Molecular marker method for identifying height character of Yunnan pine and application of molecular marker method

Abstract

The invention provides a molecular marker method for identifying the height character of Yunnan pine trees and application of the molecular marker method. The method comprises the following steps: S1, extracting DNA (deoxyribonucleic acid) of 17 population samples; s2, carrying out green-blue fluorescence labeling on 5'ends of forward primers of 17 pairs of polymorphic SSR primers; s3, calculating the allele number, the effective allele number and the Shannon information index of each site and each population, observing expected heterozygosity, the probability of sampling the same genotype at each site, the probability of sampling the same genotype at each site of a sib, and the probability of sampling the same genotype at all sites; the probability of sampling the same genotype at all sites of the sib; calculating the inbreeding coefficient of each population, the genetic difference between the populations and the gene exchange degree between the populations; the allele frequency of each site of the high tree and the short tree is analyzed, and an allele frequency distribution diagram of the PyTr06 site and the PK827 site is constructed. The DNA fingerprints of the high trees and the short trees are obtained, and the method has important significance on efficient and rapid excellent germplasm selection, genetic improvement and high-quality man-made forest construction of the pinus yunnanensis.

Description

Technical Field

[0001] The invention belongs to the technical field of fingerprints, and in particular relates to a molecular marker method for identifying high traits of Yunnan pine and an application thereof. Background Art

[0002] Yunnan pine (Pinus yunnanensis Franch) is an evergreen coniferous tree of the Pinaceae family. It is distributed in Yunnan Province and is an important forest resource in southwest China, of which artificial forests account for a large part. This species has strong adaptability to arid climate and poor soil, with a straight trunk and rapid growth. It is a pioneer tree species and a major timber species for afforestation of barren hills in southwest China. However, due to natural disasters (fires, pests and diseases, and invasion of alien species) and over-logging, Yunnan pine forests have been severely "degraded". At present, the growth rate has slowed down significantly, the cones are not fully developed, and the fruiting amount has decreased. What is particularly serious is that young forests, twisted forests, and low forests are increasing. How to effectively improve the low-yield Yunnan pine forests has become an important challenge currently faced.

[0003] The main reason for the degradation of Yunnan pine forests is the loss of high-quality seed sources. The selection of target trees is the basis for creating high-quality and high-yield plantations, and is one of the important issues that the forestry industry urgently needs to solve. Target trees refer to high-quality forest varieties whose characteristics meet the requirements of management objectives and can maintain competitiveness in various environmental disturbances for a long time. Target trees have unique genetic genes, which, combined with appropriate cultivation techniques and measures, can achieve efficient cultivation of excellent traits of forest trees. Targeted cultivation of target trees can maximize the ecological value and economic benefits of forests. The most important goal of Yunnan pine breeding is to screen and cultivate high-yield, high-quality varieties.

[0004] Marker-assisted selection (MAS), also known as marker-assisted breeding, is to analyze at the molecular level whether there is a close linkage relationship between the target gene and the important trait, providing a scientific method for the early selection of excellent germplasm. The genotype DNA markers required for molecular marker selection can be obtained from any tissue of the plant, or from the seedling stage or even the seeds. Therefore, specific traits expressed in adult plants can be screened at an early stage; DNA markers are neutral to environmental changes, and the genetic gain obtained is higher than that of phenotypic selection. In recent years, due to the improvement of molecular biology, plant breeding has begun to shift from complete reliance on phenotypic selection to molecular-assisted selection, which has significantly improved the efficiency and accuracy of selection. It has been widely used in the genetic improvement of crops such as rice, wheat and corn, and a series of crop varieties with excellent quality have been screened.

[0005] Tree height is an important characteristic of tree growth. Tree height growth is closely related to forest timber volume. They determine the economic value of forests and are important selection indicators for forest breeding. Due to the interaction between genotype-age and genotype-environment, there is usually a lack of correlation between early and late tree height growth in most coniferous species, which seriously hinders the reliability of early selection. SSRs are abundant in number, cover the entire genome, have good repeatability, co-dominance, high reliability and rich polymorphism. The use of SSR markers to construct DNA fingerprints has been effectively applied in the identification of many excellent plant germplasms.

[0006] At present, the research on Yunnan pine using SSR markers has been mainly carried out in four aspects, namely the development of Yunnan pine SSR primers (Xue et al. 2013), the analysis of genetic diversity and genetic structure of Yunnan pine populations (Xu et al. 2016) and the construction of core germplasm (Miao et al. 2023). Recently, the unique haplotypes of early-maturing cones (179bp) and non-early-maturing cones (183bp) of Yunnan pine have been successfully identified using SSR markers (Miao et al. 2023). In actual work, we found that the height of six-year-old trees of half-sibling Yunnan pine seeds from the same provenance, planted in the same cultivation environment and under the same cultivation measures, differed significantly, with the height difference between the tallest and shortest trees being 258 to 352 cm. So far, there have been no reports on the association between tree height and molecular markers for Yunnan pine. Finding molecular markers that are significantly associated with tree height, and conducting early molecular identification of "tall trees" and "short trees" through easily obtained materials such as seeds or seedling needles before breeding, and using the identified "tall trees" for breeding, will be of great significance for significantly improving the economic and ecological benefits of Yunnan pine. Summary of the invention

[0007] In order to solve the problems existing in the background technology, the present invention provides a molecular marker method for identifying the height traits of Yunnan pine and its application, so as to obtain DNA fingerprints of tall trees and short trees, which is of great significance for the efficient and rapid selection of excellent germplasm, genetic improvement and high-quality artificial forest construction of Yunnan pine.

