Method, device, equipment and medium for screening endometriosis causal genes through cross-brain region genetic regulatory integration analysis
By integrating brain region gene expression and GWAS data, and using SMR and HEIDI algorithms, key causal genes for endometriosis were screened out, solving the problem of lack of causal inference in cross-system genetic regulation in existing technologies, and realizing cross-system disease mechanism research and target discovery.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- FOSHAN MATERNAL & CHILD HEALTH CARE HOSPITAL
- Filing Date
- 2026-03-02
- Publication Date
- 2026-06-05
Smart Images
Figure CN122157765A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of biomedical testing technology, and in particular to a method, device, equipment and medium for screening causal genes of endometriosis through cross-brain region genetic regulation integration analysis. Background Technology
[0002] Endometriosis (EMs) is a chronic, hormone-dependent disease characterized by ectopic implantation and growth of endometrial-like tissue in the uterus. Common clinical manifestations include dysmenorrhea, chronic pelvic pain, and infertility. The pathogenesis of this disease is complex, involving the interaction of multiple factors such as abnormal hormonal regulation, imbalanced immune and inflammatory responses, and neural network remodeling. Epidemiological studies have shown significant familial aggregation and genetic susceptibility. In recent years, large-scale genome-wide association studies (GWAS) have identified multiple genetic loci associated with EMs risk, further validating its genetic basis.
[0003] However, current research largely focuses on the genetic regulatory mechanisms of local pelvic tissues (such as the endometrium and ovaries), lacking a systematic exploration of the role of the central nervous system in disease development. Clinical observations have revealed that endometriosis (EMs) patients commonly experience central nervous system-related symptoms such as chronic pain sensitization, mood disorders, cognitive decline, and sleep disturbances, suggesting the possible existence of cross-system regulatory mechanisms beyond pelvic lesions. However, there are currently no publicly available reports systematically assessing at the genetic level whether brain region gene expression regulation has a causal impact on the overall risk of endometriosis, nor is there a reproducible analytical procedure for integrating brain region transcriptome regulatory data with large-sample GWAS results. Therefore, current technology lacks a reproducible technical solution for integrating brain region gene regulation data with endometriosis GWAS results and conducting causal inferences. Summary of the Invention
[0004] To address the shortcomings of the existing technologies, this application provides a method, device, equipment, and medium for screening causal genes in endometriosis through cross-brain region genetic regulation integration analysis. By combining causal inference from brain region expression quantitative trait loci (eQTLs) and genome-wide association data, this method enables the identification of cross-system genetic regulatory pathways. This method can be used to explore the distant genetic mechanisms of central regulation in the occurrence of endometriosis, breaking through the limitations of traditional research that focuses on local pelvic lesions. It provides theoretical and technical support for the analysis of disease risk-related mechanisms, screening of candidate causal genes, and cross-system intervention strategies.
[0005] In a first aspect, this application provides a method for screening causal genes in endometriosis through cross-brain region genetic regulation integration analysis, including:
[0006] For eQTL data of major human brain regions, cis-regulatory sites were screened for each coding gene, candidate SNPs were selected, and a library of instrumental variables was compiled.
[0007] The GWAS summary statistics of the overall risk of EMs were used as the outcome data for standardization, and the nomenclature and effects of candidate SNPs were corrected to obtain a standardized GWAS statistical table.
[0008] Using the aforementioned instrumental variable library as the genetic exposure basis for causal inference, and targeting the standardized GWAS statistical table, the SMR algorithm is used to integrate the effect of each candidate SNP on gene expression in the brain region with the effect of the candidate SNP on the risk of EMs, to obtain the SMR screening results. The SMR screening results include estimated causal information and candidate genes.
[0009] The HEIDI test was introduced to detect local heterogeneity of the screened candidate genes, retaining high-confidence causal candidates and obtaining the HEIDI screening results.
[0010] By integrating the SMR screening results and HEIDI screening results, a set of key genes with stable causal association between the homeostatic expression level of brain regions and the risk of EMs was identified, and the set of key genes was output as the gene screening results.
