picard tool [44]. In order to reduce the impact of mapping bias, we further excluded the sites with extraordinarily high or extremely low coverage. The RealignerTargetCreator and IndelRealigner modules from GATK (version 3.8) [45] were used to improve the local alignments around the indels. The resulting alignment files were subjected to genotyping using the GATK UnifiedGenotyper at each reference locus. Finally, hard filtering was applied to the raw variant set using GATK recommended parameters, “QD < 2.0 || MQ < 40.0 || FS > 60.0 || SOR > 3.0 || MQRankSum < −12.5 || ReadPosRankSum < −8.0”. We further filtered the dataset using VCFtools (version 0.1.13) [46] to ensure that we had high-quality SNPs, removing the SNPs with a missing rate > 20% and minor allele frequencies < 0.02.

2.4. Phylogenetic Tree Construction

The maximum likelihood (ML) analysis was performed using IQtree (version 2.0.3) [47] with 1000 bootstrap [48] replicates, and the settings were as described. The results were graphically visualized and edited in FigTree (version 1.4.2).

2.5. Population Structure Analyses

Genetic structure was investigated by a PCA. A PCA was conducted using GATK, and the program Admixture [49] was used to infer the genotype structure. A population number (K) ranging from 2 to 8 was assumed, and the CV scores were used to determine the best-fit K value.

2.6. Genetic Diversity and Differentiation

A variety of genetic diversity indices were calculated using the Stacks’ populations program, including the private allele number (AP), nucleotide diversity (π), heterozygosity (HO and HE), and inbreeding coefficient (FIS) [50]. Sliding windows that were 10 kb in size were used to calculate Tajima’s D by VCFtools (version 0.1.13) [46]. To analyze the pairwise population differentiation between the wild germplasms, the FST values were also computed using the PopGenome package in R [51]. The pairwise geographic distances from longitude and latitude were identified using the R package geosphere. We used the Mantel test for the associations between the FST and geographic distance. PopLDdecay (version 3.31) was used to calculate the linkage disequilibrium between the SNP pairs within a 500 kb window [52]. The linkage disequilibrium decay was measured the distance at which the Pearson’s correlation efficient (r2) dropped to half of the maximum.

3. Results

3.1. Sequence Data Quality

For the 109 sequenced samples, 403.61 Gb of raw data with an average of 3.70 Gb per individual were generated, ranging from 1.74 to 5.29 Gb. After filtering the sequence data, a total of 371.18 Gb of clean data (1.61 Gb to 4.89 Gb for each individual, with an average of 3.37 Gb) was maintained, presenting an average effective rate of 91.97% (Supplementary Figure S1). In short, the sequencing data were of high quality and could be used for a subsequent analysis. Finally, 655,057 high-quality SNPs were selected after the filtration was performed. Nearly half of the SNPs in the population level exhibited base transitions, and the total transition-to-transversion (ts/tv) ratio was 1.36.

3.2. Genetic Diversity

When we were analyzing the variant positions for all of the polymorphic loci at the germplasm level, the observed heterozygosity (HO), expected heterozygosity (HE), nucleotide diversity (π) and Wright’s F-statistic (FIS) of the wild D. catenatum were 0.0992, 0.1575, 0.1584, and 0.3836, respectively (Table 1). The number of private alleles (AP) in the populations ranged from 1934 (FJLCTP02) to 9655 (GZDSTP30). The observed heterozygosity at the population level ranged from 0.0724 (GXXATP10) to 0.1516 (GZDSTP30); the expected heterozygosity for each population ranged from 0.0833 (FJLCTP02) to 0.1312 (GXLPTP07); the nucleotide diversity for each population ranged from 0.0961 (GDSGTP22) to 0.1484 (GXLPTP07); the inbreeding coefficient in each population ranged from −0.0434 (GZDSTP30) to 0.1135 (GXLPTP07).
Tajima’s D value was a locus-based indicator of the intraspecific polymorphism. Tajima’s D values were positive and statistically different from zero for all of the populations (Table 2). It is therefore possible to reject the null hypothesis of neutral evolution. In addition, many intermediate-frequency alleles were found in the populations, which may be a consequence of the bottleneck effects, the population structures, or the selection for balancing.

3.3. Phylogenetic Tree

In the maximum likelihood (ML) phylogenetic tree that was derived from our finalized RAD-seq matrix, D. huoshanense was the outgroup (Figure 2). There were high bootstrap values on most of the branches of the phylogenetic tree, which indicated a strong reliability of the tree. All of the samples were separated into two clades based on the ML tree (Group I and Group II). Group I mainly contained D. catenatum from the Yungui Plateau and the western part of the Nanling Mountains, while Group II mainly contained populations from the Yandangshan Mountains, Wuyishan Mountains, and the eastern part of the Nanling Mountains.
In the Group I, the YNQJTP24 accessions were the sister group to the WZSTP34 accessions, which both had 100% bootstrap values. The samples from Guizhou Dushan (GZDSTP30), Guangxi Xing’an (GXXATP10) and Hunan Yiyang (HNYYTP06) are clustered into a branch. The samples from the provenances GZSDTP29, GXGCTP09, and GXLCTP07 were divided into two branches. In the Group II, for most of the provenances of D. catenatum, the individuals of the same provenance are clustered together with high support values, with the exception of the individuals of three populations from Zhejiang Wuyi (ZJWYTP32), Zhejiang Lishui (ZJLSTP12), and Zhejiang Quzhou (ZJQZTP13). The samples from Zhejiang were gathered into a large branch, and the sister branch was composed of FJGZTP01, FJLCTP02, and JXQSTP14. The populations from Hubei Xinning (HBXNTP25), Jiangxi Xiushui (JXXSTP15), Hunan Pingjiang (HNPJTP04), and Hunan Yiyang (HNYYTP05) are clustered into a large branch, and the other three provenances being independently clustered into a small branch, with the exception of JXXSTP15. The samples from Guangdong Shaoguan (GDXGTP22) and Guangdong Heyuan (GDHYTP20) are clustered into a branch. At the same time, the distribution of the D. catenatum samples in the phylogenetic tree are closely related to their geographical locations. The base of the phylogenetic tree is represented by the population that can be located in the west.
The geographical distribution of the Dendrobium samples from the west to the east is roughly consistent with their distribution from the base to the top of the phylogenetic tree.