[0008] In order to achieve the above-mentioned purpose, in the first aspect, the present invention provides the following technical solutions:

[0009] A molecular marker method for identifying high traits of Yunnan pine trees comprises the following steps:

[0010] S1. Extract genomic DNA from 17 population samples;

[0011] S2, PCR amplification using fluorescently labeled SSR primers; the 5' end of the forward primer of 17 pairs of polymorphic SSR primers (Miao et al. 2023) was labeled with green-blue fluorescence (FAM and COUM);

[0012] S3. The number of alleles (N) at each locus and population was calculated using GenAlEx 6.5 (Peakall and Smouse, 2006) software. a ), effective number of alleles (N e ), Shannon information index (I), observed expected heterozygosity (H o ), expected heterozygosity (H e ), the probability of sampling the same genotype at each site (PI), the probability of sampling the same genotype at each site among siblings (PI sibs ), the probability of sampling the same genotype at all sites (PI c ), the probability of sampling the same genotype at all sites of siblings (PI sibsc ); the software was used to calculate the inbreeding coefficient (F is ), genetic differences among populations (F st ), and the degree of gene exchange between populations (N m ); AMOVA (analysis of molecular variance) analysis was performed to analyze the allele frequency of each locus of tall trees and dwarf trees and to construct the allele frequency distribution map of PyTr06 and PK827 loci; SPSS13.5 was used to analyze the genetic diversity parameters (N a 、N e ,I,H o , H e ) to conduct significance test.

[0013] Furthermore, the specific operation of S1 is: firstly dry the young needles to obtain completely dry Yunnan pine needles, then grind them several times with liquid nitrogen, extract genomic DNA using the CTAB method, measure the DNA concentration with a nucleic acid meter, dilute the DNA concentration to 50 ng / μL, and store them at -70°C for future use.

[0014] Furthermore, the specific operation of the drying treatment is: put the young needles into a sealed bag with 1 / 2 silica gel, place it in a laboratory, and dry it at room temperature for three weeks. During this period, the silica gel is replaced several times.

[0015] Furthermore, in S2, the PCR amplification system was 10 μL, including: 1 μL of DNA (50 ng / μL), 5 μL of PCRMasterMix (Qingke Biotechnology Co., Ltd.), 0.2 μL of upstream and downstream primers (10 pmol / μL) and 3.6 μL of ddH2O.

[0016] Furthermore, PCR amplification was performed on an MJ PTC-200 thermal cycler with the following procedure: pre-denaturation at 94°C for 3 min; 30 cycles at 94°C for 45 s, 50-61°C for 30 s, and 72°C for 30 s; extension at 72°C for 5 min; the PCR amplification products were subjected to capillary electrophoresis on an ABI 3730, and the DNA fragment length was detected using GeneScan-500 LIZ as an internal standard. The length of each allele fragment was finally manually corrected using GeneMapper 3.0 (Chatterji and Pachter, 2006).

[0017] Furthermore, in S1, 17 population samples were derived from 9 natural provenance seeds, namely Yongren (YR), Huize (HZ), Xinping (XP), Tianchi (TC), Shuangbai (SB), Lufeng (LF), Ceheng (CH), Zayu (CY) and Xichang (XC).

[0018] Furthermore, two models, GLM and MLM, using Tassel 5.0 software were used to conduct association analysis between SSR markers and tree height;

[0019] In the GLM analysis, five loci, PyTr06, PF463, P.Ma72, PK827, and PK840, were significantly correlated with tree height (P<0.001);

[0020] In the MLM analysis, two loci, PyTr06 (P<0.05) and PK827 (P<0.001), were significantly correlated with tree height traits.

[0021] Furthermore, in the GLM analysis, the explanation rates of the five loci PyTr06, PF463, P.Ma72, PK827 and PK840 for the tree height trait were 3.32%-14.85%; in the MLM analysis, the explanation rates of the two loci PyTr06 and PK827 for the tree height trait were 0.15% and 13.27%, respectively.

[0022] Furthermore, 139 tall Yunnan pine trees were amplified through the PyTr06 site, and a total of 6 different haplotypes were obtained; 196 short Yunnan pine trees were amplified through the PK827 site, and a total of 8 different haplotypes were obtained.

[0023] In the second aspect, the present invention provides the following technical solutions:

[0024] An application of the above-mentioned molecular marker method for identifying high-quality traits of Yunnan pine is used to identify high-quality traits of Yunnan pine.