[0011] Optionally, for eQTL data from major human brain regions, cis-regulatory sites are screened for each coding gene, candidate SNPs are selected, and a library of instrumental variables is compiled, including:
[0012] Retrieved eQTL data for major human brain regions from the database;
[0013] We constructed genetic proxy variables for brain region expression, and screened cis-regulatory sites as candidate SNPs in the upstream and downstream ranges for each coding gene in eQTL data;
[0014] Based on candidate SNPs, a set of independent and significant genetic proxy sites for each gene were obtained in different brain regions, and these were compiled to form a highly reliable tool variable library covering the genes.
[0015] Optionally, the GWAS summary statistics of the overall risk of EMs can be used as outcome data for standardization, and the nomenclature and effects of candidate SNPs can be corrected to obtain a standardized GWAS statistical table, including:
[0016] The overall risk of EMs is obtained from the preset project as the GWAS summary statistical results as the outcome data, which includes the GWAS summary statistical data of the case group and the control group;
[0017] The outcome data were standardized to a predetermined reference version, and SNP nomenclature and effects were corrected for consistency.
[0018] Ambiguous sites are removed from the homogenization correction data, and intersection points are extracted using PLINK or bcftools. Basic quality control is then performed to obtain a standardized GWAS statistical table that is directly integrated with the eQTL data.
[0019] Optionally, using the instrumental variable library as the genetic exposure basis for causal inference, and targeting the standardized GWAS statistical table, the SMR algorithm is used to integrate the effects of each candidate SNP with the corresponding EMs risk to obtain SMR screening results, including:
[0020] Using an instrumental variable library as the genetic exposure basis for causal inference, we model cross-system causal inference based on the SMR model;
[0021] To perform the causal inference process, for the standardized GWAS statistical table, the SMR algorithm is used to integrate the effect of each candidate SNP on gene expression in the brain region with the effect of the candidate SNP on the risk of EMs, and to estimate the potential causal effect of brain region homeostasis on the overall risk of EMs.
[0022] Based on the causal effect analysis of SMR, the causal information of each gene in the corresponding brain region is output, and candidate genes are screened.
[0023] Optionally, the HEIDI test can be introduced to detect local heterogeneity of the screened candidate genes, retaining high-confidence causal candidates and obtaining the HEIDI screening results, including:
[0024] The HEIDI test was introduced to screen candidate genes, using relevant SNPs in the vicinity of the candidate site as the detection window to assess local heterogeneity;
[0025] Based on the local heterogeneity, SMR signals that conform to the true association driven by a single pathological variant are determined, high-confidence causal candidates are screened, and candidate genes affected by multiple LD signals are removed to obtain HEIDI screening results.
[0026] Optionally, the SMR screening results and HEIDI screening results are integrated to identify a set of key genes whose homeostatic expression levels in brain regions have a stable causal association with the risk of EMs, including:
[0027] By introducing causal gene identification and functional annotation, and integrating the SMR screening results and HEIDI screening results, key genes with significant causal effects on EMs risk at the brain region expression level were identified, resulting in a set of key genes.
[0028] Optionally, each key gene in the key gene set includes at least ABO, INO80E, HCG22, and members of the YRNA family.
[0029] Secondly, this application provides a device for screening causal genes in endometriosis through cross-brain region genetic regulation integration analysis, comprising:
[0030] The variable tool construction module is used to screen cis-regulatory sites, select candidate SNPs, and summarize them into a tool variable library for eQTL data of major human brain regions, on a per-gene basis.
[0031] The data integration and standardization module is used to unify the GWAS summary statistical results of the overall risk of EMs as the outcome data, and to correct the nomenclature and effects of candidate SNPs to obtain a standardized GWAS statistical table.
[0032] The causal inference module is used to integrate the effect of each candidate SNP on gene expression in the brain region with the effect of the candidate SNP on the risk of EMs using the SMR algorithm for the standardized GWAS statistical table, and obtain the SMR screening results. The SMR screening results include estimated causal information and screened candidate genes.
[0033] The HEIDI test module is used to introduce the HEIDI test to perform local heterogeneity detection on the screened candidate genes, retain high-confidence causal candidates, and obtain HEIDI screening results.
[0034] The integration module is used to integrate SMR screening results and HEIDI screening results, identify a set of key genes that have a stable causal association between the homeostatic expression level of brain regions and the risk of EMs, and output the set of key genes as the gene screening results.
