Skip to main content
Download PDF

Genetic diversity and population structure of Caryopteris mongholica revealed by reduced representation sequencing

BMC Plant Biology volume 22, Article number: 297 (2022) Cite this article

Abstract

Background

Caryopteris mongholica Bunge is a rare broad-leaved shrub distributed in the desert and arid regions of Mongol and North China. Due to land reclamation, natural habitat deterioration and anthropogenic activities in recent years, the wild resources have sharply reduced. To effectively protect and rationally use it, we investigated the genetic diversity and population structure from 18 populations across the range of C. mongholica in China by reduced representation sequencing technology.

Results

We found the overall average values of observed heterozygosity (Ho), expected heterozygosity (He), and average nucleotide diversity (π) were 0.43, 0.35 and 0.135, respectively. Furthermore, the NM17 population exhibited higher genetic diversity than other populations. The phylogenetic tree, principal component analysis (PCA) and structure analysis showed the sampled individuals clustered into two main groups. The NM03 population, with individuals clustered in both groups, may be a transitional population located between the two groups. In addition, most genetic variation existed within populations (90.97%) compared to that among the populations (9.03%). Interestingly, geographic and environmental distances were almost equally important to the observed genetic differences. Redundancy analysis (RDA) identified optical radiation (OR), minimum temperature (MIT) and mean annual precipitation (MAP) related variables as the most important environment factors influencing genetic variation, and the importance of MIT was also confirmed in the latent factor mixed models (LFMM).

Conclusions

The results of this study facilitate research on the genetic diversity of C. mongholica. These genetic features provided vital information for conserving and sustainably developing the C. mongholica genetic resources.

Peer Review reports

Background

Caryopteris mongholica Bunge (Caryopteris, Lamiaceae) is an endangered shrub mainly distributed in the arid and semi-arid areas of Mongolia and North China (Gansu, Hebei, Inner Mongolia, Shanxi provinces etc. [1]). According to our long-term field investigations, violet-blue was the most common flower color of C. mongholica and it is rarely pink. Additionally, C. mongholica had mixed mating system with both inbreeding and outcrossing [2]. As the northernmost species within the genus Caryopteris [3], C. mongholica has an important phylogenetic position. Climate-induced local extinction is widespread among plant and animal species [4, 5]. Recent land reclamation, natural habitat deterioration and anthropogenic activities have sharply reduced the wild resources C. mongholica [6, 7]. Due to the important roles that C. mongholica plays in fixing moving sands, water and soil conservation, and delaying further desertification for the ecological-environmental stability of native habitats, the protection and research are becoming particularly important and necessary.

C. mongholica has high morphological and genetic diversity, owing to its wide geographic distribution and the survival in a broad range of environmental conditions [7, 8]. For example, the population density of C. mongholica in Saihan Tal, Inner Mongolia, is less than 20% compared with that in Shenmu, Shaanxi Province, China, where it is more than 40%. A phenotypic study of six C. mongholica populations from different areas showed that variable climatic factors and environmental conditions contributed to high trait variation, modulating photosynthetic responses and growth [8, 9]. Under the drought conditions, the growth and photosynthetic capacity of C. mongholica are inhibited, and the moisture capacity of leaves decreases [9]. Wu [7] first characterized C. mongholica germplasm with inter-simple sequence repeat markers and confirmed a high level of a genetic variation among five C. mongholica populations of in northwestern China, while genetic differentiation within populations was higher than that among populations, and clustering analysis showed populations groups geographically structured. To a certain extent, the heterogeneous environments across the geographic range of C. mongholica induced the differences in the community structure, plant morphology and genetic structure, which diversified of C. mongholica.

Single nucleotide polymorphisms (SNPs) are the most abundant and universal sequence variations in all genomes, which makes them very useful markers for genetic analysis [10]. With the rapid development of high-throughput sequencing technology, so far, scientists have developed various reduced-representation genome sequencing by restriction enzyme digestion of genomic DNA [11, 12]. Genotyping by sequencing (GBS) method is increasingly applied in numerous species [13, 14], which is a high-performance, cost-effective, and simple, allowing obtaining thousands of markers from many individual and identifying SNPs using a reduced representation library [11, 13, 15]. Furthermore, GBS detects results in SNP markers that are more informative than PCR-based markers [16, 17].

Early study relating to C. mongholica genetic diversity has relied on traditional molecular marker (inter-simple sequence repeat). However, this marker has its own limitations in resolving the geographical differentiation of population, especially when using few genetic markers. Here, we identified SNPs of C. mongholica from 18 natural populations in Northwest China using GBS to address the above drawbacks. We aimed to answer the following questions with the detected SNPs: (1) what is the level of genetic diversity and population structure of C. mongholica in these different populations? (2) does geographic and environmental distance affect the genetic diversity of C. mongholica? (3) what is the relative importance of environmental variables to the genetic diversity of C. mongholica? According to the results, at the last, we provided a reference for the protection and rational utilization of the wild C. mongholica resources.

Results

SNPs discovery, genotype and population genetic diversity

Our sequencing data showed high phred quality (Q20 > 94%, Q30 > 85%), with a stable GC content ranging from 34.38% to 38.33%. Sequencing generated 126 billion paired-end reads from 125 individuals, of which 99.99% reads passed initial quality filters (Additional file 1: Table S1 and S2). Under our parameter settings, SAM tools initially recovered 165,622 SNPs. There were 9,763 SNPs after filtering based on missing data, allele frequency, and depth that were used for all analyses. With all 125 individuals, as a whole, the Ho, He, and π ranged from 0.35(NM14) to 0.73(NM17), 0.28(NM14) to 0.70(NM17), 0.126(NX07) to 0.151(NM17), respectively, with the means of 0.43, 0.35, and 0.135, respectively (Table 1).

