Pneumoconiosis polygene genetic risk prediction system

Abstract

The invention relates to the technical field of pneumoconiosis diagnosis, in particular to a pneumoconiosis polygene genetic risk prediction system. According to the method, genetic variation related to occupational pneumoconiosis susceptibility is deeply studied, a plurality of genetic variation sites highly related to pneumoconiosis onset risks are screened out, and a multi-gene genetic risk scoring model is established in combination with macroscopic factors. The model not only considers the influence of the genetic background on the disease, but also integrates the effects of external environmental factors, thereby providing more comprehensive risk assessment. The system solves the technical problem of lack of a system for accurately predicting the incidence probability of pneumoconiosis in the prior art, and fills the blank in related fields. By inputting personal genetic information and macroscopic factor data, the system can generate personalized risk scores, help doctors and patients to better understand potential health risks and take corresponding prevention measures, and has important public health significance and practical application value.

Description

Technical Field

[0001] The present invention relates to the technical field of pneumoconiosis diagnosis, and in particular to a pneumoconiosis multi-gene genetic risk prediction system. Background Art

[0002] Pneumoconiosis is an occupational lung disease caused by chronic 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 (causing silicosis), coal dust (causing coal miner's pneumoconiosis), and fibers and particles of 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. Statistics from 2012 show that 88.28% of reported occupational disease cases were attributable to pneumoconiosis, with over 50% of these cases directly related to underground mining. Underground mining occupational exposures are complex, involving multiple factors, including silica, metal, or coal (carbon) dust generated during tunnel drilling, mining, roof bolting, and transportation. Consequently, the majority of cases of coal miner's pneumoconiosis (CWP) and iron miner's pneumoconiosis (IWP) are associated with silicosis. Exposure to silica dust has been reported to be the primary cause of pneumoconiosis. However, more than 23 million workers in China are still exposed to dust in their occupational environments. Recently, a large prospective cohort study showed that long-term exposure to silica dust is associated with increased mortality, mainly including deaths from respiratory diseases, cardiovascular diseases, and lung cancer.

[0003] Although occupational exposure to silica dust is the primary cause of pneumoconiosis, significant individual differences in pneumoconiosis susceptibility can be observed 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. In recent years, researchers have attempted to use single-gene genetic variant detection to predict the risk of pneumoconiosis. This allows for interventions to be implemented in high-risk individuals to prevent the development of pneumoconiosis. However, single-gene testing has limited accuracy and is insufficient for practical applications. Genetic risk scores (PRSs) based on genetic variation have shown promising results in improving risk prediction for complex diseases. The objectives of this study were to identify and characterize genetic variants that could be used for pneumoconiosis detection and to construct a novel polygenic risk prediction system for pneumoconiosis based on PRSs, thereby improving the accuracy of pneumoconiosis prediction. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-gene genetic risk prediction system for pneumoconiosis to solve the technical problem that the prior art lacks a risk prediction system that can accurately predict the incidence rate of pneumoconiosis.

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

[0006] A pneumoconiosis polygenic genetic risk prediction system includes a risk prediction unit, which is used to output a polygenic risk score for pneumoconiosis of a subject through a polygenic risk scoring model;

[0007] The polygenic risk score model includes genetic factors, and its formula is shown in Formula I:

[0008]

[0009] Among them, ModelScore i represents the polygenic risk score of pneumoconiosis in the subjects; a ij W1 represents the allele copy number of the j-th SNP of the subject; ij is the weight of the j-th SNP of the subject.

[0010] Furthermore, the value range of N is 1-23;

[0011] 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.

[0012] Furthermore, the allele copy number a of the SNP ij The value range is 0, 1 or 2.

[0013] Furthermore, the SNP sites used consisted 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

[0014] Furthermore, in the polygenic risk scoring model, the weight value W1 of the SNP site is 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.

[0015] Furthermore, the polygenic risk scoring model also includes macro factors, and its formula is shown in Formula II:

[0016]

[0017] 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.

[0018] Furthermore, the value of M ranges from 1 to 2; macro factors include smoking status and / or dust exposure time.

