Screening method and system for pathogenic genes and biomarkers of retinopathy

Abstract

The invention discloses a screening method and system for pathogenic genes and biomarkers of retinopathy. The screening method comprises the following steps: acquiring GWAS summarized data related to retinopathy and multi-tissue specific gene expression weight data; combining the GWAS summarized data with multi-tissue specific gene expression weight data, performing transcriptome association analysis, and outputting a TWAS positive result; combining the GWAS summarized data with multi-tissue-specificity gene expression weight data, and performing Mendel randomization analysis based on summarized genes to obtain positive associated genes based on tissue specificity; on the basis of the TWAS positive result and the positive associated gene, incorporating transcriptome cis-eQTL data or proteome pQTL data to carry out Mendel randomization analysis based on a drug target, so as to obtain a significant drug target gene or target protein; and performing screening in MR-Phewas and association of multiple diseases on the TWAS positive result, the positive associated gene positive and the first SNP of the significant drug target gene / target disease to obtain associated causal molecules.

Description

Technical Field

[0001] The present invention relates to the technical field of information processing and analysis, and specifically designs a method and system for screening pathogenic genes and biomarkers of retinal diseases. Background Art

[0002] In recent years, the incidence of diabetes has increased year by year, and it is estimated that the number of diabetic patients worldwide will increase to about 800 million in 2045. Diabetes can lead to many serious eye complications, including diabetic retinopathy (DR), neovascular glaucoma, and cataracts. Among them, DR is the most important microvascular complication and an important cause of blindness in patients. By 2045, the number of patients with DM complicated by DR is expected to increase to about 160 million worldwide. Factors that lead to the progression of DR include hypertension, obesity, hyperlipidemia, and anemia. DR is a highly specific neurovascular complication that can be divided into non-proliferative retinopathy (NPDR) and proliferative retinopathy (PDR) characterized by vision loss according to pathological progression.

[0003] At present, the main methods for evaluating drug targets and biomolecular markers from the perspective of heritability are: ① Genome Wide Association Study (GWAS), ② Transcriptome-wide Association Study (TWAS), ③ Proteome-wide Association Study (PWAS), etc.

[0004] The applicant's research found that for diabetic retinopathy, the interpretation of GWAS results usually requires further functional annotation and bioinformatics analysis to understand the specific effects of these sites on traits and the biological mechanisms behind them. Although TWAS results are presented in the form of genes, overcoming the main shortcomings of GWAS, the source of mRNA data is its most limiting aspect. Although PWAS uses protein data, it cannot identify their causal relationship with the disease.

[0005] Based on this, a method for screening pathogenic genes and biomarkers of retinal diseases is urgently needed. Summary of the invention

[0006] In order to overcome the above technical defects, the present invention provides a method and system for screening pathogenic genes and biomarkers of retinopathy.

[0007] In order to solve the above problems, the present invention is implemented according to the following technical solutions:

[0008] In a first aspect, the present invention provides a method for screening pathogenic genes and biomarkers of retinopathy, comprising the following steps:

[0009] S100: Obtain GWAS summary data related to retinopathy and multi-tissue specific gene expression weight data;

[0010] S200: combining the GWAS summary data with multi-tissue specific gene expression weight data to perform transcriptome association analysis and output TWAS positive results;

[0011] S300: combining the GWAS summary data with multiple tissue-specific gene expression weight data to perform Mendelian randomization analysis based on the summary genes to obtain tissue-specific positive associated genes;

[0012] S400: Based on the TWAS positive results and the positive associated genes, the transcriptome cis-eQTL data or the proteome pQTL data are included to perform a Mendelian randomization analysis based on drug targets to obtain significant drug target genes or target proteins;

[0013] S510: Screen the TWAS positive results, the positive associated genes and the first SNP of the significant drug target gene in MR-Phewas for association with multiple diseases to obtain the associated causal molecules; or,

[0014] S520: screening the TWAS positive result, the positive associated gene and the first SNP of the target protein in MR-Phewas and the association with multiple diseases to obtain the associated causal molecule;

[0015] Wherein, the causal molecule is a pathogenic gene or biomarker associated with retinal disease.

[0016] In combination with the first aspect, the present invention further provides a first specific implementation of the first aspect, specifically, the screening method further comprises:

[0017] S600: Acquire GWAS summary data at the biomarker level, wherein the GWAS summary data at the biomarker level includes metabolomics data and immunoomics data;

[0018] S700: A large-scale two-sample Mendelian randomization analysis was performed based on the associated causal molecules, GWAS summary data at the biomarker level, and outcome indicators to obtain significantly associated causal molecules; the outcome indicator was diabetic retinopathy.

