A System and Method for Analyzing the Association between SNP Polymorphism in Chestnut Genome and Important Traits

Abstract

The present invention discloses a system and method for analyzing the association between SNP polymorphisms and important traits of chestnut genomes, including 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 samples of chestnut genomes and the band data of SNP trait markers, a clustering dendrogram of chestnut genome marker targets is constructed simultaneously to generate the clustering analysis results of chestnut genome strain samples and SNPs; the association analysis of chestnut gene phenotype data and developed molecular markers. The present invention realizes the association analysis of important traits of qualitative traits and pseudo-qualitative traits of chestnuts, improves the processing and analysis of allelic variant sites in data mining for the association analysis of SNP polymorphisms and important traits of chestnut genomes, and improves the association analysis effect of its polymorphisms and important traits.

Description

Technical Field

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

[0002] Using SNP (Single Nucleotide Polymorphism) for association analysis between traits and markers is a widely used method 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 the present stage, in the research and analysis of the chestnut genome, chestnuts have rich variation types and unique traits. In particular, traits related to fruits are important breeding goals. Although there have been some studies on SNP mapping and association analysis in chestnuts, SNPs are only associated with traits such as single nut weight, nut shape, maturity period, and astringent skin peeling degree of chestnut nuts, that is, mainly concentrated on quantitative traits. There is still a lack of important trait association analysis for qualitative traits and pseudo-qualitative traits of chestnuts. At the same time, there is still insufficient data mining of allelic variation sites in the analysis of the association between SNP polymorphisms and important traits in the chestnut genome, which is not conducive to the subsequent development of chestnut assisted breeding and the innovative utilization of chestnut germplasm resources. For this reason, we propose a system and method for analyzing the association between SNP polymorphisms and important traits in the chestnut genome. Summary of the Invention

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

[0004] Therefore, one of the purposes of the present invention is to provide a system and method for analyzing the association between SNP polymorphisms and important traits in the chestnut genome, which realizes the important trait association analysis of qualitative traits and pseudo-qualitative traits of chestnuts 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 of allelic variation sites in the analysis of the association between SNP polymorphisms and important traits in the chestnut genome. Furthermore, it is beneficial to the subsequent development of chestnut assisted breeding and the innovative utilization of chestnut germplasm resources, and improves the association analysis effect of its polymorphisms and important traits.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] On the one hand, the present invention provides a system for analyzing the association between SNP polymorphisms and important traits in the chestnut genome, including: a genome re-sequencing module, a marker development and polymorphism verification module, a genetic diversity analysis module, and an association analysis module;

[0007] The genome re-sequencing module is used for re-sequencing the genomes of chestnut materials with at least 3 biological replicates;

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

[0009] The genetic diversity analysis module is used to construct a clustering dendrogram of the chestnut genome marker target and generate a clustering analysis result of the chestnut genome strain sample and SNP traits according to the band data of the chestnut genome strain sample and SNP trait markers;

[0010] The association analysis module is used for the association analysis between the chestnut gene phenotype data and the developed molecular markers. Specifically, genetic diversity analysis is performed based on the SNP-based mapped trait markers, and the association analysis of the traits and markers of chestnuts is respectively carried out through the MLM mixed linear model. And using the obtained single-site SNP markers of chestnuts as covariates, combined with the Lambda statistic and the permutation test method, the epistatic SNP markers of chestnuts are obtained. According to the epistatic SNP markers of chestnuts for the traits and markers of chestnuts respectively, important traits of chestnuts are screened out and then the association analysis of important traits is carried out.

[0011] As a preferred embodiment of the present invention, wherein: the strain samples of the chestnut genome are subjected to genome resequencing. After extracting the chestnut leaf DNA sample, it is randomly fragmented into fragments with a length of 350 bp, and a sequencing library kit is used for library construction. At the same time, the chestnut genome DNA fragments are subjected to end repair, adding a polyA tail, adding sequencing adapters, and purifying PCR amplification to complete the preparation of the entire chestnut genome strain sample.

