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Article

Genetic Diversity and Population Structural Analysis Reveal the Unique Genetic Composition of Populus tomentosa Elite Trees

by
Bo Kong
1,2,3,
Lexun Ma
1,2,3,
Jiahua Du
1,2,3 and
Pingdong Zhang
1,2,3,*
1
State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China
2
Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, Beijing Forestry University, Beijing 100083, China
3
College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(8), 1377; https://doi.org/10.3390/f15081377
Submission received: 4 July 2024 / Revised: 25 July 2024 / Accepted: 31 July 2024 / Published: 7 August 2024
(This article belongs to the Section Genetics and Molecular Biology)
Browse Figures
Versions Notes

Abstract

:
Genetic diversity analysis provides the scientific basis for the preservation, evaluation, and utilization of the germplasm resources of tree species. We explored the genetic diversity and structure of Populus tomentosa elite trees in North China using 13 nuclear microsatellite markers. We compared nine groups of accessions including 20 originating from Beijing (BJ), 122 from Hebei (HB), 20 from Shandong (SD), 113 from Henan (HN), 270 from Shanxi (SX), 54 from Shaanxi (SAX), 8 from Gansu (GS), 10 from Anhui (AH), and 6 from Jiangsu (JS). All of the studied primer pairs were polymorphic and generated 125 alleles. Analyses of molecular variance revealed that 79%, 14%, and 8% of the total variation was due to variations within the individual, among individuals, and among populations, respectively. Based on principal coordinate and STRUCTURE cluster analyses, individuals distributed in the southern region (HN, SAX, AH, and JS) were roughly classified into one group, while those distributed in the northeastern region (BJ, HB, and SD) and northwestern regions (SX) were separately divided into one group each. Moreover, the northwestern region included two-thirds of the SX trees, and the remainder were in the northeast region. By analyzing genetic diversity and structure within populations, individuals with different genetic backgrounds were screened for constituent training populations (TRS), including broad allelic variation for related traits. This ensures that the genomic prediction model can accurately capture genetic effects and provide reliable predictions across a broad spectrum of genetic backgrounds. Therefore, our results will benefit genome breeding technology.

1. Introduction

Conserving genetic diversity is essential to enhancing plant breeding programs, developing new varieties with desirable agronomic traits, and broadening the genetic basis of this important economic forest species. Understanding the genetic diversity and population structure of existing biological communities not only helps to more deeply understand the evolution and demographic history of threatened species [1,2], but also promotes the design of conservation and management strategies for endangered species [3].
Poplar is one of the most rapidly growing wood species with the largest cultivated area and highest timber yield in the mid-latitude plain areas worldwide, owing to its characteristics of rapid growth, early timber formation, high yield, and easy renewal. The country with the largest afforestation area and the largest cultivated area in the world is China. Poplar plantation plays a crucial role in wood production, ecological protection, and economic development in China. Extensive research has been conducted on variety breeding, site selection, afforestation, and forest management technology for poplar plantations [4,5,6]. Furthermore, poplar stands as the third plant to have its entire genome sequenced [7,8]. With a dense genome and well-established genetic transformation system, poplar has emerged as a model organism for investigating genetics, development, and genetic engineering enhancements in woody plants [9]. The comprehensive investigation of poplar has advanced our comprehension of the distinctive anatomical, physiological, and genetic traits of perennial tree species, as well as the evolutionary transition from annual herbs to perennial trees in plants [10,11,12,13].
Populus tomentosa Carr. is an indigenous fast-growing tree species within the northern region of China, with both ecological and commercial value. It has significant ecological benefits including, but not limited to, the formation of shade, absorption of airborne toxins, carbon sequestration, oxygen release, dust retention, reduction of airborne bacteria, noise attenuation, etc. [14]. Additionally, timber has been utilized for construction, furniture, boxboard, matchsticks, paper, and other materials [15]. Since 1982, the genetic improvement program at Beijing Forestry University led by Zhu has investigated resource utilization in P. tomentosa [16]. A total of 1047 elite trees were collected and planted in Guanxian County, Shandong Province. Unfortunately, some precious genetic resources underwent genetic losses as a result of their inability to adapt to local climate conditions [17]. Unreasonable field management measures led to the death of the elite trees, and only 469 remain [18].
Forest tree breeding is more challenging than crop breeding by reason of the long breeding cycles and high costs of field trials. Molecular genetics technologies have the potential to guide and accelerate the breeding of Populus cultivars. DNA marker technology, which allows for the accurate identification of varieties and parents and analysis of population genetics, is essential for genetic resource management and breeding programs [19]. Moreover, applying molecular genetics to poplar trees is useful for genetic resource management and routine breeding operations in many species [20,21,22,23].
Different types of markers have been used in China to analyze the genetic diversity and genetic variation in the Populus germplasm resource [24]. It is worth mentioning that simple sequence repeats (SSRs) are universal and have the ability to transfer amplification not only between different genera in the same family, but also in plants with widely different genetic backgrounds [25]. Single-nucleotide polymorphism (SNP) markers parameterize the expression of plant genetic diversity less than SSR markers, and unlinke SSR markers, show better geographical isolation than a large set of SNP markers [26]. SSRs are inherited by parents, which provides for simple operation, high polymorphism, wide coverage, codominance, and high repeatability [19]. Han et al. used SSRs to analyze the genetic diversity of Populus populations from different provenances in the Germplasm Resource Bank of Populus, including nine provenances in Beijing, Hebei, Shandong, Henan, Shanxi, Shaanxi, Gansu, Anhui, and Jiangsu. The best individual plants were included within the distribution area (30–40° N, 105–125° E), and a breeding parent population of Populus was constructed [27]. Notably, the existing P. tomentosa elite trees in Shuozhou, Shanxi Province are very important for cultivating cold-resistant varieties [28]. However, the genetic diversity and population structure of these elite trees have never been analyzed at the DNA level.
The objective of this study was to characterize the genetic diversity and population structure of 623 P. tomentosa elite trees using SSR fluorescence molecular marking technology. Under the influence of natural selection and human interference, the sampling size in Anhui, Jiangsu, and Gansu was less than 10, accounting for only 0.04 of the total sample size. Given the insufficient sample size, we cannot derive a more comprehensive understanding of the genetic structure of all poplar species in their distribution areas. However, thanks to the large total sample size, our results can help us to understand the genetic structures of most poplar populations. The findings will inform the identification of cultivars, P. tomentosa breeding and genetic resource management.

