243 results about "Genomic selection" patented technology

The invention discloses a sugarcane germplasm resource evaluation and breeding method for smart agriculture. The sugarcane germplasm resource evaluation and breeding method comprises the following steps: step 1, collecting materials with wide genetic diversity; establishing an intelligent incubator and a greenhouse, and monitoring environmental parameters by using an Internet of Things sensor; 2, performing data acquisition on the related germplasm resources by using an unmanned aerial vehicle, a robot and a near infrared spectrum technology, and constructing a phenotype database for data management; meanwhile, genotype identification is carried out by combining molecular marker-assisted selection and genome selection technologies, molecular markers of key genes are mined, and the genetic value of related germplasm is evaluated; step 3, constructing a hybrid combination prediction model by using an AI algorithm, optimizing parent matching of the related germplasm and the cultivated sugarcane, monitoring and collecting the filial generation at the same time, and analyzing and screening in combination with smart agriculture; 4, demonstration planting is conducted on the bred excellent sugarcane strain, and application of the excellent strain is improved through government-enterprise-scientific research institution collaborative popularization and in combination with farmer technical training.

The invention relates to the technical field of crop genetic breeding and plant molecular design breeding, in particular to a pakchoi whole genome liquid phase chip and application. The pakchoi breeding gene chip disclosed by the invention comprises a target capture probe which is designed by taking 1000 core SNP (Single Nucleotide Polymorphism) molecular markers positioned on 40KSNP sites on a pakchoi reference genome version V4.0 as templates. The pakchoi whole genome liquid phase chip provided by the invention comprises a 40K site targeted capture probe mixed solution and a hybridization capture reagent. The pakchoi 40KSNP targeted capture probe can rapidly and effectively track genetic materials of pakchoi, and is suitable for different application scenes such as accurate identification of pakchoi germplasm resources, genetic relationship analysis between materials, variety authenticity identification, QTL gene positioning and mining, molecular marker-assisted breeding, variety improvement and whole genome selective breeding application. Therefore, the method has good application prospect and important social value.

The invention relates to the technical field of bioinformatics, and provides a crop whole genome phenotype prediction method and system fused with an environmental indicator gene, and the method comprises the following steps: collecting re-sequencing data, and carrying out genome comparison to obtain variation site data; performing whole genome association analysis by using the variation site data to obtain phenotype association site information; carrying out gene expression quantity measurement on samples of the crop population material in different environments to obtain gene expression quantity data; performing differential expression analysis on the gene expression quantity data to screen environmental indicator genes to obtain an environmental indicator gene set; constructing a phenotype prediction model of double-branch fusion; and predicting a to-be-predicted material through the phenotype prediction model to obtain phenotype prediction results for different environments. According to the method, environmental factors are incorporated into the whole genome selection model, so that the phenotype prediction precision in different environments is improved.

The invention discloses a silver carp 20K liquid phase chip and application thereof, the liquid phase chip comprises a probe combination of 20,909 SNP molecular markers covering a whole genome, the physical positions of the 20,909 SNP molecular markers are determined based on sequence alignment of the silver carp genome (GCA041475455.1), and the site information of the 20,909 SNP molecular markers is shown in the specification table 1. The invention provides a silver carp first type whole genome SNP liquid phase breeding chip, which effectively reduces the application cost of silver carp in genetic diversity analysis, QTL positioning, GWAS analysis and the like in scientific research, solves the problem that no applicable product exists in silver carp molecular auxiliary breeding and whole genome selective breeding, accelerates the progress of silver carp basic research and breeding, and has a wide application prospect. The method is of great significance in improving the utilization efficiency of silver carp germplasm resources, accelerating the breeding process of excellent characters and improving the economic value and environmental adaptability of bred varieties.