[0035] Thirdly, this application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;
[0036] Memory, used to store computer programs;
[0037] When a processor executes a program stored in memory, it implements the steps of the endometriosis causal gene screening method for cross-brain region genetic regulation integration analysis as described in any embodiment of the first aspect.
[0038] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the endometriosis causal gene screening method for cross-brain region genetic regulation integration analysis as described in any embodiment of the first aspect.
[0039] In summary, this application proposes a causal inference method combining quantitative trait loci expressed in brain regions and genome-wide association data (GWAS). First, eQTL data for major human brain regions are acquired. Cis-regulatory sites are screened for each coding gene, and candidate SNPs are selected. A library of instrumental variables is compiled as the genetic exposure basis for causal inference. The GWAS summary statistical results of the overall risk of end-stage renal diseases (EMs) are used as outcome data for standardization. Candidate SNP nomenclature and effects are corrected to obtain a standardized GWAS statistical table. Then, gene expression and disease causal effect estimation using the SMR model are introduced. For the standardized GWAS statistical table, the SMR algorithm is used to integrate the effect of each candidate SNP on brain region gene expression with the effect of the candidate SNP on EMs risk, obtaining SMR screening results. The HEIDI test is introduced to control for linkage disequilibrium effects. Local heterogeneity is detected in the screened candidate genes, retaining highly reliable causal candidates, resulting in HEIDI screening results. Finally, the SMR and HEIDI screening results are integrated to determine the set of key genes with a stable causal association between the steady-state expression level of brain regions and EMs risk, and this set of key genes is output as the gene screening results. As can be seen, this application establishes for the first time a causal inference analysis process based on the integration of brain region gene regulation and endometriosis GWAS at the cross-system level, breaking through the limitations of previous studies that only focused on local lesions in the pelvic cavity, and providing a new technical path for disease mechanism research, cross-system target discovery and non-invasive genetic risk assessment research. Attached Figure Description
[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0042] Figure 1 A flowchart illustrating a method for screening causal genes in endometriosis through cross-brain region genetic regulation integration analysis, provided in an embodiment of this application.
[0043] Figure 2 This is a flowchart illustrating the steps of a method for screening causal genes in endometriosis through cross-brain region genetic regulation integration analysis, provided in an optional embodiment of this application.
[0044] Figure 3 A structural block diagram of a causal gene screening device for endometriosis based on cross-brain region genetic regulation integration analysis provided in this application embodiment;
[0045] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0046] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0047] To facilitate understanding of the embodiments of this application, further explanations and descriptions will be provided below in conjunction with the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of this application.
[0048] Figure 1 The flowchart illustrates a method for screening causal genes in endometriosis through cross-brain region genetic regulation integration analysis, which is provided for embodiments of this application. The method may specifically include the following steps:
[0049] Step 110: For eQTL data of major human brain regions, cis-regulatory sites are screened for each coding gene, candidate SNPs are selected, and the instrumental variable library is obtained by summarizing them.
[0050] In this embodiment, the GTEx v8 database was used to collect cis-expression quantitative trait loci (cis-eQTL) data from 13 major human brain regions (including the prefrontal cortex, cingulate cortex, thalamus, hippocampus, and other central regulatory core regions), which were used as eQTL data. Cis-regulatory sites, including single nucleotide polymorphisms (SNPs), were screened for each coding gene in the eQTL data to obtain candidate SNPs. Through the above processing, a set of independent and significant genetic proxy sites were obtained for each gene in different brain regions. These were then compiled into a high-reliability instrument variable library covering tens of thousands of genes, providing a genetic exposure basis for subsequent causal inference.
[0051] Step 120: The GWAS summary statistical results of the overall risk of EMs are used as the outcome data for standardization, and the nomenclature and effects of candidate SNPs are corrected to obtain a standardized GWAS statistical table.
[0052] In practice, the GWAS summary statistics of the overall risk of endometriosis from the FinnGen R11 project can be used as the outcome data. This outcome data includes 18,260 patients diagnosed with endometriosis and approximately 119,000 control individuals. The outcome data are standardized to a predetermined reference version, and SNP nomenclature, effect allele direction, and effect value meaning are consistent and corrected to achieve site alignment, resulting in a standardized GWAS statistical table that can be directly integrated with eQTL data.