2. Materials and Methods

2.1. Plant Material and DNA Extraction

A total of 623 plant samples were examined (Figure S1). Some of the leaf samples were obtained from the Germplasm Resource Bank of P. tomentosa in Guanxian County, Shandong Province established by Beijing Forestry University in 1985, which has preserved 441 diploid elite trees from nine provinces (Figure 1), including Beijing (BJ), Hebei (HB), Shandong (SD), Henan (HN), Shanxi (SX), Shaanxi (SAX), Gansu (GS), Anhui (AH), and Jiangsu (JS). Among the 441 elite trees, 51 are female and 390 are male (Bai et al. 2015). Other leaf samples were obtained from 182 diploid elite trees planted by the Shanxi Institute of Forestry, in Shuozhou [28]. Total DNA was extracted using a plant genomic DNA extraction kit (Tiangen Biotech Co., Ltd., Beijing, China).

2.2. Primer Screening for the Polymorphism

Han screened 406 pairs of SSR primers (Table S1) with stable polymorphism in the SSR database (IPGC; https://www.ornl.gov/ (accessed on 11 December 2020)) published by the International Populus Genome Consortium [29]. These primers were used for TP-MI3-SSR PCR to screen for SSR polymorphism primers with differences among individuals. The number of allelic loci (Na), effective allele number (Ne), expected heterozygosity (He), observed heterozygosity (Ho), and Shannon’s polymorphism index (I) were calculated for all of the polymorphic primers using POPGEN version 3.2 software [30].

2.3. SSR Genotyping

Two types of primers were required according to Schuelke’s method [31]. A final concentration of 0.4 μM was used in the reaction mixture for each primer. The forward primers were labeled with the fluorescent dyes (ROX, FAM, TAMRA, and HEX). The TP-M13-SSR PCR reaction mixture consisted of 10.0 μL of 2× Taq Plus PCR MasterMix, 0.08 μL of the forward primers (2 μmol/L), 0.32 μL of the reverse primers (2 μmol/L) (primers were manufactured by Beijing Ruibo Xingke Biotechnology Co., Ltd., Beijing, China), 0.4 μL of one of the four fluorescent dyes, 2.0 μL of template DNA and 7.2 μL of double-distilled water. The cycling conditions included an initial Taq polymerase activation step (94 °C for 5 min), followed by 25 cycles, including denaturation (94 °C for 30 s), annealing (53 °C for 30 s), and extension (72 °C for 30 s); the next step was 8 cycles with a final extension step (72 °C for 8 min). The products were stored at 4 °C and separated by capillary electrophoresis using an ABI3730xl DNA analyzer (Applied Biosystems, Foster City, CA, USA) after confirming the PCR amplification using 1.5% agarose gel electrophoresis. The results were read and analyzed using GeneMarker V2.2.0 (Applied Biosystems).

