Molecular identification chip for permanent resistance of rice blast and application of molecular identification chip

Abstract

The invention provides a molecular identification chip aiming at permanent resistance of rice blast and application thereof, the chip comprises 600-800 specific nucleic acid probes fixed on a solid phase carrier, each probe corresponds to a functional genetic locus subjected to multi-year, multi-point and multi-race inoculation tests and whole genome association analysis verification, and the molecular identification chip is used for identifying the permanent resistance of the rice blast. According to the method, a comprehensive resistance index (CRI) prediction model is constructed by combining high-throughput typing with machine learning. During breeding, the lasting resistance level of the material to multiple rice blast can be rapidly quantified in the seedling stage, and early-stage accurate screening is achieved. Compared with a traditional method depending on a single major R gene, the method has the advantages that the resistance broad spectrum and durability are remarkably improved, the field identification cost is reduced by 70% or above, and pesticide application reduction and green production are supported. The technology provides an efficient, intelligent and sustainable molecular tool for rice disease-resistant breeding.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of plant molecular breeding and plant pathology, and more specifically, to a molecular identification chip for persistent resistance to rice blast disease and its application. Background Technology

[0002] Rice is the staple crop for more than half of the world's population. Rice blast, caused by the ascomycete Magnaporthe oryzae, is one of the most devastating fungal diseases in rice production, causing yield losses of 10%-30% globally each year and seriously threatening food security. For a long time, breeding and promoting disease-resistant varieties has been the most economical and environmentally friendly core strategy for controlling rice blast. However, the current disease-resistant breeding and control system still faces many insurmountable bottlenecks: Current mainstream rice blast resistance breeding heavily relies on vertical resistance mediated by a single major resistance R gene. This type of resistance typically targets specific races of the pathogen and is characterized by strong antigen specificity and intuitive phenotypic identification. However, the rice blast pathogen has a rich variety of physiological races and a very rapid mutation rate, which can quickly overcome the recognition function of the R gene through gene mutation, sexual recombination, and other means.

[0003] Most disease-resistant varieties carrying a single R gene lose their resistance within 3-5 years of promotion due to the replacement of dominant races. Several disease-resistant varieties that were once widely used in my country, such as "IR26" and "Minghui 63," have been rapidly withdrawn from production due to race mutations. This cycle of "promotion of resistant varieties - pathogen mutation - resistance failure - new round of variety breeding" not only significantly increases breeding costs but also fails to provide stable and lasting disease control capabilities for rice production, making it difficult to fundamentally solve the threat of rice blast.

[0004] Currently, rice blast resistance identification mainly relies on field surveys of natural disease occurrence or artificial inoculation. These phenotypic identification methods have several drawbacks: On the one hand, the identification cycle is long, requiring waiting for the peak of disease in rice growth period, which usually accompanies the entire growth cycle. It is impossible to quickly screen materials in the early stages of breeding, which means that breeders need to retain a large amount of materials in high generations for field identification, which consumes huge land, manpower and time costs. On the other hand, phenotypic identification is easily affected by environmental conditions and differences in the small races of inoculated pathogens, resulting in poor repeatability and accuracy. Furthermore, it can only provide a binary qualitative result of "resistant / susceptible," and cannot quantitatively assess the strength and broadness of the resistance of materials. At the same time, artificial inoculation is cumbersome and makes it difficult to conduct systematic resistance screening on large-scale germplasm resources such as local variety banks and wild rice resources, thus limiting the efficiency of discovering superior resistance sources.

[0005] With resistant varieties rapidly losing their effectiveness and resistance identification lagging behind, rice farmers are forced to rely on chemical fungicides to control rice blast. In my country's major rice-producing areas, fungicides need to be sprayed 3-5 times annually to cope with peak rice blast seasons, and in some severely affected areas, even more than 8 times. The extensive use of chemical pesticides not only significantly increases planting costs but also leads to the rapid evolution of pathogen resistance, further exacerbating the vicious cycle of pesticide use.

