Pneumoconiosis susceptible gene chip detection kit

Abstract

The invention relates to the technical field of pneumoconiosis diagnosis, in particular to a pneumoconiosis risk prediction system and a pneumoconiosis susceptible gene chip detection kit thereof. The pneumoconiosis susceptible gene chip detection kit comprises a micro-fluidic chip and a PCR (Polymerase Chain Reaction) amplification reagent, a plurality of parallel sample introduction channels are arranged on the micro-fluidic chip, and two ends of each sample introduction channel are respectively communicated with a sample introduction hole and a sample discharge hole; the sample introduction channel is communicated with a plurality of connecting channels, and the connecting channels are communicated with a reaction tank; a primer combination is embedded in the reaction tank; and the PCR amplification reagent contains a universal probe. The pneumoconiosis susceptibility gene chip detection kit can be used for constructing a pneumoconiosis susceptibility risk prediction system. The pneumoconiosis risk prediction system provides a new pneumoconiosis risk prediction tool by innovatively combining genetic factors with macroscopic factors. According to the technical scheme, the technical problem that in the prior art, when the pneumoconiosis risk is predicted through genetic information, the detection operation process is tedious or the detection performance is not ideal is solved, and the ideal application and popularization prospect is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of pneumoconiosis diagnosis, and in particular to a pneumoconiosis risk prediction system and a pneumoconiosis susceptibility gene chip detection kit. Background Art

[0002] Single nucleotide polymorphism (SNP) refers to DNA sequence polymorphisms caused by variations in a single nucleotide at the genomic level and is the most common form of human genetic variation. There are various methods for detecting SNPs, each with its own characteristics and applicable scenarios. The following are some commonly used SNP detection technologies: Direct sequencing is a commonly used SNP detection technology, using Sanger sequencing or next-generation sequencing (NGS) technology to sequence specific regions to identify the specific nucleotide changes. For large-scale samples or high-throughput analysis, direct sequencing is relatively expensive, and the data analysis process is time-consuming. The TaqMan probe method is a real-time quantitative PCR method based on the principle of fluorescence resonance energy transfer. It uses a pair of specific primers and two differently fluorescently labeled probes to distinguish different allele types. This method requires the design of a specific fluorescently labeled probe for each SNP site, which increases the time and cost of experimental preparation. Therefore, how to achieve rapid, low-cost, high-throughput, and highly accurate SNP detection requires further development of detection methods and kits.

[0003] Pneumoconiosis is an occupational lung disease caused by long-term inhalation of dust particles, primarily affecting workers in industries such as mining, coal mining, metalworking, stone processing, and construction. These dust particles can include silica dust (which causes silicosis), coal dust (which causes coal workers' pneumoconiosis), and fibers and particles from other minerals or materials, such as asbestos. As dust accumulates in the lungs, it triggers an inflammatory response, ultimately leading to scarring and hardening of lung tissue, which severely impairs lung function. Although occupational exposure to silica dust is the primary cause of pneumoconiosis, individual susceptibility to pneumoconiosis varies significantly among workers with similar exposure per unit time. This suggests that, in addition to environmental factors such as silica dust exposure, genetic factors also potentially influence occupational pneumoconiosis. Previous studies using candidate gene and genome-wide association studies (GWAS) have identified and discovered several genetic variants associated with occupational pneumoconiosis susceptibility, providing some insight into the genetic pathogenesis of occupational pneumoconiosis. Rapid, efficient, and cost-effective detection of multiple pneumoconiosis-associated genetic variants is a key technological advancement in improving pneumoconiosis risk prediction. Summary of the Invention

[0004] The purpose of the present invention is to provide a pneumoconiosis susceptibility gene chip detection kit to solve the technical problems of the prior art in predicting the risk of pneumoconiosis through genetic information, such as complicated detection operation procedures or unsatisfactory detection performance.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] Pneumoconiosis susceptibility gene chip detection kit, including microfluidic chip and PCR amplification reagents;

[0007] The microfluidic chip is provided with a plurality of parallel injection channels, the two ends of which are connected to an injection hole and an outlet hole respectively; the injection channels are connected to a plurality of connecting channels, which are connected to a reaction pool; the reaction pool is embedded with a primer combination;

[0008] PCR amplification reagents contain universal probe, hot start enzyme, UNG enzyme, dUTP, dNTPs, Mg 2+ .

[0009] Furthermore, the number of reaction pools connected to the same injection channel was ≥23; among them, the 23 reaction pools were used to detect SNP sites rs3748067, rs8193036, rs4691896, rs2292832, rs2672794, rs12812500, rs2067051, rs2289477, rs26538, rs1864182, rs510432, rs7195830, rs689466, rs20417, rs2227956, rs1800470, rs11466345, rs73329476, rs4320486, rs117626015, rs1539019, rs2243250, and rs361525

[0010] Further, the 23 reaction pools are respectively embedded with a primer combination as shown in SEQ ID NO.1-SEQ ID NO.3, a primer combination as shown in SEQ ID NO.4-SEQ ID NO.6, a primer combination as shown in SEQ ID NO.7-SEQ ID NO.9, a primer combination as shown in SEQ ID NO.10-SEQ ID NO.12, a primer combination as shown in SEQ ID NO.13-SEQ ID NO.15, a primer combination as shown in SEQ ID NO.16-SEQ ID NO.18, a primer combination as shown in SEQ ID NO.19-SEQ ID NO.21, a primer combination as shown in SEQ ID NO.22-SEQ ID NO.24, a primer combination as shown in SEQ ID NO.25-SEQ ID NO.27, a primer combination as shown in SEQ ID NO.28-SEQ ID NO.30, a primer combination as shown in SEQ ID NO.31-SEQ ID NO.33, a primer combination as shown in SEQ ID NO.34-SEQ ID The primer combination shown in NO.36, the primer combination with sequences shown in SEQ ID NO.37 to SEQ ID NO.39, the primer combination with sequences shown in SEQ ID NO.40 to SEQ ID NO.42, the primer combination with sequences shown in SEQ ID NO.43 to SEQ ID NO.45, the primer combination with sequences shown in SEQ ID NO.46 to SEQ ID NO.48, the primer combination with sequences shown in SEQ ID NO.49 to SEQ ID NO.51, the primer combination with sequences shown in SEQ ID NO.52 to SEQ ID NO.54, the primer combination with sequences shown in SEQ ID NO.55 to SEQ ID NO.57, the primer combination with sequences shown in SEQ ID NO.58 to SEQ ID NO.60, the primer combination with sequences shown in SEQ ID NO.61 to SEQ ID NO.63, the primer combination with sequences shown in SEQ ID NO.64 to SEQ ID NO.66, and the primer combination with sequences shown in SEQ ID NO.67 to SEQ ID NO.69.

