Evidence optimization method and device based on Bayesian model, computer equipment and storage medium

Abstract

The invention discloses an evidence optimization method and device based on a Bayesian model, computer equipment and a storage medium, and relates to the technical field of medical care informatics. The method comprises the following steps: acquiring a text file containing a variation classification result; calculating posterior probabilities corresponding to 17 evidence combinations given by the ACMG / AMP guide, and when selected evidence combination rules meet posterior probability classification, obtaining theoretical pathogenicity probabilities corresponding to the evidence intensity grades; finely classifying the clinical significance unknown variation VUS into six levels; determining thresholds corresponding to different evidence intensity grades by comparing the positive likelihood ratio or the local positive likelihood ratio with the theoretical pathogenicity probability corresponding to each evidence intensity grade; and the variation is marked with an ACMG / AMP evidence label again, and a pathogenicity classification result of the variation is given. According to the method, the evidence optimization difficulty can be reduced, and the accuracy and consistency of genetic variation interpretation in clinical gene detection can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical care informatics, and in particular to a Bayesian model-based evidence optimization method, device, computer equipment and storage medium. Background Art

[0002] Early genetic molecular diagnosis is crucial for early intervention of patient symptoms and improving the patient's quality of life. For example, Mendelian genetic diseases are hereditary diseases caused by single gene mutations. Such diseases often bring serious health hazards to patients and may even cause congenital defects, disability or death. In clinical practice, targeted sequencing, whole exome sequencing or whole genome sequencing, combined with bioinformatics analysis and interpretation of variant pathogenicity, can identify genetic molecular causes for patients. However, how to accurately determine the pathogenicity of a large number of variants of unknown significance is one of the main challenges facing clinical genetic diagnosis.

[0003] To assist in interpreting the clinical significance of variants, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published the Standards and Guidelines for the Interpretation of Sequence Variants in 2015. Based on eight aspects including population frequency, phenotypic co-segregation, and functional prediction scores, a total of 28 evidence items (involving 5 evidence strength levels: independent, very strong, strong, moderate, and supportive) and 18 evidence combination rules, variants are classified into five categories: pathogenic (P), likely pathogenic (LP), benign (B), likely benign (LB), and uncertain significance (US). After the release of the ACMG / AMP guidelines, they have been widely recognized by international peers, greatly enhancing the global clinical gene testing and data interpretation capabilities. However, the evidence combination rules of the ACMG / AMP guidelines only qualitatively analyze the pathogenicity of variants based on a simple evidence strength level accumulation rule, lacking a mathematical model to prove its effectiveness. In 2018, ClinGen proposed that the qualitative framework of the ACMG / AMP guidelines could be transformed into a quantitative framework based on the Naive Bayes model. This quantitative framework calculates the posterior probability of variant pathogenicity (posterior probability, Post_P) according to the prior probability of variant pathogenicity and the pathogenicity odds corresponding to the evidence strength level, and then realizes variant classification. Specifically, when Post_P > 0.99, 0.90 < Post_P ≤ 0.99, 0.001 ≤ Post_P < 0.10, Post_P < 0.001, and 0.10 ≤ Post_P ≤ 0.90, the corresponding pathogenicity classifications of the variants are P, LP, LB, B, and US, respectively. Based on this quantitative framework, ClinGen SVI demonstrated that among the 18 evidence combination rules proposed by the ACMG / AMP Standards and Guidelines for the Interpretation of Sequence Variants, 16 were consistent with the classification results calculated by the Bayesian model, proving the rationality of the evidence combination rules of the ACMG / AMP guidelines. In 2020, ClinGen further converted the Bayesian framework into a scoring framework, where different evidence strength levels correspond to different scores, and variants can be classified by adding up the scores. These quantitative models provide a mathematical basis for further quantifying the evidence strength level and expanding the combination rules. Researchers quantified and expanded the strength levels of multiple evidence items, such as PS1 / PM5, PS3 / BS3, and PP3 / BP4, according to the Bayesian framework, improving the recognition rate of pathogenic variants, and proving that the standardization and stratification of evidence are crucial for establishing an objective, unbiased, and consistent variant classification.

[0004] However, there is currently a lack of software and tools specifically designed to quantify the strength of evidence and establish appropriate thresholds to improve the ACMG / AMP guidelines, resulting in low accuracy and poor consistency in the interpretation of genetic variants in clinical genetic testing. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a Bayesian model-based evidence optimization method, apparatus, computer equipment and storage medium, which aims to reduce the difficulty of evidence optimization and improve the accuracy and consistency of genetic variation interpretation in clinical genetic testing by expanding the evidence strength level of the ACMG / AMP guidelines.

