CN117778565A - Detection kit for VTE risk assessment and application - Google Patents
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- 208000004043 venous thromboembolism Diseases 0.000 title claims abstract description 73
- 238000001514 detection method Methods 0.000 title claims abstract description 33
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- 238000012360 testing method Methods 0.000 claims description 10
- 238000000034 method Methods 0.000 claims description 9
- 238000003205 genotyping method Methods 0.000 claims description 6
- 239000000523 sample Substances 0.000 claims description 6
- 102100037529 Coagulation factor V Human genes 0.000 claims description 4
- 108010014172 Factor V Proteins 0.000 claims description 4
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- 102100027378 Prothrombin Human genes 0.000 claims description 4
- 239000003153 chemical reaction reagent Substances 0.000 claims description 4
- 229940039716 prothrombin Drugs 0.000 claims description 4
- 102200142609 rs6025 Human genes 0.000 claims description 4
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- 238000001604 Rao's score test Methods 0.000 claims description 3
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract
The invention provides a detection kit for risk assessment of VTE (human immunodeficiency virus), belonging to the field of gene detection, wherein SNP (Single nucleotide polymorphism) risk feature sets related to VTE are taken as detection molecules of the kit, and the SNP risk feature sets are 44 SNPs feature data related to the VTE. The technical problems that the detection kit for VTE risk assessment in the prior art needs large SNPs, has low 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 detection kit for VTE risk assessment and application thereof.
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 estimate 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]. Thus, the use of a multigenic risk score (Polygenic risk score, PRS) model to evaluate GWAS results is beneficial for predicting complex disease genetic risk in clinical applications.
Venous thromboembolism (Venous thromboembolism, VTE) is a complex disease caused by multiple factors, with a genetic rate of about 50%, indicating that a significant portion of the risk of VTE 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. Consistently, in multiple PRS models, the accuracy of the east Asian population was reduced by about 50% compared to the prediction of European Ancestry (EA), suggesting that unmatched genetic factors lead to poor transferability of model parameters from Europe to Asian population [ PMID:31740837]. In our analysis, the largest GWAS (including 1268 cases and 17663 controls) in chinese han population found potential EA-specific SNPs, and a population-specific PRS model was constructed by 288 VTE-associated SNPs, innovatively expanding the genetic background associated with VTE. However, the deficiencies of GWAS principles 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.
Thus, to meet the practical needs, the present study aims to fuse potential chinese population-specific SNPs with SNPs reported in global studies, enabling the prediction or assessment of highly generalized risk of VTE disease by featuring fewer VTE-related SNPs.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to provide a detection kit for VTE risk assessment and application thereof. The technical problems that in the prior art, the detection kit for VTE risk assessment needs large quantity of SNPs, low accuracy and poor clinical application effect are solved.
The invention provides a technical scheme that: the detection kit for the risk assessment of the VTE comprises a SNP risk feature set related to the VTE, wherein the SNP risk feature set is a combination of 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 and 1874320.
Preferably, the detection molecule further comprises a SNP site rs1799963 of the coagulation factor V Leiden and a SNP site rs6025 of prothrombin G20210A.
Preferably, the kit further comprises a reagent for detecting genotyping of each SNP site in the detection molecule.
The invention provides another technical scheme that: the application of the SNP risk feature set in preparing a VTE risk assessment product, wherein the SNP risk feature set is the SNP risk feature set related to the VTE.
Preferably, the product comprises a chip, probe or VTE risk assessment model.
Preferably, the screening method of the SNP risk feature set 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;
a set of VTE risk features is obtained.
Preferably, in step S2, the method further comprises calculating the proportion of SNPs data reported in the first 10, 30, 50 and 100 sets of risk score ranks based on SNP risk score test results.
Preferably, in step S2.2, the filtration is performed with a standard of AUC >0.7; the supervised learning algorithm comprises logics, LASSO, ridge and Bayes.
Preferably, in step S2.2, the step stacking is to stack SNPs data step by step in descending order of association level.
