Ankylosing spondylitis multi-gene risk scoring processing method, system and device, processor and computer readable storage medium thereof

Abstract

The invention relates to an ankylosing spondylitis multi-gene risk scoring processing method which is mainly characterized by comprising the following steps: (1) collecting whole genome data of a patient, and performing corresponding data preprocessing on the whole genome data; (2) according to a preset proportion, dividing the obtained patient data and health control data into a training set and a test set, determining an association effect value between each heritable variation gene variant and ankylosing spondylitis AS, and obtaining corresponding association sites; (3) extracting single nucleotide polymorphism (SNP) related to the ankylosing spondylitis (AS), and calculating a polygene risk score (PRS) of the individual by adopting a weighted summation mode; and (4) performing ankylosing spondylitis (AS) risk assessment on the corresponding patient according to the calculated polygene risk score PRS. By adopting the multi-gene risk scoring processing method for the ankylosing spondylitis, an ankylosing spondylitis related genetic variation and onset risk assessment system suitable for East Asian people is provided.

Description

Technical Field

[0001] The present invention relates to the field of medical health, and particularly to the field of bioinformatics and genetic data analysis. Specifically, it refers to a method, system, device, processor and computer-readable storage medium for processing the polygenic risk score of ankylosing spondylitis. Background Art

[0002] Ankylosing Spondylitis (AS) is a chronic inflammatory disease that mainly affects the spine and sacroiliac joints, leading to joint ankylosis and pain. Genetic factors play a dominant role in AS, with a heritability of over 90%. Genetic factors, as stable and quantifiable lifelong markers, have long been expected to be used for disease risk assessment to promote the precise prevention, diagnosis and treatment of AS. However, current clinical risk assessment mainly relies on markers such as HLA-B27 gene detection, magnetic resonance imaging (MRI) and C-reactive protein (CRP). Although HLA-B27 is highly correlated with AS, its detection can only explain about 20% of the genetic risk, and AS can still occur in some HLA-B27 negative patients. In addition, MRI is expensive and not conveniently available in all regions, while CRP has low sensitivity and limited correlation with the disease, and cannot detect the disease in the early stage.

[0003] With the development of genome-wide association studies (GWAS), thousands of genetic variants associated with AS have been identified. Although the impact of each SNP on AS is small, the polygenic risk score (PRS) formed by comprehensively calculating these variants can significantly improve the genetic risk prediction ability for AS. Compared with HLA-B27, PRS can more comprehensively reflect an individual's genetic risk and has higher sensitivity and specificity in early diagnosis and risk screening.

[0004] The deficiencies of the current existing technologies include:

[0005] Diagnostic delay: The onset of AS is insidious, and its symptoms are extremely easy to be confused with other low back pain, resulting in an average diagnostic delay of 6 - 10 years, which also leads to poor clinical prognosis of the disease.

[0006] Limitations of HLA-B27 detection: Although HLA-B27 is highly correlated with AS, its detection can only explain part of the genetic risk, and AS can still occur in some HLA-B27 negative patients.

[0007] Deficiencies of imaging and CRP detection: MRI is expensive and not conveniently available in all regions, while CRP has low sensitivity and limited correlation with the disease, and cannot detect the disease in the early stage.

[0008] Therefore, there is an urgent need for a risk scoring system based on multi-gene data that can predict the risk of AS earlier and more accurately, providing a reference for clinical treatment. Summary of the Invention

[0009] The object of the present invention is to overcome the above-mentioned disadvantages of the prior art and provide a method, system, device, processor and computer-readable storage medium for processing the multi-gene risk score of ankylosing spondylitis.

[0010] In order to achieve the above object, the method, system, device, processor and computer-readable storage medium for processing the multi-gene risk score of ankylosing spondylitis of the present invention are as follows:

[0011] The method for processing the multi-gene risk score of ankylosing spondylitis is mainly characterized in that the method includes the following steps:

[0012] (1) Collect the whole-genome data of the patient and perform corresponding data preprocessing on it;

[0013] (2) Divide the obtained patient data and healthy control data into a training set and a test set according to a preset ratio, determine the association effect value between each genetic variant gene variant and ankylosing spondylitis AS, and obtain the corresponding associated sites;

[0014] (3) Extract the single nucleotide polymorphisms SNPs related to ankylosing spondylitis AS and calculate the polygenic risk score PRS of the individual by means of weighted summation;

[0015] (4) Perform an ankylosing spondylitis AS risk assessment on the corresponding patient according to the calculated polygenic risk score PRS.

[0016] Preferably, the step (1) is specifically:

[0017] Collect the whole-genome data of the patient and perform preprocessing of genotype data quality control and missing data filling on the obtained data.

