Site for detecting leprosy risk and application thereof

Abstract

The invention discloses a site for detecting leprosy risk and application thereof, which comprises the following steps: detecting genotypes of 25 leprosy susceptible SNP sites in a human genome of a to-be-detected individual, converting the genotypes into leprosy risk values, and predicting leprosy risk level of the to-be-detected individual according to the risk values, the risk of 28.06 is high, and the risk is 18.17 lt; the risk is medium when the GRS is less than or equal to 28.06, and the risk is low when the GRS is less than or equal to 18.17. Prediction and efficiency of the leprosy disease risk prediction model established by the invention are well verified in leprosy convalescents and close contact people, and the method can be applied to screening of the close contact people of the leprosy patients.

Description

Technical Field

[0001] The present invention belongs to the field of disease risk prediction informatics, and specifically relates to a site for detecting leprosy risk and its application. Background Art

[0002] Leprosy is a chronic infectious disease caused by Mycobacterium leprae that infects susceptible individuals, specifically destroying the skin and peripheral nerve tissue, and leading to disability in the late stages. In vitro culture and vaccine development have not been successful, and the transmission route of leprosy is still unclear. Monitoring close contacts of leprosy patients is the main preventive and control measure for early detection and diagnosis of leprosy. The main source of infection for leprosy is leprosy carriers. The incubation period of leprosy can be as long as 5-10 years. It is difficult to diagnose before the typical symptoms appear. Delayed diagnosis leads to high rates of disability in new patients. Delayed diagnosis is the main factor leading to infection and disease progression in leprosy contacts, which can increase the possibility of nerve damage and subsequent disability. Disability is often used as an indicator for delayed diagnosis. Disability is the main harm of leprosy and a major cause of social panic and discrimination. As early as the 1960s and 1970s, researchers began using single-dose dapsone (DDS), rifampicin, and minocycline for leprosy contacts. The goal was to reduce the incidence of new leprosy cases, thereby controlling the prevalence of leprosy and lowering the rate of disability. However, chemoprevention has the following challenges: 1. There is no standardized international chemoprevention regimen, and the monitoring indicators for efficacy assessment are insensitive; 2. The target population for chemoprevention is too large, resulting in high costs; 3. Large-scale implementation of chemoprevention can lead to the development of drug resistance; and 4. Chemopreventive drugs can cause adverse reactions. For example, one of the preventive drugs, dapsone (DDS), can cause severe drug hypersensitivity syndrome. Due to these issues, leprosy chemoprevention has not been effectively promoted and has achieved minimal results. Genetic epidemiology, familial clustering analyses, and twin studies have confirmed that leprosy has a strong genetic susceptibility, with a heritability of up to 57%. Therefore, there is a need for a locus that can measure leprosy risk and its application. Summary of the Invention

[0003] The purpose of the present invention is to provide a site for detecting the risk of leprosy and its application.

[0004] The present invention is achieved through the following technical solutions:

[0005] In one aspect, the application of leprosy disease risk loci includes the following SNP loci in the human genome: rs42490, rs6478109, rs7995004, rs9302752, rs9271100, rs3762318, rs2275606, rs2058660, rs6871626, rs2735591, rs2221593, rs663743, rs77061563, rs160451, rs8002861, rs76418789, rs146466242, rs780668, rs181206, rs6807915, rs55894533, rs10100465, rs13259978, rs671, and rs75680863; these SNP loci are significantly associated with the risk of leprosy disease.

[0006] In another aspect, a method for predicting the risk of leprosy disease includes the following steps:

[0007] A Detect the genotypes of 25 leprosy-susceptible SNP loci in the human genome of the individual to be tested. The 25 SNP loci are: rs42490, rs6478109, rs7995004, rs9302752, rs9271100, rs3762318, rs2275606, rs2058660, rs6871626, rs2735591, rs2221593, rs663743, rs77061563, rs160451, rs8002861, rs76418789, rs146466242, rs780668, rs181206, rs6807915, rs55894533, rs10100465, rs13259978, rs671, rs75680863; B Convert the genotypes into the risk values of leprosy disease. The calculation formula for the risk value is:

[0008]

[0009] where i represents the count of the 25 leprosy-susceptible SNP loci; SNP corresponds to the 25 leprosy-susceptible SNP loci; the risk score is the average number of risk alleles weighted by the beta value of each leprosy-susceptible SNP locus; the beta value is the ln(OR) value of each leprosy-susceptible SNP locus; the OR value is the odds ratio.