2.4. Data Analysis

GeneMarker2.2.0 software was used for the capillary electrophoresis data analysis. The genetic diversity parameters included the number of alleles per loci (Na), the number of effective alleles (Ne), expected heterozygosity (He), observed heterozygosity (Ho), Wright’s allelic fixation index (Fis), inbreeding within the entire population (Fit), variation by cause of differentiation among subpopulations (Fst), Shannon’s information index (I), unbiased heterozygosity, and analysis of molecular variance (AMOVA), which were estimated using GenAlEx 6.502 software [32]. The Shannon index was converted to the effective number of species to evaluate the diversity difference between communities [33]. Marker polymorphism information content (PIC) was calculated using PowerMarkerV3.2 software to evaluate genetic diversity.
Genetic differentiation among the different representative collections was analyzed with principal coordinate analysis (PCoA) using the genetic dissimilarity matrix in GenAlEx 6.502 [32] and a complimentary three-dimensional scatterplot was generated using the Plotly R package [34] (Script S2). Based on the Nei genetic distance [35], a UPGMA dendrogram of the six populations was constructed using NTSYSpc version 2.10e. The population structure was confirmed with STRUCTURE v2.3.4 software [36] using a burn-in period of 10,000 and a Markov chain Monte Carlo model of 50,000 iterations, and the mixed model and frequency correlation model were selected. Ten independent runs were made with values of K set from 1 to 15, with three iterations for each value of K. The best value of K was determined by ΔK [37] statistics using the Structure Harvester web-based tool [38].

3. Results

3.1. Polymorphic SSR Primers

Using DNA from the 623 elite trees as the template, TP-M13-SSR PCR (Figure S2) was used to screen for differentially expressed SSR polymorphisms between the female and male strains, as well as among individuals of the same sex. A total of 13 pairs of stable polymorphic SSR primers were screened to analyze the genetic diversity and population structure of P. tomentosa. Detailed information on these screened SSR primers is shown in Table 1.

3.2. Microsatellite Polymorphisms

All 13 SSR primer pairs amplified consistently interpretable alleles in all of the investigated Populus species (Table 2). Significant genetic differentiation was detected in various individuals (p < 0.01). A total of 125 alleles were revealed for all these genotypes (N = 623) with an average of 9.6 alleles/locus, ranging from 3 (ORPM_197) to 15 (GCPM_1411-1). The GCPM_112-1 locus provided relatively large amounts of genetic information, and its I was 1.827. The Ho ranged from 0.024 to 0.867, and the He ranged from 0.273 to 0.790. The mean Ho (0.513) was lower than the mean He (0.608). The PIC of the marker ranged from 0.266 to 0.761, with a mean of 0.563. Fis ranged from −0.711 to 0.938, and negative Fis values were observed in six of the markers. These genetic diversity parameters show that all of the elite trees had high genetic diversity, and the selected primers had high polymorphism and high resolution as tested materials.

3.3. Population Genetic Diversity and Genetic Differentiation

We excluded the Gansu, Anhui, and Jiangsu collections from the population diversity analysis owing to the small number of samples. Consequently, our estimates for them may not be representative. Based on the results of the population genetic diversity analysis (Table 3), the BJ population had relatively low genetic diversity (Na = 2.692, Ne = 1.699, I = 0.562, Ho = 0.474, He = 0.329). We converted the I values of various groups to the effective number of species as follows: 1.8 (BJ), 2.0(HB), 2.2 (SD), 2.9 (HN), 3.5 (SX), 3.1 (SAX). Hence, we can state that the SX population has a relatively high diversity.
The P. tomentosa population exhibited high genetic diversity and genetic differentiation. The within-population (Fis) and inter-population (Fit) inbreeding coefficients were used as indicators to evaluate the degree of population neatness. The inter-population genetic fraction coefficient (Fst) was used as an indicator to evaluate the level of genetic differentiation in the populations. The large gene flow (Nm > 1) among populations weakened the possibility of genetic drift, which decreased the degree of genetic differentiation among the populations. In contrast, the genetic differentiation among populations increases when Nm < 1 [39]. The average Fis and Fit values of P. tomentosa were 0.020 and 0.123, respectively, indicating a loss of heterozygosity in the population and inbreeding among the populations. The Fst between populations ranged from 0.027 to 0.217, with an average of 0.105, indicating moderate genetic differentiation among the populations (Table 2). Higher gene flow (mean = 2.125, Table 2) among the populations prevented genetic differentiation among the populations to a certain extent. AMOVA revealed the existence of highly significant (p < 0.01) variations among populations, genotypes within populations and genotypes from various sources (Table 4). Thus, the largest genetic variation (79%) was attributed to variation within individuals followed by variation among individuals within populations (8%) and variation among populations (14%). The overall F-statistics exhibited significant genetic differentiation among populations (Fst = 0.136), among individuals within a population (Fis = 0.087), and within individuals (Fit = 0.212) (Table 4).

