DOI: https://doi.org/10.21203/rs.2.10562/v1
Genetic diversity is an important component of biodiversity, and is the basis of ecosystem and species diversity [1-3]. Understanding the genetic diversity and structure of germplasm resources aids efficient and rational development, and the utilization of germplasm resources on the premise of effective protection [4-7]. The growth characteristics of trees involve reaching a large size with a long growth cycle; therefore, it is necessary to analyze their genetic diversity and population structure. To fully understanding the genetic information of germplasm resources, screening representative individuals and constructing a core germplasm bank for protection and utilization can shorten the breeding process and accelerate genetic improvement [8]. Currently, researchers regard the study of forest genetic diversity and population structure as important basic research, and such studies have been carried out on a variety of species. Moreover, many techniques and methods have been used to evaluate population genetics, for example molecular marker technology is widely used in this field. Among forest trees, the genome of Populus trichocarpa was the first to be sequenced [9]; therefore, there have been relatively more studies on its genetic information, including its genetic diversity and population structure [10-13]. In addition, similar studies have been carried out on other tree species, such as P. nigra L. [14-19], P. simonii [20], P. tremuloides [21, 22], P. balsamifera [23, 24], P. cathayana Rehd [25], P. euphratica [26, 27], P. tomentosa [28], and P. szechuanicavar. tibetica [29].
P. deltoides is widely distributed from the Mississippi River to southern Canada in North America, is one of the keystone forest tree species, and has important ecological value. This species has been widely used in forest breeding research and its genetic resources have become the main gene donors of poplar cultivars. These cultivar are mainly used to provide feedstocks for pulp, fiber, and bioenergy industries [30]. Furthermore, this tree is currently one of the most suitable for short rotation industrial timber intensive management of woody crops in the mid-latitude areas of the world. Production practice showed that the black poplar has a dominant position in the poplar plantation: P. deltoides and its hybrid with P. nigra L. have become the main commercial poplar trees in China.
There have been few studies on genetic diversity and population structure of P. deltoides. Fahrenkrog et al. [31] studied the genetic information of P. deltoides distributed in the Mississippi River Basin. However, in China, there has been little systematic and comprehensive research on the genetic information of introduced P. deltoides resources. Therefore, we have collected the germplasm resources of P. deltoides introduced by our research team and established a resource bank containing more than 1000 individuals. This bank lays the foundation for our future research on genetic diversity and population structure.
Among the methods and techniques to evaluate genetic diversity and population structure, simple sequence repeat (SSR) molecular marker technology has been widely used and is an ideal method because of its simple operation, co-dominance, high resolution, polymorphism, and repeatability [32, 33].
In the present study, we screened 15 pairs of stable, polymorphic SSR primers to analyze the genetic diversity and population structure of 384 P. deltoidesindividuals that were randomly selected from six provenances in the gene pool. The aim was to understand the genetic information of the introduced P. deltoides resources, and to provide theoretical support for the construction of a core germplasm resource bank. These resources could then be protected, managed, and utilized more scientifically and rationally.
In this study, 20 stable, polymorphic SSR primer pairs (8 pairs of primers for functional gene development and 12 pairs of universal primers) were selected from among 145 poplar SSR primer pairs for further analysis (Additional file 2: Table S1).
Microsatellite polymorphisms
The results of null allele detection revealed that some loci demonstrated an excess of homozygotes and that null alleles existed. The null allele frequency was identified and estimated using Cervus 3.0.7 [34], and those primers with a null allele frequency greater than 0.2 were discarded. Based on the intersection of the results of two detection methods (Additional file 2: Table S2), the detection data of five loci (SSR15, SSR42, SSR54, SSR65, and SSR76) were ignored in the subsequent analysis to ensure the accuracy of the results. However, five other loci (SSR47, SSR85, SSR105, SSR129, and SSR143) showed the presence of null alleles in only one provenance population, and we believed that they did not have a significant impact on the genetic diversity of the population.