[0018] Preferably, the step (2) is specifically:

[0019] Divide the patient data and healthy control data into a training set and a test set according to a preset ratio, apply the Multi-BLUP method to determine the association effect value between each genetic variant gene variant and ankylosing spondylitis AS, obtain the corresponding number of associated sites, and retain the corresponding number of sites with pairwise linkage disequilibrium r2 <= 0.9 for calculating the polygenic risk score PRS of the individual.

[0020] Preferably, the step (3) calculates the polygenic risk score PRS of the individual in the following manner:

[0021]

[0022] wherein, β i is the effect value of the i-th genetic variant gene variant, and dosage i is the number of effect alleles of the i-th genetic locus carried by the individual.

[0023] Preferably, step (4) includes:

[0024] Using the obtained number of loci in an independent test set to evaluate the prediction ability of the model with the ROC curve and AUC value, and obtaining the current ankylosing spondylitis AS risk assessment value of the input patient's genomic data according to the polygenic risk score PRS calculation formula.

[0025] The ankylosing spondylitis polygenic risk score processing system for implementing the above-described method is characterized in that the system includes:

[0026] A gene data acquisition module for acquiring the whole-genome data of a patient;

[0027] A locus screening module connected to the gene data acquisition module, configured to divide the obtained whole-genome data of the patient and a healthy control group into a training set and a test set according to the obtained whole-genome data of the patient, determine the association effect value between each genetic variant gene variant and ankylosing spondylitis AS, and obtain the corresponding associated locus;

[0028] A polygenic risk calculation module connected to the locus screening module, configured to calculate the ankylosing spondylitis AS polygenic risk assessment value of an individual according to the calculated associated locus;

[0029] A score processing module connected to the polygenic risk score calculation module, configured to divide the patient into high, medium, and low risk levels of ankylosing spondylitis AS according to the calculated ankylosing spondylitis AS risk assessment value.

[0030] The device for implementing ankylosing spondylitis polygenic risk score processing is characterized in that the device includes:

[0031] A processor configured to execute computer-executable instructions;

[0032] A memory storing one or more computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the above-described ankylosing spondylitis polygenic risk score processing method are implemented.

[0033] The processor for implementing the ankylosing spondylitis polygenic risk score processing is mainly characterized in that the processor is configured to execute computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the above-mentioned ankylosing spondylitis polygenic risk score processing method are implemented.

[0034] The computer-readable storage medium is mainly characterized in that a computer program is stored thereon, and the computer program can be executed by a processor to implement the steps of the above-mentioned ankylosing spondylitis polygenic risk score processing method.

[0035] By adopting the ankylosing spondylitis polygenic risk score processing method, system, device, processor and computer-readable storage medium of the present invention, by combining thousands of genetic variants (variants) related to ankylosing spondylitis AS, the ankylosing spondylitis polygenic risk score of an individual is calculated, so as to realize the assessment and processing of the ankylosing spondylitis onset risk of the East Asian population; its detection means is simple, the effect is remarkable, and it has relatively prominent practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is an ROC graph for evaluating and processing the ankylosing spondylitis polygenic risk score processing method of the present invention.

[0037] Figure 2 It is a schematic diagram for risk prediction of the ankylosing spondylitis polygenic risk score processing method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0038] In order to be able to more clearly describe the technical content of the present invention, the following will be further described in conjunction with specific embodiments.

[0039] Before detailing the embodiments according to the present invention, it should be noted that hereinafter, the terms "comprising", "including" or any other variant are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes these elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0040] The ankylosing spondylitis polygenic risk score processing method, wherein the method includes the following steps:

[0041] (1) Collect the whole-genome data of the patient and perform corresponding data preprocessing on it;

[0042] (2) Divide the obtained patient data and healthy control data into a training set and a test set according to a preset ratio, determine the association effect value between each genetic variant gene and ankylosing spondylitis (AS), and obtain the corresponding associated loci;

[0043] (3) Extract the single nucleotide polymorphisms (SNPs) related to ankylosing spondylitis (AS), and calculate the polygenic risk score (PRS) of an individual by means of weighted summation;

[0044] (4) Conduct an AS risk assessment for the corresponding patients based on the calculated polygenic risk score (PRS).

[0045] As a preferred embodiment of the present invention, step (1) is specifically as follows:

[0046] Collect the whole genome data of the patients, and perform preprocessing on the obtained data, including genotype data quality control and missing data imputation.

[0047] As a preferred embodiment of the present invention, step (2) is specifically as follows:

[0048] Divide the patient data and healthy control data into a training set and a test set according to a preset ratio, apply the Multi-BLUP method to determine the association effect value between each genetic variant gene and ankylosing spondylitis (AS), thereby obtaining the corresponding number of associated loci, and retain the corresponding number of loci with pairwise linkage disequilibrium r2 <= 0.9 for calculating the polygenic risk score (PRS) of an individual.