[0010] C Predict the leprosy disease risk level of the individual to be tested according to the risk value: when GRS > 28.06, it is a high risk; when 18.17 < GRS ≤ 28.06, it is a medium risk; when GRS ≤ 18.17, it is a low risk.

[0011] Furthermore, in the calculation of the risk score GRS, the number of risk alleles P at each SNP site is i The risk allele is determined as follows: 0 represents no risk allele carrier, 1 represents heterozygous carrier, and 2 represents homozygous carrier.

[0012] In another aspect, a computer-readable storage medium includes a computer program for implementing the method.

[0013] In another aspect, a computing device includes a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the method described above is implemented.

[0014] Compared with the prior art, the present invention adopts the above technical solution, and its biggest feature is:

[0015] Compared with the prior art, the present invention has the following effects:

[0016] 1. The present invention uses association analysis technology to newly explore and identify SNP sites related to the control of alpha-tocopherol content in sweet corn kernels and the related gene ZmCS2, supplementing and enriching the genetic sites for molecular identification of alpha-tocopherol content in sweet corn kernels.

[0017] 2. The present invention develops a KASP molecular marker based on the chorismate synthase gene (ZmCS2) locus, which can quickly complete genotype detection, has low cost, simple operation, and is suitable for high-throughput detection. This locus will greatly promote the biofortification breeding of sweet corn with high alpha-tocopherol content. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Age and sex were included in the model for testing. The vertical axis represents sensitivity and the horizontal axis represents specificity.

[0019] Figure 2 The GRS of patients is significantly larger than that of normal controls. A is the frequency distribution of GRS values calculated using 25 SNPs in patients and normal controls, with the vertical axis representing frequency and the horizontal axis representing GRS value. B is the frequency distribution of GRS values calculated using 7 SNPs in patients and normal controls, with the vertical axis representing frequency and the horizontal axis representing GRS value.

[0020] Figure 3 The AUC curves of the four models are shown in Figure 2. The vertical axis represents sensitivity and the horizontal axis represents specificity.

[0021] Figure 4The optimized AUC curve is shown in Figure 2. The vertical axis represents sensitivity and the horizontal axis represents specificity.

[0022] Figure 5 The GRS is the average risk of leprosy among recovered leprosy patients and their close contacts. The vertical axis represents the average risk of leprosy by GRS, and the horizontal axis represents different sample groups.

[0023] Figure 6 This is the AUC curve of the validation phase. The vertical axis represents sensitivity, and the horizontal axis represents specificity. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is further illustrated below in conjunction with embodiments and comparative examples, but they should not be construed as limiting the present invention:

[0025] The present invention will be further described in detail below in conjunction with specific embodiments. The examples provided are only for illustrating the present invention and are not intended to limit the scope of the present invention. The examples provided below can serve as a guide for further improvements by those skilled in the art and are not intended to limit the present invention in any way.

[0026] Unless otherwise specified, the experimental methods in the following examples are conventional methods and were performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials and reagents used in the following examples, unless otherwise specified, were all commercially available.

[0027] Example 1: Constructing a screening chip based on leprosy risk factors and establishing a risk prediction model.

[0028] The study cohorts in this example included two independent sample groups: (1) a sample group consisting of 1,572 Chinese leprosy patients and 2,484 Chinese normal controls collected from 2006 to 2008; and (2) a sample group consisting of 1,692 Chinese leprosy patients and 1,330 Chinese normal controls collected from 2014 to 2016, totaling 3,264 patients and 3,814 normal controls. All normal controls in this cohort were healthy individuals with no family history of leprosy or leprosy. This study was approved by the Ethics Committee of Shandong Academy of Medical Sciences / Shandong Institute of Dermatology and Venereology, and all subjects provided written informed consent.

[0029] Based on the 30 leprosy risk loci discovered by the applicant of the present invention through a previous leprosy genome-wide association study, combined with clinical related information, a leprosy risk prediction model for the Chinese population was established.