[0019] In combination with the first aspect, the present invention further provides a second specific implementation of the first aspect. Specifically, in step S400, incorporating transcriptome cis-eQTL data or proteome pQTL data into a Mendelian randomization analysis based on drug targets specifically includes:

[0020] S401: Use Steiger filtering analysis and co-localization analysis to verify the obtained significant drug target genes or target proteins, and output the verified significant drug target genes or target proteins.

[0021] In combination with the first aspect, the present invention further provides a third specific implementation of the first aspect. Specifically, in step S200, transcriptome association analysis is performed on the GWAS summary data, specifically including:

[0022] S201: Verify the TWAS positive result by combining multiple verification algorithms, and output the verified TWAS positive result;

[0023] The verification algorithm includes co-localization analysis, conditional analysis, permutation test and fine-mapping analysis.

[0024] In combination with the first aspect, the present invention also provides a fourth specific implementation manner of the first aspect, specifically, the retinopathy-related GWAS summary data are diabetic retinopathy data and proliferative diabetic retinopathy data screened from FinnGen R9 and R6 data sets.

[0025] In combination with the first aspect, the present invention also provides a fifth specific implementation scheme of the first aspect, specifically, the multi-tissue-specific gene expression weight data includes pancreas, kidney, whole blood and sCCA cross-tissue gene expression weight data summarized based on the GTEx-v8 database.

[0026] In a second aspect, the present invention provides a screening system for pathogenic genes and biomarkers of retinopathy, comprising:

[0027] A first acquisition module is used to acquire GWAS summary data related to retinopathy and multi-tissue specific gene expression weight data;

[0028] The first processing module is used to combine the GWAS summary data with multi-tissue specific gene expression weight data to perform transcriptome association analysis and output TWAS positive results;

[0029] The second processing module is used to combine the GWAS summary data with multiple tissue-specific gene expression weight data to perform Mendelian randomization analysis based on the summary genes to obtain tissue-specific positive associated genes;

[0030] The third processing module is used to perform a Mendelian randomization analysis based on drug targets by incorporating transcriptome cis-eQTL data or proteome pQTL data based on the TWAS positive results and the positive associated genes to obtain significant drug target genes or target proteins;

[0031] The fourth processing module is used to screen the TWAS positive result, the positive associated gene and the first SNP of the significant drug target gene in MR-Phewas and associate them with multiple diseases to obtain associated causal molecules; or, screen the TWAS positive result, the positive associated gene and the first SNP of the target protein in MR-Phewas and associate them with multiple diseases to obtain associated causal molecules;

[0032] Wherein, the causal molecule is a pathogenic gene or biomarker associated with retinal disease.

[0033] In combination with the second aspect, the present invention further provides a first specific implementation of the second aspect, specifically, the screening system further includes:

[0034] A second acquisition module is used to acquire GWAS summary data at the biomarker level, wherein the GWAS summary data at the biomarker level includes metabolomics data and immunoomics data;

[0035] The sixth processing module is used to perform a large-scale two-sample Mendelian randomization analysis based on the associated causal molecules, GWAS summary data of biomarker levels and outcome indicators to obtain significantly associated causal molecules; the outcome indicator is diabetic retinopathy.

[0036] In combination with the second aspect, the present invention further provides a second specific implementation of the second aspect. Specifically, the third processing module incorporates transcriptome cis-eQTL data or proteome pQTL data to perform Mendelian randomization analysis based on drug targets, and specifically performs the following steps:

[0037] The obtained significant drug target genes or target proteins are verified using Steiger filtering analysis and co-localization analysis, and the verified significant drug target genes or target proteins are output.