[0006] The genetic base of cultivated rice is becoming increasingly narrow, with most major varieties concentrated in a few lines derived from core parents, resulting in insufficient capacity to discover and utilize new resistance sources. Traditional phenotypic identification can only identify dominant resistance sources exhibiting high resistance phenotypes, while a large number of recessive resistance sources carrying minor resistance genes and phenotypically exhibiting only moderate resistance but possessing long-term stress resistance potential are overlooked. This leads to a lack of diversity of resistance sources available for breeding, making it difficult to construct a multi-layered disease resistance defense system. Furthermore, traditional breeding, primarily based on phenotypic selection, struggles to achieve precise aggregation of minor resistance genes, making it impossible to construct a stable multi-gene resistance barrier.

[0007] Traditional disease resistance breeding is independent of the improvement of other traits such as yield and quality, lacking a technical system for multi-trait synergistic selection. Meanwhile, mainstream genotyping tools are mostly developed for single-trait loci, resulting in poor data compatibility and difficulty in integrating with modern breeding technologies such as genome selection and genome-wide association studies, thus failing to support intelligent breeding models for multi-trait synergistic improvement. Furthermore, single-disease resistance breeding strategies are difficult to quickly extend to other diseases, exhibiting poor technology reusability and limiting the overall improvement of breeding efficiency.

[0008] Current crop disease resistance technologies are mostly developed for single crops and single diseases, lacking a universal framework for resistance improvement. Breeding strategies for rice blast are difficult to apply directly to other rice diseases such as sheath blight and bacterial leaf blight, let alone extend to other crops such as wheat and corn. This results in redundant investment in the research and development of disease-resistant breeding technologies and limits their industrial application.

[0009] Current rice blast resistance breeding and control technologies have failed to fundamentally overcome the inherent defects of traditional vertical resistance, thus failing to achieve the goal of durable and broad-spectrum resistance control. Phenotypic-based identification models are inefficient and cannot support large-scale germplasm resource exploration and early-generation breeding selection. High reliance on chemical control makes it difficult to meet the needs of green agriculture development. A narrow genetic base and insufficient breeding intelligence limit the improvement of breeding efficiency. Furthermore, there is a lack of a universal technical framework applicable across crops and diseases. These problems collectively restrict the improvement of rice blast control and breakthroughs in resistance breeding, urgently requiring the development of a new system for durable resistance improvement and identification. Therefore, this paper proposes a molecular identification chip for durable rice blast resistance and its application. Summary of the Invention

[0010] The purpose of this invention is to address the problems raised in the existing background technology. To achieve the above-mentioned objective, this invention provides the following technical solution: a molecular identification chip for assessing durable resistance to rice blast, the chip comprising 600 to 800 specific nucleic acid probes immobilized on a solid-phase support, each probe corresponding to a functional genetic locus validated through multi-year, multi-site, multi-race inoculation experiments and genome-wide association analysis, the locus including: Locus a is a minor resistance quantitative trait locus (QTL) that is significantly associated with durable resistance to rice blast. Site b is the coding or regulatory region of disease-related genes involved in the basic immune response; Site c is a core regulatory node gene in the rice disease resistance signaling pathway; The aforementioned chip is independent of the major resistance R gene and is used to evaluate the broad-spectrum and durable resistance of rice materials to rice blast.

[0011] As a preferred embodiment of the present invention, the low-efficacy disease-resistant QTL sites include, but are not limited to, qBR9-2, qBL3, Pi21, qMdr10-1, qPbm11 and their linked SNP markers; the basic immune-related genes include OsCERK1, OsRLCK185, OsWRKY45, OsPR1a, and OsPAL; the core node genes of the signaling pathway include NPR1, OsEDS1, OsPAD4, OsTGA2.1, and OsMAPK5.

[0012] As a preferred technical solution of the present invention, at least 70% of the 600 to 800 sites are significantly positively correlated with the resistance phenotype under the conditions of inoculation with more than three ecological zones, for three consecutive years, and with more than five rice blast races.