The invention relates to the technical field of biological breeding, in particular to a biological breeding management method and system based on big data. The method comprises the following steps: acquiring original germplasm data; multi-source heterogeneous data integration is carried out on the original germplasm data, the data integration comprises genotype sequencing integration and historical phenotype integration, and integrated germplasm data is generated and stored in a database in a distributed mode; obtaining original growth data, performing data preprocessing, generating standardized germplasm resource comprehensive data including a crop image feature set and an environment response parameter set, and synchronizing the data to the database; and performing whole genome selection prediction on the integrated germplasm data based on the standardized germplasm resource comprehensive data to generate a genotype-phenotype prediction model. By integrating germplasm resources, field management, breeding management, phenotype management, genotype management and whole genome selection, the accuracy and adjustment flexibility of biological breeding management are improved.

The invention provides a genome selective breeding method and system based on an EN-CNN model. The method comprises the following steps: capturing important feature information from genotype data of a to-be-bred object by using an auto-encoder; predicting growth traits of the to-be-bred object according to the important feature information by using a convolutional neural network model; and according to the growth traits of the to-be-bred object, determining whether to select the to-be-bred object for breeding. According to the method, the precision and robustness of genome selective breeding are improved.

The invention discloses a peanut quality character selective breeding method based on whole genome SNP and application, and relates to the technical field of crop breeding, the method comprises the specific steps of data acquisition, SNP optimization and marker screening, model construction and new strain breeding; by integrating whole genome re-sequencing, SNP optimization marker screening and mixed deep learning model construction, peanut quality character prediction and breeding value evaluation are achieved, through whole genome re-sequencing and quality control processes, an SNP variation map is obtained, ten key quality characters including protein, oil content, oleic acid and the like are covered, and the breeding value of peanuts is evaluated. According to the method, comprehensive genetic information is provided for subsequent analysis, a feature marker set is formed by screening out sites in the SNP optimization and marker screening link, interference of low-quality sites is avoided, a whole genome selection model is obtained by adjusting and training a mixed deep learning model and optimizing hyper-parameters through ten-fold cross validation, the breeding period is shortened, and the breeding efficiency is improved. And the prediction error rate is reduced.

The invention discloses a high-efficiency wheat breeding method based on a modern biotechnology, and belongs to the technical field of high-efficiency wheat breeding, and the method comprises the following steps: S1, collecting and identifying wheat germplasm resources, and screening materials with target character expressions; s2, performing phenotype identification on the screened material under a multi-ecological environment condition to obtain integrity expression data; s3, carrying out genetic typing on the identified material by adopting an SNP chip or a sequencing technology, and constructing a wheat high-density genetic map; s4, based on the genetic map and phenotype data, carrying out QTL positioning analysis on the target character, and determining a key control site; s5, establishing a whole genome selection model based on the genetic map and phenotype data, and performing predictive selection and breeding value evaluation on the germplasm material offspring; by integrating a QTL positioning result and a high-throughput SNP typing technology, target character recognition on a gene level is realized in a germplasm resource preliminary screening stage, the precision and efficiency of early-stage material selection are remarkably improved, and the screening burden of breeding populations is reduced.

The invention belongs to the technical field of gene detection and gene molecular breeding, and particularly relates to a meat duck whole genome molecular probe combination based on molecular phenotype screening, a 50K gene chip and application thereof. The molecular probe combination and the gene chip of the marker site combination for meat duck whole genome breeding simultaneously cover 7 representative meat duck varieties and 71 economic characters, have richer polymorphism and higher pertinence in meat duck groups, and are lower in cost and higher in speed compared with high-throughput sequencing detection; the breeding chip is designed according to the growth, feed efficiency, slaughtering, breeding, egg quality and various molecular phenotypes of the meat ducks, and compared with a high-throughput sequencing technology, the breeding chip is higher in seed selection accuracy, has higher breeding value, can be widely applied to breeding genotype detection of the meat ducks, and can be used for detecting the breeding genotypes of the meat ducks. And the method has creative significance in the aspect of meat duck genome selective breeding.