[0011] Furthermore, the universal probe includes a first universal fluorescent probe with a sequence as shown in SEQ ID NO.70, a first universal fluorescence quenching probe with a sequence as shown in SEQ ID NO.71, a second universal fluorescent probe with a sequence as shown in SEQ ID NO.72, and a second universal fluorescence quenching probe with a sequence as shown in SEQ ID NO.73.

[0012] This technical solution also provides an application of a pneumoconiosis susceptibility gene chip detection kit in constructing a pneumoconiosis susceptibility risk prediction system. The pneumoconiosis susceptibility gene chip detection kit is used to detect the copy number of the effect allele.

[0013] Furthermore, the pneumoconiosis susceptibility risk prediction system includes a pneumoconiosis susceptibility gene chip detection kit and a risk prediction unit; the risk prediction unit is used to output a polygenic risk score for pneumoconiosis of a subject through a polygenic risk scoring model;

[0014] The polygenic risk score model includes genetic factors, and its formula is shown in Formula I:

[0015]

[0016] Among them, ModelScore i represents the polygenic risk score of pneumoconiosis in the subjects; a ij W1 represents the allele copy number of the jth SNP of the subject, which is detected by the pneumoconiosis susceptibility gene chip detection kit; ij is the weight of the j-th SNP of the subject; 1≤N≤23;

[0017] The SNP sites used are at least one of rs3748067, rs8193036, rs4691896, rs2292832, rs2672794, rs12812500, rs2067051, rs2289477, rs26538, rs1864182, rs510432, rs7195830, rs689466, rs20417, rs2227956, rs1800470, rs11466345, rs73329476, rs4320486, rs117626015, rs1539019, rs2243250, and rs361525.

[0018] Furthermore, the polygenic risk scoring model also includes macro factors, and its formula is shown in Formula II:

[0019]

[0020] Among them, b ik is the kth macro factor value of the subject; W2 ik is the weight of the kth macro-factor of the subject; 1≤M≤2; macro-factors include smoking status and / or dust exposure time.

[0021] Furthermore, in the risk prediction unit, the allele copy number a of the SNP ij The value range is 0 or 1 or 2;

[0022] The SNP loci used were rs3748067, rs8193036, rs4691896, rs2292832, rs2672794, rs12812500, rs2067051, rs2289477, rs26538, rs1864182, rs510432, rs7195830, rs689466, rs20417, rs2227956, rs1800470, rs11466345, rs73329476, rs4320486, rs117626015, rs1539019, rs2243250, and rs361525;

[0023] The weight value W1 of the SNP site i1 -W1 i23 They are -0.210721031315653, -0.210721031315653, 0.636576829071551, 0.27002713721306, 0.198850858745165, 0.371563556432483, -0.415515443961666, -0.23572233352107, -0.544727175441672, -0.27443684570176, -0.198450938723838, and 0.4382 54930931155, -0.23572233352107, -0.127833371509885, 2.74791173452734, -0.693147180559945, 0.806475865866949, 0.774727167552368, -0.527632742082372, 0.887891257352457, 0.198850858745165, -0.22314355131421, 1.33236601909433.

[0024] Furthermore, in the risk prediction unit, macro factors consist of smoking status and dust exposure time;

[0025] b i1 is the value of the subject's first macro-factor smoking status, ranging from 0 to 1; 1 represents smoking and 0 represents non-smoking;

[0026] b i2 is the value of the second macro factor dust exposure time of the subject, which is the total number of years of dust exposure of the subject.

[0027] Furthermore, in the risk prediction unit, the weight value W2 of the macro factor smoking status i1 The weight value of the macro factor dust exposure time is -0.10719; W2 i2 It is -0.065590.

[0028] Furthermore, the modeled sample data was substituted into the polygenic risk scoring model, and the prediction critical value of the polygenic risk scoring model was determined according to the Youden index;

[0029] Comparing the subject's pneumoconiosis polygenic risk score with the predicted critical value, the risk prediction unit outputs the prediction result;

[0030] If the subject's pneumoconiosis polygenic risk score is ≤ the predicted cutoff value, the subject is at low risk for pneumoconiosis or does not have pneumoconiosis;

[0031] If the subject's pneumoconiosis polygenic risk score is greater than the predicted critical value, the subject is at high risk of pneumoconiosis or has pneumoconiosis.

[0032] The principle and beneficial effects of this technical solution are:

[0033] This technical solution combines KASP (Kompetitive Allele Specific PCR) technology and microfluidics (chip) technology (this technical solution uses the IMAP platform) for SNP detection to construct a pneumoconiosis susceptibility gene chip detection kit. It can leverage the advantages of both and provide an efficient, flexible, and high-throughput solution. The specific advantages are as follows:

[0034] (1) Improved throughput and flexibility: KASP technology itself is a flexible and efficient genotyping method that does not require the design of customized fluorescently labeled probes for each SNP, thereby reducing costs. When combined with microfluidics technology, a large number of samples and multiple SNP sites can be analyzed simultaneously on a single chip, greatly improving detection throughput and efficiency.

[0035] (2) Reduced costs: Compared to traditional probe-based microarray technology, the universal fluorescent probe system used by KASP technology reduces the need for specific probes, which helps reduce experimental costs. Applying KASP to microfluidic platforms can further optimize the cost-effectiveness ratio, especially in large-scale studies.

[0036] (3) Enhanced accuracy and reliability: KASP technology can effectively distinguish different alleles through specific primers and competitive allele-specific PCR mechanism, while microfluidics technology provides a highly parallel analysis platform, allowing a large number of SNPs to be verified in a single experiment. The combination of the two not only enhances the accuracy of the results, but also improves the reliability of the data.

[0037] (4) Simplified workflow: KASP technology is relatively simple to operate and can be easily automated. Integrating it into the microfluidic workflow can standardize and automate the entire process from sample preparation to data analysis, reducing the possibility of human error and shortening the experimental cycle.

[0038] In summary, the combination of KASP and microfluidics technology can achieve high-throughput SNP detection while maintaining low cost. The test results are suitable for pneumoconiosis risk prediction, and then a pneumoconiosis risk prediction system is constructed. The lung disease risk prediction system of this scheme is a pneumoconiosis risk prediction system based on the polygenic risk score (PRS) model. The system aims to provide a more accurate and effective method for assessing the risk of pneumoconiosis by combining individual genetic information with macro factors. The pneumoconiosis risk prediction system provides a new pneumoconiosis risk prediction tool by innovatively combining genetic factors with macro factors, which solves the problem of the lack of accurate prediction of the incidence of pneumoconiosis in existing technologies and has important theoretical significance and practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of the structure of the microfluidic chip of Example 1.

[0040] Figure 2 This is a pseudo-color image of the accuracy performance verification results of the pneumoconiosis susceptibility gene detection kit in Example 3 (for P1-P23).

[0041] Figure 3 This is a pseudo-color image of the minimum detection limit performance verification results of the pneumoconiosis susceptibility gene detection kit in Example 3 (for P1-P23).