[0025] This application has the following beneficial effects:

[0026] The present invention compared the tree heights of 6892 trees from 9 provenances with homogeneous environments, the same cultivation measures and the same age, analyzed the ability of 17 polymorphic microsatellite (SSR) markers to construct fingerprints, and used them to analyze the genetic diversity, population structure and association between SSR markers and tree height traits of 139 tall trees (tree height ≥ 300 cm) and 196 short trees (height ≤ 100 cm) in 17 artificial populations from 9 provenances. The results showed that the tree heights among the 9 provenances and within 4 provenances (TC, SB, LF and CH) were significantly different (P < 0.001). The combined PIc and PIsibsc values ​​of the 17 SSR markers were 1.1×10 -11 and 1.1×10 -5 , with strong fingerprint building capabilities.

[0027] The genetic diversity of Yunnan pine is moderate, and the tall trees (H e =0.574) and dwarf populations (H e =0.613), which is consistent with the fact that Yunnan pine is mainly wind-pollinated (F is =-0.112), high gene exchange between populations (N m =11.133) and low genetic variance (F st =0.041). Cluster analysis divided tall trees and short trees into two different groups. Analysis of molecular variance (AMOVA) showed that these two different groups accounted for 8% of the significant genetic variation. Both the general linear model (GLM) and the mixed linear model (MLM) supported that the two loci PyTr06 and PK827 were significantly associated with tree height. Haplotype and allele frequency analysis of these two groups showed that tall trees and short trees had 6 and 8 different haplotypes, respectively, and had unique alleles of 126 bp and 202 bp at the PyTr06 and PY827 loci, respectively, with allele frequencies as high as 0.597 and 0.579, respectively.

[0028] The DNA fingerprints of tall trees and short trees obtained by the present invention are of great significance to the efficient and rapid selection of high-quality germplasm, genetic improvement and high-quality artificial forest construction of Yunnan pine. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 , pictures of tall and dwarf Yunnan pine trees;

[0030] Figure 2 , tree height distribution diagram of various seed sources (PH is plant height, and the abbreviations of the nine seed sources are the same as in Table 1);

[0031] Figure 3 , the allele distribution pattern of PyTr06 and PK827 loci in tall trees and dwarf trees;

[0032] Figure 4 , Population genetic structure diagram of 17 populations of Pinus yunnanensis based on 17 polymorphic SSR markers (A is a graphical description of the relationship between K and Δk, B is a bar graph when K = 2, C is a population structure diagram sorted by Q value, and D is a UPGMA cluster dendrogram performed through Population analyses);

[0033] Figure 5 , tall trees (126bp) and dwarf trees (202bp) (A is the DNA fingerprint of tall trees and dwarf trees, B is the electrophoresis diagram of 6 haplotypes amplified by primer PyTr06 in 139 tall trees, and C is the electrophoresis diagram of 8 haplotypes amplified by primer PK827 in 196 dwarf trees). DETAILED DESCRIPTION

[0034] The present application is further described in detail below in conjunction with embodiments.

[0035] Unless otherwise specified, the raw materials used in the examples and comparative examples of the present application are all commercially available.

[0036] 1. Overall conception;

[0037] like Figure 1 The figure shows the tall and short trees of Yunnan pine. The present invention detected the ability of 17 pairs of polymorphic SSR primers to construct fingerprint maps, and used them to perform microsatellite typing on 17 populations from 9 provenances in a homogeneous garden, including 139 tall trees (tree height ≥ 300 cm) and 196 short trees (tree height ≤ 100 cm), and performed association analysis between tree height and SSR markers, aiming to construct a tree height SSR fingerprint map, provide a fast, efficient and accurate detection method for the early selection of tall tree individuals of Yunnan pine, and provide a scientific theoretical basis for the early screening and application of excellent germplasm of Yunnan pine. The details are as follows.

[0038] 2. Materials and methods;

[0039] 2.1 Materials;

[0040] The test site is located in Lufeng Forest Farm, Kunming, Yunnan, China (101°54′E, 25°8′N), with an altitude of about 1970m, belonging to the mid-subtropical monsoon climate. Its climate is characterized by warm and dry spring, no extreme heat in summer, cool and humid autumn, and no severe cold in winter. The difference between the four seasons is small, and rain and heat are in the same season. The annual average temperature is about 16.0℃, the annual rainfall is 822.9~950mm, the frost-free period is relatively long, about 261~299 days, and the annual sunshine hours are about 2198~2963h. The soil is red soil with medium fertility and excellent climatic conditions, which is suitable for the growth of Yunnan pine.

[0041] Seeds from nine natural provenances in Yunnan, Guizhou, Sichuan and Tibet provinces and autonomous regions, namely Yongren (YR), Huize (HZ), Xinping (XP), Tianchi (TC), Shuangbai (SB), Lufeng (LF), Ceheng (CH), Zayu (CY) and Xichang (XC), were collected. A provenance family experimental forest was established in Lufeng Forest Farm in 2016. A total of 269 families with 6892 trees were established (see Table 1).

[0042] The experimental design adopted a randomized block design. In order to achieve homogeneous environmental conditions, horizontal strip land preparation was used before afforestation. At the same time, each population was randomly arranged in each block, and each family was also randomly arranged in the population. After planting the seeds, the same cultivation conditions were given.