The invention discloses a grape downy mildew resistance whole genome selective breeding method based on machine learning, which comprises the following steps: randomly dividing 132 samples into a training set and a test set according to a ratio of 4: 1, and respectively extracting first 1-100 thousand variation sites as gradient training data sets according to a whole genome association analysis result; and on the basis of 10 classic regression models and the dimensionality-reduced variation data set, machine learning training and evaluation are carried out on 105 training set samples, finally, the variation data set with the highest prediction accuracy and an optimal model are screened out, and phenotype prediction is carried out on 27 test set samples by using the optimal model. The invention relates to the technical field of plant breeding, and the grape downy mildew resistance whole-genome selective breeding method based on machine learning has remarkable advantages in resistance prediction and screening, breeding efficiency improvement and multi-character comprehensive improvement, breaks through the limitation of a traditional breeding method, and is suitable for large-scale popularization and application. An innovative solution is provided for green and sustainable development of the grape industry.

The invention relates to the technical field of molecular breeding, in particular to a duck whole genome chip and a manufacturing method and application thereof. The duck whole genome chip is totally provided with 40876 probe targets, 25465 probe targets are derived from duck reference genomes, and the rest 15411 probe targets are derived from duck variety specificity and character-associated SNPs (Single Nucleotide Polymorphism). The average detection rate of the 40K whole genome liquid phase chip is 99% or above, the total number of effective markers is larger than 40,000, and the 40K whole genome liquid phase chip has high stability and is suitable for genetic improvement and resource evaluation of duck varieties. Besides, the SNP sites contained in the chip are obtained by mining important characters of injection ducks, and genome selection can be stably and efficiently performed on the important characters of the injection ducks by utilizing the sites, so that the duck breeding progress can be accelerated.

The invention belongs to the technical field of molecular biology, and particularly discloses a cotton 10K functional site breeding chip and application thereof. The breeding chip is named as' Cotton Core No.1 ', and comprises a 10K functional site targeting capture probe group designed based on a reference genome of disease-resistant, high-quality and high-yield modern upland cotton No.8, the probe group comprises 11159 SNP (Single Nucleotide Polymorphism) loci, 3981 of the SNP loci are functional loci associated with 13 important agronomic characters, and 7178 of the SNP loci are background loci reflecting genetic diversity; the SNP site information is shown in the specification table 1. The invention discloses a cotton 10K functional site breeding chip and application thereof, the breeding chip contains functional sites with more index function genetic variation, has higher detection accuracy among different cotton varieties, is applied to cotton molecular marker-assisted selection breeding, whole genome selection breeding and whole genome correlation analysis, and has higher detection accuracy. The breeding efficiency can be obviously improved, and the practical application is wider.

The invention relates to the field of bioinformatics, in particular to a genome selection method, device and equipment, a medium and a computer program product. Integrating a plurality of machine learning models, including 13 algorithms such as a support vector machine (SVM), linear regression, Ridge regression, Lasso regression and the like; by combining automatic hyper-parameter optimization, parallel computing and efficient data preprocessing technologies, the breeding prediction precision and the computing efficiency are remarkably improved. The device comprises an IASML software module and an IASML cloud platform module, wherein software supports command line operation and is suitable for large-scale data calculation; the cloud platform provides a graphical interface and supports online data uploading, model selection, real-time monitoring and result downloading. According to the method, the problems of singleness, limited data scale, poor interactivity and the like of an existing tool model are solved, and an efficient, flexible and reproducible intelligent solution is provided for animal and plant breeding and genetic research.

The invention discloses a chip for genome breeding and variety identification of tilapia mossambica. The invention provides a group of SNP (Single Nucleotide Polymorphism) marker combination for tilapia mossambica, which comprises 50000 SNP markers which are respectively SNP1-SNP50000 markers. According to the invention, the SNP marker combination related to economic characters of tilapia mossambica is integrated, representative sites with uniform coverage are selected, a biological probe for site typing is developed, and the site coverage, typing accuracy and GS accuracy of the SNP marker combination are basically consistent with those of re-sequencing; the method can be applied to the aspects of tilapia germplasm resource identification, genetic relationship identification, SNP typing, variety identification, molecular breeding, DNA fingerprint database construction, variety purity detection, germplasm resource genetic analysis, whole genome selective breeding, functional gene positioning, genetic map construction, genetic evolution analysis, whole genome association analysis and the like.

