132 results about "Population genetics" patented technology

The invention discloses an SNP (Single Nucleotide Polymorphism) molecular marker related to calving interval traits of dairy cows and application of the SNP molecular marker. The number of the SNP molecular markers is four, and the nucleotide sequences of the four markers are as shown in SEQ ID NO.1-4. Researches find that the four SNP molecular markers are significantly related to the Chinese Holstein cattle calving interval traits, and can be used for molecular marker-assisted breeding of the Chinese Holstein cattle calving interval traits to accelerate breeding of Chinese Holstein cattle with strong fecundity. The SNP molecular marker is used for identifying the calving interval character of the Chinese Holstein cattle, the four SNP loci are detected in actual breeding, seed selection and matching are guided, the breeding efficiency is improved, the non-pregnant period is shortened, the production performance of the dairy cattle can be comprehensively improved, the breeding strategy can be optimized, and the population genetic progress can be accelerated.

The invention relates to the technical field of bioinformatics, in particular to a goat selective breeding method and system based on a genetic algorithm, and the method comprises the following steps: based on goat population genetic initialization data, extracting a character characteristic index and a genetic relationship parameter of each pairing combination in a population, analyzing genetic diversity, character matching degree and genetic relationship, and calculating the genetic diversity, character matching degree and genetic relationship; and screening matched individuals and pairing hereditary characteristics, and rearranging and recombining to obtain primary pairing data of goat breeding. According to the method, genetic diversity and character matching degree are analyzed, genetic diversity and excellent character inheritance are balanced, genetic resource utilization efficiency is improved, local optimal combinations are accurately screened, genetic drift risks are reduced, character stability is enhanced, a two-point crossing rule and global character dynamic analysis are combined, and global breeding layout is optimized; according to the method, short-term improvement and long-term stability are both considered, the genetic structure is optimized through Pareto rule screening and global state updating, and the sustainability and accuracy of a breeding scheme are improved.

The invention discloses a pig breeding screening method and system based on a body type database, and particularly relates to the technical field of pig breeding screening. The system comprises a multi-modal sign data acquisition and integration module for collecting and standardizing heterogeneous data sources such as phenotypic signs, genomics and environmental parameters of breeding pigs in real time, and a multi-dimensional biological characteristic modeling and analysis module for analyzing a nonlinear action mechanism among genetic markers, phenotypic characteristics and environmental factors, the genetic potential intelligent judgment module is used for quantitatively evaluating spatial expression potential and intergenerational transmission probability of genetic advantages of the breeding pigs through a deep learning algorithm; and the intelligent screening and dynamic monitoring module is used for establishing a multi-dimensional threshold dynamic regulation and control mechanism to realize accurate elimination decision and continuous optimization of a germplasm resource library. According to the method, the screening accuracy is remarkably improved by designing multi-source data fusion and online optimization capability, meanwhile, mistaken elimination is reduced through a redundant superior species temporary storage management module, and collaborative improvement of population genetic diversity and environmental adaptability is ensured.

The invention relates to the technical field of biology, in particular to a detection method for tilapia streptococcus agalactiae resistance, a KASP primer group and application. According to the invention, the key SNP site of the ABCB4 gene, which is obviously associated with the streptococcus agalactiae disease resistance, of the tilapia is identified by a population genetics method, a typing method of the SNP site is developed by a KASP technology, efficient evaluation on disease resistance characters is realized, experimental data shows that the survival time of an AA genotype individual in a challenge test is prolonged compared with that of a GA / GG genotype individual, and the survival time of the AA genotype individual in the challenge test is obviously prolonged compared with that of a GA / GG genotype individual. The proportion of resistant fishes is remarkably increased, and genotype frequency distribution is strongly related to group disease resistance. According to the breeding strategy based on the SNP marker, the disease resistance of bred groups can be integrally improved, the morbidity and death rate are reduced, an efficient genetic improvement tool is provided for breeding of tilapia mossambica for resisting streptococcus agalactiae diseases, the loss of breeding diseases is reduced, and antibiotic dependence is reduced.

