CN117789819B - Construction method of VTE risk assessment model - Google Patents
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- CN117789819B CN117789819B CN202410212423.3A CN202410212423A CN117789819B CN 117789819 B CN117789819 B CN 117789819B CN 202410212423 A CN202410212423 A CN 202410212423A CN 117789819 B CN117789819 B CN 117789819B
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- 208000004043 venous thromboembolism Diseases 0.000 title claims abstract description 77
- 238000012502 risk assessment Methods 0.000 title claims abstract description 24
- 238000010276 construction Methods 0.000 title claims abstract description 9
- 238000012549 training Methods 0.000 claims abstract description 24
- 238000012360 testing method Methods 0.000 claims abstract description 11
- 230000003044 adaptive effect Effects 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 12
- 201000010099 disease Diseases 0.000 claims description 10
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 10
- 238000011160 research Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 13
- 108090000623 proteins and genes Proteins 0.000 abstract description 4
- 238000001514 detection method Methods 0.000 abstract description 2
- 238000004458 analytical method Methods 0.000 description 5
- 230000002068 genetic effect Effects 0.000 description 5
- 238000012795 verification Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000003234 polygenic effect Effects 0.000 description 4
- 108700028369 Alleles Proteins 0.000 description 3
- 238000007477 logistic regression Methods 0.000 description 3
- 206010014522 Embolism venous Diseases 0.000 description 2
- 208000010378 Pulmonary Embolism Diseases 0.000 description 2
- 238000001604 Rao's score test Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 238000003205 genotyping method Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000000205 computational method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000013090 high-throughput technology Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000002773 nucleotide Substances 0.000 description 1
- 125000003729 nucleotide group Chemical group 0.000 description 1
- 230000001717 pathogenic effect Effects 0.000 description 1
- 102000054765 polymorphisms of proteins Human genes 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
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Abstract
The invention provides a construction method of a VTE risk assessment model, which belongs to the field of gene detection and comprises the following steps: s1, collecting SNPs data related to VTE; s2, building a training queue, and designing a two-stage model, wherein the training queue comprises S2.1, and testing SNP risk scores based on an adaptive algorithm; s2.2, adopting a multiple supervision learning algorithm, and starting from a modeling stage of a training queue, stacking gradually to sort SNPs data; s3, obtaining a VTE risk feature set; and constructing and obtaining a VTE risk assessment model. The technical problems that the PRS model directly constructed based on the GWAS in the prior art depends on the related SNP of the VTE and has large quantity, low prediction accuracy and poor clinical application effect are solved.
Description
Technical Field
The invention belongs to the field of gene detection, and particularly relates to a construction method of a VTE risk assessment model.
Background
With the progress of high-throughput technology, the rapid development of whole genome association research (GWAS) provides a new idea for researching the polygenic genetic structure of complex traits. In the last decade, thousands of GWAS have successfully identified thousands of Single Nucleotide Polymorphisms (SNPs) associated with complex human features and diseases [ PMID:28686856]. However, conventional GWAS only estimates the effect of a single site on phenotype by genotyping individuals and mining SNP distribution differences than in case control studies, not consistent with the complex disease mechanism of polygenic effects [ PMID:29562348]. Therefore, the GWAS result is evaluated by using a polygenic risk score (Polygenic risk score, PRS) model, which is beneficial to predicting the genetic risk of complex diseases in clinical application.
Venous thromboembolism (Venous thromboembolism, VTE) is a multifactorial complex disease with a genetic rate of about 50%, indicating that a significant portion of the VTE risk is driven by genetics (PMID: 12859034). In recent years, large-scale GWAS have performed multiple risk site identifications [ PMID:31676865] [ PMID:31420334] in western countries for subjects of european or African American (AA) descent, and several computational methods have been applied in VTE for PRS analysis, such as PRSice, linear superposition, logistic regression, survival analysis, etc. Defects in the GWAS principle in mining directly related genotyping exacerbate ethnicity differences, resulting in few overlap of 288 SNPs of PRS model with reports based on european and american populations. The recently proposed PRS model screens 53 VTE-related SNPs on the basis of the existing literature and also shows a certain fitness [ PMID:37619711] in Chinese crowd simulation.
