57 results about "Mendelian randomization" patented technology

The present disclosure provides methods for determining biological pathways and nucleotide polymorphisms (NPs) shared between two diseases.

The invention discloses a novel method for food allergy intestinal flora target mining, and belongs to the technical field of target mining. The new method comprises the following steps: acquiring whole genome associated GWAS data and intestinal flora related data of food allergy; performing tool variable screening and multi-effect removal on the collected data; according to the collected tool variables, analyzing the data by adopting a bidirectional Mendel randomization method to obtain a preliminary screening result of the causal flora; the preliminary screening result is analyzed, and candidate tool variables are obtained by integrating effect values of multiple analysis methods and judgment of sensitivity analysis; establishing a food allergy animal model; carrying out targeted determination on the screened related flora having a causal relationship with food allergy by adopting a microbiological analysis method; and the active small molecules act on the target flora to determine the reliability of the active small molecules as the food allergy target. According to the method, the intestinal flora and the target flora of food allergy can be determined.

According to the screening method for the potential protein biomarkers and the drug targets of the gastric cancer, Mendel randomization analysis in a proteome range is adopted, the genetic causal relationship between circulating plasma protein and the risk of the gastric cancer is evaluated, and finally the remarkably related protein is identified. According to the screening method of the potential protein biomarker and the drug target of the gastric cancer, provided by the invention, the potential association between circulating plasma protein and the gastric cancer is systematically revealed by integrating Mendel randomization, single-cell RNA sequencing analysis, space transcriptome analysis, virtual drug screening, molecular docking, molecular dynamics simulation and other methods.

The invention discloses a liver cirrhosis microbial marker as well as a screening method and application thereof, and solves the technical problems of few markers, small clinical queue scale and unknown effectiveness in predicting the future liver cirrhosis risk in the existing intestinal flora biomarker research. The method comprises the following steps: by integrating whole genome association research data (GWAS) and 16S rRNA gene sequencing data, screening out candidate microbial differential bacteria related to liver cirrhosis by adopting difference analysis; in combination with LDSC analysis and a double-sample Mendel randomization method, MR related bacteria and LDSC genetic related bacteria are screened from the aspects of causal correlation and genetic correlation; taking an intersection of the differential bacteria, the MR related bacteria and the LDSC genetic related bacteria as a marker; finally, model training is conducted through a machine learning algorithm by means of a data set with sample labels, individual liver cirrhosis risk prediction labels are output, early screening of liver cirrhosis is achieved, and the method can be widely applied to the technical field of intestinal microorganism and data mining.

A method for identifying key targets or biomarkers of a disease at a cell specific level according to a two-step process that comprises: (1) identifying cell type-specific candidate targets / biomarkers with genetically defined causal relationships to one or more disease features by performing causal analysis (e.g., 2 sample mendelian randomization); and (2) ranking candidate targets / biomarkers by analyzing the interconnectivity between the identified candidate targets / biomarkers and common interactors / regulators in a customized cell type-specific knowledge graph.

The invention provides a Mendel randomization nonlinear causal inference method based on genome research, and relates to the technical field of biological genetic data analysis, and the method comprises the following steps: obtaining a tool variable, an exposure factor and a result variable, and carrying out data preprocessing to form a structured input matrix; respectively constructing a tool variable nonlinear model and a causal effect nonlinear model; calculating a loss function of the model, and adding a regularization item for joint optimization; optimizing model parameters by using a GBDT kernel method; updating the model based on the optimized model parameters and drawing a nonlinear response curve of the exposure factors and the result variables; replacing the tool variable with a pseudo tool variable to repeat the above steps to verify a causal inference result, and inferring a causal effect according to the verified causal inference result; according to the method, the limitation of assuming a linear relation and processing high-dimensional data in a traditional causal inference method can be overcome, and a more accurate, robust and interpretable causal inference result is provided.

The invention discloses a network pharmacology and Mendel randomization analysis method for resisting lithangiuria by chlorogenic acid, and belongs to the technical field of biomedical engineering, and the method comprises the following steps: taking an intersection of a potential action target of chlorogenic acid and a lithangiuria target as a candidate target; performing pathway enrichment analysis and screening on each candidate target spot to determine a plurality of first pathways; for each first path, calculating a target importance score of each candidate target and performing screening according to the target importance score, and obtaining the screened candidate target as a key target; respectively performing Mendel stochastic analysis on each key target spot to obtain a causal effect estimated value of each key target spot and the urinary calculus risk, screening all the first pathways according to the causal effect estimated value, taking the screened first pathways as target pathways, and taking the key target spots in the target pathways as target targets. The method can be used for efficiently and accurately screening and determining the core regulatory pathway playing the anti-lithangiuria role and the real key target to which the core regulatory pathway belongs.

The present invention discloses a multivariable Mendelian randomization method for inferring the causal relationship between images and phenotypes, including integrating genomic, radiomic, and phenomic data for causal inference between brain imaging feature metrics and phenotypes, considering the correlations of multiple brain imaging feature metrics in the causal inference and correcting possible biases, specifically including the following steps: S1: Obtain initial instrumental variables, risk factor variables, and phenotypes; S2: Bias correction, screening the initial instrumental variables and risk factor variables; S3: Use a two-stage method to infer the causal relationship and screen out the brain imaging feature metrics that have a causal relationship with the phenotype; S4: Use Sargan for horizontal pleiotropy test to check whether the part of the said phenotype that cannot be explained by the risk factor is significantly correlated with the initial instrumental variable. Compared with traditional methods, the present invention can obtain more accurate causal effect estimates, and the type I error rate of the selected risk factors is lower.

The invention relates to a screening method of anti-breast cancer key targets of a traditional Chinese medicine composition for treating triple-negative breast cancer pulmonary metastasis, which sequentially comprises the following steps: component-target preliminary screening, two sample Mendel randomization, SMR and co-localization, full phenotype association and side effect evaluation, pathway enrichment, clinical expression verification, molecular docking verification and the like. After Mendel randomization (MR) and SMR are placed in network pharmacology, population genetic variation is used as a natural randomization tool, evidence of causal relationship between exposure (target level) and outcome (breast cancer risk) is provided on a public GWAS level, traditional association analysis mixing and reverse causal risk are reduced, and the false positive rate of network pharmacology prediction is greatly reduced; pheWAS-MR is introduced to perform system identification on phenotypes of various diseases, so that a potential off-target effect can be identified in advance, the multiple effects and possible adverse reactions of potential treatment targets in traditional Chinese medicine compound targets can be evaluated, powerful data support is provided for subsequent clinical test design and drug alert strategies, and the risk of later failure of research and development is effectively reduced.

The invention provides a causal inference method and device based on genetic variation, electronic equipment and a medium. The causal inference method comprises the following steps: acquiring whole genome SNP data and proteome data of a detection sample of a target population from a detection platform; performing whole genome association analysis on the whole genome SNP data to obtain outcome SNP data associated with the target phenotype; performing protein quantitative trait site analysis based on the whole genome SNP data and the proteome data to obtain exposure SNP data associated with the target exposure factor; and performing data preprocessing on the outcome SNP data and the exposed SNP data, and performing Mendel randomization analysis on the preprocessed outcome SNP data and exposed SNP data to obtain a causal relationship between the target exposure factor and the target phenotype. According to the method, the accuracy and reliability of Mendel stochastic analysis results are improved.

This application discloses a combination of plasma protein biomarkers for predicting healthy longevity and a screening method thereof, relating to the field of bioinformatics. The method includes: acquiring genetic association data between plasma proteins and the healthy longevity phenotype; extracting single nucleotide polymorphisms (SNPs) associated with plasma protein levels as instrumental variables; using Mendelian randomization to identify plasma proteins causally associated with the healthy longevity phenotype and performing sensitivity analysis; performing mediation analysis on the causally associated plasma proteins to identify plasma proteins that influence the healthy longevity phenotype through health-related factors; and constructing a combination of plasma protein biomarkers from the identified plasma proteins. This application screens proteins causally associated with healthy longevity and mediated by health-related factors from genetic association data to construct biomarker combinations, solving the technical problem of low prediction accuracy caused by prior art relying solely on correlation analysis, and achieving the technical effect of improving the prediction accuracy of biomarkers.

The invention discloses a method for analyzing and identifying an inflammatory bowel disease related oral cancer biomarker, and belongs to the technical field of bioengineering.The method comprises the following steps that 1, IBD whole genome correlation research summary data is obtained; 2, analyzing a causal relationship between IBD and the oral cancer by using a Mendel randomization method, and determining IBD related genes; 3, collecting an oral cancer tissue sample, and carrying out single-cell RNA sequencing; according to the invention, single-cell RNA sequencing is carried out on an oral cancer specimen, and a cell cluster and a gene expression mode are described. Cell clusters and types are displayed by using t-SNE and UMAP, key modes of gene expression are defined by using a lattice diagram, and pathways related to NFKBIA expression are analyzed through GSEA. The experimental result explains the cell heterogeneity and gene expression kinetics in the oral cancer disease progression process, and reveals possible therapeutic targets related to IBD.

The invention discloses a breast cancer heterogeneity analysis system based on unicellular omics and Mendel randomization, and relates to the technical field of biological information. Comprising a data acquisition module, a single cell data processing module, a differential expression analysis module, a Mendel randomization analysis module, a survival analysis module, a function enrichment module, a drug prediction module and an output module. The system is used for integrating multiple omics data and identifying causal driving genes in breast cancer malignant epithelial cell subgroups. According to the system, unicellular omics and Mendel randomization are combined, specific causal inference of breast cancer heterogeneity cell types is achieved, key genes are identified, part of the genes are positively or negatively correlated with risks, and clinical significance is verified through survival analysis; the system provides new biomarkers and targets for accurate treatment of breast cancer, and has the advantages of high precision, strong repeatability and large clinical transformation potential.

The invention discloses a method and a device for analyzing immune metabolism and AD (Alzheimer's Disease) related genes by integrating multiple omics. The method comprises the following steps: acquiring single cell sequencing data of an AD patient; acquiring GWAS data of immune cells and metabolites of the AD patient; screening out an independent single nucleotide polymorphism (SNPS); performing Mendel randomization causal inference to obtain causal-associated immune cell characteristics and metabolite factor characteristics; analyzing the causal-associated immune cells and metabolites, and identifying the immune cells and metabolites which have important influence on AD generation, so as to obtain a metabolic pathway which affects AD; obtaining gene expression data of expression differences of metabolic pathways in different cell populations; performing differential gene analysis to obtain a gene analysis result; and screening out the gene related to the AD disease. According to the method, key biomarkers related to the Alzheimer's disease are identified by using an advanced machine learning technology, and the risk of an individual in disease development is accurately predicted.

The invention provides an application of an Oncostatin-M concentration level in diagnosis or auxiliary diagnosis of obesity-related chronic kidney diseases. By combining Mendel randomization (MR) analysis with clinical research, single cell sequencing analysis and animal experiments, the concentration of Oncostatin-M in a serum sample is determined, and the Oncostatin-M can be used as a diagnostic marker of obesity-related kidney diseases (CKD) and is used for clinical auxiliary diagnosis and disease management.

The application discloses a method and device for comprehensively analyzing immune metabolism and AD disease-related genes by multiple omics. The method comprises the following steps: obtaining single-cell sequencing data of AD patients; obtaining GWAS data of immune cells and metabolites of AD patients; screening independent single nucleotide polymorphisms (SNPs); obtaining causally related immune cell characteristics and metabolite factor characteristics through Mendelian randomization causal inference; analyzing the causally related immune cells and metabolites to identify immune cells and metabolites that have an important impact on the occurrence of AD, and then obtaining metabolic pathways that affect AD; obtaining gene expression data of expression differences of metabolic pathways in different cell populations; performing differential gene analysis to obtain gene analysis results; and screening genes related to AD disease. The method disclosed by the application uses advanced machine learning technology to identify key biomarkers related to Alzheimer's disease and accurately predict the risk of individuals in disease development.

The application discloses a Mendelian randomization analysis method for correcting sample overlap and pleiotropic bias, and is based on a joint correction framework of IVW, HDL and MR-PRESSO, in which pleiotropic abnormal SNPs are removed first, and then sample overlap bias is quantified and corrected, so that the problem that existing methods can only correct single bias is solved, synchronous and effective correction of sample overlap and pleiotropic compound bias is realized, the estimation accuracy can be ensured in a complex scene with a high sample overlap ratio and pleiotropy, meanwhile, the dependence of MR-APSS and other methods on the InSIDE assumption is abandoned, the problem that the CAUSE method is too conservative is avoided, the false positive rate can be controlled while maintaining high causal discovery sensitivity, the method can be directly packaged into a computer program, and is applied to epidemiological causal inference research of large-scale GWAS summary data, and is efficient in calculation and wide in applicability.

The application belongs to the technical field of biotechnology, and discloses a set of biomarkers for predicting the risk of idiopathic pulmonary arterial hypertension and application thereof. The application integrates expression quantitative trait locus (eQTL) data and single-cell RNA sequencing (scRNA-seq) data sets from peripheral blood and lung tissue and whole genome association analysis (GWAS) summary statistics of idiopathic pulmonary arterial hypertension, uses Mendelian randomization algorithm (MR), genome enrichment analysis and immunofluorescence experiments to identify and accurately locate related genes, and for the first time discovers biomarkers related to the risk of idiopathic pulmonary arterial hypertension, which include at least one of CST7, HLA-B, HLA-E, FKBP1A and UBB. By detecting the above biomarkers, the risk of idiopathic pulmonary arterial hypertension can be effectively diagnosed.

The invention discloses application of irbesartan in preparation of a medicine for preventing or treating osteoarthritis, and belongs to the technical field of biological medicine. Osteoarthritis single cell transcriptome data are integrated and analyzed, differential expression genes are screened, and irbesartan is screened by using a relocation method and Mendel randomization analysis to serve as a medicine for treating osteoarthritis; analysis results show that irbesartan can significantly reduce the risk of osteoarthritis. An irbesartan administration experiment is carried out by using a zebra fish bone arthritis model, and the experiment result shows that irbesartan obviously increases the joint gap of anal fins of zebra fish and has an obvious protection effect on articular cartilage; a transcriptome sequencing result shows that irbesartan plays the role by inhibiting the MAPK signal channel. The invention provides the effect of irbesartan in the development of osteoarthritis for the first time, provides a new strategy for prevention and treatment of osteoarthritis, and has a wide application prospect.

The invention belongs to but is not limited to the technical field of digital health, and particularly relates to a functional food formula and medical customized diet scheme design system and method based on a digital nutrition health evaluation system, and the system comprises a digital nutrition health evaluation module which is responsible for carrying out comprehensive, multi-dimensional and dynamic nutrition health evaluation on food, a menu and a diet mode, the method is composed of a CMFRD database and a nine-dimensional scoring algorithm. The diet generation module is used for generating a personalized menu combination scheme with nutrition science basis and disease causal associated evidence by adopting a multi-dimensional diet recommendation optimization method fusing Mendel randomization causal analysis and a genetic algorithm on the basis of the digital nutrition health evaluation module; and the functional food formula design module is used for accurately screening and designing an adaptive functional food formula combination for a target consumer group through a multi-dimensional analysis and optimization algorithm based on a digital nutrition health evaluation system NHPS.

The invention discloses a blood mtDNACN-based system, method and application for evaluating an adverse pregnancy outcome risk, and the system comprises a data obtaining module which is used for obtaining blood mtDNACN detection data; the risk assessment module is used for storing a pre-constructed causal risk assessment model; and the result output module is used for outputting a risk assessment result. The model is constructed based on a Mendel randomization method, SNP significantly related to blood mtDNACN is used as a tool variable, and the lactic acid metabolism level hybrid effect is innovatively corrected, so that the lactic acid metabolism level hybrid effect of a subject is corrected without depending on lactic acid parameters of the subject. The causal association between the blood mtDNACN and the specific adverse pregnancy outcome (including gestational hypertension, preeclampsia, premature rupture of fetal membranes and the like) is accurately determined. According to the scheme provided by the invention, more reliable and anti-hybrid individualized risk assessment can be realized, and a new tool is provided for health management in the gestation period.

A method of identifying a causal relationship between a drug and an unrelated disease, the method comprising: obtaining genetic instruments for therapeutic targets of the drug; identifying genes (or alleles, or variants thereof) associated with the genetic instruments; obtaining effect sizes of genetic instruments for targets associated with the unrelated disease; identifying genes (or alleles, or variants thereof) associated with the genetic instruments; harmonising the genes obtained previously and the genes identified previously; identifying overlapping genes in the harmonised genes obtained previously; and performing two-sample Mendelian randomization using the overlapping genes identified in step e), wherein the results obtained from the two-sample Mendelian randomization identify the causal relationship, if present.

This invention discloses a pharmaceutical composition and its application in the preparation of drugs for treating CSVD, belonging to the field of biomedical technology. The pharmaceutical composition comprises at least one GLP1R agonist and at least one VEGF-A inhibitor. Through Mendelian randomization studies, this invention discovered and revealed that the protective effect of GLP1R agonists in alleviating WMH is partly mediated by downregulating the specific molecular pathway of vascular endothelial growth factor (VEGF-A), indicating that VEGF-A is not only a pathogenic factor of CSVD but also a key downstream node for the therapeutic effect of GLP1R agonists. Based on the above findings, animal model experiments confirmed that compared to traditional single-target monotherapy, combining GLP1R agonists and VEGF-A inhibitors can synergistically enhance the effects and achieve more comprehensive and precise intervention and treatment of CSVD.

The invention discloses a biliary tract cancer risk grade assessment method, system, device, medium and product, and relates to the field of epidemiology, the method comprises the following steps: obtaining exposure factors and outcome variables of different crowds; the exposure factor is cholelithiasis related SNP (Single Nucleotide Polymorphism); the outcome variable is bile duct cancer SNP (Single Nucleotide Polymorphism); based on a Mendel randomization analysis method, screening the SNP related to cholelithiasis, and determining a tool variable; the tool variable is screened cholelithiasis related SNP (Single Nucleotide Polymorphism); based on the tool variable, the exposure factor and the outcome variable, evaluating a causal relationship between cholelithiasis and biliary tract cancer; the causal relationship comprises a strong causal relationship, a medium causal relationship and a weak causal relationship; evaluating the risk level of the biliary tract cancer disease according to the causal relationship; the risk levels of the biliary tract cancer disease comprise a high-risk level, a medium-risk level and a low-risk level. The risk levels of the biliary tract cancer disease can be accurately evaluated.

The invention discloses a systemic lupus erythematosus secondary anti-phospholipid syndrome marker and application thereof, belongs to the technical field of biomedical detection, and relates to a double-sample combined diagnosis, risk stratification and targeted therapy technology. On the basis of single cell RNA sequencing and Mendel randomization data, a skin cell-peripheral blood double-sample joint detection scheme is provided, and the diagnosis sensitivity and specificity are high; an ITGA4-MIF-PTPN22 risk layering model is constructed, and accurate risk division is achieved; a transcription factor-signal channel combined intervention preparation is developed, and the thrombus inhibition rate is high; and a rapid detection test strip is designed to meet the instant detection requirement. The method solves the problems of insufficient accuracy, single target spot and lack of layering in the prior art, and has remarkable clinical value.

The invention is suitable for the technical field of intelligent assistants, and provides an intelligent writing assistant based on a Mendel randomization system, which comprises a database, a multi-modal MR NLP integration module, a causal engine reasoning module, a content processing module and a causal writing guidance module. The NLP integration module of the multi-mode MR is used for combining genetic data with other modes through the multi-mode MR; the causal engine inference module is used for establishing a'genotype-phenotype 'relation network among text elements by simulating Mendel randomized causal inference logic, and analyzing and identifying real effective factors in writing through tool variables; the causal writing guidance module is used for identifying writing elements really influencing the reaction of the reader; the assistant identifies unique writing genes of authors by analyzing phenotypic characteristics of texts, provides personalized style strengthening suggestions, and outputs Mendel randomization to research causal relationships on the basis of a database, so that confounding factors are avoided.

The invention discloses a method for analyzing the anti-gastric cancer effect of baicalein by combining network pharmacology and Mendel randomization. The method comprises the following steps: S1, collecting potential target spots of baicalein; s2, obtaining exposure data and outcome data; s3, Mendel randomization analysis is carried out, and MR analysis is carried out by using a TwoSampleMR software package of R; s4, performing difference analysis; s5, establishing a protein-protein interaction network: importing the potential action target information obtained in the step S3 into a String database to obtain protein interaction data so as to obtain a PPI network diagram, analyzing a result by using Cytoscape Version 3.9. 1 software, constructing the protein interaction network, and screening a hub gene (TOP10) scored by Clustering Coefficient by using a Cytohubba plug-in; s6, carrying out enrichment analysis on GO and KEGG pathways; s7, molecular docking verification: taking the screened gene target as a receptor, finding a receptor 3D structure file in a PDB database, taking baicalein as a ligand, and finding a ligand 3D structure file by utilizing a PubChem database; molecular docking is carried out through AutoDock software, and a docking result is visualized through PyMOL software.

The invention discloses a method and a device for screening drugs for treating non-alcoholic fatty liver diseases and drug application. The method comprises the following steps: step S101, acquiring expression quantitative trait locus eQTLs data of drug target genes of antihypertensive drugs as exposure factor data, and acquiring whole genome association analysis GWAS data of the non-alcoholic fatty liver diseases as outcome variable data; s102, setting screening conditions according to a relevance hypothesis, an exclusiveness hypothesis, an independence hypothesis and a target spot relevance hypothesis, and extracting and screening associated SNPs from the eQTLs data according to the set screening conditions to serve as tool variables; s103, extracting the associated SNPs (Single Nucleotide Polymorphisms) which are screened out from the GWAS data; and S104, verifying the causal relationship between the antihypertensive drugs and the non-alcoholic fatty liver disease by adopting a Mendel randomization analysis method so as to select the antihypertensive drugs effective for the non-alcoholic fatty liver disease (NAFLD). Therefore, a new clinical existing medicine which can be used for treating the NAFLD can be obtained from antihypertensive medicines, and more treatment choices are provided for NAFLD patients.

The invention provides an intestinal flora composition for preventing or treating osteoporosis and application thereof, and relates to the technical field of biology. According to the invention, causal association analysis is carried out on whole genome heritable variation data and intestinal flora data of East Asian population through a Mendel randomization method, and the intestinal flora for preventing or treating osteoporosis is screened out; comprising acid amide bacteria enterobacter, bacillus haulis, kiwifruit bacteria, archaea extensa, streptococcus ricini and lysobacter pitermannii, and an accurate microbial intervention target is provided for prevention and intervention of osteoporosis. The intervention mode based on microbiomics has the advantages of noninvasiveness, high safety, good tolerance and the like, can be used for a long time, has no drug accumulation risk, and is especially suitable for early prevention and chronic management scenes of osteoporosis.

The invention discloses a sepsis risk prediction method based on intestinal flora exposure factors and a multi-machine learning algorithm, and belongs to the field of sepsis risk prediction.The sepsis risk prediction method comprises the steps that 473 intestinal flora SNP data sets and sepsis data sets are obtained from a GWAS database, a sepsis patient expression microarray data set is downloaded from a GEO database, and a sepsis risk prediction result is obtained; the method comprises the following steps: acquiring positioning information of a gene on a chromosome from a GeneCards database, and acquiring immune infiltration abundance data from CIBERSORT; according to the method, a Mendel randomization method is combined with multiple machine learning algorithms, a sepsis risk prediction model is constructed based on intestinal flora exposure factors, and the intestinal flora exposure factors and target genes closely related to sepsis are screened out through Mendel randomization analysis. A plurality of machine learning algorithms are utilized to construct a model and screen out an optimal model, the accuracy and sensitivity of sepsis early diagnosis are improved, characteristic genes are analyzed, targeted drugs are predicted, and a theoretical basis is provided for precise treatment of sepsis.