[0038] In combination with the second aspect, the present invention further provides a third specific implementation of the second aspect, specifically, the second processing module performs transcriptome association analysis on the GWAS summary data, specifically performing the following steps:

[0039] Verifying the TWAS positive result by combining multiple verification algorithms, and outputting the verified TWAS positive result;

[0040] The verification algorithm includes co-localization analysis, conditional analysis, permutation test and fine-mapping analysis.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a method for screening pathogenic genes and biomarkers of retinopathy, comprising the following steps: S100: obtaining GWAS summary data related to retinopathy and multi-tissue specific gene expression weight data; S200: combining the GWAS summary data with the multi-tissue specific gene expression weight data, performing transcriptome association analysis, and outputting a TWAS positive result; S300: combining the GWAS summary data with the multi-tissue specific gene expression weight data, performing Mendelian randomization analysis based on the summary genes, and obtaining tissue-specific positive associated genes; S400: based on the TWAS positive result and the positive associated genes, incorporating transcriptome association analysis into the transcriptome ci The s-eQTL data or the proteomic pQTL data are subjected to Mendelian randomization analysis based on drug targets to obtain significant drug target genes or target proteins; S510: the TWAS positive result, the positive associated gene positivity and the first SNP of the significant drug target gene are screened in MR-Phewas for association with multiple diseases to obtain associated causal molecules; or, S520: the TWAS positive result, the positive associated gene positivity and the first SNP of the target protein are screened in MR-Phewas for association with multiple diseases to obtain associated causal molecules; wherein the causal molecule is a pathogenic gene or biomarker associated with retinal diseases.

[0043] This screening method embodies the application of data science in the biomedical field. The core technical concept mainly uses multi-dimensional data analysis to scientifically and reliably analyze and mine the pathogenic genes and biomarkers that are significantly associated with retinopathy. The screening method of the present invention has far-reaching significance and role at the technical level, promotes the progress of retinopathy research, and also provides important scientific data support for clinical practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0045] Figure 1 It is a schematic flow chart of a method for screening pathogenic genes and biomarkers of retinopathy of the present invention;

[0046] Figure 2 This is a schematic diagram of the technical concept of the screening method of the present invention. Figure 1 ;

[0047] Figure 3 This is a schematic diagram of the technical concept of the screening method of the present invention. Figure 2 ;

[0048] Figure 4 is a TWAS-significant high-confidence gene of the present invention;

[0049] Figure 5 is a high-confidence gene of the drug-targeted MR in the eQTLgen consortium of the present invention;

[0050] Figure 6 It is the MR of the present invention that reveals the causal relationship between metabolomics and DR. DETAILED DESCRIPTION

[0051] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0052] refer to Figure 1 , the embodiment of the present invention provides a flow chart of a method for screening pathogenic genes and biomarkers of retinopathy. The core technical concept of the present invention is mainly to scientifically and reliably analyze and mine the pathogenic genes and biomarkers significantly associated with retinopathy through multi-dimensional data analysis. The screening method of the present invention has far-reaching significance and role at the technical level, promotes the progress of retinopathy research, and also provides important scientific data support for clinical practice. .

[0053] The present invention provides a method for screening pathogenic genes and biomarkers of retinal diseases. The method can be performed by a screening system for pathogenic genes and biomarkers of retinal diseases. The screening system can be implemented in the form of hardware and / or software, and the screening system can be configured in an electronic device. Figure 1 As shown, the screening method includes:

[0054] S100: Obtain GWAS summary data related to retinopathy and multi-tissue specific gene expression weight data;

[0055] S200: combining the GWAS summary data with multi-tissue specific gene expression weight data to perform transcriptome association analysis and output TWAS positive results;

[0056] S300: combining the GWAS summary data with multiple tissue-specific gene expression weight data to perform Mendelian randomization analysis based on the summary genes to obtain tissue-specific positive associated genes;

[0057] S400: Based on the TWAS positive results and the positive associated genes, the transcriptome cis-eQTL data or the proteome pQTL data are included to perform a Mendelian randomization analysis based on drug targets to obtain significant drug target genes or target proteins;

[0058] S510: Screen the TWAS positive results, the positive associated genes and the first SNP of the significant drug target gene in MR-Phewas for association with multiple diseases to obtain the associated causal molecules; or,

[0059] S520: screening the TWAS positive result, the positive associated gene and the first SNP of the target protein in MR-Phewas and the association with multiple diseases to obtain the associated causal molecule;

[0060] Wherein, the causal molecule is a pathogenic gene or biomarker associated with retinal disease.

[0061] Specifically, the screening method for pathogenic genes and biomarkers of retinopathy of the present invention is mainly for the screening method for pathogenic genes and biomarkers provided for diabetic retinopathy (DR). Specifically, each step of the screening method is described in detail in combination with diabetic retinopathy.

[0062] S100: Obtain GWAS summary data related to retinopathy and multi-tissue specific gene expression weight data.