Although VTE-associated SNPs are known to be validated in the Chinese population, model set-up is largely limited to parameters that can demonstrate the advantages of each reported SNP, without consideration of potential Chinese population-specific genetic factors. In addition, the PRS model with a large number of remarkable SNPs is constructed directly based on the GWAS, so that false positives are easy to occur, and the clinical application effect is poor. The main stream feature dimension reduction method is mainly based on supervised model training or GWAS dominance ratio gradual overlap SNPs ordering, the former depends on algorithm selection, and the latter excessively emphasizes the direct influence of single SNPs.
Therefore, to meet the practical needs, the present study aims to fuse potential chinese crowd-specific SNPs with SNPs reported in global studies, building a highly generalized PRS model featuring fewer VTE-related SNPs.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to provide a construction method of a VTE risk assessment model. The method solves the technical problems that the PRS model directly constructed based on the GWAS in the prior art depends on the related SNPs of the VTE, and has large quantity, low prediction accuracy and poor clinical application effect.
The invention provides a technical scheme that: the construction method of the VTE risk assessment model comprises the following steps:
S1, collecting SNPs data related to VTE;
s2, a training queue is established, wherein the training queue comprises a VTE case group and a control group, and a two-stage model is designed based on the training queue and comprises the following steps:
S2.1, testing SNP risk scores based on an adaptive algorithm;
S2.2, adopting a multiple supervision learning algorithm, and starting from a modeling stage of a training queue, stacking gradually to sort SNPs data;
S3, obtaining a VTE risk feature set;
s4, constructing and obtaining a VTE risk assessment model.
Preferably, the VTE risk feature set includes one or more of the following SNPs data ：rs12052817、rs7987478、rs9452114、rs182314917、rs77481115、rs73482924、rs375840470、rs140242583、rs79368348、rs4794202、rs114453875、rs17141995、rs76503183、rs758768、rs150363035、rs4843804、rs189032268、rs147567900、rs1321615、rs79753408、rs2307155、rs140972488、rs1966503、rs11022423、rs77487090、rs708362、rs12684476、rs6808492、rs7250473、rs76144234、rs12082852、rs78077609、rs144351340、rs62390610、rs75787368、rs77645935、rs75573695、rs118048568、rs11106986、rs117875424、rs117500272、rs9442580、rs35801946、rs1874320.
Preferably, in step S1, the VTE-related SNPs data includes 288 VTE-related SNPs based on the chinese VTE-related large queue GWAS study and 31 VTE-related SNPs repeated in the chinese population.
Preferably, in step S2, the method further comprises calculating the proportion of SNPs reported in the first 10, 30, 50 and 100 groups ordered by risk score based on the SNP risk score test results.
Preferably, in step S2.2, the filtration is performed at a standard of AUC > 0.7.
Preferably, in step S2.2, the supervised learning algorithm includes Logistic, LASSO, ridge, bayes.
Preferably, in step S2.2, the step stacking is to stack SNPs data step by step in descending order of association level.
Preferably, in step S2, the VTE case group is 622 cases, and the control group is 8853 disease-free controls.
Preferably, in step S3, the VTE case group is 646 cases, and the control group is 8810 disease-free controls.
The beneficial effects are that:
The construction method of the VTE risk assessment model provided by the invention is based on designing a two-stage PRS model for a training queue, and comprises the steps of testing SNP risk scores between a logistic regression algorithm and a punishment regression ordering algorithm respectively, and ordering SNPs data by adopting a multi-supervision learning algorithm from a modeling stage to gradually stack. Objective and deep research is carried out on the large-scale Chinese VTE queue, and on the basis of the GWAS result of the large-scale Chinese VTE queue, the prediction effects of different VTE risk site sets, different quantitative genetic correlation estimation methods and different VTE risk assessment models in a real environment are compared. According to the construction method, besides the specific VTE risk SNPs of the Chinese crowd found by the existing research, the potential VTE related SNPs and the VTE related SNPs aiming at the Chinese crowd queues in other GWAS researches are added, so that a VTE risk position set in a risk assessment model is enlarged to a certain extent.
The method for constructing the risk assessment model has the advantages that the method can acquire a higher fitting effect by fewer SNPs, and the constructed risk assessment model has more accurate assessment results and more universality.
Drawings
In order that the invention may be more readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings.