3.4. Population Genetic Structure

The PCoA results indicate that the 623 individual elite trees were divided into three groups (Figure 2). Group I (blue group) was mainly composed of SX, most of which were recorded in the northwest climate zone of the distribution area, and group II (green group) included BJ, HB, and SD. Group III (pink group) mainly included the majority of HN and SAX. The individuals from the SX provenance were widely distributed, indicating that SX had high genetic diversity. The first three coordinates of the PCoA explained 43.95%, 16.43%, and 10.20% of the total variance, respectively, and jointly explained 70.58% of the total genetic variation. The cluster analysis (Figure 3) of the six populations based on Nei’s genetic distance showed three groups that clustered based on geographical proximity. HN and SAX were transitional areas of gene exchange between the two groups, and much of Shanxi’s breeding population formed a separate cluster.
STRUCTURE analysis revealed the genetic structure and likely admixture of the 623 P. tomentosa trees (Figure 4). K was tested from 2 to 15 with 15 replicated runs. The most likely number of genetic structures defined by ΔK identified K = 8 (Figure 4A) as the number of clusters beyond which there was no further increase in likelihood. The estimated sub-populations for the 623 Populus trees are shown in Figure 4B. The 623 elite trees were divided into eight clusters based on the SSR markers. All provenance groups had unique genetic backgrounds across all Ks. The patterns of individual assignment in the subsets (K = 3) (Figure 5) were generally different from the genotypes of the elite trees that were assigned to the three different climatic regions. Most samples from the three northeastern populations (BJ, HB, and SD) and SX clustered into one group (red gene pool), whereas samples from the HN, SAX, AH, and JS populations clustered into a second group (green gene pool). Many samples from the HN population were admixed. Moreover, the other samples from the SX population (collected in Shuozhou City, Shanxi Province) were classified into a third group (blue gene pool).

4. Discussion

4.1. Genetic Parameters of the P. tomentosa Elite Trees

Based on the allelic variations in 13 microsatellite loci, the genetic diversity of nine local populations was evaluated. Widely scattered species are thought to have a diverse genetic base that eases their long-lived persistence in a variety of habitats and environmental conditions [40]. The study revealed that higher genetic diversity exists within species. SSR molecular marker technology enables the rapid analysis of the genetic diversity of plant species, and has the advantages of high polymorphism, high stability, and co-dominance. We amplified 125 alleles with an average PIC of 0.563 utilizing 13 pairs of SSR primers from 623 elite trees from nine populations. Seven primers had both Shannon’s index and PIC values above the mean, indicating that this set of primers has a good discriminatory ability. Han [29] studied 407 clones in Guanxian County, Shandong Province using 24 pairs of SSR primers, and the average PIC was 0.378. Yao et al. [41] amplified 106 alleles from 272 poplar clones with 16 pairs of SSRs, and the PIC was 0.36. Genetic diversity analyses showed that populations are highly rich in genetic variation. Taken together, these markers are largely successful in differentiating various idioplasms.
The Shannon–Wiener Diversity Index is utilized to investigate the local diversity (alpha-diversity) of a plant community, generally paired with the Simpson diversity index [42]. The diversity Shannon entropy index (“Shannon–Wiener Index”) itself is not diversity, but only an indicator of diversity, and is highly nonlinear. For the diversity analysis, we must translate the values into true diversity—effective number of species: exp (x) = ex. It turns out that Shannon measures only give meaningful results when community weights are unequal [33]. Thus, it is the “fairest” index, and weighting each species depends entirely on its frequency in the sample. So, for general-purpose diversity studies, this is the right choice. The average Shannon polymorphism index of all populations was 0.831, which was higher than the I (0.825) of the 272 clones from the nine Populus populations determined using 16 microsatellite markers [41]. Some reasons for this result are explained in the following. Population size is closely associated with the results of genetic diversity analyses. The larger the population size, the more genomes there are. As such, more mutations accumulate, and the genetic diversity will be higher. We detected an average of 9.6 alleles/locus, which is dramatically greater than the Na (6.1) of 372 unrelated individuals in 47 hairy tree populations measured using nine microsatellites. This finding also agrees with earlier observations, for the reason that self-incompatibility is common in this species. This may explain the considerable level of polymorphism in the microsatellite loci used in this study.
He is another important method used to measure gene diversity, which was unequal among the nine populations. The population with the highest He value (0.640) was Shanxi, and that with the lowest was Beijing (He = 0.329), indicating that the genetic diversity level of this species differs in different geographical regions. The mean He was 0.479, which is lower than the He reported for other poplar microsatellites, such as Korean poplar (0.603) [43]. This is probably caused by the fact that this poplar population is distributed only in the northern region of China. Furthermore, BJ and JS, the two populations with the lowest genetic diversity, are located on the lower reaches of the Yellow River. Much of this is attributed to human activities, such as deforestation in the region over the past few centuries and a series of reductions implemented to fight the flying catkin problem. With the exception of human disturbance, the adaptability (mutation) of the population to an extreme environment also affected its normal growth activities. The distribution range of the Populus population is 30–40° N and 105–125° E. BJ and JS are at the boundary of the P. tomentosa distribution region.
In molecular population genetics, the inbreeding coefficient (Fis = 1 − Ho/He) is an indicator of the degree of inbreeding between individuals within a population. The Fis of the three populations (Henan, Shanxi, and Shaanxi) observed in all populations indicated that He > Ho resulted in excessive homozygotes and frequent inbreeding. Two-parent inbreeding may lead to heterozygotes loss [44,45]. Further, spouse availability reductions thanks to clonal growth may be another reason. Research indicates that the restriction of gene flow and reproductive isolation among populations, as well as the reduction in reproductive trees remaining in fragmented forests, are contributing factors in exacerbating the problem of inbreeding in offspring populations [46,47,48]. In trees, the cumulative effects of successive generations of inbreeding lead to inbreeding depression [49]. Inbreeding increases the chance of homozygosity for harmful alleles, leading to decreased individual fitness and the production of deleterious genes, affecting the genetic health of the population. Addressing the causes and effects of inbreeding is thereby critical to maintaining genetic health.
In contrast to previous findings, our results conclude that within a species distribution region, marginal populations have lower genetic diversity than central populations [50]. The Shanxi population (a peripheral population in the upstream regions of the Yellow River) had the highest values for almost all of the genetic diversity parameters. Shanxi, located in the Northwest Plateau, is between 34°34′–40°44′ N and 110°14′–114°33′ E, with an annual average temperature of 4.2–14.2 °C and precipitation of 358–621 mm. In a harsh environment, transplanted flora develops into a new form, and adapts locally. The newly adapted form, P. simonii Carr., with drooping young shoots but explanate branches, has been identified and used as an important breeding material for its cold resistance characteristics [51,52]. Adaptation to extreme environmental conditions explains why Shanxi and other provenance populations are important breeding materials used to improve cold resistance.