[0042] Figure 4 This is a pseudo-color image of the specific performance verification results of the pneumoconiosis susceptibility gene detection kit in Example 3 (for N1-N3).

[0043] Figure 5 This is a pseudo-color image of the repeatability performance verification results of the pneumoconiosis susceptibility gene detection kit in Example 3 (R1 and R2 were repeated 10 times each).

[0044] Figure 6 1 is the ROC curve of different prediction models in Example 5. DETAILED DESCRIPTION

[0045] The present invention will be further described in detail below with reference to the examples, but the embodiments of the present invention are not limited thereto. Unless otherwise specified, the technical means used in the following examples and experimental examples are conventional means well known to those skilled in the art, and the materials, reagents, etc. used are all commercially available. Unless otherwise specified, the technical means used in the following examples are conventional means well known to those skilled in the art.

[0046] Example 1: Pneumoconiosis Susceptibility Gene Chip Detection Kit

[0047] This technical solution develops a pneumoconiosis risk prediction system, which includes a pneumoconiosis susceptibility gene chip detection kit and uses the test results of the pneumoconiosis susceptibility gene chip detection kit to predict the pneumoconiosis risk.

[0048] (1) Composition of the kit

[0049] This kit is used for in vitro qualitative detection of pneumoconiosis-related SNP site genotyping in human whole blood samples. This kit uses human genomic DNA as a template and employs microfluidic chip technology combined with competitive allele-specific amplification technology to detect human pneumoconiosis-related sites. The microfluidic PCR chip designed and developed using microfluidic technology can place each detection indicator in a separate reaction pool for detection; in each reaction pool, gene site-specific primers and probes with universal Tag tag sequences amplify and fluorescently label the gene fragments where the relevant gene sites are located. After the reaction is completed, the fluorescent signal is read to obtain information related to the genotyping of human pneumoconiosis-related sites. The specific description of the pneumoconiosis susceptibility gene chip detection kit is as follows:

[0050] (1) Microfluidic chip

[0051] Each chip in the pneumoconiosis susceptibility gene chip detection kit can detect 4 samples at the same time. Its structure is as follows Figure 1 As shown (the accompanying drawings mark the main structural units of the chip: injection channel A, injection hole B, outlet hole C, connecting channel D, reaction pool E. In order to more concisely mark the position of each unit, AE is marked on different injection channels A). The four injection channels A of the chip are arranged in parallel, and the injection hole B and outlet hole C are respectively located at the two ends of each injection channel A. The two ends of the injection channel A are connected to the surface of the microfluidic chip, with an injection hole B formed at one end and an outlet hole C formed at the other end. There are 28 reaction pools E (numbered 1-28) between the injection hole B and the outlet hole C, which are connected to the injection channel A through 28 connecting channels D respectively. The pneumoconiosis susceptibility gene chip detection kit also includes a sealing film for sealing the injection hole B and the outlet hole C. In order to facilitate identification of direction, a notch is provided at one corner of the rectangular microfluidic chip.

[0052] The microfluidic chip in this protocol is used to detect the following SNP sites: rs3748067, rs8193036, rs4691896, rs2292832, rs2672794, rs12812500, rs2067051, rs2289477, rs26538, rs1864182, rs510432, rs7195830, rs689466, rs20417, rs2227956, rs1800470, rs11466345, rs73329476, rs4320486, rs117626015, rs1539019, rs2243250, and rs361525. The finished pneumoconiosis susceptibility gene chip detection kit contains a total of 6 microfluidic chips with fixed primers (each chip detects 4 samples, and each sample has a total of 28 detection indicators).

[0053] In the 28 reaction pools E, a set of primers for amplification and detection of a nucleic acid target sequence was embedded and fixed using a fixative containing 1% glycerol and 0.1% low-melting-point agarose. The detection index information corresponding to each reaction pool E is detailed in Table 1.

[0054] Table 1: Chip reaction pool corresponding detection index information

[0055]

[0056]

[0057] The design of the primers used to be fixed in reaction pool E is shown in Table 2.

[0058] Table 2: Primers targeting different SNP sites

[0059]

[0060] (2) PCR amplification reagents

[0061] The PCR amplification reagent was KASP-TF V4.0 2X Master Mix 1536, Standard ROX, which contained a universal probe, hot start enzyme, UNG enzyme, dUTP, dNTPs, Mg 2+ The finished pneumoconiosis susceptibility gene chip detection kit contains 1 tube of PCR amplification reagent (500 μL / tube).

[0062] The sequence information of the probes is shown in Table 3. The fluorescence quenching probes were all coupled with conventional fluorescence quenching probes in the prior art.

[0063] Table 3: Probe sequence information

[0064] Probe Name Sequence (including fluorescent group / quencher group) serial number The first universal fluorescent probe GAAGGTGACCAAGTTCATGCT (coupled with FAM fluorescent group) SEQ ID NO.70 The first universal fluorescence quenching probe AGCATGAACTTGGTCACCTTC (coupled with a fluorescence quencher group) SEQ ID NO.71 The second universal fluorescent probe GAAGGTCGGAGTCAACGGATT (coupled with HEX fluorescent group) SEQ ID NO.72 The second universal fluorescence quenching probe AATCCGTTGACTCCGACCTTC (coupled with fluorescence quenching group) SEQ ID NO.73

[0065] (3) Positive control: human genomic DNA containing the rs3748067 mutation. The finished pneumoconiosis susceptibility gene chip detection kit contains one tube of positive control (50 μL / tube).

[0066] (4) Negative control: wild-type human genomic DNA. The finished pneumoconiosis susceptibility gene chip detection kit contains 1 tube of negative control (50 μL / tube).

[0067] (5) TE buffer (Tris-HCl-EDTA)

[0068] Preferably, it is TE buffer produced by Sangon Biotech (Shanghai) Co., Ltd. (B548106-0500).

[0069] (6) Whole blood sample extraction reagents, the following kits can be used:

[0070] Product Name: Nucleic Acid Extraction Reagent, Catalog Number: S090150, Manufacturer: Chengdu Bio-Jingxin Biotechnology Co., Ltd. Product Name: Nucleic Acid Extraction or Purification Reagent, Catalog Number: IVD3101, Manufacturer: Guangzhou Meiji Biotechnology Co., Ltd., Registration Number: Yuesui Xiebei 20150062. Product Name: TGuide Large Volume Blood Genomic DNA Extraction Kit, Catalog Number: OSR-M104, Manufacturer: Tiangen Biochemical Technology (Beijing) Co., Ltd. Product Name: Blood Genomic DNA Extraction System (0.1-20ml), Catalog Number: DP319-01, Manufacturer: Tiangen Biochemical Technology (Beijing) Co., Ltd. Product Name: Large Volume Whole Blood Genomic DNA Extraction Kit, Catalog Number: DP2201, Manufacturer: Beijing Biotech Biotechnology Co., Ltd.