Table 1 Geographic locations and genetic diversity in 18 populations of C. mongholica
Full size table

Phylogenetic relationship

To better visualize sample distribution and the relationships, a phylogenetic trees of 125 C. mongholica individuals were constructed (Fig. 2). Within the population, the individuals of each population get together except NM01, NM02, NM03, SX10, and NM20, but there are clear genetic boundaries between different populations. Four populations (NX06, NX07, NM08 and GS01) and five individuals of NM03 were clustered in a small group, which was distributed on the southwest side of the sampling locations around Helan Mountain (~ 103 – 107ºE, ~ 36 – 38ºN) (Fig. 1). While the other 13 populations and the remaining two individuals of NM03 were clustered in a large group, which was on the northeast side of the sampling locations in the Daqing Mountain and its surrounding grasslands (~ 108 – 116ºE, ~ 38 – 45ºN) (Fig. 1). Visibly, most genetic variation occurs primarily within populations of C. mongholica. For color of flowers, the “NM03-P1” was clustered in the Daqing Mountain-Grassland group, gathered on the branch with NM16 population, and “GS01-P1” was clustered in the Helan Mountain group, gathered on the branch with GS01 population (Fig. 2).

Fig. 1
figure 1

Location of the 18 natural sample sites of China collection

Full size image
Fig. 2
figure 2

Phylogenetic analysis of C. mongholica. Circular maximum likelihood tree based on SNPs showing the phylogenetic relationship of the 125 individuals. The color pattern are equivalent to the structure analysis at K = 2 (orange = group A, green = group B). The flower of the marked (pink dots) individual was pink. Numbers at the nodes are bootstrap values from 100 replications

Full size image

Some populations of C. mongholica clustered in the same group were geographically distant, such as NM02 and NM20, whereas NM08, which was more closer to NM02, clustered in different groups (Fig. 1 and Fig. 2).

Population analyses of genotyping results

As expected, analysis of unlinked SNPs for 125 individuals performed with the principal component analysis (PCA) showed two groups corresponding to the 18 populations of C. mongholica (Fig. 3). The Helan Mountain group (orange circle) included four populations (NX06, NX07, NM08 and GS01), and the Daqing Mountain-Grassland group (blue circle) included all the remaining 14 populations (NM01, NM02, NM03, NM09, SX10, SX11, NM13, NM14, NM16, NM17, NM18, NM19, NM20 and HB23). Geographically, these two genetic groups corresponded to the southwest and northeast populations, respectively (Fig. 3).

Fig. 3
figure 3

Principal component analysis (PCA)-based clustering of the 125 C. mongholica individuals (orange circle = group A, blue circle = group B)

Full size image

The structure clustering analysis estimated that two genetic groups (i.e., K = 2) were most likely, as determined by the Admixture software (Fig. 4 and Additional file 2: Fig. S1). Under K = 2, the 18 populations clustered into a small group with four populations (NX06, NX07, NM08 and GS01) and two individuals (NM03-09 and NM03-13) from the NM03, and a large second group with all the remaining populations and individuals (Fig. 4). Similar to the phylogenetic results, individuals from the NM03 population were present in both groups. Under K = 3, NX06, NX07 and GS01 populations remained unchanged, while some individuals (NM08-06, NM08-09, and NM08-19) from the NM08 population formed a new cluster with these three populations. In addition, the previous large group branched, now comprising 50 individuals, respectively.

Fig. 4
figure 4

Population structure analysis of the 125 C. mongholica accomplished from K = 2 to K = 3. Note: each inbred line is represented by a thin vertical bar, which is partitioned into two colored segments (orange = group A, green = group B) on the x-axis when K = 2, with lengths proportional to the estimated probability on the y-axis

Full size image

conStruct cross-validations (Additional file 2: Fig. S2) showed that the spatial model was marginally superior to non-spatial model continued to improve slightly as subsequent clusters were added up to K = 5, indicative of overestimating the number of potential clusters. For the spatial model, the predictive accuracy was highest K = 2. Thus, the spatial model at K = 2 sufficiently described the population structure, and the clustering patterns of spatial and non-spatial models were very similar (Fig. 5), indicating the contribution of isolation by distance (IBD) to the population structure was small.

Fig. 5
figure 5

Spatial population structure based on spatial and non-spatial construct models. (A) and (B) Population admixture estimated with K = 2 using non-spatial model and spatial model, respectively. (C) and (D) Population admixture estimated with K = 3 using non-spatial and spatial model, respectively

Full size image

The results of ML tree and PCA for the 125 individuals were basically in agreement with that of the structure cluster analysis. As a matter of convenience, we would named the four populations NX06, NX07, NM08 and GS01 as group A (Helan Mountain group), and the remaining 14 populations as group B (Daqing Mountain-Grassland group).

At the species level, the hierarchical analysis of molecular variance (AMOVA) results showed that the majority of the observed genetic variation could be attributed to differences within populations (90.97%) rather than to the variation among populations (9.03%) (Table 2).

