SNP markers associated with tobacco flowering time and uses thereof

Abstract

The present application relates to SNP markers related to tobacco flowering period and application thereof. The present application mines single nucleotide polymorphism sites located in different chromosomes and gene regions, and the nucleotide variation forms are conventional SNP types such as A / T, A / G, C / T, C / G or G / T. These SNPs can be typed by conventional molecular detection means, and these sites all show significant and stable characteristic contribution values in the XGBoost model of the present application, which can effectively reflect the genetic influence of flowering period, and can be used for rapid prediction of flowering period of tobacco materials, material screening and molecular marker assisted selection, so as to realize early identification of target flowering period materials, reduce breeding cost and accelerate new strain breeding.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology and relates to SNP markers related to tobacco flowering period and their applications. Background Technology

[0002] The flowering period trait of tobacco refers to the time required for tobacco to transition from the vegetative growth stage to the reproductive growth stage and complete flowering during its growth and development. It is one of the important agronomic traits of tobacco. The timing of flowering directly affects the length of the tobacco's growth period, field management arrangements, and adaptability to different production areas, and is of great significance for tobacco variety selection and production applications.

[0003] Currently, the evaluation of tobacco flowering time traits is mainly based on field observation and manual recording. This method requires a complete growth cycle, is time-consuming, and is easily affected by environmental factors such as temperature, light, and water. It also makes it difficult to quickly and accurately screen a large number of materials in the early stages of breeding, limiting the breeding efficiency for improving flowering time traits. To improve breeding efficiency, existing technologies have attempted to use molecular marker methods for genetic analysis of some tobacco traits. However, tobacco flowering time is a typical complex quantitative trait, its formation and regulation involving multiple biological pathways such as photoperiod response, temperature sensing, and hormone signal regulation. It is influenced by the cumulative effects of small multi-gene effects and inter-gene interactions, resulting in a complex genetic basis. Therefore, the application of traditional molecular marker-assisted selection methods in flowering time traits has limited effectiveness.

[0004] At present, the method based on genome-wide association analysis (GWAS) mainly relies on statistical significance screening for SNP loci related to flowering time. However, since flowering time traits are significantly affected by environmental factors and the genetic effects are dispersed, the number of associated loci obtained is limited and the explanatory rate is low. Furthermore, the stability and reproducibility under different genetic backgrounds or environmental conditions are insufficient, making it difficult to form a stable set of genetic molecular markers that can be directly used for breeding practices.

[0005] Meanwhile, existing technologies have failed to identify key loci that make major contributions to flowering traits from whole-genome high-dimensional SNP data, thus making it impossible to establish a set of genetic markers that can be directly used for flowering trait prediction, screening, or molecular marker-assisted selection.

[0006] In conclusion, developing high-contribution SNP genetic markers that are closely related to the flowering period trait of tobacco is of great significance. Summary of the Invention

[0007] To address the shortcomings of existing technologies and practical needs, this invention provides SNP markers related to tobacco flowering period and their applications, mines SNP markers related to tobacco flowering period, and further develops a prediction scheme based on deep learning to achieve rapid prediction of the flowering period of tobacco materials.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides SNP markers related to tobacco flowering period, the SNP markers including the SNP sites shown in Table 1.

[0009] This invention identifies high-contribution SNP markers closely related to tobacco flowering time, which can be used for rapid prediction of flowering time of tobacco materials, material screening, and molecular marker-assisted selection, thereby enabling early identification of materials with target flowering time, reducing breeding costs, and accelerating the development of new varieties.

[0010] Table 1 Secondly, the present invention provides a method for screening SNP markers related to tobacco flowering period as described in the first aspect, the screening method comprising: Different tobacco varieties were used to detect their whole genome SNP data and flowering time. The XGBoost regression model was trained using the SNP data and flowering period to construct a prediction model; Based on the prediction model, the feature contribution of each SNP is analyzed, and the SNPs are sorted from largest to smallest contribution. A specified number of top-ranked SNP sites are selected as SNP markers related to tobacco flowering period.

[0011] Optionally, the screening method further includes a step of quality control of SNP data, including filtering out sites with a deletion rate greater than 10%, deleting low polymorphic sites with a MAF (minor allele frequency) of less than 0.05, and appropriately imputing missing genotypes.

[0012] Thirdly, the present invention provides the application of the SNP markers and / or detection reagents related to tobacco flowering period described in the first aspect in predicting tobacco flowering period.

