Chinese chestnut genome SNP (Single Nucleotide Polymorphism) and important character correlation analysis system and method thereof

Abstract

The invention discloses a Chinese chestnut genome SNP (Single Nucleotide Polymorphism) and important character association analysis system and method. The system comprises a genome re-sequencing module, a marker development and polymorphism verification module, a genetic diversity analysis module and an association analysis module, according to the strain sample of the Chinese chestnut genome and the strip data of the SNP character marker, constructing a clustering tree diagram of a Chinese chestnut genome marker target at the same time, and generating a clustering analysis result of the strain sample of the Chinese chestnut genome and the SNP character; according to correlation analysis of Chinese chestnut gene phenotype data and development of molecular markers, importance character correlation analysis of quality characters and false quality characters of Chinese chestnuts is achieved, and processing analysis of data mining allelic variation sites of correlation analysis of Chinese chestnut genome SNP polymorphism and importance characters is improved; and the correlation analysis effect of the polymorphism and the importance characters is improved.

Description

Technical Field

[0001] The invention relates to the technical field of data analysis, in particular to a system and method for analyzing association between SNP polymorphism and important traits of a chestnut genome. Background Art

[0002] Using SNP (single nucleotide polymorphism) for trait-marker association analysis is a method widely used in genetic research. This method detects SNP sites in the genome, analyzes the association between these sites and specific traits, and thus identifies genes or gene regions that affect these traits. At present, in the research and analysis of the chestnut genome, chestnut has rich variation types and unique traits, especially fruit-related traits are an important goal of breeding. Although there have been some studies on SNP positioning and association analysis in chestnut, SNP is only associated with chestnut nut single-grain weight, nut shape, maturity period and degree of astringent skin peeling, that is, it is mainly concentrated on quantitative traits, and there is still a lack of association analysis of important traits of chestnut quality traits and pseudo-quality traits. At the same time, at this stage, the data mining of allelic variation sites in the association analysis of chestnut genome SNP polymorphism and important traits is still insufficient, which is not conducive to the subsequent development of chestnut assisted breeding and innovative utilization of chestnut germplasm resources. To this end, we propose a chestnut genome SNP polymorphism and important trait association analysis system and method. Summary of the invention

[0003] In view of the above-mentioned problems existing in the existing chestnut genome SNP positioning analysis, the present invention is proposed.

[0004] Therefore, one of the objects of the present invention is to provide a system and method for analyzing the association between SNP polymorphism and important traits of chestnut genome, which realizes the important trait association analysis between quality traits and pseudo-quality traits of chestnut by analyzing and processing the strain samples of chestnut genome and the band data of SNP trait markers, and improves the processing and analysis of data mining allelic variation sites in the association analysis between SNP polymorphism and important traits of chestnut genome, thereby facilitating the subsequent development of chestnut assisted breeding and innovative utilization of chestnut germplasm resources, and improving the association analysis effect between its polymorphism and important traits.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] On the one hand, the present invention provides a chestnut genome SNP polymorphism and important trait association analysis system, comprising: a genome resequencing module, a marker development and polymorphism verification module, a genetic diversity analysis module, and an association analysis module;

[0007] The genome resequencing module is used for genome resequencing of at least 3 biological replicates of chestnut materials;

[0008] The marker development and polymorphism verification module is used to select several strain samples of chestnut genome and several SNP trait markers for polymorphism verification;

[0009] The genetic diversity analysis module is used to construct a clustering dendrogram of chestnut genome marker targets based on the chestnut genome strain samples and the band data of the SNP trait markers, and generate clustering analysis results of the chestnut genome strain samples and the SNP traits;

[0010] The association analysis module is used for association analysis between chestnut gene phenotypic data and developed molecular markers, specifically, genetic diversity analysis is performed based on SNP-based positioned trait markers, association analysis is performed on chestnut traits and markers respectively through an MLM mixed linear model, and chestnut single-site SNP markers are obtained as covariates, combined with Lambda statistics and permutation test methods, chestnut interaction epistasis SNP markers are obtained, and chestnut traits and markers are respectively screened out according to the chestnut interaction epistasis SNP markers, and then association analysis of important chestnut traits is performed.