[0030] 1. Develop a susceptibility gene screening chip for people at high risk of leprosy.

[0031] The present invention uses QuantStudioTM 12K Flex A custom-made susceptibility gene screening array for individuals at high risk for leprosy was developed using microarray technology, containing 30 independent SNPs. These SNPs correspond to loci that achieved genome-wide association and a Minor Allele Frequency (MAF) greater than 0.01 in previously published GWAS studies by the applicants. These SNPs are located on the following genes in the human genome: HLA-DR, RIPK2, NOD2, CCDC122, LACC1, TNFSF15, IL23R, RAB32, IL18RAP / IL18R1, IL12B, BCL10, BATF3, CCDC88B, EGR2, CITA-SOCS1, CHD18, DEC1, FLG, NCKIPSD, CARD9, SLC29A3, IL27, TYK2, SYN2-PPARG, BBS9, CTSB, MED30, SLC7A2, ALDH2, and TCN2. The array was fabricated by Thermo Fisher Scientific in the United States.

[0032] The Assay Design Tool is used for Custom Assay design (relevant website: https: / / www.thermofisher.com / order / custom-genomic products / tools / genotyping / ). Set the file format (*.tpf or *.spf) to include each The analysis information includes gene symbol, gene name, assay ID and the position of each assay on the 96-well plate.

[0033] 2. Use susceptibility gene screening chips to perform genotyping on research samples.

[0034] (1) The genotyping data of the group samples are from the following literature: PMID: 20018961; PMID: 22019778; PMID: 23103228; PMID: 23784377; PMID: 25642632; PMID: 28842327; PMID: 27976721; PMID: 29722023. (2) The group samples were screened using the high-risk individual susceptibility gene chip developed in step 1 (QuantStudioTM 12KFlex Chip) for genotyping.

[0035] 2.1 Prepare nucleic acid samples.

[0036] 1) High molecular weight genomic DNA (gDNA) was extracted from 2 mL of venous blood from each (2) group of sample individuals using the QuickGene 610L Automatic DNA / RNA Extraction System (Fijifilm, Tokyo, Japan).

[0037] 2) Quantitative detection of gDNA by NanoDrop 8000 at 260 nm and 280 nm The ratio of the ultraviolet (UV) spectrophotometer readings at the wavelengths should be between 1.7 and 2.0 (ie, A260 / A280 = 1.7-2.0).

[0038] 3) Standardize all gDNA samples so that the same amount of sample is added to each well. Use double-distilled water to standardize the gDNA to a concentration of 30 ng / μL and add it to a 96-well plate.

[0039] 2.2Quant Studio TM 12K Flex system operation.

[0040] 1) Tracer sample:

[0041] Create a sample information file (*.csv) to track the location of samples in a 96-well sample plate. Sample tracking software automatically maps sample positions from 96-well reaction plates to 384-well sample plates and Then export the sample information in a table format (*.csv).

[0042] 2) Prepare PCR mixture and 384-well sample plate:

[0043] a) Thaw a 96-well reaction plate containing the prepared gDNA samples at room temperature; mix the gDNA samples by vortexing, then spin at 1000 rpm for 1 minute.

[0044] b) Gently invert the bottle 10 times to mix Genotyping Master Mix (ThermoFisher, 4404846).

[0045] c) Add 2 μL of Genotyping Master Mix to a 384-well sample plate.

[0046] d) Using a 12-channel pipette, transfer 2 μL of the normalized gDNA sample from step 2.1 from the 96-well reaction plate to a 384-well sample plate (batch transfer samples from four 96-well plates to one 384-well plate); cover the sample plate with aluminum foil and centrifuge at 1000 rpm for 1 minute to eliminate bubbles.

[0047] 3) Prepare QuantStudio TM 12K plate:

[0048] Using QuantStudio TM AccuFill TM The system transfers samples from 384-well plates to on the board.

[0049] a) Confirmation 384-well reaction plate (ThermoFisher, 4406947), AccuFill TM Is the system fully prepared with the gun tip and the pallet rack in place and in the correct position?

