39 results about "Genetic correlation" patented technology

The invention belongs to the technical field of biomolecular markers, and particularly relates to an SNP (Single Nucleotide Polymorphism) molecular marker related to the crown tooth number character of broiler chicken as well as a primer and application of the SNP molecular marker. Through phenotypic determination and whole genome re-sequencing related to crown tooth number characters of broiler individuals and whole genome association analysis, the excavated Chr7: 391959 locus and the crown tooth number have extremely high genetic correlation, the significance reaches 2.90 * 10 <-7 >, phenotypic variation variances capable of being explained reach 1.75%, the genetic effect is very remarkable, and the method has the advantages that the method is simple and convenient to operate, and the method is suitable for popularization and application. Therefore, the molecular marker can be effectively used for molecular marker-assisted selective breeding of the crown tooth number character of the broiler chicken, individuals with the molecular marker Chr7391959 site as the TT genotype are selected as breeding hens, the genetic selection process of the crown tooth number character of the yellow feather broiler chicken can be accelerated, the individuals are directly bred on the genome level in the early stage, the breeding efficiency is improved, and the breeding cost is reduced. The industrial competitiveness of the yellow feather broilers is effectively improved.

To clarify the genetic correlation in cold sensitivity and cold constitution, and to develop and provide a method for determining affected areas of cold sensitivity and cold constitution based on the obtained genetic information.SOLUTION: A comprehensive genome-wide SNPs analysis and a questionnaire survey related to coldness have been conducted, identifying SNPs associated with cold sensitivity and various cold constitution types. This provides a method for determining affected areas of cold sensitivity and cold constitution using these SNPs as biomarkers for cold sensitivity and the like.SELECTED DRAWING: None

The invention discloses a method for screening and genetically verifying blood and urine biomarkers for effectively predicting the onset risk of Parkinson's disease. The method comprises the following steps: S1, screening a research sample; s2, screening an optimal hematuria biomarker based on an LASSO regularization method; s3, establishing a COX proportional risk regression model under multi-covariable adjustment; s4, carrying out enrichment analysis on genetic correlation and multiple-effect property; s5, analyzing the causal relationship between the fraction of C-reactive protein, insulin growth factor-1 and immature reticulocyte and PD through Mendel randomization, and comprehensively analyzing the relationship between blood and urine biomarkers at various baseline levels and the PD onset risk by utilizing a large longitudinal queue in an British biological sample library. Through genetic analysis and a Mendel randomization method, not only are associated evidences enhanced, but also a potential genetic mechanism is deeply discussed.

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 invention relates to a multi-trait composite breeding method for fish feed conversion rate, growth and survival rate, belonging to the field of genetic breeding of aquatic animals. The method establishes composite traits by using phenotypic correlations, then establishes genomic selection methods for multiple traits, and finally uses genetic correlations to establish a comprehensive genomic breeding value calculation method and a parental selection scheme. The method of the present invention overcomes the problem of excessive subjectivity in artificially setting weighting parameters in traditional methods. Through the comprehensive evaluation of multiple important economic traits, it not only improves the objectivity and accuracy of breeding selection, but also enables more effective resource allocation and higher genetic gain in multi-trait breeding, which is beneficial to the sustainable development of aquaculture.

Exemplary methods for use in identifying crosses for use in plant breeding are disclosed. One exemplary method includes generating population prediction scores for each potential cross within a set of potential crosses, where each population prediction score is associated with a prediction of commercial success for the associated potential cross within the set of potential crosses. The method also includes selecting a subgroup of potential crosses, based on thresholds associated with the population prediction scores for the set of potential crosses. The exemplary method further includes selecting multiple target crosses from the subgroup of potential crosses based on a genetic relatedness of the parents in the subgroup of potential crosses, and directing ones of the selected target crosses into a breeding pipeline, thereby providing crosses to the breeding pipeline based, at least in part, on commercial success of parents included in the selected ones of the filtered crosses.

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.

The invention belongs to the technical field of molecular markers, and particularly discloses a primer of a molecular marker of an ACBD6 gene for detecting chicken lateral crown branch defects and application of the primer of the molecular marker of the ACBD6 gene for detecting the chicken lateral crown branch defects. Phenotype determination and whole genome re-sequencing related to 300-day-old chicken lateral crown branches are carried out on 1496 pure-line Qingyuan partridge chicken individuals, whole genome association analysis is carried out, and the molecular marker of the ACBD6 gene for detecting the chicken lateral crown branch defects is obtained. The excavated Chr8: 5934516 (GRCg7b) (rs737527050) site has extremely high genetic correlation (p = 3.49207e-10) with the lateral crown branch character phenotype, so that the excavated Chr8: 5934516 (GRCg7b) (rs737527050) site can be effectively used for molecular marker-assisted selective breeding of the lateral crown branch defect of the chicken; individuals with the molecular marker Chr8: 5934516 site as the GG genotype are selected as breeding hens, the cockscomb lateral branch defect alleles in breeding populations and the crown lateral branch occurrence rate of subsequent generations of populations can be rapidly reduced, and meanwhile the carcass uniformity of breeding core populations can be indirectly improved.

