Application of RAD Sequencing for Evaluating the Genetic Diversity of Domesticated Panax notoginseng (Araliaceae)

Yuezhi Pan; Xueqin Wang; Guiling Sun; Fusheng Li; Xun Gong

doi:10.1371/journal.pone.0166419

Abstract

Panax notoginseng, a traditional Chinese medicinal plant, has been cultivated and domesticated for approximately 400 years, mainly in Yunnan and Guangxi, two provinces in southwest China. This species was named according to cultivated rather than wild individuals, and no wild populations had been found until now. The genetic resources available on farms are important for both breeding practices and resource conservation. In the present study, the recently developed technology RADseq, which is based on next-generation sequencing, was used to analyze the genetic variation and differentiation of P. notoginseng. The nucleotide diversity and heterozygosity results indicated that P. notoginseng had low genetic diversity at both the species and population levels. Almost no genetic differentiation has been detected, and all populations were genetically similar due to strong gene flow and insufficient splitting time. Although the genetic diversity of P. notoginseng was low at both species and population levels, several traditional plantations had relatively high genetic diversity, as revealed by the H_e and π values and by the private allele numbers. These valuable genetic resources should be protected as soon as possible to facilitate future breeding projects. The possible geographical origin of Sanqi domestication was discussed based on the results of the genetic diversity analysis.

Citation: Pan Y, Wang X, Sun G, Li F, Gong X (2016) Application of RAD Sequencing for Evaluating the Genetic Diversity of Domesticated Panax notoginseng (Araliaceae). PLoS ONE 11(11): e0166419. https://doi.org/10.1371/journal.pone.0166419

Editor: Tzen-Yuh Chiang, National Cheng Kung University, TAIWAN

Received: April 5, 2016; Accepted: October 29, 2016; Published: November 15, 2016

Copyright: © 2016 Pan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All the data files are available from the National Center for Biotechnology Information (NCBI) Sequence-Read Archive (SRA) database with the accession numbers SRR3123274, SRR3123435 - SRR3123442, SRR3123444, SRR3123447 and SRR3123450.

Funding: This project was supported by grant 31570339 from the National Natural Science Foundation of China and grant 2011FZ207 from Natural Science Foundation of Yunnna province, China (YP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Crop species were first domesticated from their wild relatives approximately 10000 years ago [1]. Cornille et al[2] defined domesticated species as those segments of evolutionary lineages that diverge from their wild progenitors in response to artificial selection pressure and human control over reproduction. Crop domestication can lead to dramatic changes in agronomic traits. At the same time, the genetic bottleneck that occurs during this process can reduce the genetic diversity in cultivated plants and lead to a loss of genetic variation relative to the species’ wild ancestors [1,3]. Understanding the makeup and distribution of this genetic diversity has been our priority as we consider the process of crop genetic resources conservation and improvement. The assessment of the level and patterns of crop genetic diversity will also be helpful for estimating any possible loss of genetic diversity during conservation programs. Moreover, this assessment will be helpful for evaluating the effects of evolutionary forces (mutation, natural selection, gene flow and genetic drift) on population properties, such as effective population size, breeding systems, population structure, and dispersal mechanisms[4–6].

Panax notoginseng(Burkill) F. H. Chen ex C. Y. Wu et K M. Feng, commonly known as “Sanqi” in China, is a diploid (2n = 2x = 24) species[7]that belongs to the family Araliaceae and has a genome of approximately 2400 Mb in size [8]. It is a traditional Chinese medicinal plant that is widely used for cardiovascular diseases and has been domesticated and cultivated for approximately 400 years [9]. However, the serious root rot disease caused by pathogens limits the production of this herb [10]. Phylogenetic analysis confirmed its taxonomic position in the genus Panax [11,12]; however, this taxon was initially named according to cultivated rather than wild individuals, and no wild populations had ever been found until now [13].The genetic resources available in the farms are of the utmost importance for both breeding practices and resource conservation. A sustainable Sanqi-growing industry will rely on the access to and use of Sanqi’s genetic diversity to develop improved disease-resistant cultivars through marker-assisted breeding, genome-wide association studies (GWAS) and genomic selection (GS) [14,15]. Well-powered GWAS and GS require a genome-wide assessment of genetic diversity and population structure [14]. In addition, genetic management for the remnant Sanqi resources requires an assessment of the genetic diversity pattern.

Panax notoginsengis cultivated in some plantations of the Wenshan Autonomous Prefecture of Yunnan province and the Jingxi County of the Guangxi Zhuang Autonomous Region of China. Cultivated P. notoginseng displays a wide range of morphological diversity, such as white-yellow or dark red tuberous roots, green, dark red or mixed color stems, red or yellow fruits [16]. However, it exhibits low genetic diversity at the species level compared to a wild relative, P. stipuleanatus, as evidenced by ITS sequencing and AFLP polymorphism analysis [17]. No sequence variation in the ITS segment was detected among 24 individuals of P. notoginseng from three populations, and nine sites (1.30%) were variable in 51 accessions sampled from eight populations of P. stipuleanatus. The percentage of AFLP polymorphic sites was 76.9% in P. notoginseng and 96.5% in P. stipuleanatus [17]. P. notoginseng also harbored less DNA variation than did its two cultivated tetraploid relatives, P. ginseng and P. quinquefolius, as revealed by the screening of 36 single copy nuclear loci [18].This type of comparison of closely related species can potentially reveal the processes by which genetic diversity has recently or historically been altered. However, there is no guarantee that the mutation rate of a locus in one species will match that of another, which makes interspecific comparisons very challenging [19]. In addition, the data used in the P. notoginseng studies mentioned above were not sufficient for fully assessing the genetic structure and diversity, especially at the population level.

Next-generation sequencing (NGS) technology provides the opportunity to generate large-scale molecular marker data to study genetic diversity at a much higher resolution. Restriction-site associated DNA (RAD) sequencing is a method based on NGS technology that can create a reduced representation of the genome and identify thousands of genetic markers that are randomly distributed across the target genome. It promises to generate high-resolution population genomic data for model and non-model organisms [20]. For RAD sequencing, genomic DNA is digested by using a restriction enzyme such as EcoRI or SbfI, or a combination of two enzymes, and is then sequenced from the restriction sites to yield a vast number of short reads [20–23]. RAD sequencing has been successfully applied to generate genome-wide SNP data to address questions in population genomics, phylogenetics and speciation studies [21,24–29]. Bioinformatic tools such as Stacks [30,31] and pyRAD can greatly [32] facilitate the analysis of the RAD short reads.

Sanqi currently faces severe pathogen pressures, and long-term sustainability projects and associated medical industries will rely on the exploitation of the existing natural genetic diversity. The specific objectives of the present study were to determine the genetic diversity, population divergence and structure at both the species and population levels by using RAD sequencing technology. The generated knowledge would be beneficial to breeding and germplasm conservation efforts of this medicinal crop.

Materials and Methods

Plant materials

The materials included 36 samples from 12 plantations. Twenty-seven accessions were obtained from nine populations that were distributed in five Yunnan counties, and nine accessions were obtained from three populations distributed in the Jingxi county of Guangxi (Fig 1 and S1 Table). Three samples were randomly collected from each plantation. No specific permissions were required during the sample collection.

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Fig 1. Sample locations of Panax notoginseng.

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Creation and sequencing of the RAD libraries and sequence analysis

Total genomic DNA was extracted from silica gel-dried leaf material using a modified CTAB procedure [33]. The genomic DNA samples were digested with EcoRI, and 36 RAD sequencing libraries were prepared according to the methods described previously [21,34]. In brief, the libraries were prepared following DNA digestion using the EcoRI enzyme, P1 adapter/barcode ligation and DNA purification, size selection, P2 adapter ligation and RAD tag amplification. Single-end sequencing was aimed to produce approximately 1,000 Mb raw data for each library using Illumina HiSeq2000. The steps mentioned above were carried out by Majorbio Pharm Technology Co., Ltd., Shanghai. The raw sequence data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence-Read Archive (SRA) database with the accession numbers SRR3123274, SRR3123435—SRR3123442, SRR3123444, SRR3123447 and SRR3123450.

The raw data were analyzed de novo using the Stacks1.0 pipeline [31,35]to reconstruct loci. The process_radtags program was used to demultiplex and sort the raw data according to the barcodes used in each sample. During this process, the adapter contamination was filtered out, and the raw reads with low average quality score bases (phred score ≤ 10) were discarded. The clean data for each sample were grouped into loci using ustackswith a stack depth parameter (-m) of 5, a mismatch parameter (-M) of 2, and maximum stacks allowed per locus (—max_locus_stacks) of 3. The loci data of all of the samples were merged into a catalog using cstacks, and then the loci of each sample were matched against the catalog so as to determine the allele status in each sample using sstacks. To evaluate the genetic diversity of P. notoginseng at the species level using populations, we treated all 36 samples as a whole population. To include a locus in this analysis, we required it to be present in at least 67% of the samples. When we evaluated the genetic diversity at the population level, we treated each plantation as a population and required a locus to be present in all individuals (r = 1) in at least six populations (p = 6).