[0050] b) Take out from refrigerator Plate and place in room temperature for 10 minutes;

[0051] c) Prepare the Immersion Fluid syringe, and connect the needle tip to the syringe, ready for use;

[0052] d) Close QuantStudio TM AccuFill TM Operate the gate and then start QuantStudio TM AccuFill TM Software. When When the Plate window is open, open the instrument door and carefully remove the designated plate and then use immediately Seal the plate with the housing cover and use the prepared Immersion Fluid Syringe Closure plate and seal it with the special screws.

[0053] 4) The sample will be reproduced The plate is loaded into the instrument and run plate:

[0054] a) Place the tablet on the tablet adapter. Make sure: The plate is properly aligned in the adapter with the plate barcode facing up and toward the front of the instrument;

[0055] b) Click Get Board ID" to import our The barcode of the plate. The plate's barcode will be populated and matched;

[0056] c) Click on the Quant Studio 12K Flex platform instrument to run. The temperature conditions are 93°C for 10 minutes, followed by 50 cycles of (95°C for 45 seconds, 94°C for 13 seconds, and 53.5°C for 2 minutes), and then incubate at 25°C for 2 minutes.

[0057] 2.3 Transfer and analyze experimental results.

[0058] 1) After the experiment is finished, close the run window and reopen the *.eds file to display the allelic discrimination screen. Then import the sample names and assay IDs into the generated *.eds file.

[0059] 2) Export the experimental results to another computer and analyze the experimental results using Taqman Genotype Analyzer v1.3.1 software.

[0060] 3) Review the QC criteria flags, such as NOSIGNAL, NOAMP, and OFFSCALE. Samples marked with BADROX and AMPSCORE are not genotyped by default and need to be manually removed. Review and change the analysis settings as needed. Click Edit Settings and specify the typing data.

[0061] 4) Export analysis data and select QuantStudio TM 12K Flex format (*.csv) for further analysis.

[0062] 3. Quality control (QC) of SNPs and samples.

[0063] The SNP filtering criteria were as follows: 1. Remove five SNPs with call rates < 95% (rs145562243, rs58600253, rs73058713, rs4720118, rs55882956); 2. Remove those with Hardy-Weinberg Equilibrium (HWE) P values < 10 in the control cohort; –3Finally, 25 of the 30 SNPs passed the quality control (Table 1).

[0064] Samples with missing values in the 25 SNPs genotyping data were not included in the next analysis. After this filtering step, all sample cohorts in groups (1) and (2) remained with 2,144 leprosy patients and 2,671 normal controls.

[0065] 4. Efficacy testing of leprosy susceptibility gene screening chip.

[0066] The present invention used PLINK v1.07 software to calculate the relationship between leprosy phenotype and 25 SNPs based on a logistic regression model. The results showed that the P values for these 25 SNPs were all < 0.05 (P value e* in Table 1), and the OR values were consistent with those reported in previous articles. This result demonstrates that the leprosy susceptibility gene screening chip in step 1 has good detection efficiency.

[0067] Table 1. Relationships between phenotypes and 25 SNPs calculated based on the logistic regression model.

[0068]

[0069]

[0070]

[0071]

[0072] 6. Construction of leprosy risk model.

[0073] 6.1 Examination of variables included in the risk prediction model.

[0074] The present invention uses T test and Pearson chi-square test to test the age and gender of leprosy patients and normal controls respectively. The results show that there is no significant difference in age and gender between patients and controls (P>0.05). When basic information such as age and gender is included in the model for screening, the contribution of age and gender to the discriminative ability of the model is almost zero (see Figure 1 In the Model 1_Age_Gender and Model 2_Age_Gender_GRS, there was no statistical difference in age and gender between leprosy cases and healthy controls. Therefore, age and gender were not included in the subsequent models for further study, and only 25 SNPs were finally included in the model. The GRS of leprosy patients was significantly right-skewed (i.e., larger) compared to the GRS of healthy controls ( Figure 2), there was a statistical difference between the two P = 1.01E-152. In the modeling stage, the risk allele count (number of risk sites) of the risk factor and GRS were compared to see which variable was more optimized and had the strongest discrimination ability. The results showed that GRS ( Figure 1 :Model4_GRS) is better than risk all lele count( Figure 1 : Model5_Risk_Allele_Count), GRS was selected as the risk factor and included in the model.

[0075] The basic information of the modeling queue is shown in Table 2.

[0076]

[0077] 6.2 Construction of risk prediction model.