The invention discloses a polar region environment-oriented two-stage micro-siting optimization method for a wind generating set, and relates to the technical field of polar region renewable energy planning and wind power generation, the optimization method comprises the following steps: polar region environment parameter initialization and scene modeling; the first stage is grid primary selection based on mixed integer linear programming; the second stage is coordinate refinement based on a multi-population genetic algorithm; and scheme output and efficiency evaluation. According to the method, a sector reweighting mechanism of extremely cold correction parameters is realized through reweighting of biochemical parameters and scenes in an environment special for a polar region, the extremely cold correction parameters are directly embedded into a wind regime scene set and a power model, and the problem that a conventional land model can obviously underestimate or overestimate single-machine power and wake flow propagation distance under extremely low temperature and low roughness conditions, so that the reliability of the model is improved is solved. Therefore, the power prediction and wake flow loss evaluation are more practical under the polar region conditions such as the south pole, so that the engineering reliability of site selection decision and the accuracy of generating capacity prediction are improved.

The invention discloses a corn selective breeding effect analysis method integrating population inheritance and deep learning, and the method comprises the steps: collecting corn germplasm resource data, including phenotype data, genotype data and environmental data; performing standardization processing and missing value filling on the phenotypic data, and calculating population genetic parameters including allele frequency, linkage imbalance and population structure stability; performing correlation analysis on the population genetic parameters and phenotypic data by using a deep learning model, predicting phenotypic values of candidate parents by using the trained model, calculating breeding indexes in combination with the population genetic parameters, and providing guidance for corn breeding decisions based on prediction results; through organic combination of a deep learning technology and a population genetics principle, accurate prediction and evaluation of the corn selective breeding effect are realized, the breeding efficiency is improved, and a new technical path is provided for corn variety improvement.

The invention discloses a subgrade structure parameter intelligent optimization method and device fusing population inheritance and reverse learning, equipment and a storage medium, and relates to the technical field of land traffic engineering subgrade structure design, and the method comprises the steps: obtaining a subgrade engineering to-be-optimized parameter set, a subgrade engineering parameter optimization model and a self-adaptive hyper-heuristic algorithm; based on the roadbed engineering parameter set to be optimized, the roadbed engineering parameter optimization model and the adaptive hyper-heuristic algorithm, population inheritance and reverse learning are carried out to generate a population, and target population individual information is determined; based on the roadbed engineering parameter set to be optimized, the roadbed engineering parameter optimization model, the adaptive hyper-heuristic algorithm and the target population individual information, correlation analysis is carried out to adjust the search direction and the search step length, and target optimization population information is determined; and completing intelligent optimization of roadbed structure parameters based on the target optimization population information. The subgrade parameters are optimized by fusing population heredity and reverse learning, and the optimization efficiency is improved.

The invention discloses a satellite planning and scheduling method, medium and equipment based on enhanced multi-population inheritance, and relates to the field of multi-satellite imaging task scheduling, and the method comprises the steps: constructing a constraint model and a target function of multi-satellite task scheduling; calculating the conflict degree of the visible time window of each satellite resource, and allocating the visible time window with the conflict degree of 0 to a task to complete scheduling preprocessing; generating satellite resources and visible time window initial solution individuals of satellite task scheduling by adopting a bisection method; a double-population genetic algorithm is adopted, one population is used for exploring a new allocation scheme, the other population is used for optimizing a task planning result under a specific allocation scheme, and population communication is carried out based on reinforcement learning; and performing task insertion based on time relaxation according to a multi-satellite task scheduling solution obtained by the double-population genetic algorithm, and if task insertion cannot be performed, performing task replacement to obtain a final scheduling scheme. According to the method, the problem of complex multi-satellite imaging scheduling can be solved.

The invention discloses a Dazu black goat gene polymorphism and lambing trait association analysis method, and relates to the field of genes. Comprising the following steps: sample collection and data collection: collecting Dazu black goats as well as blood samples and ovary samples of the Dazu black goats, respectively collecting the lambing number and birth weight of the black goats, and then calculating the average birth weight of the first birth and the average lambing number of the first three births; extracting blood DNA, namely extracting the blood DNA by adopting a traditional phenol-chloroform method; designing a PCR primer and an amplification system; and analyzing a population genetic structure, and carrying out genetic analysis and screening on SNP sites of the goat AMH gene. The molecular marker-assisted technology is utilized to screen out the marker of the targeted gene polymorphic site which is strongly related to the breeding traits of the Dazu black goats, the breeding period can be shortened in molecular breeding, the breeding and breeding efficiency can be effectively improved, and the economic value of the Dazu black goats is further improved.