[0011] As a preferred embodiment of the present invention, the strain sample of the chestnut genome is subjected to genome resequencing, and the chestnut leaf DNA sample is extracted and randomly broken into fragments of 350 bp in length, and a sequencing library kit is used to build a library. At the same time, the chestnut genome DNA fragment is end-repaired, ployA-tailed, sequenced, and purified by PCR amplification to complete the preparation of the strain sample of the entire chestnut genome.

[0012] As a preferred embodiment of the present invention, the MLM mixed linear model is used to perform association analysis on chestnut traits and markers, and specifically, the chestnut genome strain unit locus SNP markers with significant effects are obtained after determining the unit locus SNP markers with significant effects. The chestnut genome strain unit locus SNP markers with significant effects are analyzed as follows:

[0013]

[0014] Among them, Q ij is the phenotypic observation value of the jth strain of the i-th trait of chestnut; μ ik is the population mean of the i-th trait of chestnut under environment k; a ic is the cth covariate effect value of the i-th trait of chestnut, α is the cth covariate effect value of the i-th trait of chestnut ic The coefficient of n is the total number of permutations of the i-th trait of chestnut; b kc is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k, and β is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k.kc The coefficient of m is the total number of permutations of the chestnut environment k; ε ij is the random residual value of the jth strain of the i-th trait.

[0015] As a preferred embodiment of the present invention, the obtained strain unit site SNP markers of the chestnut genome are used as covariates, and the two-interaction epistatic SNP markers with significant interaction effects are obtained by Lambda statistics and permutation test methods, as follows:

[0016]

[0017] Among them, Q ij is the phenotypic observation value of the jth strain of the i-th trait of chestnut; μ ik is the population mean of the i-th trait of chestnut under environment k; a ic is the cth covariate effect value of the i-th trait of chestnut, α is the cth covariate effect value of the i-th trait of chestnut ic The coefficient of n is the total number of permutations of the i-th trait of chestnut; b kc is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k, and β is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k. kc The coefficient of m is the total number of permutations of the chestnut environment k; ε ij is the random residual value of the jth strain of the i-th trait.

[0018] As a preferred embodiment of the present invention, wherein: the chestnut genome strain with significant effect is obtained by using the permutation test method to determine the threshold of significant effect; then the relationship between the Lambda statistic and the threshold is judged; when the Lambda statistic of a pair of two-interaction epistatic SNP markers is greater than or equal to the threshold, the pair of SNP markers is a two-interaction epistatic SNP marker with significant interaction effect.

[0019] As a preferred embodiment of the present invention, the SNP site evaluation is based on the allele frequency, and the evaluation indicators of the strain unit site SNP markers of the chestnut genome include PIC, minor gene frequency, detection rate, and heterozygosity rate SNP site evaluation; wherein the site evaluation and screening utilize sites that are less than a preset threshold to distinguish the differences between chestnut varieties.

[0020] As a preferred embodiment of the present invention, the chestnut properties include: thorn bud shape, cracking mode, thorn bundle branching angle, thorn bundle density, thorn bundle color, edge fruit shape, nut color, nut color uniformity, nut gloss, fruit top and shoulder, hair distribution, hair color, hair density, obvious degree of tendon line, base size, base smoothness, base connection, difficulty of peeling astringent skin, and kernel color;

[0021] The important chestnut traits were screened out, and the chestnut trait membership was calculated after statistics were performed based on the standard threshold of chestnut trait grading assignment. The important chestnut traits were screened out after analyzing and comprehensively evaluating the average membership.