[0012] As a preferred embodiment of the present invention, wherein: the association analysis of the traits and markers of chestnuts is respectively carried out through the MLM mixed linear model. Specifically, after determining the single-site SNP markers with significant effects, the single-site SNP markers of the chestnut genome strain with significant effects are obtained. The analysis of the single-site SNP markers of the chestnut genome strain with significant effects is as follows:

[0013]

[0014] Wherein, Q ij is the phenotypic observation value of the j-th 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 covariate effect value of the c-th covariate of the i-th trait of chestnut, and α is the coefficient of the covariate effect value a ic of the c-th covariate of the i-th trait of chestnut, and 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 c-th covariate of chestnut under environment k, and β is the additive effect value bkc The coefficient, m is the total number of permutations of chestnut environment k; ε ij is the random residual value of the j-th strain of the i-th trait.

[0015] As a preferred embodiment of the present invention, wherein: the single-site SNP markers of the obtained chestnut genome strains are used as covariates, and through the Lambda statistic and permutation test method, the significant epistatic SNP markers of two-way interactions are obtained as follows:

[0016]

[0017] wherein, Q ij is the phenotypic observation value of the j-th 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 c-th covariate effect value of the i-th trait of chestnut, and α is the c-th covariate effect value a of the i-th trait of chestnut ic The coefficient, 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 c-th covariate of chestnut under environment k, and β is the additive effect value b of the SNP marker of the c-th covariate of chestnut under environment k kc The coefficient, m is the total number of permutations of chestnut environment k; ε ij is the random residual value of the j-th strain of the i-th trait.

[0018] As a preferred embodiment of the present invention, wherein: the permutation test method is used to determine the threshold of significant effect for obtaining the strains of the chestnut genome with significant effects; then the size relationship between the Lambda statistic and the threshold is judged; when the Lambda statistic of a pair of two-way epistatic SNP markers is greater than or equal to the threshold, this pair of SNP markers is the significant epistatic SNP markers of two-way interactions.

[0019] As a preferred embodiment of the present invention, wherein: the SNP site evaluation is based on allele frequency, and the evaluation indexes of the single-site SNP markers of the chestnut genome strains include SNP site evaluation of PIC, minor allele frequency, detection rate, and heterozygosity rate; among them, the site evaluation and screening use sites smaller than the preset threshold to distinguish the differences between chestnut varieties.

[0020] As a preferred embodiment of the present invention, wherein: the traits of the chestnut include: burr shape, cracking method, angle of burr branch, burr density, burr color, shape of side fruit, nut color, uniformity of nut color, nut luster, fruit top and fruit shoulder, distribution of hairs, hair color, hair density, obviousness of tendon line, size of base, smoothness of base, base connection line, ease of peeling of astringent skin, color of kernel;

[0021] Select the important traits of chestnuts. According to the grading assignment standard thresholds of chestnut traits, calculate the membership degree of chestnut traits after counting the chestnut traits, and screen out the important chestnut traits by analyzing and comprehensively evaluating the average membership degree.

[0022] As a preferred embodiment of the present invention, among them: According to the grading assignment standard thresholds of chestnut traits, calculate the membership degree of chestnut traits after counting the chestnut traits, and screen out the important chestnut traits as follows by analyzing and comprehensively evaluating the average membership degree:

[0023]

[0024] Among them, y k,xi is the sample data of the i-th trait sample of chestnuts under the chestnut environment k after being processed by the evaluation index threshold for SNP locus evaluation, and P(X i ) is the proportion value of the i-th trait sample of chestnuts;

[0025] When calculating the membership degree of chestnut traits, it also includes calculating the entropy value of the evaluation index of SNP locus evaluation. Through the obtained entropy value of the evaluation index of SNP locus evaluation, calculate the weight coefficient of chestnut environment k and then calculate the evaluation membership degree value. Specifically, the calculation of the entropy value of the evaluation index is as follows:

[0026]

[0027] Among them, E j is the entropy value of the j-th SNP locus evaluation index, ln(p ij ) is the natural logarithm of p ij , ln(n) is the natural logarithm of n. When

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

[0029]

[0030] Calculate the evaluation membership degree value as follows:

[0031]

[0032] Among them, Qi avg is the comprehensive evaluation average membership degree value of the i-th trait sample of chestnuts, Q(X i ) is the evaluation membership degree value of the i-th trait sample of chestnuts, w i is the weight of the evaluation membership degree of the i-th trait sample of chestnuts, and w i is greater than 0.