The invention discloses a western China cattle parameter-free Bayesian genome selection method. The method comprises the following steps: step 1, constructing a reference group and measuring important classification characters; step 2, collecting blood from the western China cattle, preserving, extracting DNA, and performing genetic typing to obtain the genotype of each SNP site; step 3, for the individual Chinese western cattle, using plink2 software to associate the genotype file and the phenotype file to obtain summarized statistical data; partitioning each SNP site according to the position of a chromosome to obtain an LD reference file; 4, the summarized statistical data and the LD reference file serve as input files of a parameter-free Bayesian model, and an effect value of the SNP site is obtained through calculation; and multiplying the genotype vector of the candidate individual by the locus effect value vector to obtain a genome estimation breeding value. The invention aims to establish a breeding method for classification characters of the Huaxi cattle based on a parameter-free Bayesian genome selection technology, and provides a molecular breeding method for breeding high-quality Huaxi cattle so as to promote rapid development of beef cattle breeding industry.

The invention provides a target character prediction method suitable for low-depth sequencing and a whole genome selection model suitable for low-depth sequencing, and relates to the technical field of biology. According to the target character prediction method suitable for low-depth sequencing and the whole-genome selection model provided by the invention, the target character of the to-be-detected sample can be accurately predicted under a limited data volume by combining low-depth whole-genome re-sequencing data with a machine learning technology, and the genome selection efficiency can be improved by matching the two; the time and economic cost of breeding or medical research are reduced, and the method is particularly suitable for large-scale genetic analysis scenes needing rapid iteration.

The invention discloses a Chinese rose vase life whole genome SNP molecular marker combination and application thereof, the SNP locus combination comprises 1679 SNP markers significantly associated with Chinese rose vase life, and the SNP markers are uniformly distributed on 14 chromosomes of Chinese rose. The SNP loci are obtained through genome-wide association analysis and screening, a genome-wide selection model constructed by the SNP loci can accurately predict the vase life of the Chinese rose, and the prediction accuracy can reach 0.710 or above. The invention also relates to specific probes designed aiming at the SNP loci and a liquid phase breeding chip containing the probes, which can be used for rapidly and efficiently screening individuals with excellent vase life characters in a Chinese rose seed or seedling stage, so that the breeding period is remarkably shortened, the breeding efficiency is improved, and the breeding cost is reduced. Powerful technical support is provided for molecular breeding and variety improvement of the Chinese roses.

A GAN model construction method, and a data fitting, phenotype prediction, sample augmentation and breeding method based on a GAN model. The construction of a GAN model comprises: first, entering a real multi-omics data generator G1 on the basis of input real multi-omics data, extracting features, and then entering a real phenotype discriminator D1, and training G1 and D1; and then randomly generating a group of noise, inputting same into a fitting multi-omics data generator G2, inputting real data into G1, outputs of G2 and G1 entering a fitting multi-omics data discriminator D2, and training G2 and D2. The method can be at least used for making up for an insufficient data amount and also fully exerting the advantages of a deep learning algorithm, thereby comprehensively improving the accuracy of genome selection.

The invention discloses a China rose inflorescence whole genome SNP molecular marker combination and application thereof. A traditional breeding method for improving China rose inflorescence characters is time-consuming and labor-consuming, and ideal plants are difficult to obtain quickly. The SNP molecular marker technology is utilized, 358 Chinese rose materials and inflorescence phenotype data are analyzed through whole genome association, 1574 SNP loci remarkably related to inflorescence are screened out, a whole genome selection model with the prediction accuracy larger than 0.743 is constructed, and inflorescence characters can be effectively predicted. Meanwhile, a corresponding probe is synthesized to be used for capturing and sequencing, the capturing efficiency is high, the coverage depth is good, the SNP sites related to the rosa chinensis inflorescence can be accurately detected, an efficient technical means is provided for rosa chinensis genetic improvement and new variety cultivation, and the development of the rosa chinensis industry is assisted.