The invention discloses a compression and decompression method based on generic genome expression, and relates to the technical field of compression and decompression of DNA next-generation sequencing data, in particular to the compression and decompression method based on generic genome expression. The method aims at solving the problems that in the prior art, the capacity of processing population genetic diversity is insufficient, original sequencing quality information cannot be effectively restored during decompression, and memory occupation is too high during large-scale data processing. Obtaining a to-be-compressed sequencing sequence data file, a reference genome sequence and a thousand-person genome variation sample; obtaining a haplotype list, a variation list and a haplotype offset list corresponding to each window block; storing the window number, the haplotype number, the haplotype offset, the head and tail unmatched sequences, the current sequence name and the quality score character string into a single compression block; carrying out binding storage; completing the compression processing of the mass fraction; and obtaining each to-be-compressed sequencing sequence based on the result of the compressed part.

The invention belongs to the field of plant molecular genetics, and relates to a spirodela polymorphic microsatellite molecular marker primer and application thereof. The kit comprises 10 pairs of microsatellite primers which form 5 groups of duplex PCR systems, and the kit can be applied to population genetic diversity analysis of spirodela polyrhiza. The population genetic diversity analysis comprises the following steps: collecting a sample of spirodela polyrhiza to be identified; extracting spirodela polyrhiza genome DNA from the spirodela polyrhiza sample; taking the extracted DNA as a template, and carrying out PCR (Polymerase Chain Reaction) amplification on the DNA template by using the screened 10 pairs of microsatellite primers; carrying out electrophoresis detection on a PCR product, and then carrying out silver staining; the silver staining result is digitally analyzed, the UPGMA clustering analysis chart is drawn by genetic analysis software, the clustering analysis chart of the spirodela polyrhiza is identified according to the clustering analysis result, and the method has huge popularization and utilization value.

The invention provides an AISPN marker combination and a multi-dimensional ancestral and geographic inference model, and relates to the technical field of forensic medicine. According to the method, firstly, seven groups of ancestral inference SNP (Single Nucleotide Polymorphism) sites (AISNPs, AISNPs) of East Asia and Southeast Asia crowds are screened, the screened sites are respectively subjected to efficiency evaluation based on principal component analysis, ADMIXTURE and t-SNE methods, and a set of multi-dimensional ancestral and geographic source inference model is constructed in combination with six different machine learning methods. The AISPN marker combination provided by the invention comprises at least 50 AISPN sites, has relatively high differentiation in East Asia and Southeast Asia crowds, can distinguish nine crowds based on relatively few sites, provides site information for preparation of kits, and provides valuable information for research of forensic medicine, anthropology and population genetics.

According to the multi-distribution-center collaborative medicine distribution model and the double-population genetic simulated annealing algorithm provided by the invention, aiming at the timeliness, multi-center collaboration and customer satisfaction requirements of medicine distribution, a mathematical model taking the total cost minimization as a target is constructed. The model comprehensively considers the vehicle fixed cost, the transportation cost, the customer satisfaction cost and the goods damage cost, introduces a soft time window function based on cosine distribution to quantify the influence of delivery delay on the cost, optimizes resource allocation through a multi-center order splitting strategy, and solves the problems of low delivery efficiency and high cost of a traditional single-center mode. According to the characteristics of the model, a double-population genetic simulated annealing algorithm (DPGSA) is designed. According to the algorithm, the global search capability is enhanced through a double-population coevolution mechanism, the local optimization efficiency is improved in combination with the Metropolis criterion of the simulated annealing algorithm, the defect of premature convergence of a traditional genetic algorithm is effectively avoided, the drug transportation and distribution path is optimized, and the distribution cost is minimized.