[0033] As a preferred embodiment of the present invention, wherein: the correlation analysis uses the correlation coefficient calculation method to perform the correlation analysis of the important traits of chestnuts.

[0034] On the one hand, the present invention provides a method for the association analysis of chestnut genome SNP polymorphism and important traits, comprising the following steps:

[0035] Step 1, re-sequencing the genomes of chestnut materials with at least 3 biological replicates;

[0036] Step 2, selecting several strain samples of chestnut genomes and several SNP trait markers for polymorphism verification;

[0037] Step 3, according to the band data of the strain samples of the chestnut genome and the SNP trait markers, simultaneously construct a clustering dendrogram of the chestnut genome marker targets to generate the clustering analysis results of the chestnut genome strain samples and SNPs;

[0038] Step 4, perform genetic diversity analysis based on the SNP-based mapped trait markers, perform association analysis on the traits and markers of chestnuts respectively through the MLM mixed linear model, and use the obtained chestnut single-site SNP markers as covariates, combined with the Lambda statistic and the permutation test method to obtain the chestnut epistatic SNP markers, and according to the chestnut epistatic SNP markers, respectively analyze the traits and markers of chestnuts, screen out the important traits of chestnuts and then perform the association analysis of important traits.

[0039] Compared with the prior art, the beneficial effects of the present invention are: by analyzing and processing the band data of the strain samples of the chestnut genome and the SNP trait markers, the present invention realizes the association analysis of the important traits of the qualitative traits and pseudo-qualitative traits of chestnuts, and improves the processing and analysis of mining allelic variation sites in the association analysis of chestnut genome SNP polymorphism and important traits, which is conducive to the subsequent development of chestnut assisted breeding and the 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 will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings. Among them:

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

[0042] Figure 2 It is a flowchart of the method of the present invention. Detailed implementation manners

[0043] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention fall within the scope of protection of the present invention.

[0044] Referring to Figure 1 - Figure 2 , which is an embodiment of the present invention. This embodiment provides a system and method for analyzing the association between SNP polymorphisms and important traits in the chestnut genome. The system includes: a genome re-sequencing module 100, a marker development and polymorphism verification module 101, a genetic diversity analysis module 102, and an association analysis module 103;

[0045] The genome re-sequencing module 100 is used for re-sequencing the genomes of at least 3 biological replicates of chestnut materials;

[0046] The marker development and polymorphism verification module 101 is used for selecting several strain samples of the chestnut genome and several SNP trait markers for polymorphism verification;

[0047] The genetic diversity analysis module 102 is used for simultaneously constructing a clustering dendrogram of the chestnut genome marker targets based on the band data of the strain samples of the chestnut genome and the SNP trait markers, and generating a clustering analysis result of the strain samples of the chestnut genome and the SNPs;

[0048] The association analysis module 103 is used for the association analysis between the chestnut gene phenotype data and the developed molecular markers. Specifically, genetic diversity analysis is performed based on the SNP-based mapped trait markers. Association analysis of the traits and markers of chestnuts is performed respectively through the MLM mixed linear model, and the obtained single-site SNP markers of chestnuts are used as covariates. Combining the Lambda statistic and the permutation test method, the epistatic SNP markers of chestnuts are obtained. Based on the epistatic SNP markers of chestnuts, the traits and markers of chestnuts are respectively screened for important traits, and then the association analysis of the 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 the between-group differences among 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 DNA sequence polymorphism caused by single nucleotide variation in the genome.

[0052] As further illustrated in this embodiment, the strain samples of the chestnut genome are subjected to genome resequencing. After extracting the chestnut leaf DNA samples, they are randomly fragmented into fragments with a length of 350 bp, and a sequencing library kit is used to construct the library. At the same time, the chestnut genome DNA fragments are subjected to end repair, adding a poly(A) tail, adding sequencing adapters, and purifying PCR amplification to complete the preparation of the strain samples of the entire chestnut genome. Among them, the Poly(A) tail is an important feature of eukaryotic mRNA, which consists of multiple adenosine acids and is located at the 3' end of 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 traits and markers of chestnuts. Specifically, after determining the single-site SNP markers with significant effects, the single-site SNP markers of the chestnut genome strains with significant effects are obtained. The analysis of the single-site SNP markers of the chestnut genome strains with significant effects is as follows:

[0054]

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

[0056] Preferably in this embodiment, the single-site SNP markers of the chestnut genome strains obtained are used as covariates, and through the Lambda statistic and permutation test method, the two-locus epistatic SNP markers with significant interaction effects are obtained as follows:

[0057]

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

[0059] In this embodiment, the acquisition of strains with significant simultaneous effects in the Chinese chestnut genome uses a permutation test method to determine the threshold of significant effects; then, the magnitude relationship between the Lambda statistic and the threshold is judged; when the Lambda statistic of a pair of two-way epistatic SNP markers is greater than or equal to the threshold, this pair of SNP markers is a pair of two-way epistatic SNP markers with significant epistatic effects.

[0060] Specifically, in this embodiment, the evaluation indexes of single-site SNP markers of strains in the Chinese chestnut genome include the evaluation of SNP sites such as PIC, minor allele frequency, detection rate, and heterozygosity rate; the SNP site evaluation is based on allele frequency, and among them, sites with values less than the preset threshold are used for site evaluation and screening to distinguish differences between Chinese chestnut varieties.

[0061] Specifically, the traits of Chinese chestnut include: burr shape, cracking method, thorn bundle branch angle, thorn bundle density, thorn bundle color, side fruit shape, nut color, nut color uniformity, nut luster, fruit top and fruit shoulder, hair distribution, hair color, hair density, obviousness of tendon lines, base size, base smoothness, base connection, ease of peeling the astringent skin, and kernel color;

[0062] When screening out important traits of Chinese chestnut in this embodiment, according to the grading assignment standard threshold of Chinese chestnut traits, the membership degree of Chinese chestnut traits is calculated after statistics on Chinese chestnut traits, and important traits of Chinese chestnut are screened out by analyzing and comprehensively evaluating the average membership degree.

[0063] Furthermore, according to the grading assignment standard threshold of Chinese chestnut traits, the membership degree of Chinese chestnut traits is calculated after statistics on Chinese chestnut traits, and important traits of Chinese chestnut screened out by analyzing and comprehensively evaluating the average membership degree are as follows:

[0064]

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

[0066] In calculating the membership of chestnut traits in this embodiment, 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 based on 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:

[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 i, 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 explore molecular markers significantly associated with chestnut phenotypic traits, this example identified 14 phenotypic data of 185 chestnut samples, screened out 32 molecular markers with polymorphisms for genetic diversity analysis, and used the model in the chestnut genome SNP polymorphism and important trait association analysis system of the example to perform association analysis on the chestnut phenotypic data. The association analysis used a correlation coefficient calculation method to perform correlation analysis on 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 kernel weight were all above 30%, indicating a high degree of genetic variation. The coefficients of variation of fruit shape index and water content were both below 10%, indicating stable genetic characteristics. There was a strong correlation between the appearance traits of the bract and the nut, with the correlation coefficients being above 0.76.

[0075] In addition, in this embodiment, by measuring the quantity and quality important traits of 233 chestnut varieties (lines), and using SNP mapping markers for genetic diversity and association analysis, it is found that 25 SNP mapping markers are extremely significantly correlated with the characteristics of 23 important traits of chestnut, and the ratio of phenotypic variation explained ranges from 12.22% to 32.11%.

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

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

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

[0079] Meanwhile, this embodiment also provides a method for the association analysis of SNP polymorphism and important traits of the chestnut genome, including the following steps:

[0080] Step 1, re-sequencing the genomes of chestnut materials with at least 3 biological replicates;

[0081] Step 2, selecting several strain samples of the chestnut genome and several SNP trait markers for polymorphism verification;

[0082] Step 3, according to the band data of the strain samples of the chestnut genome and the SNP trait markers, simultaneously constructing a clustering dendrogram of the chestnut genome marker targets to generate the clustering analysis results of the chestnut genome strain samples and SNPs;

[0083] Step 4: Conduct genetic diversity analysis based on SNP-based trait markers. Use the MLM mixed linear model to perform association analysis on the traits and markers of Chinese chestnut respectively. Take the obtained single-locus SNP markers of Chinese chestnut as covariates, and combine the Lambda statistic and the permutation test method to obtain the epistatic SNP markers of Chinese chestnut. Based on the epistatic SNP markers of Chinese chestnut for the traits and markers of Chinese chestnut respectively, screen out the important traits of Chinese chestnut and then conduct association analysis of the important traits.

