46 results about "Phenotype correlation" patented technology

To provide a technique that enables direct seeding cultivation of seed potatoes in the field of production of sweet potato tuberous roots (sweet potato cultivation), which offers significant labor-saving benefits, wherein the technique enables efficient development of sweet potato plants having desired traits and exhibiting low enlargement of seed potatoes.SOLUTION: A method for identifying a sweet potato plant exhibiting low enlargement of seed potatoes, includes the steps of: detecting a nucleotide site indicating the "Kyushu 199-type variant allele" associated with a DNA marker from a genomic DNA of the sweet potato plant to be identified, with respect to "the DNA marker that shows relevance to the phenotype of low enlargement of seed potatoes" in the genomic DNA of sweet potato plants; and determining that the sweet potato plant to be identified is a sweet potato plant exhibiting low enlargement of seed potatoes if a nucleotide site indicating the "Kyushu 199-type variant allele" associated with the DNA marker is detected in the detection step.SELECTED DRAWING: Figure 2

The invention discloses a scophthalmus maximus anti-edwardsiella tarda SNP molecular marker and application thereof, and belongs to the technical field of molecular breeding and biology. The nucleotide sequence of the SNP molecular marker is shown as SEQ ID No.1, and the polymorphic site of the SNP molecular marker is A / C. The invention further provides application of the SNP molecular marker in screening of turbots with the Edwardsiella tarda resistant character, and the CC genotype turbots are individuals with the Edwardsiella tarda resistant character. By utilizing the SNP molecular marker provided by the invention, DNA level selection can be carried out in the early stage of fish fries, interference of environmental factors is avoided, the accuracy and efficiency of breeding are remarkably improved, and the breeding period is shortened. The molecular marker is remarkably verified in an independent verification group through linear regression analysis, genotype-phenotype association is stable and reliable, false positive is eliminated, and the molecular marker has a good market application prospect.

The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method identifying a covariant of interest with respect to drug response phenotype (DRP) of a treatment is disclosed.

The invention discloses a Chinese rose petal outer edge notch whole genome SNP molecular marker combination and application thereof, and aims to overcome the defects in the prior art, 879 SNP markers significantly related to petal outer edge notch are identified by fusing 358 Chinese rose whole genome re-sequencing data and petal outer edge notch phenotype correlation analysis and applying a multi-model combined screening technology, and the SNP markers are used for identifying the petal outer edge notch. And a whole genome selection model with high prediction accuracy is established. The markers are distributed on 14 chromosomes, high-throughput and rapid detection can be achieved, the detection period is shortened, the petal wave character is directly predicted through seedling genotype data, and the breeding process is accelerated. The method has the advantages of low cost, high throughput and industrialization potential, provides key data support for Chinese rose petal outer edge notch genetic law analysis and molecular marker assisted breeding, and significantly improves the breeding efficiency and precision.

The application discloses a high-throughput KASP molecular marker significantly related to plant height of Gossypium hirsutum and application, and belongs to the technical field of cotton molecular breeding. The KASP marker is successfully developed based on cotton plant type related genes and resequencing data of germplasm resources. Through genotyping and phenotype correlation analysis in 246 natural Gossypium hirsutum populations, four KASP markers (PH-TK1-PH-TK4) significantly related to plant height in three environments are screened. The regulation effect and aggregation effect of the markers on plant height are verified in a separation population. Experiments prove that the KASP marker can be used for high-throughput and accurate genotyping of plant height in early cotton breeding, and provides an effective tool for molecular marker assisted selection of cotton ideal plant type, and helps to accelerate the breeding process of cotton varieties suitable for mechanical harvesting.

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.

The application provides a kind of genotypes and phenotypic association analysis method based on multiple phenotypes, belongs to the technical field of bioinformatics, and establishes SNP-gene-phenotype three-layer network to explore the relationship between genotypes and phenotypes using statistical data, and the internal correlation between genomics and genomics, such as genes and genes, and phenotypes and phenotypes, is considered in the three-layer network construction process, which is more consistent with biological reality;Solve the problem that clinical data is difficult to obtain and SNP and phenotype correlation cannot be predicted;The biological pathway correlation between SNP-gene-phenotype different omics layers is analyzed.Through the analysis of various database data, it is found that the internal correlation of each database omics has quantitative value, while the correlation between omics generally has qualitative value, i.e. the correlation is 1 and the non-correlation is 0.Through the establishment of three-layer model, the key correlation genes can be predicted using the quantitative value of the internal omics and the qualitative value of the inter-omeric relationship, and the pathway relationship score between each layer is calculated to analyze the pathway relationship between SNP-gene-phenotype.