[0053] Step 130: Using the instrumental variable library as the genetic exposure basis for causal inference, and targeting the standardized GWAS statistical table, the SMR algorithm is used to integrate the effect of each candidate SNP on gene expression in the brain region with the effect of the candidate SNP on the risk of EMs to obtain the SMR screening results.
[0054] The SMR screening results include estimated causal information and candidate genes.
[0055] After completing site alignment, this embodiment performs cross-system causal inference modeling based on the SMR model. Specifically, the summary-data based Mendelian randomization (SMR) algorithm is used to integrate the effect of each SNP on gene expression in the brain region with the effect of the SNP on the risk of EMs. Based on the results of the SMR analysis (including estimated causal information), a predetermined strategy is used to screen preliminary candidate genes.
[0056] Step 140: Introduce the HEIDI test to perform local heterogeneity detection on the screened candidate genes, retain high-confidence causal candidates, and obtain the HEIDI screening results.
[0057] In practice, to eliminate spurious causal associations caused by linkage disequilibrium (LD), the candidate genes obtained in the preceding steps are further subjected to HEIDI (Heterogeneity In Dependent Instruments) testing. Significantly associated loci corresponding to the candidate genes are used as candidate loci. HEIDI analysis selects SNPs from these candidate loci to assess local heterogeneity, thereby detecting local heterogeneity and retaining high-confidence causal candidates as the HEIDI screening results. Non-high-confidence causal candidates are discarded.
[0058] Step 150: Integrate the SMR screening results and HEIDI screening results to determine the set of key genes that have a stable causal association between the homeostatic expression level of brain regions and the risk of EMs, and output the set of key genes as the gene screening results.
[0059] Based on the above steps, this embodiment obtained the screening results of SMR and HEIDI. By integrating the two screening results, key genes with stable causal association between the steady-state expression level of brain regions and the risk of EMs can be obtained, forming a set of key genes, which are used as the gene screening results.
[0060] As can be seen, the embodiments of this application establish for the first time a causal inference analysis process based on the integration of brain region gene regulation and endometriosis GWAS at the cross-system level, breaking through the limitations of previous studies that only focused on local lesions in the pelvic cavity, and providing a new technical path for disease mechanism research, cross-system target mining and non-invasive genetic risk assessment research.
[0061] Reference Figure 2 This illustration shows a flowchart of a method for screening causal genes in endometriosis through cross-brain region genetic regulation integration analysis, provided in an optional embodiment of this application. The method may specifically include the following steps:
[0062] Step 210: For eQTL data of major human brain regions, cis-regulatory sites are screened for each coding gene, candidate SNPs are selected, and the instrumental variable library is obtained by summarizing them.
[0063] In an optional embodiment, the above-mentioned eQTL data for major human brain regions, on a per-gene basis, screens cis-regulatory sites, selects candidate SNPs, and summarizes them to obtain an instrumental variable library. Specifically, this may include: obtaining eQTL data for major human brain regions from a database; constructing brain region expression genetic proxy variables, screening cis-regulatory sites in the upstream and downstream ranges of each coding gene in the eQTL data as candidate SNPs; and based on the candidate SNPs, obtaining a set of independent and significant genetic proxy sites for each gene in different brain regions, and summarizing them to form a highly reliable instrumental variable library covering genes.
[0064] In practice, for each coding gene in the eQTL data, candidate SNPs that meet predetermined criteria are screened within a certain range upstream and downstream. Preferably, for each coding gene, SNPs that meet the criteria are screened within a ±2000kb range upstream and downstream.
[0065] Candidate SNPs can meet the following criteria (or requirements): (1) minor allele frequency (MAF) greater than 1%; (2) eQTL association significance P value less than (3) It meets the requirements of site uniqueness and independence.
[0066] Ultimately, we obtain highly reliable instrumental variables (IVs) covering tens of thousands of genes, thus creating an instrumental variable library.
[0067] Step 220: The GWAS summary statistics of the overall risk of EMs are used as the outcome data for standardization, and the nomenclature and effects of candidate SNPs are corrected to obtain a standardized GWAS statistical table.
