Ankylosing spondylitis polygenic risk score processing method, system, device, processor and computer readable storage medium thereof

Abstract

The present application relates to a kind of ankylosing spondylitis polygenic risk score processing method, its main features are, the method includes the following steps: (1) the whole genome data of patient is collected, and it is carried out corresponding data preprocessing;(2) according to the preset proportion, the patient data and healthy control data obtained are divided into training set and test set, determine the association effect value between each genetic variant and ankylosing spondylitis AS, and obtain the corresponding association site;(3) extract the single nucleotide polymorphism SNP associated with ankylosing spondylitis AS, and the polygenic risk score PRS of individual is calculated using the weighted summation method;(4) the ankylosing spondylitis AS risk assessment of corresponding patient is carried out according to the polygenic risk score PRS obtained by calculation.This ankylosing spondylitis polygenic risk score processing method of the present application provides an ankylosing spondylitis related genetic variation and onset risk assessment system suitable for east Asian population.

Description

Technical Field

[0001] This invention relates to the field of medical and health care, and more particularly to the field of bioinformatics and genetic data analysis, specifically to a method, system, device, processor, and computer-readable storage medium for processing multi-gene risk scores for ankylosing spondylitis. Background Technology

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

[0003] With the development of genome-wide association studies (GWAS), thousands of genetic variants associated with ankylosing spondylitis (AS) have been identified. Although the impact of each individual SNP on AS is small, the polygenic risk score (PRS), which is calculated by combining these variants, can significantly improve the predictive ability of AS genetic risk. 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 shortcomings of current technologies include:

[0005] Delayed diagnosis: AS has an insidious onset, and its symptoms are easily confused with other back pain, resulting in an average delay of 6-10 years in diagnosis, which also leads to poor clinical prognosis.

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

[0007] Limitations of imaging and CRP testing: MRI is expensive and not readily available in all areas, while CRP has low sensitivity, limited correlation with disease, and cannot detect disease in its early stages.

[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, so as to provide a reference for clinical treatment. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, device, processor and computer-readable storage medium for processing multigene risk scores of ankylosing spondylitis.

[0010] To achieve the above objectives, the ankylosing spondylitis multigene risk score processing method, system, device, processor, and computer-readable storage medium of the present invention are as follows:

[0011] The main feature of this polygenic risk scoring method for ankylosing spondylitis is that the method includes the following steps:

[0012] (1) Collect the patient's whole genome data and perform corresponding data preprocessing;

[0013] (2) According to the preset ratio, the obtained patient data and healthy control data are divided into training set and test set, the association effect value between each genetic variant and ankylosing spondylitis AS is determined, and the corresponding association sites are obtained.

[0014] (3) Extract single nucleotide polymorphisms (SNPs) associated with ankylosing spondylitis (AS) and calculate the individual's polygenic risk score (PRS) using a weighted summation method;

[0015] (4) Based on the calculated polygenic risk score PRS, the corresponding patients were assessed for ankylosing spondylitis (AS) risk.

[0016] Preferably, step (1) specifically comprises:

[0017] Collect patients' whole genome data, and perform genotypic data quality control and missing data imputation preprocessing on the acquired data.

[0018] Preferably, step (2) specifically includes:

[0019] According to a preset ratio, patient data and healthy control data are divided into training set and test set. The Multi-BLUP method is applied to determine the association effect value between each genetic variant and ankylosing spondylitis (AS), thereby obtaining the corresponding number of associated loci. Among them, the corresponding number of loci with pairwise linkage disequilibrium r2 <= 0.9 are retained for calculating the individual's polygenic risk score (PRS).

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

[0021]

[0022] Where, β i The effect value of the i-th genetic variant gene is called dosage. i The number of effect alleles at the i-th genetic locus carried by an individual.

[0023] Preferably, step (4) includes:

[0024] The number of loci obtained was used to evaluate the predictive ability of the model in an independent test set using ROC curves and AUC values. The patient's ankylosing spondylitis (AS) risk assessment value was obtained by calculating the multigene risk score PRS for the input patient's genomic data.