[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 processes or functions according to the present application are 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", etc. means that the specific features, structures, materials, or characteristics described in connection 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 can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0086] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of these features. In the description of the present application, "a plurality" means two or more unless otherwise specifically defined.

[0087] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed in the present application, and these should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A system for analyzing the association between SNP polymorphisms and important traits in the chestnut genome, characterized in that: Including: A genome re-sequencing module, a marker development and polymorphism verification module, a genetic diversity analysis module, and an association analysis module; The genome re-sequencing module is used for genome re-sequencing of at least 3 biological replicates of chestnut materials; The marker development and polymorphism verification module is used to select several strain samples of the chestnut genome and several SNP trait markers for polymorphism verification; The genetic diversity analysis module is used to construct a clustering dendrogram of the chestnut genome marker target based on the band data of the strain samples of the chestnut genome and SNP trait markers, and generate a clustering analysis result of the chestnut genome strain samples and SNPs; The association analysis module is used for the association analysis of chestnut gene phenotype data and developed molecular markers. Specifically, genetic diversity analysis is performed based on SNP-based mapped trait markers. Association analysis of the traits and markers of chestnuts is performed respectively through the MLM mixed linear model, and the obtained single-site SNP markers of chestnuts are used as covariates. Combining the Lambda statistic and the permutation test method, the epistatic SNP markers of chestnuts are obtained. According to the epistatic SNP markers of chestnuts for the traits and markers of chestnuts respectively, important traits of chestnuts are screened out and then association analysis of important traits is performed.

2. The system for analyzing the association between chestnut genome SNP polymorphism and important traits according to claim 1, wherein For the strain samples of the chestnut genome, genome re-sequencing is carried out. After extracting the chestnut leaf DNA samples, they are randomly fragmented into fragments with a length of 350 bp, and a sequencing library kit is used for library construction. At the same time, the chestnut genome DNA fragments are subjected to end repair, adding a polyA tail, adding sequencing adapters, and purifying PCR amplification to complete the preparation of the entire strain samples of the chestnut genome.

3. The system for analyzing the association between chestnut genome SNP polymorphisms and important traits according to claim 1, wherein Association analysis of the traits and markers of chestnuts is performed respectively through the MLM mixed linear model. Specifically, after determining the single-site SNP markers with significant effects, the single-site SNP markers of the chestnut genome strain with significant effects are obtained. The analysis of the single-site SNP markers of the chestnut genome strain with significant effects is as follows: Among them, Q ij is the phenotypic observation value of the j-th 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 c-th covariate effect value of the i-th trait of chestnut, and α is the coefficient of the c-th covariate effect value a ic of the i-th trait of chestnut, and n is the total number of arrangements of the i-th trait of chestnut; b kc is the additive effect value of the SNP marker of the c-th covariate of chestnut under environment k, and β is the coefficient of the additive effect value b kc of the c-th covariate of chestnut under environment k; ε ij is the random residual value of the j-th strain of the i-th trait.

4. The system for analyzing the association between chestnut genome SNP polymorphisms and important traits according to claim 1, wherein Using the obtained single-site SNP markers of the chestnut genome strain as covariates, through the Lambda statistic and the permutation test method, the two-epistatic SNP markers with significant interaction effects are obtained, as follows: Among them, Q ij is the phenotypic observation value of the j-th 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 c-th covariate effect value of the i-th trait of chestnut, and α is the coefficient of the c-th covariate effect value a ic of the i-th trait of chestnut, and n is the total number of arrangements of the i-th trait of chestnut; b kc is the additive effect value of the SNP marker of the c-th covariate of chestnut under environment k, and β is the coefficient of the additive effect value b kc of the c-th covariate of chestnut under environment k, and m is the total number of arrangements of chestnut environment k; ε ij is the random residual value of the j-th strain of the i-th trait.