[0043] Table 1. Information of 9 seed sources and 17 population samples

[0044]

[0045]

[0046] In Table 1, S s , seed source; N f , number of families; N t , the total number of individuals in the family; H a , average tree height (cm); P a , population abbreviation; T, tall trees with a height greater than or equal to 300 cm; D, short trees with a height less than or equal to 100 cm; N s , the total number of tall or short trees from each seed source; N e , the number of experiments with tall or short trees from which the seeds came.

[0047] 2.2. Measurement and statistics of provenance tree height;

[0048] The tree heights of 6892 6-year-old samples from 9 provenances were measured with a ruler (accurate to 1.0 cm), and the tree height distribution of the 9 provenances was plotted using Origin 2021. Figure 2 SPSS13.0 (Green and Salkind, 2003) was used to conduct multiple comparative analysis of tree height differences among provenances, and the software was also used to detect the significance of tree height differences between and within provenances.

[0049] 2.3, SSR analysis of 17 populations;

[0050] 2.3.1. Collection of needles;

[0051] Young needles of all tall trees (tree height ≥ 300 cm) and all short trees (tree height ≤ 100 cm) were collected from each provenance as much as possible. Since there were many short trees (YR-D) in Yongren and tall trees (LF-T) in Lufeng, 90 and 119 trees respectively, we selected 30 and 25 trees respectively. The final experimental materials included 17 populations and 335 trees, including 196 short trees and 139 tall trees, as shown in Table 1.

[0052] The young needles of each tree were placed in a sealed bag filled with 1 / 2 silica gel and dried at room temperature in the laboratory for three weeks. The silica gel was replaced several times during this period until the leaves were completely dry for DNA extraction.

[0053] 2.3.2. Extraction of genomic DNA;

[0054] Completely dried Yunnan pine needles were ground several times with liquid nitrogen, and genomic DNA was extracted using the modified CTAB method (Bai Qingsong et al., 2013). The DNA concentration was measured using a nucleic acid analyzer (Thermo Scientific NanoProp 2000), and the DNA concentration was uniformly diluted to 50 ng / μL and stored at -70°C for future use.

[0055] 2.3.3, SSR genotyping;

[0056] The 5′ end of the forward primer of 17 pairs of polymorphic SSR primers (Miao et al. 2023) was labeled with green-blue fluorescence (FAMand COUM), and the PCR amplification system was 10 μL, including: 1 μL of DNA (50 ng / μL), 5 μL of PCR MasterMix (Qingke Biotechnology Co., Ltd.), 0.2 μL of upstream and downstream primers (10 pmol / μL) and 3.6 μL of ddH2O.

[0057] PCR amplification was performed on an MJ PTC-200 amplification instrument, and the amplification program was as follows:

[0058] Pre-denaturation: This is the first step of PCR amplification, the purpose of which is to completely denature the DNA template, that is, to separate the double-stranded DNA into single strands. This step is performed at 94°C for 3 minutes.

[0059] Loops: This is followed by 30 loops, each consisting of three steps:

[0060] ① Denaturation: 45 seconds at 94°C to denature the DNA into single strands again;

[0061] ② Annealing: 30s at 50-61℃, during which the primers bind to the template DNA;

[0062] ③ Extension: Perform at 72℃ for 30s. During this step, DNA polymerase begins to synthesize new DNA chains.

[0063] Extension: The last step is extension, which is carried out at 72°C for 5 min to ensure that all newly synthesized DNA strands are fully synthesized.

[0064] The PCR amplification products were subjected to capillary electrophoresis on ABI 3730, and the DNA fragment length was detected using GeneScan-500LIZ as an internal standard. The length of each allele fragment was finally manually corrected using GeneMapper 3.0 (Chatterji and Pachter, 2006).

[0065] 2.3.4, Genetic diversity analysis of 17 SSR loci and 17 populations;

[0066] The number of alleles (N) at each locus and population was calculated using GenAlEx 6.5 (Peakall and Smouse, 2006) software. a ), effective number of alleles (N e ), Shannon information index (I), observed expected heterozygosity (H o ), expected heterozygosity (H e ), the probability of sampling the same genotype at each site (PI), the probability of sampling the same genotype at each site among siblings (PI sibs ), the probability of sampling the same genotype at all sites (PI c ), the probability of sampling the same genotype at all sites of siblings (PI sibsc ); the software was used to calculate the inbreeding coefficient (F is ), genetic differences among populations (F st ), and the degree of gene exchange between populations (N m ); AMOVA (analysis of molecular variance) analysis was performed to analyze the allele frequency of each locus of tall trees and dwarf trees and to construct the allele frequency distribution map of PyTr06 and PK827 loci; SPSS13.5 was used to analyze the genetic diversity parameters (N a 、N e ,I,H o , H e ) to conduct significance test.

