55 results about "Genomewide association" patented technology

The invention discloses an SNP (Single Nucleotide Polymorphism) marker-based rice disease resistance character whole genome association analysis method, which relates to the technical field of biology, and is characterized by comprising the following steps: selecting a representative genetic recombination population, carrying out high-density SNP genetic typing on the population, and combining recombination event positioning and linkage analysis to obtain the disease resistance character whole genome association analysis of rice. Obtaining genetic parameters reflecting recombination frequencies of different genome segments; on the basis of the obtained original genetic data, parent and offspring genotype information is further integrated, fine positioning and statistics are carried out on recombination breaking points, and a high-resolution genetic map is constructed. By constructing a high-resolution genetic map and combining feature engineering and machine learning, accurate identification and dynamic SNP encryption of a high-recombination region are realized, the marker coverage and associated signal capture capability of the region is enhanced, the functional site leak detection risk is reduced, and the detection efficiency is improved. The positioning accuracy and the molecular breeding application effect of the rice disease resistance character related candidate gene are improved.

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 a penaeus monodon high temperature resistance character related SNP marker primer based on whole genome association analysis and application, and belongs to the field of molecular biology. The nucleotide sequence of the primer pair is shown in SEQ ID NO.1-2. An SNP molecular marker is located at the 27494780 site of the 27th chromosome of a penaeus monodon genome and is shown as Cgt; a mutation, and the A mutation is associated with a high temperature resistant character. The invention further provides a kit for detecting the high-temperature-resistant character of penaeus monodon, and high-temperature-resistant individuals can be screened by utilizing the primers and are used for breeding new varieties.

The invention discloses a gene detection system which comprises a sample collection module, a gene extraction module, a sequencing module, a data analysis module and a report generation module. The gene extraction module is used for extracting high-quality DNA (Deoxyribonucleic Acid) or RNA (Ribonucleic Acid) from a sample, and the data analysis module is used for processing, analyzing and explaining original data obtained by sequencing by integrating various bioinformatics algorithms and tools. The game theory is integrated into the whole genome association analysis GWAS model to improve the whole genome association analysis GWAS model, the game theory is combined to enable the SNP sites to be more and more detailed, the relevance between deeper hereditary characters and defective genes can be mined, the false positive probability of the result obtained by the association model is smaller, and the accuracy of the result obtained by the association model is improved. A genetic-defect gene association map is constructed according to a whole genome association analysis GWAS model fused with the game theory thought, a defect gene risk prediction model is trained, and the most possible defect gene type is predicted according to genetic characteristics, so that the accuracy is greatly improved.

The invention discloses a grape cold resistance character whole genome selective breeding method based on machine learning, which comprises the following steps: carrying out whole genome re-sequencing and freezing point detection on 352 grape germplasm resources, and carrying out whole genome association analysis, so as to obtain a variation site data set related to cold resistance characters. Then, ten classic regression models are utilized to train a variation site data set after dimension reduction, and phenotypic training and evaluation of the grape cold resistance character are realized. Finally, a Bayesian regression model and 4189 variation points are adopted, the prediction accuracy rate of 82.2% is achieved in a training set, and the Pearson correlation coefficient between a predicted value and a true value in a test set reaches 95%. The invention relates to the technical field of grape cold resistance character whole genome selective breeding. According to the machine learning-based grape cold-resistant character whole-genome selective breeding method, the screening cost of grape filial generations is remarkably reduced, the cold-resistant breeding efficiency is improved, and the method has an important breeding application value.

This invention relates to the fields of genomics, molecular biology, bioinformatics, and genome-wide selection breeding, specifically a 20k liquid-phase breeding chip for the whole genome of *S. esculenta* and its applications. The liquid-phase chip contains background SNPs and functional SNPs located on the *S. esculenta* reference genome; wherein the background SNPs are uniformly distributed within the genome; and the functional SNPs are associated with important economic traits of *S. esculenta*; these important economic traits include one or more of the following: saponin content, polysaccharide content, and heat tolerance. The chip can be applied to the assessment of genetic diversity in *S. esculenta*, identification of germplasm resources and phylogenetic relationships, genome-wide association analysis of important economic traits, and genome-wide selection breeding. This chip has advantages such as high throughput, high region coverage, high locus detection rate, and high flexibility, providing powerful tool support for molecular breeding of *S. esculenta*.