[0006] In a first aspect, the present invention provides an evidence optimization method based on a Bayesian model, comprising the following steps: S10: Obtain a text file containing a variant classification result, wherein the variant classification result includes variant, ACMG / AMP evidence, and variant pathogenicity classification information; S20: When there is an exponential relationship between the pathogenicity probabilities of each evidence strength level, the posterior probability Post_P corresponding to the 17 evidence combinations given in the ACMG / AMP guidelines under a given prior probability Prior_P is calculated according to the preset number of cycles. When the selected evidence combination rule satisfies the posterior probability Post_P classification, the minimum pathogenicity probability value corresponding to each evidence strength level is obtained, and the minimum pathogenicity probability value is used as the theoretical pathogenicity probability corresponding to each evidence strength level; S30: VUS of unknown clinical significance is subclassified into first-level hot, second-level warm, third-level tepid, fourth-level cool, fifth-level cold and sixth-level ice cold. VUS of unknown clinical significance classified as first-level hot, second-level warm and third-level tepid are considered as PL-VUS of unknown clinical significance with a tendency to cause disease. VUS of unknown clinical significance classified as fourth-level cool, fifth-level cold and sixth-level ice cold are considered as BL-VUS of unknown clinical significance with a tendency to cause benign disease. When optimizing evidence, PL-VUS of unknown clinical significance with a tendency to cause disease are filtered out. S40: For categorical variables, calculate the positive likelihood ratio LR + For continuous variables, the local positive likelihood ratio lr+ is calculated by comparing the positive likelihood ratio LR + or local positive likelihood ratio lr + The theoretical pathogenicity probability corresponding to each level of evidence strength is used to determine the thresholds corresponding to different levels of evidence strength; S50: According to the optimized evidence strength corresponding level and corresponding threshold, the ACMG / AMP evidence label is re-labeled for the variant, and the pathogenicity classification results of the variant are given according to the evidence combination rules, Bayesian framework and scoring system given in the ACMG / AMP guidelines.

[0007] Further, in step S20, the posterior probability Post_P classification is specifically as follows: the posterior probability Post_P of the pathogenic combination satisfies: posterior probability Post_P>0.99; the posterior probability Post_P of the possible pathogenic combination satisfies: 0.90< posterior probability Post_P ≤ 0.99; the posterior probability Post_P of the possible benign combination satisfies: 0.001 ≤ posterior probability Post_P<0.10; the posterior probability Post_P of the benign combination satisfies: posterior probability Post_P<0.001; The calculation formula for the theoretical pathogenicity probability corresponding to each level of evidence strength is: ; OP: Odds Path, the pathogenicity probability obtained by combining ACMG / AMP evidence; O PVSt : The probability of pathogenicity corresponding to the Very Strong pathogenicity evidence strength level; N PVSt : The number of evidences with a very strong level of pathogenicity evidence; N PSt : The number of evidences with strong pathogenicity evidence level; N PM : The number of evidences with moderate pathogenicity strength level; N PSu : The number of evidences with the pathogenicity evidence strength level as supporting (Supporting); N BSt : The number of evidences with a benign evidence strength level of strong (Strong); N BSu : The number of evidences with the benign evidence strength level as supporting; The calculation formula of the posterior probability Post_P is: ; Among them, Post_P: posterior probability of variant pathogenicity; Prior_P: prior probability of variant pathogenicity.

[0008] Further, in step S40, for categorical variables, various statistical indicators for each test condition are calculated by calculating the number of P / LP and BL-VUS / B / LB variants that meet or do not meet the test condition, including true positive, false positive, true negative, accuracy, true positive rate, true negative rate, positive predictive value, negative predictive value, F1 score and positive likelihood ratio LR +, the positive likelihood ratio LR is obtained by bootstrapping algorithm + The 95% confidence interval estimate of + The evidence strength level and threshold are determined by combining the 95% confidence interval lower limit value and the theoretical pathogenicity probability corresponding to each evidence strength level obtained in step S20.

[0009] Furthermore, the positive likelihood ratio LR + The calculation formula is: ; Among them, TP: True positive, the number of P / LP variants that meet the test conditions; FP: False positive, the number of BL-VUS / B / LB variants that meet the test conditions; TN: True negative, the number of BL-VUS / B / LB variants that do not meet the test conditions; FN: False negative, the number of P / LP variants that do not meet the test conditions; The local positive likelihood ratio lr + The calculation formula is: ; Among them, p: probability; s: test value; v: variation; : represents the density of the benign variation score under a certain test value; : represents the density of the distribution of pathogenic variant scores under a certain test value.