[0013] An application of a molecular identification chip for durable resistance to rice blast includes the following steps: (i) Extract genomic DNA from the rice materials to be tested; (ii) Hybridize the DNA with the chip or amplify and then hybridize to obtain genotype data for each functional locus; (iii) Input the genotype data into the pre-trained Comprehensive Resistance Index (CRI) prediction model and output the CRI value of the material; (iv) Quantitatively assess and rank the level of durable resistance of rice materials based on CRI values.

[0014] As a preferred technical solution of the present invention, the CRI prediction model is constructed based on a machine learning algorithm, which is selected from random forest, XGBoost, support vector regression or neural network. The model training data comes from field resistance phenotypes and high-throughput genotype datasets of no less than 3,000 rice materials in at least 3 ecological zones, for 3 consecutive years, and inoculated with more than 5 representative rice blast races.

[0015] As a preferred technical solution of the present invention, the CRI value is a standardized continuous value of 0–1, and materials with CRI ≥ 0.70 are judged to have high background resistance and are suitable as core parents for breeding long-lasting resistance to rice blast.

[0016] As a preferred technical solution of the present invention, a molecular identification chip for persistent resistance to rice blast is used to perform high-throughput genotyping of breeding populations and calculate CRI values, and individuals with the top 15% CRI values ​​are preferentially selected for subsequent hybridization, backcrossing or variety breeding.

[0017] As a preferred technical solution of the present invention, the breeding population includes F2 to F6 segregating generations, recombinant inbred lines (RILs), chromosome segment replacement lines (CSSLs), or multi-parent advanced crossbreeding populations (MAGIC).

[0018] A system for predicting the durable resistance of rice varieties to rice blast includes: (a) A molecular identification chip for persistent resistance to rice blast disease; (b) Genotype data reading module; (c) A computational module with an integrated CRI prediction model, used to receive genotype data and output CRI values; (d) Visual output interface for displaying resistance ratings, key contributing sites and breeding recommendations.

[0019] Application of a molecular identification chip for persistent resistance to rice blast in reducing the use of chemical fungicides in rice production and promoting green plant protection and ecological agriculture.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention breaks away from the traditional vertical resistance model that relies on a single major disease-resistance gene (R gene), shifting towards a horizontal or background resistance system centered on multiple minor QTLs, basic immune genes, and signaling pathway nodes. This strategy significantly improves the broad-spectrum, stable, and persistent resistance of rice to rice blast, effectively avoiding the problem of rapid resistance failure caused by pathogen race variation, and fundamentally extending the lifespan of resistant varieties.

[0021] This invention transforms the durable resistance of rice to rice blast into a calculable Comprehensive Resistance Index (CRI), realizing a shift from the traditional binary qualitative assessment of resistance / susceptibility to a continuous, multidimensional, and comparable quantitative evaluation. Breeders can accurately rank hundreds or thousands of materials based on CRI values, significantly improving selection efficiency and accuracy and avoiding subjective experience bias.

[0022] This invention enables high-throughput typing of 600–800 functional loci using a chip during the seedling stage, eliminating the need to wait for field disease development or perform tedious manual inoculation identification. Combined with the CRI model, it can rapidly eliminate low-resistance materials in the early F2–F4 generations, reducing the workload of field resistance identification by more than 70% and shortening the breeding cycle of disease-resistant varieties by 2–3 years.

[0023] The high CRI variety of this invention has a strong immune base and exhibits good resistance to natural disease pressure. It can significantly reduce the frequency and amount of chemical fungicide application. Field trials show an average reduction of more than 50%, reducing production costs, environmental pollution, and pesticide residue risks, and strongly supporting the national strategy of reducing chemical fertilizers and pesticides and promoting ecological agriculture.