A total of 108 alleles were detected using 15 SSR primer pairs in 384 P. deltoides individuals. The number of alleles (Na) per locus ranged from 2 (SSR85) to 19 (SSR126), with a mean of 7.2. The average number of effective alleles (Ne) per locus was 3.476, ranging from 1.076 at SSR85 to 10.397 at SSR126. Locus SSR126 provided more genetic information than the other loci and its Shannon information index was the largest (I = 2.533). The means of the observed heterozygosity(Ho) and expected heterozygosity (He) were 0.509 and 0.579, respectively, and except for locus SSR104, Ho was higher than He, indicating that the false-homozygous phenomenon existed in some loci. The polymorphic information content (PIC), as one measurement of genetic diversity, was between 0.068 (SSR85) and 0.896 (SSR120), with an average of 0.535. Six loci showed significant deviations from the Hardy-Weinberg equilibrium (HWE), possibly because of differences in the number and structure of the P. deltoides resources in each provenance, which indicted that there was genetic differentiation among the different provenances (Additional file 2: Table S3). The analysis results showed that the selected SSR primers had abundant polymorphisms and could effectively reflect the genetic diversity of the P. deltoides population.
The heat map of polymorphic SSR loci revealed the genetic diversity information of the loci, with the color richness being related to the degree of polymorphism. Analysis of the results (Fig. 1) showed that loci SSR85, SSR104, and SR143 had more monotonous colors, indicating relatively poor levels of polymorphism; whereas other loci were rich in polymorphisms. In addition, homozygous and heterozygous information for the loci were also clearly and intuitively expressed in the heat map.
Based on the result of the population genetic diversity analysis (Additional file 2: Table S4), we found that the ‘Was’ population had the lowest genetic diversity (Na = 2.600, Ne = 1.962, I = 0.617, Ho = 0.365, He = 0.358). The I values of the ‘Que’, ‘Lou’, and ‘Ten’ populations were greater than 1.0, and these three populations had private alleles; therefore, their genetic diversities were higher than the others. The HWE results showed that the ‘Que’ and ‘Ten’ populations deviated significantly from the equilibrium, suggesting that genetic differentiation might have occurred within the populations. The fixation index (F) reflects whether the actual frequency of a genotype deviates from the proportion of the genetic equilibrium theory. The F value of the ‘Mis’ population was 0.210 and its He (0.502) was higher than its Ho (0.389), suggesting that there was a genetic deletion in this population, which may be related to the small number of samples contained in the population.
The abundance of amplified fragments and the differences in the amplified fragments at each point in each population could be displayed visually using a heat map of the population genetic diversity (Fig. 2), which reflected the genetic variation of the population. The individuals from ‘Was’ populations expressed as homozygote at SSR58, SSR80, SSR85, SSR104, SSR105, and SSR143 loci, and its genetic diversity was poor. The size and proportion of amplified fragments could reveal the main genotypes and their differentiation in each population at each locus. Taking locus SSR143 as an example, single amplified fragments were obtained from the ‘Mis’, ‘Iow’, and ‘Was’ populations; some clones in the ‘Que’ and ‘Ten’ populations were deleted on the basis of the main amplified fragments; and some clones in the ‘Lou’ population were lengthened.
Population genetic differentiation and genetic variation
The P. deltoides population showed abundant 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, which was caused by a lack of heterozygosity from non-random mating among individuals. The inter-population genetic fraction coefficient (Fst) was used as an indicator to evaluate the level of genetic differentiation of populations: The larger the value, the greater the genetic differentiation among the populations. The existence of large gene flow (Nm > 1) among populations weakened the possibility of genetic drift, which would decrease the degree of genetic differentiation among populations. By contrast, when Nm< 1, the genetic differentiation among populations increases [36]. The average of Fis and Fit values of P. deltoides were 0.058 and 0.182, respectively, indicating that there was a loss of heterozygosity in the population, and there was inbreeding among the populations. The Fst between populations ranged from 0.062 to 0.205, with an average of 0.129, indicating that there was moderate genetic differentiation among the populations (Table 1). Meanwhile, higher gene flow (mean = 1.931, Table 1) among populations prevented genetic differentiation among populations to a certain extent.