Table 2 Analysis of molecular variance for among and within populations of the studied C. mongholica individuals
Full size table

Partitioning genomic variation to IBD and IBE

Gene flow patterns depend on environmental or geographic conditions; therefore, we tested IBD and environment (IBE). A significant correlation between pairwise FST/(1-FST) and geographic distance (Mantel r = 0.5856, p = 0.00009; Fig. 6A) was detected by the Mantel test, indicating a significant pattern of IBD. We also identified a significant pattern of IBE(Mantel r = 0.4582, p = 0.0204; Fig. 6B). The autocorrelation between environmental and geographic distances was also strong (Mantel r = 0.5569, p = 0.0003; Fig. 6C).

Fig. 6
figure 6

Isolation by distance and environment using Mantel test. (A)Pairwise genetic distance FST/(1-FST) is significantly associated with environmental distance and (B)geographic distance. (C)Geographic distance is significantly correlated with environmental distance

Full size image

The variance inflation factor (VIF) was less than 10 for all variables included in the RDA. The first axis of RDA accounted for 87.54%. the second axis of RDA accounted for 82.83%, while the third accounted for 77.28% of the environmental variables’ relations (Fig. 7). In addition, MIT showed the greatest number of associated SNPs according to the latent factor mixed models (LFMM) (Additional file 2: Fig. S4).

Fig. 7
figure 7

Biplot of redundancy analysis (RDA) of the relationship between genomic variation and three environment variables. The SNPs were in red (in the center of each plot), and the individuals were the black circles. The blue vectors were the environmental predictors

Full size image

Discussion

Recently, genomic resources have allowed us to study genetic mechanism of evolution in more detail. Many researchers have been studying the genetic information of C. mongholica, unravelling the molecular basis for protecting its genetic diversity [6, 17]. C. mongholica is a plant species with a small genome size (< 400 Mb), for which no complete reference genome exists. Therefore, studies of C. mongholica genomics using a genome resequencing-based approach that requires a complete reference genome are limited. SNPs are currently markers of choice for several types of genetic diversity studies in C. mongholica. Using the GBS methodology on genomic the DNA of C. mongholica, we developed the first genomic-SNP markers for this important drought-tolerant shrub species.

Genetic diversity

Previous studies have shown that the genetic diversity of the widespread plant species is higher than that of the narrowly distributed [18], and compared to annual and perennial herbaceous plants, perennial woody plants tend to have higher genetic diversity at the species level and within populations [19,20,21]. As a long-lived perennial shrub plants widely distributed in arid and semi-arid areas, the NM17 population of C. mongholica was found to have highest He value (0.70), whereas the NM14 had the lowest (0.28) (Table 1). This finding suggests that individuals from NM17 are potential source of useful and rare genes for widening the genetic base of breeding populations derived from C. mongholica. Thus, a greater effort should be directed toward collecting specimens from these regions. Compared to coordinal plants, the He value of Salvia yunnanensis and Salvia miltiorrhiza as revealed by Simple Sequence Repeats markers were 0.28 and 0.15, respectively [22], while C. mongholica showed slightly higher genetic variation at the species level.

Gene flow plays an important role in the genetic differentiation of population. It increases the genetic variation within populations and a decrease in differentiation among them, which is the opposite effect of the genetic drift [23]. In addition, the genetic differentiation of natural populations is thought to be a dynamic process which relies on an equilibrium relationship between gene flow and selection [23]. In this study, the estimation of gene flow of these populations of C. mongholica populations was > 1 (Table 1), indicating it was high enough to resist the genetic differentiation among them caused by genetic drift, thus achieving a homogenizing effect [24].

Our results revealed that higher genetic variations in the C. mongholica germplasm were due to differences within the populations and were in line with those of a previous study [7]. Wu characterized the C. mongholica in China using ISSR markers and found higher variations within populations (78.98%) than among them (21.02%). Pollen spread and seed dispersal are the most important determinants of gene flow. In nature, C. mongholica is mainly pollinated by wind and insects [25], and the long-distance transmission of pollen provides the chances of long-distance pollen transmission opens opportunities for genetic migration among different populations [26]. In the natural communities, C. mongholica usually depends on seed propagation, and long-distance seed dispersal must depend on seed propagation, thus, on frugivorous animals [2]. Frugivores have a limited home range and typically disperse only a few seeds [27]. Therefore, we presume that the contribution of seed dispersal to the homogenization of C. mongholica populations is likely small and that the frequent gene flow between them might be caused by wind-dispersed pollen or pollinators.

Population structure

Our assignment test results based on the ML tree separate C. mongholica individuals into two groups. This division is consistent with their geographical origins and genetic backgrounds [3]. C. mongholica may have evolved from ancestors initially distributed in the Hengduan mountain region of western Sichuan and northwestern Yunnan and gradually expanded to the Loess Plateau and Inner-Mongolia Plateau [3]. The migratory history of C. mongholica is complicated, especially when migrating from the Hengduan mountains region in southwest of the Loess Plateau in northwest China, which explains why the genetic relationship of some C. mongholica individuals did not well correlate with the geographical origins in this study. For example, “NM02-18” and “NM03-25” were from Dalad Banner Nei Mongol and Otog Banner Nei Mongol, clustered in the same clade and displayed relatively close genetic distance (Fig. 1 and Fig. 2). However, “NM03-13” and “NM03-25” were both from Otog Banner Nei Mongol, but clustered in two different subgroups and displayed relatively distant genetic distances (Fig. 1 and Fig. 2). conStruct gave little support to an overall pattern of IBD in the sampled populations, as also shown by geographically adjacent populations were not always more similar than geographically distant population, such as NM02 and NM08, NM02, and NM20. These results suggest that some populations of C. mongholica were isolated in the past, likely as a result of historical events (e.g. geographic isolation or refugia). Geological events [28,29,30] and subsequent climatic changes during the Pliocene–Pleistocene in northern and western China shaped the distribution and genetic differentiation of species in northern parts of the country [31]. In addition, human activity has also contributed to the fragmentation of C. mongholica.