[0010] Furthermore, in step S40, for continuous variables, all observations are sorted, and each observation is taken as the center. For a given sliding window, the local posterior probability of pathogenicity of each test value in the interval is calculated, and the 95% confidence interval of the local posterior probability of each test value is estimated through a preset number of iterations to evaluate the threshold corresponding to the evidence strength level.

[0011] Furthermore, the calculation formula of the pathogenicity local posterior probability Local posterior probability is: ; Among them, Local posterior probability: local posterior probability; #P / LP variants ininterval: the number of P / LP variants in a given sliding window; #non-pathogenic variants in interval: the number of BL-VUS / B / LB variants in a given sliding window; Weight: the probability of BL-VUS / B / LB variants occurring in the dataset.

[0012] Furthermore, the test value of the categorical variable is a binary variable; and the test value of the continuous variable is a continuous variable.

[0013] In a second aspect, the present invention provides an evidence optimization device based on a Bayesian model, comprising: An acquisition module, used to acquire a text file containing variant classification results, wherein the variant classification results include variants, ACMG / AMP evidence, and variant pathogenicity classification information; A calculation and selection module is used to calculate the posterior probability Post_P corresponding to the 17 evidence combinations given in the ACMG / AMP guidelines under a given prior probability Prior_P according to a preset number of cycles when there is an exponential relationship between the pathogenicity probabilities of each evidence strength level. When the selected evidence combination rule satisfies the posterior probability Post_P classification, the minimum pathogenicity probability value corresponding to each evidence strength level is obtained, and the minimum pathogenicity probability value is used as the theoretical pathogenicity probability corresponding to each evidence strength level; A subclassification module is used to subclassify clinically unknown variants VUS into first-level hot, second-level warm, third-level tepid, fourth-level cool, fifth-level cold, and sixth-level ice cold, and to classify clinically unknown variants VUS classified as first-level hot, second-level warm, and third-level tepid as clinically unknown variants PL-VUS with a tendency to cause disease, and to classify clinically unknown variants VUS classified as fourth-level cool, fifth-level cold, and sixth-level ice cold as clinically unknown variants BL-VUS with a tendency to benign. When optimizing evidence, clinically unknown variants PL-VUS with a tendency to cause disease are filtered out; Threshold determination module, used to calculate the positive likelihood ratio LR for categorical variables + ; For continuous variables, calculate the local positive likelihood ratio lr + , by comparing the positive likelihood ratio LR + or local positive likelihood ratio lr + The theoretical pathogenicity probability corresponding to each level of evidence strength is used to determine the thresholds corresponding to different levels of evidence strength; The result output module is used to re-label the variants with ACMG / AMP evidence labels according to the optimized evidence strength corresponding levels and corresponding thresholds, and to give the pathogenicity classification results of the variants according to the evidence combination rules, Bayesian framework and score system given in the ACMG / AMP guidelines.

[0014] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0015] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the processor executes the steps of the above method.

[0016] The beneficial effects of the present invention are: The present invention first obtains a text file containing the variant classification results, and calculates the posterior probability corresponding to the 17 evidence combinations given by the ACMG / AMP guidelines under a given prior probability. When the selected evidence combination rule satisfies the posterior probability Post_P classification, the theoretical pathogenicity probability corresponding to each evidence strength level is obtained. For categorical variables, the positive likelihood ratio LR is calculated. + ; For continuous variables, calculate the local positive likelihood ratio lr + By comparing the positive likelihood ratio LR + or local positive likelihood ratio lr + The theoretical pathogenicity probability corresponding to each level of evidence strength can be determined, and the thresholds corresponding to different levels of evidence strength can be determined, thereby improving the flexibility and convenience of quantifying ACMG / AMP evidence strength levels and establishing corresponding thresholds, which can promote the transition from subjective evaluation to quantitative and objective evaluation.

[0017] Then, according to the optimized evidence strength corresponding level and corresponding threshold, the ACMG / AMP evidence label is re-marked on the variant, and the pathogenicity classification results of the variant are given according to the evidence combination rules, Bayesian framework and scoring system given in the ACMG / AMP guidelines; based on the evidence optimization results, the pathogenicity of the variant is reclassified, and the classification is not limited to using the ACMG / AMP combination rules, but can also be classified according to the posterior probability calculated according to the Bayesian framework. By expanding the evidence strength level of the ACMG / AMP guidelines, the present invention is expected to further improve the accuracy and consistency of variant interpretation in clinical genetic testing and improve the classification of variants of unknown clinical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of an evidence optimization method based on a Bayesian model provided by an embodiment of the present invention; Figure 2 is a corresponding relationship diagram of variant pathogenicity classification, posterior probability and evidence combination provided by an embodiment of the present invention; Figure 3 It is a functional module block diagram of an evidence optimization device based on a Bayesian model provided in an embodiment of the present invention.