The result of AMOVA showed that 84.88% of the total genetic variation originated from individuals, while 11.49% came from populations, and only 3.64% was ascribed to differences among individuals within the populations (Table 2). The results were consistent with the previous analysis results, which suggested that the genetic diversity of P. deltoides was mainly caused by genetic differences among individuals.
Analysis using the Mantel test showed that there was no significant relationship between genetic distance and geographical distance (R2 = 0.0065, P = 0.333, Fig. 3), indicating that geographical location was not the main factor leading to the genetic differences among the populations.
The results of PCoA showed that the 384 clones of P. deltoides could be divided into two groups (Fig. 4a). Group I mainly included the clones from ‘Que’ and ‘Was’, and a few clones from the ‘Iow’ provenance. Group II mainly included the clones of the ‘Ten’, ‘Lou’, ‘Mis,’ and ‘Iow’ provenances. At the same time, the clones from the ‘Que’ provenance were widely distributed, which indicated that ‘Que’ has abundant genetic diversity. STRUCTURE cluster analysis showed that when K = 2, the ΔK value was the largest (Fig. 4b), which indicated that the 384 P. deltoides individuals were suitable to be divided into two different groups. The genetic structure map (Fig. 4c) showed that most individuals from ‘Que’ and ‘Was’ belonged to the red group (Group I), while most individuals from ‘Ten’, ‘Lou’, and ‘Iow’ belonged to the green group (Group II). Based on the analysis of the matrix of its estimated membership probability (Q-matrix) when K = 2, we could determine the composition of the red and green groups (Additional file 2: Table S5).
To understand the genetic structure among the six populations of P. deltoides, we carried out PCoA and cluster analysis between the populations, based on the unweighted pair-group method with arithmetic means (UPGMA) on the provenance populations (Fig. 5a, b). In addition, an unrooted tree was drawn (Fig. 5c). The results showed that the ‘Lou’ and ‘Ten’ provenances were clustered together; the ‘Mis’, ‘Was,’ and ‘Que’ provenances were clustered together; and ‘Iow’ was independent of the other provenance populations.
SSR molecular markers have been widely used to study population genetic diversity and genetic structure. However, the existence of invalid alleles may reduce the population genetic diversity and increase genetic differentiation among populations, which will have a significant impact on the results of the study [22, 39, 40]. Therefore, when using molecular marker technology to carry out related research, the null alleles of the locus should be tested to ensure the accuracy and reliability of the results. In this study, two pieces of software with different calculation methods (Micro-Checker and Cervus) were used to detect the null alleles of the loci, and the commonly detected results were selected to improve the credibility of the results. Five pairs of primers with high frequency of null alleles were identified, and the data for these five loci were excluded in the subsequent analysis to ensure the reliability of the results.
To clearly understand the effect of loci containing null alleles on the population genetic analysis results, we compared the changes in of major genetic parameters before and after deletion of these loci. We found that the difference between Ho and He increased, the number of Fst increased, and the Nm decreased in the presence of the null allele loci (Table 3). These changes indicated that the null allele loci increase the false-homozygous phenomenon in populations, expand genetic differentiation, and weaken gene exchange among populations, thus concealing genetic diversity of the populations to some extent [41, 42].
The SSR primers used in this study were partly derived from functional gene primers developed by other researchers. The amplification motifs of the two primer pairs, SSR15 and SSR42, showed some differences from the original study.