Population genetic statistics, including the private allele number, heterozygosity (H), nucleotide diversity (π) and Wright’s F statistics F_IS and F_ST, were calculated for every SNP using the populations program in Stacks. For bi-allelic SNP markers, π is a useful overall measure of genetic diversity in a population, and the F statistic measures the distribution of genetic variation within and among populations [30,36]. To test whether there is a hidden population structure within each population, we examined the inbreeding coefficient F_IS, which measures the reduction in heterozygosity due to inbreeding [37,38]. To assess the genetic relatedness of the populations, we calculated the average F_ST for pairwise comparisons of all sampled populations in the present study. We then used these average pairwise F_ST values to cluster populations by a neighbor-joining method implemented in the Mega6.0 program [39].

To analyze the organization of the populations using multilocus genotypic information, the populations program in Stacks was used to output SNP data across all RAD sites into Structure-format files [40–42]to analyze the genetic structure at the population level and into Genepop-format files to estimate the gene flow among populations using the Genepop4.0 software (http://genepop.curtin.edu.au/). During this data outputting process, only the first SNP per locus was written in both the Genepop-format and Structure-format outputs to avoid tight linkage SNPs [35]with the output parameters r = 1 and p = 6.

The distribution of genetic variation was analyzed by AMOVA analysis using the Arlequin3.5 software [43] after converting the Genepop-format files into an Arlequin-compatible format.

Sample assignment analysis was performed using the software Structure2.3 [40] on the complete data produced by the populations program. For this analysis, 10000 burn-in steps and 100000 iterations were used, with 10 replicates for each value of K, where K is the number of genotypic groups, which ranged from 1 to 12. Output data were processed in Structure Harvester v0.693 [44] (http://taylor0.biology.ucla.edu/structureHarvester/). The optimal K for each analysis was chosen using the delta K method of Evanno et al. [45], as implemented in Structure Harvester. Genetic relationships among the studied individuals were also assessed by principal coordinates analysis (PCoA) in R software package adegenet (http://adegenet.r-forge.r-project.org/files/montpellier/practical-MVAintro.1.0.pdf). based on the Euclidian distances between individual genotypes.

Gene flow (Nm) at the species level was estimated using the software Genepop4.0 (http://genepop.curtin.edu.au/), and the pairwise Nm values at the population level were measured using the formula Nm = (1—F_ST) / 4 F_ST[46,47] and based on the F_ST values derived from the populations program.

To estimate the migration rates and effective population size for Guangxi and Yunnan groups as well as ancestral group, IM model was performed using IMa [48]. The Phylip–format SNP data was output by populations with the parameters p = 1 and r = 24, in which one locus was present in at least 24 individuals was used as the input file after manually editing. Demographic parameters including effective population sizes (θ₁, θ_2, and θ_A) and migration rates (m₁ and m₂) were estimated by 20000000 steps following 200000 burn-in periods. To verify convergence upon the same values, the analysis was repeated three times using the same priors but different seeds in each one of the runs.

Results

Sequence data quality and processing

The raw sequence data of most samples were around or greater than 1000Mb, whereas three samples had less than 700 Mb of raw data. After filtering by process_radtags, the clean data derived from each sample ranged from 595Mb to 1880Mb, and most of them were approximately 1000Mb (S1 Table). All of the clean data were of high quality, as assessed by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The loci of each sample were produced after clean data processing analysis done using the ustacks-cstacks-sstacks program (S1 Table).

To evaluate the genetic diversity of P. notoginseng at the species level using populations, all 36 samples were treated as a whole population. After requiring loci to be present in at least 67% of samples, 25543 RAD loci were retained. Each plantation was treated as a population when evaluating genetic diversity at the population level. After requiring loci to be present in all individuals of at least six populations, 13216 loci were retained.

Genetic diversity at the species and population (plantation) levels

For all loci that were polymorphic in the entire data set at the species level, the observed heterozygosity (H_o) was 0.1523, the expected heterozygosity (H_e) was 0.1554, the nucleotide diversity (π) was 0.159, and the inbreeding coefficient (F_IS) was 0.0591. When considering all nucleotide positions, including the non-polymorphic ones, the observed heterozygosity decreased to 0.0016, the expected heterozygosity to 0.0016, the nucleotide diversity to 0.0017, and the inbreeding coefficient to 0.0006.

The statistics for each population were shown in Table 1 and Figs 2 and 3. For all loci that were polymorphic in at least one population in the entire data set, the average observed heterozygosity ranged from 0.1489 to 0.1997, the expected heterozygosity ranged from 0.1197 to 0.1650, the nucleotide diversity ranged from 0.1473 to 0.2020, and the inbreeding coefficient ranged from -0.0114 to 0.0282. When considering all nucleotide positions, including the non-polymorphic ones anywhere in the dataset, the observed heterozygosity decreased to 0.0011 to 0.0017, the expected heterozygosity decreased to 0.0009 to 0.0014, the nucleotide diversity ranged from 0.0011 to 0.0017, and the inbreeding coefficient ranged from -0.0001 to 0.0002. The private allele number of each population ranged from 182 to 824. As indicated in Table 1and Figs 2 and 3, the NP population showed the highest genetic diversity, as revealed by the observed heterozygosity (H_o), the expected heterozygosity (H_e), the nucleotide diversity (π) and the private allele numbers. The MT and PL populations had relatively higher genetic diversity than did the other populations. In contrast, the CF population showed the lowest heterozygosity and nucleotide diversity values, and the DH population had the fewest private alleles.

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Fig 2. The distribution of private allele numbers among populations with p = 6/r = 1.

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Fig 3. Distribution of genetic diversity indices, including observed heterozygosity (H_o), expected heterozygosity (H_e) and nucleotide diversity (π) with p = 6/r = 1.

(A)Genetic diversity indices were based on variant position data, and on (B) all position data.

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Table 1. The statistical values of genetic diversity within populations from variant and all positions data with p = 6/r = 1.

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Genetic distances within and among populations

When considering all polymorphic loci, the average F_IS values were very close to zero (ranging from -0.0114 to 0.0282 as shown in Table 1), thus indicating a lack of genetic structure or assortative mating within populations [30].

The F_ST values shown in Table 2 were very close to zero and therefore may not be biologically significant, indicating that there was nearly no differentiation among populations of P. notoginseng. The neighbor-joining tree based on the F_ST values revealed that three populations (NP, RL and CF) from the Guangxi province grouped into a branch (Fig 4).

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Fig 4. A neighbor-joining (NJ) tree created using pairwise F_ST values as distance metrics with p = 6/r = 1.

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Table 2. Pairwise comparison of genetic distances (F_ST values) among P. notoginseng populations with p = 6/r = 1.

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Population structure and gene flow

We used an AMOVA approach to more precisely partition the genetic variation across populations with 6418 SNPs that were produced by Stacks (see description in methods). Most (~96.5%) of the genetic variation occurred within populations, whereas only approximately 3.5% of the variation occurred among populations(Table 3). These results confirmed the conclusions deduced from the F_ST analysis.

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Table 3. The results of the AMOVA analysis.

https://doi.org/10.1371/journal.pone.0166419.t003

As a further test of potential population structure, we analyzed these 6418 SNPs using the software Structure2.3 [40] with an “admixture model” and a “correlated alleles frequencies model”. Because loci in tight linkage should be avoided in Structure analyses, only one SNP was chosen from each RAD site, which means that these 6418 SNPs came from 6418 RAD sites. By examining the change in LnP(D), and using the deltaK approach of Evanno [45], we found that a model with K = 2 best fits the data (S1 and S2 Figs). However, an examination of the posterior probabilities plot (Fig 5) did not show two distinct clusters; all samples were genetically intermingled and had admixed ancestry. Principal coordinates analysis did not produce any distinct groupings either (Fig 6), which was consistent with the results of Structure analysis. These results further supported the hypothesis that no genetic differentiation occurred among Sanqi populations.

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Fig 5. Bayesian inference of the number of clusters (K) of Panax notoginseng based on Structure analysis.

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Fig 6. Principal coordinates analysis (PCoA) plot generated by adegenet.

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Genepop analyses revealed that the overall number of migrants (Nm) per generation was 1.4. The pairwise population Nm values calculated from Wright’s analysis indicated that the level of gene flow between populations was substantially high, with the largest being 156.0 between the NP and DH populations and the smallest being 12.7 between the GH and DL populations.