[0019] icon: Acquisition module 100; Calculation and selection module 200; Sub-classification module 300; Threshold determination module 400; Result output module 500.

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Example

[0021] like Figure 1 As shown, an evidence optimization method based on a Bayesian model provided by an embodiment of the present invention may include the following steps S10 to S40: Step S10: Obtain a text file containing variant classification results.

[0022] The variant classification result at least includes the variant, ACMG / AMP evidence and variant pathogenicity classification information, and the variant, ACMG / AMP evidence and variant pathogenicity classification information are separated by a tab character.

[0023] Step S20: When there is an exponential relationship between the pathogenicity probabilities of each evidence strength level, the posterior probability Post_P corresponding to the 17 evidence combinations given in the ACMG / AMP guidelines under a given prior probability Prior_P is calculated according to a preset number of cycles. When the selected evidence combination rule meets the posterior probability Post_P classification, the minimum pathogenicity probability value corresponding to each evidence strength level is obtained, and the minimum pathogenicity probability value is used as the theoretical pathogenicity probability corresponding to each evidence strength level.

[0024] In this embodiment, it is first assumed that there is an exponential relationship between the pathogenicity probabilities of each evidence strength level. During implementation, the preset number of cycles is preferably 10,000 times. Within the preset number of cycles, the posterior probability Post_P corresponding to the 17 evidence combinations provided by the ACMG / AMP guidelines is calculated under a given prior probability Prior_P. When the selected evidence combination rule satisfies the posterior probability Post_P classification, the minimum pathogenicity probability value corresponding to each evidence strength level is obtained, and the minimum pathogenicity probability value is used as the theoretical pathogenicity probability corresponding to each evidence strength level.

[0025] In one embodiment, the posterior probability Post_P classification is specifically divided into the following cases: the posterior probability Post_P of the pathogenic combination satisfies: posterior probability Post_P>0.99; the posterior probability Post_P of the possible pathogenic combination satisfies: 0.90<posterior probability Post_P ≤ 0.99; the posterior probability Post_P of the possible benign combination satisfies: 0.001 ≤ posterior probability Post_P<0.10; the posterior probability Post_P of the benign combination satisfies: posterior probability Post_P<0.001.

[0026] Furthermore, the calculation formula for the theoretical pathogenicity probability corresponding to each level of evidence strength is: ; OP: Odds Path, the pathogenicity probability obtained by combining ACMG / AMP evidence; O PVSt : The probability of pathogenicity corresponding to the Very Strong pathogenicity evidence strength level; N PVSt : The number of evidences with a very strong level of pathogenicity evidence; N PSt : The number of evidences with strong pathogenicity evidence level; N PM : The number of evidences with moderate pathogenicity strength level; N PSu : The number of evidences with the pathogenicity evidence strength level as supporting (Supporting); N BSt : The number of evidences with a benign evidence strength level of strong (Strong); N BSu : The number of evidences with the benign evidence strength level as Supporting.

[0027] Furthermore, the calculation formula of the posterior probability Post_P is: ; Among them, Post_P: posterior probability of variant pathogenicity; Prior_P: prior probability of variant pathogenicity.

[0028] In this embodiment, the corresponding relationship between the variant pathogenicity classification, the posterior probability Post_P and the evidence combination is as follows: Figure 2 As shown, the evidence is combined into 17 combinations. Figure 2 In, O PVSt : Very Strong pathogenicity evidence strength level corresponding to the probability of pathogenicity; O PSt : Strong pathogenicity evidence strength level corresponding to the probability of pathogenicity; O PM: The probability of pathogenicity corresponding to the strength of evidence of moderate pathogenicity; O PSu : Supporting pathogenicity evidence strength level corresponding to the probability of pathogenicity; O BSt : Strong: the probability of pathogenicity corresponding to the strength level of benign evidence; O BSu : Supporting the probability of pathogenicity corresponding to the strength level of evidence for benignity.

[0029] It should be noted that the pathogenicity classification of variants includes: P (pathogenic), LP (likely pathogenic), B (benign), LB (likely benign) and US (uncertain significance).