[0078] Four risk models were constructed based on 25 leprosy risk loci.

[0079] Model 1: constructed using 25 SNPs based on the GRS method;

[0080] Model 2: constructed based on the GRS method using the 7 SNPs with P values < 5E-8 in Table 1;

[0081] Model 3: constructed using 25 SNPs based on the Bayesian network method;

[0082] Model 4: constructed based on the Bayesian network method using the 7 SNPs with P values < 5E-8 in Table 1.

[0083] The performance of Bayesian networks was implemented using the R package bnlearn, where structure learning was performed using the score-based structure algorithm (HC method, hc function) and parameter learning was performed using the bn.fit function.

[0084] 6.3 Evaluation of risk prediction models.

[0085] The present invention uses the Hosmer-Lemeshow goodness of fit test as an indicator to evaluate the goodness of fit of the above models. Model fit refers to the degree of agreement between the model prediction results and the actual observed occurrence. The more consistent the two are, the more "successful" the model establishment is. In all models, the P value of the Hosmer-Lemeshow test is >0.05 (from model 1 to 4, the P values are 0.060, 0.093, 0.627, and 0.712, respectively), indicating that the four risk models all have a certain fitting effect; the receiver operating characteristic (ROC) curve and the area under the curve (AUC) are used to evaluate the prediction effect of the model. Figure 3 shown.

[0086] The prediction effect of the model was evaluated by receiver operating characteristic (ROC) curve and area under the curve (AUC). The best risk prediction model was established based on GRS and 25 SNPs, with an area under the curve (AUC) of 0.743 ( Figure 3 (as shown in the GRS basedon 25 variants).

[0087] Whether based on the GRS method or the Bayesian network method, the 25 SNPs model ( Figure 3 GRS based on 25 variants and BN based on 25 variants) are better than 7 SNPs ( Figure 3 ), when modeling with 25 SNPs, the GRS method was used ( Figure 3 GRSbased on 25 variants, AUC = 0.743) is better than the Bayesian network ( Figure 3 Medium BN based on 25 variants, AUC = 0.731) (P value = 5.81E-4).

[0088] DeLong's test was used to test the statistical significance of the AUC obtained by each model. Model 1 and Model 3 (using 25 SNPs based on the GRS method and Bayesian method, P value = 5.81E-4) showed that Model 1 was superior to Model 3. The above tests were performed using the R software package pROC. The distribution of GRS obtained by Model 1 in leprosy patients and normal controls ( Figure 2 Middle (A) shows that the peak of the case group is significantly shifted to the right, which also shows that the model has good discrimination ability.

[0089] In summary, the leprosy risk prediction model established using Model 1 is the optimal leprosy risk prediction model, with the optimal AUC = 0.743 ( Figure 4 (as shown in GRS based on 25 variants).

[0090] 6.4. GRS calculation and logistic regression analysis.

[0091] 6.4.1 Calculate the beta (β) value of the SNP.

[0092] The GRS calculation formula calculates SNPs based on allele carrier status. The genotype of each sample at each SNP is represented by (0, 1, 2), which is the number of risk SNPs present. 0 represents no carrier of the risk allele, 1 represents heterozygous carrier, and 2 represents homozygous carrier. For example, at SNP rs42490, the risk allele is G. No carrier of the SNP is 0, heterozygous carrier is 1, and homozygous carrier is 2.

[0093] After calculation, the mean of all SNP betas (the beta value of each SNP is equal to the ln value of the OR value of each SNP in Table 1, that is, beta = ln (OR)) is 0.255.

[0094] 6.4.2SNP risk score (Genetic risk score, GRS).

[0095] A weighted approach was used to calculate the SNP risk score. When risk alleles were combined, the individual effect of each SNP on disease development was considered. Assuming that each locus had a different effect on leprosy susceptibility but was related to its OR, the GRS was calculated as the average number of risk alleles weighted by the β value (ln(OR)) of each SNP.

[0096] The β value is derived from the coefficient of the logistic regression model using the 25 SNPs, that is, the (ln(OR)) value for each risk SNP locus. The number of risk SNPs at each of the 25 leprosy susceptibility SNPs in each sample was calculated. Based on the β value (ln(OR)) for each risk SNP locus, the beta value for each leprosy susceptibility SNP locus in the sample was calculated.