[0025] The ankylosing spondylitis multigene risk scoring system for implementing the above-described method is characterized in that the system comprises:

[0026] The gene data acquisition module is used to collect the patient's whole genome data;

[0027] The site screening module, connected to the gene data acquisition module, is used to divide the acquired patient's whole genome data into a training set and a test set with the healthy control group, determine the association effect value between each genetic variant and ankylosing spondylitis (AS), and obtain the corresponding association site.

[0028] The polygenic risk calculation module, connected to the site screening module, is used to calculate the polygenic risk assessment value of an ankylosing spondylitis (AS) in an individual based on the calculated associated sites.

[0029] The scoring processing module, connected to the multi-gene risk scoring calculation module, is used to classify patients into high, medium, and low risk levels for ankylosing spondylitis (AS) based on the calculated AS risk assessment value.

[0030] The main feature of this device for implementing multi-gene risk scoring for ankylosing spondylitis is that the device comprises:

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

[0032] The memory stores one or more computer-executable instructions, which, when executed by the processor, implement the steps of the ankylosing spondylitis multigene risk score processing method described above.

[0033] The processor used to implement the polygenic risk score processing for ankylosing spondylitis is characterized in that the processor is configured to execute computer-executable instructions, which, when executed by the processor, implement the steps of the polygenic risk score processing method for ankylosing spondylitis described above.

[0034] The computer-readable storage medium is characterized in that it stores a computer program that can be executed by a processor to implement the steps of the ankylosing spondylitis multigene risk score processing method described above.

[0035] The present invention employs a polygenic risk score processing method, system, device, processor, and computer-readable storage medium for ankylosing spondylitis (AS). By combining thousands of genetic variants associated with AS, an individual's polygenic risk score for AS is calculated, thereby enabling the assessment and processing of the risk of developing AS in East Asian populations. The detection method is simple, effective, and has significant practical value. Attached Figure Description

[0036] Figure 1 The ROC plot is used to evaluate the ankylosing spondylitis multigene risk scoring method of the present invention.

[0037] Figure 2 This is a schematic diagram illustrating the risk prediction process of the polygenic risk scoring method for ankylosing spondylitis according to the present invention. Detailed Implementation

[0038] To more clearly describe the technical content of the present invention, the following description is provided in conjunction with specific embodiments.

[0039] Before describing the embodiments of the present invention in detail, it should be noted that, in the following, the terms “comprising,” “including,” or any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0040] The method for processing polygenic risk scores for ankylosing spondylitis includes the following steps:

[0041] (1) Collect the patient's whole genome data and perform corresponding data preprocessing;

[0042] (2) According to the preset ratio, the obtained patient data and healthy control data are divided into training set and test set, the association effect value between each genetic variant and ankylosing spondylitis AS is determined, and the corresponding association sites are obtained.

[0043] (3) Extract single nucleotide polymorphisms (SNPs) associated with ankylosing spondylitis (AS) and calculate the individual's polygenic risk score (PRS) using a weighted summation method;

[0044] (4) Based on the calculated polygenic risk score PRS, the corresponding patients were assessed for ankylosing spondylitis (AS) risk.

[0045] In a preferred embodiment of the present invention, step (1) specifically comprises:

[0046] Collect patients' whole genome data, and perform genotypic data quality control and missing data imputation preprocessing on the acquired data.

[0047] In a preferred embodiment of the present invention, step (2) specifically comprises:

[0048] According to a preset ratio, patient data and healthy control data are divided into training set and test set. The Multi-BLUP method is applied to determine the association effect value between each genetic variant and ankylosing spondylitis (AS), thereby obtaining the corresponding number of associated loci. Among them, the corresponding number of loci with pairwise linkage disequilibrium r2 <= 0.9 are retained for calculating the individual's polygenic risk score (PRS).

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

[0050]

[0051] Where, β i The effect value of the i-th genetic variant gene is called dosage. i The number of effect alleles at the i-th genetic locus carried by an individual.

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

[0053] The number of loci obtained was used to evaluate the predictive ability of the model in an independent test set using ROC curves and AUC values. The patient's ankylosing spondylitis (AS) risk assessment value was obtained by calculating the multigene risk score PRS for the input patient's genomic data.