[0030] Step S30: subclassify the clinically unknown variants VUS into the first level hot, the second level warm, the third level tepid, the fourth level cool, the fifth level cold and the sixth level ice cold; classify the clinically unknown variants VUS classified as the first level hot, the second level warm and the third level tepid as the clinically unknown variants PL-VUS with a tendency to be pathogenic; classify the clinically unknown variants VUS classified as the fourth level cool, the fifth level cold and the sixth level ice cold as the clinically unknown variants BL-VUS with a tendency to be benign; when optimizing the evidence, filter out the clinically unknown variants PL-VUS with a tendency to be pathogenic.

[0031] Step S40: For categorical variables, calculate the positive likelihood ratio LR + ; For continuous variables, calculate the local positive likelihood ratio lr + , by comparing the positive likelihood ratio LR + or local positive likelihood ratio lr + The theoretical pathogenicity probability corresponding to each level of evidence strength is used to determine the thresholds corresponding to different levels of evidence strength.

[0032] In one embodiment, for categorical variables, various statistical indicators for each test value are calculated by calculating the number of P / LP and BL-VUS / B / LB variants that meet or do not meet the test conditions, including true positive, false positive, true negative, accuracy, true positive rate, true negative rate, positive predictive value, negative predictive value, F1 score and positive likelihood ratio LR + , the positive likelihood ratio LR is obtained by bootstrapping algorithm + The 95% confidence interval estimate of+ The evidence strength level and threshold are determined by combining the 95% confidence interval lower limit value and the theoretical pathogenicity probability corresponding to each evidence strength level obtained in step S20.

[0033] It should be noted that the Bootstrapping process is formally described as: for a given natural language processing task, a specific guided method for training a classification model is selected; then two data sets are required, generally a small amount of labeled data set L and an unlabeled data set U; finally, the labeled data set is gradually expanded through the unlabeled data set U, so as to train the final classifier to achieve a specific natural language processing task. Bootstrapping is an iterative process that can use a small amount of labeled corpus to obtain a large amount of labeled corpus with high confidence. The Bootstrapping method is implemented through two main processes. The first is to provide a heuristic rule or other classification method that can effectively classify a small amount of corpus, and the second is to evaluate the new labeled corpus generated by the classifier. Through evaluation, a labeled corpus with high confidence is obtained, so that a larger labeled data set can be obtained through iteration. The iterative termination condition is to give a threshold of the number of iterations, or the number of new labeled data generated is too small.

[0034] The categorical variable refers to a binary variable, for example, whether the variant is marked with a certain evidence label (such as PM2, PS1, etc.), such as whether the variant is significantly enriched in the population (PS4 evidence, the corresponding test value is OR>5 and p<0.05). For categorical variables, a four-cell table will be obtained, and then the positive likelihood ratio LR will be calculated. + value.

[0035] Furthermore, the positive likelihood ratio LR + The calculation formula is: ; TP: True positive, the number of P / LP variants above the test value; FP: False positive, the number of BL-VUS / B / LB variants above the test value; TN: True negative, the number of BL-VUS / B / LB variants below the test value; FN: False negative, the number of P / LP variants above the test value.

[0036] The local positive likelihood ratio lr + The calculation formula is: ; Among them, p: probability; s: test value; v: variation; : represents the density of the benign variation score under a certain test value; : represents the density of the distribution of pathogenic variant scores under a certain test value.

[0037] In one embodiment, for continuous variables, all observations are sorted, and each observation is centered. For a given sliding window, the local posterior probability of pathogenicity for each test value in the interval is calculated, and the 95% confidence interval of the local posterior probability of each test value is estimated through a preset number of iterations to evaluate the threshold corresponding to the evidence strength level.

[0038] Among them, the continuous variable means that the test value is a continuous variable, for example: PM2 / BS1 evidence corresponds to the threshold of the frequency of the variant in the population, PP3 / BP4 corresponds to the threshold of the pathogenicity score of the variant predicted by the software, and when determining the thresholds corresponding to the different evidence strength levels of these evidence, it is necessary to test each frequency value or score value, and these values ​​are continuous and have a range. During implementation, the preset number of times is set by the user, preferably 100 times, 1000 times and 5000 times.

[0039] Furthermore, the calculation formula of the pathogenicity local posterior probability Local posterior probability is: ; Among them, Local posterior probability: local posterior probability; #P / LP variants ininterval: the number of P / LP variants in a given sliding window; #non-pathogenic variants in interval: the number of BL-VUS / B / LB variants in a given sliding window; Weight: the probability of BL-VUS / B / LB variants occurring in the dataset.

[0040] This embodiment improves the flexibility and convenience of quantifying ACMG / AMP evidence strength levels and establishing corresponding thresholds by determining thresholds corresponding to evidence strength levels, and can promote the transition from subjective assessment to quantitative and objective assessment.