3.4. Population Structure

Based on the ML tree (Figure 2), we calculated the population structure with K values that range from two to eight (Figure 3). In the clustering analysis using the Admixture software, K = 2 was the most likely genetic cluster number, because its cross-validation error (CV) was the lowest. However, the CV values were very close when K = 3 and K = 4. According to the principal component analysis (Figure 4), when the K value equaled two, 109 materials were not clearly assigned to the two groups, which was inconsistent with the results of the phylogenetic tree analysis. Due to the differences in the population structure and phylogenetic tree results, it was necessary to analyze the population structure under different K values.
When K = 2, the Group I and Group II accessions could be separated. When K = 3, the Yunnan populations in Group I were distinguished, which was inconsistent with K = 4. In conjunction with the phylogenetic tree and structure analysis, these results clearly classified D. catenatum into two groups.

3.5. Genetic Relationships

A principal component analysis (PCA) was performed with 109 individuals. According to the first component, the accessions were divided into two groups: Group I and Group II. Overall, GXXATP10 was clustered separately, and other D. catenatum accessions could be divided into two groups. Among them, the distribution of Group II was more concentrated (Figure 4).
The linkage disequilibrium (LD) decay, which was measured by the physical distance, at which the pairwise correlation coefficient dropped to half of its maximum value, occurred at 48.2 kb in Group I (r2 = 0.277) and 13.6 kb in Group II (r2 = 0.242) (Figure 5), respectively. Group II had the higher linkage disequilibrium decay rate.

3.6. Genetic Differentiation

The pairwise FST values between the wild populations of D. catenatum varied from 0.0533 (HNYLTP06 with GXGCTP09) to 0.4755 (FJLCTP02 with GZDSTP30), with 110 of the 276 population pairs having FST values that are greater than 0.25 (Figure 6). It became clear that, GXXATP10 had the highest genetic differentiation from the other 24 populations which was consistent with the PCA results (Figure 4). GXLPTP07 was in the western part of the Nanling Mountains, and it obtained the lowest mean pairwise FST value, 0.1504; low values represent the genetic flow between the populations and show a greater genetic difference between the accessions within a population than it does between the populations. The pairwise genetic distance between the locations FST was highly correlated with the geographic distance (Figure 7); the level of significance was p < 0.05.

4. Discussion

Genomic data provide a novel perspective for studying the genetic diversity and method of identification of medicinal Dendrobium species. In the past, scholars have been authenticated eight wild populations of D. officinale by ISSR [26]. Because of the low number of microsatellite markers, marker ascertainment bias, and there being a high variance in the microsatellite-derived estimates, the genetic differentiation among the populations (FST) estimates of microsatellites were significantly larger than those from the SNPs [31]. The simple PCR methods are simple and quite effective and cheap. However, their weakness is obvious at the same time. Firstly, these data are not reproducible enough, and it is difficult to share and reference them, and they can only be analyzed and used in the same experimental project. Secondly, these markers cannot be mapped to the genome for further analysis and applications. The data that are obtained by RAD not only have the advantages of being convenient and highly reproducible, but also, they can be accurately located in the genome position for more applications. For example, the candidate genes with known functions can be identified by annotating the outlier loci through the selection elimination analysis [53,54]. At the same time, the SNPs that are obtained by RAD-seq can provide more accurate and reliable information for a genetic and evolutionary analysis. Researchers utilized anadromous pike (Esox lucius) to assess the microsatellite and RAD-seq results of a study of population differentiation and genetic structure, and they discovered that the full RAD-seq dataset can provide the most accurate detection of the finer-scaled genetic structuring [55]. The nuclear microsatellites and the RAD-seq data for a threatened freshwater fish species were compared by other researchers. The results showed that RAD-seq more clearly and consistently identified the hierarchical phylogenetic structure [56].
In this research, a whole-genome restriction enzyme digestion of 109 accessions from 24 provenances was performed to obtain accurate variation information. Except for GXGCTP09, the clean data size of each sample in the other 23 populations was at least 2 times the D. catenatum genome size (1.11 Gb), and some individual populations reached 4 times this (Supplementary Figure S1). The average sequencing volume of each sample was 3.37 Gb. The amount of sequencing data was sufficient to meet the requirements of the subsequent analysis, thus ensuring the accuracy of population genetic analysis. In terms of the result of the RAD-seq, we detected 655,057 SNPs, which exceeded the number of genetic variations that were detected by amplified fragment length polymorphism (AFLP) and the random amplified polymorphic DNA (RAPD) markers [57,58]. These data not only can be used in this experimental analysis, but they are also convenient for researchers for other studies.
The number of transitions was predicted to be much larger than the number of transversions due to the biased mutational processes within the plant genomes. This nucleotide mutation pattern is also observed in other plants, such as peanuts [59], maize [60], Amorphophallus paeoniifolius [39], Arabidopsis, and apricots [61,62]. Consistent with this prediction, the ts/tv ratio of the D. catenatum populations was 1.36, indicating that there was a strong transition bias. This value was higher than the result (1.34) that was reported by Zhang et al. [42], and lower than that for D. huoshanense (1.47) [63].
Based on the ML tree, the principal component analysis, the genetic structure analysis, and the population differentiation analysis divided the 24 wild populations of D. catenatum into two groups. The two groups where the provenances of the wild D. catenatum are arranged from the west to the east are very consistent with the geographical distribution of the samples. In another study, Ding et al. used RAPD to study eight wild D. catenatum populations [58]. Among them, Shaowu and Shunchang in Fujian were clustered into a small branch, which was the sister branch of Jiangxi Nanfeng, and Tian’e in Guangxi and Guangnan in Yunnan were clustered into a small group. Our results in this study are consistent with the previous findings. In a systematic geographical study of D. catenatum and four related taxa, the regional evolution of D. catenatum was divided into six populations [64]. They were the South Yungui Plateau, the East Yungui Plateau, Nanling Mountain, Wuyishan Mountain, Dabieshan Mountain, and Yandang Mountain. In this study, Group I contained the Yungui Plateau and the western part of the Nanling Mountains, while Group II contained the Yandangshan Mountains, Wuyishan Mountains, and the eastern part of the Nanling Mountains. This is largely consistent with the division that had been made previously.
Genetic diversity forms during the evolution of a species, and it often plays a key role in the gradual evolution and long-term survival of the species in a changeable environment. The average nucleotide diversity (π) of the D. catenatum population mutation parameters was 0.1584 at the population level (Table 2). D. catenatum had a smaller mean nucleotide variation than other crops did such as A. paeoniifolius (π = 0.3592) [39]. At the population level, the expected heterozygosity (HE) was 0.1575, which was very close to the previous results (0.1477) [57]. Overall, the observed heterozygosity and the expected heterozygosity were relatively low.
Among all of the wild populations, GXLPTP07 had the most significant private allele number (AP) of 5221, and this indicated the presence of substantial genetic variation, which could guide genetic improvement in the future. The genetic diversity indices (HE, AP, and π) of the GXLPTP07 accessions were higher than those of the accessions in the other ecological groups (Table 2). We speculate that the GXLPTP07 was closer to the origin center of Group I in terms of genetic distance. In the principal component analysis, GXXATP10 was clustered separately. Combined with the analysis of genetic diversity, GXXATP10 had the highest Tajima’s D values, indicating that the population underwent a balanced selection or a sudden contraction. The results of the HO, HE, and inbreeding coefficient (FIS) show that HO < HE, FIS > 0, which may have resulted from the heterozygote loss and inbreeding, and the selection pressure is high (Figure 3, Table 2). Based on PC1, we speculate that GXXATP10 was a descendant that was formed by the intersection of the East Yungui Plateau population and the western part of the Nanling Mountains population in the past, or a nonnative species that had been introduced from another place.
The Mantel test discovered a significant positive relationship between the genetic and geographical distances in the total distribution (r = 0.47, p < 2.2 × 10−16), suggesting that isolation by distance plays an important role in genome-wide variation. Combined with the results of FST, the phylogenetic tree and the Linkage disequilibrium analysis speculated that the Group I population is closer to the origin center in terms of its genetic distance, which supports that it originated in the Nanling Mountains and the Yungui Plateau before migrating eastward [64].
In the wild, Dendrobium species are either epiphytic or lithophytic. Based on this habit, there is no need to compete for the niche of most terrestrial plants. However, people over-excavate and conduct commercial trade for profit, thus causing serious damage to the wild resources [65]. This leads to a decline in the genetic differentiation among the Dendrobium species [66].