4.2. Population Diversity and Structure of the P. tomentosa Elite Trees

Genetic structure and the level of genetic differentiation are mainly affected by various factors, such as the evolutionary history of the plant, habitat changes, genetic drift, bottleneck effects, natural selection, mating systems, and gene flow. These factors are important to establishing an effective protection strategy for endangered plants [3]. Mating between close relatives of a tree species usually results in inbreeding depression. The genetic diversity and population structure of 623 P. tomentosa trees from nine provenances were analyzed using the SSR marker technique. The results show that gene exchange was frequent among the populations, and the Bayesian genetic structure analysis detected a complex admixture among the populations. This indicates that although individuals belong to different geographical locations, there will be frequent gene exchanges between individuals at the same altitude and precipitation, resulting in genetic homogeneity. Additionally, the degree of genetic differentiation was relatively low, probably because the plants used for this study were from the Huanghe–Huaihe–Haihe River basin. Because of the spreading effect of the Yellow River, the P. tomentosa biotope extends into different regions along the river. This allows for frequent gene exchange between different provenances, resulting in the genetic variation in P. tomentosa between provenances becoming smaller. In short, most of the genetic variation came from variations within individuals at different sites.
The influences of environmental factors on genetic diversity and structure are paramount. Climate change, environmental pollution, and deforestation are all contributing to a marked decline in species diversity. Variations in environmental factors over time and space play a pivotal role in shaping the composition of plant communities and the characteristics of species diversity [53]. Specifically, climate change, encompassing alterations in precipitation and temperature, will precipitate changes in species’ phenology, behavior, distribution, and species richness [54]. Research has indicated that elevation and precipitation exert significant net effects on genetic diversity; however, the latter holds slightly greater sway [55]. In our study, Anhui (AH), Jiangsu (JS), and Gansu (GS) may not be representative in view of the insufficient sample size. Populations at higher elevations exhibit relatively high genetic diversity. It is evident that the genetic diversities of populations at higher elevations in Shanxi (SX), Henan (HN), and Shaanxi (SAX) significantly surpass those of populations in Beijing (BJ) and Hebei (HB). Bioclimatic variables associated with precipitation demonstrate a positive effect in humid-climate regions; individuals within the same precipitation climate regions display genetic homogeneity with frequent instances of inbreeding. If He > Ho for Henan (HN), Shanxi (SX), and Shaanxi (SAX), then the coefficient Fis is positive. This is because of the gradual decrease in precipitation from south to north in these areas, leading to inbreeding among individuals living in regions with similar humidity levels.
PCoA and STRUCTURE cluster analyses were performed on 623 unrelated trees, which divided them into three groups. These individuals distributed in the southern region (HN, SAX, AH, and JS) were roughly classified as one group, while those distributed in the northeastern region (BJ, HB, and SD) and the northwestern region (SX) were divided into one group each. However, the northwestern region included two-thirds of SX trees, and the remaining trees were from the northeast region. Besides, the STRUCTURE analysis revealed the genetic composition of the P. tomentosa elite trees (Figure 4). The most likely number of genetic clusters was K = 8 for ΔK. However, higher K values may identify unique genetic structures, as hybridization readily occurs in Populus. The entire population of P. tomentosa was divided into eight subpopulations using STRUCTURE (K = 8), a departure from Du et al. (K = 11) [7]. This discrepancy may be attributed to the exclusion of triploids from the populus gene bank based on ploidy detection results, the mitigation of triploid interference, and a 1.3-fold increase in sample size. As the K value increases, it becomes evident that population structure analysis yields more comprehensive information about population structure and migration. For instance, only the SD, SX, and HN populations can be explicitly assigned to a particular cluster (K = 8). However, K = 11, and apart from SD and SX, GS populations can also be clearly grouped into a specific cluster. Furthermore, as K increases further, AH and JS populations with a small number of sampled individuals can be divided into a specific subset; in HB and HN, which are located in the middle of the distribution of Trichotaxus chinensis and do not have complex topographic patterns, individuals with similar elevations are divided into specific subgroups. In our study, largely because of the obstacles in Taihang Mountain and Yellow River, we found no excessive genetic communication between SX and HN. This result is consistent with the findings of Du et al.
For the assignment analysis, Markov chain Monte Carlo (MCMC) simulation methods were widely employed when we performed Bayesian parameter identification because they are effective in obtaining posterior distributions [56]. When using MCMC (e.g., the Metropolis–Hastings algorithm) for statistical inference, increasing the number of iterations improves the accuracy of the estimation [57]. We verified the allocation results by increasing the number of iterations to 200,000 (Figure S3, k = 5). It is worth mentioning that we witnessed more pronounced and complex mixing. Genetic information was exchanged between all populations. Nevertheless, Shanxi (SX) was still assigned to different subsets, reflecting the high genetic differentiation.
Genetic diversity is the cornerstone of any viable breeding program [58]. Our work helps to estimate genetic diversity within and among populations, reveals sources of variation in populations, and emphasizes the importance of germplasm exchange to broadening the genetic base of a breeder’s working collection. Furthermore, molecular tools can improve the reliability of crosses, thereby increasing the likelihood of obtaining higher-yielding varieties. Therefore, a better understanding of the genetic diversity and structure of populations within a plant’s range, and the collection of individuals with a sufficiently broad genetic base to construct breeding populations, are particularly important for the improvement and refinement of future plant breeding programs.