[0068] Optionally, the GWAS summary statistical results of the overall risk of EMs can be used as outcome data for standardization, and the nomenclature and effects of candidate SNPs can be corrected to obtain a standardized GWAS statistical table. Specifically, this may include: obtaining the GWAS summary statistical results of the overall risk of EMs from a preset project as outcome data, wherein the outcome data includes the GWAS summary statistical data of the case group and the control group; standardizing the outcome data to a predetermined reference version and performing consistency correction on the nomenclature and effects of SNPs; removing ambiguous sites from the consistency-corrected data, extracting intersection sites through PLINK or bcftools, performing basic quality control, and obtaining a standardized GWAS statistical table that is directly integrated with eQTL data.
[0069] In practice, outcome data includes EM-diagnosed patients and control individuals. To achieve precise matching with brain region eQTL data and ensure high statistical power, precise site alignment and cross-platform compatibility between eQTL and GWAS data can be achieved through methods such as unified genome versioning, standardized allele nomenclature, and removal of palindromic SNPs.
[0070] Specifically, this embodiment unifies the outcome data and eQTL data to the hg38 / GRCh38 reference version, and performs consistency correction on SNP nomenclature, effect allele direction, and effect value meaning to obtain consistency-corrected data. Ambiguous sites of palindromic SNPs and MAFs are further removed from the consistency-corrected data to avoid directional errors; in actual experiments, nearly 50% of ambiguous sites can be removed.
[0071] Next, intersection sites are extracted using PLINK or bcftools, and basic quality control such as missing rate is performed to obtain a standardized GWAS statistical table that can be directly integrated with eQTL data.
[0072] Step 230: Using the instrumental variable library as the genetic exposure basis for causal inference, model cross-system causal inference based on the SMR model.
[0073] Step 240: Perform the causal inference process. For the standardized GWAS statistical table, use the SMR algorithm to integrate the effect of each candidate SNP on brain region gene expression with the effect of candidate SNP on EMs risk, and estimate the potential causal effect of brain region homeostasis expression on the overall risk of EMs.
[0074] Step 250: Based on the causal effect of SMR analysis, output the causal information of each gene in the corresponding brain region and screen candidate genes.
[0075] A unified explanation of steps 230-250 is provided below:
[0076] In practical implementation, a SMR algorithm driven by aggregated statistical data is used for analysis, integrating the effect values of each SNP on gene expression (i.e., ) and the effect size on disease risk (i.e. A causal inference model of gene expression level on the risk of endometriosis is constructed, which can be used to perform the causal inference analysis process.
[0077] The causal inference process is performed, and the potential causal effect of brain region homeostasis on the overall risk of endometriosis is estimated gene by gene for the two integrated effect values. The effect estimate, standard error and significance level of each gene in the corresponding brain region output by SMR analysis are obtained as causal information.
[0078] By introducing a dual threshold, the P-value of SMR can be set to <0.05 and the false discovery rate (FDR) to <0.05. The dual threshold strategy is used to screen preliminary candidate genes (which can be understood as significant candidate pathogenic genes), i.e. SMR P <0.05 and FDR <0.05.
[0079] Step 260: The HEIDI test is introduced to evaluate the local heterogeneity of the screened candidate genes by using the relevant SNPs in the vicinity of the candidate site as the detection window.
[0080] Step 270: Based on the local heterogeneity, determine the SMR signal that matches the true association driven by a single pathological variant, screen high-confidence causal candidates, and remove candidate genes affected by multiple LD signals to obtain the HEIDI screening results.
[0081] A unified explanation of steps 260-270 is provided below:
[0082] To eliminate false positive associations caused by linkage disequilibrium, the HEIDI test was introduced to detect local heterogeneity of significant candidate genes in all SMR analyses. Specifically, during the analysis, 5-20 relevant SNPs in the vicinity of the candidate site were used as the detection window to assess local heterogeneity. Only when HEIDI P>0.05 was the SMR signal determined to be a true association driven by a single pathological variant, and thus retained as a high-confidence causal candidate; the rest were considered to be possibly affected by multiple LD signals and were eliminated to ensure that the identified causal effect originated from a single genetic variant rather than a complex LD structure.