[0024] This invention's chip can systematically mine the resistance potential of local varieties, wild rice, and international germplasm resources, identifying latent resistance sources with low phenotypic characteristics but high CRI values, providing a new pathway for broadening the genetic base of cultivated rice. Simultaneously, it supports the targeted aggregation of multiple minor beneficial alleles in breeding, constructing a multi-gene superposition resistance barrier and enhancing the overall stress resistance of varieties.

[0025] The chip of this invention is compatible with mainstream high-throughput genotyping platforms, and the data can be seamlessly connected to genomic selection (GS), genome-wide association analysis (GWAS), and intelligent breeding information systems. This facilitates joint selection with other traits such as yield, quality, and stress resistance, and promotes a new intelligent breeding model for the synergistic improvement of multiple traits.

[0026] The technical framework of this invention is not only applicable to rice blast, but its background resistance plus multi-gene aggregation plus indexation assessment approach can be extended to other rice diseases, including sheath blight, bacterial blight, and even major crops such as wheat and corn. It has cross-crop and cross-disease application potential and has broad industrialization prospects. Attached Figure Description

[0027] Figure 1 A data parameter block diagram provided for this invention; Figure 2 This is a block diagram of chip detection data provided by the present invention; Figure 3 A data flowchart of varieties provided by the present invention; Figure 4 A data flowchart for site classification provided by this invention. Detailed Implementation

[0028] 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. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention.

[0029] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely illustrates some embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments and features and technical solutions in the embodiments of the present invention can be combined with each other. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0030] Example 1: A molecular identification chip for assessing durable resistance to rice blast disease. The chip contains 600 to 800 specific nucleic acid probes immobilized on a solid-phase support. Each probe corresponds to a functional genetic locus validated through multi-year, multi-site, multi-race inoculation trials and genome-wide association analysis. The loci include: Locus a is a minor resistance quantitative trait locus (QTL) that is significantly associated with durable resistance to rice blast. Site b is the coding or regulatory region of disease-related genes involved in the basic immune response; Site c is a core regulatory node gene in the rice disease resistance signaling pathway; The chip is independent of the major disease resistance R gene and is used to evaluate the broad-spectrum and durable resistance of rice materials to rice blast.

[0031] Mildly effective disease-fighting QTL sites include, but are not limited to, qBR9-2, qBL3, Pi21, qMdr10-1, qPbm11 and their linked SNP markers; basic immune-related genes include OsCERK1, OsRLCK185, OsWRKY45, OsPR1a, and OsPAL; core node genes of signaling pathways include NPR1, OsEDS1, OsPAD4, OsTGA2.1, and OsMAPK5.

[0032] Of the 600 to 800 loci, at least 70% of the loci showed a significant positive correlation with the resistance phenotype under inoculation conditions of more than three ecoregions, for three consecutive years, and with more than five rice blast races.

[0033] An application of a molecular identification chip for durable resistance to rice blast includes the following steps: (i) Extract genomic DNA from the rice materials to be tested; (ii) Hybridize the DNA with the microarray or amplify and then hybridize to obtain genotype data for each functional locus; (iii) Input the genotype data into the pre-trained Comprehensive Resistance Index (CRI) prediction model and output the CRI value of the material; (iv) Quantitatively assess and rank the level of durable resistance of rice materials based on CRI values.

[0034] The CRI prediction model is built based on machine learning algorithms, which are selected from random forest, XGBoost, support vector regression or neural network. The model training data comes from field resistance phenotypes and high-throughput genotype datasets of no less than 3,000 rice materials in at least 3 ecological zones, for 3 consecutive years, and inoculated with more than 5 representative rice blast races.

[0035] The CRI value is a standardized continuous value of 0–1. Materials with a CRI ≥ 0.70 are considered to have high background resistance and are suitable as core parents for breeding long-lasting resistance to rice blast.

[0036] A molecular identification chip for persistent resistance to rice blast disease is used to perform high-throughput genotyping of breeding populations and calculate CRI values. Individuals with the top 15% CRI values ​​are selected for subsequent hybridization, backcrossing, or variety breeding.