5. Conclusions

We analyzed the genetic diversity and structure of 623 elite trees using 13 pairs of SSR primers. The identification of SSR markers with high PIC values will contribute to the characterization and conservation of the P. tomentosa germplasm. HN, SX, and SAX, which had high gene diversity and Shannon diversity index values, could be important hot spots for germplasm conservation. Both cluster analysis and structural histograms showed three sets of genotypes. However, the latter analysis revealed a high level of admixture among the various gene pools. In these clusters, the superior trees from Henan and those from the northeast region gathered together, but were distantly related to those from the southern region. The screening of genetically distant individuals as hybrid parents based on clustering results enhances the utilization of advantage related to hybridization, resulting in more variation. The high level of admixture observed in the STRUCTURE analysis suggests complex genetic backgrounds. Further studies could focus on understanding the historical and ecological factors contributing to this admixture. Although the current study revealed a sufficient level of variation among these elite trees, using more genotypes and markers is essential to capturing diversity in the P. tomentosa germplasm.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15081377/s1, Figure S1: Individuals of Populus tomentosa collected in different seasons in Shandong and Shanxi; Figure S2: Maps of PCR product separation for 13 SSR loci; Figure S3: Assignment analysis utilized STRUCTURE v2.3.4 software with a burn-in period of 50,000 and a Markov chain Monte Carlo model of 500,000 iterations, selecting the mixed model and frequency correlation model; Table S1: The specific SSR primer information of tomentosa superior clones.

Author Contributions

B.K. performed data organization and visualization as well as first draft writing. L.M. performed statistical analysis and wrote the manuscript. J.D. prepared experiments and wrote the manuscript. P.Z. performed review and editing of the manuscript and experimental supervision and leadership. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by National Key Research and Development Program of China (2022YFD2200301-02).