The invention discloses a Chinese rose petal outer edge wave whole genome SNP (Single Nucleotide Polymorphism) molecular marker combination and application thereof, and aims to solve the problems that the traditional Chinese rose breeding period is long, the Chinese rose breeding is easily interfered by the environment, and the petal wave character cannot be predicted in a seedling or seed stage. According to the method, 358 Chinese rose whole genome re-sequencing data and petal outer edge wave phenotype association analysis are integrated, a multi-model combined screening strategy is adopted, 1453 SNP markers obviously associated with petal outer edge waves are determined, and a whole genome selection model with high prediction precision is constructed. By combining a specific probe and chip technology, high-throughput and rapid detection is realized, the detection period can be shortened, the petal wave traits can be directly predicted by utilizing genotype data in a seedling stage, the breeding process is accelerated, and core data support is provided for Chinese rose petal outer edge wave genetic law analysis and molecular marker-assisted breeding; the method has outstanding theoretical value and application potential.

The invention relates to the technical field of biological information, and provides a whole genome selection method, device and equipment of a graph neural network and a storage medium, and the whole genome selection method of the graph neural network comprises the following steps: mapping single nucleotide polymorphism (SNP) to a gene level, and converting the SNP into a gene embedding vector; constructing a gene interaction network based on preset multi-source biological priori knowledge; inputting the target character into a graph neural network model to obtain a predicted value of the target character output by the graph neural network model; wherein the graph neural network model is determined based on a gene embedding vector and a gene interaction network. According to the method, the SNP is mapped to the gene level, and the gene interaction network is combined, so that the prediction result has clear biological significance; and the gene interaction network is constructed based on the preset multi-source biological priori knowledge, so that interaction information between genes can be fully utilized, and the accuracy of target character prediction is improved.

The invention relates to the technical field of genomics, molecular biology, bioinformatics and whole-genome selective breeding, in particular to a whole-genome liquid chip for apostichopus japonicus and application of the whole-genome liquid chip. The liquid phase chip contains sequences of background SNP loci and functional SNP loci which are used for gene analysis and are positioned on an apostichopus japonicus reference genome; wherein the functional SNP sites are associated with important economic characters of the apostichopus japonicus. The invention also discloses application of the liquid chip in apostichopus japonicus genome selective breeding, important economic character gene positioning, genetic diversity analysis, germplasm resource improvement and protection.

The invention discloses a lutjanus erythropterus breeding auxiliary method and system based on multi-character collaborative selection, and the method comprises the steps: carrying out phenotype data collection and gene detection, and constructing a standardized phenotype matrix and a genotype matrix; performing genetic parameter estimation and breeding value prediction based on the standardized phenotype matrix and the genotype matrix to obtain breeding value prediction information; the method comprises the following steps: analyzing an association relationship between characters of lutjanus rubripes to be bred, constructing a character association network, and carrying out multi-character dynamic weight configuration to obtain a dynamic weight set; performing genome selection index calculation and parent priority analysis according to the breeding value prediction information and the dynamic weight set to generate a parent selection priority list; and generating a candidate parent pool through the parent selection priority list, performing hybridization combination analysis, performing genetic gain prediction on the generated hybridization combination, and generating a hybridization auxiliary scheme. The genetic gain accuracy and balance of breeding of the lutjanus erythropterus are improved, the limitation of traditional experience selection and matching is broken through, and the breeding efficiency is improved.

The invention discloses a genome selection method and system based on natural language processing. According to the method, genes arranged according to chromosomes in a genome of a sample are expressed as embedded vectors by utilizing natural language processing, and the embedded vectors are input into a deep learning model, so that the phenotype prediction performance of the sample selected by the genome is remarkably improved by adopting a deep learning framework combining a convolutional neural network, a bidirectional long-short term memory network and a self-attention mechanism.