The invention aims at finding out the genetic correlation between the cold constitution and the cold intolerance and developing and providing a method for judging the disease site of the cold constitution and the cold intolerance according to the obtained genetic information. Questionnaire survey is carried out on coldness, comprehensive SNP analysis of a whole genome is carried out, and SNPs only related to cold constitution and various cold intolerance types are found out. Also provided is a method for determining the site of onset of cold constitution and cold intolerance, in which the SNP is used as a biomarker for cold constitution and the like.

The invention relates to a fish feed conversion rate, growth rate and survival rate multi-character composite breeding method, and belongs to the field of aquatic animal genetic breeding. According to the method, composite characters are established by using phenotypic correlation, and then a genome selection method of multiple characters is established; and finally, establishing a comprehensive genome breeding value calculation method and a parent selection scheme by utilizing genetic correlation. According to the method, the problem that in a traditional method, the subjectivity of manually setting weighting parameters is too high is solved. Through comprehensive evaluation of a plurality of important economic characters, not only are the objectivity and accuracy of breeding selection improved, but also more effective resource allocation and higher genetic gain can be realized in multi-character breeding, and sustainable development of aquaculture is facilitated.

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.

The invention belongs to the technical field of molecular markers, and particularly discloses a molecular marker primer for detecting the shank girth of a cast chicken and application of the molecular marker primer, and the molecular marker primer is characterized in that 300-day-old shank girth-related phenotype determination and whole genome re-sequencing are carried out on 297 cast chicken-Yao chicken F2 generation resource group individuals (the average sequencing depth gt of the individuals; the molecular marker Chr1: 68713248 (GRCg7b) has the advantages that the molecular marker Chr1: 68713248 (GRCg7b), which is excavated by carrying out whole genome correlation analysis on the molecular marker Chr1: 68713248 (GRCg7b), has extremely high genetic correlation with 300-day-old shin girth, and is very remarkable in genetic effect, so that the molecular marker Chr1: 68713248 (GRCg7b) can be effectively used for molecular marker-assisted selective breeding of Dast chicken thick shin girth related specialized strain breeding, individuals with the molecular marker Chr1: 68713248 (GRCg7b) sites being AA or AT genotypes are selected as breeding hens, and the breeding hens can be bred and the genetic selection progress of Dongshin chicken containing Dongshin chicken blood relationship related specialized strain Dongshin chicken can be accelerated.

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 embodiment of the invention discloses a livestock and poultry dynamic character genome association analysis method and system based on a stochastic regression model, and relates to the technical field of data processing. The method comprises the following steps: constructing a target stochastic regression model to fit an individual phenotype trajectory, and estimating a regression coefficient of an individual phenotype curve; taking the regression coefficient as a phenotypic value, and positioning a major gene of a dynamic character growth trajectory by adopting a multivariable mixed model association analysis method; wherein the target stochastic regression model comprises a first processing layer and a second processing layer, the first processing layer is used for optimizing an individual phenotype curve according to a Bayesian information criterion so as to reduce the number of dimensions of repeated measurement, and the second processing layer is used for eliminating genetic correlation among individuals by using spectral decomposition so as to reduce the number of dimensions of repeated measurement. And the multivariate hybrid model is converted into a plurality of independent univariate hybrid models through typical transformation so as to optimize the solving method of the multi-gene variance component.

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 invention discloses a method for identifying complex disease susceptible protein based on multi-phenotype conjoint analysis, and relates to the technical field of susceptible protein identification. The method comprises the following steps: obtaining multiple phenotypes related to a target complex disease, and searching GWAS summarized statistical data corresponding to the phenotypes; calculating genetic correlation among different phenotypes of the disease, and screening out a remarkably related phenotype combination; carrying out conjoint analysis by utilizing MTAG to obtain a phenotypic specificity correlation estimation value of each SNP (Single Nucleotide Polymorphism); establishing a protein expression model based on SNP, screening an optimal prediction model, integrating the optimal prediction model with the phenotype specificity correlation estimation value, and analyzing the relationship between different proteins and phenotype comprehensive characteristics; inferring a causal relationship between the protein and the disease; according to the method, the interaction among various phenotypes and the common influence of the phenotypes on disease susceptibility are considered, the identification method is optimized, the identification capability is improved, and the accuracy of an identification result is guaranteed.

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.

The invention discloses a model and method for predicting biological characters by integrating genetic background and variation information, and the model comprises an embedded Siamese network which generates and extracts genetic background and genetic correlation information between samples, and builds an embedded vector for each sample to represent the genetic background information; the genetic variation embedded network is used for extracting complex high-order information and establishing an embedded vector by using a deep learning network and the whole genome variation information of each sample so as to characterize the cumulative effect of genetic variation on characters; and the fusion and character prediction module organically fuses embedded vectors output by the two modules to realize accurate prediction of characters. According to the method, the problem that genetic background information and genome variation information cannot be effectively fused in the prior art can be solved, the nonlinear effect between genes is effectively captured, and the accuracy of genome selection and the genetic architecture analysis capability of complex characters are greatly improved.

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 present disclosure relates to a SatSeq technology which couples saturation mutagenesis, DNA barcoding, and deep sequencing. Disclosed are vectors, systems and methods for analysis of protein functions, validating drug targets, identifying on-target drug effects, identifying drugs that bind to genetically relevant variants, and / or identifying pathway-specific modulators of a pleiotropic gene, by applying the SatSeq technology.