[0071] (2) Use of the kit

[0072] This protocol utilizes microfluidic chip technology in combination with KASP (Kompetitive Allele Specific PCR) technology to enable typing detection of multiple SNP / InDel sites, solving the bias and compatibility issues of multiplex PCR.

[0073] (1) Human genomic DNA extracted from whole blood using a whole blood sample extraction reagent is used as the sample to be tested (sample DNA). The collected blood should not be stored for more than one week at 2°C-8°C; should not be stored for more than two years at -20°C±5°C; and can be stored for a long time below -70°C. It is recommended to use fresh blood for DNA extraction whenever possible. The genomic DNA to be tested must meet the concentration of 2ng / μL-20ng / μL. The extracted human genomic DNA needs to be measured for concentration. If the concentration is higher than 20ng / μL, it must be diluted to meet the above requirements before subsequent experiments can be carried out.

[0074] (2) PCR amplification

[0075] (2.1) Aliquoting PCR amplification reagents: Prepare a corresponding number of 0.2 mL centrifuge tubes based on the number of samples and label the tubes with the sample number. Remove the PCR amplification reagents from the kit in this protocol, thaw them completely at room temperature (natural thawing), vortex to mix thoroughly, and centrifuge briefly to the bottom of the tube. Aliquot 20 μL / tube of the thawed and mixed PCR amplification reagent into 0.2 mL centrifuge tubes with the same sample number.

[0076] (2.2) Sample Mixing: Sample DNA must be thawed at room temperature before use, then mixed thoroughly and centrifuged briefly. Add 10 μL of each test sample DNA to a 0.2 mL centrifuge tube containing PCR amplification reagent, followed by 10 μL of 1×TE (Product Number: B548106-0500, Manufacturer: Sangon Biotech (Shanghai) Co., Ltd.). The total volume of each PCR reaction system is 40 μL (PCR amplification system). The reaction volume of each reaction pool is as follows: 0.5 μL of PCR amplification reagent, 0.25 μL of test sample DNA, and 0.25 μL of 1×TE (total volume 1 μL).

[0077] (2.3) Chip loading: Remove the chip from the kit and allow it to return to room temperature. Open the packaging on a clean bench and place the chip horizontally with the missing corner at the bottom left. Use a pipette to draw 38 μL of the prepared PCR amplification system. Vertically inject the liquid into the chip from the chip inlet until the liquid reaches the corresponding outlet through the inlet channel. Stop loading the sample at this point. Wipe any remaining liquid from the inlet and outlet wells with lint-free paper. Finally, seal the inlet and outlet wells with sealing film.

[0078] (2.4) Chip centrifugation: Turn on the centrifuge power, fix the sample-loaded chip on the centrifuge and level it. Place it with the chip's broken corner on top. Centrifuge at 4,000 rpm for 60 seconds and remove it. If there are still bubbles in the chip reaction pool, extend the centrifugation time until the bubbles disappear.

[0079] (2.5) Chip heat sealing: Turn on the heat sealer. After the temperature stabilizes, click the in-and-out button. After the chip cartridge is removed from the cartridge, insert the chip with the missing corner located in the upper left corner. A maximum of 4 chips can be inserted at a time. After each chip is correctly inserted, the corresponding chip position on the display interface turns green. After the chip cartridge is placed in the cartridge, click "start" to heat seal. After the interface shows that the heat sealing is complete, the chip can be removed. Generally, the pressure cannot be lower than the required minimum pressure. The heat sealing pressure and temperature vary slightly depending on the chip batch and instrument. Please refer to the test results of the instrument and chip for details. Generally, the heat sealing pressure is greater than 0.5MPa; the temperature is 170-175℃, and the time is 4s / 5s. The main purpose of heat sealing is to make each reaction well an independent reaction pool to prevent cross contamination between different reaction wells.

[0080] (2.6) PCR Amplification: Place the chip, film-coated side down, in a PCR amplifier and perform the PCR amplification reaction according to the thermal cycling program in Table 4. The pneumoconiosis susceptibility gene chip detection kit of this protocol can be used with the following equipment (other existing instruments compatible with the kit of this protocol can also be used for detection): Gene PCR amplification instrument manufactured by Hangzhou Langji Scientific Instrument Co., Ltd., product number: A300.

[0081] Table 4: Nucleic acid amplification reaction procedure

[0082]

[0083] (3) Chip scanning

[0084] The pneumoconiosis susceptibility gene chip detection kit of this protocol can be used in conjunction with the following equipment (other existing instruments and equipment that are compatible with the kit of this protocol can also be used for detection):

[0085] The Luxscan 10K / D microarray scanner, part number Y010010, is manufactured by Chengdu Bio-Jingxin Biotechnology Co., Ltd. Signal reading and result interpretation are performed using the LuxScan 10K / D microarray scanner and the pneumoconiosis susceptibility gene detection and reporting system. For scanning, place the microarray chip face-up, with the missing corner facing the upper right corner. Scan according to the instrument's pre-set parameters. After scanning, extract the raw data and save an LSR file, pseudo-color image, and tif file. The scan file name generally matches the chip number. The LSR file includes information such as the test information value and background value. The S-B_Median-635 data in the LSR file represents the amplified signal value. The pseudo-color image allows for intuitive distinction between sample loci with or without deletions. The tif file is the original black-and-white scanned image. The scanned data requires analysis to determine the genotype for each sample's corresponding locus. Bio-Jingxin's proprietary typing software, the "IMAP Reporting System," can be used to analyze test results and generate test reports.

[0086] (3) Result determination

[0087] The positive judgment value of each site in this kit is calculated using the ROC method. When the detection signal value of each site is greater than or equal to the positive judgment value of the site, the site is judged to be positive; when the detection signal value of each site is less than the positive judgment value of the site, the site is judged to be negative.

[0088] (IV) Interpretation of test results

[0089] (1) Kit reference substance

[0090] Positive control: If the test result of the positive control sample at the rs3748067 site shows the TC heterozygous genotype, the positive control is normal. Negative control: If the test result of the negative control sample at the rs8193036 site shows the CC genotype, the negative control is normal.

[0091] (2) Internal control quality control points and positive quality control points in the chip

[0092] Each chip in this kit can test four samples, with one internal control and one positive control for each sample. According to the result interpretation criteria, if both the internal control and the positive control are normal, then the chip quality control is normal, indicating that the test result is valid. However, if any of the quality controls show an abnormality, the sample test result is invalid and requires retesting.

[0093] (3) Genetic test results are divided into three categories:

[0094] A red fluorescent signal is displayed for a FAM homozygous genotype; a green fluorescent signal is displayed for a HEX homozygous genotype; and a yellow fluorescent signal is displayed for a FAM-HEX heterozygous genotype. The test results indicate the copy number of the target SNP allele in the sample, which can be 0, 1, or 2.