[0063] In the present invention, GWAS summary data generally refers to the summary statistics of genome-wide association study (Genome-WideAssociation Study), and these data include the analysis results of genetic variation related to specific phenotype or disease in the whole genome. GWAS summary data is statistical genetics data, which relates to the detection of genetic variation (marker) polymorphism of multiple individuals in the whole genome, usually for single nucleotide polymorphism (SNPs), to obtain the genotype of each individual. These data are further subjected to statistical analysis of population level with phenotype, and the genetic variation most likely to affect specific traits is screened out by statistics or significance (p value), so as to mine the gene related to trait variation. The data type of GWAS mainly includes genotype data and phenotypic data. Genotype data usually exists in vcf file format, while phenotypic data exists in txt file form containing sample name and trait. It can be obtained from public databases, such as GWAS Catalog and OpenGWAS, which have collected published GWAS data.

[0064] Specifically, GWAS summary data mainly include genetic variation and genotype data, trait and disease related data, and meta-analysis data. In addition, GWAS summary data also include genetic variation identifiers (SNP ids), allele information, effect size, statistical significance (p-value), sample information, etc., which are of great significance for understanding the relationship between genetic variation and phenotype. (1) Genetic variation and genotype data: These data are usually presented in the form of single nucleotide polymorphisms (SNPs) and are associated with various traits, diseases and characteristics. The GWAS Catalog provides information on these SNPs, including genotype frequency, statistical association coefficient, p-value, etc. (2) Trait and disease related data: The database contains data related to various traits and diseases, such as cardiovascular disease, cancer, autoimmune disease, metabolic disease, etc. These data are crucial for studying the relationship between genes and diseases. (3) Meta-analysis data: The GWAS Catalog also provides meta-analysis data from multiple independent studies. These data integrate the results of multiple studies and provide a more comprehensive perspective to analyze the relationship between genes and diseases.

[0065] In the present invention, the GWAS summary data related to retinopathy are the diabetic retinopathy data and proliferative diabetic retinopathy data screened from the FinnGen R9 and R6 data sets.

[0066] In a specific implementation, S100 obtained SNPs associated with diabetic retinopathy (DR) from the FinnGen R9 and R6 datasets, where the SNPs associated with diabetic retinopathy (DR) included 10,413 cases of DR and 10,860 cases of proliferative diabetic retinopathy (PDR).

[0067] In the present invention, multi-tissue specific gene expression weight data generally refer to the data of gene expression levels in different tissues or cell types, and these data can reveal the activity pattern of genes in specific tissues. Multi-tissue specific gene expression weight data comprises the weight of tissue-specific genes and gene set test results. Multi-tissue specific gene expression weight data is calculated based on the tissue-specific gene activity information in the human protein atlas, and these data reflect the specific weights of gene expression in different tissues. Utilize these data, tissue-specific gene set weights can be generated, and these weights reflect the expression specificity and importance of genes in different tissues.

[0068] In the present invention, multi-tissue specific gene expression weight data include pancreas, kidney, whole blood and sCCA cross-tissue gene expression weight data summarized based on the GTEx-v8 database.

[0069] In the present invention, cross-tissue gene expression weight data, obtained through sCCA analysis, refers to the weight of each gene when forming the maximum correlation linear combination in the gene expression data across multiple tissues or samples. These weights reflect the importance of different genes in the association pattern, that is, which gene expression changes contribute the most to the association between different tissues or samples.

[0070] Specifically, sCCA cross-tissue gene expression weights data include:

[0071] (1) Gene weight: The weight of each gene in the linear combination obtained by sCCA analysis, indicating the importance of the gene in the association pattern.

[0072] (2) Association between tissues or samples: sCCA analysis can reveal the association patterns of gene expression between different tissues or samples, which may be related to specific biological processes or disease states.

[0073] (3) Sparsity: A key feature of sCCA is sparsity, that is, the obtained linear combinations usually contain fewer non-zero weights, which helps to identify genes that play a key role in the association pattern.

[0074] S200: combining the GWAS summary data with multi-tissue specific gene expression weight data to perform transcriptome association analysis and output TWAS positive results.

[0075] In the present invention, Transcriptome-Wide Association Studies (TWAS) is a method that uses genotype data and gene expression data to identify genetic variants that affect specific traits. TWAS usually uses summary statistics from genome-wide association studies (GWAS) to infer the association between gene expression and specific traits.

[0076] In a specific implementation, positive TWAS P values ​​for each tissue analyzed were <0.05 / number of features (pancreas: 5734, whole blood: 7938, renal cortex: 1203) and the P value for sCCA was <1.32e-06.