[0019] Furthermore, macro factors consisted of smoking status and dust exposure duration;

[0020] 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;

[0021] 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.

[0022] Furthermore, in the polygenic risk scoring model, the weight value W2 of the macro factor smoking status is i1 The weight value of the macro factor dust exposure time is -0.10719; W2 i2 It is -0.065590.

[0023] 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;

[0024] Comparing the subject's pneumoconiosis polygenic risk score with the predicted critical value, the risk prediction unit outputs the prediction result;

[0025] If the subject's pneumoconiosis polygenic risk score is ≤ the predicted cutoff value, the risk of developing pneumoconiosis is low;

[0026] If the subject's pneumoconiosis polygenic risk score is greater than the predicted critical value, the risk of developing pneumoconiosis is high.

[0027] The principle and beneficial effects of this technical solution are:

[0028] This paper provides a polygenic genetic risk prediction system for pneumoconiosis. Based on a polygenic risk score (PRS) model, this system integrates information from multiple individual single-nucleotide polymorphisms (SNPs) and macro-environmental factors to predict and assess the risk of developing pneumoconiosis. This system not only integrates the results of multidisciplinary research in genetics, statistics, and epidemiology, but also overcomes the limitations of traditional single-environment exposure assessments in predictive accuracy.

[0029] One of the keys to implementing this technical solution is the selection of SNP sites for model building. Although existing studies have identified multiple genetic variations associated with pneumoconiosis susceptibility through genome-wide association analysis (GWAS), these candidate SNPs are often unable to be directly converted into a risk scoring system with practical value in actual modeling. Dozens to hundreds of disease-related SNPs are usually identified in GWAS studies, but the effects of their individual sites are small. If they are included in the model without screening, they may cause "noise" interference, "overfitting" or model instability. In response to the above challenges, the present invention has finally identified 23 core SNP sites with significant predictive power from the 64 candidate SNPs initially screened out through a large amount of data mining and experimental research (and some of the SNP sites used to build the model do not belong to the scope of the 64 candidate SNPs). The selection of these sites does not simply rely on previous literature reports or biological function annotations, but is through strict statistical screening, model effectiveness testing and ROC curve area under the curve (AUC) optimization to ensure their effectiveness and accuracy in the prediction model.

[0030] Prior to the present invention, although studies have attempted to use single-gene genetic variation detection or construct risk scoring models based on a wider set of candidate SNPs to predict the risk of pneumoconiosis, their accuracy and reliability are often limited. By screening these 23 SNP sites, our polygenic genetic risk score (PRS) model achieved a diagnostic efficacy of an AUC value of 0.79. The polygenic risk scoring model constructed by the present invention not only takes genetic factors into account, but also incorporates two key macro factors, smoking status and dust exposure time, into the scoring system, thereby achieving a more comprehensive and personalized risk prediction. When the two macro factors of smoking status and dust exposure time are combined, the AUC value of the model is further improved to 0.82. This significant improvement in predictive performance was not foreseen by previous technologies.

[0031] The 23 SNPs ultimately selected included several new loci not widely reported in previous literature, such as rs2292832, rs2289477, rs689466, rs20417, rs2227956, and rs361525. Experiments showed that the model's predictive power declined significantly without these SNPs, demonstrating that these loci are crucial for accurate prediction of pneumoconiosis and that their inclusion significantly enhanced the model's performance. By limiting the number of SNPs to 23, the team ensured model prediction accuracy while reducing the complexity and cost of genetic testing, facilitating large-scale application.

[0032] In summary, this invention, through a scientific and rational SNP site screening and model building strategy, has successfully developed a multi-gene genetic risk prediction system for pneumoconiosis with high prediction accuracy, good stability, and broad application prospects. This system not only addresses the lack of accurate prediction tools in the existing technology but also provides a practical technical means for early screening of occupational pneumoconiosis, identification of high-risk populations, and personalized intervention, with significant social benefits and clinical application value.