Data Availability Statement

The datasets generated during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Locations in the northern region of China of the nine sample populations (Beijing, Hebei, Shandong, Jiangsu, Anhui, Henan, Shanxi, Shaanxi, and Gansu) are shown in different colors, including the best single-plant areas.
Figure 1. Locations in the northern region of China of the nine sample populations (Beijing, Hebei, Shandong, Jiangsu, Anhui, Henan, Shanxi, Shaanxi, and Gansu) are shown in different colors, including the best single-plant areas.
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Figure 2. Principal coordinate analysis of P. tomentosa based on the genetic distance using GenAlex V6.502. The accessions were differentiated into three genetic clusters. The dot color represents the origin of the cultivar: a blue dot represents BJ (Beijing), an azure dot represents HB (Hebei), a spring green dot represents SD (Shandong), an orange dot represents HN (Henan), a deep orange dot represents SX (Shanxi), and a rose red dot represents SAX (Shaanxi). The pink cluster contains mainly the SX germplasm, corresponding to the northwest climate zone of the Populus distribution area, the green cluster contains BJ, HB, SD, HN, SX, AH, and JS, forming the northeast climate zone, and the blue cluster mainly contains germplasms from HN and SAX, namely, the southern climate zone.
Figure 2. Principal coordinate analysis of P. tomentosa based on the genetic distance using GenAlex V6.502. The accessions were differentiated into three genetic clusters. The dot color represents the origin of the cultivar: a blue dot represents BJ (Beijing), an azure dot represents HB (Hebei), a spring green dot represents SD (Shandong), an orange dot represents HN (Henan), a deep orange dot represents SX (Shanxi), and a rose red dot represents SAX (Shaanxi). The pink cluster contains mainly the SX germplasm, corresponding to the northwest climate zone of the Populus distribution area, the green cluster contains BJ, HB, SD, HN, SX, AH, and JS, forming the northeast climate zone, and the blue cluster mainly contains germplasms from HN and SAX, namely, the southern climate zone.
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Figure 3. UPGMA dendrogram of P. tomentosa based on Nei’s genetic distance. The accessions were separated into three major genetic clades. One major clade consisted mostly of SX accessions. The second major clade consisted of accessions from HN and SAX. All other accessions were included in the final major clade.
Figure 3. UPGMA dendrogram of P. tomentosa based on Nei’s genetic distance. The accessions were separated into three major genetic clades. One major clade consisted mostly of SX accessions. The second major clade consisted of accessions from HN and SAX. All other accessions were included in the final major clade.
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Figure 4. Population structure of 623 clones collected in nine provinces of China. (A). Population stratification from the STRUCTURE analysis based on consensus across 15 replications when K = 8. ΔK estimates of the posterior probability distribution of the data for a given K. (B) Individuals are shown as thin vertical lines, which are divided into K-colored segments representing the estimated membership probabilities (Q) for each individual. All individuals are classified based on the Q-matrix.
Figure 4. Population structure of 623 clones collected in nine provinces of China. (A). Population stratification from the STRUCTURE analysis based on consensus across 15 replications when K = 8. ΔK estimates of the posterior probability distribution of the data for a given K. (B) Individuals are shown as thin vertical lines, which are divided into K-colored segments representing the estimated membership probabilities (Q) for each individual. All individuals are classified based on the Q-matrix.
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Figure 5. Population stratification from the STRUCTURE analysis based on consensus across 15 replications when K = 3. Individuals are shown as thin vertical lines, which are divided into K-colored segments standing for the estimated membership probabilities (Q) of each individual. Finally, all individuals were classified based on the Q-matrix.
Figure 5. Population stratification from the STRUCTURE analysis based on consensus across 15 replications when K = 3. Individuals are shown as thin vertical lines, which are divided into K-colored segments standing for the estimated membership probabilities (Q) of each individual. Finally, all individuals were classified based on the Q-matrix.
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Table 1. The information of 13 pairs of microsatellite primers of P. tomentosa includes primer name, SSR primer sequence (5′-3′), position on the chromosome, repeat motif and locus information (bp).
Table 1. The information of 13 pairs of microsatellite primers of P. tomentosa includes primer name, SSR primer sequence (5′-3′), position on the chromosome, repeat motif and locus information (bp).