The results of structure was the most likely when K = 2, which was supported by the results of ML tree and PCA (Fig. 2 and Fig. 3), confirming the effectiveness of the SNP markers. We used three methods to separate the 18 populations of C. mongholica into two groups, the Helan Mountain group (group A) and the Daqing Mountain-Grassland group (group B). Figure 1 clearly shows that NX06, NX07, NM08 and GS01 are located on both sides of the Helan Mountains, and these four populations clusters within the same group of structure analysis. There was a possibility of gene flow among these populations which resulted in genetic similarity and their grouping under the same population. The non-spatial and spatial clustering identified by conStruct was largely congruent with each other also showing a clear east–west transition (Fig. 5). Daqing Mountain-Grassland group clustered a total of 14 C. mongholica populations from the northeast side of sampling sites. They were located in relatively wide geographical locations, including grasslands and plains, which may have promoted gene flow among them. In addition, of seven individuals from the NM03 location were used as plant material, two (NM03-09, NM03-13) showed a membership coefficient of more than 50% and clustered in group A, the other five individuals were grouped in the group B (Fig. 4). The intermediate positions of the NM03 population suggest a probability of gene exchange between the two groups. It is speculated that the NM03 may be a transitional population between the two groups.

During the sample collection, we noticed that pink flowers were very rare. Violet-blue color is more favored by insects than pink and has a higher seed-setting rate [25]. However, the population structure indicated that the grouping was not significantly related to the flower colors. Molecular markers reflect the genotype differences in gene expression under specific environmental conditions; thus, they are a product of gene-environment interactions. The phenotype was complex and diverse because of gene regulation and interaction events, which was the main reason why the phenotypic classification and molecular marker classification in many species were partially inconsistent [32,33,34]. In summary, although identifying C. mongholica by color appears intuitive, this trait was susceptible to environmental effects and should be used with caution.

Environmental adaptation

The testing result of IBD and IBE revealed that geographic and environmental distances were almost equally important to the observed genetic differences implying a certain degree of IBD (Fig. 6). Environment has been widely reported as a strong selective pressure on natural populations [35, 36]. We thus further applied RDA to estimate the impact of environment on genetic variation. Local adaptation studies on climate change contribute to understanding the ability of populations to sustain or adapt to rapid climate change [37]. Adaptive variation is partially drove by environmental factors, which may be mostly driven by temperature, precipitation and minimum temperature for C. mongholica [38]. RDA indicated that optical radiation (OR), minimum temperature (MIT) and mean annual precipitation (MAP) were by far the most important variable associated with genetic variation, and the importance of MIT was also confirmed in the LFMM method.

The OR indirectly reflects the temperature, which is a key factor influencing growth and phenology of various species, including C. mongholica [8]. OR influences the growth of plants by affecting the metabolic processes such as photosynthesis, respiration, and transpiration, as well as the metabolic processes that affect the synthesis and transportation of organic matter [39]. Additionally, OR can directly affect soil temperature, thus affecting the absorption and transport of water and nutrients. In a previous study of genetic diversity, Jia et al. [14] found that temperature was the most influential factor on genetic variation of Platycladus orientalis.

Water availability is commonly recognized as another critical factor delimitating species' distribution in northern China [31, 40]. The precipitation had important implications for the genetic diversity of vascular plants species according to the statistical data of 79 vascular plants [41]. Although MAP is the limitation for plant growth, after long-term adaptive evolution, plants in arid environments can developed different drought-resistant mechanisms (such as resistance genes) as well as genetic structures adapted to the environment [42, 43]. The genetic variation and diversity of the population in arid environments is predicted to increase as a consequence of drought stress, and the rate of evolution of plant populations in these environments is higher than that of the populations in humid environments [43]. On the other hand, the cost of sexual reproduction increased as climatic drought stress increased [44]. For genetic diversity, the effect of aridity should be discussed with caution due to its two aspects.

MIT seemed to be an indispensable factor, which is a limiting factor for the survival [14]. However, the physiological mechanism of C. mongholica responding to low temperature is not yet understood. Dissection of this adaptive mechanism should be the objective of future studies.

Implications for ecological restoration

C. mongholica has excellent tolerance to drought and barren soil because of its well-developed roots, and is a frequent pioneer shrub species for sand fixation. It is of great significance to accelerate the greening of northwest China and improve the ecological environment. Owing to its drought resistance, it plays a vital role in forming the landscape of China, especially in the northwestern Loess Plateau [6]. Climate change and human activity cause soil erosion, negatively affecting biodiversity and ecosystem stability. Therefore, protecting and rationally utilizing the wild resources of C. mongholica is crucial, as is putting effort into the ecological restoration of its habitats. Genetic resource conservation of wild C. mongholica populations should be considered mainly for the ecological restoration of desert areas. In addition to the in situ conservation of the populations with high genetic diversity, ex situ germplasm collection for different purposes, such as breeding and conservation, is also significant for achieving our goals. Furthermore, given that the genetic variation mainly existed within populations, more individuals should be selected and propagated within those used for ecological restoration in these 18 sampling areas. Finally, environmental protection should be advocated in order to increase the local farmers’ awareness of the protection of this valuable species.