The invention discloses 36 pairs of polymorphic primers and 84 pairs of SNP molecular markers used for amplifying the swallow aculeatus, and an application of the polymorphic primers in genetic diversity analysis of the swallow aculeatus, and particularly discloses an application of the polymorphic primers in the genetic diversity analysis of the swallow aculeatus and application of the polymorphic primers in the genetic diversity analysis of the swallow aculeatus, the number of the polymorphic primers is 36, and the number of the swallow aculeatus SNP molecular markers used for amplifying the 36 pairs of the primers is 84. According to the invention, the SNP molecular markers with abundant polymorphism are established on the swallow-wing fish in batches for the first time, 36 pairs of polymorphic primers are screened out, and an efficient analysis technical tool is provided for research on genetic diversity, family identification, population genetic structure, genetic breeding, enhancement and release and the like of the swallow-wing fish; the method for developing the SNP marker is simple, convenient, rapid and efficient, and manpower and cost are saved.

This invention discloses a goose double-enzyme digestion simplified genome sequencing library construction kit, library construction method, and application. The kit includes Kit A, Kit B, and Kit C, and magnetic beads. The invention uses two conventional endonucleases to control the location and number of target fragments, and uses improved enrichment primers to significantly increase the number of library pools for GBS sequencing. This leverages the large amount of NGS data and makes goose population genetics research more feasible. In theory, this library construction strategy can exponentially increase the number of individuals to be tested simply by modifying the corresponding adapter and index sequences.

The present invention provides a sensor layout method and system for large-sized deployable antennas, including the following steps: establishing an antenna model, extracting effective vibration modes, and constructing a modal matrix; establishing a mathematical model for the optimal configuration of sensors as the objective function; initializing the configured sensors, and simultaneously configuring the sensors through population one and population two to generate two initial populations; establishing a fitness function by mapping the objective function in the above step according to the objective function, evaluating the advantages and disadvantages of individuals through the fitness function, and performing selection and optimization using a multi-population genetic algorithm; evaluating the configuration result to determine whether the optimization criterion is satisfied. The present invention can achieve the optimal sensor layout for on-orbit structural state measurement of large-sized deployable antennas, and meet the goal of maximizing the acquisition of structural state information with limited measurement points.

This invention discloses an artificial intelligence-based method and system for predicting and intelligently replenishing SPD consumable demand, belonging to the field of medical consumable supply chain management technology. It obtains key influencing factors of SPD consumables by clustering analysis of historical SPD consumable demand correlation data and identifying influencing factors. Furthermore, through the acquisition and training of an initial SPD consumable demand prediction model, an accurate SPD consumable demand prediction model is obtained. This model is then used to predict real-time SPD consumable demand correlation data, outputting SPD consumable demand prediction results that better match actual needs and applications. In addition, the replenishment objective function is iteratively optimized using a multi-population genetic algorithm to obtain optimal SPD control parameters, thereby automating the generation of intelligent replenishment strategies.

The invention discloses a pig whole genome low-density 6K SNP (Single Nucleotide Polymorphism) chip, and the chip contains 5,866 SNP loci. The SNP chip provided by the invention is suitable for a plurality of fields of pig whole genome association analysis, whole genome selective breeding, population genetics analysis, character-related candidate gene and QTL site analysis and positioning and the like, and has relatively high value in production application and scientific research.

The invention discloses an SSR (Simple Sequence Repeat) primer composition for evaluating genetic diversity of channel catfish breeding population and an application method of the SSR primer composition, and belongs to the technical field of fish genetic breeding. The method comprises the following steps: modifying 5'ends of forward primers of the primers by using FAM fluorophores respectively, amplifying genome DNA of each breeding group by using the 10 pairs of microsatellite site primers respectively, analyzing amplification products, reading genotypes, analyzing parameters, and determining the genotype of the breeding group. The analysis result is used for evaluating the population polymorphism, the variation condition of the population genetic structure and the genetic differentiation degree among the populations. By applying the evaluation method disclosed by the invention, the population genetic diversity and genetic structure of continuous breeding generations of channel catfish can be systematically evaluated. The SSR primer and the genetic diversity evaluation method can be used for genetic improvement and new variety breeding of channel catfish.

The invention belongs to the technical field of molecular biology, and relates to an SNP (Single Nucleotide Polymorphism) molecular marker related to peak daily milk quantity traits of dairy cows and application. Mainly discloses five SNP (Single Nucleotide Polymorphism) molecular markers (SEQ ID NO.1-5) which are obviously related to the peak daily milk quantity character of the Chinese Holstein cattle. Whole genome association analysis and identification show that the markers can be used for molecular marker-assisted breeding of Chinese Holstein cattle. The five SNP loci are detected in actual breeding, seed selection and matching can be guided, the breeding strategy is optimized, and the population genetic progress is accelerated. The traditional breeding bottleneck is broken through, the seed selection accuracy and efficiency are remarkably improved, the genetic improvement process of high-yield cows is accelerated, and milk enterprises are assisted to improve economic benefits.