[0077] In a preferred implementation, in step S200, the GWAS summary data is combined with multi-tissue specific gene expression weight data to perform transcriptome association analysis, specifically including:

[0078] S201: Verify the TWAS positive result by combining multiple verification algorithms, and output the verified TWAS positive result;

[0079] The verification algorithm includes co-localization analysis, conditional analysis, permutation test and fine-mapping analysis.

[0080] In a specific implementation, regarding the TWAS post-validation analysis:

[0081] (1) Colocalization analysis: The present invention evaluates the probability that the TWAS association reflects the following two situations: (1) PP.H3: linkage between different causal single nucleotide polymorphisms (SNPs). (2) PP.H4: a single causal SNP. By comparing PP.H3 and PP.H4, the present invention distinguishes whether there is a true causal relationship between the trait and diabetic retinopathy (DR). Furthermore, the present invention defines positive genes as genes with PP.H4 exceeding 0.9.

[0082] (2) Permutation test: In order to verify the analysis results of the present invention, a permutation test was used, and a P value < 0.05 was considered a positive indicator.

[0083] (3) Conditional analysis: Conditional / marginal features were significantly associated with DR only in the unadjusted model, and their associations were entirely dependent on the expression of other adjacent features. After correction, independent / jointly significant features were still associated with the phenotype at a significant level (p < 0.05 was defined as passing the conditional analysis test).

[0084] (4) Fine-mapping analysis: FOCUS estimates the posterior inclusion probability (PIP) of causal association for each feature within the associated region. The PIP of a single element is also of interest, with values ​​> 0.5 indicating that the locus is more likely to be causally associated with DR than any other locus in the region. FOCUS naturally allows for multiple causal SNPs and genes while incorporating gene effect sizes through the use of conjugate priors. In this example, we defined genes with PIP > 0.8, significant TWAS P values, and significant conditional analysis P values ​​as high confidence genes.

[0085] In the specific implementation, the analysis can be performed by using plink and FOCUS software on Linux.

[0086] like Figure 4 As shown, based on the above analysis process, the present invention screened out 10 positive sites, namely TWAS positive results.

[0087] S300: combining the GWAS summary data with multiple tissue-specific gene expression weight data to perform Mendelian randomization analysis based on summary genes to obtain tissue-specific positive associated genes.

[0088] In the present invention, the Summary databased Mendelian randomization method (SMR) is a tool for inferring the causal relationship between genetic variants closely related to exposure and outcomes. Another advantage of SMR is that it can integrate data from two independent samples.

[0089] In a specific implementation, SMR can establish a connection between a quantitative trait locus (eQTL) and a phenotype. Specifically, the present invention uses SMR for analysis. In order to assess linkage disequilibrium (LD) and estimate potential co-localization, we performed a HEIDI test using an external reference. To this end, the present invention defines the criteria for the final significant gene in this step as follows:

[0090] (1) SMR-FDR: Genes with SMR-FDR-P value < 0.05 were considered significant.

[0091] (2) Genome-wide significance: We required that the eQTL and genome-wide association study (GWAS) results were significantly different at the genome-wide level (P < 1 × 10 -5 ).

[0092] (3) HEDI test result: If the HEDI test showed a result greater than 0.05, the gene was retained. These strict criteria enabled us to identify genes associated with diabetic retinopathy.

[0093] In the specific implementation, for the two diseases DR and PDR, we found 92 key DR regulatory genes that showed 446 significant positives in 49 tissues, and 55 key PDR regulatory genes that showed 327 significant positives in 49 tissues.

[0094] S400: Based on the TWAS positive results and the positive associated genes, the transcriptome cis-eQTL data or the proteome pQTL data are included to perform a Mendelian randomization analysis based on drug targets to obtain significant drug target genes or target proteins.

[0095] In the present invention, transcriptome cis-eQTL data or proteome pQTL data are included. In the drug development process, the use of cis-expression quantitative trait loci (cis-eQTL) can be better combined with key target genes. The present invention obtains filtered significant cis-eQTL data from the eQTLgen consortium. The eQTLgen consortium collected and measured peripheral blood samples from 31,684 projects. These data provide valuable insights into the genetic regulation of gene expression. The positive genes extracted from the eQTLgen consortium were verified by extracting multidimensional genomics data from the PsychENCODE project, which focuses on the developing and adult brain, including healthy and diseased states.