The findings of this study provide latest ideas and guidance for the protection, rational development and utilization of C. mongholica resources.

Methods

Sample collection and DNA isolation

In this study, all of C. mongholica shrub materials were originally collected from 18 locations spanning 9º of latitude (~ 36 – 45ºN) and 13º of longitude (~ 103 – 116ºE) across Northwest and North China (Fig. 1 and Additional file 1: Table S1). The sample collection was approved by the Academy of Forestry Science, the Inner Mongolia Autonomous Region, China. Among these materials, the flowers of “NM03-P1” and “GS01-P1” were rare pink in the wild, while those of remaining materials were common violet-blue (Additional file 2: Fig. S3). Each leaf sample was represented by one shrub, spaced at least 25 m apart. The samples were carefully identified by Professor Meng Ji of Academy of Forestry Science based on the descriptions in Flora of China, a voucher specimen was deposited in the Herbarium of Plant Biology Department, Beijing Forestry University with an accession number BJFU-CM117. The leaf materials were dried in silica gel kept in zip-lock bags until DNA extraction. Total genomic DNA was extracted using a Plant Genomic DNA kit (Tiangen, Beijing, China), and its integrity was evaluated on 1% (w/v) agarose gel. The purity and concentration of DNA in each sample were determined using the NanoPhotometer spectrophotometer (IMPLEN, CA, USA) and Qubit DNA Assay Kit in Qubit 2.0 Flurometer (Life Technologies, CA, USA), respectively.

Library construction and GBS analysis

GBS libraries were constructed in accordance with the modified protocol [45]. Genomic DNA was incubated at 37℃ with MspI (New England Biolabs, NEB), T4 DNA ligase (NEB), ATP (NEB), and MspI Y adapter N-containing barcode. Restriction-ligation reactions were heat-inactivated at 65℃ and digested with the additional restriction enzyme EcoRI (NEB) at 37℃. The restricted ligation samples were purified using Agencourt AMPure XP (Beckman). The purified samples were PCR amplified with Phusion Master Mix (NEB) universal primer and index primer to add the index. The PCR products were purified, pooled, and electrophoresed on 2% agarose gel. Fragments between 265 and 315 bps were selected and purified. Pair-end sequencing was performed on the selected tags using the Illumina PE150 platform.

Processing of Illumina data

Adapter sequences and low-quality bases (base quality < 20) from the tail of each read were removed using Trimmomatic v0.36 [46]. Because the public databases does not contain full genome information for C. mongholica, under these circumstances, de novo generation of a GBS reference was constructed following [47]. We selected the sample “NM01-09” with the most tags for stack clustering as the reference sequence. Clean Reads were aligned against the reference sequence using bowtie2 software [48], and then genotyping was performed applying Unified program in the GATK software [49] to predict SNP sites in samples. The predicted results were screened using the SelectVariants program of the GATK software (Key parameters: -restricelesto biallelic-select QD > 10.0). Subsequently, the variant dataset was further filtered using the software vcftools (main parameter -maf 0.01 -minDP 4 -max-missing 0.6).

Population structure analyses

The genetic structure was characterized using phylogenetic tree construction, PCA, and population structure analysis. The ML phylogeny tree was inferred based on the filtered SNPs using RAxML [50] to determine the evolutionary relationship between populations, and a rapid bootstrapping analysis with 100 bootstraps was conducted. PCA was used to evaluate the structure and calculate eigenvectors and eigenvalues using the software GCTA software [51]. PCA distribution maps were drawn using the R script. Frappe software [52] was used to analyze the group structure, which implements an expectation–maximization algorithm for estimating individual membership in clusters [53]. Briefly, the input file of PLINK—Ped file was created, and the algorithm was used to construct population genetic structure and population lineage information. The number of assumed genetic clusters K ranged from 2 to 3. To avoid overestimating the number of potential clusters caused by the presence of IBD, as is often found in continuous populations, we further used conStruct [54] to identify structure in a spatially aware context. conStruct allows for explicit test of discrete versus continuous spatial patterns by estimating the ancestral components of each population. We tested both non-spatial and spatial models. To identify the most favorable fit for the number of clusters(K), Admixture software [55] was used to investigate the cross-validation (CV) error, and the minimum CV error, which corresponded to the most favourable K value. The genetic structure of each identified population was assessed by calculating nucleotide diversity per base pair (π), observed heterozygosity (Ho) and expected heterozygosity (He) were determined for each population identified. The Stack software [56] was used to calculate π and inbreeding coefficient (FIS). Arlequin software [57] was used to calculate the Ho and He. DnaSP software was used to calculate the pairwise FST/(1-FST) and gene flow (Nm) from Hudson et al. [58].

AMOVA

AMOVA was conducted to quantify genetic variation at two different hierarchical levels: among population within the germplasm group and within populations. Genetic variations were further tested by assigning populations to genetic clusters identified by population structure analyses. The analyses were conducted in Arlequin [57], and the significance levels for the variance components were tested using 1,000 permutation steps.

Isolation by distance and isolation by environment

To investigate the role of geographic and environmental factors in shaping the spatial genetic differentiation, we calculated:(A) the correlation between environmental and geographic distance, (B) IBD, and (C) IBE. According to the geographic coordinates of the different C. mongholica collection sites, the climate data for each sample site were obtained from the WorldCLIM global high-resolution climate database (http://www.worldclim.org/). A total of 17 bioclimatic variables were used to calculate IBE (Additional file 1: Table S3 and S4), including temperature, precipitation, optical radiation and soil pH. The Mantel test was used to assess associations between linearized FST/(1-FST) and geographic distance and environmental distance with significance determined using 999 permutations in the R package vegan [59].