[0067] 2.3.5. Genetic structure among populations;

[0068] First, Structure 2.3.3 (Pritchard et al. 2000) was used to construct the genetic relationship between populations. The number of groups K was set to 1-19, and 10 replicates were performed for each K value. The burn-in and MCMC (Markov Chain Monte Carlo) were both set to 300,000. All replicates used a mixed model with correlated allele frequencies. Usually, the most likely number of groups is the minimum number of groups that can capture the maximum genetic structure. However, many studies have shown that the genetic structure value L(K) has been in a gradual increase with the number of groups. In this case, the optimal number of groups cannot be found based solely on the maximum genetic structure value. Therefore, Evanno et al. (2005) further proposed the ad hoc statistical parameter ΔK. ΔK is a further transformation value of L(K), ΔK = m(|L(K+1)–2L(K)+L(K–1)|) / sL(K)(Wang et al. 2022). The application of ΔK resulted in the highest L(K) in the trend line of L(K), further confirming the possible number of groupings. ΔK successfully captured the optimal number of groupings in many species studies (Liu et al. 2023; Wu et al. 2024).

[0069] Secondly, Nei's genetic distance D a (Nei et al. 1983), through Populations1.2.30( http: / / bioinformatics.org / ~tryphon / populations / #ancre_telecharge ment ) and MEGA 6.06 (Tamura et al. 2013) were used to construct the UPGMA (Unweighted Pair Group Method with Arithmetic mean) clustering tree between populations.

[0070] 2.4. Association analysis between tree height and SSR;

[0071] The general linear model (GLM) and mixed linear model (MLM) in Tassel 5.0 (Bradbury et al. 2007) were used to analyze the association between tree height and SSR markers.

[0072] In the MLM analysis, SPAGeDi 1.3 (Hardy and Vekemans, 2002) was used to calculate the kinship matrix between individuals (Loiselle et al. 1995); the population structure Q value when K = 2 was calculated using the Structure software, and the SSR loci associated with tree height and the explanation rate of the associated loci for phenotypic variation were determined based on the combined phenotypic data, molecular data, population structure and kinship data.

[0073] By constructing haplotype maps of 139 tall trees at the PyTr06 locus and 196 short trees at the PK827 locus, DNA fingerprints specific to tall and short trees were identified.

[0074] 3. Results;

[0075] 3.1. Comparison of tree height differences between and within provenances;

[0076] like Figure 2 As shown in the tree height distribution diagram of the nine provenances, the tree height of the LF provenance is the highest, and the tree height of the YR provenance is the lowest. This is consistent with the total number of tall and short trees in the provenance (N s , Table 1) statistical results are consistent, LF provenance has 119 tall trees and 7 short trees, accounting for 14.66% and 0.86% of the total number of trees in the provenance, respectively, and the average tree height is 233cm (Table 1); YR provenance tree height is opposite to LF, there are no tall trees in this provenance, the total number of short trees is the highest among the 9 provenances, up to 90, accounting for 10.94% of the total number of trees in the provenance, and the average tree height is 156cm. Multiple analyses showed that there were significant differences in tree height among most provenances.

[0077] See Table 2, analysis of tree height differences among and within 9 provenances. There were significant differences in tree height among the 9 provenances (P<0.001), and there were significant differences in tree height between individuals within 4 provenances (XP, TC, SB and LF) (P<0.01). There were no significant differences in tree height between individuals within the remaining 5 provenances (YR, HZ, CH, CY and XC) (P>0.05).

[0078] Table 2. Analysis of differences in tree height between and within seed sources (significant differences)

[0079] Source Sum of Squares df MeanSquare F Sig. Betweenseedsources 94315287.5 521 181027.4 190184.5 0.000 Y 37.0 33 1.1 1.2 0.255 HZ 39.0 30 1.3 1.4 0.118 XP 58.0 29 2.0 2.1 0.002 TC 90.0 32 2.8 3.0 0.000 SB 108.0 35 3.1 3.2 0.000 LF 61.0 23 2.7 2.8 0.000 CH 17.5 14 1.3 1.3 0.207 CY 33.0 30 1.1 1.2 0.283 XC 16.5 13 1.3 1.3 0.201

[0080] 3.2, Genetic diversity of 17 SSR loci;

[0081] A total of 110 alleles were detected in 335 samples from 17 populations using 17 pairs of polymorphic SSR primers. See Table 3 for the number of alleles at each locus (N a ) ranges from 2 to 12; the effective number of alleles (N e) and Shannon index (I) ranged from 1.900 to 5.207 and 0.665 to 1.793, with average values ​​of 2.804 and 1.167, respectively. The average observed heterozygosity (H o =0.677) is higher than the expected heterozygosity (H e =0.593), ranging from 0.213 to 0.985 and from 0.430 to 0.797 respectively; the probability of sampling the same genotype at each site (PI) and the probability of sampling the same genotype at each site of siblings (PI sibs ) ranged from 0.071 to 0.391 and 0.369 to 0.625, with average values ​​of 0.234 and 0.512, respectively; the probability of sampling the same genotype at all sites (PI c ) is 1.1E-11; the probability of sampling the same genotype at all sites of siblings (PI sibsc ) is 1.1E-05.

[0082] Table 3. Summary of genotype information for each SSR locus

[0083]

[0084]

[0085] In Table 3, N a , number of alleles; N e , effective number of alleles; I, Shannon information index; H o , observe heterozygosity; H e , expected heterozygosity; PI, probability of identity at a site; PI sibs , the probability of sibling identity at a locus; PI c , PI of all joint sites; PI sibsc , PI of all joint sites sibs .