[0096] In a specific implementation, in order to identify gene-related instrumental variables, the present invention selects SNPs within 100 kb. The P value of these SNPs to the target gene is less than 5e-8. And set r 2 =0.001, a total of 2499 genes were screened from eQTLgen and 1367 genes were screened from PsychENCODE. The screening method of the present invention utilizes these eQTL datasets to enhance the understanding of gene regulation and potential drug targets.

[0097] In another implementation, pQTL data may also be included, and the data processing method is similar to the above. This example uses eQTL data.

[0098] In a specific implementation, given a sufficient number of high confidence SNPs, the present invention avoids using proxies in this step. To ensure instrument robustness, the present invention sets the F value statistic to >10 in this step, excluding SNPs with insufficient statistical power. For traits or genes with a single SNP as an instrumental variable, the present invention uses the Wald ratio to analyze causal relationships in this step. When dealing with two SNPs, the present invention uses inverse variance weighted (IVW) analysis in this step.

[0099] For more than two SNPs, the present invention uses IVW as the center at this step, supplemented by MR Egger, weighted median, simple mode and weighted mode analysis. In order to enhance robustness and minimize confounding, the present invention uses FDR to correct the p value at this step. The MR Egger intercept test enables us to observe horizontal pleiotropy. Specifically, the heterogeneity of the causal relationship between each instrument and diabetic retinopathy was assessed using the Cochran Q heterogeneity test.

[0100] In a specific implementation, MR analysis can be performed using the R software package TwoSampleMR v0.5.11.

[0101] In a specific implementation, in step S400, incorporating transcriptome cis-eQTL data or proteome pQTL data into drug target Mendelian randomization analysis specifically includes:

[0102] S401: Use Steiger filtering analysis and co-localization analysis to verify the obtained significant drug target genes or target proteins, and output the verified significant drug target genes or target proteins.

[0103] Specifically, the present invention conducted MR Steiger test to verify the direction and explore the reverse causal relationship. All the results were tested by co-localization analysis, and a total of 6 positive genes, i.e., significant drug target genes, were found. Figure 5 shown.

[0104] S510: Screen the TWAS positive results, the positive associated genes and the first SNP of the significant drug target gene in MR-Phewas for association with multiple diseases to obtain the associated causal molecules; or,

[0105] S520: The TWAS positive result, the positive associated gene and the first SNP of the target protein are screened and associated with multiple diseases in MR-Phewas to obtain the associated causal molecules.

[0106] Wherein, the causal molecule is a pathogenic gene or biomarker associated with retinal disease.

[0107] In the specific implementation of the present invention, the functional significance of the drug target sites significantly associated with DR in step S510 / S520 (with a significance threshold of P < 0.05) can be explored by using MR PheWAS. The top SNPs of each gene are selected as input to explore their association with different trait GWAS summary data on a large scale. In the specific implementation, the analysis was performed using the ieugwasr v0.2.2R software package.

[0108] In a preferred embodiment of the present invention, the screening method further comprises a further analysis and screening step, specifically comprising:

[0109] S600: Obtaining GWAS summary data at the biomarker level, wherein the GWAS summary data at the biomarker level includes metabolomics data and immunoomics data.

[0110] In the present invention, the GWAS summary data at the biomarker level included metabolome exposure data of 8229 individuals from the Canadian Longitudinal Study on Aging (CLSA) cohort, which was used to select 1091 metabolites and 309 metabolite ratios. As exposure data from 3757 individuals in Sardinia, 731 immune cell (118 absolute cell (AC) counts, 389 median fluorescence intensity (MFI) reflecting surface antigen levels, 32 morphological parameters, and 192 relative cell (RC) counts) related features were selected. Of, DR-related features were used as results. The present invention screened SNPs associated with 1400 metabolites (P = 5E-08) and SNPs associated with 731 immune markers (P = 1E-05). r^2 was set to 0.001, and clump was within 100kb. These biomarkers were selected to study whether there is a casual association between them and DR, which will help us understand the deeper mechanism of DR progression.

[0111] S700: A large-scale two-sample Mendelian randomization analysis was performed based on the associated causal molecules, GWAS summary data at the biomarker level, and outcome indicators to obtain significantly associated causal molecules; the outcome indicator was diabetic retinopathy.

[0112] In the specific implementation, in order to ensure that there are enough SNPs for analysis, the present invention uses the European reference sample of the 1000 Genomes Project. The screening threshold is R^2=0.8. SNPs in the MHC region are not removed. NSNP<=3 will be deleted.