[0022] As a preferred embodiment of the present invention, according to the threshold value of chestnut trait classification, the chestnut trait membership is calculated after statistics are taken, and the important chestnut traits are screened out by analyzing and comprehensively evaluating the average membership as follows:

[0023]

[0024] Among them, y k,xi is the sample data of chestnut trait sample i under chestnut environment k that reaches the evaluation index threshold of SNP site evaluation, P(X i ) is the proportion of the chestnut trait samples;

[0025] When calculating the membership of chestnut traits, the entropy value of the evaluation index of the SNP site evaluation is also calculated. The weight coefficient of the chestnut environment k is calculated by the obtained entropy value of the evaluation index of the SNP site evaluation, and then the evaluation membership value is calculated. Specifically, the entropy value of the evaluation index is calculated as follows:

[0026]

[0027] Among them, E j For the j The entropy value of the evaluation index of each SNP site, ln(p ij ) is p ij The natural logarithm of ln(n) is the natural logarithm of n. lnp ij =0

[0028] Calculate the weight coefficient of the chestnut environment k as follows:

[0029]

[0030] The evaluation membership value is calculated as follows:

[0031]

[0032] Among them, Qi avg is the average membership value of the comprehensive evaluation of the chestnut trait sample, Q(X i ) is the evaluation membership value of the chestnut trait sample i, w i is the weight of the assessed membership of the chestnut trait sample i, and w i Greater than 0.

[0033] As a preferred embodiment of the present invention, the association analysis adopts a correlation coefficient calculation method to perform correlation analysis of important chestnut traits.

[0034] In one aspect, the present invention provides a method for analyzing the association between SNP polymorphism and important traits of chestnut genome, comprising the following steps:

[0035] Step 1: resequencing the genome of chestnut materials with at least three biological replicates;

[0036] Step 2: Select several chestnut genome strain samples and several SNP trait markers for polymorphism verification;

[0037] Step 3: Based on the chestnut genome strain samples and the band data of the SNP trait markers, a clustering dendrogram of the chestnut genome marker targets is constructed to generate clustering analysis results of the chestnut genome strain samples and the SNP traits;

[0038] Step 4: Perform genetic diversity analysis based on SNP positioned trait markers, perform association analysis on chestnut traits and markers using the MLM mixed linear model, and use the obtained chestnut single-site SNP markers as covariates, combine the Lambda statistic and permutation test method to obtain chestnut interaction epistasis SNP markers, and use the chestnut interaction epistasis SNP markers to screen out important chestnut traits and perform association analysis on the important traits.

[0039] Compared with the prior art, the present invention has the following beneficial effects: the present invention realizes the important trait association analysis of chestnut quality traits and pseudo-quality traits by analyzing and processing the strain samples of the chestnut genome and the band data of SNP trait markers, and improves the processing and analysis of data mining allele variation sites in the association analysis of chestnut genome SNP polymorphism and important traits, which is beneficial to the subsequent development of chestnut assisted breeding and innovative utilization of chestnut germplasm resources, and improves the association analysis effect of its polymorphism and important traits. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0041] Figure 1 It is a schematic diagram of the modular structure of the system of the present invention;

[0042] Figure 2 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0044] Reference Figure 1-Figure 2 , is an embodiment of the present invention, which provides a chestnut genome SNP polymorphism and important trait association analysis system and method, the system comprising: a genome resequencing module 100, a marker development and polymorphism verification module 101, a genetic diversity analysis module 102, and an association analysis module 103;

[0045] A genome resequencing module 100 is used for genome resequencing of at least three biological replicates of chestnut materials;

[0046] The marker development and polymorphism verification module 101 is used to select a number of chestnut genome strain samples and a number of SNP trait markers for polymorphism verification;

[0047] Genetic diversity analysis module 102, for simultaneously constructing a clustering dendrogram of chestnut genome marker targets based on chestnut genome strain samples and band data of SNP trait markers, and generating clustering analysis results of chestnut genome strain samples and SNP traits;

[0048] The association analysis module 103 is used for the association analysis between chestnut gene phenotypic data and developed molecular markers, specifically, genetic diversity analysis is performed based on the SNP-based positioned trait markers, and association analysis is performed on chestnut traits and markers respectively through the MLM mixed linear model, and chestnut single-site SNP markers are obtained as covariates, combined with Lambda statistics and permutation test methods, chestnut interaction epistasis SNP markers are obtained, and chestnut traits and markers are respectively analyzed based on the chestnut interaction epistasis SNP markers, and important chestnut traits are screened out and then association analysis of important traits is performed.