5. Conclusions

In this study, a genetic information database of D. catenatum was established, which confirmed that RAD-seq technology has the potential to be applied in the identification of medicinal Dendrobium of different origins. The level of genetic diversity in the population of wild D. catenatum was relatively low. The 24 wild populations were divided into two groups, and the Group I population is closer to the origin center in terms of its genetic distance. The isolation by distance plays an important role in genome-wide variation. This study provides information for the development of identification technology for medicinal Dendrobium species in order to provide a new perspective for the research of genuine regional drugs. Genetic diversity research provides a theoretical basis for the protection of Dendrobium, and it also lays a solid foundation for the breeding of fine Dendrobium provenances. Overall, our study provides abundant genomic resources for wild Dendrobium species and makes important contributions to its genetic improvement and molecular breeding.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes13112093/s1, Figure S1: Average clean data size per sample in 24 populations.

Author Contributions

Conceptualization, X.-Y.W. and J.-X.H.; data curation, X.-Y.W., T.-Z.L. and F.Z.; writing—original draft, X.-Y.W.; writing—review and editing, Y.-H.Y. and J.-X.H.; supervision, J.-B.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Department of Wildlife Conservation, National Forestry and Grassland Administration, China (15 June 2018).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All genomic data generated in this study are deposited in NCBI database (https://www.ncbi.nlm.nih.gov/) (BioProject accession: PRJNA774562). These data will remain private until the related manuscript has been accepted. All other data generated in this manuscript are available from the corresponding author upon reasonable request.