Climatic association analysis

To estimate the degree to which genomic variation is influenced by environmental variables, we performed a series of redundancy analyses (RDAs) in the R package vegan [59]. RDA involves a multiple linear regression followed by a PCA on the matrix of regression-fitted values. A dependent matrix of minor allele frequencies for each population and a independent matrice of environmental variables were included. To avoid high collinearity, we excluded those with a VIF over 10 [60]. Finally, we reserved three environmental variables, including OR, MAP and MIT to explain population variation using the RDA function in the vegan package [59]. The other software, LFMM [61], was also used for gene-climate association analysis. As it estimates the hidden impact of population structure, LFMM permits the presence of background levels of population structure (latent factors). The detected SNPs that exhibit an association with the environment were determined according to the z-score. Bonferroni adjustment was used on the z-score values for multiple tests.

Availability of data and materials

The plant materials were collected from natural population in geographic distribution of C. mongholica. The datasets generated for this study can be found in the NCBI SAR. Bioproject #PRJNA779263. All data generated during the current study are included in this published article and its supplementary information as Additional files 1, 2.

Abbreviations

Ho :

Observed heterozygosity

He :

Expected heterozygosity

π :

Average nucleotide diversity

PCA:

Principal component analysis

RDA:

Redundancy analysis

OR:

Optical radiation

MIT:

Minimum temperature

MAP:

Mean annual precipitation

LFMM:

Latent factor mixed models

GBS:

Genotyping by sequencing

SNP:

Single nucleotide polymorphism

MAF:

Minor allele frequency

ML:

Maximum likelihood

IBD:

Isolation by distance

AMOVA:

Analysis of molecular variance

IBE:

Isolation by environment

VIF:

Variance inflation factor

F IS :

Inbreeding coefficient

Nm :

Gene flow

CV:

Cross-validation

References

  1. Wu ZY, Raven PH, Larsen K. Flora of China. Vol. 17. Verbenaceae through Solanaceae. Science Press, Beijing, and Missouri Botanical Garden Press. Nord J Bot. 1995;15:522.

  2. Guo CY. Study on biology of Caryopteris mongholica Bunge. China: phD Thesis, Inner Mongolia Agricultural University; 2009.

  3. Zhao YZ. On Floristic Geographical Distribution of Caryopteris mongholica. Acta Scientiarum Naturalium Universitatis NeiMonggol. 1995;02:195–7.

    Google Scholar 

  4. Urban MC. Accelerating extinction risk from climate change. Science. 2015;348(6234):571–3.

    Article  CAS  PubMed  Google Scholar 

  5. Wiens JJ. Climate-related local extinctions are already wide-spread among plant and animal species. PLoS Biol. 2016;14(12): e2001104.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. He YM. Studies on ecological adaptation and reproductive strategy of Caryopteris mongolica. China: phD Thesis, Inner Mongolia Agricultural University; 2018.

  7. Wu M. The ISSR genetic diversity analysis on the Caryopteris mongholica an endemic plant in Mongolian Plateau. China: Master’s Thesis, Inner Mongolia Agricultural University; 2018.

  8. Bai XQ, Liu C, Ji RX, Shen C, Wang XP. Effects of origin climate on light response characteristics of Caryopteris mongholica. Acta Ecol Sin. 2018;38(23):8425–33.

    Google Scholar 

  9. Xie QJ. Studies on ecophysiological characteristics of drought resistance in Caryopteris mongholica Bunge. China: Master’s Thesis, Beijing Forestry University; 2011.

  10. Wang S, Chen JD, Zhang WP, Hu Y, Chang LJ, Fang L, et al. Sequence-based ultra-dense genetic and physical maps reveal structural variations of allopolyploid cotton genomes. Genome Biol. 2015;16(1):108–108.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE. 2011;6(5): e19379.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Altshuler D, Pollara VJ, Cowles CR, Van Etten WJ, Baldwin J, Linton L, et al. An SNP map of the human genome generated by reduced representation shotgun sequencing. Nature. 2000;407(6803):513–6.

    Article  CAS  PubMed  Google Scholar 

  13. Ariani A, Berny Mier y Teran JC, Gepts P. Genome-wide identification of SNPs and copy number variation in common bean (Phaseolus vulgaris L.) using genotyping-by-sequencing (GBS). Molecular Breeding. 2016;36(7):1–11.

  14. Jia KH, Zhao W, Maier PA, Hu XG, Jin YQ, Zhou SS, et al. Landscape genomics predicts climate change-related genetic offset for the widespread Platycladus orientalis (Cupressaceae). Evol Appl. 2020;13(4):665–76.

    Article  PubMed  Google Scholar 

  15. Schröder S, Mamidi S, Lee R, McKain MR, McClean PE, Osorno JM. Optimization of genotyping by sequencing (GBS) data in common bean (Phaseolus vulgaris L.). Molecular Breeding. 2016;36(1):1–9.

  16. Bhatia D, Wing RA, Singh K. Genotyping by sequencing, its implications and benefits. Crop Improv. 2013;40(2):101–11.

    Google Scholar 

  17. Nadeem MA, Guizado SJV, Shahid MQ, Nawaz MA, Habyarimana E, Ercisli S, et al. In-depth genetic diversity and population structure of endangered peruvian amazon rosewood germplasm using genotyping by sequencing (GBS) technology. Forests. 2021;12(2):197.