[0086] 3.2, genetic diversity of 17 populations;

[0087] The genetic diversity of 17 populations was analyzed using 17 pairs of polymorphic SSR primers. The results are shown in Table 4, showing the number of alleles (N a ) ranged from 2.941 to 5.529 and from 3.824 to 5.706, with average values ​​of 4.772 and 5.275, respectively; the effective allele number (N e) ranged from 2.444 to 3.145 and 2.639 to 3.434, with average values ​​of 2.704 and 2.892, respectively. The Shannon index (I) ranged from 0.910 to 1.250 and 1.059 to 1.380, with average values ​​of 1.109 and 1.218, respectively. The heterozygosity (H o ) ranged from 0.628 to 0.706 and from 0.653 to 0.765, with average values ​​of 0.657 and 0.695, respectively; the expected heterozygosity (H e ) ranged from 0.533 to 0.615 and 0.576 to 0.682, with average values ​​of 0.574 and 0.613, respectively. a , N e , I, H o , H e ) After T test, except H o Except for the significant difference between tall tree and short tree populations (P<0.05), the other four genetic parameters were not significant. is ) All 17 populations were negative, with an overall average of -0.128. The genetic differences among the 17 populations were small (F st =0.041), but the degree of gene exchange was high (N m =11.133).

[0088] Table 4. Genetic diversity indices of 17 Yunnan pine populations based on SSR markers

[0089] Population <![CDATA[N a ]]> <![CDATA[N e ]]> I <![CDATA[H o * ]]> <![CDATA[H e ]]> <![CDATA[F is ]]> <![CDATA[F st-t ]]> <![CDATA[N m-t ]]> HZ-T 5.471 3.145 1.250 0.628 0.609 -0.034 XP-T 3.941 2.629 1.041 0.647 0.557 -0.160 TC-T 5.471 2.681 1.149 0.643 0.615 -0.110 SB-T 5.176 2.637 1.117 0.655 0.561 -0.156 LF-T 5.529 2.586 1.121 0.656 0.556 -0.164 CH-T 4.118 2.632 1.088 0.686 0.576 -0.169 CY-T 2.941 2.444 0.910 0.706 0.533 -0.300 XC-T 5.529 2.879 1.193 0.638 0.588 -0.079 Mean 4.772 2.704 1.109 0.657 0.574 -0.146 YR-D 5.706 2.993 1.284 0.653 0.629 -0.017 HZ-D 5.118 2.791 1.190 0.706 0.605 -0.145 XP-D 5.353 2.647 1.144 0.657 0.576 -0.113 TC-D 5.529 3.080 1.290 0.706 0.643 -0.084 SB-D 5.294 2.730 1.189 0.687 0.596 -0.132 LF-D 3.824 2.639 1.059 0.723 0.580 -0.216 CH-D 5.706 2.872 1.218 0.675 0.605 -0.089 CY-D 5.294 2.845 1.207 0.681 0.605 -0.098 XC-D 5.647 3.434 1.380 0.765 0.682 -0.114 Mean 5.275 2.892 1.218 0.695 0.613 -0.112 0.041 11.133

[0090] In Table 4, N a , number of alleles; N e , effective number of alleles; I, Shannon information index; H o , observe heterozygosity; H e , expected heterozygosity; F is , inbreeding coefficient; F st-t , the total average genetic differentiation among the 17 populations; N m-t , the total average gene flow degree among 17 populations; *, P < 0.05.

[0091] AMOVA (see Table 5) revealed that most of the genetic variation (92%) existed within populations, the molecular variation between tall trees and short trees accounted for 8% of the total variation, and the molecular variation between the two was significant (P<0.001).

[0092] Table 5. Analysis of molecular variance (AMOVA) between and within populations

[0093]

[0094] Depend on Figure 3 It can be seen that the allele frequencies of the two loci PyTr06 and PK827 in tall trees and short trees are 0.068-0.597, 0.000-0.309 and 0.000-0.421, 0.003-0.579, respectively.

[0095] 3.4. Genetic structure among populations;

[0096] The trend of population genetic structure value L(K) changing with the number of groups is shown in Figure 4 In Aa, it is obvious that the maximum genetic structure cannot be found by the L(K) value. This is because the L(K) values ​​of the genetic structures captured when the grouping number K = 2, 3, 4, and 5 are similar, namely -14456, -14424, -14446, and -14460, respectively. However, when ΔK is used to determine the optimal grouping number, when K = 2, ΔK captures the maximum value of 80.33 (see Figure 4 In structure analysis, the populations were clustered (see Figure 4 B) and Q-value clustering (see Figure 4 C) and Population analysis (see Figure 4 The results of Figure 5 (D) are consistent with those of Figure 5 (D), that is, the 8 tall tree populations (T) are clustered into one group (Group 1), and the 9 short tree populations (D) are clustered into another group (Group 2).