Acknowledgments

We would like to thank Zhong-Jian Liu at the Key Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization at College of Landscape Architecture, Fujian Agriculture and Forestry University, for identifying the samples in the present study and other help.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cribb, P.; Govaerts, R. Just how many orchids are there? In Proceedings of the 18th World Orchid Conference, Dijon, France, 11–20 March 2005; Rayanl-Roques, A., Roguenant, A., Prat, D., Eds.; Naturalia: Dijon, France, 2005; pp. 161–172. [Google Scholar]
  2. Pridgeon, A.M.; Cribb, P.J.; Chase, M.W.; Rasmussen, F.N. Genera Orchidacearum Volume 6: Epidendroideae (Part 3); OUP: Oxford, UK, 2014; ISBN 978-0-19-100389-9. [Google Scholar]
  3. Chinese Pharmacopoeia Commission. Pharmacopoeia of the People’s Republic of China; Chinese Medicine Science and technology Publishing House: Beijing, China, 2020.
  4. Bao, X.S.; Shun, Q.S.; Chen, L.Z. The Medicinal Plants of Dendrobium (Shi-Hu) in China; Shanghai Medical University Press, Fudan University Press: Shanghai, China, 2001; pp. 56–60. [Google Scholar]
  5. He, T.G.; Yang, L.T.; Li, Y.R.; Wang, C.Q.; Huang, D.P. Physicochemical properties and antitumor activity of polysaccharide dcpp1a-1 from suspension-cultured protocorms of Dendrobium candidum. Nat. Prod. Res. Dev. 2007, 578–583. [Google Scholar] [CrossRef]
  6. Liu, X.-F.; Zhu, J.; Ge, S.-Y.; Xia, L.-J.; Yang, H.-Y.; Qian, Y.-T.; Ren, F.-Z. Orally Administered Dendrobium Officinale and Its Polysaccharides Enhance Immune Functions in BALB/c Mice. Nat. Prod. Commun. 2011, 6, 867–870. [Google Scholar] [CrossRef] [Green Version]
  7. Huang, K.; Li, Y.; Tao, S.; Wei, G.; Huang, Y.; Chen, D.; Wu, C. Purification, Characterization and Biological Activity of Polysaccharides from Dendrobium Officinale. Molecules 2016, 21, 701. [Google Scholar] [CrossRef] [Green Version]
  8. Zha, X.Q.; Luo, J.P.; Luo, S.Z.; Jiang, S.T. Structure Identification of a New Immunostimulating Polysaccharide from the Stems of Dendrobium Huoshanense. Carbohydr. Polym. 2007, 69, 86–93. [Google Scholar] [CrossRef]
  9. Zhang, J.; Guo, Y.; Si, J.-P.; Sun, X.-B.; Sun, G.-B.; Liu, J.-J. A Polysaccharide of Dendrobium Officinale Ameliorates H2O2-Induced Apoptosis in H9c2 Cardiomyocytes via PI3K/AKT and MAPK Pathways. Int. J. Biol. Macromol. 2017, 104, 1–10. [Google Scholar] [CrossRef] [PubMed]
  10. Tian, C.-C.; Zha, X.-Q.; Luo, J.-P. A Polysaccharide from Dendrobium Huoshanense Prevents Hepatic Inflammatory Response Caused by Carbon Tetrachloride. Biotechnol. Biotechnol. Equip. 2015, 29, 132–138. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Zeng, Q.; Ko, C.-H.; Siu, W.-S.; Li, K.-K.; Wong, C.-W.; Han, X.-Q.; Yang, L.; Lau, C.B.-S.; Hu, J.-M.; Leung, P.-C. Inhibitory Effect of Different Dendrobium Species on LPS-Induced Inflammation in Macrophages via Suppression of MAPK Pathways. Chin. J. Nat. Med. 2018, 16, 481–489. [Google Scholar] [CrossRef]
  12. Zhang, Q.; Li, J.; Luo, M.; Xie, G.-Y.; Zeng, W.; Wu, Y.; Zhu, Y.; Yang, X.; Guo, A.-Y. Systematic Transcriptome and Regulatory Network Analyses Reveal the Hypoglycemic Mechanism of Dendrobium Fimbriatum. Mol. Ther.-Nucleic Acids 2020, 19, 1–14. [Google Scholar] [CrossRef]
  13. Pan, L.-H.; Li, X.-F.; Wang, M.-N.; Zha, X.-Q.; Yang, X.-F.; Liu, Z.-J.; Luo, Y.-B.; Luo, J.-P. Comparison of Hypoglycemic and Antioxidative Effects of Polysaccharides from Four Different Dendrobium Species. Int. J. Biol. Macromol. 2014, 64, 420–427. [Google Scholar] [CrossRef]
  14. Si, J.-P.; Wang, Q.; Liu, Z.-J.; Liu, J.-J.; Luo, Y.-B. Breakthrough in key science and technologies in Dendrobium catenatum industry. China J. Chin. Mater. 2017, 42, 2223–2227. [Google Scholar] [CrossRef]
  15. Lam, Y.; Ng, T.B.; Yao, R.M.; Shi, J.; Xu, K.; Sze, S.C.W.; Zhang, K.Y. Evaluation of Chemical Constituents and Important Mechanism of Pharmacological Biology in Dendrobium Plants. Evid.-Based Complement. Altern. Med. 2015, 2015, e841752. [Google Scholar] [CrossRef] [Green Version]
  16. Ma, G.; Guo, Y.; Xu, G.; Xu, L.; An, D. Microscopic ldentification Studies on Dendrobium Stems By Clustering Analysis. J. China Pharm. Univ. 1996, 27, 208–210. [Google Scholar]
  17. Zha, X.-Q.; Pan, L.-H.; Luo, J.-P.; Wang, J.-H.; Wei, P.; Bansal, V. Enzymatic Fingerprints of Polysaccharides of Dendrobium Officinale and Their Application in Identification of Dendrobium Species. J. Nat. Med. 2012, 66, 525–534. [Google Scholar] [CrossRef] [PubMed]
  18. Wei, G.; Shun, Q.; Huang, Y.; Yang, M.; Chen, L.; Li, M.; Ye, J.; Wang, H.; Jiang, R.; He, Z. Comparative study of hplc characteristic spectrum of Dendrobium officinale from three provenance sources. Tradit. Chin. Drug Res. Clin. Pharmacol. 2014, 25, 467–471. [Google Scholar]