The beneficial effects are that:
the detection kit for the VTE risk assessment provided by the invention takes the SNP risk feature set related to VTE as a detection molecule of the kit, obtains higher fitting effect by fewer SNPs features, and prepares related assessment or prediction products, including the kit and a chip. The kit targets 44 SNPs risk characteristics, improves the accuracy of genetic risk of venous thromboembolism, and provides new ideas, strategies and choices for disease risk assessment, prevention, clinical guidance and the like of venous thromboembolism. The monitoring result is more accurate and has more universality. The risk assessment model constructed by the invention 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 the result of proving the validity of SNPs sequencing results according to the invention 1;
FIG. 2 is a schematic diagram of the result of proving the validity of SNPs sequencing results according to the present invention 2;
FIG. 3 is a training set analysis result of a risk assessment model constructed based on 44 SNPs risk feature data in embodiment 3 of the present invention;
FIG. 4 is a verification set analysis result of a risk assessment model constructed based on 44 SNPs risk feature data in embodiment 3 of the present invention;
fig. 5 is a test result of the VTE risk assessment model of this embodiment 3.
Detailed Description
The invention will be described in detail below with reference to the drawings in connection with embodiments. The principles and features of the present invention are described below with reference to the drawings, and it should be noted that the embodiments 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 invention.
The invention relates to a detection kit for evaluating the risk of venous thromboembolism, which takes SNP risk feature set related to VTE as detection molecule of the kit, the SNP risk feature set is rs12052817, rs7987478, rs9452114, rs182314917, rs77481115, rs73482924, rs375840470, rs140242583, rs79368348 rs4794202, rs114453875, rs17141995 rs4794202, rs114453875, rs17141995 rs17141995, rs 17141995.
The 44 SNPs feature data are shown in Table 1 below.
TABLE 1 VTE-associated 44 SNPs characterization data
Wherein, variant: variants, i.e., SNP site numbering; chr_position: the position of the locus; alles: an allele; alt Allele: alternative genes.
Further, the detection molecules in the kit also comprise an SNP locus rs1799963 of the coagulation factor V Leiden and an SNP locus rs6025 of the prothrombin G20210A.
The detection molecules are detected by the kit, wherein the detection molecules comprise SNP risk feature sets related to VTE, and the high specificity and the high sensitivity of the detection molecules bring more efficient and convenient superiority for predicting or evaluating the risk of VTE; support is provided to help clinicians quickly and accurately evaluate risk of VTE. The SNP risk feature set related to the VTE is obtained by screening by the following method:
s1, collecting SNPs data related to VTE; 288 VTE-related SNPs based on global GWAS study and 31 VTE-related SNPs repeated in Chinese population; 288 VTE-associated SNPs obtained for global GWAS-based studies; 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 Table 2 below, by comparing the ordering results of the different algorithms in parallel, the penalty regression model (L2) is better. 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 2 SNPs composition for the first 100 inclusion in different ranking algorithms
Thereby, a set of SNP risk features related to VTE is obtained.
Further, a set of SNP risk features associated with the VTE 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. 1 and FIG. 2; in fig. 1, train represents the model training effect in the training phase, and test in fig. 2 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. Therefore, the random method extracts 44 SNPs for modeling test, and the training test effect is poor. The validity, namely non-contingency, of the SNP risk feature set related to VTE obtained by the invention is demonstrated.
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 step by step. 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. 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, and 44 SNPs characteristic data are obtained by adopting the screening method, so that the result is more accurate, rapid and universal in the constructed VTE risk assessment model and the prepared related products.
Example 1
The embodiment is a detection kit for risk assessment of VTE, and the detection is performed by using a time-of-flight mass spectrometer. And (3) carrying out MassEXTEND single-gene extension reaction on the target sequence amplified by the PCR, wherein a section of probe is designed next to the SNP locus, dNTPs are replaced by ddNTPs in a reaction system, so that the probe extends for one base at the SNP locus to terminate, and the probe is combined with different ddNTPs according to the SNP locus in the reaction system to generate products with different molecular weights. The extended product is combined with a chip matrix to form a compound, and then genotyping analysis is performed by means of a mass spectrometry.