[0095] Example 2: Study on the positive judgment value of the pneumoconiosis susceptibility gene chip detection kit

[0096] Fifty clinical samples were tested, and the samples were tested according to the method of Example 1. The positive judgment value was determined using the ROC curve method. The positive judgment values ​​for specific sites are shown in Table 5. If only the FAM fluorescence value is positive and the HEX fluorescence value is negative, the copy number of the SNP allele corresponding to the probe containing the FAM group is 2, and the copy number of the other allele is 0; if only the HEX fluorescence value is positive and the FAM fluorescence value is negative, the copy number of the SNP allele corresponding to the probe containing the HEX group is 2, and the copy number of the other allele is 0; if both the HEX fluorescence value and the FAM fluorescence value are positive, the copy number of the SNP allele corresponding to the probe containing the HEX group and the SNP allele corresponding to the probe containing the FAM group are both 1.

[0097] Table 5: Positive judgment values ​​for pneumoconiosis susceptibility gene detection

[0098]

[0099] Example 3: Performance Study of Pneumoconiosis Susceptibility Gene Chip Detection Kit

[0100] (1) Performance testing

[0101] Accuracy: Positive reference samples (P1-P23) were diluted to 3 ng / μL and the procedure in Example 1 was followed. Minimum detection limit: Positive reference samples (P1-P23) were diluted to 2 ng / μL and the procedure in Example 1 was followed. Specificity: Negative reference samples (N1-N3) were diluted to 3 ng / μL and the procedure in Example 1 was followed. Repeatability: Repeatability reference samples (R1-R2) were tested in parallel and the procedure in Example 1 was followed. Each sample was tested 10 times.

[0102] Among them, the above samples are specifically as follows: P1: containing at least rs3748067 heterozygous mutant; P2: containing at least rs8193036 heterozygous mutant; P3: containing at least rs4691896 heterozygous mutant; P4: containing at least rs2292832 heterozygous mutant; P5: containing at least rs2672794 heterozygous mutant; P6: containing at least rs12812500 heterozygous mutant; P7: containing at least rs2067051 heterozygous mutant; P8: containing at least rs2289477 heterozygous mutant; P9: containing at least rs26538 heterozygous mutant; P10: containing at least rs1864182 heterozygous mutant; P11: containing at least rs510432 heterozygous mutant; P12: containing at least rs71 95830 heterozygous mutant type; P13: at least containing rs689466 heterozygous mutant type; P14: at least containing rs20417 heterozygous mutant type; P15: at least containing rs2227956 heterozygous mutant type; P16: at least containing rs1800470 heterozygous mutant type; P17: at least containing rs11466345 heterozygous mutant type; P18: at least containing rs73329476 heterozygous mutant type; P19: at least containing rs4320486 heterozygous mutant type; P20: at least containing rs117626015 heterozygous mutant type; P21: at least containing rs1539019 heterozygous mutant type; P22: at least containing rs2243250 heterozygous mutant type; P23: at least containing rs361525 heterozygous mutant type. N1: Wild type (rs20417); N2: Wild type (rs117626015); N3: Wild type (rs2067015). R1: Contains at least the rs8193036 heterozygous mutant; R2: Contains at least the rs2227956 heterozygous mutant. P-P23, N1-N3, R1, and R2 are all nucleic acid samples. It has been verified that the performance of the pneumoconiosis susceptibility gene detection kit meets the expected results, and the results are consistent with the corresponding genotype. For details of the pseudo-color image of the accuracy performance verification results of the pneumoconiosis susceptibility gene detection kit (for P1-P23), please see Figure 2 The pseudo-color diagram of the minimum detection limit performance verification results (for P1-P23) is available at Figure 3 Specificity performance verification results pseudo-color map (for N1-N3) see Figure 4 Repeatability performance verification results (R1 and R2 repeated 10 times each) are shown in pseudo-color. Figure 5 .

[0103] (2) First-generation sequencing validation: 50 clinical samples were used to perform first-generation sequencing validation on the aforementioned 23 loci. It was confirmed that the first-generation sequencing results were 100% consistent with the test results of this protocol for the 50 leading samples.

[0104] Example 4: Application of a pneumoconiosis susceptibility gene chip detection kit in constructing a pneumoconiosis susceptibility risk prediction system

[0105] The SNP site effect allele in the sample was detected using a pneumoconiosis susceptibility gene chip detection kit to obtain the copy number of the allele, which was then substituted into the pneumoconiosis susceptibility risk prediction system to predict the risk of pneumoconiosis.

[0106] The pneumoconiosis susceptibility risk prediction system also includes a risk prediction unit. Genetic factor data (SNP site data) or genetic factor data and macro-factor data are input into the risk prediction unit, and the risk score is calculated through the polygenic risk scoring model (PRS model, polygenic risk scoring model, Polygenic Risk Score) formula to obtain the risk score and then determine the polygenic genetic risk of pneumoconiosis.

[0107] The formula of the polygenic risk score model is shown in Formula I (genetic factors):

[0108]

[0109] Among them, ModelScore i Represents the polygenic risk score of the i-th individual. i represents the i-th individual, j is the j-th SNP, W1 ij is the weight of the jth SNP of the i-th individual, a ij is the allele copy number of the jth SNP of the i-th individual (a ij = 0 / 1 / 2, the human body is diploid, and at a single SNP locus, the copy numbers of the disease-associated effect / variant alleles are 0 / 1 / 2. The number of SNPs used for scoring is N, preferably N = 23 (j = 1, 2, 3, ..., 23), and the SNP loci shown in Table 6 are used. Formula I considers only genetic factors to assess the risk of pneumoconiosis.

[0110] Table 6: 23 SNP sites used for the prediction and diagnosis of pneumoconiosis

[0111]

[0112] In Table 6, the major allele is on the left side of the “>” in the Allele column, and the minor allele is on the right side of the “>”. The major allele refers to the most common or highest frequency nucleotide at a specific SNP position in a population. In other words, it is the dominant form at that position. The minor allele refers to the nucleotide that appears less frequently at the same position, representing a relatively rare form of variation. MAF stands for Minor Allele Frequency, which refers to the proportion of alleles that appear less frequently at a specific SNP position in a given population. MAF Case refers to the frequency of the minor allele at a specific SNP position in a population of individuals with a certain disease or condition (i.e., the case group). MAF Control refers to the frequency of the minor allele at the same SNP position in a population of individuals who do not have the disease or are not in the condition (i.e., the control group). Beta (β) refers to the regression coefficient and represents the estimated magnitude of the effect on the phenotype for each additional copy of an allele (minor allele) of a particular SNP. The beta value reflects the extent of the influence of a genetic variant on a particular trait or disease. The sign of the beta value indicates the direction of the allele's influence. A positive value indicates that an increase in the allele is associated with an increase in the phenotype (for example, an increased risk of disease), while a negative value indicates the opposite relationship (for example, a decreased risk of disease). A P value ≤ 0.05 indicates that the association between the SNP and the phenotype is unlikely to occur by chance. The OR value, or odds ratio, is used to assess the relationship between a specific genetic variant (such as an allele of a SNP) and the risk of disease. The OR value for rs3748067 is 0.81, which means that for each additional copy of the effect allele, the individual's chance of developing pneumoconiosis is reduced by 19% compared to individuals who do not carry the allele.