[0037] Breeding populations include F2 to F6 segregating generations, recombinant inbred lines (RILs), chromosome segment replacement lines (CSSLs), or multiparent advanced crossbreeding populations (MAGIC).

[0038] A system for predicting the durable resistance of rice varieties to rice blast includes: (a) A molecular identification chip for persistent resistance to rice blast disease; (b) Genotype data reading module; (c) A computational module with an integrated CRI prediction model, used to receive genotype data and output CRI values; (d) Visual output interface for displaying resistance ratings, key contributing sites and breeding recommendations.

[0039] Application of a molecular identification chip for persistent resistance to rice blast in reducing the use of chemical fungicides in rice production and promoting green plant protection and ecological agriculture.

[0040] The molecular identification chip for durable resistance to rice blast disease described in this invention is based on the host background resistance theory. It integrates multi-omics data and field phenotypic validation to construct a high-throughput molecular evaluation system centered on minor resistance loci. Its working principle is as follows: Traditional disease resistance breeding relies on major R genes, such as Pi-ta and Pik, but these genes are easily overcome by the rice blast fungus Magnaporthe oryzae. This invention abandons this approach and instead focuses on a background resistance system composed of multiple minor QTLs, basic immune genes, and signaling pathway nodes.

[0041] Through continuous inoculation trials involving ≥10 representative physiological races of multiple races conducted over 3–5 years in major rice-growing regions of South China, Central China, and Northeast China, combined with large-scale field resistance phenotypic data including leaf blast index, neck blast incidence, and lesion expansion rate, genome-wide association studies (GWAS) and transcriptome co-expression networks were used to screen 600–800 functional SNPs / InDel loci that were stable and significantly associated with durable resistance under multiple environmental and pathogenic pressures. These loci, after functional annotation, were clearly classified into three biological modules: Mildly effective disease resistance QTL regions include qBR9-2, Pi21, etc., which contribute broad-spectrum but mild resistance; Basic immune-related genes include OsCERK1-mediated PAMP recognition and OsWRKY45-regulated defense gene expression; The core nodes of the disease-fighting signaling pathway include the NPR1-mediated salicylic acid pathway and the OsMAPK5-mediated signal transduction.

[0042] The allelic variant sequences corresponding to the aforementioned 600–800 functional sites are used as specific oligonucleotide probes and immobilized on a solid-phase carrier, including a glass chip or microbead array, to form a high-density molecular identification chip. This chip is compatible with mainstream genotyping platforms, including Illumina Infinium or domestically produced microarray systems, and can perform a single hybridization of rice genomic DNA samples to simultaneously detect the genotypes of hundreds of resistance-related loci, achieving high-throughput, low-cost, and high-accuracy molecular genotyping.

[0043] Based on historical phenotypic and genotypic big data, machine learning algorithms, including XGBoost or Random Forest, are used to train a Comprehensive Resistance Index (CRI) prediction model. This model weights and integrates the allelic effects at each locus, outputting a continuous value between 0 and 1. The higher the CRI value, the more favorable alleles the material carries, and the stronger its broad-spectrum activity and stability under various pathogen races and environmental conditions. A low CRI value indicates weak background resistance; even if a major R gene is carried, the resistance may quickly become ineffective due to a lack of resistance buffer.

[0044] The model underwent cross-validation, and the correlation coefficient between the prediction accuracy and the actual multi-year, multi-point resistance mean reached r≥0.85, demonstrating strong generalization ability.

[0045] Breeders submit DNA samples, including F3 lines, DH lines, or local varieties, for testing. After the microarray returns genotype data, the system automatically calculates the CRI value and generates a resistance rating report. Breeders then use this information to: Precisely remove individuals with low CRI in early generations, including seedling stage, to avoid wasting resources in later field resistance identification; Prioritize the selection of high CRI materials as parents, aggregate multiple minor resistance sites, and cultivate new disease-resistant varieties that are not easily overcome by pathogens; Combining the main R gene with the requirement of short-term resistance and high CRI background, a dual protection strategy of main effect plus background is achieved.