Demographic parameters estimated using the IM model

The demographic parameters of IMa analysis are shown in Table 4, and the marginal distributions of the probabilities of the parameters are shown in supporting file S3. The effective population size of Guangxi group (θ₁) was smaller than that of Yunnan group (θ₂), and both were much smaller than that of the ancestral population (θ_A). The migration rates between the Guangxi and Yunnan groups (m1 and m2) were not significant.

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Table 4. The demographic parameters estimated using IM model.

https://doi.org/10.1371/journal.pone.0166419.t004

Discussion

Low genetic diversity of P. notoginseng

Nucleotide diversity (π)is known as the average pairwise difference between two DNA sequences, and it is a measure of expected heterozygosity for bi-allelic SNP markers [30,49]. Different DNA fragments in one species may not have the same π values due to variation in evolutionary rates [50]. Shi et al.[18] selected 36 single-copy nuclear genes to infer the phylogenetic relationships of the Panax species and evaluate whether the same ortholog exhibits heterogeneous evolutionary rates in diploid and tetraploid species. The π values of P. notoginseng ranged from zero to 0.0139, with anaverage value of 0.0045,whereas the tetraploid P. ginseng and P. quinquefolius had an average π value of 0.0097 and 0.0104, respectively. The π values of these selected genes are not more representative than those from RAD tags when used to describe the genetic diversity of a species. Our study revealed that the π values P. notoginseng estimated using RAD sequencing data were 0.0017 at a species-wide level and 0.0011 ~ 0.0017 at a population level. These data indicated that the nucleotide diversity level of Sanqi was low. Using RAD tags, Xiao [51] reported the genetic diversity of Phoebe zhennan, an endemic and endangered species in China. The π values of this tree species ranged from 0.0010 to 0.0016 among different populations[51], which is very similar to the estimated Sanqi range. The genomic nucleotide diversity of cultivated soybean (Glycine max) was 0.0005 ~ 0.0010[52], as estimated by SLAF-seq, which is another reduced-representation sequencing technology similar to RAD sequencing [53]; the estimated values were even lower than those of P. notoginseng and P. zhennan.

Heterozygosity(H), including observed heterozygosity(H_o) and expected heterozygosity(H_e), is another important statistic in population genetics. Although the heterozygosity values estimated using different DNA markers will vary in plants [54], we made a comparison of the heterozygosity values estimated with different markers in P. notoginseng and two relatives, P. ginseng and P. quinquefolius. The H_e value of P. notoginseng estimated from polymorphic sites using RAD sequencing was 0.1554. The total genetic diversity of wild P. ginseng estimated from allozyme data was low at the species level (H_e = 0.0230) [55], but its average expected heterozygosity estimated using the RAPD method was 0.1348 [56]. For cultivated populations of P. quinquefolius, the H_e values were 0.1637 based on RAPD data [56] and 0.3100 based on allozyme data [57].

The effective size (N_e)of a population, reflecting the rate at which genetic diversity will be lost, will be reduced by a population bottleneck [46]. The IM analysis revealed that the effective population sizes of Guangxi group (θ₁) and Yunnan group (θ₂) were both much smaller than that of the ancestral population (θ_A), which meaning that serious population contraction has occurred in the two distribution areas of Sanqi.

All the findings mentioned above indicated that P. notoginseng probably experienced domestication bottlenecks [58,59] and thus lost a certain amount of genetic diversity. This bottleneck probably occurred in the early domestication process when only a limited number of individuals of the progenitor species were used by the early farmers, which left most of the genetic diversity in the progenitor behind. During the subsequent cultivation process, weak artificial selection for special agronomic traits has been carried out in Sanqi, but only seeds from the strongest plants in each generation were chosen to give rise to the next generation. This winnowing can also cause a severe loss of genetic diversity [3,58]. Scrophularia singpoensis is a famous medicinal plant in China with a domestication history about 1000 years. The cpDNA nucleotide diversity is 0.00076 and 0.00301 of cultivated and wild populations, respectively [60]. Genetic diversity of the cultivated species is usually low whether it has been domesticated for a long time or not [60–63].

Almost no genetic differentiation among populations

Genetic differentiation usually results from a long evolutionary period and is affected by biological features, such as mating systems, life history traits and gene flow. The differentiation index (F_ST) among Sanqi populations ranged from 0.0016 to 0.0193, which is significantly lower than the average F_ST values of species with mixed mating systems or with short-lived perennial history [54], suggesting that all populations of Sanqi were genetically similar. Wright [64] explained that if F_{ST <} 0.0500, there is almost no differentiation between populations. Furthermore, AMOVA analysis revealed that only 3.47% of the genetic variation occurred between populations and that approximately 96.5% of the genetic variation occurred within populations. The results of the Structure analysis and Principal Coordinates Analysis also supported this conclusion. Wang et al. [65] analyzed the chemical variation of P. notoginseng and found no significant differences in saponin concentration among different groups; however, the saponin concentration exhibited great variation among individual samples. This distribution pattern of chemical variation coincided with the pattern of genetic variation revealed in the present study. A mixed NJ tree estimated from 11713 bp further supported this conclusion (S4 Fig), too. Population structure is strongly influenced by genetic exchange patterns (gene flow) within and between populations [66], and only when the lack of gene flow occurs, will the mutation and genetic drift cause populations to genetically diverge from one another [46]. The overall gene flow of P. notoginseng, Nm, is> 1, suggesting that frequent genetic exchange among populations could hold back the genetic differentiation from occurring [67]. Similar to most medicinal plants, the strong gene flow in P. notoginseng comes from the frequent seed exchanges among different farms. In addition, there was a lack of breeding selection during the domestication process such that no cultivars or landraces have been created, and P. notoginseng is still a mixed population of individuals with heterogeneous phenotypic features such as red or yellow seeds and green or dark red stems [68,69]. This kind of lack of strong artificial selection on special agronomic traits is also the cause of the absence of genetic differentiation in Sanqi. In addition, Sanqi has relatively short cultivation history, and insufficient splitting time between populations should be a cause of the lack of differentiation, too [60]. Some cultivated medicinal plants usually have no genetic differentiation with most genetic variation occurring within populations even though they have longer cultivation history than Sanqi or have phenotypic or agro-ecological groups [70–72]. And the insufficient splitting time also resulted in the lack of the differentiation between the Guangxi group and Yunnan group although the migration rates (demographic parameters, m1 and m2) between them were not significant.

Implications for the Conservation of Genetic Resources

Reduced genetic variation might restrict the fitness of domesticated individuals. P.notoginseng faces serious recurrent cropping obstacles. After a three-year cropping period, the farmers must wait for 7~10 years until the soil can be used to plant Sanqi again. In addition, as a medicinal plant, the planting scale of P. notoginseng is seriously influenced by the market demand. When the price is low, the number and size of plantations decrease quickly. For example, the Guangxi Zhuang Autonomous Region of China used to be the main P. notoginseng production district, and there were many plantations in the Jingxi, Napo and Debo counties of Guangxi before 1988 [73]. However, with the price of Sanqi falling sharply on a large scale in the late 1980s, most of the plantations have disappeared and only three populations (NP, RL and CF) were found in Jingxi county when we collected the materials used in the present study in 2011. Some genetic resources have likely been lost with the disappearance of most plantations. Therefore, germplasm nurseries and banks should be built as soon as possible to maintain and protect the existing genetic resources of P. notoginseng.

Although the genetic diversity of P. notoginseng was low at both the species and population levels, several traditional plantations such as NP and PL had a relatively higher genetic diversity level than did others based on H_e and π values as well as on the private allele numbers (shown in Table 1).The NP plantation is located in the Jingxi county of Guangxi, and the owners of this plantation have been cultivating Sanqi for approximately 40 years. It is likely that continuous cultivation has allowed large amounts of genetic variation to still be available today, though most such plantations disappeared in the 1980s. Furthermore, all of the seeds used to propagate the crops were collected from their own plantation during cultivation, which suggests that there was almost no seed flow between NP and other plantations. This cultivation model ensured that the NP population had the most private alleles. Thus, the NP plantation should be the first choice for us to collect the genetic resources for breeding or conservation.

In addition to the NP population, the PL plantation, which has a traditional Sanqi cultivation history, had high genetic diversity. This plantation, located in the Wenshan county of the Yunnan province, is a large planting base for a Sanqi production company. Many individuals of P. notoginseng with various excellent agricultural traits (e.g., purple roots) [68] have been collected from other plantations and planted in this one. In other words, the samples from this plantation not only harbored higher genetic diversity but also had the most variable agricultural traits. These valuable resources should be protected as soon as possible so they can be used in future breeding projects.