[0049] The Lambda statistic is a statistic used in multivariate statistical analysis, mainly used in multivariate analysis of variance (MANOVA) to evaluate the significance of inter-group differences between multiple dependent variables.

[0050] The MLM mixed linear model is a statistical model that combines fixed effects and random effects and is used to analyze data with correlation and non-independence.

[0051] SNP (Single Nucleotide Polymorphism) refers to the DNA sequence polymorphism caused by the variation of a single nucleotide in the genome.

[0052] This embodiment further illustrates that the strain sample of the chestnut genome is subjected to genome resequencing, and the chestnut leaf DNA sample is extracted and randomly broken into fragments of 350 bp in length, and a sequencing library kit is used to build a library. At the same time, the chestnut genomic DNA fragment is end-repaired, polyA tailed, sequencing adapter added, and purified PCR amplified to complete the preparation of the strain sample of the entire chestnut genome. The Poly (A) tail is an important feature of eukaryotic mRNA, which is composed of multiple adenosines and is located at the 3 end of the mRNA. PCR (Polymerase Chain Reaction) is a molecular biology technique used to rapidly amplify specific DNA fragments in vitro.

[0053] It should be emphasized in this embodiment that the MLM mixed linear model is used to perform association analysis on the chestnut traits and markers, and specifically, the chestnut genome strain unit locus SNP markers with significant effects are obtained after determining the unit locus SNP markers with significant effects. The chestnut genome strain unit locus SNP markers with significant effects are analyzed as follows:

[0054]

[0055] Among them, Q ij is the phenotypic observation value of the jth strain of the i-th trait of chestnut; μ ik is the population mean of the i-th trait of chestnut under environment k; a ic is the cth covariate effect value of the i-th trait of chestnut, α is the cth covariate effect value of the i-th trait of chestnut ic The coefficient of n is the total number of permutations of the i-th trait of chestnut; b kc is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k, and β is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k. kc The coefficient of m is the total number of permutations of the chestnut environment k; ε ij is the random residual value of the jth strain of the i-th trait.

[0056] In this embodiment, the obtained chestnut genome strain single-site SNP markers are used as covariates, and the two-interaction epistatic SNP markers with significant interaction effects are obtained by Lambda statistics and permutation test methods, as follows:

[0057]

[0058] Among them, Qij is the phenotypic observation value of the jth strain of the i-th trait of chestnut; μ ik is the population mean of the i-th trait of chestnut under environment k; a ic is the cth covariate effect value of the i-th trait of chestnut, α is the cth covariate effect value of the i-th trait of chestnut ic The coefficient of n is the total number of permutations of the i-th trait of chestnut; b kc is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k, and β is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k. kc The coefficient of m is the total number of permutations of the chestnut environment k; ε ij is the random residual value of the jth strain of the i-th trait.

[0059] In this embodiment, the chestnut genome strains with significant simultaneous effects are obtained by using a permutation test method to determine the threshold of significant effects; then the relationship between the Lambda statistic and the threshold is determined; when the Lambda statistic of a pair of two-interaction epistatic SNP markers is greater than or equal to the threshold, the pair of SNP markers is a two-interaction epistatic SNP marker with significant interaction effect.

[0060] Specifically, in this embodiment, the evaluation indicators of the strain unit site SNP markers of the chestnut genome include SNP site evaluation of PIC, minor gene frequency, detection rate, and heterozygosity rate; the SNP site evaluation is based on the allele frequency, wherein the site evaluation and screening utilize sites that are less than a preset threshold to distinguish the differences between chestnut varieties.

[0061] Specifically, the chestnut properties include: thorn bud shape, cracking mode, thorn bundle branching angle, thorn bundle density, thorn bundle color, edge fruit shape, nut color, nut color uniformity, nut gloss, fruit top and shoulder, hair distribution, hair color, hair density, obvious degree of tendon line, base size, base smoothness, base connection, difficulty of peeling astringent skin, and kernel color;

[0062] When screening out important chestnut traits, this embodiment calculates chestnut trait membership after statistics of chestnut traits according to the standard threshold of chestnut trait classification assignment, and screens out important chestnut traits by analyzing and comprehensively evaluating the average membership.