  19. Huang, L.; Zhang, X.; Chen, S. Research Progress in Quality Ecology of Genuine Regional Drugs. Mod. Tradit. Chin. Med. Mater. Med.-World Sci. Technol. 2019, 21, 844–853. [Google Scholar] [CrossRef]
  20. Xia, B.; Zhou, Y.; Tan, H.S.; Ding, L.S.; Xu, H.X. Advanced Ultra-Performance Liquid Chromatography–Photodiode Array–Quadrupole Time-of-Flight Mass Spectrometric Methods for Simultaneous Screening and Quantification of Triterpenoids in Poria Cocos. Food Chem. 2014, 152, 237–244. [Google Scholar] [CrossRef]
  21. Tan, H.-S.; Hu, D.-D.; Song, J.-Z.; Xu, Y.; Cai, S.-F.; Chen, Q.-L.; Meng, Q.-W.; Li, S.-L.; Chen, S.-L.; Mao, Q.; et al. Distinguishing Radix Angelica Sinensis from Different Regions by HS-SFME/GC–MS. Food Chem. 2015, 186, 200–206. [Google Scholar] [CrossRef]
  22. Zhang, X.; Gu, C.; Ahmad, B.; Huang, L. Optimization of Extract Method for Cynomorium Songaricum Rupr. by Response Surface Methodology. J. Anal. Methods Chem. 2017, 2017, 6153802. [Google Scholar] [CrossRef] [Green Version]
  23. Ding, X.; Wang, Z.; Xu, H.; Xu, L.; Zhou, K. Database establishment of the whole rDNA ITS region of Dendrobium species of “fengdou” and authentication by analysis of their sequences. Acta Pharm. Sin. B. 2002, 37, 567–573. [Google Scholar]
  24. Asahina, H.; Shinozaki, J.; Masuda, K.; Morimitsu, Y.; Satake, M. Identification of Medicinal Dendrobium Species by Phylogenetic Analyses Using matK and rbcL Sequences. J. Nat. Med. 2010, 64, 133–138. [Google Scholar] [CrossRef]
  25. Liu, H.; Fang, C.; Zhang, T.; Guo, L.; Ye, Q. Molecular Authentication and Differentiation of Dendrobium Species by rDNA ITS Region Sequence Analysis. AMB Expr. 2019, 9, 53. [Google Scholar] [CrossRef] [PubMed]
  26. Shen, J.; Ding, X.; Liu, D.; Ding, G.; He, J.; Li, X.; Tang, F.; Chu, B. Intersimple Sequence Repeats (ISSR) Molecular Fingerprinting Markers for Authenticating Populations of Dendrobium Officinale Kimura et Migo. Biol. Pharm. Bull. 2006, 29, 420–422. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Ding, G.; Xu, G.; Zhang, W.; Lu, S.; Li, X.; Gu, S.; Ding, X.-Y. Preliminary Geoherbalism Study of Dendrobium Officinale Food by DNA Molecular Markers. Eur. Food Res. Technol. 2008, 227, 1283–1286. [Google Scholar] [CrossRef]
  28. Hodel, R.G.J.; Chen, S.; Payton, A.C.; McDaniel, S.F.; Soltis, P.; Soltis, D.E. Adding Loci Improves Phylogeographic Resolution in Red Mangroves despite Increased Missing Data: Comparing Microsatellites and RAD-Seq and Investigating Loci Filtering. Sci. Rep. 2017, 7, 17598. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Hale, M.L.; Burg, T.M.; Steeves, T.E. Sampling for Microsatellite-Based Population Genetic Studies: 25 to 30 Individuals per Population Is Enough to Accurately Estimate Allele Frequencies. PLoS ONE 2012, 7, e45170. [Google Scholar] [CrossRef]
  30. Zimmerman, S.J.; Aldridge, C.L.; Oyler-McCance, S.J. An Empirical Comparison of Population Genetic Analyses Using Microsatellite and SNP Data for a Species of Conservation Concern. BMC Genom. 2020, 21, 382. [Google Scholar] [CrossRef]
  31. Fischer, M.C.; Rellstab, C.; Leuzinger, M.; Roumet, M.; Gugerli, F.; Shimizu, K.K.; Holderegger, R.; Widmer, A. Estimating Genomic Diversity and Population Differentiation—An Empirical Comparison of Microsatellite and SNP Variation in Arabidopsis Halleri. BMC Genom. 2017, 18, 69. [Google Scholar] [CrossRef] [Green Version]
  32. Shendure, J.; Ji, H. Next-Generation DNA Sequencing. Nat. Biotechnol. 2008, 26, 1135–1145. [Google Scholar] [CrossRef]
  33. Ganal, M.W.; Altmann, T.; Roder, M.S. SNP Identification in Crop Plants. Curr. Opin. Plant Biol. 2009, 12, 211–217. [Google Scholar] [CrossRef]
  34. Ma, B.; Liao, L.; Peng, Q.; Fang, T.; Zhou, H.; Korban, S.S.; Han, Y. Reduced Representation Genome Sequencing Reveals Patterns of Genetic Diversity and Selection in Apple. J. Integr. Plant Biol. 2017, 59, 190–204. [Google Scholar] [CrossRef]
  35. Davey, J.W.; Hohenlohe, P.A.; Etter, P.D.; Boone, J.Q.; Catchen, J.M.; Blaxter, M.L. Genome-Wide Genetic Marker Discovery and Genotyping Using next-Generation Sequencing. Nat. Rev. Genet. 2011, 12, 499–510. [Google Scholar] [CrossRef] [PubMed]
  36. Andolfatto, P.; Davison, D.; Erezyilmaz, D.; Hu, T.T.; Mast, J.; Sunayama-Morita, T.; Stern, D.L. Multiplexed Shotgun Genotyping for Rapid and Efficient Genetic Mapping. Genome Res. 2011, 21, 610–617. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Liu, N.; Li, M.; Hu, X.; Ma, Q.; Mu, Y.; Tan, Z.; Xia, Q.; Zhang, G.; Nian, H. Construction of High-Density Genetic Map and QTL Mapping of Yield-Related and Two Quality Traits in Soybean RILs Population by RAD-Sequencing. BMC Genom. 2017, 18, 466. [Google Scholar] [CrossRef] [PubMed]