[0033] This solution has the following technical advantages:

[0034] Improved prediction accuracy: Compared with traditional risk assessment methods that rely solely on environmental and occupational exposure, the system provided by the present invention significantly improves the accuracy and reliability of pneumoconiosis risk prediction by integrating genetic information.

[0035] Personalized medical applications: It provides the possibility of realizing personalized medicine, enabling medical resources to be more effectively allocated to high-risk groups, thereby improving the effectiveness of public health management.

[0036] Promote early intervention: Early identification of high-risk individuals can help take timely and effective preventive measures, reduce the incidence of pneumoconiosis, and alleviate the social medical burden.

[0037] In summary, the present invention provides a new risk prediction tool for pneumoconiosis by innovatively combining genetic factors with macro factors (23 specific SNP sites and 2 macro factors), which solves the problem of the lack of accurate prediction methods for the incidence of pneumoconiosis in the existing technology and has important theoretical significance and practical application value.

[0038] Figures in the specification

[0039] Figure 1 2 are ROC curves of different prediction models in Example 2. DETAILED DESCRIPTION

[0040] 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.

[0041] Example 1: SNP sites used in a polygenic genetic risk prediction system for pneumoconiosis

[0042] This technical solution investigated and screened susceptibility genetic variants associated with pneumoconiosis, providing candidate SNP sites for the construction of a Chinese population-specific polygenic genetic risk score (PRS) for pneumoconiosis, and providing ideas for the identification of 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 1. These SNP sites all met the following conditions: the genetic variation association P value was <0.05, and the frequency of the effect allele in the control population was greater than 0.01. However, the direct application of the above 62 SNP sites to the construction of the polygenic genetic risk score model for pneumoconiosis did not yield ideal prediction accuracy, and further screening, adjustment, and research of the SNP sites are needed.

[0043] After further site-association studies, 23 SNP sites were finally used for the prediction and diagnosis of pneumoconiosis, as shown in Table 2.

[0044] Table 1: 62 SNPs associated with pneumoconiosis

[0045]

[0046]

[0047] Table 2: 23 SNP sites used for the prediction and diagnosis of pneumoconiosis

[0048]

[0049] In Table 2, 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 less common 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 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.

[0050] Comparing Table 2 with Table 1, it is clear that not all of the 62 candidate SNPs associated with pneumoconiosis obtained through screening of published literature are suitable for pneumoconiosis risk prediction. Table 2 incorporates only the 18 SNPs from Table 1 into the model, and additional SNPs for pneumoconiosis prediction are added, such as rs2292832, rs2289477, rs689466, rs20417, rs2227956, and rs361525. Selecting appropriate SNPs for model construction is crucial for model prediction accuracy. As can be seen from the above, while existing technologies can provide some information on SNPs associated with pneumoconiosis, selecting valid and applicable SNPs from this vast amount of information is challenging. Furthermore, the aforementioned screening approach (screening from previously published information) can also miss some SNPs that play a significant role in building risk prediction models. After extensive research, the SNPs for pneumoconiosis prediction, shown in Table 2, were ultimately identified.

[0051] Example 2: Construction of a polygenic genetic risk prediction system for pneumoconiosis

[0052] By inputting genetic factor data (SNP locus data) or genetic factor data and macro-factor data into the risk prediction unit of the polygenic genetic risk prediction system for pneumoconiosis, and calculating through the risk prediction model (PRS model, polygenic risk score model, Polygenic Risk Score) formula, a risk score can be obtained, thereby determining the polygenic genetic risk of pneumoconiosis. Genetic factor data (SNP locus data) can be obtained through conventional sequencing methods (first-generation sequencing) or other methods. Macro-factor data can be obtained through conventional epidemiological statistical methods. The risk prediction model formula of the polygenic genetic risk prediction system for pneumoconiosis is now explained.

[0053] (1) Sample situation

[0054] The samples were from our group's GWAS database, with 202 cases and 198 controls. Among the 202 cases, 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 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).