[0041] Step S50: According to the optimized evidence strength corresponding level and corresponding threshold, the ACMG / AMP evidence label is re-marked on the variant, and the pathogenicity classification results of the variant are given according to the evidence combination rules, Bayesian framework and scoring system given in the ACMG / AMP guidelines.

[0042] This embodiment reclassifies the pathogenicity of the variant based on the evidence optimization results, and the classification is not limited to using the ACMG / AMP combined rules, but can also be classified according to the posterior probability calculated according to the Bayesian framework. By expanding the evidence strength level of the ACMG / AMP guidelines, the present invention is expected to further improve the accuracy and consistency of variant interpretation in clinical genetic testing and improve the classification of variants of unknown clinical significance, for example, providing strong support for early intervention and personalized treatment of Mendelian genetic diseases.

[0043] Example 2 like Figure 3 As shown, an embodiment of the present invention also provides an evidence optimization device based on a Bayesian model, which may include an acquisition module 100, a calculation and selection module 200, a detailed classification module 300, a threshold determination module 400 and a result output module 500.

[0044] The acquisition module 100 is used to acquire a text file containing a variation classification result, wherein the variation classification result includes variation, ACMG / AMP evidence, and variation pathogenicity classification information.

[0045] The calculation and selection module 200 is used to calculate the posterior probability Post_P corresponding to the 17 evidence combinations given in the ACMG / AMP guidelines under a given prior probability Prior_P according to a preset number of cycles when there is an exponential relationship between the pathogenicity probabilities of each evidence strength level. When the selected evidence combination rule satisfies the posterior probability Post_P classification, the minimum pathogenicity probability value corresponding to each evidence strength level is obtained, and the minimum pathogenicity probability value is used as the theoretical pathogenicity probability corresponding to each evidence strength level.

[0046] The subclassification module 300 is used to subclassify clinically unknown variants VUS into first level hot, second level warm, third level tepid, fourth level cool, fifth level cold and sixth level ice cold, and classify clinically unknown variants VUS classified as first level hot, second level warm and third level tepid as clinically unknown variants PL-VUS with a tendency to cause disease, and classify clinically unknown variants VUS classified as fourth level cool, fifth level cold and sixth level ice cold as clinically unknown variants BL-VUS with a tendency to benign, and filter out clinically unknown variants PL-VUS with a tendency to cause disease during evidence optimization.

[0047] The threshold determination module 400 is used to calculate the positive likelihood ratio LR for the categorical variable. + ; For continuous variables, calculate the local positive likelihood ratio lr + , by comparing the positive likelihood ratio LR + or local positive likelihood ratio lr+ The theoretical pathogenicity probability corresponding to each level of evidence strength is used to determine the thresholds corresponding to different levels of evidence strength.

[0048] The result output module 500 is used to re-label the variant with ACMG / AMP evidence labels according to the optimized evidence strength corresponding level and corresponding threshold, and to give the pathogenicity classification results of the variant according to the evidence combination rules, Bayesian framework and score system given in the ACMG / AMP guidelines.

[0049] In detail, each module in the Bayesian model-based evidence optimization device provided in the embodiment of the present invention adopts the same technical means as the Bayesian model-based evidence optimization method provided in the above-mentioned Example 1 when used, and can produce the same technical effects, which will not be repeated here.

[0050] Example 3 The embodiment of the present invention further provides a computer device, which includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method described in Embodiment 1.

[0051] In some embodiments, the processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes various functions and processes data by running or executing programs or modules stored in the memory (such as evidence optimization programs based on Bayesian models, etc.), and calling data stored in the memory.

[0052] In some embodiments, the memory may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the memory may also include both an internal storage unit of the electronic device and an external storage device. The memory can be used not only to store application software and various types of data installed in the electronic device, such as the code of the evidence optimization program based on the Bayesian model, but also to temporarily store data that has been output or is to be output.

[0053] Example 4 An embodiment of the present invention further provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes the steps of the method described in Example 1.

[0054] The storage medium is a readable storage medium, which may include a flash memory, a mobile hard disk, a multimedia card, a card-type memory (eg, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc.