[0097] The calculation formula for GRS is as follows:

[0098]

[0099] In formula I, i represents 25 leprosy susceptibility SNP sites (25 human genome SNP sites shown in Table 1: rs42490, rs6478109, rs7995004, rs9302752, rs9271100, rs3762318, rs2275606, rs2058660, rs6871626, rs2735591, rs222159 3, rs663743, rs77061563, rs160451, rs8002861, rs76418789, rs146466242, rs780668, rs181206, rs6807915, rs55894533, rs10100465, rs13259978, rs671, rs75680863);

[0100] The SNPs correspond to each leprosy susceptibility SNP locus;

[0101] The formula for calculating the beta value is beta i =ln(OR i ).

[0102] 6.5 Determine risk thresholds and group them based on GRS risk assessment.

[0103] A positive likelihood ratio greater than 5 is considered robust evidence for diagnosing disease, while a negative likelihood ratio less than 0.2 is considered robust evidence for excluding disease. Using a GRS greater than 28.06 (positive likelihood ratio > 5.0) as the threshold, the sensitivity is 12.5%, the specificity is 97.5%, and the positive predictive value is 21.25%. This means that if an individual's GRS is greater than 28.06, there is a 21.25% probability that they have leprosy. The negative predictive value is 95.38%, meaning that if an individual's GRS is 28.06 or less, there is a 95.38% chance that they do not have leprosy (see Table 3). Furthermore, a GRS threshold of 22.38 yields the best sensitivity and specificity, while a GRS threshold of 18.17 yields a negative likelihood ratio of 0.2. The corresponding likelihood ratios and positive / negative predictive values are shown in Table 3.

[0104] Table 3. Genetic risk analysis based on the GRS method using 25 SNPs modeling.

[0105]

[0106] Note: The “&” threshold corresponds to the best sensitivity and specificity.

[0107] Based on the model's positive and negative likelihood ratios, individuals in the modeling cohort were divided into three groups: high-risk, intermediate-risk, and low-risk. The high-risk group included individuals with a GRS greater than 28.06 (PLR>5.0), the low-risk group included individuals with a GRS ≤18.17 (NLR<0.20), and the intermediate-risk group included individuals with a GRS between 18.17 and 28.06. The results showed that individuals in the high-risk group had a significantly increased odds of developing leprosy (OR=24.65, 95% CI: 17.57-34.60) compared to individuals in the low-risk group: the highest-risk group (GRS greater than 28.06) had a 24.65-fold increased risk compared to the lowest-risk group (GRS<=18.17) (Table 4).

[0108] According to the selection of thresholds, high-risk individuals can be screened out accordingly. When a low-risk threshold of 18.17 is selected, 2038 leprosy patients need to be monitored, and 95.06% of the patients can be detected. If a low-risk threshold of 22.38 corresponding to the best sensitivity and specificity is selected, there are 1438 (67.07%) high-risk individuals among the 2144 leprosy patients (as shown in Table 5).

[0109]

[0110]

[0111] In summary, the present invention successfully constructed a leprosy risk prediction model (leprosy risk prediction model 1 constructed using 25 SNPs based on the GRS method, AUC = 0.743), which can be used to evaluate high-risk populations and lay a theoretical foundation for "precision" chemical prevention.

[0112] In the above application, the substances for detecting the polymorphism (ie, allele) or genotype of the 25 SNP sites in the human genome can be PCR primers and single-base extension primers for amplifying genomic DNA fragments including the 25 SNP sites mentioned above.

[0113] The material for detecting the polymorphism (i.e., allele) or genotype of the 25 SNP sites in the human genome can be a reagent and / or instrument required to determine the polymorphism (i.e., allele) or genotype of the 25 SNP sites by at least one of the following methods: DNA sequencing, restriction fragment length polymorphism, single-strand conformation polymorphism, denaturing high-performance liquid chromatography, and SNP chip. Among them, the SNP chip includes a chip based on nucleic acid hybridization reaction, a chip based on single base extension reaction, a chip based on allele-specific primer extension reaction, a chip based on "one-step" reaction, a chip based on primer ligation reaction, a chip based on restriction endonuclease reaction, a chip based on protein DNA binding reaction, and a chip based on fluorescent molecule DNA binding reaction.