The present invention provides a Przewalski's gazelle SSR molecular marker combination, a primer combination, a kit and an application, and relates to the technical field of molecular biology. Based on the whole genome sequencing data of Przewalski's gazelle, the present invention performs SSR primer design and SSR site screening, and the obtained 26 pairs of SSR primers can stably amplify the target product and are highly polymorphic, and can be used for the detection of genetic diversity of Przewalski's gazelle population, the analysis of population genetic structure, and the study of evolution and kinship. The polymorphic SSR molecular marker combination of Przewalski's gazelle obtained by screening can be used for individual identification of Przewalski's gazelle with high accuracy. The experimental results show that the SSR molecular marker combination of Przewalski's gazelle of the present invention can meet the needs of individual identification, and 33 fecal samples of Przewalski's gazelle subadults are individually identified, which come from 24 different individuals, which is consistent with the number of Przewalski's gazelle subadults in the Jiangxigou Przewalski's gazelle rescue center.

The application discloses a high-throughput genotype intelligent analysis method, which comprises data acquisition and preprocessing, molecular marker identification and prediction of genetic relationship and hybridization advantage; high-throughput genotype data are subjected to standardization treatment, three quality evaluation standards of coverage, transversion / transversion ratio and error rate are established, and batch effect is corrected through a position effect index; then genetic similarity matrix between samples is calculated based on the pretreated data, a label SNP with high centrality is identified as a molecular marker through linkage disequilibrium network analysis; the population genetic relationship is estimated by using the marker, and a phylogenetic tree is constructed, and the hybridization advantage is predicted based on the relationship index of heterozygosity and genetic distance. The application significantly improves the quality of genotype data, optimizes the screening of molecular markers, accurately estimates the population genetic relationship and accurately predicts the hybridization advantage, and provides an efficient bioinformatics solution for modern breeding.

The present invention belongs to the fields of biotechnology and agricultural science, and particularly relates to a liquid-phase chip detection method for the identification of Leymus chinensis varieties. The method of the present invention uses a 50K liquid-phase chip of the whole genome of Leymus chinensis for the identification of Leymus chinensis varieties. This method combines high-throughput liquid-phase chip technology with machine learning algorithms, and through high-throughput SNP detection, realizes the accurate detection of SNP sites in the whole genome, innovatively constructs an efficient and stable classification model, and significantly improves the accuracy and efficiency of variety identification. By screening specific SNP sites, combining population genetic analysis with the random forest algorithm, the reliability of the model is enhanced, and problems such as low efficiency of traditional methods are solved.

The invention provides a primer group, a kit and a detection method for individual identification of treponema pallidum, the primer group comprises 12 pairs of primers with nucleotide sequences respectively corresponding to SEQ ID NO.1-SEQ ID NO.24. The invention provides a specific DNA fingerprint spectrum of treponema pallidum strains formed by 12 loci, a variable number tandem repeat sequence (microsatellite sequence) of a genome is analyzed, and the specific DNA fingerprint spectrum of the treponema pallidum strains is obtained. The method is used for realizing individual identification of treponema pallidum strains. The primer group provided by the invention has high sensitivity, accuracy and specificity, and the detection method based on the primer group can carry out individual identification, genetic identification and genetic typing analysis at the same time, and has wide application prospects in the fields of population genetics and the like.

The application provides an analysis method for complex evolutionary history based on deep learning, comprising: according to a preset species evolutionary history simulation sequence data, respectively as a model training set and a test set; determining the topological structure of the training set data, and labeling the training data in different topological structure proportions; constructing a convolutional neural network, training and testing the convolutional neural network with the data set, so that the error between the data prediction value and the label value is minimized; based on the trained convolutional neural network, analyzing real genomic sequence data, and combining other population genetics analysis methods, determining the evolutionary relationship and introgression site between different biological groups. The application uses comparative genome or population genome data, infers the topological structure between sequences through a deep learning algorithm, further evaluates the evolutionary relationship at the genome level, and identifies local introgression signals through the difference of the topological structure between different regions.