[0055] (2) Risk prediction model formula

[0056] The formula of the scoring model of the risk prediction unit is shown in Formula I (genetic factors):

[0057]

[0058] Among them, ModelScore i represents the polygenic risk score of the i-th individual. i represents the i-th individual, j represents 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 SNP locus, the copy number of the effect / variant allele associated with the disease is 0 / 1 / 2). The number of SNPs used for scoring is N. Based on the results of the study in Example 1, N = 23 (j = 1, 2, 3, ... 23), and the SNP loci shown in Table 2 are used. Formula I only considers genetic factors to assess the risk of pneumoconiosis. The copy number of the SNP allele can be obtained by conventional methods such as first-generation sequencing in the prior art, or by other conventional methods in the prior art to obtain information on the copy number of the SNP allele in the sample.

[0059] The formula of the scoring model of the risk prediction unit can also be shown as Formula II (genetic factors + macro factors):

[0060]

[0061] Among them, ModelScore i represents the polygenic risk score of the i-th individual. i represents the i-th individual, j represents 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 number of the effect allele associated with the disease is 0 / 1 / 2). The number of SNPs used for scoring is N. According to the results of the study in Example 1, N=23 is preferred, and the SNP sites shown in Table 2 are used.

[0062] K represents the kth macro factor, W2 ik is the weight of the kth macro factor of the ith individual, b ikThen 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.

[0063] More specifically, for 23 SNP sites and macro factors, the W1 ij Weight and W2 ik The weight values ​​are shown in Table 3. ij The specific value of the weight is derived from the variable risk effect value in the largest sample size pneumoconiosis GWAS published in the Chinese population. ik The weights (for the two macro factors) are derived from regression analysis of a large number of samples. The corresponding regression coefficients are calculated to obtain the weights of the two macro factors. The statistically calculated weights are used as constants in the formula of the risk prediction unit's scoring model.

[0064] Table 3: Weight values ​​of the PRS model

[0065]

[0066] (3) Model effectiveness

[0067] The polygenic risk score (ModelScore) of each sample was calculated according to the above formula (Formula I or Formula II). i). The model performance is evaluated by the ROC curve (Receiver Operating Characteristic Curve). TPR (True Positive Rate) and FPR (False Positive Rate) are calculated at different classification thresholds, and these points are plotted with FPR as the X-axis and TPR as the Y-axis. The area under the ROC 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. More specifically, the following criteria are generally used to judge the diagnostic effect of the model: AUC ≥ 0.7, the diagnostic efficacy of the model is feasible, and the larger the value, the more ideal the model efficacy; 0.7> AUC> 0.5, the diagnostic efficacy of the model is not ideal.

[0068] Substituting the weight value into formula I, we obtain formula I-I, which is as follows:

[0069] The polygenic risk score of the i-th subject:

[0070] ModelScore i =-0.210721031315653a i1 -0.210721031315653a i2 +0.636576829071551a i3

[0071] +0.27002713721306a i4 +0.198850858745165a i5 +0.371563556432483a i6

[0072] -0.415515443961666a i7 -0.23572233352107a i8 -0.544727175441672a i9

[0073] -0.27443684570176a i10 -0.198450938723838a i11 +0.438254930931155a i12

[0074] -0.23572233352107a i13 +-0.127833371509885a i14 +2.74791173452734a i15

[0075] -0.693147180559945a i16 +0.806475865866949a i17 +0.774727167552368a i18

[0076] -0.527632742082372a i19 +0.887891257352457a i20 +0.198850858745165a i21

[0077] -0.22314355131421a i22 +1.33236601909433a i23 .

[0078] The corresponding test data of the aforementioned 202 patients + 198 controls (a ij The polygenic risk score of each patient can be calculated by substituting the polygenic risk score of each patient (value: number of allele copies of SNP) into the above formula Ⅰ-Ⅰ. Then, according to the polygenic risk score value 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 according to the AUC value of the ROC curve. The AUC value of the model shown in formula Ⅰ-Ⅰ is specifically 0.79, and the corresponding ROC curve can be seen in Figure 1 (Model: 23SNP). It can be seen that the diagnostic effect of the model shown in formula I-I is acceptable and can be used to predict pneumoconiosis. Medical staff can 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 cutoff value is determined by substituting the known sample data into the formula, calculating the Youden's Index according to the ROC curve, and selecting 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 normal from abnormal results. ModelScore i The test results below the Cutoff value are considered to be the subjects with a low risk of pneumoconiosis, while the ModelScore i A test result higher than the Cutoff value is considered to indicate that the subject has a higher risk of pneumoconiosis.