    Article  Google Scholar 

  18. Liu LX, Zhang CY, Wang YW, Dong MF, Shang FD, Li P. The complete chloroplast genome of Caryopteris mongholica and phylogenetic implications in Lamiaceae. Conserv Genet Resour. 2018;10(3):281–5.

    Article  Google Scholar 

  19. Hamrick JL, Linhart YB, Mitton JB. Relationships between life history characteristics and electrophoretically detectable genetic variation in plants. Annu Rev Ecol Syst. 1979;10(1):173–200.

    Article  Google Scholar 

  20. Hamrick JL, Godt MJW, Sherman-Broyles SL. Factors influencing levels of genetic diversity in woody plant species. Population genetics of forest trees. Springer, Dordrecht, 1992. p. 95–124.

  21. Chung MY, Son S, Herrando-Moraira S, Tang CQ, Maki M, Kim Y, et al. Incorporating differences between genetic diversity of trees and herbaceous plants in conservation strategies. Conserv Biol. 2020;34(5):1142–51.

    Article  PubMed  Google Scholar 

  22. Tian HY, Fei JQ, Zou Z, Xu SZ. Assessment of genetic diversity and genetic relationship of Salvia yunnanensis C.H.W.Wright germplasm resources based on SSR marker. Molecular Plant Breeding. 2021;1–15. (In Chinese)

  23. Whitlock MC, McCauley DE. Indirect measures of gene flow and migration: FST not equal to 1/(4Nm + 1). Heredity. 1999;82(2):117–25.

    Article  PubMed  Google Scholar 

  24. Wright S. The genetic structure of populations. Ann Eugen. 1949;15(1):323–54.

    Article  Google Scholar 

  25. Wang YN. The reproductive characteristics and spatial pattern of Caryopteris mongholica Bunge with different colors. China: Master’s Thesis, Northwest Normal University; 2018.

  26. Nakanishi A, Takeuchi T, Ueno S, Nishimura N, Tomaru N. Spatial variation in bird pollination and its mitigating effects on the genetic diversity of pollen pools accepted by Camellia japonica trees within a population at a landscape level. Heredity. 2020;124(1):170–81.

    Article  CAS  PubMed  Google Scholar 

  27. Li Q, Zhao J. Genetic diversity of Phyllanthus emblica populations in dry-hot valleys in Yunnan. Biodivers Sci. 2007;15(1):84–91.

    Article  Google Scholar 

  28. Ge JY, Guo ZT, Zhan T, Yao ZQ, Deng CL, Oldfield F. Magnetostratigraphy of the Xihe loess-soil sequence and implication for late Neogene deformation of the West Qinling Mountains. Geophys J Int. 2012;189(3):1399–408.

    Article  Google Scholar 

  29. Renner SS. Available data point to a 4-km-high Tibetan Plateau by 40 Ma, but 100 molecular-clock papers have linked supposed recent uplift to young node ages. J Biogeogr. 2016;43:1479–87.

    Article  Google Scholar 

  30. Sengupta A, McConkey KR, Kwit C. The influence of provisioning on animal‐mediated seed dispersal. Oikos. 2022;2022(2).

  31. Xia HH, Wang BS, Zhao W, Pan J, Mao JF, Wang XR. Combining mitochondrial and nuclear genome analyses to dissect the effects of colonization, environment, and geography on population structure in Pinus tabuliformis. Evol Appl. 2018;11(10):1931–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Schut JW, Qi X, Stam P. Association between relationship measures based on AFLP markers, pedigree data and morphological traits in barley. Theor Appl Genet. 1997;95(7):1161–8.

    Article  CAS  Google Scholar 

  33. Ude G, Pillay M, Ogundiwin E, Tenkouano A. Genetic diversity in an African plantain core collection using AFLP and RAPD markers. Theor Appl Genet. 2003;107(2):248–55.

    Article  CAS  PubMed  Google Scholar 

  34. Panahi B, Afzal R, Neghab MG, Paymard B. Relationship among AFLP, RAPD marker diversity and Agromorphological traits in safflower (Carthamus tinctorius L.). Progress in Biological Sciences. 2013;(1):90–9.

  35. Joshi J, Schmid B, Caldeira MC, Dimitrakopoulos PG, Good J, Harris R, et al. Local adaptation enhances performance of common plant species. Ecol Lett. 2001;4(6):536–44.

    Article  Google Scholar 

  36. Mosca E, Eckert AJ, Di Pierro EA, Rocchini D, La Porta N, Belletti P, et al. The geographical and environmental determinants of genetic diversity for four alpine conifers of the European Alps. Mol Ecol. 2012;21(22):5530–45.

    Article  CAS  PubMed  Google Scholar 

  37. Fournier-Level A, Korte A, Cooper MD, Nordborg M, Schmitt J, Wilczek AM. A map of local adaptation in Arabidopsis thaliana. Science. 2011;334(6052):86–9.

    Article  CAS  PubMed  Google Scholar 

  38. Ji RX, Yu X, Chang Y, Shen C, Bai XQ. Xia XL. et al. Leaf anatomical structure and geographic environmental dataset of Caryopteris mongholica from seven regions in north and northwest China. J Glob Change Data Discov. 2021;5(1):99–107.

  39. Wahid A, Gelani S, Ashraf M, Foolad MR. Heat tolerance in plants: An overview. Environmental and Experimental Botany. 2007;61(3):199–223.

  40. Mao JF, Wang XR. Distinct niche divergence characterizes the homoploid hybrid speciation of Pinus densata on the Tibetan Plateau. Am Nat. 2011;177(4):424–39.