  38. Valdisser, P.A.M.R.; Pappas, G.J.; de Menezes, I.P.P.; Müller, B.S.F.; Pereira, W.J.; Narciso, M.G.; Brondani, C.; Souza, T.L.P.O.; Borba, T.C.O.; Vianello, R.P. SNP Discovery in Common Bean by Restriction-Associated DNA (RAD) Sequencing for Genetic Diversity and Population Structure Analysis. Mol. Genet. Genom. 2016, 291, 1277–1291. [Google Scholar] [CrossRef] [PubMed]
  39. Gao, Y.; Yin, S.; Wu, L.; Dai, D.; Wang, H.; Liu, C.; Tang, L. Genetic Diversity and Structure of Wild and Cultivated Amorphophallus Paeoniifolius Populations in Southwestern China as Revealed by RAD-Seq. Sci. Rep. 2017, 7, 14183. [Google Scholar] [CrossRef] [Green Version]
  40. Hirase, S.; Tezuka, A.; Nagano, A.J.; Kikuchi, K.; Iwasaki, W. Genetic Isolation by Distance in the Yellowfin Goby Populations Revealed by RAD Sequencing. Ichthyol. Res. 2020, 67, 98–104. [Google Scholar] [CrossRef]
  41. Martin Cerezo, M.L.; Kucka, M.; Zub, K.; Chan, Y.F.; Bryk, J. Population Structure of Apodemus Flavicollis and Comparison to Apodemus Sylvaticus in Northern Poland Based on RAD-Seq. BMC Genom. 2020, 21, 241. [Google Scholar] [CrossRef] [Green Version]
  42. Zhang, G.-Q.; Xu, Q.; Bian, C.; Tsai, W.-C.; Yeh, C.-M.; Liu, K.-W.; Yoshida, K.; Zhang, L.-S.; Chang, S.-B.; Chen, F.; et al. The Dendrobium Catenatum Lindl. Genome Sequence Provides Insights into Polysaccharide Synthase, Floral Development and Adaptive Evolution. Sci. Rep. 2016, 6, 19029. [Google Scholar] [CrossRef] [Green Version]
  43. Li, H.; Durbin, R. Fast and Accurate Short Read Alignment with Burrows–Wheeler Transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef] [Green Version]
  44. McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M.; et al. The Genome Analysis Toolkit: A MapReduce Framework for Analyzing next-Generation DNA Sequencing Data. Genome Res. 2010, 20, 1297–1303. [Google Scholar] [CrossRef] [Green Version]
  45. Van der Auwera, G.A.; Carneiro, M.O.; Hartl, C.; Poplin, R.; del Angel, G.; Levy-Moonshine, A.; Jordan, T.; Shakir, K.; Roazen, D.; Thibault, J.; et al. From FastQ Data to High-Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline. Curr. Protoc. Bioinform. 2013, 43, 11.10.1–11.10.33. [Google Scholar] [CrossRef] [Green Version]
  46. Danecek, P.; Auton, A.; Abecasis, G.; Albers, C.A.; Banks, E.; DePristo, M.A.; Cornelis, A.; Handsaker, R.E.; Lunter, G.; Marth, G.T.; et al. The Variant Call Format and VCFtools. Bioinformatics 2011, 27, 2156–2158. [Google Scholar] [CrossRef] [PubMed]
  47. Nguyen, L.-T.; Schmidt, H.A.; von Haeseler, A.; Minh, B.Q. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Mol. Biol. Evol. 2015, 32, 268–274. [Google Scholar] [CrossRef] [PubMed]
  48. Minh, B.Q.; Nguyen, M.A.T.; von Haeseler, A. Ultrafast Approximation for Phylogenetic Bootstrap. Mol. Biol. Evol. 2013, 30, 1188–1195. [Google Scholar] [CrossRef] [Green Version]
  49. Alexander, D.H.; Novembre, J.; Lange, K. Fast Model-Based Estimation of Ancestry in Unrelated Individuals. Genome Res. 2009, 19, 1655–1664. [Google Scholar] [CrossRef] [Green Version]
  50. Catchen, J.; Hohenlohe, P.A.; Hohenlohe, P.A.; Bassham, S.; Amores, A.; Cresko, W.A. Stacks: An Analysis Tool Set for Population Genomics. Mol. Ecol. 2013, 22, 3124–3140. [Google Scholar] [CrossRef] [Green Version]
  51. Pfeifer, B.; Ulrich Wittelsbürger; Ramos-Onsins, S.E.; Lercher, M.J. PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses in R. Mol. Biol. Evol. 2014, 31, 1929–1936. [Google Scholar] [CrossRef] [Green Version]
  52. Chi, Z.; Shan-Shan, D.; Jun-Yang, X.; Wei-Ming, H.; Tie-Lin, Y. PopLDdecay: A Fast and Effective Tool for Linkage Disequilibrium Decay Analysis Based on Variant Call Format Files. Bioinformatics 2018, 35, 1786–1788. [Google Scholar] [CrossRef]
  53. Bay, R.A.; Harrigan, R.J.; Underwood, V.L.; Gibbs, H.L.; Smith, T.B.; Ruegg, K. Genomic Signals of Selection Predict Climate-Driven Population Declines in a Migratory Bird. Science 2018, 359, 83–86. [Google Scholar] [CrossRef] [Green Version]
  54. Xie, H.; Hou, J.; Fu, N.; Wei, M.; Li, Y.; Yu, K.; Song, H.; Li, S.; Liu, J. Identification of QTL Related to Anther Color and Hull Color by RAD Sequencing in a RIL Population of Setaria Italica. BMC Genom. 2021, 22, 556. [Google Scholar] [CrossRef]
  55. Sunde, J.; Yıldırım, Y.; Tibblin, P.; Forsman, A. Comparing the Performance of Microsatellites and RADseq in Population Genetic Studies: Analysis of Data for Pike (Esox Lucius) and a Synthesis of Previous Studies. Front. Genet. 2020, 11, 218. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Bohling, J.; Small, M.; Von Bargen, J.; Louden, A.; DeHaan, P. Comparing Inferences Derived from Microsatellite and RADseq Datasets: A Case Study Involving Threatened Bull Trout. Conserv. Genet. 2019, 20, 329–342. [Google Scholar] [CrossRef]