[0049] As a preferred embodiment of the present invention, step (3) calculates the polygenic risk score (PRS) of an individual in the following manner:

[0050]

[0051] where β i is the effect value of the i-th genetic variant gene, and dosage i is the number of effect alleles of the i-th genetic locus carried by the individual.

[0052] As a preferred embodiment of the present invention, step (4) includes:

[0053] Use the ROC curve and AUC value in an independent test set to evaluate the prediction ability of the model for the obtained number of loci, and obtain the AS risk assessment value of the current patient according to the polygenic risk score (PRS) calculation formula for the input genomic data of the patient.

[0054] The ankylosing spondylitis polygenic risk score processing system for implementing the above-mentioned method, wherein the system includes:

[0055] A gene data collection module for collecting the whole genome data of patients;

[0056] A locus screening module, connected to the gene data collection module, for dividing the obtained whole genome data of patients and a healthy control group into a training set and a test set according to the obtained whole genome data of patients, determining the association effect value between each genetic variant gene and ankylosing spondylitis (AS), and obtaining the corresponding associated loci;

[0057] A polygenic risk calculation module, connected to the locus screening module, for calculating the polygenic risk assessment value of ankylosing spondylitis (AS) for an individual according to the calculated associated loci;

[0058] A score processing module, connected to the polygenic risk score calculation module, for classifying patients into high, medium, and low risk levels of ankylosing spondylitis (AS) according to the calculated risk assessment value of ankylosing spondylitis (AS).

[0059] The device for implementing the ankylosing spondylitis polygenic risk score processing, wherein the device includes:

[0060] A processor configured to execute computer-executable instructions;

[0061] A memory storing one or more computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the above-mentioned ankylosing spondylitis polygenic risk score processing method are implemented.

[0062] The processor for implementing the ankylosing spondylitis polygenic risk score processing, wherein the processor is configured to execute computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the above-mentioned ankylosing spondylitis polygenic risk score processing method are implemented.

[0063] The computer-readable storage medium, wherein a computer program is stored thereon, and the computer program can be executed by a processor to implement the steps of the above-mentioned ankylosing spondylitis polygenic risk score processing method.

[0064] The following will further elaborate on the technical solution in detail. In a specific embodiment of the present invention:

[0065] Research design process and research population:

[0066] This technical solution developed a polygenic risk score (PRS) for AS in 5,401 AS patients and 4,449 healthy controls, and validated it in 600 AS patients and 494 healthy controls. The diagnosis of ankylosing spondylitis patients in the training set and test set strictly followed the 1984 New York Revised Criteria.

[0067] All studies were approved by the Ethics Review Committee of Shanghai Changzheng Hospital. Each participant signed an informed consent form.

[0068] Genetic variant site selection and genotyping:

[0069] This technical solution used the CoreExome chip of Illumina to perform genotyping to obtain genetic variant information of the detection sites.

[0070] Construction of PRS:

[0071] (1) Extract Variant effect values from the GWAS cohort data and calculate PRS:

[0072] Apply the Multi-BLUP method based on the Bayesian linear model. Its core goal is to estimate the effect value (β) of each genetic variant, rather than performing traditional hypothesis tests (such as calculating p-values). Estimate parameters (such as effect values) through prior and posterior distributions, rather than judging whether a variant is "significant" through significance tests. Determine the association effect value of each variant with AS in 5,401 AS patients and 4,449 healthy individuals in East Asia, and obtain the effect allele and effect value corresponding to AS for each locus obtained from the genome-wide association study of the East Asian population in this technical solution. First, divide the genome into 75,000-base pair blocks, calculate the association between each block and the target trait, screen the blocks according to the significance threshold and merge them into regions, estimate the genetic variance of each region, calculate the effect of genetic variation, obtain 7,579 associated loci within the significant regions, and retain 6,671 loci with pairwise linkage disequilibrium r2 <= 0.9 for calculating individual AS PRS.

[0073] Risk assessment of polygenic genetic risk score for AS:

[0074] According to the results of the training cohort of the genome-wide association study of ankylosing spondylitis in the East Asian population, the number of individual genetic variant risk alleles (0, 1, or 2) is weighted and summed according to their corresponding effect values to obtain the AS PRS score of the test cohort samples. According to different thresholds, predict whether the test samples have AS, judge the prediction ability through the area under the curve of the ROC graph, and the positive risk prediction value and negative risk prediction value of this PRS model under different incidence rates (see Figure 1 ,Figure 2 )。

[0075] In practical applications, such as Figure 1 shown, the discrimination ability of AS PRS and HLA-B27 in the diagnosis of AS is demonstrated. Among them, the AS PRS AUC is significantly better than the AUC of HLA-B27 (P = 3.04x10 -13 ). As Figure 2 shown, when the AS prevalence is 30%, the positive risk prediction value (ordinate in red) and negative risk prediction value (ordinate in blue) of AS PRS for judging whether an individual has AS, and the abscissa is the percentile of AS PRS in the distribution.