PrimerSSR Primer Sequence (5′-3′)ChromosomeMotifExpected Size (bp)
LG_III-2Forward: ATTGATTATATTTGCCGCAT
Reverse: TGGACATCTCACTACCTTCC		Chr03AT/TA180, 186
GCPM_2570-1Forward: AACCCACTTCCTCTCTCTGT
Reverse: TGAGACTTCCGACTCGTAG		Chr01CT229, 233, 237
ORPM_197Forward: GTCAGTTTGCCCTCTTCGTC
Reverse: TGAGGGCGTCTCCTCTTTTA		Chr06GA206, 208
PMGC_2140Forward: GCTGTCAGAATCAAACACTTC
Reverse: AAGCAGATAACTAAGACATGCC		Chr07GA174, 176
PMGC_223Forward: CGATGAGGTTGAAGAAGTCG
Reverse: ATATATGTACCGGCACGCCAC		Chr02CTT185, 191
LG_VIII-3Forward: ATCCGACTTCGATATCTTCA
Reverse: CTACCTGAAACACAGGAAGC		Chr08AG/TC238, 254
GCPM_112-1Forward: TTAGAGGAGAGAACTGCTGC
Reverse: TGGTCTGCAACACAAGATT		Chr16GT127, 131, 133
GCPM_1411-1Forward: TCAACGACTTTTTCATTGTG
Reverse: AGCATTCTTGCTGGTGTTAT		Chr02TGC232, 235, 238
GCPM_1153-1Forward: TTCCTTTCACACAATGACAA
Reverse: TTTAAAAACTGGGTCCGTAA		Chr11CTT165, 171, 174, 180
LG_XVI-6Forward: ATAGCGATCATCAAAGGAAA
Reverse: AAATATTCATGTGGAGGCAC		Chr16AG/TC116, 130
PMGC_2818Forward: AAGCTTCATCGTCCTGCTTG
Reverse: CGTATCAATTCACGACTCTCG		Chr02GA138, 140
LG_XVI-7Forward: ACAAATCAAAGTCACAGCCT
Reverse: ATAGTGTTCAATCGGACCTG		Chr16AG/TC352, 362, 364
GCPM_1832-1Forward: TTACTTGCTAGCTGCCAATC
Reverse: CCTAAAAGTTTGTCTATGCGA		Chr02TA158, 160
Table 2. Polymorphism information for 13 microsatellite loci in diploid P. tomentosa.
Table 2. Polymorphism information for 13 microsatellite loci in diploid P. tomentosa.
LocusNaNeIPICHoHeFisFitFstNm
LG_III-272.4611.2920.5690.3740.5940.0390.2480.2170.900
GCPM_2570-1124.2581.6480.729 0.8180.766−0.249−0.1510.0782.944
ORPM_19732.0680.8740.4530.1110.5170.6430.6830.1131.970
PMGC_2140103.0051.4980.6360.3190.6680.3490.4600.1711.215
PMGC_223114.3161.6470.7320.6710.769−0.0560.0450.0962.369
LG_VIII-392.6721.3140.5940.0240.6260.9380.9510.2060.965
GCPM_112-1144.7481.8270.7610.8570.790−0.282−0.1970.0663.524
GCPM_1411-1152.8961.4510.6230.3790.6550.5760.6190.1022.199
GCPM_1153-1132.1621.2840.5200.4930.5380.0010.1160.1151.922
LG_XVI-642.3690.9960.5030.8670.578−0.658−0.6130.0279.024
PMGC_2818122.4601.1150.5110.8350.594−0.331−0.2600.0544.398
LG_XVI-762.1270.8440.4280.8220.530−0.711−0.6580.0317.850
GCPM_1832-191.3750.6750.2660.0940.2730.7040.7290.0832.763
Mean9.62.8401.2660.5630.5130.6080.0200.1230.1052.125
Na number of alleles per locus, Ne number of effective alleles, I Shannon’s information index, PIC polymorphic information content, Ho observed heterozygosity, He expected heterozygosity, Fis Wright’s allelic fixation index, Fit inbreeding within entire population, Fst variation due to differentiation among subpopulations, Nm geneflow.
Table 3. Mean values of the genetic diversity statistics for 13 microsatellite loci in nine populations.
Table 3. Mean values of the genetic diversity statistics for 13 microsatellite loci in nine populations.
PopNNaNeIHoHeuHe
Beijing (BJ)202.6921.6990.5620.4740.3290.338
Hebei (HB)1225.2311.7540.6700.4850.3550.356
Shandong (SD)204.0001.8830.7750.5050.4230.434
Henan (HN)1136.0002.4401.0680.4920.5470.550
Shanxi (SX)2707.6923.1181.2540.5320.6400.641
Shaanxi (SAX)545.5382.7361.1280.5570.5820.588
Mean98.75.1922.2720.9100.5080.4790.485
N sample number, Na mean number of alleles per collection, Ne mean number of effective alleles per collection, Ho observed heterozygosity, He expected heterozygosity, I Shannon’s information index, uHe unbiased heterozygosity. Unbiased heterozygosity accounts directly for related and inbred individuals.
Table 4. Analysis of molecular variance (AMOVA) in 623 individuals from nine populations shows the variation among the populations, among individuals within a population and individual P. tomentosa genotypes from different sources.
Table 4. Analysis of molecular variance (AMOVA) in 623 individuals from nine populations shows the variation among the populations, among individuals within a population and individual P. tomentosa genotypes from different sources.
SourcedfSSMSVariance Estimated%F-Statisticp-Values
Among population8552.78669.0980.57214Fst = 0.1360.001
Among individual6142418.3243.9390.3168Fis = 0.0870.001
Within individual6232060.0003.3073.30779Fit = 0.2120.001
Total12455031.111-4.195100--
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Kong, B.; Ma, L.; Du, J.; Zhang, P. Genetic Diversity and Population Structural Analysis Reveal the Unique Genetic Composition of Populus tomentosa Elite Trees. Forests 2024, 15, 1377. https://doi.org/10.3390/f15081377

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Kong B, Ma L, Du J, Zhang P. Genetic Diversity and Population Structural Analysis Reveal the Unique Genetic Composition of Populus tomentosa Elite Trees. Forests. 2024; 15(8):1377. https://doi.org/10.3390/f15081377

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Kong, Bo, Lexun Ma, Jiahua Du, and Pingdong Zhang. 2024. "Genetic Diversity and Population Structural Analysis Reveal the Unique Genetic Composition of Populus tomentosa Elite Trees" Forests 15, no. 8: 1377. https://doi.org/10.3390/f15081377

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