  57. Li, X.; Ding, X.; Chu, B.; Zhou, Q.; Ding, G.; Gu, S. Genetic Diversity Analysis and Conservation of the Endangered Chinese Endemic Herb Dendrobium Officinale Kimura et Migo (Orchidaceae) Based on AFLP. Genetica 2008, 133, 159–166. [Google Scholar] [CrossRef]
  58. Ding, G.; Ding, X.; Shen, J.; Tang, F.; Liu, D.; He, J.; Li, X.; Chu, B. Genetic diversity and molecular authentication of wild populations of Dendrobium officinale by RAPD. Yao Xue Xue Bao 2005, 40, 1028–1032. [Google Scholar] [PubMed]
  59. Gupta, S.K.; Baek, J.; Carrasquilla-Garcia, N.; Penmetsa, R.V. Genome-Wide Polymorphism Detection in Peanut Using next-Generation Restriction-Site-Associated DNA (RAD) Sequencing. Mol. Breed. 2015, 35, 145. [Google Scholar] [CrossRef]
  60. Jiao, Y.; Zhao, H.; Ren, L.; Song, W.; Zeng, B.; Guo, J.; Wang, B.; Liu, Z.; Chen, J.; Li, W.; et al. Corrigendum: Genome-Wide Genetic Changes during Modern Breeding of Maize. Nat. Genet. 2014, 46, 1039–1040. [Google Scholar] [CrossRef] [Green Version]
  61. Dillon, M.M.; Sung, W.; Lynch, M.; Cooper, V.S. The Rate and Molecular Spectrum of Spontaneous Mutations in the GC-Rich Multichromosome Genome of Burkholderia Cenocepacia. Genetics 2015, 200, 935–946. [Google Scholar] [CrossRef] [Green Version]
  62. Li, W.; Liu, L.; Wang, Y.; Zhang, Q.; Fan, G.; Zhang, S.; Wang, Y.; Liao, K. Genetic Diversity, Population Structakure, and Relationships of Apricot (Prunus) Based on Restriction Site-Associated DNA Sequencing. Hortic. Res. 2020, 7, 69. [Google Scholar] [CrossRef]
  63. Ye, M.; Wang, X.; Zhou, Y.; Huang, S.; Liu, A. Genetic Diversity and Population Structure of Cultivated Dendrobium Huoshanense (C.Z. Tang et S.J. Cheng) Using SNP Markers Generated from GBS Analysis. Pak. J. Bot 2021, 53, 1683–1690. [Google Scholar] [CrossRef]
  64. Hou, B.; Luo, J.; Zhang, Y.; Niu, Z.; Xue, Q.; Ding, X. Iteration Expansion and Regional Evolution: Phylogeography of Dendrobium Officinale and Four Related Taxa in Southern China. Sci. Rep. 2017, 7, 43525. [Google Scholar] [CrossRef] [Green Version]
  65. Hinsley, A.; de Boer, H.J.; Fay, M.F.; Gale, S.W.; Gardiner, L.M.; Gunasekara, R.S.; Kumar, P.; Masters, S.; Metusala, D.; Roberts, D.L.; et al. A Review of the Trade in Orchids and Its Implications for Conservation. Bot. J. Linn. Soc. 2018, 186, 435–455. [Google Scholar] [CrossRef]
  66. Niu, Z.; Hou, Z.; Wang, M.; Ye, M.; Zhang, B.; Xue, Q.; Liu, W.; Ding, X. A Comparative Plastomics Approach Reveals Available Molecular Markers for the Phylogeographic Study of Dendrobium Huoshanense, an Endangered Orchid with Extremely Small Populations. Ecol. Evol. 2020, 10, 5332–5342. [Google Scholar] [CrossRef] [PubMed]
Genes 13 02093 g001 550
Figure 1. Sampling locations of D. catenatum species used in this study.
Figure 1. Sampling locations of D. catenatum species used in this study.
Genes 13 02093 g001
Genes 13 02093 g002 550
Figure 2. ML phylogenetic tree of the D. catenatum accessions and model-based clustering with K from 2. Numbers near the nodes are bootstrap percentages.
Figure 2. ML phylogenetic tree of the D. catenatum accessions and model-based clustering with K from 2. Numbers near the nodes are bootstrap percentages.
Genes 13 02093 g002
Genes 13 02093 g003 550
Figure 3. Genetic structure of cultivated 109 D. catenatum for K = 2–8 based on the Admixture software (K = 2 with cross validation error is 0.18580, while K = 3 with ross validation error is 0.19141).
Figure 3. Genetic structure of cultivated 109 D. catenatum for K = 2–8 based on the Admixture software (K = 2 with cross validation error is 0.18580, while K = 3 with ross validation error is 0.19141).
Genes 13 02093 g003
Genes 13 02093 g004 550
Figure 4. PCA of 109 samples in D. catenatum. Red shapes indicate Group I, while blue shapes indicate Group II.
Figure 4. PCA of 109 samples in D. catenatum. Red shapes indicate Group I, while blue shapes indicate Group II.
Genes 13 02093 g004
Genes 13 02093 g005 550
Figure 5. Linkage disequilibrium decay patterns of different D. catenatum group.
Figure 5. Linkage disequilibrium decay patterns of different D. catenatum group.
Genes 13 02093 g005
Genes 13 02093 g006 550
Figure 6. Genetic distance (FST) for D. catenatum. The samples from Group I were in red font, and the samples from Group II were in blue font.
Figure 6. Genetic distance (FST) for D. catenatum. The samples from Group I were in red font, and the samples from Group II were in blue font.
Genes 13 02093 g006
Genes 13 02093 g007 550