[0046] The working principle of this chip is essentially to transform complex and persistent resistance phenotypes into quantifiable and predictable molecular indicators. Through a multi-gene aggregation-model prediction-intelligent screening technology chain, it realizes a paradigm shift from passively responding to pathogen mutations to actively strengthening the host's immune resilience, providing core genetic tools to support green and sustainable rice production.

[0047] This invention discloses a practical application process for a molecular identification chip for persistent resistance to rice blast disease. It is a standardized operating procedure integrating molecular detection, data analysis, and breeding decision-making. The specific working process is as follows: Step 1: Sample preparation Fresh leaf tissues were collected from rice materials to be evaluated, including breeding lines, germplasm resources, or candidate varieties. High-quality genomic DNA was extracted using the conventional CTAB method or a commercial DNA extraction kit. DNA concentration and purity are measured using Nanodrop or Qubit to ensure that the requirements for microarray hybridization are met, typically with a concentration ≥20 ng / μL and an A260 / A280 ratio of ≈1.8–2.0.

[0048] Step 2: Microarray hybridization and genotyping Qualified DNA samples were fragmented, labeled, including fluorescent labeling and amplification, according to the chip platform operation manual; Hybridization of the processed DNA with the molecular identification chip described in this invention is typically performed in a temperature-controlled hybridization apparatus under conditions including 42–65°C for 16–24 hours. After hybridization, the signal intensity of each probe site is read through a high-resolution chip scanner after elution, scanning and other steps. Using accompanying analysis software, including GenomeStudio or customized genotyping algorithms, the genotypes of 600–800 target loci, including AA, AB, and BB, can be automatically determined.

[0049] Step 3: Data upload and calculation of the Comprehensive Resistance Index (CRI) The obtained genotype data is imported into an analysis system that integrates a pre-trained CRI prediction model; The system automatically matches the allelic effect weights corresponding to each locus based on training with multi-location phenotypes over many years. The model weights and integrates the contributions of all sites to output the overall resistance index (CRI) of the material, with a value range of 0–1. Simultaneously, create visual reports, including: CRI values ​​and resistance grades include high resistance (CRI ≥ 0.70), medium resistance (0.50 ≤ CRI < 0.70), and low resistance (CRI < 0.50). The list of key contributing sites includes QTLs or immune genes carrying multiple favorable alleles; Comparison with the CRI of reference varieties including Kongyu 131 and 93-11.

[0050] Step 4: Breeding Decisions and Material Screening Based on CRI reports, breeders conducted efficient initial screening of large segregating populations in early generations, including F2 or F3; Prioritize retaining individuals with the top 10%–15% CRI values ​​and discard low-CRI materials to significantly reduce the workload of subsequent field resistance identification. For high CRI materials, further compounding or testing can be conducted by combining them with agronomic traits, yield potential, and other indicators. If strong resistance is required in the short term, known major R genes, including those introduced into Pik or Pi54 through molecular marker-assisted selection, can be superimposed on a high CRI background to achieve durable and highly effective dual resistance.

[0051] Step 5: Verification and Variety Breeding The selected high CRI candidate lines were validated in multi-location rice blast natural induction areas or artificial inoculation test fields the following year; The validation results are fed back to the CRI model database for model iteration and optimization, thereby improving prediction accuracy. After 2–3 years of stable performance, strains with high CRI and excellent agronomical characteristics can enter regional trials or variety approval procedures.

[0052] The working process of this invention achieves a seamless connection from DNA samples to breeding decisions, transforming complex and persistent resistance into operable, quantifiable, and comparable molecular indicators, providing an efficient, intelligent, and sustainable technical path for modern rice disease resistance breeding.

[0053] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described herein. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or equivalent substitutions to the present invention, as well as all technical solutions and improvements that do not depart from the spirit and scope of the invention, are covered within the scope of the claims of the present invention.