The invention discloses a carbapenem drug resistance marker screening method and system based on a cross-species compressed Debrueine diagram and a medium. The method comprises the following steps: starting from whole genome sequencing data of gram-negative bacteria belonging to different species and carbapenem drug phenotypes of the gram-negative bacteria, constructing a compressed Debrueine graph based on cross-species joint data, and taking existence / deletion of nodes in the graph as unified genetic variation characteristics. Performing correlation analysis on the nodes and the drug resistance phenotypes by using a linear hybrid model to obtain a candidate node set related to the phenotypes; k-mer is extracted based on the candidate node sequence, and secondary statistical screening is completed in combination with chi-square test and mutual information; and finally determining a group of carbapenem drug-resistant genetic markers which can be applicable across species through a hierarchical feature selection strategy of random forest and XGBoost. Efficient dimension reduction of large-scale cross-species genome data, cross-species consistent variation representation and high-interpretability marker screening are achieved.

The invention relates to the technical field of molecular biology and plant breeding, and provides a black cabbage yellowing phenotype related KASP molecular marker, the marker corresponds to a specific SNP site of a black cabbage BcCHL27 gene, the SNP site is located at the 217bp position of the black cabbage BcCHL27 gene, and the basic group is mutated from A to G. The invention also provides a primer group for detecting the molecular marker. Meanwhile, the invention also provides application of the molecular marker or the primer group. Based on the KASP molecular marker, genotype identification and character screening of the yellow phenotype of the black cabbage are carried out, the accuracy, specificity and screening efficiency of yellow character detection of the black cabbage can be remarkably improved, rapid high-throughput identification of the yellow phenotype of the black cabbage in the seedling stage is achieved, an efficient tool is provided for genetic research of the yellow character of the black cabbage and molecular assisted breeding, and the application prospect is wide. Therefore, breeding cycle is shortened, resources are saved, and genetic modification of black cabbage is accelerated.

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.

The present invention provides a genomic prediction method and model that integrates cross-modal features of corn genotypes and environments. It uses deep learning methods to model the complex nonlinear transformation relationship between different omics data, including genome and environment groups. By constructing a GECF_GP model to integrate the characteristics of corn cross-genotype and environment interactions, it better explores temporal characteristics and important growth periods related to phenotypes, and realizes the function of predicting the phenotypes of complex agronomic traits of corn. The present invention demonstrates strong robustness in different experimental settings, has superiority in genomic prediction of G×E interactions, explores important growth periods related to phenotypes and the performance of predicting phenotypes across different years, and provides new ideas for genomic prediction and analysis methods. It further provides theoretical and application value references for the adaptability analysis of corn in different environments and the exploration of key growth periods, accelerating the development of the field of smart breeding.

The invention discloses a disease community discovery and patient layering method based on gene information guidance, and belongs to the technical field of biological information.According to the method, an initial disease set is created through full phenotype correlation research, and the diseases are expressed in a high-dimensional embedding space of electronic health record data; vector representation of diseases is optimized and extracted by learning individual features of the diseases and a topological structure of a co-disease network of the diseases through a graph auto-encoder, the diseases are divided into different communities with consistent interiors by embedding vectors into the optimized diseases and applying a clustering algorithm, and the method is provided based on the individual health history, and is suitable for the individual health history and the co-disease network. According to the method for classifying new patients into a certain community, accurate patient layering is achieved, a complex multi-disease network is disclosed, a brand-new systematic perspective is provided for disease understanding, a traceable and data-driven bridge from genes to patient subgroups is established, and a novel framework is provided for medical conversion.

A method for capturing distinct higher order interactions of a dataset relevant to biological inferences includes deriving Hamiltonian parameters for the dataset, wherein the dataset includes data responsive to a phenotype of interest. The method further includes generating a partition function responsive to the Hamiltonian parameters; calculating cumulant moments of the partition function and deriving higher order cumulants using the Hamiltonian parameters, wherein the higher order cumulants are responsive to the phenotype of interest.

