22 results about "Genetic correlation" patented technology

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.

The invention relates to the technical field of animal genetic breeding, and provides a double-character genome selection platform based on local genetic correlation (LGC). The platform is composed of a phenotype data processing module, a genotype data processing module and a genome selection module (comprising a model selection sub-module and a parameter selection sub-module). The platform performs quality control, data normality test and correction on phenotypic data; performing quality control, sequence alignment, variation detection and genotype filling on the genotype data; and identifying whole genome local genetic correlation, checking the significance of LGC, selecting a model based on a whole genome LGC estimation result of a character pair and a corresponding P value, setting a corresponding model parameter threshold, and realizing section weighted double-character genome selection analysis. According to the method, the genetic evaluation accuracy of complex characters, especially low heritability characters, can be effectively improved, and an efficient and stable genome selection tool is provided for livestock and poultry breeding.

The invention discloses a breeding method for improving grouper feed protein utilization efficiency and hypoxia-resistant selection efficiency based on a whole genome, which comprises the following steps: (1) determining phenotypic values of grouper breeding population individuals, and screening individuals with excellent characters as primary selection objects; and (2) constructing an additive genetic correlation matrix based on liquid phase chip genotype data and a molecular correlation coefficient method, calculating breeding values of feed protein utilization efficiency and hypoxia-resistant selection efficiency of individuals by utilizing a linear mixed model, and screening parent breeding values according to the breeding values to obtain candidate individuals. According to the invention, a whole genome selection technology flow of grouper feed protein utilization and hypoxia resistance is established, the prediction effect of the whole genome selection technology on the feed protein utilization and hypoxia resistance of groupers, especially epinephelus fuscoguttatus, is researched, and the selection number of reference parent groups is reduced, so that the production cost is reduced, and the economic benefit is increased. Therefore, an economical and effective whole genome selection technology is established.

The present application belongs to the field of aquatic animal genetics and breeding, and particularly relates to a breeding method of a new line of palm-shaped and high-meat-yield long oyster. The method comprises the following steps: (1) determining the genetic distance of each population of long oysters from different geographical sources; (2) obtaining the variation coefficient of the shell length and meat yield of long oysters; (3) calculating the genetic force of the shell length and meat yield of long oysters, and the genetic correlation between the two indexes; (4) calculating the family breeding value of the two traits, setting the weight value of the shell length and meat yield respectively, calculating the comprehensive breeding value, and screening the optimal family in proportion; (5) pairing the families among populations to improve the genetic diversity of the offspring; (6) taking the shell length and meat yield as the breeding target, and retaining the seed oysters according to the set seed retention rate every generation, the number of female and male seed oysters is maintained above the set number, and the breeding population retains the matching line.

The invention discloses a method for auxiliary identification of Duroc pig growth traits based on a BTG1 region SNP marker, and belongs to the technical field of animal genetic breeding and reproduction. Comprising the following steps: step S1, acquiring a growth trait record of a Duroc pig to be detected, performing data preprocessing, and calculating genetic correlation and phenotypic correlation between heritability and growth traits based on a pedigree; step S2, performing genetic typing on the Duroc pig to be detected, and detecting genotypes of the Duroc pig in a BTG1 region and other key SNP sites; s3, carrying out auxiliary identification on the growth traits of the to-be-detected Duroc pigs on the basis of a pre-established relationship between genotypes and the growth traits; and S4, selecting a corresponding individual as a breeding object according to an auxiliary identification result. The method can be used for early screening and genetic improvement of growth traits such as different day-age weights and staged average daily gain of Duroc pigs, and is suitable for breeding pig breeding practice of large-scale pig farms, live pig breeding enterprises and scientific research institutions.