The invention discloses an intelligent analysis method based on high-throughput phenotypic data, which comprises the following steps: preprocessing high-throughput genotypic data, calculating the deletion rate and heterozygosity of a sample and an SNP (Single Nucleotide Polymorphism) site, and filtering; principal component analysis is carried out based on the filtered data, and the data dimension is reduced through orthogonal transformation; constructing an evolutionary tree, and analyzing an evolutionary relationship among samples in the group; an analysis result is output and visually displayed; the method supports genotype data files in HMP or VCF formats, provides various visual displays of MAF distribution diagrams, SNP density diagrams, PCA scatter diagrams, evolutionary tree diagrams and the like, and can output analysis result files in various formats; the method improves the efficiency and accuracy of genome data analysis, and has important value in population genetics research and molecular breeding application.

The invention belongs to the technical field of molecular markers, and particularly relates to a group of SNP (Single Nucleotide Polymorphism) molecular markers for genetic analysis of hypoxia genes of Himalayan marmots and application of the SNP molecular markers. The SNP molecular marker comprises one or more than two of 11 SNP loci which are all located in an amplified fragment of a Himalayan marmot EPO gene with a nucleotide sequence as shown in SEQ ID NO.1, molecular technical support is provided for Himalayan marmot population genetics research, and the SNP molecular marker can be used for research on Himalayan marmot hypoxia adaptation, population evolution and the like. Moreover, the invention also provides a primer pair capable of identifying the SNP molecular marker, the primer pair can perform specific amplification on Himalayan marmot EPO hypoxia gene sequences of different geographical populations, has high polymorphism, can be applied to the research fields of Himalayan marmot environmental adaptation, genetic diversity, pedigree geography and the like, and has wide application prospects.

The present invention provides a SNP molecular marker combination for constructing the grouping of eggplant lines. Based on the whole-genome resequencing data of 577 collected eggplant lines, 1009 polymorphic SNP marker combinations were screened out from millions of SNPs, and the SNP marker combination for constructing the grouping of eggplant lines was successfully constructed. This marker combination is evenly distributed on 12 chromosomes of eggplant and has high polymorphism. Based on the characteristics of high marker density, strong automation, and high detection throughput of the liquid-phase probe chip based on this SNP marker, it is more conducive to establishing an eggplant population grouping system. This marker combination can provide theoretical and data support for work such as the identification of new eggplant lines, molecular marker-assisted selection breeding, genome-wide selection, and population genetic diversity analysis, and has relatively important theoretical value and application significance.

The present disclosure discloses a method for reconstructing the working face of coal mine based on search space pruning and multi-population dynamic adjustment, which involves the technical field of working face reconstruction of coal mine under incomplete projection conditions, comprising: based on prior knowledge of the working face reconstruction model structure, an initial grid partitioning of the exploration area is performed. Subsequently, multi-scale grid partitioning is achieved through search space pruning, guided by the sum of ray intercepts within each grid unit; to meet the requirements of population diversity and rapid convergence in the multi-population genetic algorithm, a multi-scale reconstruction objective function is constructed based on the partitioned multi-scale grids, this objective function is then solved using a dynamic multi-population genetic algorithm. Therefore, the above-mentioned method for reconstructing the working face of coal mine based on search space pruning and multi-population dynamic adjustment may dynamically adjust and optimize the number of populations, to ensure that the diversity of populations may be maintained, and the search efficiency may be improved, thereby improving the stability and convergence speed of the working face reconstruction operation.

The application discloses a water surface platform combat force generation method based on natural language processing and intelligent optimization algorithm, relates to the field of natural language processing, and combines intelligent search algorithms with optimization strategies, adopts an improved multi-population genetic algorithm, that is, utilizes multiple populations to perform parallel optimization search, and realizes diversified search strategies by assigning different control parameters to different populations; information exchange is performed between various populations through a migration operator, collaborative evolution is promoted, and convergence is judged by reserving optimal individuals of each generation and using the optimal individuals to maintain generation number, so that correctness and global optimality of a final solution are ensured. The application improves the quality and response speed of combat decision.