The SSR15 locus was located in the promoter region of the PsHsf16 gene of the P. simonii Hsf family. Its repeat unit was ATTT [43]. In this study, the repeat motif of the SSR15 primer was ATT (Fig. 6a). The SSR42 locus was in the intron region of the PtCesA6 gene in P. tomentosa, and its repeating motif was TTCTCC [44]. In this study, the amplifying motif of the SSR42 primer was TC (Fig. 6b). At the same time, by calculating the null allele frequencies of different loci, we observed that the molecular markers of the functional genes of other poplar varieties screened in this study were likely to generate null alleles. These phenomena indicate that the marker information and characteristics of the same primers may change when they are applied to different experimental materials [45]. Therefore, the stability and polymorphism of primers should be further verified when screening functional gene development primers. Of course, it is better to develop polymorphic loci independently, based on the experimental materials, which is conducive to further research.
A better understanding of the genetic diversity, genetic variation, and genetic regulation of a population is essential for its proper management and conservation, especially in the face of current climate change, and the genetic evaluation of groups such as forests is particularly important [46]. Studies on the genetic diversity of P. deltoides populations have been carried out. Fahrenkrog et al. performed the first population genomics study for 425 unrelated individuals of P. deltoides distributed in 13 states of the southeastern United States. By assessing population structure, population differentiation, genetic diversity, LD, and adaptation in P. deltoides, they found that the differentiation between subpopulations of the natural P. deltoides population was weak (Fst = 0.022-0.106); however, the genetic diversity was high [31]. Furthermore, genome-wide association studies (GWASs) were also used to dissect the genetic regulation of eight growth and wood composition traits in P. deltoides, and single-nucleotide polymorphisms were detected by targeted resequencing of 18,153 genes in a population of 391 unrelated individuals. The authors found that both common and low frequency variants need to be considered to understand the genetic regulation of complex traits [47]. In the present study, the genetic diversity and population structure of 384 individuals of P. deltoides from six provenances in the Saint Lawrence River Basin (Quebec, Canada), Columbia River Basin (Washington, USA) and Mississippi River Basin (Missouri, Iowa, Tennessee and Louisiana, USA) were analyzed using the SSR marker technique. The results showed that population had abundant genetic diversity and the average of Shannon's information index (I) was 0.910. Moreover, there was moderate genetic differentiation (Fs t= 0.062-0.205, mean = 0.129), which was higher than the results of Fahrenkrog et al. [31]. This phenomenon may have been caused by the wide distribution of the clones selected in this study, which can more effectively reflect the genetic information of P. deltoides populations. In addition, our AMOVA results showed that 84.88% of the genetic variation originated from individuals, and the difference between populations was small. Based on the results of the present study, we can build a core germplasm resource bank to rationally manage, preserve, and utilize the introduced P. deltoides resources.
The P. deltoides population has high genetic diversity and moderate genetic differentiation. To further understand its population structure, association studies in natural populations are required [48]. In this study, we analyzed the population structure of P. deltoides based on different levels of analysis. At the individual level, PCoA and STRUCTURE cluster analysis were performed on 384 clones, which divided them into two groups. Most of the clones distributed in the Mississippi River Basin were grouped together (Group II, Fig. 4a, c), which was consistent with the results of Fahrenkrog et al’s. [31, 47] analysis. The genetic differentiation between different states in the Mississippi River Basin is weak; however, it is larger than other basins, indicating that geographical isolation restricts gene exchange among populations, a conclusion reached by certain other researchers [11, 49]. By contrast individuals distributed in the Saint Lawrence River Basin and the Columbia River Basin, with similar climatic conditions and latitudes, were grouped into the other group (Group I, Fig. 4a, c). Clones distributed between the two locations may have the same type of genetic variation because of the similarities between the external environments, or there may be introducing behavior