Disclosed herein is a system and method for increasing the fidelity of measured genetic data, for making allele calls, and for determining the state of aneuploidy, in one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available. Poorly or incorrectly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related individuals. In accordance with one embodiment, incomplete genetic data from an embryonic cell are reconstructed at a plurality of loci using the more complete genetic data from a larger sample of diploid cells from one or both parents, with or without haploid genetic data from one or both parents. In another embodiment, the chromosome copy number can be determined from the measured genetic data, with or without genetic information from one or both parents.

Disclosed herein is a system and method for increasing the fidelity of measured genetic data, for making allele calls, and for determining the state of aneuploidy, in one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available. Poorly or incorrectly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related individuals. In accordance with one embodiment, incomplete genetic data from an embryonic cell are reconstructed at a plurality of loci using the more complete genetic data from a larger sample of diploid cells from one or both parents, with or without haploid genetic data from one or both parents. In another embodiment, the chromosome copy number can be determined from the measured genetic data, with or without genetic information from one or both parents.

Disclosed herein is a system and method for increasing the fidelity of measured genetic data, for making allele calls, and for determining the state of aneuploidy, in one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available. Poorly or incorrectly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related individuals. In accordance with one embodiment, incomplete genetic data from an embryonic cell are reconstructed at a plurality of loci using the more complete genetic data from a larger sample of diploid cells from one or both parents, with or without haploid genetic data from one or both parents. In another embodiment, the chromosome copy number can be determined from the measured genetic data, with or without genetic information from one or both parents.

The application discloses a kind of respiratory system disease data processing method, system, equipment and computer readable storage medium, method includes: obtaining single cell sequencing sequence data to be analyzed;Using the method of machine learning to the single cell sequencing sequence data to be analyzed is handled, obtains the PAS score matrix of cell pathway and the PAS of cell pathway;Obtain the genetic correlation data of respiratory tract disease, process the genetic correlation data, obtain the pathway data with SNPs annotation;The PAS of the cell pathway and the pathway data with SNP annotation are statistically analyzed and handled, obtain the estimated coefficient;The estimated coefficient is multiplied by PAS and summed to obtain the genetic correlation pathway activity score gPAS of cell;Output the genetic correlation pathway activity score gPAS.

This invention discloses a pleiotropic gene sharing genetic structure with obesity and mental disorders, along with its screening method and applications, belonging to the field of biomedical technology. The pleiotropic gene includes RERE, NEGR1, DENND1B, CTNNB1, TMEM106B, SP4, TSNARE1, DENND1A, CNNM2, NT5C2, BMAL1, MPHOSPH9, CCDC92, YLPM1, TAOK2, RTN4RL1, and ARFGEF2. The process includes collecting GWAS summary statistics on obesity-related phenotypes and mental disorders, assessing genetic correlation, screening pleiotropic genetic loci and candidate pleiotropic genes, using TWAS to screen pleiotropic genes, functional analysis and drug target prediction, and bidirectional causal association genetic validation. These methods are used to prepare drugs for the treatment of obesity and mental disorders or to construct a risk prediction model for obesity-mental disorder comorbidity.

Disclosed herein is a system and method for increasing the fidelity of measured genetic data, for making allele calls, and for determining the state of aneuploidy, in one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available. Poorly or incorrectly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related individuals. In accordance with one embodiment, incomplete genetic data from an embryonic cell are reconstructed at a plurality of loci using the more complete genetic data from a larger sample of diploid cells from one or both parents, with or without haploid genetic data from one or both parents. In another embodiment, the chromosome copy number can be determined from the measured genetic data, with or without genetic information from one or both parents.

The invention provides a multisystem disease analysis method based on gastrin plasma proteomics, which comprises the following steps of: A, analyzing a large number of adult individuals subjected to baseline evaluation by using queue research data of a biological sample library, and measuring a plurality of plasma proteins; b, analyzing a standardized protein expression (NPX) value of gastrin (GAST) by adopting a multivariable Cox proportional risk model, and correcting by using Bonferroni so as to reduce a false positive result; and C, exploring the correlation between the GAST-PRS and the disease phenotype by adopting full phenotype correlation analysis (PheWAS) based on polygene risk score (PRS), and further analyzing the genetic correlation between the GAST and the disease by using linkage imbalance score regression (LDSC). According to the method, the GAST can be redefined as a cross-system risk regulation factor, and the method is of great significance to layering of patients with shared pathway imbalance. The GAST has multi-system correlation with morbidity and mortality in the outcome of 168 diseases, and challenges the classical normal form of the gastrointestinal specific function of the GAST. These findings establish the location of GAST as a multi-system risk indicator.