[0079] Substituting the weight values ​​into Formula II, we obtain Formula II-I, which is as follows:

[0080] The polygenic risk score of the i-th subject:

[0081] ModelScore i =-0.210721031315653a i1 -0.210721031315653a i2 +0.636576829071551a i3

[0082] +0.27002713721306a i4 +0.198850858745165a i5 +0.371563556432483a i6

[0083] -0.415515443961666a i7 -0.23572233352107a i8 -0.544727175441672a i9

[0084] -0.27443684570176a i10 -0.198450938723838a i11 +0.438254930931155a i12

[0085] -0.23572233352107a i13 +-0.127833371509885a i14+2.74791173452734a i15

[0086] -0.693147180559945a i16 +0.806475865866949a i17 +0.774727167552368a i18

[0087] -0.527632742082372a i19 +0.887891257352457a i20 +0.198850858745165a i21

[0088] -0.22314355131421a i22 +1.33236601909433a i23 -0.10719b i1 -0.065590b i2 .

[0089] Substituting the corresponding test data of the aforementioned 202 patients + 198 controls (aij value: number of allele copies of SNP; bi1 value: whether smoking; bi2 value: dust exposure time) into the above formula II-I, 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 suffering from pneumoconiosis), the ROC curve is obtained. The predictive power of the above model is evaluated according to the AUC value of the ROC curve. The AUC value of the model shown in formula II-I is specifically 0.82, and the corresponding ROC curve can be found in Figure 1 (Model: Smoke+DustTime+23SNP) It can be seen that the diagnostic effect of the model shown in Formula II-I is relatively ideal and can be used for risk prediction of pneumoconiosis.

[0090] In addition, if only two 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 1 (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.

[0091] It can be seen that the risk prediction model formulas (Formula I, Formula II, Formula I-I, Formula II-I) 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.

[0092] Comparative Example 1: Effectiveness evaluation of scoring models constructed based on different SNP sites

[0093] 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.

[0094] Test 1: Use the 62 SNP sites shown in Table 1 to establish a polygenic risk scoring model and a scoring model for the risk prediction unit (i.e., the form of Formula I). ​​The weight values ​​of the 62 SNP sites are directly based on the variable risk effect values ​​in the largest sample size pneumoconiosis GWAS in the Chinese population reported in the prior art, which will not be described in detail here. The corresponding test data of the aforementioned 202 patients + 198 controls (a ij Substituting the ROC curve (value: SNP allele copy number) into the formula established in this test, the model effectiveness was evaluated using the AUC value of <0.7. This shows that the prediction effect of the scoring model established using 62 SNP loci is not ideal and is inferior to the scoring model established using 23 SNP loci in this technical solution. Therefore, the risk prediction model in the risk prediction unit of this solution should be constructed using the 23 SNP loci shown in Table 2.

[0095] Test 2: Use the SNP sites in Table 2 except rs2292832, rs2289477, rs689466, rs20417, rs2227956, and rs361525 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 SNP sites 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 1). Of the 23 SNP sites in this patent solution, only 17 SNP sites in Table 1 were used to add to the model, and additional SNP sites for pneumoconiosis prediction (the aforementioned 6 sites) were added.

[0096] Specifically, the new model obtained by omitting the terms corresponding to the above 6 SNP sites in formula Ⅰ-Ⅰ (omitting a i4 Item, a i8 Item, ai13 Item, a i14 Item, a i15 Item, a i23 Item). The corresponding test data of the aforementioned 202 patients + 198 controls (a ij Substituting the AUC (value: SNP allele copy number) into the formula established for this test yielded a receiver operating characteristic (ROC) curve to evaluate model effectiveness. The AUC value for this test model was <0.65. This demonstrates that the inclusion of the six SNPs as genetic factors in this protocol and the subsequent model development is crucial to the model's diagnostic and predictive efficacy. These SNPs are not readily available through conventional screening (see Example 1).