[0113] TwoSampleMR v0.5.11 was used to perform MR analysis of metabolomics and immunoomics biomarkers for DR. The other steps of the method were the same as those for drug target MR unless otherwise mentioned.

[0114] In a specific implementation, in addition to the Egger intercept test, the present invention also uses MR-PRESSO as a test of horizontal multiple validity. The linkage disequilibrium (LD) between variants is taken into account by leveraging the power of multiple genetic variants. In order to understand whether the association between genetic variants and outcomes is weakened after adjusting for exposure, a "leave one out" analysis was then performed, each SNP was gradually deleted, the meta-effects of the remaining SNPs were calculated, and whether the results changed after deleting each SNP was observed. The results were mainly positive metabolomics results, and the immunogenomic results were negative after correction. Specifically, the results can be seen in Figure 6 , and obtain the significantly associated causal molecules.

[0115] The present invention also provides a screening system for pathogenic genes and biomarkers of retinopathy, comprising:

[0116] A first acquisition module is used to acquire GWAS summary data related to retinopathy and multi-tissue specific gene expression weight data;

[0117] The first processing module is used to combine the GWAS summary data with multi-tissue specific gene expression weight data to perform transcriptome association analysis and output TWAS positive results;

[0118] The second processing module is used to combine the GWAS summary data with multiple tissue-specific gene expression weight data to perform Mendelian randomization analysis based on the summary genes to obtain tissue-specific positive associated genes;

[0119] The third processing module is used to perform a drug target Mendelian randomization analysis based on the TWAS positive results and the positive associated genes, incorporating transcriptome cis-eQTL data or proteome pQTL data, to obtain significant drug target genes or target proteins;

[0120] The fourth processing module is used to screen the TWAS positive result, the positive associated gene and the first SNP of the significant drug target gene in MR-Phewas and associate them with multiple diseases to obtain associated causal molecules; or, screen the TWAS positive result, the positive associated gene and the first SNP of the target protein in MR-Phewas and associate them with multiple diseases to obtain associated causal molecules;

[0121] Wherein, the causal molecule is a pathogenic gene or biomarker associated with retinal disease.

[0122] Preferably, the screening system further comprises:

[0123] A second acquisition module is used to acquire GWAS summary data at the biomarker level, wherein the GWAS summary data at the biomarker level includes metabolomics data and immunoomics data;

[0124] The sixth processing module is used to perform a large-scale two-sample Mendelian randomization analysis based on the associated causal molecules, GWAS summary data of biomarker levels and outcome indicators to obtain significantly associated causal molecules; the outcome indicator is diabetic retinopathy.

[0125] Preferably, the third processing module incorporates transcriptome cis-eQTL data or proteome pQTL data to perform drug target Mendelian randomization analysis, and specifically performs the following steps:

[0126] The obtained significant drug target genes or target proteins are verified using Steiger filtering analysis and co-localization analysis, and the verified significant drug target genes or target proteins are output.

[0127] Preferably, the second processing module performs transcriptome association analysis on the GWAS summary data, specifically performing the following steps:

[0128] Verifying the TWAS positive result by combining multiple verification algorithms, and outputting the verified TWAS positive result;

[0129] The verification algorithm includes co-localization analysis, conditional analysis, permutation test and fine-mapping analysis.

[0130] For other structures of the screening method and system for pathogenic genes and biomarkers of retinopathy described in this embodiment, refer to the prior art.

[0131] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Therefore, any modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for screening pathogenic genes and biomarkers of retinopathy, characterized in that: The following steps are involved: S100: Obtain GWAS summary data related to retinopathy and multi-tissue specific gene expression weight data; S200: combining the GWAS summary data with multi-tissue specific gene expression weight data to perform transcriptome association analysis and output TWAS positive results; S3 00: The GWAS summary data are combined with the multi-tissue specific gene expression weight data to perform Mendelian randomization analysis based on the summary genes to obtain tissue-specific positive associated genes; S400: Based on the TWAS positive results and the positive associated genes, the transcriptome cis-eQTL data or the proteome pQTL data are included to perform a Mendelian randomization analysis based on drug targets to obtain significant drug target genes or target proteins; S510: Screen the TWAS positive results, the positive associated genes and the first SNP of the significant drug target gene in MR-Phewas for association with multiple diseases to obtain the associated causal molecules; or, S520: screening the TWAS positive result, the positive associated gene and the first SNP of the target protein in MR-Phewas and the association with multiple diseases to obtain the associated causal molecule; Wherein, the causal molecule is a pathogenic gene or biomarker associated with retinal disease.