The present invention discloses a genome-wide association analysis algorithm at the gene level based on an EMS population, which relates to the technical field of bioinformatics. The present invention conducts association analysis with genes as the basic unit. The MAF index of SNPs in the EMS population is much lower than that of the normal population, but the MAF value at the gene level is higher than that at the SNP level. Weights are assigned according to the mutation effects of each mutation site, and the total weighted value of all mutations in each gene of a single sample is statistically calculated as the basis for association analysis, greatly reducing the false positive rate of the results and increasing the statistical power of the analysis. At the same time, multiple statistical methods are used for comprehensive evaluation to find the most reliable candidate genes. Compared with GWAS that can only locate a fuzzy interval that may contain multiple genes, the present invention can directly and accurately locate to a single gene, improving the experimental efficiency of functional verification of candidate genes and effectively solving the problems that the prior art cannot afford large-scale analysis of the EMS population and has low analysis power.

The invention relates to the technical field of biological information, in particular to a non-centralized whole genome association aggregation analysis method, which comprises the following steps that: each data source converts individual-level data of the data source into universal report data; importing the versatile report data into a least square formula to obtain a whole genome association analysis model; and the organization center carries out polymerization analysis to synthesize a required effect value. According to the method, each data source converts own data into versatile report data, then the least square algorithm is reconstructed, the versatile report data is imported into the least square formula to obtain a full-genotype association analysis model, the non-centralized design can meet the data privacy requirement, the data leakage risk is reduced, and the analysis efficiency is improved. And the organization center performs aggregation analysis to synthesize the required effect value, so that the problems of model selection and parameter adjustment between the data source and the organization center are solved, and the efficiency of aggregation analysis is improved.

The present invention relates to the field of molecular detection technology, and specifically discloses a 10K liquid-phase chip for Pyropia yezoensis, its design method and application; the genotyping sites of the 10K liquid-phase chip for Pyropia yezoensis include 12,942 SNP sites; the physical positions of the 12,942 SNP sites are determined based on the whole-genome sequence alignment of the reference genome of Pyropia yezoensis, and the version number of the whole-genome sequence of the reference genome of Pyropia yezoensis is GCA_009829735.1. The present invention can achieve population structure analysis and genome-wide association analysis of Pyropia yezoensis.

The application discloses a method for quickly exploring quantitative trait genes of rice, and the method comprises the following steps: performing whole genome association analysis on a rice nested linkage mapping population and linkage analysis on each subpopulation, and exploring quantitative trait loci (QTL) which can be identified by both methods; comprehensively analyzing the function, variation and expression of candidate genes in the QTL; performing weighted scoring and sorting on the number of keyword matches in the annotation information of the candidate genes, the influence of the variation sites of the candidate genes on the gene function, the expression amount of the candidate genes in specific tissues and the like, and selecting three genes with the highest scores as the candidate genes of the target quantitative trait.

The application provides a molecular marker related to cold tolerance of arrowhead pea on chromosome 1 and application, and belongs to the technical field of biology. Through whole genome association analysis, an InDel site is located in the arrowhead pea and named as Vs_Chr1_34169935, and the nucleotide sequence of the insertion or deletion fragment is shown in the sequence table SEQ ID NO. 3. A primer pair for amplifying the molecular marker is shown in the nucleotide sequences of SEQ ID NO. 1-2. The molecular marker and the primer pair thereof can identify or assist in identifying the cold tolerance of the arrowhead pea. Meanwhile, the application also provides a method for rapidly identifying the cold tolerance of the arrowhead pea by using the molecular marker. The method is simple and fast, the identification result is accurate, and has a good popularization and application prospect.

The invention provides a genome method and an SNP (Single Nucleotide Polymorphism) marker combination for a pig to adapt to related traits in a high-temperature and high-humidity environment, and belongs to the technical field of genome prediction, the method comprises the following steps: taking a local pig SNP marker combination as a prior fixing effect; standard miscalculation of a p value, a beta value and a beta value based on whole genome association analysis; performing interval dimension reduction based on linkage imbalance; searching an optimal parameter by using a genetic algorithm, and adaptively grouping the SNPs; performing weighted summation on the G matrix; the SNP marker combination is used as a fixed effect, the weighted G matrix is used as a random effect, a genetic evaluation model is configured, and genome prediction is carried out on target characters. According to the method, the large-effect SNP and the minor-effect multi-gene effect which are causally associated with the target prediction character can be accurately captured, redundant information is removed through dimension reduction in the interval, the optimal parameter combination is found through the genetic algorithm, and the calculation speed is greatly increased while the accuracy of genome prediction is improved.