The possible geographical origins of Sanqi domestication

The first historical record of Sanqi can be found in the “Compendium of Materia Medica”, a book on Chinese herbal medicine published in 1596. In this book, Sanqi is described to have been discovered in the mountains of west Guangxi. Approximately 150 years later, cultivated Sanqi has been shown to be sold in Wenshan, Yunnan, based on observations recorded in the “Annals of Kaihua Prefecture”, published in 1757. The possible geographical origin of Sanqi domestication is therefore still controversial [73]. Private alleles may provide evidence on the center of origin of this crop [62]. The results of the present study revealed that the NP population located in Guangxi had the highest private allele number, which suggests that the primitive domestication of Sanqi probably occurred first in Guangxi and then dispersed to Yunnan. Although Sanqi has been cultivated for just approximately 400 years, no wild resources can be found today. The wild individuals were probably driven to extinction by over harvesting in the past years. On the other hand, the cultivated Sanqi probably originated from the hybrid events of wild relative species, which could be deduced by its owning of admixed ancestry as revealed by structure analysis [74–79]. Fortunately, wild populations of several generic species such as P. stipuleanatus, P. zingiberensis, P. japonicus and P. vietnamensis still exist in Yunnan, which could potentially provide genetic resources for the improvement of cultivated Sanqi and the further research of Sanqi origin.

Supporting Information

S1 Fig. The mean posterior probability of each given K (10replicates)estimated from Bayesian analysis as implemented by Structure.

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(EPS)

S2 Fig. The distribution of ΔK estimated from Bayesian analysis as implemented by Structure.

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(EPS)

S3 Fig. Marginal distribution of the posterior probability of five demographic parameters estimated by the IM model.

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(EPS)

S4 Fig. The NJ tree of P. notoginseng.

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(EPS)

S1 Table. Summary statistics for each RAD-sequencing data sample, including reads length, clean read number, base count and the loci number matched to the catalog produced by Stacks.

https://doi.org/10.1371/journal.pone.0166419.s005

(DOCX)

Author Contributions

Conceptualization: YP XG FL.
Data curation: YP.
Formal analysis: YP GS XW.
Funding acquisition: YP XG.
Project administration: YP.
Visualization: YP.
Writing – original draft: YP GS XW FL XG.
Writing – review & editing: YP GS XW FL XG.