Claims

1. A molecular identification chip for assessing persistent resistance to rice blast, characterized in that, The chip contains 600 to 800 specific nucleic acid probes immobilized on a solid support. Each probe corresponds to a functional genetic locus validated through multi-year, multi-site, multi-race inoculation experiments and genome-wide association analysis. These loci include: Locus a is a minor resistance quantitative trait locus (QTL) that is significantly associated with durable resistance to rice blast. Site b is the coding or regulatory region of disease-related genes involved in the basic immune response; Site c is a core regulatory node gene in the rice disease resistance signaling pathway; The aforementioned chip is independent of the major resistance R gene and is used to evaluate the broad-spectrum and durable resistance of rice materials to rice blast.

2. The molecular identification chip for persistent resistance to rice blast disease according to claim 1, characterized in that, The low-efficacy disease-resistant QTL sites include, but are not limited to, qBR9-2, qBL3, Pi21, qMdr10-1, qPbm11 and their linked SNP markers; the basic immune-related genes include OsCERK1, OsRLCK185, OsWRKY45, OsPR1a, and OsPAL; the core node genes of the signaling pathway include NPR1, OsEDS1, OsPAD4, OsTGA2.1, and OsMAPK5.

3. The molecular identification chip for persistent resistance to rice blast disease according to claim 2, characterized in that, Of the 600 to 800 loci, at least 70% of the loci showed a significant positive correlation with the resistance phenotype under inoculation conditions of more than three ecological zones, for three consecutive years, and with more than five rice blast races.

4. The application of a molecular identification chip for durable resistance to rice blast disease according to any one of claims 1 to 3, characterized in that, Includes the following steps: (i) Extract genomic DNA from the rice materials to be tested; (ii) Hybridize the DNA with the chip or amplify and then hybridize to obtain genotype data for each functional locus; (iii) Input the genotype data into the pre-trained Comprehensive Resistance Index (CRI) prediction model and output the CRI value of the material; (iv) Quantitatively assess and rank the level of durable resistance of rice materials based on CRI values.

5. The application of the molecular identification chip for durable resistance to rice blast disease according to claim 4, characterized in that, The CRI prediction model is built based on machine learning algorithms, which are selected from random forest, XGBoost, support vector regression or neural network. The model training data comes from field resistance phenotypes and high-throughput genotype datasets of no less than 3,000 rice materials in at least 3 ecological zones, for 3 consecutive years, and inoculated with more than 5 representative rice blast races.

6. The application of the molecular identification chip for durable resistance to rice blast disease according to claim 5, characterized in that, The CRI value is a standardized continuous value of 0–1. Materials with a CRI ≥ 0.70 are considered to have high background resistance and are suitable as core parents for breeding long-lasting resistance to rice blast.

7. The application of the molecular identification chip for durable resistance to rice blast disease according to claim 6, characterized in that, Using a molecular identification chip for persistent resistance to rice blast as described in any one of claims 1 to 3, high-throughput genotyping of the breeding population is performed, and CRI values ​​are calculated. Individuals with the top 15% CRI values ​​are preferentially selected for subsequent hybridization, backcrossing, or variety breeding.

8. The application of the molecular identification chip for persistent resistance to rice blast disease according to claim 7, characterized in that, The breeding populations include F2 to F6 segregating generations, recombinant inbred lines (RILs), chromosome segment replacement lines (CSSLs), or multiparent advanced crossbreeding populations (MAGIC).

9. A system for predicting the durable resistance of rice varieties to rice blast, characterized in that, include: (a) A molecular identification chip for persistent resistance to rice blast disease as described in any one of claims 1 to 3; (b) Genotype data reading module; (c) A computational module with an integrated CRI prediction model, used to receive genotype data and output CRI values; (d) Visual output interface for displaying resistance ratings, key contributing sites and breeding recommendations.

10. A molecular identification chip for persistent resistance to rice blast disease according to any one of claims 1 to 3, which reduces the use of chemical fungicides in rice production.

Images