FIG. 1 is a schematic diagram of a queue of 288 VTE-related SNPs obtained based on a large-scale queue GWAS study related to VTE in example 1 of the present invention;
FIG. 2 is a schematic diagram 1 showing the result of the validity of the ranking result in example 1 SNPs of the present invention;
FIG. 3 is a schematic diagram of the result of the verification of the effectiveness of the sorting result in example 1 SNPs of the present invention;
FIG. 4 is a training set analysis result of a risk assessment model constructed based on 44 SNPs risk feature data in embodiment 2 of the present invention;
FIG. 5 is a verification set analysis result of a risk assessment model constructed based on 44 SNPs risk feature data in embodiment 2 of the present invention;
Fig. 6 is a test result of the VTE risk assessment model of this embodiment 2.
Detailed Description
The application will be described in detail below with reference to the drawings in connection with embodiments. The principles and features of the present application are described below with reference to the drawings, and it should be noted that embodiments of the present application and features of the embodiments may be combined with each other without conflict. The examples are given solely for the purpose of illustration and are not intended to limit the scope of the application.
Example 1
The embodiment is a method for constructing a VTE risk assessment model, which comprises the following steps:
s1, collecting SNPs data related to VTE; 288 VTE-related SNPs based on a Chinese VTE-related large-scale queue GWAS study and 31 repeated VTE-related SNPs in Chinese population; referring to FIG. 1, 288 VTE-related SNPs obtained based on a China VTE-related Large queue GWAS study; the repeated 31 VTE-related SNPs in Chinese population are obtained based on the prior art published literature;
S2, a training queue is established, wherein the training queue comprises a VTE case group and a control group, the VTE case group is 622 cases, the queue is collected from large-scale pulmonary embolism registration (CURES) research in China, the control group is 8853 disease-free controls, and a two-stage model is designed based on the training queue and comprises the following steps:
S2.1, testing SNP risk scores based on an adaptive algorithm;
S2.2, adopting a multiple supervision learning algorithm, including a Logistic regression (Logistic), a LASSO regression, a Ridge regression (Ridge) and a Bayes algorithm, and sequencing SNPs data by gradually stacking from a modeling stage of a training queue, wherein the gradually stacking is to gradually stack the SNP data in a descending order of association level, and the requirement AUC is more than 0.7;
The proportions of SNPs reported in the first 10, 30, 50 and 100 groups ranked by risk score were calculated based on SNP risk score test results, comparing the ability of different ranking algorithms to mine for weak correlation effects. Referring to the following table 1, the penalty regression model (L2) is better by comparing the ordering results of the different algorithms in parallel. It can be seen that: GLM captures more known genes overall, but because it fits more in china population, pathogenic sites based on analysis of the european and american population are not ranked in the front. In contrast, the algorithm of L2 regularization can better capture nonlinear correlations.
TABLE 1 SNPs composition for the first 100 inclusion in different ranking algorithms
S3, obtaining a VTE risk feature set;
the set of VTE risk features includes one or more SNP signature data in rs12052817、rs7987478、rs9452114、rs182314917、rs77481115、rs73482924、rs375840470、rs140242583、rs79368348、rs4794202、rs114453875、rs17141995、rs76503183、rs758768、rs150363035、rs4843804、rs189032268、rs147567900、rs1321615、rs79753408、rs2307155、rs140972488、rs1966503、rs11022423、rs77487090、rs708362、rs12684476、rs6808492、rs7250473、rs76144234、rs12082852、rs78077609、rs144351340、rs62390610、rs75787368、rs77645935、rs75573695、rs118048568、rs11106986、rs117875424、rs117500272、rs9442580、rs35801946、rs1874320. Specific information is given in table 2 below.
TABLE 2 SNP characterization data for VTE Risk characterization set
Wherein, variant: variants, i.e., SNP site numbering; chr_position: the position of the locus; alleles: an allele; ALT ALLELE: alternative genes.
The VTE risk feature set is validated. Randomly extracting SNPs for modeling, repeatedly extracting 1000 times of 50 groups, and randomly extracting a box diagram of AUC distribution of 1000 groups of 44 SNPs under a linear regression algorithm and a ridge regression algorithm, referring to FIG. 2 and FIG. 3; in fig. 2, train represents the model training effect in the training phase, and test in fig. 3 represents the model test effect in the verification phase; the corresponding points above each box plot represent the estimated effects of the 44-SNPs selected by effect ordering under the algorithm, specific to the corresponding algorithm. The effectiveness, i.e. non-contingency, of the VTE risk feature set of the present invention was demonstrated.
And constructing and obtaining a VTE risk assessment model based on the VTE risk feature set.
Further, verifying the constructed VTE risk assessment model.