[0063] To further illustrate, according to the threshold value of chestnut trait classification, chestnut trait membership was calculated after statistics were collected, and the important chestnut traits were screened out by analyzing and comprehensively evaluating the average membership as follows:

[0064]

[0065] Among them, y k,xiis the sample data of chestnut trait sample i under chestnut environment k that reaches the evaluation index threshold of SNP site evaluation, P(X i ) is the proportion of the chestnut trait samples;

[0066] When calculating the membership of chestnut traits, this embodiment also includes calculating the entropy value of the evaluation index of the SNP site evaluation. The obtained entropy value of the evaluation index of the SNP site evaluation is used to calculate the weight coefficient of the chestnut environment k and then calculate the evaluation membership value. Specifically, the entropy value of the evaluation index is calculated as follows:

[0067]

[0068] Among them, E j is the entropy value of the evaluation index of the j-th SNP site, ln(p ij ) is p ij The natural logarithm of ln(n) is the natural logarithm of n. lnp ij =0

[0069] Calculate the weight coefficient of the chestnut environment k as follows:

[0070]

[0071] The evaluation membership value is calculated as follows:

[0072]

[0073] Among them, Qi avg is the average membership value of the comprehensive evaluation of the chestnut trait sample, Q(X i ) is the assessed membership value of the chestnut trait sample i, w i is the weight of the assessed membership of the chestnut trait sample i, and w i Greater than 0.

[0074] In order to mine molecular markers significantly associated with chestnut phenotypic traits, this embodiment identified 14 phenotypic data of 185 chestnut samples, screened out 32 molecular markers with polymorphism for genetic diversity analysis, and used the model in the chestnut genome SNP polymorphism and important trait association analysis system of the embodiment to perform association analysis on the chestnut phenotypic data. The association analysis used a correlation coefficient calculation method to perform correlation analysis of important chestnut traits. In the quantitative trait analysis, the coefficient of variation ranged from 4.25% to 37.8%, and the coefficients of variation of bract weight, total bract weight and single-grain weight were all above 30%, with a high degree of genetic variation. The coefficients of variation of fruit shape index and water content were both below 10%, with stable genetic characteristics. There was a strong correlation between the appearance traits of the total bract and the nut, and the correlation coefficients were all above 0.76.

[0075] In addition, this example also measured the quantity and quality important traits of 233 chestnut varieties (lines), and used SNP positioning markers to perform genetic diversity and association analysis, and found that 25 SNP positioning markers were extremely significantly correlated with the characteristics of 23 important chestnut traits, and the ratio of explaining phenotypic variation ranged from 12.22% to 32.11%.

[0076] In summary, the present invention realizes the important trait association analysis of chestnut quality traits and pseudo-quality traits by deeply analyzing the strain samples of chestnut genome and the band data of SNP trait markers. This process not only improves the data mining capability of the association analysis of chestnut genome SNP polymorphism and important traits, but also optimizes the processing and analysis method of allelic variant sites. Researchers can more accurately identify key genes and variant sites related to important chestnut traits, thereby providing important genetic markers and candidate genes for molecular assisted breeding of chestnut.

[0077] In addition, the invention also promotes the innovative use of chestnut germplasm resources. Through in-depth research on the chestnut genome, we can better understand the genetic diversity and evolutionary history of chestnuts, and provide a theoretical basis and practical guidance for breeding new varieties with better traits. Researchers can discover the expansion or contraction of gene families related to specific traits in chestnuts, and then reveal the genetic basis of these traits.

[0078] In conclusion, the present invention not only improves the effect of the association analysis between SNP polymorphism and important traits in the chestnut genome, but also provides strong technical support for the auxiliary breeding of chestnut and the innovative utilization of germplasm resources, and has important scientific significance and application value.