between the two provenances. Taking the population as the research object, PCoA and UPGMA cluster analysis were carried out, and the unrooted trees among the provenance populations were plotted. The results showed that the ‘Lou’ and ‘Ten’ provenances were clustered into one group, the ‘Mis’, ‘Was’ and ‘Que’ provenances were clustered into another group, and the ‘Iow’ provenance formed a third group (Fig. 5). Meanwhile, there was no correlation between genetic distance and geographical distance among populations. To determine the reasons for the differences between the results of individual and population analysis, we analyzed Nm and Fst among the six provenance populations (Table 4). We observed that the Nm between ‘Iow’ and the others was relatively small and the Fst was large, which could explain why the ‘Iow’ provenance was separately classified into one group [50, 51]. Eighteen individuals from ‘Iow’ were introduced, and their Ho (0.456) was significantly higher than the He (0.390) (Additional file 2: Table S4). Moreover, PCoA of 384 individuals of P. deltoides showed that 18 clones from ‘Iow’ distributed widely in the dimension of the first principal component, and most clones were on the edge of Group II (Fig. 4a). Therefore, we have two different hypotheses. One is that there may be more clones of P. deltoides in ‘Iow’, but in this study, only 18 introduced clones were studied. The genetic information reflected by these clones differed greatly from that of other provenances. However, these clones were reasonably classified into one group at the individual level. The other hypothesis is that individuals from ‘Iow’ may be subjected to some external selection pressure, which has resulted in genetic variation of this P. deltoides population in a certain direction. This selection pressure may exist in other provenances, but the selection pressure is relatively weak. Therefore, in the analysis of population structure, we should not neglect the influence of external environmental conditions on the structure.
The cuttings of P. deltoides were collected from the germplasm resource bank that established through international exchange and cooperation of germplasm resources, among which the resources came from its main distribution areas. The materials have been formally identified by the State Forestry and Grassland Administration, People’s Republic of China, under the identification number 2009-56. A total of 384 clones were selected in this research. Among them, 6, 18, 75, and 108 individuals were from Missouri (‘Mis’), Iowa (‘Iow’), Tennessee (‘Ten’), and Louisiana (‘Lou’), respectively. The four provenances were located in Mississippi River basin. Seventeen individuals were from Washington (‘Was’), which is located in the Columbia River basin. In addition, 160 clones were from the Saint Lawrence River basin (Quebec, Canada, ‘Que’) (Additional file 1: Fig. S1, Additional file 2: Table S6).
Plant leaves were collected during the growing season and stored in a refrigerator at −40 C for DNA extraction. Total genomic DNA was extracted according to the highly efficient modified cetyltrimethylammonium bromide (CTAB) method [52]. The quality and integrity of the extracted genomic DNA were assessed using 1% agarose gel electrophoresis. The concentration was then determined using a NanoDrop-2000 ultramicro-spectrophotometer (Thermo Fisher Scientific Waltham, MA, USA). Finally, the DNA was diluted to 50 ng·μL-1 and stored at -20 C for polymerase chain reaction (PCR) amplification.
In this study, 20 representative clonal DNAs of P. deltoides were selected randomly for SSR primer screening, which included 75 pairs of primers for functional gene development of poplar [43, 44, 53-56] and 70 pairs of universal primers [57-60]. Using PCR amplification, 2% agarose gel electrophoresis, and 8% non-denaturing polyacrylamide gel electrophoresis, the SSR primers were screened for their stability and polymorphism content.
The PCR amplification reaction system for all SSR markers comprised a 25-μL mixed system containing 2.5 μL of 10 × buffer (Mg2+ plus), 1.8 μL of dNTP mixture, 1 μL of forward primer (10 μmol·L-1), 1 μL of reverse primer (10 μmol·L-1), 0.25 μL of Taq polymerase (5 U·μL-1), 1 μL of DNA template (50 ng·μL-1), and 17.45 μL of ddH2O. The PCR amplification procedure comprised 94 C for 3 minutes; 35 cycles of 94 C for 30 seconds, annealing at the annealing temperature of each primer pair for 30 seconds, elongation at 72 C for 45 seconds; and a final extension at 72 C for 10 minutes. The PCR products of the polymorphic SSR primers were separated by capillary electrophoresis using an ABI 3730xl DNA analyzer (Applied Biosystems, Foster City, CA, USA) and the peak size map of amplified fragments was obtained.