[0113] The formula of the polygenic risk scoring model can also be shown as Formula II (genetic factors + macro factors):

[0114]

[0115] Among them, ModelScore i Represents the polygenic risk score of the i-th individual. i represents the i-th individual, j is the j-th SNP, W1 ij is the weight of the jth SNP of the i-th individual, a ij is the allele copy number of the jth SNP of the i-th individual (a ij=0 / 1 / 2, and the copy numbers of the effect alleles associated with the disease are 0 / 1 / 2). The number of SNPs used for scoring is N, preferably N=23, and the SNP sites shown in Table 6 are used.

[0116] K represents the kth macro factor, W2 ik is the weight of the kth macro factor of the ith individual, b ik Then it is the kth macro-factor value of the ith individual. Formula II takes into account the macro-factors to evaluate the risk of pneumoconiosis. Preferably, the macro-factors include smoking status (yes / no) and dust exposure time, that is, M=2(k=1, 2). The value of the first macro-factor "smoking status (yes / no)" is 1 / 0 (the value 1 is smoking, the value 0 is non-smoking); the value of the second macro-factor "dust exposure time" is the total number of years of exposure. Formula II takes into account genetic factors and macro-factors to evaluate the risk of pneumoconiosis. The values ​​of the two macro-factors "smoking status (yes / no)" and "dust exposure time" can be obtained through relevant conventional epidemiological research statistics.

[0117] More specifically, for the 23 SNP sites, the W1 ij Weight and W2 ik The weight values ​​are shown in Table 7. ij The specific weights are derived from the variable risk effect values ​​in the largest published GWAS of pneumoconiosis in the Chinese population. The W2ik weights (two macro-factors) are derived from regression analysis results across a large sample size. The corresponding regression coefficients are calculated to determine the weights of the two macro-factors. The statistically calculated weights are used as constants in the scoring model formula for the risk prediction unit.

[0118] Table 7: Weight values ​​of the PRS model

[0119]

[0120] Example 5: Polygenic risk score model effectiveness

[0121] The polygenic risk score (ModelScore) of each sample was calculated according to the above formula (Formula I or Formula II). i). The model performance was evaluated by the ROC curve (Receiver Operating Characteristic Curve). TPR (True Positive Rate) and FPR (False Positive Rate) were calculated at different classification thresholds, and these points were plotted with FPR as the X-axis and TPR as the Y-axis. The area under the ROC curve (Area Under the Curve, AUC) is a single numerical indicator used to quantify the overall performance of the classifier. The AUC value ranges from 0 to 1, and the larger the value, the better the performance of the classifier. An AUC of 1 means a completely correct classifier, while an AUC of 0.5 is equivalent to random guessing. By comparing the ROC curves or AUC values ​​of different models, it can be intuitively seen which model has better classification performance. Among them, the sample situation used for model validation is as follows: the samples come from the GWAS database of the research group, and the cases include 202 patients and 198 controls. Among the 202 patients, 68 had no smoking history and 134 had a smoking history, with an average dust exposure of 10.30 ± 11.91 years (mean ± SD). Among the 198 healthy controls, 62 had no smoking history and 136 had a smoking history, with an average dust exposure of 21.57 ± 12.83 years (mean ± SD). More specifically, the diagnostic efficacy of the model was generally judged by the following criteria: AUC ≥ 0.7 indicated a feasible diagnostic efficacy, with larger values ​​indicating an optimal model efficacy; 0.7 > AUC > 0.5 indicated an unsatisfactory diagnostic efficacy.

[0122] Substitute the weight value into formula I and add the corresponding test data of the aforementioned 202 patients + 198 controls (a ij Substituting the polygenic risk score (value: SNP allele copy number) into the above formula I, the polygenic risk score for each patient can be calculated. Then, based on the polygenic risk score values ​​of each patient and control and the actual test results of the sample (whether or not there is pneumoconiosis), the ROC curve is obtained. The predictive power of the above model is evaluated based on the AUC value of the ROC curve. The AUC value of the model is specifically 0.79. The corresponding ROC curve can be found in Figure 6 (Model: 23SNP), it can be seen that the diagnostic effect of the model is acceptable and can be used to predict pneumoconiosis. Medical staff will use the prediction results (ModelScore i For example, the corresponding cutoff value (critical value) can be calculated based on the ROC curve, ModelScore iIf the score exceeds the cutoff value, the individual has a certain risk of developing pneumoconiosis and needs to pay attention to the prevention and treatment of pneumoconiosis in work and daily life. More specifically, the method commonly used in the prior art to determine the cutoff value is: substitute the known sample data into the formula, calculate the Youden's Index according to the ROC curve, and select the Cutoff value that maximizes the Youden's Index. At this value, the best performance can be achieved while balancing sensitivity and specificity. The Cutoff value is used to distinguish between normal and abnormal results. A test result below the Cutoff value is considered to indicate that the subject has a lower risk of developing pneumoconiosis, while a test result above the Cutoff value is considered to indicate that the subject has a higher risk of developing pneumoconiosis. Calculated according to the ROC curve

[0123] Substitute the weight value into formula II and add the corresponding test data of the aforementioned 202 patients + 198 controls (a ij Value: Allele copy number of SNP; b i1 Value: whether smoking; b i2 Substituting the value (value: dust exposure time) into the above formula II, the polygenic risk score of each patient can be calculated. Then, based on the polygenic risk score values ​​of each patient and control and the actual test results of the sample (whether or not suffering from pneumoconiosis), the ROC curve is obtained. The predictive power of the above model is evaluated based on the AUC value of the ROC curve. The AUC value of the model is specifically 0.82. The corresponding ROC curve can be found in Figure 6 (Model: Smoke+DustTime+23SNP), it can be seen that the diagnostic effect of the model is relatively ideal and can be used for the prediction of pneumoconiosis.

[0124] In addition, if only macro factors are used to construct the model, the corresponding test data of the aforementioned 202 patients + 198 controls (b i1 Value: whether smoking; b i2 The AUC value of the test model is 0.71, and the corresponding ROC curve can be found in Figure 6 (Model: Smoke + DustTime) It can be seen that the effectiveness of predicting pneumoconiosis risk using only macroeconomic factors is somewhat insufficient, and appropriate genetic factors need to be added to ensure better prediction results.

[0125] It can be seen that the risk prediction model formulas (Formula I and Formula II) used in this plan can better distinguish between pneumoconiosis patients and healthy people, and thus can effectively and accurately reflect the health risks of pneumoconiosis. Medical personnel can adopt appropriate health management methods or certain treatment plans for relevant personnel based on the risk situation.