[0079] At the same time, this embodiment also provides a method for analyzing the association between SNP polymorphism and important traits of chestnut genome, comprising the following steps:

[0080] Step 1: resequencing the genome of chestnut materials with at least three biological replicates;

[0081] Step 2: Select several chestnut genome strain samples and several SNP trait markers for polymorphism verification;

[0082] Step 3: Based on the chestnut genome strain samples and the band data of the SNP trait markers, a clustering dendrogram of the chestnut genome marker targets is constructed to generate clustering analysis results of the chestnut genome strain samples and the SNP traits;

[0083] Step 4: Genetic diversity analysis was performed based on SNP-located trait markers. Association analysis was performed on chestnut traits and markers using the MLM mixed linear model. The chestnut single-site SNP markers obtained were used as covariates. Combined with the Lambda statistic and permutation test method, chestnut interaction epistasis SNP markers were obtained. Based on the chestnut interaction epistasis SNP markers, the chestnut traits and markers were analyzed separately, and important chestnut traits were screened out for association analysis.

[0084] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0085] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0086] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0087] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A chestnut genome SNP polymorphism and important trait association analysis system, characterized by: include: Genome resequencing module, marker development and polymorphism validation module, genetic diversity analysis module, and association analysis module; The genome resequencing module is used for genome resequencing of at least 3 biological replicates of chestnut materials; The marker development and polymorphism verification module is used to select several strain samples of chestnut genome and several SNP trait markers for polymorphism verification; The genetic diversity analysis module is used to construct a clustering dendrogram of chestnut genome marker targets based on the chestnut genome strain samples and the band data of the SNP trait markers, and generate clustering analysis results of the chestnut genome strain samples and the SNP traits; The association analysis module is used for association analysis between chestnut gene phenotypic data and developed molecular markers, specifically, genetic diversity analysis is performed based on SNP-based positioned trait markers, association analysis is performed on chestnut traits and markers respectively through an MLM mixed linear model, and chestnut single-site SNP markers are obtained as covariates, combined with Lambda statistics and permutation test methods, chestnut interaction epistasis SNP markers are obtained, and chestnut traits and markers are respectively screened out according to the chestnut interaction epistasis SNP markers, and then association analysis of important chestnut traits is performed.

2. A chestnut genome SNP polymorphism and important trait association analysis system as claimed in claim 1, characterized in that: The strain sample of the chestnut genome was subjected to genome resequencing. After extracting chestnut leaf DNA samples, they were randomly broken into fragments with a length of 350 bp, and a sequencing library kit was used to build a library. At the same time, the chestnut genome DNA fragments were end-repaired, ployA-tailed, sequenced, and purified by PCR amplification to complete the preparation of the strain sample of the entire chestnut genome.

3. The chestnut genome SNP polymorphism and important trait association analysis system according to claim 1, characterized in that: The MLM mixed linear model was used to perform association analysis on chestnut traits and markers. Specifically, the chestnut genome strain unit-site SNP markers with significant effects were obtained by determining the unit-site SNP markers with significant effects. The chestnut genome strain unit-site SNP markers with significant effects were analyzed as follows: Among them, Q ij is the phenotypic observation value of the jth strain of the i-th trait of chestnut; μ ik is the population mean of the i-th trait of chestnut under environment k; a ic is the cth covariate effect value of the i-th trait of chestnut, α is the cth covariate effect value of the i-th trait of chestnut ic The coefficient of n is the total number of permutations of the i-th trait of chestnut; b kc is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k, and β is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k. kc The coefficient of m is the total number of permutations of the chestnut environment k; ε ij is the random residual value of the jth strain of the i-th trait.

4. The chestnut genome SNP polymorphism and important trait association analysis system according to claim 1, characterized in that: The obtained chestnut genome strain single-site SNP markers were used as covariates, and the Lambda statistic and permutation test method were used to obtain the two-interaction epistatic SNP markers with significant interaction effects, as follows: Among them, Q ij is the phenotypic observation value of the jth strain of the i-th trait of chestnut; μ ik is the population mean of the i-th trait of chestnut under environment k; a ic is the cth covariate effect value of the i-th trait of chestnut, α is the cth covariate effect value of the i-th trait of chestnut ic The coefficient of n is the total number of permutations of the i-th trait of chestnut; b kc is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k, and β is the additive effect value of the SNP marker of the cth covariate in chestnut under environment k. kc The coefficient of m is the total number of permutations of the chestnut environment k; ε ij is the random residual value of the jth strain of the i-th trait.