2. The method for screening pathogenic genes and biomarkers of retinopathy according to claim 1, characterized in that: The screening method further comprises: S600: Acquire GWAS summary data at the biomarker level, wherein the GWAS summary data at the biomarker level includes metabolomics data and immunoomics data; S700: A large-scale two-sample Mendelian randomization analysis was performed based on the associated causal molecules, GWAS summary data at the biomarker level, and outcome indicators to obtain significantly associated causal molecules; the outcome indicator was diabetic retinopathy.

3. The method for screening pathogenic genes and biomarkers of retinopathy according to claim 1, characterized in that: In step S400, the transcriptome cis-eQTL data or the proteome pQTL data are incorporated into a drug target-based Mendelian randomization analysis, specifically including: S401: Use Steiger filtering analysis and co-localization analysis to verify the obtained significant drug target genes or target proteins, and output the verified significant drug target genes or target proteins.

4. The method for screening pathogenic genes and biomarkers of retinopathy according to claim 1, characterized in that: In step S200, transcriptome association analysis is performed on the GWAS summary data, specifically including: S201: Verify the TWAS positive result by combining multiple verification algorithms, and output the verified TWAS positive result; The verification algorithm includes co-localization analysis, conditional analysis, permutation test and fine-mapping analysis.

5. The method for screening pathogenic genes and biomarkers of retinopathy according to claim 1, characterized in that: The retinopathy-related GWAS summary data are diabetic retinopathy data and proliferative diabetic retinopathy data screened from FinnGen R9 and R6 data sets.

6. The method for screening pathogenic genes and biomarkers of retinopathy according to claim 1, characterized in that: The multi-tissue-specific gene expression weight data include pancreas, kidney, whole blood and sCCA cross-tissue gene expression weight data summarized based on the GTEx-v8 database.

7. A screening system for pathogenic genes and biomarkers of retinopathy, characterized in that: include: A first acquisition module is used to acquire GWAS summary data related to retinopathy and multi-tissue specific gene expression weight data; The first processing module is used to combine the GWAS summary data with multi-tissue specific gene expression weight data to perform transcriptome association analysis and output TWAS positive results; The second processing module is used to combine the GWAS summary data with multiple tissue-specific gene expression weight data to perform Mendelian randomization analysis based on the summary genes to obtain tissue-specific positive associated genes; The third processing module is used to carry out a Mendelian randomization analysis based on drug targets by incorporating transcriptome cis-eQTL data or proteome pQTL data based on the TWAS positive results and the positive associated genes to obtain significant drug target genes or target proteins; The fourth processing module is used to screen the TWAS positive result, the positive associated gene and the first SNP of the significant drug target gene in MR-Phewas and associate them with multiple diseases to obtain associated causal molecules; or, screen the TWAS positive result, the positive associated gene and the first SNP of the target protein in MR-Phewas and associate them with multiple diseases to obtain associated causal molecules; Wherein, the causal molecule is a pathogenic gene or biomarker associated with retinal disease.

8. The screening system for pathogenic genes and biomarkers of retinopathy according to claim 7, characterized in that: The screening system further comprises: A second acquisition module is used to acquire GWAS summary data at the biomarker level, wherein the GWAS summary data at the biomarker level includes metabolomics data and immunoomics data; The sixth processing module is used to perform a large-scale two-sample Mendelian randomization analysis based on the associated causal molecules, GWAS summary data of biomarker levels and outcome indicators to obtain significantly associated causal molecules; the outcome indicator is diabetic retinopathy.

9. The screening system for pathogenic genes and biomarkers of retinopathy according to claim 7, characterized in that: The third processing module incorporates transcriptome cis-eQTL data or proteome pQTL data to perform Mendelian randomization analysis based on drug targets, and specifically performs the following steps: The obtained significant drug target genes or target proteins are verified using Steiger filtering analysis and co-localization analysis, and the verified significant drug target genes or target proteins are output.

10. The screening system for pathogenic genes and biomarkers of retinopathy according to claim 7, characterized in that: The second processing module performs transcriptome association analysis on the GWAS summary data, specifically performing the following steps: Verifying the TWAS positive result by combining multiple verification algorithms, and outputting the verified TWAS positive result; The verification algorithm includes co-localization analysis, conditional analysis, permutation test and fine-mapping analysis.

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