The invention relates to the technical field of molecular biology, in particular to application of an MEB2 gene in negative regulation of sclerotiniose resistance of plants. Whole genome correlation analysis is carried out to identify that the site of the BnMEB2 gene is a sclerotiniose resistance key regulation site in a natural rape population, and meanwhile, arabidopsis thaliana meb2 mutant, overexpression and BnMEB2 susceptible haplotype are utilized to prove that the disease resistance of plants can be improved due to MEB2 function deficiency, the susceptibility is enhanced due to overexpression, and the BnMEB2 gene can be used as the sclerotiniose resistance key regulation site. And a dose effect relationship between gene expression and disease-resistant phenotype is established. The invention discloses a variable shear transcript AtMEB2.2 of an arabidopsis MEB2 gene. An overexpression experiment shows that the transcript significantly enhances sclerotinia sclerotiorum susceptibility. Besides, a haplotype typing method is developed aiming at 16 SNP loci in the rape BnMEB2 gene region, and the haplotype typing method has an important application prospect in rapid screening of disease-resistant phenotypes.

The invention provides a staphylococcus aureus drug resistance prediction method based on whole genome association analysis and machine learning, and belongs to the technical field of bioinformatics and artificial intelligence crossing. Five common antibiotics such as erythromycin, ciprofloxacin, penicillin, fusidic acid and methicillin are selected as modeling objects. Performing comparison and SNP site detection on a strain genome by using a reference genome, and constructing a variation matrix; then, a pyseer tool is adopted for GWAS analysis, and genetic loci significantly related to the drug resistance of each antibiotic are screened as modeling features; and for each antibiotic, respectively constructing an independent machine learning model, inputting the SNP features screened by the GWAS, and outputting a drug resistance / sensitivity classification label. Through cross validation and independent test set evaluation, the model has excellent accuracy and F1 score on multiple antibiotics. According to the method, drug resistance prediction can be achieved based on genome data without traditional drug sensitivity experiments, and the method has the remarkable time advantage, popularization value and scientific research significance.

The present invention discloses a SNP molecular marker of Larix olgensis and a whole-genome liquid chip prepared therefrom and its applications. The SNP molecular marker of Larix olgensis includes at least one of 61,680 SNP molecular markers, and the physical positions of the 61,680 SNP molecular markers are determined by sequence alignment based on the Japanese larch reference genome GCA_027924585.1; the SNP molecular marker of Larix olgensis of the present invention can be used for specific identification of Larix olgensis trees. Using the loci to prepare a whole-genome liquid chip for Larix olgensis can achieve low-cost genotyping, assessment of genetic diversity of Larix olgensis, identification of germplasm resources and genetic relationships, molecular design breeding, construction of genetic maps and functional gene mapping, genome-wide association analysis and intelligent breeding, and has high application value in multiple fields of Larix olgensis breeding.

The invention discloses a whole genome gene-gene and gene-environment interaction detection method based on an interpretable genetic information neural network, which comprises the following steps of: 1, training a deep neural network model by using genotype data and phenotype data to obtain a trained deep neural network model; 2, inputting genome data of a sample, and predicting a phenotype value of the sample by using the trained deep neural network model; and step 3, calculating the xPI value of each genome variation site, and carrying out whole genome association analysis and genetic site detection through the xPI values. By constructing the deep neural network model and combining the genotype data and phenotype data of the sample, the target character is accurately predicted. Through trained network analysis, the xAI-GWAS not only quantifies the importance of each genotype data, but also can detect the additive effect, the gene interaction effect and the gene-environment interaction effect of the genotype data.