The invention discloses a genome and phenotype group fused phenotype prediction method and system, and the method comprises the steps: firstly obtaining genotype data and phenotype data of a to-be-detected crop, then respectively building a genome selection prediction model and a phenotype group selection prediction model, and carrying out the prediction through the genotype data and the phenotype data, obtaining a genome selection prediction result and a phenotype group selection prediction result; and finally, calculating the weights of the genome selection prediction result and the phenotype group selection prediction result by using a Deoptim method or a FastW method, and carrying out weighted summation to obtain the phenotype prediction result of the crop to be detected. According to the method, the advantages of genome and phenotype group prediction are fused, the prediction precision is improved, and the calculation time is shortened.

The invention belongs to the technical field of whole genome gene chips, and particularly relates to a cattle whole genome liquid phase chip and application thereof. Based on an SNP molecular marker combination (position information is shown in a specification table 1) provided by the invention, cattle genotyping can be realized through a targeted capture sequencing technology, and cattle genotypes can be detected in a short time and at high throughput, so that cattle breeding work is promoted. Furthermore, based on the cattle SNP molecular marker combination provided by the invention, a probe is designed, and a whole genome breeding chip is constructed, so that large-scale typing site detection rate of cattle is high, stability is good, and platform wide adaptability is achieved. The method can be applied to germplasm resource identification and mining, genetic diversity analysis, population structure analysis, genetic relationship identification and whole genome selective breeding of cattle varieties, especially Anhui province local cattle varieties, and has important significance in improving the cattle core provenance autonomous breeding efficiency.

The application relates to the technical field of gene chips, in particular to a SNP molecular marker combination of Pinus massoniana and application thereof. The SNP molecular marker combination is composed of 113,709 SNP molecular markers, wherein the physical position of the SNP molecular markers is determined by sequence alignment based on a reference genome Pinus.tabuliformis V1.0 of Pinus tabulaeformis. The application provides a whole genome chip of Pinus massoniana containing 113,709 SNP sites for the first time, and the chip has the advantages of high efficiency, low cost, good genetic stability and the like, and can be widely applied to different application scenarios such as identification of Pinus massoniana germplasm resources, genetic background analysis of breeding materials, gene positioning, whole genome association analysis, whole genome selection breeding and intelligent design breeding.

The invention discloses a China rose fragrance whole genome SNP molecular marker combination and application thereof, 2068 SNP markers significantly related to fragrance are screened out by analyzing 358 parts of China rose re-sequencing materials and flower fragrance phenotype data, and a whole genome selection model is constructed. The method can accurately predict the fragrance character of the China rose, improve the breeding efficiency and accuracy, shorten the breeding period and reduce the cost. Meanwhile, a new direction is provided for China rose fragrance improvement and breeding, the method has reference value for character improvement of other horticultural crops, has efficient and stable capture efficiency, and can be used for subsequent sample detection and evaluation.

The invention belongs to the technical field of aquatic genetic breeding, and particularly relates to a grass carp disease-resistant character genome breeding method and related application thereof. According to the method, filial generation juvenile fish families produced by non-family grass carp parents from different geographical sources are used for constructing an anti-hemorrhagic disease reference group, the disease-resistant SNP site provided by the invention is used for calculating a genome estimation breeding value of a candidate group, and individuals with high breeding value are relatively high in disease resistance and can be used for breeding disease-resistant improved varieties of grass carp.

The invention belongs to the technical field of animal molecular breeding, and relates to a cattle low-temperature adaptability related molecular marker, a screening method and application thereof. Specifically, a cattle high-density SNP chip is utilized to carry out genetic typing on cattle varieties in different regions, two genomic selection signal analysis methods of FLK and hapFLK are integrated, genomic selection signals among cattle in different temperature regions or in the cattle varieties are compared, regions subjected to strong selection and positive selection genes are identified, and a CRISPR / cas9 gene knockout mouse model is utilized to verify the function of the HSPA12B gene. According to the method, a functional gene and molecular markers (g.51906414T > C, g.51910621A > G and g.51913699G > A) suitable for low temperature are screened out. According to the invention, a new target is provided for analyzing a molecular mechanism adapted to cattle at low temperature, a scientific basis and a simple and rapid detection method are provided for screening and cultivating special cattle varieties with low-temperature resistance, and meanwhile, a new reference is provided for cultivating new low-temperature-resistant varieties of other livestock and poultry.