The Gene-Marker 2.2.0 software (SoftGenetics LLC, USA) was used to read the peak maps of the amplified fragments of each polymorphic locus. To exclude the effect of null alleles on the population genetic diversity, the null allele frequencies of each locus were detected using Cervus 3.0.7 [34] and Microchecker 2.2.3 [35] software, and the Cervus 3.0.7 software was also used to calculate PIC of each locus.
The GeneAlEx 6.503 [61, 62] software was used to convert various file formats for different analysis and to calculate genetic diversity parameters, including the number of observed alleles (Na), the number of effective alleles (Ne), Shannon’s information index (I), observed heterozygosity (Ho), expected heterozygosity (He), the number of private alleles, population fixed index (F), gene flow (Nm), and F-Statistics (Fis, Fit, Fst). HWE tests across all populations were performed using Genepop 4.7.0 [63, 64]. To more intuitively reflect the genetic diversity of loci and populations, heat maps of every locus were drawn using OmicShare tools ( www.omicshare.com/tools). With the help of the Arlequin 3.5.2.2 [65] and GeneAlEx 6.503 software, AMOVA was carried out to partition the genetic variances into three levels: Among populations, among individuals within populations, and within individuals.
We further calculated the genetic distance between individuals and populations using the GeneAlEx 6.503 software for PCoA. A cluster analysis between populations, based on UPGMA, was also developed using the NTSYS-pc 2.10e [66] software. An unrooted tree was constructed based on pairwise standard genetic distances [67], using the least squares algorithm with 10,000 bootstrap replicates, and these processes were generated and analyzed using PHYLIP 3.6 [68] software.
The population genetic structure was analyzed using STRUCTURE 2.3.4 [38] software, using a model-based clustering algorithm that implements a Bayesian framework and the Markov chain Monte Carlo (MCMC) algorithm. To confirm the optimum number of subpopulations (K), 10 independent runs for each value of K, ranging from 2 to 5, were conducted. Each run consisted of a burn-in period of 100,000 steps followed by 1000,000 MCMC iterations. The ΔK parameter, which was based on the rate of change in the log probability of data between successive K values, was estimated to determine the best K, based on the model developed by Evanno et al. [37].
To determine whether further geographical distance can cause genetic differences, a Mantel test was performed using the GeneAlEx 6.503 software to analyze the correlation between Nei’s genetic distance and geographical distance. The geographical distance between different provenances was calculated according to the latitude and longitude using Vincenty’s formula ( www.movable-type.co.uk/scripts/latlong-vincenty.html).
SSR: simple sequence repeat; PIC: polymorphic information content; AMOVA: analysis of molecular variance; PCoA: principal coordinate analysis; ‘Mis’: the provenance population in Missouri, USA; ‘Iow’: the provenance population in Iowa, USA; ‘Was’: the provenance population in Washington State, USA; ‘Que’: the provenance population in Quebec, Canada; ‘Lou’: the provenance population in Louisiana, USA; ‘Ten’ : the provenance population in Tennessee, USA; HWE: Hardy-Weinberg equilibrium; Fis: inbreeding coefficient within-population; Fit: inbreeding coefficient inter-population; Fst: inter-population genetic fraction coefficient; Nm: gene flow; UPGMA: unweighted pair-group method with arithmetic means.
The experimental materials used in this study have been formally identified by the State Forestry and Grassland Administration, People’s Republic of China, under the identification number 2009-56.
Consent for publication
Not applicable.
All data generated or analyzed during this study are included in this published article [and its additional files].
The authors declare that they have no competing interests.
This study was supported by the Basic Research Fund of RIF (Grant No. CAFYBB2018SY007). The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
QH XS conceived and designed the experiments. CC CD collected the experimental materials. YC assisted in screening primers. CC performed the experiments, analyzed the data, and was a major contributor in writing the manuscript. All authors read and approved the final manuscript.
Not applicable.
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