[0097] 3.5. Association analysis between tree height and SSR markers;

[0098] Tassel 5.0 software GLM and MLM models were used to conduct association analysis between SSR markers and tree height, and the results are shown in Table 6;

[0099] In the GLM analysis, five loci, PyTr06, PF463, P.Ma72, PK827 and PK840, were significantly correlated with tree height (P<0.001); the explanation rate of these five loci, PyTr06, PF463, P.Ma72, PK827 and PK840, for tree height traits was 3.32%-14.85%.

[0100] In the MLM analysis, two loci, PyTr06 (P<0.05) and PK827 (P<0.001), were significantly correlated with tree height traits, and the explanation rates of these two loci, PyTr06 and PK827, for tree height traits were 0.15% and 13.27%, respectively.

[0101] Both GLM and MLM model analyses support that the two loci PyTr06 and PK827 are significantly associated with tree height.

[0102] Table 6. Analysis of SSR loci associated with tree height in 139 tall trees and 196 short trees using GLM and MLM models in Tassel

[0103]

[0104] 3.6, DNA fingerprint of tree height;

[0105] 139 tall Yunnan pine trees were amplified by PyTr06 locus, and a total of 6 different haplotypes were obtained, see Figure 5 In A (H1-H6), they are 126 bp, 120 / 126 bp, 123 / 126 bp, 129 / 126 bp, 132 / 126 bp and 135 / 126 bp, respectively. The capillary electrophoresis patterns of the six haplotypes are shown in Figure 1. Figure 5 As shown in B (H1~H6).

[0106] Eight different haplotypes were detected in 196 Yunnan pine dwarf trees by amplification of the PK827 locus. Figure 5 In A (H7-H14), the fragment lengths are 202 bp, 214 / 202 bp, 190 / 202 bp, 206 / 202 bp, 198 / 202 bp, 210 / 202 bp, 186 / 202 bp and 194 / 202 bp, respectively. The capillary electrophoresis patterns corresponding to the eight haplotypes are shown in Figure 5 As shown in C (H7~H14).

[0107] The PyTr06 and PK827 loci detected 126bp and 202bp allele fragments specific to tall and short trees of Yunnan pine, respectively.

[0108] 4. Results / Conclusions;

[0109] 4.1. Genetic diversity levels of tall and short trees;

[0110] Genetic diversity is the basis for the long-term survival and evolution of species. The degree of genetic diversity within a species determines its evolutionary potential and ability to resist adverse environmental factors (Chung et al. 2023). For plants, the study of genetic diversity is crucial to understanding the level of genetic variation and genetic structure within species, and is the basis for the protection, utilization, and germplasm improvement of excellent resources.

[0111] The genetic diversity level of tall trees (H e =0.574) and dwarf trees (H e There was no significant difference between the two populations (P = 0.062, Table 4). Their genetic diversity was at a medium level in the genus Pinus, because their genetic diversity level was higher than that of Korean pine (P. koraiensis; H e=0.47; Wei et al. 2022), Pinus tabuliformis (H e =0.524; Yang et al. 2023) and European mountain pine (P. mugo; H e =0.53; Komárková et al. 2024), but higher than P. densiflora (H e =0.673; Ahn et al. 2021), Turkish pine and Mediterranean pine (P. brutia, H e =0.84; P. halepensis, H e＝ 0.71; Kurt et al. 2023) and European black pine (P. nigra; H e =0.76; Dias et al. 2020) low.

[0112] There is no difference in genetic diversity between tall and short tree populations. This is mainly because Yunnan pine is distributed continuously, has two air sacs in its pollen, and has winged seeds. It is a typical wind-pollinated outcrossing plant (Miao et al. 2023). The inbreeding coefficients within the population are all negative (-0.300 to -0.017), and the genetic differences between populations are small (F st-t =0.041), and the gene exchange was high (N m-t =11.133).

[0113] 4.2, Tree height DNA fingerprint;

[0114] In fingerprint map construction, PI refers to the probability that two individuals randomly selected from a population have the same genotype at multiple gene loci, and is an indicator of the ability of molecular markers to construct DNA fingerprints (Tarroux et al. 2014). However, Waits et al. (2001) believed that since the substructure of plant populations is formed by the environment and human selection, the assumption of independent separation between molecular loci is not valid, which may lead to an overestimation of the PI value, and therefore proposed PI sibs (Sibling PI value) can be used as a conservative upper limit of PI. At the same time, it is proposed that the PI value of the locus must be between 0.01 and 0.0001 to be sufficient to distinguish individuals.

[0115] The probability of sampling the same genotype at 17 sites in the present invention (PI c ) and the probability of sampling the same genotype at 17 loci among siblings (PI sibsc ) values ​​are 1.1×10 -11 and 1.1×10 -5 , which is far lower than the standard value (0.01-0.0001), indicating that the 17 SSR markers used in the present invention have a high ability to construct fingerprint maps.

[0116] AMOVA analysis of the two groups of tall trees and short trees showed that there were significant molecular differences between them (P<0.001), and the molecular differences accounted for 8% of the total variation (Table 5). The association analysis of SSR loci and tree height, both GLM and MLM analysis supported that the PyTr06 and PK827 loci were significantly correlated with tree height (Table 6). The allele frequency distribution diagram of tall trees and short trees at the PyTr06 and PY827 loci also showed that tall trees had a unique allele of 126bp at the PyTr06 locus, and the frequency of this allele was as high as 0.597; while short trees had a private allele of 202bp at the PY827 locus, and the frequency of this allele was also as high as 0.579 ( Figure 3 ). Haplotype analysis showed that the tall tree had 6 haplotypes, each containing a 126bp fragment, while the short tree had 8 haplotypes, each containing a 202bp fragment ( Figure 5 ).