5. The system for analyzing the association between chestnut genome SNP polymorphisms and important traits according to claim 2, characterized in that, The method of permutation test is used to determine the threshold of significant effects for obtaining the chestnut genome strain with significant effects; then judge the size relationship between the Lambda statistic and the threshold; when the Lambda statistic of a pair of two-epistatic SNP markers is greater than or equal to the threshold, this pair of SNP markers is the two-epistatic SNP markers with significant interaction effects.

6. The system for analyzing the association between SNP polymorphisms and important traits of chestnut genome according to claim 1, characterized in that The evaluation of SNP sites is based on allele frequency. The evaluation indexes of the single-site SNP markers of the chestnut genome strain include SNP site evaluation of PIC, minor allele frequency, detection rate, and heterozygosity rate; Among them, site evaluation and screening use sites smaller than the preset threshold to distinguish the differences between chestnut varieties.

7. The association analysis system for chestnut genome SNP polymorphism and important traits according to claim 1, wherein, The traits of the chestnut include: burr shape, cracking method, thorn bundle branching angle, thorn bundle density, thorn bundle color, side fruit shape, nut color, nut color uniformity, nut gloss, fruit top and fruit shoulder, hair distribution, hair color, hair density, obviousness of tendon lines, base size, base smoothness, base connection, ease of separating the astringent skin, and kernel color; Select the important traits of chestnuts. According to the grading assignment standard thresholds of chestnut traits, calculate the membership degree of chestnut traits after statistics, and screen out the important traits of chestnuts by analyzing and comprehensively evaluating the average membership degree.

8. The association analysis system for chestnut genome SNP polymorphism and important traits according to claim 7, characterized in that, According to the grading assignment standard thresholds of chestnut traits, calculate the membership degree of chestnut traits after statistics, and screen out the following important traits of chestnuts by analyzing and comprehensively evaluating the average membership degree: Among them, y k,xi is the sample data of the chestnut at the k-th chestnut environment after the evaluation index threshold of the i-th trait sample of the chestnut reaches the SNP locus evaluation, and P(X i ) is the proportion value of the i-th trait sample of the chestnut; When calculating the membership degree of chestnut traits, it also includes calculating the entropy value of the evaluation index of SNP locus evaluation. Through the entropy value of the evaluation index of the obtained SNP locus evaluation, calculate the weight coefficient of chestnut environment k and then calculate the evaluation membership degree value. Specifically, the calculation of the entropy value of the evaluation index is as follows: Among them, E j is the entropy value of the evaluation index of the j-th SNP site, ln(p ij ) is the natural logarithm of p ij , ln(n) is the natural logarithm of n. When Calculate the weight coefficient of chestnut environment k as follows: Calculate the evaluation membership degree value as follows: Among them, Qi avg is the comprehensive evaluation average membership degree value of the i-th trait sample of chestnut, and Q(X i ) is the evaluation membership degree value of the i-th trait sample of chestnut. w i is the weight of the evaluation membership degree of the i-th trait sample of chestnut, and w i is greater than 0.

9. The SNP polymorphism of chestnut genome and the correlation analysis system and method for important traits according to claim 1, characterized in that, The correlation analysis of important chestnut traits is carried out by using the correlation coefficient calculation method in the association analysis.

10. A method applied to a chestnut genome SNP polymorphism and important trait association analysis system as described in claim 1, characterized in that, It includes the following steps: Step 1, perform genome resequencing on chestnut materials with at least 3 biological replicates; Step 2, select several strain samples of chestnut genomes and several SNP trait markers for polymorphism verification; Step 3, according to the band data of the strain samples of chestnut genomes and SNP trait markers, construct a clustering dendrogram of the chestnut genome marker target at the same time, and generate the clustering analysis results of the chestnut genome strain samples and SNPs; Step 4, conduct genetic diversity analysis based on SNP-based mapped trait markers. Use the MLM mixed linear model to perform association analysis on the traits and markers of chestnuts respectively, and use the obtained single-site SNP markers of chestnuts as covariates. Combine the Lambda statistic and the permutation test method to obtain the epistatic SNP markers of chestnuts. According to the epistatic SNP markers of chestnuts for the traits and markers of chestnuts respectively, screen out the important traits of chestnuts and then conduct association analysis of important traits.

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