[0013] Fourthly, the present invention provides a kit for predicting the flowering period of tobacco, the kit comprising reagents for detecting the SNP markers associated with the flowering period of tobacco as described in the first aspect.

[0014] Fifthly, the present invention provides a tobacco flowering period prediction model, which is obtained by training a machine learning model using SNP markers related to tobacco flowering period as described in the first aspect and flowering period data.

[0015] Optionally, the machine learning model includes the XGBoost regression model.

[0016] Optionally, the training includes using a 5x cross-validation hyperparameter.

[0017] Optionally, the hyperparameters include n_estimators, learning_rate, and max_depth.

[0018] Optionally, n_estimators is 200, learning_rate is 0.01, and max_depth is 8.

[0019] Sixthly, the present invention provides a method for predicting the flowering period of tobacco, the prediction method comprising: Information on SNP markers related to tobacco flowering period as described in the first aspect is obtained in the tobacco to be predicted, and the SNP information is input into the tobacco flowering period prediction model described in the fifth aspect to predict the tobacco flowering period.

[0020] In a seventh aspect, the present invention provides an electronic device comprising one or more processors and a memory for storing executable instructions, the one or more processors being configured to invoke the executable instructions stored in the memory to implement the steps of the tobacco flowering period prediction method described in the sixth aspect.

[0021] Eighthly, the present invention provides a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the steps of the tobacco flowering period prediction method described in the sixth aspect.

[0022] Compared with the prior art, the present invention has at least the following beneficial effects: This invention identifies single nucleotide polymorphism (SNP) sites located in different chromosomes and gene regions. The nucleotide variations are in the form of conventional SNPs such as A / T, A / G, C / T, C / G, or G / T. These SNPs can be genotyped using conventional molecular detection methods. In the XGBoost model of this invention, these sites all show significant and stable feature contribution values, which can effectively reflect the genetic influence of flowering time. This invention can be used for rapid prediction of flowering time of tobacco materials, material screening, and molecular marker-assisted selection, thereby achieving early identification of materials with target flowering time, reducing breeding costs, and accelerating the development of new lines. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the technical route of the present invention.

[0024] Figure 2 A graph showing the mean absolute SHAP value results for each SNP.

[0025] Figure 3 The graph shows the prediction performance of the prediction model on the test sets of all features and core SNP features, respectively.

[0026] Figure 4This is a field planting experiment diagram of 200 tobacco materials in Example 3.

[0027] Figure 5 The value represents the flowering period of the tobacco material in Example 3. Detailed Implementation

[0028] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments. However, the following examples are merely simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention is determined by the claims.

[0029] Where specific techniques or conditions are not specified in the examples, they shall be performed in accordance with the techniques or conditions described in the literature in this field, or in accordance with the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased from legitimate channels.

[0030] Unless otherwise defined, scientific and technical terms and their abbreviations used in conjunction with this invention shall have the meanings commonly understood by one of ordinary skill in the art to which this invention pertains. Some of the terms and abbreviations used in this invention are listed below.

[0031] SNP: Single nucleotide polymorphism.

[0032] XGBoost: The ultimate gradient boosting tree model.

[0033] n_estimators: The number of weak learners.

[0034] learning_rate: learning rate.

[0035] max_depth: Maximum depth.

[0036] SHAP value: Shapley additive interpretation value.

[0037] This invention mines high-contribution SNP genetic markers closely related to tobacco flowering period (hereinafter referred to as the "flowering period-related SNP marker set"). These SNP loci are derived from a systematic analysis of tobacco whole-genome data and can significantly explain the genetic variation in tobacco flowering period. The overall technical approach is as follows: Figure 1 As shown.

[0038] Based on 5500 tobacco germplasm resources, genomic DNA was extracted, and whole-genome SNP data were obtained using high-throughput sequencing, with an initial total of 1,326,450 SNPs. Subsequently, quality control was performed on the SNP data, including filtering out sites with a deletion rate greater than 10%, deleting low-polymorphism sites with a MAF (minor allele frequency) below 0.05, and appropriately imputing missing genotypes, ultimately retaining 95,308 high-quality SNP sites. Furthermore, using flowering phenotypic data corresponding to the above materials, an XGBoost regression model was constructed to predict flowering time. Model training employed 5-fold cross-validation, and key hyperparameters such as learning rate (0.01–0.3), maximum depth (3–12), and subsample ratio (0.5–1.0) were determined through grid search. After model training, the tree structure within XGBoost automatically captures nonlinear interaction effects and additive effects between SNPs. The feature contribution of each SNP is extracted based on the trained XGBoost model, including the feature importance provided by the model itself and the SHAP value calculated by the SHAP framework for each SNP, so as to obtain the average contribution of each site in flowering prediction.