References

1. Tanksley SD, McCouch SR. Seed banks and molecular maps: unlocking genetic potential from the wild. Science. 1997; 277: 1063–1066. pmid:9262467
- View Article
- PubMed/NCBI
- Google Scholar
2. Cornille A, Giraud T, Smulders MJM, Roldan-Ruiz I, Gladieux P. The domestication and evolutionary ecology of apples. Trends Genet. 2014; 30: 57–65. pmid:24290193
- View Article
- PubMed/NCBI
- Google Scholar
3. Doebley JF, Gaut BS, Smith BD. The molecular genetics of crop domestication. Cell. 2006; 127: 1309–1321. pmid:17190597
- View Article
- PubMed/NCBI
- Google Scholar
4. Frankel OH, Brown AHD, Burdon JJ. The conservation of plant diversity. Cambridge: Cambridge University Press; 1995.
5. Frankel OH, Hawkes JG. Crop genetic resources for today and tomorrow. Cambridge: Cambridge University Press; 1975.
6. Mutegi E, Sagnard F, Semagn K, Deu M, Muraya M, Kanyenji B, et al. Genetic structure and relationships within and between cultivated and wild sorghum (Sorghum bicolor (L.) Moench) in Kenya as revealed by microsatellite markers. Theor Appl Genet. 2011; 122: 989–1004. pmid:21153801
- View Article
- PubMed/NCBI
- Google Scholar
7. Yi TS, Lowry PP, Plunkett GM, Wen J. Chromosomal evolution in Araliaceae and close relatives. Taxon. 2004; 53: 987–1005.
- View Article
- Google Scholar
8. Pan YZ, Zhang YC, Gong X, Li FS. Estimation of genome size of four Panax species by flow cytometry. Plant Diver and Resour. 2014; 36: 233–236.
- View Article
- Google Scholar
9. Zheng GZ, Yang CR. Biology of Panax notoginseng and its applications. Beijing: Science Press; 1994.
10. Miao Z, Li S, Liu X, Chen Y, Li Y, Wang Y, et al. The causal microorganisms of Panax notoginseng root rot disease. Sci Agric Sin.2006; 39: 1371–1378.
- View Article
- Google Scholar
11. Lee C, Wen J. Phylogeny of Panax using chloroplast trnC-trnD intergenic region and the utility of trnC-trnD in interspecific studies of plants. Mol Phylogenet Evol. 2004; 31: 894–903. pmid:15120387
- View Article
- PubMed/NCBI
- Google Scholar
12. Wen J, Zimmer EA. Phylogeny and biogeography of Panax L (the ginseng genus, Araliaceae): Inferences from ITS sequences of nuclear ribosomal DNA. Mol Phylogenet Evol. 1996; 6: 167–177. pmid:8899721
- View Article
- PubMed/NCBI
- Google Scholar
13. Wen J. Species diversity, nomenclature, phylogeny, biogeography, and classification of ginseng genus (Panax L., Araliaceae). In: Punja ZK, editor. Utilization of biotechnological, genetic and cultural approaches for North American and Asian ginseng improvement. Burnaby: Simon Fraser University Press. 2001; pp. 67–88.
14. Myles S, Boyko AR, Owens CL, Brown PJ, Grassi F, Aradhya MK, et al. Genetic structure and domestication history of the grape. Proc Natl Acad Sci U S A. 2011; 108: 3530–3535. pmid:21245334
- View Article
- PubMed/NCBI
- Google Scholar
15. Spindel JE, Begum H, Akdemir D, Collard B, Redona E, Jannink JL, et al. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity. 2016; 116: 395–408. pmid:26860200
- View Article
- PubMed/NCBI
- Google Scholar
16. Xiao H, Jin H, Zhang JJ, Xiao F, Duan C, Cui X. Study on phenotypic diversity of Panax notoginseng. Southwest China J Agricul Sci. 2008; 21: 147–151.
- View Article
- Google Scholar
17. Zhou SL, Xiong GM, Li ZY, Wen J. Loss of genetic diversity of domesticated Panax notoginseng F H Chen as evidenced by ITS sequence and AFLP polymorphism: A comparative study with P. stipuleanatus H T Tsai et K M Feng. J Integr Plant Biol. 2005; 47: 107–115.
- View Article
- Google Scholar
18. Shi FX, Li MR, Li YL, Jiang P, Zhang C, Pan YZ, et al. The impacts of polyploidy, geographic and ecological isolations on the diversification of Panax (Araliaceae). BMCPlant Biol.2015;15: 297–308.
- View Article
- Google Scholar
19. Lozier JD. Revisiting comparisons of genetic diversity in stable and declining species: assessing genome-wide polymorphism in North American bumble bees using RAD sequencing. Mol Ecol. 2014; 23: 788–801. pmid:24351120
- View Article
- PubMed/NCBI
- Google Scholar
20. Davey JL, Blaxter MW. RADSeq: next-generation population genetics. Brief Func Genomics. 2010; 9: 416–423.
- View Article
- Google Scholar
21. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoSONE. 2008; 3(10): e3376.
- View Article
- Google Scholar
22. DaCosta JM, Sorenson MD. ddRAD-seq phylogenetics based on nucleotide, indel, and presence-absence polymorphisms: Analyses of two avian genera with contrasting histories. Mol Phylogenet Evol. 2016; 94: 122–135. pmid:26279345
- View Article
- PubMed/NCBI
- Google Scholar
23. Peterson BK, Weber JN, Kay EH, Fisher HS, Hoeksra HE. Double digest RADseq: an inespensive method for De Novo SNP discovery and genotyping in model and non-model species. PLoS ONE. 2012; 7: e37135. pmid:22675423
- View Article
- PubMed/NCBI
- Google Scholar
24. Bruneaux M, Johnston SE, Herczeg G, Merila J, Primmer CR, Vasemagi A. Molecular evolutionary and population genomic analysis of the nine-spined stickleback using a modified restriction-site-associated DNA tag approach. Mol Ecol. 2013; 22: 565–582. pmid:22943747
- View Article
- PubMed/NCBI
- Google Scholar
25. Hohenlohe PA, Amish SJ, Catchen JM, Allendorf FW, Luikart G. Next-generation RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow and westslope cutthroat trout. Mol Ecol Resour. 2011; 11: 117–122. pmid:21429168
- View Article
- PubMed/NCBI
- Google Scholar
26. Hohenlohe PA, Bassham S, Currey M, Cresko WA. Extensive linkage disequilibrium and parallel adaptive divergence across threespine stickleback genomes. Philos Trans R Soc B. 2012; 367: 395–408.
- View Article
- Google Scholar
27. Hohenlohe PA, Bassham S, Etter PD, Stiffler N, Johnson EA, Cresko WA. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoSGenet. 2010; 6(2): e100086.
- View Article
- Google Scholar
28. Ogden R, Gharbi K, Mugue N, Martinsohn J, Senn H, Davey JW, et al. Sturgeon conservation genomics: SNP discovery and validation using RAD sequencing. Mol Ecol. 2013; 22: 3112–3123. pmid:23473098
- View Article
- PubMed/NCBI
- Google Scholar
29. Xu P, Xu S, Wu X, Tao Y, Wang B, Wang S, et al. Population genomic analyses from low-coverage RAD-Seq data: a case study on the non-model cucurbit bottle gourd. Plant J. 2014; 77: 430–442. pmid:24320550
- View Article
- PubMed/NCBI
- Google Scholar
30. Catchen J, Bassham S, Wilson T, Currey M, O'Brien C, Yeates Q, et al. The population structure and recent colonization history of Oregon threespine stickleback determined using restriction-site associated DNA-sequencing. Mol Ecol. 2013; 22: 2864–2883. pmid:23718143
- View Article
- PubMed/NCBI
- Google Scholar
31. Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH. Stacks: building and genotyping loci De Novo from short-read sequences. G3. 2011; 1: 171–182. pmid:22384329
- View Article
- PubMed/NCBI
- Google Scholar
32. Eaton DAR. PyRAD: assembly of de novo RADseq loci for phylogenetic analyses. Bioinformatics. 2014; 30: 1844–1849. pmid:24603985
- View Article
- PubMed/NCBI
- Google Scholar
33. Doyle JJ, Doyle JL. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull. 1987; 19(1): 11–15.
- View Article
- Google Scholar
34. Miller MR, Atwood TS, Eames BF, Eberhart JK, Yan Y-L, Postlethwait JH, et al. RAD marker microarrays enable rapid mapping of zebrafish mutations. Genome Biol. 2007; 8: R105. pmid:17553171
- View Article
- PubMed/NCBI
- Google Scholar
35. Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA. Stacks: an analysis tool set for population genomics. Mol Ecol. 2013; 22: 3124–3140. pmid:23701397
- View Article
- PubMed/NCBI
- Google Scholar
36. Frankham R, Ballou JD, Briscoe DA. Introduction to conservation genetics. Cambridge: Cambridge University Press; 2002.
37. Hartl DL, Clark AG. Principles of population genetics. Sunderland, Massachusetts: Sinauer Associates; 2007.
38. Wright S. Evolution and the genetics of populations.Vol. 4: Variability within and among natural populations. Chicago: University of Chicago Press; 1978.
39. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013; 30: 2725–2729. pmid:24132122
- View Article
- PubMed/NCBI
- Google Scholar
40. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000; 155: 945–959. pmid:10835412
- View Article
- PubMed/NCBI
- Google Scholar
41. Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003; 164: 1567–1587. pmid:12930761
- View Article
- PubMed/NCBI
- Google Scholar
42. Hubisz MJ, Falush D, Stephens M, Pritchard JK. Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour. 2009; 9: 1322–1332. pmid:21564903
- View Article
- PubMed/NCBI
- Google Scholar
43. Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010; 10: 564–567. pmid:21565059
- View Article
- PubMed/NCBI
- Google Scholar
44. Earl DA, vonHoldt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conser Genet Resour. 2012; 4: 359–361.
- View Article
- Google Scholar
45. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005; 14: 2611–2620. pmid:15969739
- View Article
- PubMed/NCBI
- Google Scholar
46. Freeland J, Kirk H, Petersen S. Molecular ecology. 2 nd ed. Chichester: Wiley—Blackwell; 2011.
47. Wright S. The genetical structure of populations. Ann Eugen. 1951; 15: 323–354. pmid:24540312
- View Article
- PubMed/NCBI
- Google Scholar
48. Hey J, Nielsen R. Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics. Proc Natl Acad Sci U S A. 2007;104: 2785–2790. pmid:17301231
- View Article
- PubMed/NCBI
- Google Scholar
49. Hamilton MB. Population genetics. Chichester: Wiley—Blackwell; 2009.
50. Wright SI, Lauga B, Charlesworth D. Subdivision and haplotype structure in natural populations of Arabidopsis lyrata. Mol Ecol. 2003; 12: 1247–1263. pmid:12694288
- View Article
- PubMed/NCBI
- Google Scholar
51. Xiao JH. Genetic diversity of valuable timber tree Phoebe zhennan using RAD tags: M.Sc. Thesis, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences; 2015.
52. Han Y, Zhao X, Liu D, Li Y, Lightfoot DA, Yang Z, et al. Domestication footprints anchor genomic regions of agronomic importance in soybeans. New phytol. 2016; 209: 871–884. pmid:26479264
- View Article
- PubMed/NCBI
- Google Scholar
53. Sun X, Liu D, Zhang X, Li W, Liu H, Hong W, et al. SLAF-seq: an efficient method of large-scale de novo SNP discovery and genotyping using high-throughput sequencing. PLoS ONE. 2013;8: e58700. pmid:23527008
- View Article
- PubMed/NCBI
- Google Scholar
54. Nybom H. Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants. Mol Ecol. 2004; 13: 1143–1155. pmid:15078452
- View Article
- PubMed/NCBI
- Google Scholar
55. Zhuravlev YN, Koren OG, Reunova GD, Muzarok TI, Gorpenchenko TY, Kats I, et al. Panax ginseng natural populations: their past, current state and perspectives. Acta Pharmacol Sin. 2008; 29: 1127–1136. pmid:18718182
- View Article
- PubMed/NCBI
- Google Scholar
56. Artyukova EV, Kozyrenko MM, Koren OG, Muzarok TI, Reunova GD, Zhuravlev YN. RAPD and allozyme analysis of genetic diversity in Panax ginseng C.A. Meyer and P. quinquefolius L. Russ J Genet. 2004; 40: 178–185.
- View Article
- Google Scholar
57. Grubbs HJ, Case MA. Allozyme variation in American ginseng (Panax quinquefolius L.): Variation, breeding system, and implications for current conservation practice. Conserv Genet. 2004; 5: 13–23.
- View Article
- Google Scholar
58. Eyre-Walker A, Gaut RL, Hilton H, Feldman DL, Gaut BS. Investigation of the bottleneck leading to the domestication of maize. Proc Natl Acad Sci U S A. 1998; 95: 4441–4446. pmid:9539756
- View Article
- PubMed/NCBI
- Google Scholar
59. Hyten DL, Song Q, Zhu Y, Choi I-Y, Nelson RL, Costa JM, et al. Impacts of genetic bottlenecks on soybean genome diversity. Proc Natl Acad Sci U S A. 2006;103: 16666–16671. pmid:17068128
- View Article
- PubMed/NCBI
- Google Scholar
60. Chen C, Li P, Wang RH, Schaal BA, Fu CX. The population genetics of cultivation: domestication of a traditional Chinese medicine, Scrophularia ningpoensis Hemsl. (Scrophulariaceae). PLoS ONE. 2014;9: e105064. pmid:25157628
- View Article
- PubMed/NCBI
- Google Scholar
61. Barrett SCH, Kohn JR. Genetic and evolutionary consequences of small population-size in plants: implications for conservation In: Falk DA, Holsinger KE, editors. Genetics and Conservation of Rare Plants; 1991. pp. 3–30.
- View Article
- Google Scholar
62. Guo Y, Chen S, Li Z, Cowling WA. Center of origin and centers ofdiversity in an ancient crop, Brassica rapa (Turnip Rape). J Hered. 2014; 105: 555–565.
- View Article
- Google Scholar
63. Jakob SS, Rodder D, Engler JO, Shaaf S, Ozkan H, Blattner FR, et al. Evolutionary history of wild barley (Hordeum vulgare subsp. spontaneum) analyzed using multilocus sequence data and paleodistribution modeling. Genome Biol Evol. 2014; 6: 685–702. pmid:24586028
- View Article
- PubMed/NCBI
- Google Scholar
64. Wright S. Evolution and the genetics of populations. Chicago: University of Chicago; 1978.
65. Wang D, Koh H-L, Hong Y, Zhu H-T, Xu M, Zhang YJ, et al. Chemical and morphological variations of Panax notoginseng and their relationship. Phytochemistry. 2013; 93: 88–95. pmid:23566718
- View Article
- PubMed/NCBI
- Google Scholar
66. Schaal BA, Hayworth DA, Olsen KM, Rauscher JT, Smith WA. Phylogeographic studies in plants: problems and prospects. Mol Ecol. 1998; 7: 465–474.
- View Article
- Google Scholar
67. Slatkin M. Gene flow in natural populations. Ann Rev Ecol Syst. 1985; 16: 393–430.
- View Article
- Google Scholar
68. Chen Z, Wang Y, Zeng J, Li Z, Tian Y. Studies on characteristic distinction and its affect on yield and quality of Panax notoginseng. Chinese Traditional and Herbal Drugs. 2001; 32: 357–359.
- View Article
- Google Scholar
69. Guo HB, Cui XM, An N, Cai GP. Sanchi ginseng (Panax notoginseng (Burkill) F. H. Chen) in China: distribution, cultivation and variations. Genet Resour Crop Evol. 2010; 57: 453–460.
- View Article
- Google Scholar
70. Tiwari G, Singh R, Singh N, Choudhury DR, Paliwal R, Kumar A, et al. Study of arbitrarily amplified (RAPD and ISSR) and gene targeted (SCoT and CBDP) markers for genetic diversity and population structure in Kalmegh [Andrographis paniculata (Burm. f.) Nees]. Ind Crops Prod. 2016; 86: 1–11.
- View Article
- Google Scholar
71. Yao X, Deng J, Huang H. Genetic diversity in Eucommia ulmoides (Eucommiaceae), an endangered traditional Chinese medicinal plant. Conser Genet. 2012; 13: 1499–1507.
- View Article
- Google Scholar
72. Zhang JJ, Xing C, Tian H, Yao X H. Microsatellite genetic variation in the Chinese endemic Eucommia ulmoides (Eucommiaceae): implications for conservation. Bot J Linn Soc. 2013; 175: 775–785.
- View Article
- Google Scholar
73. Huang RS, Yang HJ, He ZJ, Chen CJ, Li ZW, Zhang CL. Textual research on the origin areas of Panax notoginseng. Lishizhen Medicine and Materia Medica Research. 2007; 18: 1610–1611.
- View Article
- Google Scholar
74. Bolibox-Bragoszewska H, Targonska M, Bolibox L, Kilian A, Rakoczy-Trojanowska M. Genome-wide characterization of genetic diversity and population structure in Secale. BMC Plant Biol. 2014; 14: 184–199. pmid:25085433
- View Article
- PubMed/NCBI
- Google Scholar
75. Emanuelli F, Lorenzi S, Grzeskowiak L, Catalano V, Stefanini M, Troggo M, et al. Genetic diversity and population structure assessed by SSR and SNP markers in a large germplasm collection of grape. BMC Plant Biol. 2013; 13: 39–56. pmid:23497049
- View Article
- PubMed/NCBI
- Google Scholar
76. Liu W, Xiao Z, Bao X, Yang X, Fang J, Xiang X, et al. Identifying Litchi (Litchi chinensis Sonn.) cultivars and their genetic relationships using single nucleotide polymorphism (SNP) markers. PLoS ONE. 2015; 10: e0135390. pmid:26261993
- View Article
- PubMed/NCBI
- Google Scholar
77. Meegahakumbura MK, Wambulwa MC, Thapa KK, Li MM, Möller M, Xu JC, et al. Indications for three independent domestication events for the tea plant (Camellia sinensis (L.) O. Kuntze) and new insights into the origin of tea germplasm in China and India revealed by nuclear microsatellites. PloS ONE. 2016; 11: e0155369. pmid:27218820
- View Article
- PubMed/NCBI
- Google Scholar
78. Migicovsky Z, Sawler J, Money D, Eibach R, Miller AJ, Luby JJ, et al. Genomic ancestry estimation quantifies use of wild species in grape breeding. BMC Genomics. 2016; 17: 478–485. pmid:27357509
- View Article
- PubMed/NCBI
- Google Scholar
79. Wu GA, Prochnik S, Jenkins J, Salse J, Hellsten U, Murat F, et al. Sequencing of diverse mandarin,pummelo and orange genomes reveals complex history of admixture during citrus domestication. Nat Biotechnol. 2014; 32: 656–662. pmid:24908277
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Tanksley SD, McCouch SR. Seed banks and molecular maps: unlocking genetic potential from the wild. Science. 1997; 277: 1063–1066. pmid:9262467
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Cornille A, Giraud T, Smulders MJM, Roldan-Ruiz I, Gladieux P. The domestication and evolutionary ecology of apples. Trends Genet. 2014; 30: 57–65. pmid:24290193
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Doebley JF, Gaut BS, Smith BD. The molecular genetics of crop domestication. Cell. 2006; 127: 1309–1321. pmid:17190597
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Frankel OH, Brown AHD, Burdon JJ. The conservation of plant diversity. Cambridge: Cambridge University Press; 1995.