[0126] Comparative Example 1: Screening of SNP sites for polygenic risk scoring models

[0127] This technical solution investigated and screened susceptibility genetic variants associated with pneumoconiosis, providing candidate SNP sites for constructing a Chinese-specific polygenic genetic risk score (PRS) for pneumoconiosis and providing ideas for identifying high-risk populations and early screening for the disease. After screening, 62 SNP sites were found to be associated with pneumoconiosis, as shown in Table 8. These SNP sites all met the following conditions: genetic variation association P value <0.05, and the frequency of the effect allele in the control population was greater than 0.01. However, directly applying these 62 SNP sites to construct a polygenic genetic risk score model for pneumoconiosis did not yield ideal prediction accuracy, and further screening, adjustment, and research of the SNP sites were required. After further site-related correlation studies, 23 SNP sites were ultimately used for the prediction and diagnosis of pneumoconiosis, as detailed in Table 6 above. Comparing Table 8 with Table 6, it can be seen that not all of the 62 candidate SNPs associated with pneumoconiosis obtained through screening of published literature are suitable for pneumoconiosis risk prediction. In Table 6, only 17 SNP sites in Table 8 were used to join the model, and additional SNP sites for pneumoconiosis prediction were added, such as rs2292832, rs2289477, rs689466, rs20417, rs2227956, and rs361525. Selecting appropriate SNP sites for model construction is very critical for the prediction accuracy of the model. As can be seen from the above content, although some SNP information related to pneumoconiosis can be obtained through the guidance of existing technologies, it is difficult to screen out some effective and applicable SNP sites from the massive amount of information. In addition, the aforementioned screening method will also miss some SNP sites that play a relatively important role in the process of building a risk prediction model. After a lot of research, the SNP sites for pneumoconiosis prediction shown in Table 6 were finally determined.

[0128] Table 8: 62 SNPs associated with pneumoconiosis

[0129]

[0130] Comparative Example 2: Effectiveness evaluation of scoring models constructed based on different SNP sites

[0131] Before determining the 23 SNP sites of this scheme, the inventors evaluated different SNP sites that may be associated with pneumoconiosis and attempted to model them.

[0132] Test 1: Use the 62 SNP sites shown in Table 8 to establish a polygenic risk scoring model, and establish a scoring model for the risk prediction unit (i.e., in the form of Formula I). ​​The weight values ​​of the 62 SNP sites directly adopt the variable risk effect values ​​in the pneumoconiosis GWAS with the largest sample size in the Chinese population reported in the prior art, which will not be elaborated here. Substitute the corresponding test data of the aforementioned 202 patients + 198 controls (aij value: the number of allele copies of the SNP) into the formula established in this test, obtain the ROC curve and then evaluate the effectiveness of the model. The AUC value of this test model is <0.7. It can be seen that the prediction effect of the scoring model established using 62 SNP sites is not ideal, and is not as good as the scoring model established using 23 SNP sites in this technical solution. Therefore, the risk prediction model in the risk prediction unit of this solution should be constructed using the 23 SNP sites shown in Table 6.

[0133] Test 2: A polygenic risk scoring model was established using the SNP sites in Table 6 except rs2292832, rs2289477, rs689466, rs20417, rs2227956, and rs361525, to establish a scoring model for the risk prediction unit (i.e., in the form of Formula I). ​​rs2292832, rs2289477, rs689466, rs20417, rs2227956, and rs361525 are newly included SNP sites associated with pneumoconiosis in this study and have not been reported in the prior art (reported SNP sites that may be associated with pneumoconiosis can be found in Table 8). Of the 23 SNP sites in this patent solution, only 17 of the SNP sites in Table 8 were added to the model, and additional SNP sites for pneumoconiosis prediction (the aforementioned 6 sites) were added.

[0134] Specifically, the new model obtained by omitting the terms corresponding to the above-mentioned six SNP sites in Formula I (omitting terms ai4, ai8, ai13, ai14, ai15, and ai23). The corresponding test data (aij value: SNP allele copy number) of the aforementioned 202 patients + 198 controls were substituted into the formula established for this test to obtain an ROC curve to evaluate the model effectiveness. The AUC value of this test model was <0.65. This shows that the above-mentioned six SNP sites added in this scheme as genetic factors to establish the model are very critical to the diagnostic and predictive effectiveness of the model, and the above-mentioned SNP sites cannot be obtained through conventional screening (see Example 1).

[0135] Test 3: A polygenic risk score model was established using the six SNPs (rs2292832, rs2289477, rs689466, rs20417, rs2227956, and rs361525) to create a risk prediction unit score model (i.e., the form of Formula I). ​​Specifically, the six SNPs in Formula I were retained to create a new model (retaining items ai4, ai8, ai13, ai14, ai15, and ai23, while omitting all other items). The corresponding test data (aij value: SNP allele copy number) from the 202 patients and 198 controls described above were substituted into the formula established for this test. A receiver operating characteristic (ROC) curve was used to evaluate the model's effectiveness. The AUC value for this model was <0.6. This demonstrates that the risk prediction performance of the model using only the six SNPs is not ideal. Therefore, all 23 SNPs need to be combined for model construction.

[0136] In addition, the inventors also tried to add the SNP sites in Table 8 to the 23 SNPs in this solution (the added SNP sites were different from the 23 SNPs in this solution) to construct a model. However, after multiple attempts (trying to add multiple SNP sites that appear in Table 8 but not in Table 6 to construct multiple models), the AUC values ​​of the ROC curves of the obtained multiple models were difficult to significantly improve on the AUC value (0.79) of Formula I. Therefore, using the 23 SNP sites in this solution to construct a prediction model is the optimal approach. In this way, the risk of pneumoconiosis can be effectively predicted, the complexity of experimental operations can be reduced, and effective risk prediction can be achieved by detecting the copy number of as few SNP sites as possible.

[0137] It can be seen that the selection of 23 specific SNP sites in this solution is very critical for constructing an ideal prediction model. Although there are many SNP sites related to pneumoconiosis reported in the prior art, after being used for model construction, it was found that the model effectiveness was not ideal. By using the 23 SNP sites in this solution, the AUC value of the constructed model can reach a level close to 0.8. If further combined with two macro factors, the AUC value of the model can reach above 0.8. This technical solution provides a new pneumoconiosis risk prediction tool, which solves the problem of the lack of a method for accurately predicting the incidence of pneumoconiosis in the prior art, and has important theoretical significance and practical application value.

Claims

1. A pneumoconiosis susceptibility gene chip detection kit, characterized by: Including microfluidic chips and PCR amplification reagents; The microfluidic chip is provided with a plurality of parallel injection channels, the two ends of which are connected to an injection hole and an outlet hole respectively; the injection channels are connected to a plurality of connecting channels, which are connected to a reaction pool; the reaction pool is embedded with a primer combination; PCR amplification reagents contain universal probe, hot start enzyme, UNG enzyme, dUTP, dNTPs, Mg 2+ .