[0039] Using the top 0.1% of SNPs by contribution as candidate loci (Table 1), further independent population validation, effect direction consistency checks, and contribution stability analysis were conducted to finally obtain the flowering-related SNP marker set described in this invention. The top 0.1% of high-contribution SNPs were then used for secondary model training, and the XGBoost regression model was reconstructed using these SNPs as input. Experiments show that using this simplified SNP set as input improves the model's prediction accuracy compared to the whole-genome model and significantly reduces computational resource consumption during model training and inference, demonstrating the advantages of this SNP marker set in practical applications.

[0040] In one specific embodiment of the present invention, a kit for predicting tobacco flowering time is provided, the kit comprising reagents for detecting SNP markers associated with tobacco flowering time. Specifically, these may be high-throughput sequencing reagents.

[0041] In one specific embodiment of the present invention, a method for predicting the flowering period of tobacco is provided. The prediction method includes: obtaining information on SNP markers related to the flowering period of tobacco in the tobacco to be predicted, inputting the SNP information into a tobacco flowering period prediction model, and performing tobacco flowering period prediction.

[0042] In another specific embodiment of the present invention, an electronic device is provided, the electronic device including one or more processors and a memory for storing executable instructions, the one or more processors being configured to invoke the executable instructions stored in the memory to implement the steps of the tobacco flowering period prediction method.

[0043] Those skilled in the art will understand that the device of this application can be obtained using various forms of hardware, software, firmware, dedicated processors, or combinations thereof.

[0044] In another specific embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, wherein when the computer program instructions are executed by a processor, the steps of the tobacco flowering period prediction method are implemented.

[0045] Example 1 This implementation involves obtaining a set of SNP markers related to flowering period.

[0046] (1) Experimental materials and phenotypic data Using 5,500 tobacco germplasm resources, covering various types including flue-cured, burley, and aromatic tobacco, representing a broad genetic background, flowering phenotypic data were obtained through field surveys under uniform cultivation and management conditions, recording the number of days required for each material to flower from transplanting to 50% of the plants.

[0047] (2) Acquisition and quality control of SNP data DNA extraction and sequencing: Genomic DNA was extracted from the material, and simplified genome sequencing was used to obtain whole-genome SNP data. The initial total number of SNPs was 1,326,450.

[0048] Genotype coding: SNP genotypes are digitized using a 0 / 1 / 2 additive model.

[0049] Quality control: Sites with a deletion rate >10% were filtered out, low polymorphism sites with a minor allele frequency (MAF) of less than 0.05% were removed, and missing genotypes were imputed. Finally, 95,380 high-quality SNP sites were retained for subsequent analysis.

[0050] Example 2 This embodiment describes the construction and performance evaluation of the initial model.

[0051] Data partitioning: The 5,500 materials in Example 1 were randomly divided into a training set (80%, 4,400 materials) and a test set (20%, 1,100 materials).

[0052] XGBoost Model Construction: An XGBoost regression model was constructed, and grid search optimization was performed on hyperparameters such as learning rate and maximum depth using 5x cross-validation. The final optimal hyperparameters were determined as: n_estimators = (200), learning_rate = (0.01), max_depth = (8).

[0053] Performance evaluation: The model made predictions on 1100 test sets, and the performance metric was a Pearson correlation coefficient of 0.45.

[0054] SHAP value calculation: Based on the trained initial XGBoost model, the SHAP value of 95536 SNPs is calculated using the SHAP framework. The mean absolute SHAP value of each feature is calculated as its final contribution to flowering time prediction.

[0055] Screening: Select the top 0.1% of SNPs by contribution as the candidate label set. Figure 2 The final set of high-contribution SNP markers and their details are shown in Table 1.

[0056] Secondary training: Using only the high-contribution SNPs selected above as input features, the XGBoost regression model is reconstructed and trained.

[0057] Performance Comparison: The model was tested on the same 1100 test samples, and its performance was compared with the initial model. The results show that using core SNPs significantly improved the prediction accuracy from 0.53 to 0.68 with a substantial reduction in the number of features, and also increased the training and inference speed by 5 times, confirming the high efficiency and accuracy of this marker set in practical breeding applications. Figure 3 ).