And establishing a verification queue comprising a VTE case group and a control group, wherein the VTE case group is 646 cases, the queue collected from large-scale pulmonary embolism registration (CURES) research in China is another group of Chinese people independent of the training queue, and the control group is 8810 non-disease controls for model test.
Example 2
The present embodiment is a VTE risk assessment model, which includes 44 SNPs data obtained by screening in embodiment 1, and a VTE risk assessment model constructed by a Ridge regression (Ridge) algorithm. Referring to fig. 4 and 5, fig. 4 shows that the training set AUC reaches 0.831, and fig. 5 shows that the validation set AUC reaches 0.739. And an evaluation result with higher accuracy is realized. Referring to FIG. 6, the ROC curve for the specific set of 44-SNPs was selected in order under the ridge regression algorithm. The thick line is formed by the circles and the thin line is the model training effect in the training stage, and the thin line is the model testing effect in the verification stage.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (6)
- The construction method of the VTE risk assessment model is characterized by comprising the following steps:S1, collecting SNPs data related to VTE;s2, a training queue is established, wherein the training queue comprises a VTE case group and a control group, and a two-stage model is designed based on the training queue and comprises the following steps:s2.1, testing SNP risk scores based on an adaptive algorithm, and calculating the proportion of SNPs data reported in the first 10, 30, 50 and 100 groups sequenced according to the risk scores based on SNP risk score testing results;S2.2, adopting a multiple supervision learning algorithm, and starting from a modeling stage of a training queue, stacking gradually to sort SNPs data;S3, obtaining a VTE risk feature set;S4, constructing and obtaining a VTE risk assessment model;wherein the VTE risk feature set includes one or more of the following SNPs data ：rs12052817、rs7987478、rs9452114、rs182314917、rs77481115、rs73482924、rs375840470、rs140242583、rs79368348、rs4794202、rs114453875、rs17141995、rs76503183、rs758768、rs150363035、rs4843804、rs189032268、rs147567900、rs1321615、rs79753408、rs2307155、rs140972488、rs1966503、rs11022423、rs77487090、rs708362、rs12684476、rs6808492、rs7250473、rs76144234、rs12082852、rs78077609、rs144351340、rs62390610、rs75787368、rs77645935、rs75573695、rs118048568、rs11106986、rs117875424、rs117500272、rs9442580、rs35801946、rs1874320;The VTE-related SNPs data comprise 288 VTE-related SNPs data obtained based on a China VTE-related large-scale queue GWAS research and 31 repeated VTE-related SNPs data in China crowd.
- 2. The method of constructing a VTE risk assessment model according to claim 1, wherein in step S2.2, filtering is performed with a standard of AUC > 0.7.
- 3. The method according to claim 2, wherein in step S2.2, the supervised learning algorithm comprises Logistic, LASSO, ridge, bayes.
- 4. The method of claim 3, wherein in step S2.2, the stepwise stacking is stacking SNPs data stepwise in descending order of association level.
- 5. The method according to claim 1, wherein in step S2, the VTE case group is 622 cases and the control group is 8853 disease-free controls.
- 6. The method according to claim 1, wherein in step S3, the VTE case group is 646 cases, and the control group is 8810 disease-free controls.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111739641A (en) * | 2020-06-23 | 2020-10-02 | 杭州和壹医学检验所有限公司 | Gastric cancer risk prediction method and system, computer equipment and readable storage medium |
| CN112553327A (en) * | 2020-12-30 | 2021-03-26 | 中日友好医院(中日友好临床医学研究所) | Construction method of pulmonary thromboembolism risk prediction model based on single nucleotide polymorphism, SNP site combination and application |
| CN113012761A (en) * | 2021-04-07 | 2021-06-22 | 中国医学科学院阜外医院 | Method and device for constructing stroke polygene genetic risk comprehensive score and application |
| CN113506594A (en) * | 2021-05-26 | 2021-10-15 | 中国医学科学院阜外医院 | Construction method, device and application of multi-gene genetic risk comprehensive score of coronary heart disease |
| CN113517069A (en) * | 2021-07-13 | 2021-10-19 | 赛福解码(北京)基因科技有限公司 | Method for establishing genetic risk assessment model of focal epilepsy |