5. A chestnut genome SNP polymorphism and important trait association analysis system as claimed in claim 2, characterized in that: The chestnut genome strains with significant effects are obtained by using a permutation test method to determine the threshold of significant effects; then the relationship between the Lambda statistic and the threshold is determined; when the Lambda statistic of a pair of two-interaction epistatic SNP markers is greater than or equal to the threshold, the pair of SNP markers is a two-interaction epistatic SNP marker with significant interaction effect.

6. The chestnut genome SNP polymorphism and important trait association analysis system according to claim 1, characterized in that: The SNP site evaluation is based on the allele frequency, and the evaluation indicators of the strain unit site SNP markers of the chestnut genome include PIC, minor gene frequency, detection rate, and heterozygosity rate SNP site evaluation; The site evaluation and screening utilize sites smaller than the preset threshold to distinguish the differences between chestnut varieties.

7. The chestnut genome SNP polymorphism and important trait association analysis system according to claim 1, characterized in that: The chestnut properties include: thorn bud shape, cracking mode, thorn bundle branching angle, thorn bundle density, thorn bundle color, side fruit shape, nut color, nut color uniformity, nut gloss, fruit top and shoulder, hair distribution, hair color, hair density, obvious degree of tendon line, base size, base smoothness, base connection, difficulty of peeling astringent skin, and kernel color; The important chestnut traits were screened out, and the chestnut trait membership was calculated after statistics were performed based on the standard threshold of chestnut trait grading assignment. The important chestnut traits were screened out after analyzing and comprehensively evaluating the average membership.

8. The chestnut genome SNP polymorphism and important trait association analysis system according to claim 1, characterized in that: According to the threshold value of chestnut trait classification, the chestnut trait membership was calculated after statistics were collected. The important chestnut traits were selected by analyzing and comprehensively evaluating the average membership as follows: Among them, y k,xi is the sample data of chestnut trait sample i under chestnut environment k that reaches the evaluation index threshold of SNP site evaluation, P(X i ) is the proportion of the chestnut trait samples; When calculating the membership of chestnut traits, the entropy value of the evaluation index of the SNP site evaluation is also calculated. The weight coefficient of the chestnut environment k is calculated by the obtained entropy value of the evaluation index of the SNP site evaluation, and then the evaluation membership value is calculated. Specifically, the entropy value of the evaluation index is calculated as follows: Among them, E j is the entropy value of the evaluation index of the j-th SNP site, ln(p ij ) is p ij The natural logarithm of ln(n) is the natural logarithm of n. Calculate the weight coefficient of the chestnut environment k as follows: The evaluation membership value is calculated as follows: Among them, Qi avg is the average membership value of the comprehensive evaluation of the chestnut trait sample, Q(X i ) is the assessed membership value of the chestnut trait sample i, w i is the weight of the assessed membership of the chestnut trait sample i, and w i Greater than 0.

9. A chestnut genome SNP polymorphism and important trait association analysis system and method as claimed in claim 1, characterized in that: The association analysis adopts a correlation coefficient calculation method to perform correlation analysis of important chestnut traits.

10. A method for applying to the chestnut genome SNP polymorphism and important trait association analysis system as claimed in claim 1, characterized in that: The following steps are involved: Step 1: resequencing the genome of chestnut materials with at least three biological replicates; Step 2: Select several chestnut genome strain samples and several SNP trait markers for polymorphism verification; Step 3: Based on the chestnut genome strain samples and the band data of the SNP trait markers, a clustering dendrogram of the chestnut genome marker targets is constructed to generate clustering analysis results of the chestnut genome strain samples and the SNP traits; Step 4: Perform genetic diversity analysis based on SNP positioned trait markers, perform association analysis on chestnut traits and markers using the MLM mixed linear model, and use the obtained chestnut single-site SNP markers as covariates, combine the Lambda statistic and permutation test method to obtain chestnut interaction epistasis SNP markers, and use the chestnut interaction epistasis SNP markers to screen out important chestnut traits and perform association analysis on the important traits.

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