[0054] The ankylosing spondylitis multigene risk scoring system for implementing the above-described method includes:

[0055] The gene data acquisition module is used to collect the patient's whole genome data;

[0056] The site screening module, connected to the gene data acquisition module, is used to divide the acquired patient's whole genome data into a training set and a test set with the healthy control group, determine the association effect value between each genetic variant and ankylosing spondylitis (AS), and obtain the corresponding association site.

[0057] The polygenic risk calculation module, connected to the site screening module, is used to calculate the polygenic risk assessment value of an ankylosing spondylitis (AS) in an individual based on the calculated associated sites.

[0058] The scoring processing module, connected to the multi-gene risk scoring calculation module, is used to classify patients into high, medium, and low risk levels for ankylosing spondylitis (AS) based on the calculated AS risk assessment value.

[0059] The device for implementing a multi-gene risk score for ankylosing spondylitis includes:

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

[0061] The memory stores one or more computer-executable instructions, which, when executed by the processor, implement the steps of the ankylosing spondylitis multigene risk score processing method described above.

[0062] The processor for implementing ankylosing spondylitis multigene risk score processing is configured to execute computer-executable instructions, which, when executed by the processor, implement the steps of the ankylosing spondylitis multigene risk score processing method described above.

[0063] The computer-readable storage medium contains a computer program that can be executed by a processor to implement the steps of the ankylosing spondylitis multigene risk score processing method described above.

[0064] The technical solution will be described in further detail below, in a specific embodiment of the present invention:

[0065] Research design process and research population:

[0066] This technical approach developed a polygenic risk score (PRS) for ankylosing spondylitis (AS) in 5401 AS patients and 4449 healthy controls, and validated it in 600 AS patients and 494 healthy controls. Diagnosis of ankylosing spondylitis patients in both the training and test sets strictly followed the 1984 New York revised criteria.

[0067] All studies were approved by the Ethics Review Committee of Shanghai Changzheng Hospital. Informed consent was obtained from each participant.

[0068] Selection of genetic variant sites and genotyping:

[0069] This technical solution uses Illumina's CoreExome chip for genotyping to obtain genetic variation information at the detection sites.

[0070] Construction of PRS:

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

[0072] The Multi-BLUP method, based on a Bayesian linear model, aims to estimate the effect size (β) of each genetic variant, rather than performing traditional hypothesis testing (such as calculating p-values). Parameters (e.g., effect sizes) are estimated using prior and posterior distributions, rather than relying on significance tests to determine if a variant is "significant." In 5401 AS patients and 4449 healthy individuals in East Asia, the association effect size between each variant and AS was determined, and the effect alleles and effect sizes corresponding to AS at each locus were obtained from a genome-wide association study in the East Asian population. First, the genome was divided into 75,000-base-pair blocks. The association between each block and the target trait was calculated. Blocks were screened and merged into regions based on a significance threshold. The genetic variance of each region was estimated, and the effect of the genetic variant was calculated. 7579 associated loci were obtained within the significant regions, and 6671 loci with pairwise linkage disequilibrium r² <= 0.9 were retained for calculating individual AS PRS.

[0073] Polygenic genetic risk score for AS risk assessment:

[0074] Based on the training cohort results of the GWAS (Genetic Genetic Assay) for ankylosing spondylitis in East Asian populations, the number of risk alleles (0, 1, or 2) for individual genetic variations was weighted and summed according to their corresponding effect values ​​to obtain the AS PRS score for the test cohort samples. Based on different thresholds, the test samples were used to predict whether they had AS. The predictive ability was evaluated using the area under the ROC curve. Furthermore, the positive and negative risk predictive values ​​of this PRS model under different incidence rates were also analyzed (see...). Figure 1 , Figure 2 ).

[0075] In practical applications, such as Figure 1 As shown, the differential diagnostic ability of AS PRS and HLA-B27 in diagnosing AS is demonstrated. The AUC of AS PRS is significantly superior to that of HLA-B27 (P = 3.04 x 10⁻⁶). -13 ).like Figure 2 As shown, when the AS prevalence is 30%, the AS PRS predicts the positive risk (red on the y-axis) and negative risk (blue on the y-axis) of whether an individual has AS. The x-axis represents the percentile of the AS PRS in the distribution.