The invention discloses an X-ray machine pet type recognition method based on machine vision, and relates to the technical field of machine vision and deep learning, the technical path of X-ray pet recognition is reconstructed through the machine vision technology, and the technical path of X-ray pet recognition is obtained based on a rib dip angle distribution curve of Gaussian kernel smoothing and skull polar coordinate sampling. Converting the discrete anatomical marker into continuous function description; the mechanism captures progressive morphological variation caused by variety hybridization, such as a rib dip angle gradient sequence of a golden feather * Labrudl hybrid dog, eliminates feature step distortion in traditional discrete classification, and provides a geometric basis for hybrid pedigree analysis; the mixed density network is combined with a Gaussian mixture model to generate a variety contribution degree probability vector, and continuous anatomical parameters are mapped into quantifiable genetic relevance measurement; spatial distribution of variety prototype vectors is restrained through graph Laplacian regularization, adjacent distribution of associated varieties on feature manifolds is forced, and smooth diffusion of probability vectors in a spectrum gradual change area is guaranteed.

The application belongs to the technical field of animal genetics and breeding, and provides a genomic selection analysis method considering trait local genetic correlation significance, comprising a phenotype data processing module, a genotype data processing module and a genomic selection module; the application performs quality control on the phenotype data; performs quality control, sequence alignment and variation detection on the genotype data; performs whole genome correlation analysis to generate summary statistical data; identifies whole genome local genetic correlation, tests the significance of LGC, and realizes double-trait genomic selection analysis with segment weighting; and the application can effectively improve the genetic evaluation accuracy of complex traits, especially low heritability traits, and provides an efficient and robust genomic selection tool for livestock breeding.

The application discloses a model and method for predicting biological traits by comprehensively integrating genetic background and variation information, and the model comprises: a Siamese network which generates and extracts genetic background and genetic correlation information between samples, establishes an embedding vector for each sample to represent the genetic background information; a genetic variation embedding network which uses a deep learning network to extract complex high-order information using whole genome variation information of each sample, establishes an embedding vector to represent the cumulative effect of genetic variation on traits; and a fusion and trait prediction module which realizes accurate prediction of traits by organically fusing the embedding vectors output by the above two modules. The application can solve the problem that genetic background information and genomic variation information cannot be effectively fused in the prior art, effectively capture the nonlinear effect between genes, and greatly improve the accuracy of genomic selection and the genetic architecture analysis capability of complex traits.

The invention discloses a mineralization potential level evaluation method based on deep learning, and relates to the technical field of mineral resource exploration, and the method comprises the steps: obtaining and preprocessing multi-source geological data of a target exploration region, and generating a standardized data cube of the target region; performing cause feature extraction on the standardized data cube to generate a cause correlation distribution map representing mineralization control elements; based on a small number of known ore occurrences in the target area, extracting a target area metallogenic mode prototype representing a regional metallogenic law from the cause correlation distribution map, and constructing a structured metallogenic knowledge map based on domain knowledge; taking the target area metallogenic mode prototype as guidance, querying a metallogenic knowledge graph to perform knowledge reasoning, and generating a knowledge-guided distant view area focus graph; and performing information enhancement on the standardized data cube by using the knowledge-guided distant view area focus image to generate a knowledge enhanced feature cube. According to the invention, the problem of insufficient evaluation reliability of traditional deep learning under the condition of sample scarcity in the initial stage of exploration is solved.