[0055] In summary, the embodiment of the present invention first obtains a text file containing variant classification results, and calculates the posterior probabilities corresponding to the 17 evidence combinations given by the ACMG / AMP guidelines under a given prior probability. When the selected evidence combination rule satisfies the posterior probability Post_P classification, the theoretical pathogenicity probability corresponding to each evidence strength level is obtained. For categorical variables, the positive likelihood ratio LR is calculated. + ; For continuous variables, calculate the local positive likelihood ratio lr + By comparing the positive likelihood ratio LR + or local positive likelihood ratio lr + The theoretical pathogenicity probability corresponding to each level of evidence strength can be determined, and the thresholds corresponding to different levels of evidence strength can be determined, thereby improving the flexibility and convenience of quantifying ACMG / AMP evidence strength levels and establishing corresponding thresholds, which can promote the transition from subjective evaluation to quantitative and objective evaluation.

[0056] Then, according to the optimized evidence strength corresponding level and corresponding threshold, the ACMG / AMP evidence label is re-marked on the variant, and the pathogenicity classification results of the variant are given according to the evidence combination rules, Bayesian framework and scoring system given in the ACMG / AMP guidelines; based on the evidence optimization results, the pathogenicity of the variant is reclassified, and the classification is not limited to using the ACMG / AMP combination rules, but can also be classified according to the posterior probability calculated according to the Bayesian framework. By expanding the evidence strength level of the ACMG / AMP guidelines, the present invention is expected to further improve the accuracy and consistency of variant interpretation in clinical genetic testing and improve the classification of variants of unknown clinical significance.

[0057] In the embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation. The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, the functional modules in the various embodiments of the present invention may be integrated in a processing unit, or each unit may exist physically separately, or two or more units may be integrated in one unit. The above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An evidence optimization method based on a Bayesian model, characterized in that: The following steps are involved: S10: Obtain a text file containing variant classification results, wherein the variant classification results include variants, ACMG / AMP evidence, and variant pathogenicity classification information; S20: When there is an exponential relationship between the pathogenicity probabilities of each evidence strength level, the posterior probability Post_P corresponding to the 17 evidence combinations given in the ACMG / AMP guidelines under a given prior probability Prior_P is calculated according to the preset number of cycles. When the selected evidence combination rule satisfies the posterior probability Post_P classification, the minimum pathogenicity probability value corresponding to each evidence strength level is obtained, and the minimum pathogenicity probability value is used as the theoretical pathogenicity probability corresponding to each evidence strength level; S30: VUS of unknown clinical significance is subclassified into first-level hot, second-level warm, third-level tepid, fourth-level cool, fifth-level cold and sixth-level ice cold. VUS of unknown clinical significance classified as first-level hot, second-level warm and third-level tepid are considered as PL-VUS of unknown clinical significance with a tendency to cause disease. VUS of unknown clinical significance classified as fourth-level cool, fifth-level cold and sixth-level ice cold are considered as BL-VUS of unknown clinical significance with a tendency to cause benign disease. When optimizing evidence, PL-VUS of unknown clinical significance with a tendency to cause disease are filtered out. S40: For categorical variables, calculate the positive likelihood ratio LR + ; For continuous variables, calculate the local positive likelihood ratio lr + , by comparing the positive likelihood ratio LR + or local positive likelihood ratio lr + The theoretical pathogenicity probability corresponding to each level of evidence strength is used to determine the thresholds corresponding to different levels of evidence strength; S50: According to the optimized evidence strength corresponding level and corresponding threshold, the ACMG / AMP evidence label is re-labeled for the variant, and the pathogenicity classification results of the variant are given according to the evidence combination rules, Bayesian framework and scoring system given in the ACMG / AMP guidelines.

2. The Bayesian model-based evidence optimization method according to claim 1, characterized in that: In step S20, the posterior probability Post_P classification is specifically as follows: the posterior probability Post_P of the pathogenic combination satisfies: posterior probability Post_P>0.99; the posterior probability Post_P of the possible pathogenic combination satisfies: 0.90 < posterior probability Post_P ≤ 0.99; the posterior probability Post_P of the possible benign combination satisfies: 0.001 ≤ posterior probability Post_P < 0.10; the posterior probability Post_P of the benign combination satisfies: posterior probability Post_P < 0.001; The calculation formula for the theoretical pathogenicity probability corresponding to each level of evidence strength is: ； OP: Odds Path, the pathogenicity probability obtained by combining ACMG / AMP evidence; O PVSt : The probability of pathogenicity corresponding to the Very Strong pathogenicity evidence strength level; N PVSt : The number of evidences with a pathogenicity strength level of Very Strong; N PSt : The number of evidences with strong pathogenicity evidence level; N PM : The number of evidences with moderate pathogenicity strength level; N PSu : The number of evidences with the pathogenicity evidence strength level as supporting (Supporting); N BSt : The number of evidences with a benign evidence strength level of strong (Strong); N BSu : The number of evidences with the strength level of benign evidence being supporting; The calculation formula of the posterior probability Post_P is: ； Among them, Post_P: posterior probability of variant pathogenicity; Prior_P: prior probability of variant pathogenicity.