[0076] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

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

[0078] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0079] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0080] In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "embodiment," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

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

[0082] The present invention employs a polygenic risk score processing method, system, device, processor, and computer-readable storage medium for ankylosing spondylitis (AS). By combining thousands of genetic variants associated with AS, an individual's polygenic risk score for AS is calculated, thereby enabling the assessment and processing of the risk of developing AS in East Asian populations. The detection method is simple, effective, and has significant practical value.

[0083] In this specification, the invention has been described with reference to specific embodiments thereof. However, it will be apparent that various modifications and variations can be made without departing from the spirit and scope of the invention. Therefore, the specification and drawings should be considered illustrative rather than restrictive.

Claims

1. A method for processing polygenic risk scores in ankylosing spondylitis, characterized in that, The method includes the following steps: (1) Collect whole genome data of individual patients in East Asian populations and perform corresponding data preprocessing; (2) According to the preset ratio, the obtained patient data and healthy control data are divided into training set and test set, the association effect value between each genetic variant and ankylosing spondylitis AS is determined, and the corresponding association sites are obtained. Step (2) specifically refers to: According to a preset ratio, patient data and healthy control data were divided into training and test sets. Using the Multi-BLUP method, the genome was first divided into 75,000-base-pair blocks. The association between each block and the target trait was calculated. Blocks were then selected based on a significance threshold and merged into regions. The genetic variance of each region was estimated to determine the association effect value between each genetic variant and ankylosing spondylitis (AS), thereby obtaining the corresponding number of associated loci and retaining pairwise linkage disequilibrium (r). 2 The corresponding number of loci <= 0.9 are used to calculate the individual's polygenic risk score (PRS). (3) Extract single nucleotide polymorphisms (SNPs) associated with ankylosing spondylitis (AS) and calculate the individual's polygenic risk score (PRS) using a weighted summation method; Step (3) involves calculating the individual's polygenic risk score (PRS) as follows: Where, β i The effect value of the i-th genetic variant gene is called dosage. i This represents the number of effect alleles at the i-th genetic locus carried by an individual. (4) Based on the calculated polygenic risk score PRS, the corresponding patients were assessed for ankylosing spondylitis (AS) risk. Step (4) includes: The number of loci obtained was used to evaluate the predictive ability of the model in an independent test set using ROC curves and AUC values. The genomic data of newly input East Asian individuals were used to calculate the current ankylosing spondylitis (AS) risk assessment value for the patient according to the polygenic risk score (PRS) formula.

2. The method for processing polygenic risk scores for ankylosing spondylitis according to claim 1, characterized in that, The specific steps (1) are as follows: Collect patients' whole genome data, and perform genotypic data quality control and missing data imputation preprocessing on the acquired data.

3. A polygenic risk scoring system for ankylosing spondylitis used to implement the method of any one of claims 1 to 2, characterized in that, The system includes: The gene data acquisition module is used to collect whole genome data from individual patients in the East Asian population. The locus screening module, connected to the gene data acquisition module, is used to divide the acquired patient's whole genome data into a training set and a test set, along with a healthy control group. It determines the association effect value between each genetic variant and ankylosing spondylitis (AS), obtains the corresponding association loci, and performs linkage disequilibrium analysis. 2 Filtering for values ​​≤0.9; The polygenic risk calculation module, connected to the site screening module, is used to calculate the polygenic risk assessment value of an ankylosing spondylitis (AS) in an individual based on the calculated associated sites. The scoring processing module, connected to the multi-gene risk scoring calculation module, is used to classify patients into high, medium, and low risk levels for ankylosing spondylitis (AS) based on the calculated AS risk assessment value.

4. A device for implementing multi-gene risk scoring for ankylosing spondylitis, characterized in that, The device includes: A processor is configured to execute computer-executable instructions; The memory stores one or more computer-executable instructions, which, when executed by the processor, implement the steps of the ankylosing spondylitis multigene risk score processing method as described in any one of claims 1 to 2.

5. A processor for implementing multi-gene risk scoring for ankylosing spondylitis, characterized in that, The processor is configured to execute computer-executable instructions, which, when executed by the processor, implement the steps of the ankylosing spondylitis multigene risk score processing method as described in any one of claims 1 to 2.

6. A computer-readable storage medium, characterized in that, It stores a computer program that can be executed by a processor to implement the steps of the ankylosing spondylitis multigene risk score processing method according to any one of claims 1 to 2.

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