The application discloses a kind of molecular marker related to oat salt tolerance and its application, belong to molecular biology technical field.The application takes 144 forages oat as material, by whole genome resequencing and germination rate phenotype determination under salt stress, carries out whole genome association analysis, identifies one SNP site significantly related to oat salt tolerance, which is located at the 375175364 base of chromosome chr1D, and the polymorphism is G / A.According to the SNP site, the application further develops KASP primer combination.Using the molecular marker and primer combination, the salt tolerance of oat seed stage can be quickly identified: oat material with genotype GG has higher germination rate under salt stress, and shows strong salt tolerance, and material with genotype AA is contrary.The method in the application is stable, and has good repeatability, and can be used for early salt tolerance screening and molecular assisted breeding, to significantly improve breeding efficiency and accuracy.

The invention discloses a whole genome association analysis method and device for bifidobacterium traits and a medium, and the method comprises the following steps: constructing a generalized linear hybrid GLMM full model which represents an additive effect of quantitative trait loci QTLs of an analysis object, an additive episomic effect between the QTLs and an interaction effect between the QTLs and the environment; constructing a standardized genotype matrix, wherein the standardized genotype matrix comprises an additive effect standardized coefficient matrix and an additive effect standardized coefficient matrix; carrying out correlation analysis on the QTL to obtain a significant additive QTL pair and a significant epistatic QTLs pair; and constructing a generalized linear hybrid QTL full model, solving by adopting an average information amount restrictive maximum likelihood estimation iteration method, and estimating each effect parameter of the QTL. The calculation process is simplified, and the calculation efficiency is improved; genetic variation sites related to characters can be more comprehensively mined; and the method has both the accuracy of accurate inspection and the rapidity of approximate inspection.

The invention discloses an optimal prediction system for selecting black carp growth traits based on a whole genome and a construction method and application thereof.The system is composed of an optimal GS statistical model GBLUP model and an optimal SNP genotype site set with the number of 12000, the construction method comprises the steps that genome re-sequencing and genetic typing are conducted on 499 black carps, and 237730 high-quality SNP sites are obtained; carrying out whole genome association analysis by combining the phenotypic data of the growth traits of the black carps to obtain 9 SNP genotype site sets; 80% of a black carp group is used as a training group, black carp growth character phenotype data, five GS statistical models and nine SNP genotype site set data are utilized to establish a black carp growth character GS prediction model, a black carp growth character optimal prediction system is obtained through verification and screening, and breeding values of growth characters in the black carp group are rapidly and accurately calculated. The early prediction of the growth traits of the black carps is realized, and the black carps with good growth potential are screened.

The invention provides a construction method and application of a chrysanthemum aphid resistance prediction system based on whole genome selection. According to the method, SNP loci remarkably related to the aphid resistance of the chrysanthemum are identified through whole genome association analysis, the influence of different statistical models and SNP markers with different densities on the whole genome prediction precision is compared, the method for rapidly, efficiently and accurately predicting the aphid resistance of the chrysanthemum based on GWAS-assisted whole genome selection is established, and the prediction accuracy can reach 0.882. The whole genome selection system constructed based on the method can realize early selection of the aphid resistance of the chrysanthemum, accelerates the breeding process of the excellent new variety of aphid resistance of the chrysanthemum, saves the breeding cost, effectively overcomes the technical problems that the field aphid resistance identification work is tedious, long in period and easy to be influenced by environmental factors and human subjective factors and the like, and improves the identification efficiency of the aphid resistance of the chrysanthemum. The method has important theoretical and practical significance.

The invention discloses an SNP (Single Nucleotide Polymorphism) marker related to poplar leaf rust and application, and relates to the technical field of genetic breeding of poplar. The marker is located at the 14699534th bp position on a chromosome 13 of a reference sequence of a 4.1 version of a populus trichocarpa genome. Through multi-model whole genome association analysis (GWAS), it is determined that the gene is a key regulation and control site affecting the leaf rust of the poplar, meanwhile, expression of genes related to the leaf rust is affected, a new choice is provided for breeding leaf rust resistant poplar germplasm, and the gene has important application value in accelerating the poplar breeding process.