[ref5] 5. Frankel OH, Hawkes JG. Crop genetic resources for today and tomorrow. Cambridge: Cambridge University Press; 1975.

[ref6] 6. Mutegi E, Sagnard F, Semagn K, Deu M, Muraya M, Kanyenji B, et al. Genetic structure and relationships within and between cultivated and wild sorghum (Sorghum bicolor (L.) Moench) in Kenya as revealed by microsatellite markers. Theor Appl Genet. 2011; 122: 989–1004. pmid:21153801
View Article
PubMed/NCBI
Google Scholar

[16] View Article

[17] PubMed/NCBI

[18] Google Scholar

[ref7] 7. Yi TS, Lowry PP, Plunkett GM, Wen J. Chromosomal evolution in Araliaceae and close relatives. Taxon. 2004; 53: 987–1005.
View Article
Google Scholar

[20] View Article

[21] Google Scholar

[ref8] 8. Pan YZ, Zhang YC, Gong X, Li FS. Estimation of genome size of four Panax species by flow cytometry. Plant Diver and Resour. 2014; 36: 233–236.
View Article
Google Scholar

[23] View Article

[24] Google Scholar

[ref9] 9. Zheng GZ, Yang CR. Biology of Panax notoginseng and its applications. Beijing: Science Press; 1994.

[ref10] 10. Miao Z, Li S, Liu X, Chen Y, Li Y, Wang Y, et al. The causal microorganisms of Panax notoginseng root rot disease. Sci Agric Sin.2006; 39: 1371–1378.
View Article
Google Scholar

[27] View Article

[28] Google Scholar

[ref11] 11. Lee C, Wen J. Phylogeny of Panax using chloroplast trnC-trnD intergenic region and the utility of trnC-trnD in interspecific studies of plants. Mol Phylogenet Evol. 2004; 31: 894–903. pmid:15120387
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref12] 12. Wen J, Zimmer EA. Phylogeny and biogeography of Panax L (the ginseng genus, Araliaceae): Inferences from ITS sequences of nuclear ribosomal DNA. Mol Phylogenet Evol. 1996; 6: 167–177. pmid:8899721
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref13] 13. Wen J. Species diversity, nomenclature, phylogeny, biogeography, and classification of ginseng genus (Panax L., Araliaceae). In: Punja ZK, editor. Utilization of biotechnological, genetic and cultural approaches for North American and Asian ginseng improvement. Burnaby: Simon Fraser University Press. 2001; pp. 67–88.

[ref14] 14. Myles S, Boyko AR, Owens CL, Brown PJ, Grassi F, Aradhya MK, et al. Genetic structure and domestication history of the grape. Proc Natl Acad Sci U S A. 2011; 108: 3530–3535. pmid:21245334
View Article
PubMed/NCBI
Google Scholar

[39] View Article

[40] PubMed/NCBI

[41] Google Scholar

[ref15] 15. Spindel JE, Begum H, Akdemir D, Collard B, Redona E, Jannink JL, et al. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity. 2016; 116: 395–408. pmid:26860200
View Article
PubMed/NCBI
Google Scholar

[43] View Article

[44] PubMed/NCBI

[45] Google Scholar

[ref16] 16. Xiao H, Jin H, Zhang JJ, Xiao F, Duan C, Cui X. Study on phenotypic diversity of Panax notoginseng. Southwest China J Agricul Sci. 2008; 21: 147–151.
View Article
Google Scholar

[47] View Article

[48] Google Scholar

[ref17] 17. Zhou SL, Xiong GM, Li ZY, Wen J. Loss of genetic diversity of domesticated Panax notoginseng F H Chen as evidenced by ITS sequence and AFLP polymorphism: A comparative study with P. stipuleanatus H T Tsai et K M Feng. J Integr Plant Biol. 2005; 47: 107–115.
View Article
Google Scholar

[50] View Article

[51] Google Scholar

[ref18] 18. Shi FX, Li MR, Li YL, Jiang P, Zhang C, Pan YZ, et al. The impacts of polyploidy, geographic and ecological isolations on the diversification of Panax (Araliaceae). BMCPlant Biol.2015;15: 297–308.
View Article
Google Scholar