The present application relates to a kind of detection methods of strong putrefaction Pseudomonas lundensis, comprising extracting nucleic acid in the sample to be measured;Then with nucleic acid as template, using specific RAA primer pair is recombined enzyme-mediated amplification, and obtains amplification product;RAA primer pair targets the aprX gene of Pseudomonas lundensis comprising single nucleotide polymorphism site associated with strong putrefaction phenotype;Finally, the amplification product is added to CRISPR / Cas detection system, incubated at constant temperature, then real-time detects its fluorescence signal;Detection system includes Cas protein, specific crRNA and fluorescent reporter probe, and crRNA can recognize single nucleotide polymorphism site associated with strong putrefaction phenotype in Pseudomonas lundensis aprX gene.The present application improves the detection accuracy, and detection process is simple to operate, and reaction system is simple, not only improves detection efficiency, also reduces detection cost.

The invention discloses application of a mutant gene related to ASD, the mutant gene is at least one of the following genes: RAB5A, EFNB2, NEDD9, WNT9B, LIN7C, FOLR1, BNIP3, CAMSAP2, ADCY10, FCSK, UGDH and PIGH, the genes are related to ASD phenotype related pathways, but the genes are not recorded in an SFARI / HPO database before. The findings are beneficial to clarification of genetic causes of ASD and provide favorable help for early diagnosis and treatment research of ASD.

The invention discloses a special scientific research disease library system based on hepatolenticular degeneration, which belongs to the field of medical information technology and clinical scientific research and comprises a case data acquisition module, a database management module, a clinical scientific research analysis module, a follow-up management module and a standardized quality control module. Structured input of copper metabolism indexes, clinical manifestation, genetic information and treatment data is realized through a disease specificity data acquisition template; semantic fusion and efficient retrieval of multi-source heterogeneous data are completed by utilizing a database architecture based on a knowledge graph; a copper metabolism kinetic model, a nervous system curative effect evaluation model and a genotype-phenotype association model are embedded, so that automatic analysis of scientific research data is realized; and generating a personalized follow-up plan and dynamically adjusting the follow-up period in combination with a rule engine. The system realizes data acquisition standardization, scientific research analysis intelligence and follow-up management systematization, and is suitable for high-quality accumulation and comprehensive research of hepatolenticular degeneration scientific research data.

The invention discloses a gene variation frequency calculation system based on personalized phenotypic crowds. The gene variation frequency calculation system comprises a data acquisition module, a clinical phenotypic category module, a crowd phenotypic construction module, a data classification module, a data extraction module, a personalized combination module and a mark calculation module. According to the method, gene variation population frequency related to a specific phenotype can be provided, gene variation frequency data rich in phenotype can be provided by combining the genotype with the specific phenotype, reference result data of genome variation frequency of different populations can be obtained through calculation, the data can be stored and transmitted, and an existing database can be perfected.

The present disclosure relates to systems and methods for identifying genes associated with phenotypes. A list of phenotypes is provided as input and the systems and methods automatically provide an output with a list of genes associated with the phenotypes provided. The systems and method analyze assertions linking a gene to a phenotype using a graph-based algorithm to identify the genes associated with the phenotypes.

The application discloses a transcription factor regulation network modeling method based on single cell transcriptome data, comprising the following steps: S1, extracting multi-phenotype single cell omics data, performing data cleaning, and integrating the cleaned data; S2, based on a biological knowledge base, analyzing the data processed in S1, and constructing a prior inter-molecular interaction relationship network; S3, based on a multi-factor non-negative matrix factorization algorithm, establishing a multi-dimensional molecular synergistic interaction relationship module according to the data processed in S1 and the prior inter-molecular interaction relationship network in S2; S4, calculating the interaction relationship module related to the phenotype of the multi-dimensional molecular synergistic interaction relationship module; and S5, visualizing and exporting the interaction relationship between the multi-dimensional molecular synergistic interaction relationship module and the phenotype. With the aid of single cell sequencing technology and prior biological knowledge, the application extracts multi-dimensional molecular synergistic regulation relationship in high-dimensional single cell omics data, and obtains a transcription factor-gene function synergistic regulation interaction mechanism closely related to phenotype changes.