A system and method for determining the genetic data for one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available, are disclosed. Genetic data for the target individual is acquired and amplified using known methods, and poorly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related subjects. In accordance with one embodiment of the invention, incomplete genetic data is acquired from embryonic cells, fetal cells, or cell-free fetal DNA isolated from the mother's blood, and the incomplete genetic data is reconstructed using the more complete genetic data from a larger sample diploid cells from one or both parents, with or without genetic data from haploid cells from one or both parents, and / or genetic data taken from other related individuals.

The invention discloses a deep learning genome prediction method and system based on a retrieval enhancement mechanism. The method comprises the following steps: acquiring genome data; generating an individual embedding representation through a gene feature extraction network, and optimizing embedding space distribution by using a gene specific discriminator to capture a potential genetic structure; retrieving a reference individual most related to the target sample based on the similarity of the embedded space; performing weighted fusion on the retrieved reference sample features and the target sample to form enhanced representation; and finally predicting the phenotypic value of the target sample through the regression network. According to the method, a retrieval enhancement mechanism is introduced, so that the model can dynamically utilize information of genetic related individuals in a prediction process, and genetic related characteristics of an individual level are extracted from a group. Different from a traditional genome prediction model which is only based on independent sample learning, the method provided by the invention structurally fuses genetic similarity among individuals, and can more accurately model a complex nonlinear genetic effect.

This invention belongs to the field of animal molecular genetic detection and bioinformatics analysis technology, and relates to a method and application for constructing low-density SNP marker combinations for equine population attribution determination, population genetic structure assessment, and genetic correlation assessment. Specifically, based on equine whole-genome autosomal biallelic SNP data, a low-density differential information marker combination is obtained through multi-population differentiation information screening and supervised feature optimization. Results show that the marker combination maintains high population attribution determination performance in both outer cross-validation and external independent test sets, and can well reproduce the population genetic structure reflected by PCA, ADMIXTURE, and genomic relationship matrices; it is suitable for equine population attribution determination, genetic group identification, germplasm resource assessment, population genetic structure analysis, kinship determination, and the development of related molecular detection products.

One or more genetic family trees are formed that connect a plurality of individuals based on measures of genetic relatedness between the individuals. A plurality of the individuals are grouped into a cluster that comprises those individuals who are genetically related to (1) a target individual according to first data and (2) each other according to second data. The first data comprises genetic relatedness measures for a plurality of the individuals relative to the target individual, and the second data comprises genetic relatedness measures between a plurality of the individuals. The cluster is deconstructed into a tree structure based on the first data, and family tree(s) are iteratively formed based on pairs of adjacent nodes from the tree structure until the nodes are positioned in family tree(s) based on the second data. The family tree(s) can be connected to the target individual based on the first data.

A system and method for determining the genetic data for one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available, and also for predicting likely phenotypic outcomes using mathematical models and given genetic, phenotypic and / or clinical data of an individual, and also relevant aggregated medical data consisting of genotypic, phenotypic, and / or clinical data from germane patient subpopulations. Genetic data for the target individual is acquired and amplified using known methods, and poorly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related subjects.

This invention discloses a method and system for estimating genetic correlations based on a high-precision likelihood function system, relating to the field of data analysis. By constructing a reference panel feature mapping model and an accuracy mapping model, this invention overcomes the subjectivity and blindness of traditional manual selection and achieves precise learning of the correlation between quality control indicators and estimation accuracy, providing a quantitative basis for optimizing quality control strategies. Secondly, an error verification mechanism is introduced to rigorously judge the quality of the results. When the accuracy is insufficient, the reference panel is dynamically updated first, and then various influencing indicators are iteratively adjusted and continuously verified until the accuracy requirements are met. Finally, the parameters are replaced and the final result is obtained by recalculation. This approach balances the adaptability of the reference panel with the rationality of the quality control parameters, effectively reducing interference factors such as population background bias and genotyping quality fluctuations, and significantly improving the accuracy, robustness, and repeatability of genetic correlation estimation results.