2. The pneumoconiosis susceptibility gene chip detection kit according to claim 1, characterized in that: The number of reaction pools connected to the same injection channel was ≥23; among them, the 23 reaction pools were used to detect SNP sites rs3748067, rs8193036, rs4691896, rs2292832, rs2672794, rs12812500, rs2067051, rs2289477, rs26538, rs1864182, rs510432, rs7195830, rs689466, rs20417, rs2227956, rs1800470, rs11466345, rs73329476, rs4320486, rs117626015, rs1539019, rs2243250, and rs361525.

3. The pneumoconiosis susceptibility gene chip detection kit according to claim 2, characterized in that: The 23 reaction pools are respectively embedded with a primer combination as shown in SEQ ID NO.1-SEQ ID NO.3, a primer combination as shown in SEQ ID NO.4-SEQ ID NO.6, a primer combination as shown in SEQ ID NO.7-SEQ ID NO.9, a primer combination as shown in SEQ ID NO.10-SEQ ID NO.12, a primer combination as shown in SEQ ID NO.13-SEQ ID NO.15, a primer combination as shown in SEQ ID NO.16-SEQ ID NO.18, a primer combination as shown in SEQ ID NO.19-SEQ ID NO.21, a primer combination as shown in SEQ ID NO.22-SEQ ID NO.24, a primer combination as shown in SEQ ID NO.25-SEQ ID NO.27, a primer combination as shown in SEQ ID NO.28-SEQ ID NO.30, a primer combination as shown in SEQ ID NO.31-SEQ ID NO.33, a primer combination as shown in SEQ ID NO.34-SEQ The primer combination shown in SEQ ID NO.36, the primer combination shown in SEQ ID NO.37-SEQ ID NO.39, the primer combination shown in SEQ ID NO.40-SEQ ID NO.42, the primer combination shown in SEQ ID NO.43-SEQ ID NO.45, the primer combination shown in SEQ ID NO.46-SEQ ID NO.48, the primer combination shown in SEQ ID NO.49-SEQ ID NO.51, the primer combination shown in SEQ ID NO.52-SEQ ID NO.54, the primer combination shown in SEQ ID NO.55-SEQ ID NO.57, the primer combination shown in SEQ ID NO.58-SEQ ID NO.60, the primer combination shown in SEQ ID NO.61-SEQ ID NO.63, the primer combination shown in SEQ ID NO.64-SEQ ID NO.66, and the primer combination shown in SEQ ID NO.67-SEQ ID NO.

69.

4. The pneumoconiosis susceptibility gene chip detection kit according to claim 4, characterized in that: The universal probes include a first universal fluorescent probe with a sequence as shown in SEQ ID NO.70, a first universal fluorescence quenching probe with a sequence as shown in SEQ ID NO.71, a second universal fluorescent probe with a sequence as shown in SEQ ID NO.72, and a second universal fluorescence quenching probe with a sequence as shown in SEQ ID NO.

73.

5. Use of the pneumoconiosis susceptibility gene chip detection kit according to any one of claims 1 to 4 in constructing a pneumoconiosis susceptibility risk prediction system, characterized in that: The pneumoconiosis susceptibility gene chip detection kit is used to detect the copy number of the effect allele.

6. Use of the pneumoconiosis susceptibility gene chip detection kit according to claim 5 in constructing a pneumoconiosis susceptibility risk prediction system, characterized in that: The pneumoconiosis susceptibility risk prediction system includes a pneumoconiosis susceptibility gene chip detection kit and a risk prediction unit; the risk prediction unit is used to output the subject's pneumoconiosis polygenic risk score through a polygenic risk scoring model; The polygenic risk score model includes genetic factors, and its formula is shown in Formula I: Among them, ModelScore i represents the polygenic risk score of pneumoconiosis in the subjects; a ij W1 represents the allele copy number of the jth SNP of the subject, which is detected by the pneumoconiosis susceptibility gene chip detection kit; ij is the weight of the j-th SNP of the subject; 1≤N≤23; The SNP sites used are at least one of rs3748067, rs8193036, rs4691896, rs2292832, rs2672794, rs12812500, rs2067051, rs2289477, rs26538, rs1864182, rs510432, rs7195830, rs689466, rs20417, rs2227956, rs1800470, rs11466345, rs73329476, rs4320486, rs117626015, rs1539019, rs2243250, and rs361525.

7. Use of the pneumoconiosis susceptibility gene chip detection kit according to claim 6 in constructing a pneumoconiosis susceptibility risk prediction system, characterized in that: The polygenic risk scoring model also includes macro factors, and its formula is shown in Formula II: Among them, b ik is the kth macro factor value of the subject; W2 ik is the weight of the kth macro-factor of the subject; 1≤M≤2; macro-factors include smoking status and / or dust exposure time.

8. Use of the pneumoconiosis susceptibility gene chip detection kit according to claim 7 in constructing a pneumoconiosis susceptibility risk prediction system, characterized in that: In the risk prediction unit, the allele copy number a of the SNP ij The value range is 0 or 1 or 2; The SNP loci used were rs3748067, rs8193036, rs4691896, rs2292832, rs2672794, rs12812500, rs2067051, rs2289477, rs26538, rs1864182, rs510432, rs7195830, rs689466, rs20417, rs2227956, rs1800470, rs11466345, rs73329476, rs4320486, rs117626015, rs1539019, rs2243250, and rs361525; The weight value W1 of the SNP site i1 -W1 i23 They are -0.210721031315653, -0.210721031315653, 0.636576829071551, 0.27002713721306, 0.198850858745165, 0.371563556432483, -0.415515443961666, -0.23572233352107, -0.544727175441672, -0.27443684570176, -0.198450938723838, and 0.4382 54930931155, -0.23572233352107, -0.127833371509885, 2.74791173452734, -0.693147180559945, 0.806475865866949, 0.774727167552368, -0.527632742082372, 0.887891257352457, 0.198850858745165, -0.22314355131421, 1.33236601909433.

9. Use of the pneumoconiosis susceptibility gene chip detection kit according to claim 8 in constructing a pneumoconiosis susceptibility risk prediction system, characterized in that: In the risk prediction unit, macro factors consist of smoking status and dust exposure time; b i1 is the value of the subject's first macro-factor smoking status, ranging from 0 to 1; 1 represents smoking and 0 represents non-smoking; b i2 is the value of the second macro factor dust exposure time of the subject, which is the total number of years of dust exposure of the subject.

10. Use of the pneumoconiosis susceptibility gene chip detection kit according to claim 9 in constructing a pneumoconiosis susceptibility risk prediction system, characterized in that: In the risk prediction unit, the weight value W2 of the macro factor smoking status i1 is -0.10719; Weight value W2 of macro-factor dust exposure time i2 It is -0.065590.

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