The invention discloses a method for selecting single nucleotide polymorphism (SNP) for phenotype prediction based on feature importance, which comprises the following steps of: performing missing filling on SNP data, and dividing a sample into a training set and a test set before phenotype-related screening to avoid data leakage; then, by taking the gene as a unit, fitting phenotypes of the SNPs positioned on the promoter, the exon and the intron by adopting a regression model, calculating correlation coefficients and carrying out multiple inspection correction, and taking a significant correlation gene as a candidate; selecting one SNP (Single Nucleotide Polymorphism) from each gene in the candidate genes on the basis of feature importance to form an Important-SNP set; and encoding the set, and inputting the encoded set into a prediction model to obtain a phenotype prediction result. According to the method, the feature dimension is remarkably reduced while the prediction accuracy is maintained or improved, and the method has relatively high interpretability and engineering availability, is suitable for phenotype prediction of crops, can be used as a core marker for design and optimization of a breeding chip, and provides efficient and interpretable technical support for molecular breeding and genome selection.

The invention broadly relates to methods for marker assisted identification and / or selection of subjects having one or more genetic markers associated with a phenotype of interest. More particularly, the invention relates to methods of marker assisted identification and / or selection or rejection of non-human subjects, and particularly bovine subjects, for one or more traits associated with productivity and / or worth, for example fertility such as sperm and / or semen quality, by determining the presence of one or polymorphisms associated with the one or more productivity and / or worth traits.

The application provides a SNP molecular marker for detecting cucumber fruit top shape, a KASP primer and application thereof. The SNP molecular marker is closely linked to the cucumber fruit top shape, is located at the position of 25221828 of a Chinese Long v3 genome Chr01 chromosome, and is a C to G SNP mutation, and is related to the phenotype of the cucumber fruit top shape. A KASP primer group is designed through the SNP marker, and the KASP primer group comprises CsFAS-FAM, CsFAS-HEX and CsFAS-COMMON, and the cucumber fruit top shape can be accurately identified through PCR amplification and genotyping technology. The molecular marker can be widely applied to molecular assisted selection of the fruit top shape in cucumber breeding, and has high accuracy and efficiency. In the cucumber breeding, the technology can be screened through genotyping at an early stage, avoids uncertainty and time cost problems existing in traditional phenotype identification, and provides a simple and efficient tool for the cucumber breeding. The technical method of the application is simple and rapid, can be popularized and applied in the cucumber breeding, and has important economic value and social value.

The invention belongs to the technical field of plant heredity and genetic engineering, and particularly relates to soybean RING type E3 ubiquitin ligase GmSSE1 and application thereof. According to the invention, the soybean RING type E3 ubiquitin ligase gene GmSSE1 is separated from soybean Williams 82 (Williams 82), and tests prove that the expression of the gene is induced by salt stress, and the salt tolerance of soybean can be negatively regulated. Besides, according to haplotype analysis and phenotype correlation research, the single plant grain weights of different haplotypes (GmSSE1Hap1, GmSSE1Hap2 and GmSSE1Hap3) of the GmSSE1 in a natural population are obviously different. The invention not only provides an important gene resource for clarification of a plant salt-tolerant molecular mechanism, but also provides an effective molecular target and technical support for crop salt-tolerant genetic improvement, and has important theoretical significance and application potential.

The present application relates to the technical field of intelligent agriculture, and particularly relates to a cadmium low-adsorption potato cultivation system, which comprises: a genotype-phenotype prediction module, which is used for receiving genotype data of potato germplasm resources, processing the genotype data by using a basic genotype-phenotype correlation model based on machine learning, and outputting cadmium adsorption potential rating data of the potato germplasm resources to a selection and screening decision module; the selection and screening decision module is used for outputting a selection scheme data containing optimal germplasm identification and cultivation parameters by using a multi-objective optimization algorithm; the selection and screening decision module is further used for iteratively optimizing the basic genotype-phenotype correlation model and / or the multi-objective optimization algorithm according to cadmium adsorption capacity evaluation result data, so as to form a closed-loop feedback. The present application adopts a basic model combined with online iterative optimization, realizes rapid prediction of cadmium adsorption potential of potatoes and full-process automation and remote management of the low-cadmium high-yield target, and significantly improves breeding efficiency and precision.