[0083] Step 280: Integrate the SMR screening results and HEIDI screening results to determine the set of key genes that have a stable causal association between the homeostatic expression level of brain regions and the risk of EMs, and output the set of key genes as the gene screening results.
[0084] Optionally, the above-mentioned integration of SMR screening results and HEIDI screening results to determine the set of key genes that have a stable causal association between the steady-state expression level of brain regions and the risk of EMs includes: introducing causal gene identification and functional annotation, integrating the SMR screening results and HEIDI screening results, identifying key genes that have a significant causal effect on the risk of EMs at the brain region expression level, and obtaining the set of key genes.
[0085] In practical implementation, the key gene set includes at least ABO, INO80E, HCG22, and members of the YRNA family. See Table 1:
[0086] In this application, by combining the screening results of SMR and HEIDI, a set of key genes with a stable causal association between the homeostatic expression level of brain regions and the risk of endometriosis was identified. Ultimately, four genes with significant causal effects on the risk of endometriosis at the brain region expression level were selected:
[0087] ① ABO (ENSG00000175164): Encodes a blood group-related glycosyltransferase that is involved in the regulation of cerebrovascular function and immune barrier, including angiogenesis, cell adhesion and inflammation regulation. Its high expression is associated with reduced risk (OR=0.875, FDR=0.0001).
[0088] ② INO80E (ENSG00000169592): A core subunit of the chromatin remodeling complex, regulating neural development and transcriptional activity, including regulating gene transcription and neural plasticity. High expression has a protective effect (OR=0.805, FDR=0.011).
[0089] ③ HCG22 (ENSG00000228789): a long non-coding RNA that regulates immune responses and post-transcriptional mechanisms and has immunomodulatory potential. Its high expression increases risk (OR=1.163, FDR=0.005).
[0090] ④ YRNA family member (ENSG00000201451): a small RNA gene involved in RNA stability and stress response, and high expression is associated with increased risk (OR=1.204, FDR=0.036).
[0091] The results of the cross-brain region genetic regulation integration analysis to screen for causal candidate genes (i.e., causal genes) for endometriosis are summarized in Table 1 below:
[0092]
[0093] Table 1
[0094] In summary, this application's embodiments first establish a new paradigm for cross-system causal inference. Specifically, this embodiment integrates quantitative trait loci expressed in brain regions with GWAS statistical data on endometriosis for the first time, constructing a cross-brain region-pelvic genetic causal analysis pathway. This breaks through the limitations of previous studies that focused only on the genetic risk of local pelvic tissues, providing a new tool for exploring the neuro-immune-endocrine interaction mechanism. Second, the technical solution of this embodiment establishes a complete process from data extraction, instrumental variable screening, model construction to linkage disequilibrium correction. The screening parameters and algorithms are publicly available, reproducible, and scalable, facilitating rapid application by other researchers and clinical translation teams. The standardized process has strong reproducibility. Third, the key gene screening results obtained through the technical solution of this embodiment have clinical and research value. This embodiment identifies key brain region expressed genes such as ABO, INO80E, HCG22, and YRNA, revealing a directional causal link between central regulation and the risk of endometriosis, providing a clear basis for subsequent target research, drug screening, and genetic risk assessment.
[0095] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should know that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps may be performed in other orders or simultaneously.
[0096] like Figure 3 As shown in the embodiments of this application, an endometriosis causal gene screening device 300 for cross-brain region genetic regulation integration analysis is also provided, comprising:
[0097] The variable tool construction module 310 is used to screen cis-regulatory sites, select candidate SNPs, and summarize the eQTL data of major human brain regions on a per-coding gene basis to obtain a tool variable library.
[0098] The data integration and standardization module 320 is used to unify the GWAS summary statistical results of the overall risk of EMs as the outcome data, and to correct the nomenclature and effects of candidate SNPs to obtain a standardized GWAS statistical table.
[0099] The causal inference module 330 is used to integrate the effect of each candidate SNP on gene expression in the brain region with the effect of the candidate SNP on the risk of EMs using the SMR algorithm for the standardized GWAS statistical table, and obtain the SMR screening results. The SMR screening results include estimated causal information and screened candidate genes.