[0097] Test 3: Use the six SNP sites rs2292832, rs2289477, rs689466, rs20417, rs2227956, and rs361525 to establish a polygenic risk scoring model and establish a scoring model for the risk prediction unit (i.e., the form of Formula I). ​​That is, the new model obtained by retaining the above six SNP sites in Formula I-I (retaining a i4 Item, a i8 Item, a i13 Item, a i14 Item, a i15 Item, a i23 Item, other items are omitted). The corresponding test data of the aforementioned 202 patients + 198 controls (a ij Substituting the AUC (value: SNP allele copy number) into the formula established for this test yielded a receiver operating characteristic (ROC) curve to assess model effectiveness. The AUC for this model was <0.6. This demonstrates that using only the six SNPs in the model is not ideal for risk prediction. This suggests that all 23 SNPs need to be combined for model construction.

[0098] In addition, the inventors also tried to add the SNP sites in Table 1 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 1 but not in Table 2 to construct multiple models), the AUC values ​​of the ROC curves of the obtained multiple models were difficult to significantly improve on the basis of the AUC value of Formula I-I. Therefore, using the 23 SNP sites in this solution to construct a prediction model is the best way. In this way, the risk of pneumoconiosis can be effectively predicted, the complexity of the experimental operation can be reduced, and effective risk prediction can be achieved by detecting the copy number of as few SNP sites as possible.

[0099] 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.

[0100] The above is only an embodiment of the present invention, and the common knowledge such as the specific structure and characteristics of the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several variations and improvements can be made without departing from the structure of the present invention, and these should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A polygenic genetic risk prediction system for pneumoconiosis, characterized by: It includes a risk prediction unit, which is used to output a polygenic risk score for pneumoconiosis of a subject 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 j-th SNP of the subject; ij is the weight of the j-th SNP of the subject.

2. The pneumoconiosis multi-gene genetic risk prediction system according to claim 1, characterized in that: The value range of N is 1-23; 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.

3. The pneumoconiosis multi-gene genetic risk prediction system according to claim 2, characterized in that: Allele copy number of SNP a ij The value range is 0, 1 or 2.

4. The polygenic genetic risk prediction system for pneumoconiosis according to claim 3, characterized in that: The SNP sites used are rs3748067, rs8193036, rs4691896, rs2292832, rs2672794, rs12812500, rs2067051, rs2289477, rs26538, rs1864182, rs510432, rs7195830, rs689466, rs20417, rs2227956, rs1800470, rs11466345, rs73329476, rs4320486, rs117626015, rs1539019, rs2243250, and rs361525 5. The pneumoconiosis multi-gene genetic risk prediction system according to claim 4, characterized in that: In the polygenic risk scoring model, the weight value W1 of the SNP site is 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.

6. The pneumoconiosis multi-gene genetic risk prediction system according to any one of claims 1 to 5, 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.

7. The pneumoconiosis multi-gene genetic risk prediction system according to claim 6, characterized in that: The value of M ranges from 1 to 2; macro factors include smoking status and / or dust exposure time.

8. The pneumoconiosis multi-gene genetic risk prediction system according to claim 7, characterized in that: Macro factors consist of smoking status and dust exposure duration; 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.

9. The pneumoconiosis multi-gene genetic risk prediction system according to claim 8, characterized in that: In the polygenic risk scoring model, 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.

10. The pneumoconiosis multi-gene genetic risk prediction system according to claim 9, characterized in that: Substitute the modeling sample data into the polygenic risk scoring model and determine the prediction critical value of the polygenic risk scoring model according to the Youden index; Comparing the subject's pneumoconiosis polygenic risk score with the predicted critical value, the risk prediction unit outputs the prediction result; If the subject's pneumoconiosis polygenic risk score is ≤ the predicted cutoff value, the risk of developing pneumoconiosis is low; If the subject's pneumoconiosis polygenic risk score is greater than the predicted critical value, the risk of developing pneumoconiosis is high.

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