[0076] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent a module, segment, or part of code including one or more executable instructions for implementing a specific logical function or process. And the scope of the preferred embodiments of the present invention includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the technical field of the embodiments of the present invention.

[0077] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device.

[0078] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0079] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc.

[0080] In the description of this specification, the descriptions referring to terms such as "an embodiment", "some embodiments", "example", "specific example", or "embodiment", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0081] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0082] By adopting the ankylosing spondylitis polygenic risk score processing method, system, device, processor, and computer-readable storage medium of the present invention, by combining thousands of genetic variants related to ankylosing spondylitis (AS), the polygenic risk score of an individual for ankylosing spondylitis is calculated, thereby realizing the assessment and processing of the onset risk of ankylosing spondylitis in the East Asian population; its detection means is simple, the effect is remarkable, and it has relatively prominent practical value.

[0083] In this specification, the present invention has been described with reference to its specific embodiments. However, it is obvious that various modifications and transformations can still be made without departing from the spirit and scope of the present invention. Therefore, the specification and drawings should be regarded as illustrative rather than restrictive.

Claims

1. A method for processing ankylosing spondylitis polygenic risk score, characterized in that: The method comprises the following steps: (1) Collect the patient's whole genome data and perform corresponding data preprocessing; (2) Divide the acquired patient data and healthy control data into training sets and test sets according to the preset ratio, determine the association effect value between each genetic variant gene variant and AS, and obtain the corresponding association sites; (3) Extract single nucleotide polymorphisms (SNPs) associated with ankylosing spondylitis (AS) and calculate the individual polygenic risk score (PRS) using a weighted summation method; (4) Based on the calculated polygenic risk score (PRS), the risk of ankylosing spondylitis (AS) was assessed for the corresponding patients.

2. The method for processing ankylosing spondylitis polygenic risk score according to claim 1, characterized in that: The step (1) is specifically as follows: The whole genome data of the patients were collected, and the acquired data were preprocessed for quality control of genotype data and filling of missing data.

3. The method for processing ankylosing spondylitis polygenic risk score according to claim 1, characterized in that: The step (2) is specifically as follows: According to the preset ratio, the patient data and healthy control data were divided into training set and test set. The Multi-BLUP method was applied to determine the association effect value between each genetic variant gene variant and ankylosing spondylitis AS, so as to obtain the corresponding number of associated sites, and retain the corresponding number of sites with pairwise linkage disequilibrium r2<=0.9 to calculate the individual's polygenic risk score PRS.

4. The method for processing ankylosing spondylitis polygenic risk score according to claim 3, characterized in that: The step (3) calculates the individual's polygenic risk score PRS in the following manner: Among them, β i is the effect value of the i-th genetic variant gene variant, dosage i is the number of effect alleles of the i-th genetic locus carried by the individual.

5. The method for processing ankylosing spondylitis polygenic risk score according to claim 4, characterized in that: The step (4) comprises: The obtained number of sites was used in an independent test set to evaluate the predictive ability of the model using the ROC curve and AUC value, and the input patient's genomic data was calculated according to the polygenic risk score PRS formula to obtain the patient's current ankylosing spondylitis AS risk assessment value.

6. A polygenic risk score processing system for ankylosing spondylitis for implementing the method according to any one of claims 1 to 5, characterized in that: The system comprises: Gene data collection module, used to collect the patient's whole genome data; A site screening module is connected to the gene data collection module and is used to divide the patient's whole genome data and the healthy control group into a training set and a test set according to the acquired patient's whole genome data, determine the association effect value between each genetic variant gene variant and ankylosing spondylitis AS, and obtain the corresponding association site; A polygenic risk calculation module, connected to the site screening module, is used to calculate an individual's ankylosing spondylitis (AS) polygenic risk assessment value based on the calculated associated sites; The scoring processing module is connected to the polygenic risk score calculation module and is used to classify patients into high, medium and low risk levels of ankylosing spondylitis AS according to the calculated ankylosing spondylitis AS risk assessment value.

7. A device for implementing ankylosing spondylitis polygenic risk scoring processing, characterized in that: The device comprises: a processor configured to execute computer executable instructions; A memory stores one or more computer executable instructions, and when the computer executable instructions are executed by the processor, the steps of the ankylosing spondylitis polygenic risk score processing method according to any one of claims 1 to 5 are implemented.

8. A processor for implementing ankylosing spondylitis polygenic risk scoring processing, characterized in that: The processor is configured to execute computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the ankylosing spondylitis polygenic risk score processing method described in any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program can be executed by a processor to implement the steps of the ankylosing spondylitis polygenic risk scoring processing method according to any one of claims 1 to 5.

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