[0114] There are no special sequence requirements for the PCR primers, as long as they can amplify a genomic DNA fragment including the 25 SNP sites. The extension primers can be designed based on the upstream of each of the 25 SNP sites in the human genome (excluding the SNP site), and the last nucleotide of the extension primer can correspond to the first nucleotide of each of the 25 SNP sites in the human genome, as long as the 3′ end of the single-base extension primer can extend beyond the nucleotide of the SNP site.

[0115] In an embodiment of the present invention, the product may be a reagent or a kit. The active ingredient of the product may be a substance that detects the polymorphism or genotype of 25 SNP sites in the human genome. The active ingredient of the product may also include a substance that detects the polymorphism or genotype of other SNP sites in the human genome. Based on the previous invention, the present invention intends to develop a high-risk individual susceptibility gene screening chip based on the 32 leprosy risk sites that have been located. The screening chip will be used to type approximately 2,000 leprosy patients and normal controls who have been clearly typed. The typing results will be compared with the GWAS database to determine the sensitivity and specificity of the screening chip. A leprosy incidence risk prediction model will be constructed through mathematical analysis using the susceptibility gene risk sites as risk factors. In order to evaluate the effectiveness of the model, close contacts of leprosy in counties with a high incidence of leprosy in Shandong Province will be screened to study the infection status of leprosy bacteria in close contacts of leprosy. Finally, 2,210 recovered patients and 9,972 close contacts will be genetically typed, and the effectiveness of the prediction model will be cross-validated, in order to provide a diagnostic basis for screening high-risk groups for leprosy and determining patients with subclinical infection, and lay the foundation for achieving "precise" chemical prevention of leprosy and eliminating the harm of leprosy.

[0116] Example 2: Efficacy evaluation of leprosy close contact screening and risk prediction model.

[0117] 1Screening population and methods.

[0118] 1.1 Follow-up population:

[0119] Based on the National Leprosy Prevention and Control System, 20 counties and cities with a high incidence of leprosy were selected, representing approximately 80% of the total number of leprosy cases in history. Follow-up was conducted on 2,210 recovered leprosy patients currently registered in the National Leprosy Prevention and Control System, as well as 8,709 close family contacts and 1,740 neighbors. Close contacts of leprosy were defined as first-, second-, and third-degree relatives of leprosy patients, regardless of whether they lived with the leprosy patient for a long period of time. Neighbors were defined as non-blood relatives living within 200 meters of the leprosy patient's home. All participants in the follow-up study provided signed informed consent (children received informed consent from their guardians).

[0120] 1.2. Follow-up expert group:

[0121] Four leprosy prevention and control workers and two medical graduate students conducted home follow-up.

[0122] 1.3. Follow-up items:

[0123] The questionnaire filled out by close contacts within the household mainly includes age, gender, ethnicity, family income, education level, living situation, medical history, tuberculosis vaccination history, skin condition, allergy history, and history of contact with leprosy. Each patient will undergo routine skin testing, ophthalmology, and superficial nerve examinations.

[0124] 1.4. Diagnosis of leprosy:

[0125] According to the national diagnostic criteria of GB15973-1995, at least two or more of the following criteria must be met: (1) dermatological symptoms; (2) superficial nerve involvement; (3) positive tissue fluid bacteria; and (4) histological pathology reveals foam cells or epithelioid cell granulomas, S-100 staining reveals broken nerves, and positive bacteria. All leprosy cases collected had no history of tuberculosis or other infectious diseases, and no history of autoimmune diseases such as psoriasis and herpes. Clinical subtyping criteria were completed according to the Ridley and Jopling criteria.

[0126] 1.5. Statistical Analysis

[0127] The data were uniformly entered into Microsoft Excel XP and analyzed using STATISTICA (release 6.1, StatSoft, USA) software.

[0128] 2. Effectiveness evaluation of leprosy risk prediction model.

[0129] 2.1 Genotyping

[0130] The susceptible gene screening chip prepared in Example 1 is used to perform genotyping on the leprosy survivors, close contacts and neighbors in step 1 to determine the risk factor carrier status, which is used to introduce the optimized risk prediction model and screen high-risk groups.