[53] View Article

[54] Google Scholar

[ref19] 19. Lozier JD. Revisiting comparisons of genetic diversity in stable and declining species: assessing genome-wide polymorphism in North American bumble bees using RAD sequencing. Mol Ecol. 2014; 23: 788–801. pmid:24351120
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref20] 20. Davey JL, Blaxter MW. RADSeq: next-generation population genetics. Brief Func Genomics. 2010; 9: 416–423.
View Article
Google Scholar

[60] View Article

[61] Google Scholar

[ref21] 21. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, et al. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoSONE. 2008; 3(10): e3376.
View Article
Google Scholar

[63] View Article

[64] Google Scholar

[ref22] 22. DaCosta JM, Sorenson MD. ddRAD-seq phylogenetics based on nucleotide, indel, and presence-absence polymorphisms: Analyses of two avian genera with contrasting histories. Mol Phylogenet Evol. 2016; 94: 122–135. pmid:26279345
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref23] 23. Peterson BK, Weber JN, Kay EH, Fisher HS, Hoeksra HE. Double digest RADseq: an inespensive method for De Novo SNP discovery and genotyping in model and non-model species. PLoS ONE. 2012; 7: e37135. pmid:22675423
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref24] 24. Bruneaux M, Johnston SE, Herczeg G, Merila J, Primmer CR, Vasemagi A. Molecular evolutionary and population genomic analysis of the nine-spined stickleback using a modified restriction-site-associated DNA tag approach. Mol Ecol. 2013; 22: 565–582. pmid:22943747
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref25] 25. Hohenlohe PA, Amish SJ, Catchen JM, Allendorf FW, Luikart G. Next-generation RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow and westslope cutthroat trout. Mol Ecol Resour. 2011; 11: 117–122. pmid:21429168
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref26] 26. Hohenlohe PA, Bassham S, Currey M, Cresko WA. Extensive linkage disequilibrium and parallel adaptive divergence across threespine stickleback genomes. Philos Trans R Soc B. 2012; 367: 395–408.
View Article
Google Scholar

[82] View Article

[83] Google Scholar

[ref27] 27. Hohenlohe PA, Bassham S, Etter PD, Stiffler N, Johnson EA, Cresko WA. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoSGenet. 2010; 6(2): e100086.
View Article
Google Scholar

[85] View Article

[86] Google Scholar

[ref28] 28. Ogden R, Gharbi K, Mugue N, Martinsohn J, Senn H, Davey JW, et al. Sturgeon conservation genomics: SNP discovery and validation using RAD sequencing. Mol Ecol. 2013; 22: 3112–3123. pmid:23473098
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref29] 29. Xu P, Xu S, Wu X, Tao Y, Wang B, Wang S, et al. Population genomic analyses from low-coverage RAD-Seq data: a case study on the non-model cucurbit bottle gourd. Plant J. 2014; 77: 430–442. pmid:24320550
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref30] 30. Catchen J, Bassham S, Wilson T, Currey M, O'Brien C, Yeates Q, et al. The population structure and recent colonization history of Oregon threespine stickleback determined using restriction-site associated DNA-sequencing. Mol Ecol. 2013; 22: 2864–2883. pmid:23718143
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref31] 31. Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH. Stacks: building and genotyping loci De Novo from short-read sequences. G3. 2011; 1: 171–182. pmid:22384329
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref32] 32. Eaton DAR. PyRAD: assembly of de novo RADseq loci for phylogenetic analyses. Bioinformatics. 2014; 30: 1844–1849. pmid:24603985
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref33] 33. Doyle JJ, Doyle JL. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bull. 1987; 19(1): 11–15.
View Article
Google Scholar

[108] View Article

[109] Google Scholar

[ref34] 34. Miller MR, Atwood TS, Eames BF, Eberhart JK, Yan Y-L, Postlethwait JH, et al. RAD marker microarrays enable rapid mapping of zebrafish mutations. Genome Biol. 2007; 8: R105. pmid:17553171
View Article
PubMed/NCBI
Google Scholar

[111] View Article

[112] PubMed/NCBI

[113] Google Scholar

[ref35] 35. Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA. Stacks: an analysis tool set for population genomics. Mol Ecol. 2013; 22: 3124–3140. pmid:23701397
View Article
PubMed/NCBI
Google Scholar

[115] View Article

[116] PubMed/NCBI

[117] Google Scholar

[ref36] 36. Frankham R, Ballou JD, Briscoe DA. Introduction to conservation genetics. Cambridge: Cambridge University Press; 2002.

[ref37] 37. Hartl DL, Clark AG. Principles of population genetics. Sunderland, Massachusetts: Sinauer Associates; 2007.

[ref38] 38. Wright S. Evolution and the genetics of populations.Vol. 4: Variability within and among natural populations. Chicago: University of Chicago Press; 1978.

[ref39] 39. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013; 30: 2725–2729. pmid:24132122
View Article
PubMed/NCBI
Google Scholar

[122] View Article

[123] PubMed/NCBI

[124] Google Scholar

[ref40] 40. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000; 155: 945–959. pmid:10835412
View Article
PubMed/NCBI
Google Scholar

[126] View Article

[127] PubMed/NCBI

[128] Google Scholar

[ref41] 41. Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003; 164: 1567–1587. pmid:12930761
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref42] 42. Hubisz MJ, Falush D, Stephens M, Pritchard JK. Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour. 2009; 9: 1322–1332. pmid:21564903
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref43] 43. Excoffier L, Lischer HEL. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010; 10: 564–567. pmid:21565059
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref44] 44. Earl DA, vonHoldt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conser Genet Resour. 2012; 4: 359–361.
View Article
Google Scholar

[142] View Article

[143] Google Scholar

[ref45] 45. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005; 14: 2611–2620. pmid:15969739
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref46] 46. Freeland J, Kirk H, Petersen S. Molecular ecology. 2 nd ed. Chichester: Wiley—Blackwell; 2011.

[ref47] 47. Wright S. The genetical structure of populations. Ann Eugen. 1951; 15: 323–354. pmid:24540312
View Article
PubMed/NCBI
Google Scholar

[150] View Article

[151] PubMed/NCBI

[152] Google Scholar

[ref48] 48. Hey J, Nielsen R. Integration within the Felsenstein equation for improved Markov chain Monte Carlo methods in population genetics. Proc Natl Acad Sci U S A. 2007;104: 2785–2790. pmid:17301231
View Article
PubMed/NCBI
Google Scholar

[154] View Article

[155] PubMed/NCBI

[156] Google Scholar

[ref49] 49. Hamilton MB. Population genetics. Chichester: Wiley—Blackwell; 2009.

[ref50] 50. Wright SI, Lauga B, Charlesworth D. Subdivision and haplotype structure in natural populations of Arabidopsis lyrata. Mol Ecol. 2003; 12: 1247–1263. pmid:12694288
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref51] 51. Xiao JH. Genetic diversity of valuable timber tree Phoebe zhennan using RAD tags: M.Sc. Thesis, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences; 2015.

[ref52] 52. Han Y, Zhao X, Liu D, Li Y, Lightfoot DA, Yang Z, et al. Domestication footprints anchor genomic regions of agronomic importance in soybeans. New phytol. 2016; 209: 871–884. pmid:26479264
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref53] 53. Sun X, Liu D, Zhang X, Li W, Liu H, Hong W, et al. SLAF-seq: an efficient method of large-scale de novo SNP discovery and genotyping using high-throughput sequencing. PLoS ONE. 2013;8: e58700. pmid:23527008
View Article
PubMed/NCBI
Google Scholar

[168] View Article

[169] PubMed/NCBI

[170] Google Scholar

[ref54] 54. Nybom H. Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants. Mol Ecol. 2004; 13: 1143–1155. pmid:15078452
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref55] 55. Zhuravlev YN, Koren OG, Reunova GD, Muzarok TI, Gorpenchenko TY, Kats I, et al. Panax ginseng natural populations: their past, current state and perspectives. Acta Pharmacol Sin. 2008; 29: 1127–1136. pmid:18718182
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref56] 56. Artyukova EV, Kozyrenko MM, Koren OG, Muzarok TI, Reunova GD, Zhuravlev YN. RAPD and allozyme analysis of genetic diversity in Panax ginseng C.A. Meyer and P. quinquefolius L. Russ J Genet. 2004; 40: 178–185.
View Article
Google Scholar

[180] View Article

[181] Google Scholar

[ref57] 57. Grubbs HJ, Case MA. Allozyme variation in American ginseng (Panax quinquefolius L.): Variation, breeding system, and implications for current conservation practice. Conserv Genet. 2004; 5: 13–23.
View Article
Google Scholar