Figure 7. Relationships between the pairwise genetic distance (FST) and geographic distance with blue bars and shading represent the 95% highest posterior density.
Figure 7. Relationships between the pairwise genetic distance (FST) and geographic distance with blue bars and shading represent the 95% highest posterior density.
Genes 13 02093 g007
Table
Table 1. In this study, the population locations and voucher information of D. catenatum and D. huoshanense.
Table 1. In this study, the population locations and voucher information of D. catenatum and D. huoshanense.
Sample NameLocationSpecimen CodePopulation CodeIndividual Number
D. catenatumGuangze, Fujian ProvinceZ.J. Liu 10572FJGZTP015
Liancheng, Fujian ProvinceZ.J. Liu 10583FJLCTP024
Pingjiang, Hunan ProvinceZ.J. Liu 10580HNPJTP045
Yiyang, Hunan ProvinceZ.J. Liu 10582HNYYTP055
Yuanling, Hunan ProvinceZ.J. Liu 10578HNYLTP065
Xinning, Hunan ProvinceZ.J. Liu 9506HNXNTP363
Lipu, Guangxi ProvinceZ.J. Liu 10574GXLPTP075
Gongcheng, Guangxi ProvinceZ.J. Liu 9497GXGCTP094
Xing’an, Guangxi ProvinceZ.J. Liu 9518GXXATP105
Lishui, Zhengjiang ProvinceZ.J. Liu 9520ZJLSTP125
Wuyi, Zhengjiang ProvinceZ.J. Liu 9511ZJWYTP325
Quzhou, Zhengjiang ProvinceZ.J. Liu 9510ZJQZTP135
Yanshan, Jiangxi ProvinceZ.J. Liu 10576JXQSTP145
Xiushui, Jiangxi ProvinceZ.J. Liu 10573JXXSTP154
Huichang, Jiangxi ProvinceZ.J. Liu 10581JXHCTP165
Xingguo, Jiangxi ProvinceZ.J. Liu 9517JXXGTP174
Nanxiong, Guangdong ProvinceZ.J. Liu 10584GDNXTP195
Heyuan, Guangdong ProvinceZ.J. Liu 10585GDHYTP204
Shaoguan, Guangdong ProvinceZ.J. Liu 9504GDSGTP225
Qujing, Yunnan ProvinceZ.J. Liu 10579YNQJTP244
Wangzishan, Yunnan ProvinceZ.J. Liu 7462WZSTP344
Xianning, Hubei ProvinceZ.J. Liu 9514HBXNTP255
Sandu, Guizhou ProvinceZ.J. Liu 11147GZSDTP294
Dushan, Guizhou ProvinceZ.J. Liu 11148GZDSTP304
D. huoshanenseHuangshan, Anhui ProvinceZ.J. Liu 9508AHHSHS265
Longhushan, Jiangxi Province Z.J. Liu 9500JXLHSHS285
Table
Table 2. The statistical values of genetic diversity within the populations from variants and all of the positions data. (AP, private allele number; HO, observed heterozygosity; HE, expected heterozygosity; π, nucleotide diversity; FIS, inbreeding coefficient of an individual relative to the subpopulation.)
Table 2. The statistical values of genetic diversity within the populations from variants and all of the positions data. (AP, private allele number; HO, observed heterozygosity; HE, expected heterozygosity; π, nucleotide diversity; FIS, inbreeding coefficient of an individual relative to the subpopulation.)
TaxonPopulationAPHOHEπFISTajima’s D
D. catenatum 0.09920.15750.15840.3836
FJGZTP0132540.07860.09690.11650.07250.6534
FJLCTP0219340.07290.08330.0990.04960.3964
HNPJTP0428710.08950.11310.12970.08240.4221
HNYYTP0542630.08180.10820.12230.0840.4411
HNYLTP0646890.09760.11960.13710.0810.5556
GXLPTP0755120.09520.13120.14840.11350.4684
GXGCTP0926790.09650.10410.13550.0660.7907
GXXATP1029400.07240.08910.10740.06280.8556
ZJLSTP1223550.09940.11820.13410.07370.3298
ZJQZTP1330840.11230.11050.12450.02560.3962
JXQSTP1438440.1230.11150.12430.00530.3384
JXXSTP1525520.07830.1070.12570.09220.3767
JXHCTP1634170.07780.10370.11880.08660.3404
JXXGTP1742150.09190.0960.11340.0390.5287
GDNXTP1935150.08480.10220.11540.06590.3163
GDHYTP2052210.10190.11550.1330.06380.2443
GDSGTP2238450.10780.08620.0961−0.02080.4748
YNQJTP2439330.11440.11530.13430.0390.4327
HBXNTP2527570.09790.10570.11850.04260.5005
GZSDTP2944610.09750.11470.13260.06960.4221
GZDSTP3096550.15160.11210.1288−0.04340.4646
ZJWYTP3229320.08850.1060.11910.06640.2539
WZSTP3448200.1250.11650.13420.02080.4762
HNXNTP3629670.10340.09940.12180.03320.4315
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Wu, X.-Y.; Li, T.-Z.; Zheng, F.; Chen, J.-B.; Yan, Y.-H.; Huang, J.-X. Population Diversity Analysis Provide Insights into Provenance Identification of Dendrobium catenatum. Genes 2022, 13, 2093. https://doi.org/10.3390/genes13112093

AMA Style

Wu X-Y, Li T-Z, Zheng F, Chen J-B, Yan Y-H, Huang J-X. Population Diversity Analysis Provide Insights into Provenance Identification of Dendrobium catenatum. Genes. 2022; 13(11):2093. https://doi.org/10.3390/genes13112093

Chicago/Turabian Style

Wu, Xin-Yi, Ting-Zhang Li, Fang Zheng, Jian-Bing Chen, Yue-Hong Yan, and Jiu-Xiang Huang. 2022. "Population Diversity Analysis Provide Insights into Provenance Identification of Dendrobium catenatum" Genes 13, no. 11: 2093. https://doi.org/10.3390/genes13112093

APA Style

Wu, X. -Y., Li, T. -Z., Zheng, F., Chen, J. -B., Yan, Y. -H., & Huang, J. -X. (2022). Population Diversity Analysis Provide Insights into Provenance Identification of Dendrobium catenatum. Genes, 13(11), 2093. https://doi.org/10.3390/genes13112093

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop