32 results about "Genome-wide association study" patented technology

The invention discloses a molecular marker-assisted efficient breeding method for fresh corn, which comprises the following steps: S1, target character confirmation: determining a target character needing to be improved, and positioning a gene or quantitative character locus (QTL) related to the target character through linkage analysis or whole genome association research (GWAS); the method is reasonable in design, existence and transmission of target genes can be accurately identified, the selection accuracy is improved, target character genotypes can be screened in seedling stages or early generations through molecular marker-assisted selection, the breeding cycle is shortened, and good varieties can be more quickly derived. By means of molecular markers, genotype combination and target character expression of filial generations can be known in advance, a breeding scheme is reasonably planned, the probability of obtaining good varieties is improved, multiple target characters can be selected at the same time, individuals with target genotypes can be rapidly screened out from a large number of groups, and the breeding efficiency is improved. The workload of field trials and phenotype identification is reduced, and breeders can concentrate resources to cultivate good varieties.

In various embodiments, the disclosure provides compositions, such as polypeptides, polynucleotides, gene editing systems, small molecules, vectors, or host cells, comprising and / or modulating the expression or activity of an immunomodulatory-related protein, such as a cytokine. In various embodiments, the disclosure also provides for the treatment of a disease or condition (e.g., a disease or disorder) using an agent comprising and / or modulating the expression or activity of an immunomodulatory-related protein. The present invention relates to methods for identifying such agents (e.g., diseases or conditions associated with whole genome association studies (GWAS), cancer genome maps (TCGA), whole genome sequencing, whole phenotype group association studies (PheWAS), expression quantitative trait locus (eQTL) studies, or combinations thereof), and methods for identifying such agents.

The invention discloses a non-alcoholic fatty liver disease treatment drug target screening method and application thereof, and the method comprises the following steps: screening a drug-acceptable gene, obtaining cis-eQTL data of the drug-acceptable gene, obtaining whole genome correlation research data of a non-alcoholic fatty liver disease, and constructing an end database and a verification database. Screening potential drug targets having a causal relationship with the non-alcoholic fatty liver disease by using a Mendel randomization analysis method, and determining a target drug target by using a co-localization analysis method; potential drug targets related to NAFLD pathogenesis are screened and identified by using a Mendel randomization analysis method, and a causal relationship is established between exposure and results, so that the accuracy of the drug targets is improved, common driving factors between the potential drug targets and NAFLD risks are confirmed by performing co-localization analysis, and the risk of the NAFLD is determined. And screening, identifying and determining a drug target for treating the NAFLD so as to realize targeted treatment of the NAFLD.

Methods comprising an integrated, multiscale artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional sub-networks are described. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of a drug pharmacogenomic network is made through the selection of candidate SNPs by imputation, determination of the predicted causality of the SNPs using machine learning and deep learning, use of the causal SNPs to probe the spatial genome as determined by chromosome conformation capture analysis, combining targeted genes controlled by the same cell and tissue-specific enhancers, and reconstruction of the pharmacogenomic network using diverse data sources and metrics based on the results of genome-wide association studies. Knowledge-based segmentation methods are used to deconstruct the pharmacogenomic network into its constituent efficacy and adverse event sub-networks for applications in clinical decision support, drug re-purposing, and in silico drug discovery.

The invention relates to the technical field of life science. The method for analyzing the relation between the red blood cells and the rheumatoid arthritis comprises the following steps that research data are collected and comprise whole genome correlation research data related to the red blood cells and whole genome correlation research data related to the rheumatoid arthritis; performing tool variable screening on the research data; according to the method, two-way two-sample Mendel randomization analysis is carried out according to screened tool variables, the degree of influence of exposure on the outcome is obtained, and therefore the relation between the red blood cells and the rheumatoid arthritis is obtained, two-sample Mendel randomization and meta analysis are used for researching the potential two-way causal relation between the red blood cells and the rheumatoid arthritis, and the relation between the red blood cells and the rheumatoid arthritis is obtained. Results show that the average red blood cell hemoglobin, the average red blood cell hemoglobin concentration, the average red blood cell volume, the red blood cell distribution width and the reticulocyte count have a causal relationship with RA.

A method for constructing a disease prediction model is provided. First, a genome-wide association study (GWAS) is conducted on patients with the target disease to identify relevant SNP loci. Next, two SNP loci are randomly selected as a first SNP combination, and a first machine learning model is trained for disease prediction, with its accuracy verified. Subsequently, the remaining SNP loci are sequentially added to the first combination to generate multiple second SNP combinations, and the corresponding disease prediction models are trained and validated. Among these second combinations, the one with the highest prediction accuracy is selected as the third combination. This process is repeated until all SNP loci are included, ultimately determining the optimal SNP target combination for the final training and prediction of the disease prediction model.

Example embodiments relate to analyzing and merging data from genome-wide association studies. An example embodiment includes a method. The method includes receiving, by a processor from a memory, a candidate data set including data from a genetic study conducted within a population. The data from the genetic study includes a plurality of gene variants determined within the population. The method also includes removing one or more of the plurality of gene variants from the candidate data set in order to generate a revised candidate data set based on one or more variant-level quality metrics. Further, the method includes determining whether the revised candidate data set satisfies one or more study-level quality metrics. Additionally, the method includes establishing data set metadata based on whether the revised candidate data set satisfies one or more study-level quality metrics. Further, the method includes storing, within the memory, the data set metadata.

Example embodiments relate to analyzing and merging data from genome-wide association studies. An example embodiment includes a method. The method includes receiving, by a processor from a memory, a candidate data set including data from a genetic study conducted within a population. The data from the genetic study includes a plurality of gene variants determined within the population. The method also includes removing one or more of the plurality of gene variants from the candidate data set in order to generate a revised candidate data set based on one or more variant-level quality metrics. Further, the method includes determining whether the revised candidate data set satisfies one or more study-level quality metrics. Additionally, the method includes establishing data set metadata based on whether the revised candidate data set satisfies one or more study-level quality metrics. Further, the method includes storing, within the memory, the data set metadata.

According to one embodiment of the present disclosure, a method for estimating a polygenic risk score (PLS) includes the steps of obtaining data related to a genome-wide association study (GWAS) of an individual for whom the polygenic risk score is to be estimated, obtaining a plurality of primary estimates of the individual's polygenic risk score from the data via a plurality of deep learning models, generating a second test set for a metalearning model using the plurality of primary estimates, and obtaining a final estimate of the polygenic risk score from the second test set via the metalearning model.

Provided are methods, systems and computer program product embodiments for analyzing multi-omic data using a tensor regression model for genome-wide association studies in the life sciences. The unique structure of tensor covariates is leveraged to find associations between the omics data and complex diseases. Within this framework, the excessive dimensionality is reduced to a manageable level, leading to efficient estimations and predictions. The method is superior to using classical regression techniques in genome-wide association studies, which are challenged by analyzing multi-dimensional and uniquely structured data from the health and life sciences, in which covariates can take on more intricate forms such as multi-dimensional arrays. Embodiments have multiple uses in genomics, proteomics, metabolomics, multi-omics data integration, drug discovery, personalized medicine and predictive modeling, demonstrating the versatility and importance of tensor regression models to understand the associations between omics data and complex diseases.

The present invention relates to the field of computational biology, and discloses a GWAS-based social isolation and neuroplasm analysis system, which comprises: a data collection and processing module, which comprises a database interface for extracting single nucleotide polymorphism from a whole genome association research database and a data processing unit; the tool variable selection module comprises an SNP (Single Nucleotide Polymorphism) screening algorithm; the causal analysis module comprises a calculation unit used for executing double-sample Mendel randomization analysis; the heterogeneity and multi-effect test module comprises a heterogeneity detection unit and a multi-effect detection unit; and the result display and interpretation module comprises a visual interface and a report generator. According to the method, the correlation in the GWAS data can be effectively converted into causal inference by integrating a double-sample Mendel randomization (TSMR) method, and the system can identify the potential causal relationship between social isolation and neuroplasm by selecting SNP sites meeting strict tool variable standards and combining multiple robustness analysis methods such as inverse variance weighting (IVW).

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 application discloses a TWAS statistical method for horizontal pleiotropic correction in underrepresented populations, and relates to the technical field of whole genome association study, and comprises the following steps: for each gene, the effect of the expression prediction value of the gene in GWAS research on a phenotype and the horizontal pleiotropic effect of cis-SNPs on the phenotype are considered, and thus a TWAS model of underrepresented populations is constructed; based on the TWAS model of underrepresented populations, a non-zero effect is selected by introducing a smooth truncated absolute deviation penalty function, and least square estimation is combined to estimate and infer the effect and the horizontal pleiotropic effect of individual horizontal data and aggregate statistical data respectively. In the TWAS framework, cis-SNPs with horizontal pleiotropy are accurately identified, and accurate statistical inference and effect estimation of the predicted gene expression on a phenotype are realized.

The present invention provides an application of a reagent for detecting the SNP locus rs2246690 in the preparation of a screening product for AMS susceptible populations, which relates to the field of biomedical technologies. Through genome-wide association studies, it has been found that the genotype AA of the SNP locus rs2246690 at 9p24.1 is the susceptible genotype for AMS. By detecting the SNP locus rs2246690, it is possible to effectively achieve the auxiliary screening of AMS susceptible populations and conduct scientific interventions to specifically reduce the risk of AMS occurrence in these susceptible populations.

The disclosure provides, in various embodiments, compositions, such as polypeptides, polynucleotides, gene editing systems, small molecules, vectors or host cells, that comprise and / or modulate expression or activity of immune regulation-associated proteins, such as cytokines. The disclosure also provides, in various embodiments, methods of treating a disease or condition (e.g., a disease or condition associated with the Genome-Wide Association Study (GWAS), the Cancer Genome Atlas (TCGA), whole genome sequencing, phenome-wide association study (PheWAS), expression quantitative trait locus (cQTL) studies, or a combination thereof) using an agent that comprises and / or modulates expression or activity of an immune regulation-associated protein, and methods of identifying said agent.

The invention provides an irritable bowel syndrome risk marker based on genomics. The risk marker comprises the following six pathogenic genes: CADM2, PHF2, PCLO, SHISA6, LRP1B and TANK. According to the invention, not only is the effect of the latest large-scale whole genome association research (GWAS) on the aspect of analyzing the genetic cause of the irritable bowel syndrome shown, but also five new genetic risk variation, potential unreported pathogenic genes and treatment targets of the irritable bowel syndrome are found; a new insight is provided for the cause of the irritable bowel syndrome, and a potential therapeutic intervention target is highlighted. The invention also provides an application based on the risk marker of the irritable bowel syndrome and a corresponding early screening kit.

The invention provides application of a reagent for detecting an SNP (Single Nucleotide Polymorphism) site rs2246690 in preparation of a product for screening AMS susceptible population, and relates to the technical field of biomedicine. Whole genome association research finds that the genotype AA of the SNP site rs2246690 of 9p24.1 is a susceptible genotype of AMS, and by detecting the SNP site rs2246690, auxiliary screening of AMS susceptible population can be effectively realized, and scientific intervention is carried out, so that the risk of AMS occurrence of the susceptible population is reduced in a targeted manner.

The invention provides application of a reagent for detecting an SNP (Single Nucleotide Polymorphism) site rs6802921 in preparation of a product for screening AMS susceptible population, and relates to the technical field of biomedicine. Whole genome association research finds that the genotype GG of the SNP site rs6802921 of 3p25.3 is a susceptible genotype of AMS, and by detecting the SNP site rs6802921, auxiliary screening of AMS susceptible population can be effectively realized, and scientific intervention is carried out to reduce the risk of AMS occurrence of the susceptible population in a targeted manner.

Improved methods for determining full-genome associations with phenotype data represented by medical images, ECG traces, spirometry-traces, or other high-dimensional phenotype-representing physiosignals are provided. These methods include training an encoder, as pan of an autoencoder, to project input physiosignals into a phenotypically representative set of lower-dimensional latent variables. In some examples, the latent variables are augmented by clinical correlates of the input physiosignals (e.g., a. force vital capacity-determined from a spirometry trace), The latent variables and / or clinical correlates are then used to determine genetic loci that are associated with each of the latent variables. These associations can then be used to focus drug development and / or to predict polygenic scores tor ram diseases for which sufficient, data, may-not be available for a full genome-wide association study or other genomic data-to-phenotype association.

Methods comprising an integrated, multiscale artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional sub-networks are described. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of a drug pharmacogenomic network is made through the selection of candidate SNPs by imputation, determination of the predicted causality of the SNPs using machine learning and deep learning, use of the causal SNPs to probe the spatial genome as determined by chromosome conformation capture analysis, combining targeted genes controlled by the same cell and tissue-specific enhancers, and reconstruction of the pharmacogenomic network using diverse data sources and metrics based on the results of genome-wide association studies. Knowledge-based segmentation methods are used to deconstruct the pharmacogenomic network into its constituent efficacy and adverse event sub-networks for applications in clinical decision support, drug re-purposing, and in silico drug discovery.

The application provides an rs10045697 related SNP marker combination for predicting the risk and susceptibility of high myopia based on equivalent spherical lenses and an application thereof, and relates to the field of biological medicine. Specifically, the application screens a key SNP marker rs10045697 and a plurality of SNP sites related to high myopia in a high myopia queue based on whole genome association study (GWAS), and a model constructed based on the SNP marker combination can realize risk stratification and susceptibility detection of high myopia, is suitable for ophthalmic screening, high-risk population identification and precise prevention and control intervention, and has important clinical transformation value.

The invention provides a myopia progress prediction method based on a dynamic weight multi-gene risk score (PRS). The myopia progress prediction method comprises the following steps of: selecting a myopia progress prediction model according to the dynamic weight multi-gene risk score (PRS); the method comprises the following steps of: inputting a whole genome association study (GWAS) effect value of a basic population and individual genotype data of a target population, carrying out dynamic correction on a single nucleotide polymorphism (SNP) effect value in combination with a population differentiation index (Fst) and a linkage disequilibrium (LD) parameter of the target population, and constructing a PRS model with a population generalization ability. The method further collects individual environmental behavior data, including close-range eye load and outdoor exposure, calculates environmental interaction factors, and introduces the environmental interaction factors into a risk scoring model to comprehensively generate an individual dynamic risk score (PRSdynamic). The method has the characteristics of cross-racial adaptability, genetic-environment interaction modeling ability and optimization along with time change, can be used for individualized accurate prediction of myopia progress risks, and provides a scientific basis for myopia prevention and control research and intervention strategy formulation.

The present invention discloses a Mendelian randomization causal inference method based on the summary statistical data of whole genome association study, comprising: S1, integrating the inference model, the polygenic effect model and the estimation error model, adjusting the LD effect, and obtaining the MR-MU model; S2, collecting the GWAS data of the studied phenotype and its risk factor candidate set; S3, screening the risk factor candidate set; S4, excluding the characteristics showing weak pleiotropy evidence; S5, selecting appropriate SNPs from the GWAS data as instrumental variables; S6, performing parameter estimation on the conditional likelihood function of the MR-MU model, respectively obtaining two sets of parameter estimation values ​​under the conditions of fixed and unfixed causal effects, and the estimation value of the causal effect under unfixed conditions, and calculating the function values ​​of the conditional likelihood function corresponding to the two sets of parameter estimation values; S7, calculating the likelihood ratio test statistic, identifying the risk factors of the studied phenotype, and statistically inferring whether there is a causal effect, so as to improve the accuracy and reliability of the causal inference.

The disclosure relates to the field of molecular marker-assisted breeding of maize, and specifically an application of a Zm00001d012005 gene in regulating starch content of maize kernels. Specifically, an application of a gene related to starch content of maize kernels in molecular marker-assisted breeding of maize is provided in the disclosure. A sequence of the Zm00001d012005 gene is as shown in SEQ ID NO: 1. In the disclosure, genome-wide association study (GWAS) analysis and genetic linkage analysis are utilized to co-localize SNP_166371888 which is on chromosome 8 and significantly associated with kernel starch content, and a functional gene Zm00001d012005 that regulates the kernel starch content is further identified. The gene Zm00001d012005 can explain 10.19% of phenotypic variation in the kernel starch content.

The present invention provides a reagent for detecting the SNP site rs1531641 in the preparation of a screening product for AMS-susceptible populations, relating to the field of biomedical technology. A genome-wide association study found that the genotype TT of the SNP site rs1531641 on 3q25.1 is a susceptible genotype for AMS. Detection of the SNP site rs1531641 can effectively assist in screening AMS-susceptible populations and enable scientific intervention to specifically reduce the risk of AMS in these susceptible populations.

The invention discloses a computer device and method for detecting target phenotype related genes or candidate genes based on low-depth sequencing data and application. The present invention uses a 29-mer sequence representing each SSP gene at the generic genome level to reveal gene diversity that has not been explored between different wheat varieties and modern cultivated varieties. The SSP gene related to the processing quality is identified based on the whole genome association study of k-mer so as to improve the wheat processing quality. The device or the method provided by the invention can be applied to screening, mining and cloning of phenotypic character related candidate genes with insufficient gene annotation caused by numerous coding genes, complex structures, existence of a large number of long and short repetitive sequences in the genes and existence of a large number of non-communicated regions in coding sequences and flanking sequences; and annotation of related gene functions, correlation analysis and molecular marker development are carried out.

Methods are described that include an integrated, multi-scale, artificial intelligence-based system that reconstructs drug-specific pharmacogenomic networks and their constituent functional subnetworks. The system uses features of the functional topology of the three-dimensional architecture of drug-modulated spatial contacts in chromatin space. Discovery of drug pharmacogenomic networks is performed by selecting candidate SNPs with the aid of imputation, determining predictive causal relationships of the SNPs using machine learning and deep learning, probing spatial genomes as determined by chromosome conformation capture analysis using causal relationship SNPs, combining targeted genes controlled by the same cell and tissue-specific enhancers, and using different data sources and metrics to reconstruct pharmacogenomic networks based on results of genome-wide association studies. The pharmacogenomic networks are deconstructed into their constituent functional and adverse event subnetworks using a knowledge-based segmentation approach for application in clinical decision support, drug repurposing, and in silico drug discovery.

The invention provides an rs10045697 related SNP (Single Nucleotide Polymorphism) marker combination for predicting high myopia risk and susceptibility based on an equivalent spherical mirror and application of the rs10045697 related SNP marker combination, and relates to the field of biological medicines. Specifically, a key SNP marker rs10045697 and a plurality of SNP loci related to high myopia are screened based on equivalent spherical power in a high myopia queue through genome-wide association study (GWAS), and a model constructed based on the SNP marker combination can realize risk stratification and susceptibility detection of high myopia. The method is suitable for ophthalmology screening, high-risk group identification and accurate prevention and control intervention, and has important clinical transformation value.

Genome wide association studies (GWAS) have demonstrated the power of genetics to detect previously unrecognized contributions to disease biology. To date one limitation has been the modest effect sizes of many of the detected loci, and the lack of a predictable relationship between the locus and specific mechanisms. These features have prevented the large-scale identification of new genetic targets for therapeutic intervention for all but a small number of GWAS loci. To directly address these limitations and build on the success of GWAS, the present disclosure presents a series of specific biological tools and analytics, which when combined with relevant clinical data at scale, enable the systematic discovery of previously unknown disease subsets, previously unknown disease genes of significant effect, previously unknown therapeutic targets, and disease-to-pharmacology matching at the individual patient level.

Method and kits for diagnosing propensity to non-contact cranial cruciate ligament rupture (CCLR) in a dog are described. The method includes isolating genomic DNA from a dog and then analyzing the genomic DNA from step for a single nucleotide polymorphism occurring in selected loci that have been determined to be associated with the CCLR phenotype via a genome-wide association study.