| CN113593630A (en) * | 2021-08-23 | 2021-11-02 | 北京果壳生物科技有限公司 | Family coronary heart disease risk assessment and risk factor identification system |
| CN115058511A (en) * | 2022-07-20 | 2022-09-16 | 中日友好医院(中日友好临床医学研究所) | Venous thromboembolism recurrence risk assessment model based on polygenic mutation characteristics, construction method and application thereof |
| CN115295075A (en) * | 2022-07-20 | 2022-11-04 | 北京携云启源科技有限公司 | Construction method of complex disease genetic risk assessment model, model and application thereof |
| WO2022247903A1 (en) * | 2021-05-26 | 2022-12-01 | 中国医学科学院阜外医院 | Polygenic risk score for coronary heart disease, construction method therefor, and application thereof in combination with clinical risk assessment |
| CN116052770A (en) * | 2022-07-20 | 2023-05-02 | 中日友好医院(中日友好临床医学研究所) | VTE risk assessment model based on polygenic mutation, construction method and application |
| CN116287204A (en) * | 2023-02-24 | 2023-06-23 | 中日友好医院(中日友好临床医学研究所) | Application of mutation condition of detection characteristic gene in preparation of venous thromboembolism risk detection product |
| CN116469552A (en) * | 2022-07-28 | 2023-07-21 | 复旦大学附属肿瘤医院 | Method and system for breast cancer polygene genetic risk assessment |
| CN116564421A (en) * | 2023-06-08 | 2023-08-08 | 苏州卫生职业技术学院 | Method for constructing prognosis model related to copper death of acute myelogenous leukemia patient |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200294618A1 (en) * | 2019-03-12 | 2020-09-17 | Ambry Genetics Corporation | Method to perform medical procedures on breast cancer patients guided by an snp derived polygenic risk score |
| US20210118571A1 (en) * | 2019-10-18 | 2021-04-22 | Board Of Trustees Of Michigan State University | System and method for delivering polygenic-based predictions of complex traits and risks |
-
2024
- 2024-02-27 CN CN202410212423.3A patent/CN117789819B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111739641A (en) * | 2020-06-23 | 2020-10-02 | 杭州和壹医学检验所有限公司 | Gastric cancer risk prediction method and system, computer equipment and readable storage medium |
| CN112553327A (en) * | 2020-12-30 | 2021-03-26 | 中日友好医院(中日友好临床医学研究所) | Construction method of pulmonary thromboembolism risk prediction model based on single nucleotide polymorphism, SNP site combination and application |
| CN113012761A (en) * | 2021-04-07 | 2021-06-22 | 中国医学科学院阜外医院 | Method and device for constructing stroke polygene genetic risk comprehensive score and application |
| WO2022247903A1 (en) * | 2021-05-26 | 2022-12-01 | 中国医学科学院阜外医院 | Polygenic risk score for coronary heart disease, construction method therefor, and application thereof in combination with clinical risk assessment |
| CN113506594A (en) * | 2021-05-26 | 2021-10-15 | 中国医学科学院阜外医院 | Construction method, device and application of multi-gene genetic risk comprehensive score of coronary heart disease |
| CN113517069A (en) * | 2021-07-13 | 2021-10-19 | 赛福解码(北京)基因科技有限公司 | Method for establishing genetic risk assessment model of focal epilepsy |
| CN113593630A (en) * | 2021-08-23 | 2021-11-02 | 北京果壳生物科技有限公司 | Family coronary heart disease risk assessment and risk factor identification system |
| CN115058511A (en) * | 2022-07-20 | 2022-09-16 | 中日友好医院(中日友好临床医学研究所) | Venous thromboembolism recurrence risk assessment model based on polygenic mutation characteristics, construction method and application thereof |
| CN115295075A (en) * | 2022-07-20 | 2022-11-04 | 北京携云启源科技有限公司 | Construction method of complex disease genetic risk assessment model, model and application thereof |
| CN116052770A (en) * | 2022-07-20 | 2023-05-02 | 中日友好医院(中日友好临床医学研究所) | VTE risk assessment model based on polygenic mutation, construction method and application |
| CN116469552A (en) * | 2022-07-28 | 2023-07-21 | 复旦大学附属肿瘤医院 | Method and system for breast cancer polygene genetic risk assessment |
| CN116287204A (en) * | 2023-02-24 | 2023-06-23 | 中日友好医院(中日友好临床医学研究所) | Application of mutation condition of detection characteristic gene in preparation of venous thromboembolism risk detection product |
| CN116564421A (en) * | 2023-06-08 | 2023-08-08 | 苏州卫生职业技术学院 | Method for constructing prognosis model related to copper death of acute myelogenous leukemia patient |
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