3. The Bayesian model-based evidence optimization method according to claim 1, characterized in that: In step S40, for categorical variables, various statistical indicators for each test condition are calculated by counting the number of P / LP and BL-VUS / B / LB variants that meet or do not meet the test condition, including true positive, false positive, true negative, accuracy, true positive rate, true negative rate, positive predictive value, negative predictive value, F1 score and positive likelihood ratio LR + , the positive likelihood ratio LR is obtained by bootstrapping algorithm + The 95% confidence interval estimate of + The evidence strength level and threshold are determined by combining the 95% confidence interval lower limit value and the theoretical pathogenicity probability corresponding to each evidence strength level obtained in step S20.

4. The Bayesian model-based evidence optimization method according to claim 3, characterized in that: The positive likelihood ratio LR + The calculation formula is: ； Among them, TP: True positive, the number of P / LP variants that meet the test conditions; FP: False positive, the number of BL-VUS / B / LB variants that meet the test conditions; TN: True negative, the number of BL-VUS / B / LB variants that do not meet the test conditions; FN: False negative, the number of P / LP variants that do not meet the test conditions; The local positive likelihood ratio lr + The calculation formula is: ； Among them, p: probability; s: test value; v: variation; : represents the density of the benign variation score under a certain test value; : represents the density of the distribution of pathogenic variant scores under a certain test value.

5. The Bayesian model-based evidence optimization method according to claim 1, characterized in that: In step S40, for continuous variables, all observations are sorted, and each observation is taken as the center. For a given sliding window, the local posterior probability of pathogenicity of each test value in the interval is calculated, and the 95% confidence interval of the local posterior probability of each test value is estimated through a preset number of iterations to evaluate the threshold corresponding to the evidence strength level.

6. The Bayesian model-based evidence optimization method according to claim 5, characterized in that: The calculation formula of the pathogenicity local posterior probability Local posterior probability is: ； Among them, Local posterior probability: local posterior probability; #P / LP variants in interval: the number of P / LP variants in a given sliding window; #non-pathogenic variants in interval: the number of BL-VUS / B / LB variants in a given sliding window; Weight: the probability of BL-VUS / B / LB variants occurring in the dataset.

7. The method for optimizing evidence based on the Bayesian model according to claim 1, characterized in that: The test value of the categorical variable is a binary variable; the test value of the continuous variable is a continuous variable.

8. An evidence optimization device based on a Bayesian model, characterized in that: include: An acquisition module, used to acquire a text file containing variant classification results, wherein the variant classification results include variants, ACMG / AMP evidence, and variant pathogenicity classification information; A calculation and selection module is used to calculate the posterior probability Post_P corresponding to the 17 evidence combinations given in the ACMG / AMP guidelines under a given prior probability Prior_P according to a preset number of cycles when there is an exponential relationship between the pathogenicity probabilities of each evidence strength level. When the selected evidence combination rule satisfies the posterior probability Post_P classification, the minimum pathogenicity probability value corresponding to each evidence strength level is obtained, and the minimum pathogenicity probability value is used as the theoretical pathogenicity probability corresponding to each evidence strength level; A subclassification module is used to subclassify clinically unknown variants VUS into first-level hot, second-level warm, third-level tepid, fourth-level cool, fifth-level cold, and sixth-level ice cold, and to classify clinically unknown variants VUS classified as first-level hot, second-level warm, and third-level tepid as clinically unknown variants PL-VUS with a tendency to cause disease, and to classify clinically unknown variants VUS classified as fourth-level cool, fifth-level cold, and sixth-level ice cold as clinically unknown variants BL-VUS with a tendency to benign. When optimizing evidence, clinically unknown variants PL-VUS with a tendency to cause disease are filtered out; Threshold determination module, used to calculate the positive likelihood ratio LR for categorical variables + ; For continuous variables, calculate the local positive likelihood ratio lr + , by comparing the positive likelihood ratio LR + or local positive likelihood ratio lr + The theoretical pathogenicity probability corresponding to each level of evidence strength is used to determine the thresholds corresponding to different levels of evidence strength; The result output module is used to re-label the variants with ACMG / AMP evidence labels according to the optimized evidence strength corresponding levels and corresponding thresholds, and to give the pathogenicity classification results of the variants according to the evidence combination rules, Bayesian framework and score system given in the ACMG / AMP guidelines.

9. A computer device, characterized in that: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the method according to any one of claims 1 to 7.

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