The application discloses a Rhodococcus double oxygenase deletion library and a construction method and application thereof. Based on strict bioinformatics screening, the Rhodococcus double oxygenase gene is screened, different double oxygenase genes are knocked out, the Rhodococcus double oxygenase deletion mutant library is constructed, and the library is used for systematic degradation function screening and genotype-phenotype correlation analysis of aromatic compounds, so that key double oxygenase genes necessary or redundant for degradation of specific aromatic compounds can be rapidly identified, the substrate utilization spectrum and functional division spectrum of the double oxygenase family can be drawn, and the application prospect in multiple fields such as high value of lignin, synthesis of fine chemicals, and environmental biological remediation is potential.

The invention relates to a method for evaluating the uterus-nest double-rearing effect of fish maw based on phenotype driving, and belongs to the field of evaluation of the uterus-nest double-rearing effect of the fish maw, and the method comprises the following steps: 1, carrying out the standardized collection of phenotype data, and carrying out the multi-dimensional phenotype definition of uterus-nest double-rearing; 2, intelligent integration of multi-source evidences and association mining of fish maw-nest phenotypes; step 3, automatic generation and priority ranking of verifiable hypotheses; and 4, performing experimental verification and closed-loop feedback. According to the invention, through multidimensional phenotypic correlation analysis of uterus and nest cultivation, it is clear that the fish maw plays a role in improving uterus blood flow, regulating ovarian hormone synthesis or repairing cell apoptosis; clinical research and experimental evidence are integrated, it is assumed that the support degree score is larger than or equal to 0.7, and the conclusion is improved by 40% compared with a traditional method; the limitation of traditional isolated evaluation of the uterus or the ovary is broken through, and the uterus-nest synergistic effect is verified through the function association phenotype; according to the method, key functional components and action targets are quickly locked through evidence priority ranking.

Methods and systems for CRISPR forward selection are described herein. According to CRISPR forward selection, DNA sequencing is used for identifying phenotype-related genes with disturbance caused by CRISPR guide RNA (Ribonucleic Acid).

The invention discloses an interpretable crop genome prediction deep learning model, puts forward a deep learning framework Cropform, fuses a convolutional neural network (CNN) and a multi-head self-attention mechanism, constructs a technical scheme integrating phenotype prediction and gene mining, automatically extracts local features of genome data through the CNN, and provides an explainable crop genome prediction deep learning model. In combination with a multi-head self-attention mechanism, global association among features is captured to realize high-precision prediction of complex phenotypes, and the prediction accuracy is maximally improved by 7.5% compared with CropGBM, DEM and the like. Key SNPs and genes can be accurately positioned through attention weight and SHAP value analysis, a genetic variation mechanism is disclosed, and multi-modal data fusion of SNP, InDel, gene expression and the like is supported to further improve performance. In order to improve practicability and convenience, the Cropform provides a free online network server. According to the method, the black box limitation of a traditional deep learning model is broken through, analysis of gene-phenotype association is assisted, and an efficient tool is provided for crop genome design and breeding.

Highly sensitive assays for pathogen detection, identification and / or analysis including, but not limited to, sensing of metabolite patterns associated with high-risk drug resistance phenotypes.

The invention discloses a zhou chicken molecular marker genotype and phenotype correlation analysis and breeding method. The method comprises the following steps: firstly, collecting zhou chicken samples, recording growth, slaughter and reproduction phenotype indexes, extracting genome DNA, screening specific SNP markers on GH, IGF-1 and PRL genes, judging genotypes through PCR amplification and Sanger sequencing, and screening AA (GH), GG (IGF-1) and TT (PRL) dominant genotypes through GLM model correlation analysis; and screening a core parent based on the dominant genotype, and carrying out artificial hybridization, hatching egg incubation, chick breeding, offspring genotype and phenotype double screening and multi-generation breeding to obtain an excellent new strain. According to the new strain, the weight of the 12-week-old cocks is larger than or equal to 1650 g, the weight of the hens is larger than or equal to 1300 g, the slaughter rate is larger than or equal to 88%, the first laying day age of the hens is smaller than or equal to 140 d, the annual egg yield is larger than or equal to 200, the growth speed and the annual egg yield are increased by 25% and 18% respectively compared with a traditional variety, the problems that traditional breeding is long in period and low in accuracy are solved, and accurate and efficient breeding is achieved.