[0117] Therefore, the present invention established DNA fingerprints of 126 bp at PyTr06 locus for 139 tall trees and 202 bp at PK827 locus for 196 short trees. Using the SSR fingerprints of tall trees and short trees in the present invention, effective early selection of Yunnan pine can be carried out in the early seedling stage or even in the seed stage, and the survival of the fittest can be achieved, which provides a theoretical basis for the selection of Yunnan pine varieties and the construction of high-quality artificial forests.

[0118] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0119] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A molecular marker method for identifying high traits of Yunnan pine, characterized in that: The steps include: S1. Extract genomic DNA from 17 population samples; S2, PCR amplification using fluorescently labeled SSR primers; 5' end of the forward primers of 17 pairs of polymorphic SSR primers were labeled with green-blue fluorescence; S3. The number of alleles, effective number of alleles, Shannon information index, observed expected heterozygosity, expected heterozygosity, probability of sampling the same genotype at each locus, probability of sampling the same genotype at each locus of siblings, probability of sampling the same genotype at all loci, and probability of sampling the same genotype at all loci of siblings were calculated using GenAlEx 6.5 software for each locus and each population; the inbreeding coefficient of each population, genetic differences between populations, and degree of gene exchange between populations were also calculated using the software; AMOVA analysis was performed to analyze the allele frequencies at each locus in tall and short trees and to construct allele frequency distribution maps for PyTr06 and PK827 loci; SPSS13.5 was used to perform significance tests on genetic diversity parameters of tall and short tree populations.

2. The molecular marker method for identifying high traits of Yunnan pine according to claim 1, characterized in that: The specific operation of S1 is: first dry the young needles to obtain completely dry Yunnan pine needles, then grind them several times with liquid nitrogen, extract genomic DNA using the CTAB method, measure the DNA concentration with a nucleic acid meter, dilute the DNA concentration to 50 ng / μL, and store it at -70°C for later use.

3. The molecular marker method for identifying high traits of Yunnan pine according to claim 2, characterized in that: The specific operation of the drying treatment is: put the young needles into a sealed bag with 1 / 2 silica gel, put it in the laboratory, and dry it at room temperature for three weeks. During this period, the silica gel is replaced several times.

4. The molecular marker method for identifying high traits of Yunnan pine according to claim 1, characterized in that: In S2, the PCR amplification system was 10 μL, including: 1 μL of DNA, 5 μL of PCR Master Mix, 0.2 μL of upstream and downstream primers, and 3.6 μL of ddH2O.

5. The molecular marker method for identifying high traits of Yunnan pine according to claim 1 or 4, characterized in that: PCR amplification was carried out on a thermal cycler with the following procedure: pre-denaturation at 94°C for 3 min; 30 cycles at 94°C for 45 s, 50-61°C for 30 s, and 72°C for 30 s; extension at 72°C for 5 min; the PCR amplification products were subjected to capillary electrophoresis on ABI 3730, and the DNA fragment length was detected using GeneScan-500 LIZ as the internal standard. The length of each allele fragment was finally manually corrected using GeneMapper 3.

0.

6. The molecular marker method for identifying high traits of Yunnan pine according to claim 1, characterized in that: In S1, 17 population samples came from 9 natural provenance seeds, namely Yongren, Huize, Xinping, Tianchi, Shuangbai, Lufeng, Ceheng, Zayu and Xichang.

7. The molecular marker method for identifying high traits of Yunnan pine according to claim 1, characterized in that: Tassel 5.0 software GLM and MLM models were used to conduct association analysis between SSR markers and tree height; In the GLM analysis, five loci, PyTr06, PF463, P.Ma72, PK827, and PK840, were significantly correlated with tree height (P<0.001); In the MLM analysis, two loci, PyTr06 (P<0.05) and PK827 (P<0.001), were significantly correlated with tree height traits.

8. The molecular marker method for identifying high traits of Yunnan pine according to claim 7, characterized in that: In the GLM analysis, the explanation rates of five loci, PyTr06, PF463, P.Ma72, PK827 and PK840, for tree height traits were 3.32%-14.85%. In the MLM analysis, the explanation rates of two loci, PyTr06 and PK827, for tree height traits were 0.15% and 13.27%, respectively.

9. The molecular marker method for identifying high traits of Yunnan pine according to claim 1, characterized in that: A total of 6 different haplotypes were obtained by amplifying the PyTr06 locus in 139 tall Yunnan pine trees, and a total of 8 different haplotypes were obtained by amplifying the PK827 locus in 196 short Yunnan pine trees.

10. An application of the molecular marker method for identifying high traits of Yunnan pine as claimed in any one of claims 1 to 9, characterized in that: Used to identify the height traits of Yunnan pine.

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