[0058] Example 3 This embodiment verifies the prediction of flowering-related SNP marker sets in independent breeding materials.

[0059] An additional 200 tobacco breeding materials with different genetic backgrounds from the model training set (5500 materials) were selected as an independent validation set. None of these materials were used in the model construction and optimization process.

[0060] High-throughput sequencing or specific SNP genotyping techniques were used to obtain the genotypic information of flowering-related SNP markers (SNPs screened in Example 2) in these 200 materials.

[0061] The SNP genotype information obtained above was input into the XGBoost regression model trained twice based on the core SNP set in Example 2 to predict the flowering period of these 200 materials. Based on the model's predictions, the flowering periods of these 200 materials were sorted from low to high.

[0062] These 200 samples were used in a field planting trial. Figure 4 Using a field survey under uniform cultivation and management conditions, the actual flowering period of each material was accurately determined by recording the number of days required for each material to flower from transplanting to 50% of the plants.

[0063] Based on the ranking of model predictions, 50 materials predicted to flower very early were identified. Upon comparison, 40 of the 50 early-flowering materials predicted by the model (i.e., an 80% accuracy rate) were actually confirmed as early-flowering materials by actual measurements. Figure 5 (Flowering period is measured in days). This fully demonstrates that this marker set can serve as a core tool for marker-assisted selection, greatly accelerating the breeding process of new early-flowering tobacco varieties.

[0064] In summary, this invention mines flowering-related SNP markers, including single nucleotide polymorphism (SNP) sites located in different chromosomes and gene regions, with nucleotide variations in common SNP types such as A / T, A / G, C / T, C / G, or G / T. These SNPs can all be genotyped using conventional molecular detection methods. The flowering-related SNP markers consist of several SNPs located in gene regions (including coding regions, promoters, enhancers, and introns) and key regulatory regions of metabolic pathways. These sites all exhibit significant and stable feature contribution values ​​in the XGBoost model of this invention, with their SHAP values ​​ranking in the top 0.1% of all SNP features. This effectively reflects the genetic influence of flowering time and can be used for rapid prediction of flowering time in tobacco materials, material screening, and marker-assisted selection, thereby achieving early identification of materials with target flowering times, reducing breeding costs, and accelerating the development of new lines.

[0065] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A SNP marker associated with tobacco flowering period, characterized in that, The SNP markers include the SNP sites shown in Table 1.

2. The method for screening SNP markers related to tobacco flowering period as described in claim 1, characterized in that, The screening method includes: Different tobacco varieties were used to detect their whole genome SNP data and flowering time. The XGBoost regression model was trained using the SNP data and flowering period to construct a prediction model; Based on the prediction model, the feature contribution of each SNP is analyzed, and the SNPs are sorted from largest to smallest contribution. A specified number of top-ranked SNP sites are selected as SNP markers related to tobacco flowering period.

3. The application of the SNP markers and / or detection reagents related to tobacco flowering period as described in claim 1 in predicting tobacco flowering period.

4. A kit for predicting the flowering period of tobacco, characterized in that, The kit includes a reagent for detecting the SNP markers associated with tobacco flowering period as described in claim 1.

5. A tobacco flowering period prediction model, characterized in that, The tobacco flowering period prediction model is obtained by training a machine learning model using the SNP markers related to tobacco flowering period as described in claim 1 and flowering period data.

6. The tobacco flowering period prediction model according to claim 5, characterized in that, The machine learning model includes the XGBoost regression model.

7. The tobacco flowering period prediction model according to claim 5 or 6, characterized in that, The training includes: using a 5x cross-validation hyperparameter; Optionally, the hyperparameters include n_estimators, learning_rate, and max_depth.

8. A method for predicting the flowering period of tobacco, characterized in that, The prediction method includes: Information on SNP markers related to tobacco flowering period as described in claim 1 is obtained from the tobacco to be predicted. The SNP information is then input into the tobacco flowering period prediction model as described in claim 5 to predict the tobacco flowering period.

9. An electronic device comprising one or more processors and a memory for storing executable instructions, characterized in that, The one or more processors are configured to invoke executable instructions stored in the memory to implement the steps of the tobacco flowering period prediction method of claim 8.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the steps of the tobacco flowering period prediction method according to claim 8.

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