[0100] HEIDI test module 340 is used to introduce HEIDI test to perform local heterogeneity detection on the screened candidate genes, retain high-confidence causal candidates, and obtain HEIDI screening results;
[0101] The integration module 350 is used to integrate the SMR screening results and the HEIDI screening results, identify the set of key genes that have a stable causal relationship between the steady-state expression level of brain regions and the risk of EMs, and output the set of key genes as the gene screening results.
[0102] It should be noted that the endometriosis causal gene screening device for cross-brain region genetic regulation integration analysis provided in the embodiments of this application can execute the endometriosis causal gene screening method for cross-brain region genetic regulation integration analysis provided in any embodiment of this application, and has the corresponding functions and beneficial effects of the method.
[0103] In a specific implementation, the aforementioned device can be integrated into a device that can combine eQTL and GWAS data. Through causal effect estimation using an SMR model and the HEIDI test, it can analyze key genes with significant causal effects on endometriosis risk at the brain region expression level. As an electronic device, it enables cross-system causal inference and can identify potential pathogenic pathways and candidate causal genes of centrally expressed factors. This electronic device can consist of two or more physical entities, or it can consist of a single physical entity. For example, an electronic device can be a personal computer (PC), a computer, a server, etc. This application does not impose specific limitations on this.
[0104] like Figure 4As shown, this application provides an electronic device including a processor 111, a communication interface 112, a memory 113, and a communication bus 114. The processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114. The memory 113 is used to store computer programs. When the processor 111 executes the program stored in the memory 113, it implements the steps of the endometriosis causal gene screening method based on cross-brain region genetic regulation integration analysis provided in any of the aforementioned method embodiments. For example, the method may include the following steps: For eQTL data of major human brain regions, cis-regulatory sites are screened for each coding gene, candidate SNPs are selected, and a library of instrumental variables is compiled; the GWAS summary statistical results of the overall risk of EMs are used as outcome data for standardization, and the nomenclature and effects of candidate SNPs are corrected to obtain a standardized GWAS statistical table; using the instrumental variable library as the genetic exposure basis for causal inference, the SMR algorithm is used to integrate the effect of each candidate SNP on gene expression in the brain region with the effect of the candidate SNP on the risk of EMs, to obtain SMR screening results, which include estimated causal information and candidate genes; the HEIDI test is introduced to detect local heterogeneity of the screened candidate genes, retaining highly reliable causal candidates, to obtain HEIDI screening results; the SMR screening results and HEIDI screening results are integrated to determine the set of key genes that have a stable causal association between the steady-state expression level of the brain region and the risk of EMs, and the set of key genes is output as the gene screening results.
[0105] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the endometriosis causal gene screening method based on cross-brain region genetic regulation integration analysis as provided in any of the foregoing method embodiments.
[0106] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof 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 elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0107] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.
Claims
1. A method for screening causal genes in endometriosis through cross-brain region genetic regulation integration analysis, characterized in that, include: For eQTL data of major human brain regions, cis-regulatory sites were screened for each coding gene, candidate SNPs were selected, and a library of instrumental variables was compiled. The GWAS summary statistics of the overall risk of EMs were used as the outcome data for standardization, and the nomenclature and effects of candidate SNPs were corrected to obtain a standardized GWAS statistical table. Using the aforementioned instrumental variable library as the genetic exposure basis for causal inference, and targeting the standardized GWAS statistical table, the SMR algorithm is used to integrate the effect of each candidate SNP on gene expression in the brain region with the effect of the candidate SNP on the risk of EMs, to obtain the SMR screening results. The SMR screening results include estimated causal information and candidate genes. The HEIDI test was introduced to detect local heterogeneity of the screened candidate genes, retaining high-confidence causal candidates and obtaining the HEIDI screening results. By integrating the SMR screening results and HEIDI screening results, a set of key genes with stable causal association between the homeostatic expression level of brain regions and the risk of EMs was identified, and the set of key genes was output as the gene screening results.
2. The method according to claim 1, characterized in that, For eQTL data from major human brain regions, cis-regulatory sites were screened for each coding gene, candidate SNPs were selected, and a library of instrumental variables was compiled, including: Retrieved eQTL data for major human brain regions from the database; We constructed genetic proxy variables for brain region expression and screened cis-regulatory sites as candidate SNPs in the upstream and downstream ranges for each coding gene in eQTL data. Based on candidate SNPs, a set of independent and significant genetic proxy sites for each gene were obtained in different brain regions, and these were compiled to form a highly reliable tool variable library covering the genes.