[0131] 2.2 Prediction of leprosy risk.

[0132] Using the optimal leprosy risk prediction model established in Example 1, 2210 close contacts of leprosy survivors who were followed up (including 5983 blood relatives, 2726 spouses of patients and their relatives, and 1740 non-blood-related neighbors) were screened to predict the high-risk population for leprosy.

[0133] The risk model was validated in leprosy survivors and close contacts, and the low risk threshold was selected as 18.17. The average risk of leprosy survivors and close contacts was ( Figure 5 ) showed that the GRS increased with the distance of blood relationship: first-degree relatives had the highest GRS (GRS=22.03), with the highest risk of disease; followed by second-degree relatives (GRS=21.62), with the second highest risk of disease; third-degree relatives (GRS=21.15), with the third highest risk of disease; and close contacts without blood relationship (GRS=20.92) had the lowest risk of disease. The ROC curve results showed that the model had good discrimination ability (AUC=0.707( Figure 6 ).

[0134] According to GRS risk assessment, the risk of leprosy in the high-risk group (GRS>28.06) of close contacts was 10.68 times that of the low-risk group (GRS≤18.17).

[0135] Table 6. Validation of the risk model constructed based on 25 variant sites

[0136]

[0137]

[0138] The above results show that monitoring and following up 29 close contacts can detect one leprosy patient (NNT value), which greatly narrows the scope of the monitored population and lays a theoretical foundation for "precision" chemical prevention.

[0139] In summary, the predictive efficacy of the leprosy risk prediction model established by the present invention has been well verified in leprosy survivors and close contacts. This model will contribute to the implementation of "precision" chemical prevention in the future and can be applied to the screening of close contacts of leprosy patients for early detection and diagnosis, and to reduce the rate of deformities. The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art who, within the technical scope disclosed by the present invention, makes equivalent substitutions or changes based on the technical solutions and inventive concepts of the present invention is covered within the scope of protection of the present invention.

Claims

1. Application of leprosy risk sites, characterized by: It includes the following SNP sites in the human genome: rs42490, rs6478109, rs7995004, rs9302752, rs9271100, rs3762318, rs2275606, rs2058660, rs6871626, rs2735591, rs2221593, rs663743, rs77061563, rs160451, rs8002861, rs76418789, rs146466242, rs780668, rs181206, rs6807915, rs55894533, rs10100465, rs13259978, rs671, and rs75680863; and these SNP sites are significantly associated with the risk of leprosy.

2. A method for predicting the risk of leprosy, characterized in that: It includes the following steps: A Detect the genotypes of 25 leprosy-susceptible SNP sites in the human genome of the individual to be tested. The 25 SNP sites are: rs42490, rs6478109, rs7995004, rs9302752, rs9271100, rs3762318, rs2275606, rs2058660, rs6871626, rs2735591, rs2221593, rs663743, rs77061563, rs160451, rs8002861, rs76418789, rs146466242, rs780668, rs181206, rs6807915, rs55894533, rs10100465, rs13259978, rs671, rs75680863; B Convert the genotypes into risk values for leprosy. The formula for calculating the risk value is: where i represents the count of the above 25 leprosy-susceptible SNP sites; SNP corresponds to the 25 leprosy-susceptible SNP sites; the risk score is the average number of risk alleles weighted by the beta value of each leprosy-susceptible SNP site; the beta value is the ln(OR) value of each leprosy-susceptible SNP site; the OR value is the odds ratio. C Predict the leprosy risk level of the individual to be tested according to the risk value: when GRS > 28.06, it is a high risk; when 18.17 < GRS ≤ 28.06, it is a medium risk; when GRS ≤ 18.17, it is a low risk.

3. A method for predicting the risk of leprosy according to claim 2, characterized in that: In the calculation of the risk score GRS, the number of risk alleles P at each SNP site is i The values were determined as follows: 0 for no risk allele, 1 for heterozygous carrier, and 2 for homozygous carrier.

4. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a computer program, and the computer program is used to implement the method described in any one of claims 2-3.

5. A computing device, characterized in that The computing device includes a memory and a processor. The memory is used to store a computer program, and when the computer program is executed by the processor, the method described in any one of claims 2-3 is implemented.

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