The kit of this embodiment includes:
(1) The SNP risk feature set related to VTE is used as a detection molecule of the kit, the SNP risk feature set is rs12052817, rs7987478, rs9452114, rs182314917, rs77481115, rs73482924, rs375840470, rs140242583, rs79368348 rs4794202, rs114453875, rs17141995 rs4794202, rs114453875, rs17141995 rs17141995, rs 17141995;
(2) Specific amplification primers, namely performing multiplex PCR (polymerase chain reaction) amplification on the detection molecules to obtain upstream and downstream primer pairs and single-base extension primer sequences, wherein primer design can be performed by adopting primer design software, such as Oligo primer design software;
(3) Other conventional reagents required for PCR techniques.
The experimental procedure includes:
1. designing a primer;
2. enrichment of the target fragment can be performed after amplification by the PCR primer;
3. SAP digestion is carried out to remove redundant reagents such as enzyme, buffer, mg2+, dNTP and the like;
4. hybridization extension amplification;
5. single base extension amplification (termination at A/T/C/G).
Finally, the typing result can be intuitively read out by detecting the peak diagram of a single base through mass spectrum.
The detection sample of the kit can be peripheral blood, and the genotyping result of SNP loci is obtained through detection, so that the risk of VTE is predicted or estimated, thereby providing favorable support for clinically making a treatment scheme for patients, being beneficial to realizing personalized treatment for the patients and improving the treatment effect.
Example 2
The embodiment is a detection kit for risk assessment of VTE, based on embodiment 1, the detection molecule comprises 44 SNPs sites in a SNP risk feature set related to VTE, and also comprises SNP site rs1799963 of coagulation factor V Leiden and SNP site rs6025 of prothrombin G20210A. The other components are the same as those in embodiment 1, and will not be described in detail here.
Example 3
The present embodiment is a VTE risk assessment model, including the 44 SNPs risk feature data of embodiment 1, and a VTE risk assessment model constructed by a Ridge regression (Ridge) algorithm. Referring to fig. 3 and 4, fig. 3 shows that the training set AUC reaches 0.831, and fig. 4 shows that the validation set AUC reaches 0.739. And an evaluation result with higher accuracy is realized. Referring to FIG. 5, 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 (9)
1. A detection kit for VTE risk assessment, which is characterized in that SNP risk feature sets related to VTE are used as detection molecules of the kit, the SNP risk feature set is rs12052817, rs7987478, rs9452114, rs182314917, rs77481115, rs73482924, rs375840470, rs140242583, rs79368348 rs4794202, rs114453875, rs17141995 rs4794202, rs114453875, rs17141995 rs17141995, rs 17141995.
2. The kit of claim 1, wherein the detection molecule further comprises SNP site rs1799963 of coagulation factor V Leiden and SNP site rs6025 of prothrombin G20210A.
3. The kit according to claim 1 or 2, further comprising a reagent for detecting genotyping of each SNP site in the detection molecule.
4. Use of a set of SNP risk features for the preparation of a VTE risk assessment product, characterized in that the set of SNP risk features is the set of SNP risk features described in claim 1 in relation to VTE.
5. The use of claim 4, wherein the product comprises a chip, probe, or VTE risk assessment model.
6. The use according to claim 4, wherein the screening method of the SNP risk feature set comprises the steps of:
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;
a set of VTE risk features is obtained.
7. The use according to claim 6, further comprising calculating the proportion of SNPs data reported in the first 10, 30, 50 and 100 sets of risk score ranks based on SNP risk score test results in step S2.
8. The use according to claim 6, wherein in step S2.2, the filtering is performed with a standard of AUC >0.7; the supervised learning algorithm comprises logics, LASSO, ridge and Bayes.
9. The use according to claim 6, wherein in step S2.2, the stepwise stacking is stepwise stacking SNPs data in descending order of association level.
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