3. The method according to claim 1, characterized in that, The GWAS summary statistics of the overall risk of EMs were used as outcome data for standardization, and the nomenclature and effects of candidate SNPs were corrected to obtain a standardized GWAS statistical table, including: The overall risk of EMs is obtained from the preset project as the GWAS summary statistical results as the outcome data, which includes the GWAS summary statistical data of the case group and the control group; The outcome data were standardized to a predetermined reference version, and SNP nomenclature and effects were corrected for consistency. Ambiguous sites are removed from the homogenization correction data, and intersection points are extracted using PLINK or bcftools. Basic quality control is then performed to obtain a standardized GWAS statistical table that is directly integrated with the eQTL data.
4. The method according to claim 1, characterized in that, Using the aforementioned instrumental variable library as the genetic exposure basis for causal inference, and targeting the standardized GWAS statistical table, the SMR algorithm is used to integrate the effects of each candidate SNP with the corresponding EMs risk to obtain the SMR screening results, including: Using an instrumental variable library as the genetic exposure basis for causal inference, we model cross-system causal inference based on the SMR model. To perform the causal inference process, for the standardized GWAS statistical table, the SMR algorithm is used to integrate the effect of each candidate SNP on gene expression in the brain region with the effect of the candidate SNP on the risk of EMs, and to estimate the potential causal effect of brain region homeostasis on the overall risk of EMs. Based on the causal effect analysis of SMR, the causal information of each gene in the corresponding brain region is output, and candidate genes are screened.
5. The method according to claim 1, characterized in that, The HEIDI test is introduced to detect local heterogeneity in the screened candidate genes, retaining high-confidence causal candidates and obtaining HEIDI screening results, including: The HEIDI test was introduced to screen candidate genes, using relevant SNPs in the vicinity of the candidate site as the detection window to assess local heterogeneity. Based on the local heterogeneity, SMR signals that conform to the true association driven by a single pathological variant are determined, high-confidence causal candidates are screened, and candidate genes affected by multiple LD signals are removed to obtain HEIDI screening results.
6. The method according to claim 1, characterized in that, By integrating SMR and HEIDI screening results, a set of key genes with a stable causal association between brain region homeostasis and EM risk was identified, including: By introducing causal gene identification and functional annotation, and integrating the SMR screening results and HEIDI screening results, key genes with significant causal effects on EMs risk at the brain region expression level were identified, resulting in a set of key genes.
7. The method according to any one of claims 1-6, characterized in that, The key gene set includes at least ABO, INO80E, HCG22, and members of the YRNA family.
8. A device for screening causal genes in endometriosis through cross-brain region genetic regulation integration analysis, characterized in that, include: The variable tool construction module is used to screen cis-regulatory sites, select candidate SNPs, and summarize them into a tool variable library for eQTL data of major human brain regions, on a per-gene basis. The data integration and standardization module is used to unify the GWAS summary statistical results of the overall risk of EMs as the outcome data, and to correct the nomenclature and effects of candidate SNPs to obtain a standardized GWAS statistical table. The causal inference module is used to integrate the effect of each candidate SNP on gene expression in the brain region with the effect of the candidate SNP on the risk of EMs using the SMR algorithm for the standardized GWAS statistical table, and obtain the SMR screening results. The SMR screening results include estimated causal information and screened candidate genes. The HEIDI test module is used to introduce the HEIDI test to perform local heterogeneity detection on the screened candidate genes, retain high-confidence causal candidates, and obtain HEIDI screening results. The integration module is used to integrate SMR screening results and HEIDI screening results, identify a set of key genes that have a stable causal association between the homeostatic expression level of brain regions and the risk of EMs, and output the set of key genes as the gene screening results.
9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements the steps of the endometriosis causal gene screening method according to any one of claims 1-7, which involves cross-brain region genetic regulation integration analysis.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the endometriosis causal gene screening method according to any one of claims 1-7, which involves cross-brain region genetic regulation integration analysis.