    Article  PubMed  Google Scholar 

  41. Tan J, Wan J, Luo F, Yu F. Relationships between genetic diversity of vascular plant species and climate factors. J Resour Ecol. 2018;9(6):663–72.

    Google Scholar 

  42. Ceccarelli S, Grando S. Drought as a challenge for the plant breeder. Plant Growth Regul. 1996;20(2):149–55.

    Article  CAS  Google Scholar 

  43. Stebbins GL. Aridity as a stimulus to plant evolution. Am Nat. 1952;86(826):33–44.

    Article  Google Scholar 

  44. Wang ZW, Xie LN, Prather CM, Guo HY, Han GD, Ma CC. What drives the shift between sexual and clonal reproduction of Caragana stenophylla along a climatic aridity gradient? BMC Plant Biol. 2018;18(1):1–10.

    Article  CAS  Google Scholar 

  45. Poland JA, Brown PJ, Sorrells ME, Jannink J. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE. 2012;7(2): e32253.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bolger AM, Lohse M, Usadel B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Qi P, Gimode D, Saha D, Schröder S, Chakraborty D, Wang X, et al. UGbS-flex, a novel bioinformatics pipeline for imputation-free SNP discovery in polyploids without a reference genome: finger millet as a case study. BMC Plant Biol. 2018;18(1):117.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9(4):357–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88(1):76–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Bryc K, Velez C, Karafet T, Moreno-Estrada A, Reynolds A, Auton A, et al. Genome-wide patterns of population structure and admixture among Hispanic/Latino populations. PNAS. 2010;107(Supplement 2):8954–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Tang H, Peng J, Wang P, Risch NJ. Estimation of individual admixture: Analytical and study design considerations. Genet Epidemiol. 2005;28(4):289–301.

    Article  PubMed  Google Scholar 

  54. Bradburd G, Coop G, Ralph P. Inferring continuous and discrete population genetic structure across space. Genetics. 2018;210(1):33–52.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Alexander DH, Lange K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinformatics. 2011;12(1):1–6.

    Article  Google Scholar 

  56. Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA. Stacks: an analysis tool set for population genomics. Mol Ecol. 2013;22(11):3124–40.

    Article  PubMed  PubMed Central  Google Scholar 

  57. 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 Res. 2010;10(3):564–7.

  58. Hudson RR, Boos DD, Kaplan NL. A statistical test for detecting population subdivision. Mol Biol Evol. 1992;9(1):138–51.

    CAS  PubMed  Google Scholar 

  59. Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: Community Ecology Package. R Package Version 2.5–2. Available at: https://CRAN.R-project.org/package=vegan (Published: November 28, 2020).

  60. Borcard D, Gillet F, Legendre P. Spatial analysis of ecological data. Numerical ecology with R. Springer, Cham. 2018. p. 299–367.

  61. Frichot E, Schoville SD, Bouchard G, François O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol Biol Evol. 2013;30:1687–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank Qingzhang Du and Jianbo Xie at College of Biological Sciences and Biotechnology, Beijing Forestry University, for experimental methods guidance and valuable discussion. Jianfeng Mao at College of Biological Sciences and Biotechnology, Beijing Forestry University, for his help in using the software. Meng Ji and Yuewen Yang at Inner Mongolia Academy of Forestry Science, for their help in plant identification.

Funding

This work was supported by the National Science and Technology Support Project of the Ministry of Science and Technology of China (2015BAD07B01) and the National Natural Science Foundation of China (32071734, 31770649, 31600484).

Author information

Authors and Affiliations

  1. College of Biological Sciences and Biotechnology, National Engineering Research Center of Tree Breeding, Beijing Forestry University, Beijing, China

    Ruoxuan Ji, Xiao Yu, Tianmeng Ren, Yuan Chang, Zheng Li, Xinli Xia, Weilun Yin & Chao Liu

Authors
  1. Ruoxuan Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiao Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianmeng Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuan Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xinli Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Weilun Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CL, XLX, and WLY planned and designed the research. RXJ, XY, YC, TMR and CL contributed to the investigation. ZL, XY and RXJ processed the figures. RXJ and CL were in charge of data curation. RXJ analyzed data and wrote the original draft, and CL reviewed. All authors agree to be accountable for the final manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Chao Liu.

Ethics declarations

Ethics Approval and Consent to Participate

The sample collection in this study was carried out in accordance to local and national guidelines.

Consent for Publication

Not applicable.

Competing interests

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

 Table S1. List of 125 C. mongholica individuals tested in this study. Table S2. Summary of GBS data. Table S3. Environmental parameters used in this study, and the mean (±standard deviation) values of 18 locations. Table S4. 17 bioclimatic variables in the 18 locations of C. mongholica

Additional file 2: Fig S1.

Plot of ADMIXTURE cross-validation error. Fig S2. Cross-validation results comparing the non-spatial and spatial models. Fig S3. Pink and viole-blue flowers of C. mongholica. Fig S4. Manhattan plots representing the distribution of significance values -log10(p-value) obtained by the genetic-environment association approach LFMM for 17 bioclimatic variables.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ji, R., Yu, X., Ren, T. et al. Genetic diversity and population structure of Caryopteris mongholica revealed by reduced representation sequencing. BMC Plant Biol 22, 297 (2022). https://doi.org/10.1186/s12870-022-03681-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12870-022-03681-y

Keywords

BMC Plant Biology

ISSN: 1471-2229

Contact us