[183] View Article

[184] Google Scholar

[ref58] 58. Eyre-Walker A, Gaut RL, Hilton H, Feldman DL, Gaut BS. Investigation of the bottleneck leading to the domestication of maize. Proc Natl Acad Sci U S A. 1998; 95: 4441–4446. pmid:9539756
View Article
PubMed/NCBI
Google Scholar

[186] View Article

[187] PubMed/NCBI

[188] Google Scholar

[ref59] 59. Hyten DL, Song Q, Zhu Y, Choi I-Y, Nelson RL, Costa JM, et al. Impacts of genetic bottlenecks on soybean genome diversity. Proc Natl Acad Sci U S A. 2006;103: 16666–16671. pmid:17068128
View Article
PubMed/NCBI
Google Scholar

[190] View Article

[191] PubMed/NCBI

[192] Google Scholar

[ref60] 60. Chen C, Li P, Wang RH, Schaal BA, Fu CX. The population genetics of cultivation: domestication of a traditional Chinese medicine, Scrophularia ningpoensis Hemsl. (Scrophulariaceae). PLoS ONE. 2014;9: e105064. pmid:25157628
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref61] 61. Barrett SCH, Kohn JR. Genetic and evolutionary consequences of small population-size in plants: implications for conservation In: Falk DA, Holsinger KE, editors. Genetics and Conservation of Rare Plants; 1991. pp. 3–30.
View Article
Google Scholar

[198] View Article

[199] Google Scholar

[ref62] 62. Guo Y, Chen S, Li Z, Cowling WA. Center of origin and centers ofdiversity in an ancient crop, Brassica rapa (Turnip Rape). J Hered. 2014; 105: 555–565.
View Article
Google Scholar

[201] View Article

[202] Google Scholar

[ref63] 63. Jakob SS, Rodder D, Engler JO, Shaaf S, Ozkan H, Blattner FR, et al. Evolutionary history of wild barley (Hordeum vulgare subsp. spontaneum) analyzed using multilocus sequence data and paleodistribution modeling. Genome Biol Evol. 2014; 6: 685–702. pmid:24586028
View Article
PubMed/NCBI
Google Scholar

[204] View Article

[205] PubMed/NCBI

[206] Google Scholar

[ref64] 64. Wright S. Evolution and the genetics of populations. Chicago: University of Chicago; 1978.

[ref65] 65. Wang D, Koh H-L, Hong Y, Zhu H-T, Xu M, Zhang YJ, et al. Chemical and morphological variations of Panax notoginseng and their relationship. Phytochemistry. 2013; 93: 88–95. pmid:23566718
View Article
PubMed/NCBI
Google Scholar

[209] View Article

[210] PubMed/NCBI

[211] Google Scholar

[ref66] 66. Schaal BA, Hayworth DA, Olsen KM, Rauscher JT, Smith WA. Phylogeographic studies in plants: problems and prospects. Mol Ecol. 1998; 7: 465–474.
View Article
Google Scholar

[213] View Article

[214] Google Scholar

[ref67] 67. Slatkin M. Gene flow in natural populations. Ann Rev Ecol Syst. 1985; 16: 393–430.
View Article
Google Scholar

[216] View Article

[217] Google Scholar

[ref68] 68. Chen Z, Wang Y, Zeng J, Li Z, Tian Y. Studies on characteristic distinction and its affect on yield and quality of Panax notoginseng. Chinese Traditional and Herbal Drugs. 2001; 32: 357–359.
View Article
Google Scholar

[219] View Article

[220] Google Scholar

[ref69] 69. Guo HB, Cui XM, An N, Cai GP. Sanchi ginseng (Panax notoginseng (Burkill) F. H. Chen) in China: distribution, cultivation and variations. Genet Resour Crop Evol. 2010; 57: 453–460.
View Article
Google Scholar

[222] View Article

[223] Google Scholar

[ref70] 70. Tiwari G, Singh R, Singh N, Choudhury DR, Paliwal R, Kumar A, et al. Study of arbitrarily amplified (RAPD and ISSR) and gene targeted (SCoT and CBDP) markers for genetic diversity and population structure in Kalmegh [Andrographis paniculata (Burm. f.) Nees]. Ind Crops Prod. 2016; 86: 1–11.
View Article
Google Scholar

[225] View Article

[226] Google Scholar

[ref71] 71. Yao X, Deng J, Huang H. Genetic diversity in Eucommia ulmoides (Eucommiaceae), an endangered traditional Chinese medicinal plant. Conser Genet. 2012; 13: 1499–1507.
View Article
Google Scholar

[228] View Article

[229] Google Scholar

[ref72] 72. Zhang JJ, Xing C, Tian H, Yao X H. Microsatellite genetic variation in the Chinese endemic Eucommia ulmoides (Eucommiaceae): implications for conservation. Bot J Linn Soc. 2013; 175: 775–785.
View Article
Google Scholar

[231] View Article

[232] Google Scholar

[ref73] 73. Huang RS, Yang HJ, He ZJ, Chen CJ, Li ZW, Zhang CL. Textual research on the origin areas of Panax notoginseng. Lishizhen Medicine and Materia Medica Research. 2007; 18: 1610–1611.
View Article
Google Scholar

[234] View Article

[235] Google Scholar

[ref74] 74. Bolibox-Bragoszewska H, Targonska M, Bolibox L, Kilian A, Rakoczy-Trojanowska M. Genome-wide characterization of genetic diversity and population structure in Secale. BMC Plant Biol. 2014; 14: 184–199. pmid:25085433
View Article
PubMed/NCBI
Google Scholar

[237] View Article

[238] PubMed/NCBI

[239] Google Scholar

[ref75] 75. Emanuelli F, Lorenzi S, Grzeskowiak L, Catalano V, Stefanini M, Troggo M, et al. Genetic diversity and population structure assessed by SSR and SNP markers in a large germplasm collection of grape. BMC Plant Biol. 2013; 13: 39–56. pmid:23497049
View Article
PubMed/NCBI
Google Scholar

[241] View Article

[242] PubMed/NCBI

[243] Google Scholar

[ref76] 76. Liu W, Xiao Z, Bao X, Yang X, Fang J, Xiang X, et al. Identifying Litchi (Litchi chinensis Sonn.) cultivars and their genetic relationships using single nucleotide polymorphism (SNP) markers. PLoS ONE. 2015; 10: e0135390. pmid:26261993
View Article
PubMed/NCBI
Google Scholar

[245] View Article

[246] PubMed/NCBI

[247] Google Scholar

[ref77] 77. Meegahakumbura MK, Wambulwa MC, Thapa KK, Li MM, Möller M, Xu JC, et al. Indications for three independent domestication events for the tea plant (Camellia sinensis (L.) O. Kuntze) and new insights into the origin of tea germplasm in China and India revealed by nuclear microsatellites. PloS ONE. 2016; 11: e0155369. pmid:27218820
View Article
PubMed/NCBI
Google Scholar

[249] View Article

[250] PubMed/NCBI

[251] Google Scholar

[ref78] 78. Migicovsky Z, Sawler J, Money D, Eibach R, Miller AJ, Luby JJ, et al. Genomic ancestry estimation quantifies use of wild species in grape breeding. BMC Genomics. 2016; 17: 478–485. pmid:27357509
View Article
PubMed/NCBI
Google Scholar

[253] View Article

[254] PubMed/NCBI

[255] Google Scholar

[ref79] 79. Wu GA, Prochnik S, Jenkins J, Salse J, Hellsten U, Murat F, et al. Sequencing of diverse mandarin,pummelo and orange genomes reveals complex history of admixture during citrus domestication. Nat Biotechnol. 2014; 32: 656–662. pmid:24908277
View Article
PubMed/NCBI
Google Scholar

[257] View Article

[258] PubMed/NCBI

[259] Google Scholar

Figures

Abstract

Introduction

Materials and Methods

Plant materials

Creation and sequencing of the RAD libraries and sequence analysis

Results

Sequence data quality and processing

Genetic diversity at the species and population (plantation) levels

Genetic distances within and among populations

Population structure and gene flow

Demographic parameters estimated using the IM model

Discussion

Low genetic diversity of P. notoginseng

Almost no genetic differentiation among populations

Implications for the Conservation of Genetic Resources

The possible geographical origins of Sanqi domestication

Supporting Information

S1 Fig. The mean posterior probability of each given K (10replicates)estimated from Bayesian analysis as implemented by Structure.

S2 Fig. The distribution of ΔK estimated from Bayesian analysis as implemented by Structure.

S3 Fig. Marginal distribution of the posterior probability of five demographic parameters estimated by the IM model.

S4 Fig. The NJ tree of P. notoginseng.

S1 Table. Summary statistics for each RAD-sequencing data sample, including reads length, clean read number, base count and the loci number matched to the catalog produced by Stacks.

Author Contributions

References