The invention discloses an SNP (Single Nucleotide Polymorphism) site related to maize cadmium tolerance and a transcript gene ZmEIV related to the SNP site, and relates to the technical field of biochemistry, the technical key points are as follows: the SNP site and the transcript gene ZmEIV related to the SNP site are obtained by carrying out whole genome association analysis and screening, one transcript gene ZmEIV5 can enhance the cadmium tolerance of organisms, and the other transcript gene ZmEIV5 can enhance the cadmium tolerance of the organisms; the corn with the overexpression gene ZmEIV5 is cultivated, so that the corn shows better adaptability on the cadmium-polluted land; according to the invention, the agricultural environment can be improved, the influence of heavy metal pollution on land can be reduced, the tolerance of crops to heavy metal pollution can be obviously improved, and the food safety can be ensured.

The invention relates to the field of gene data analysis, and provides a corn whole genome association analysis method, device and equipment and a readable storage medium, the method comprises the following steps: integrating a genotype data matrix and a phenotype data matrix of a corn sample to obtain a whole genome association analysis data matrix; training a convolutional neural network based on the whole genome association analysis data matrix; combining the trained convolutional neural network with the member contribution model to construct a whole genome association analysis model; and based on the whole genome association analysis model, obtaining an influence result of each gene locus of the to-be-analyzed corn on the corn phenotype. According to the corn whole genome correlation analysis method, the whole genome correlation analysis model is constructed through the combination of the convolutional neural network and the member contribution model, correlation analysis is performed on the corn whole genome, and the precision and efficiency of corn whole genome analysis are improved.

The invention discloses a grape titratable acid whole genome selective breeding method. The method comprises the steps of data acquisition and preprocessing, variation site analysis, whole genome association analysis and model analysis and prediction. The invention relates to the technical field of grape genetic breeding. Aiming at quantitative character characteristics (influenced by multiple genes and environment) of titratable acidic characters, more than 10000 significant variation sites and key gene areas (such as candidate genes for coding malic enzyme and tartaric acid synthetase) covering tartaric acid and malic acid metabolism related pathways are positioned through whole genome association analysis, so that the one-sidedness of single gene analysis is avoided, and the quantitative character characteristics of the titratable acidic characters are obtained. The probability of false positive results is remarkably reduced, a model is constructed based on two-year phenotype data, and the influence of environmental noise is eliminated through cross validation, so that a prediction result is closer to a real genetic effect, and the problem that traditional phenotype selection is interfered by environmental fluctuation is solved.

The present invention belongs to the field of plant genetic breeding and specifically discloses a primer set of a molecular marker for identifying peanut stalk strength and its application. A site significantly associated with stalk strength is identified by whole-genome association analysis. The molecular marker is SNP site 2_31358672, located on peanut chromosome A02 at 31358672 bp. The sequence of 100 bp before and after the SNP site 2_31358672 is shown in SEQ ID NO. 1. When the genotype of the SNP site 2_31358672 is C / C, the corresponding peanut has high stalk strength, while when the genotype is A / C, the corresponding peanut has poor stalk strength. This molecular marker can be used to identify and screen peanut varieties with high stalk strength and suitable for mechanization at an early stage, thereby greatly saving production costs and improving production efficiency.

The invention provides a crop insect resistance character high-throughput screening method based on transboundary RNA stability, and the method comprises the following steps: firstly, inoculating standardized aphids to crop leaves to be detected, and limiting feeding time; then, time sequence sampling is carried out at a specific time point after the aphids are removed; then, extracting total RNA (Ribonucleic Acid) of a plant, and detecting abundance of target transboundary RNA by utilizing an aphid specific primer through a real-time fluorescent quantitative PCR (Polymerase Chain Reaction) technology; then, calculating the stability index of the transboundary RNA, calculating the resistance index of the crop based on the established mathematical model, and judging the resistance grade according to the resistance index; and finally, integrating the steps on a high-throughput automatic platform, and further applying the obtained resistance phenotype data to whole genome association analysis so as to mine resistance gene loci.

The invention discloses a grape soluble solid whole genome selective breeding method. The method comprises the following steps: step 1, obtaining whole genome sequencing data and soluble solid phenotype data of grape germplasm resources; 2, screening genetic variation sites significantly associated with the soluble solids through whole genome association analysis; and 3, predicting the phenotype of the soluble solid based on a machine learning model, and screening the high-sugar-degree germplasm. The invention relates to the technical field of agriculture. According to the method, association sites are screened through whole genome association analysis, SSC efficient prediction is achieved in combination with a ridge regression model, the accuracy rate reaches 77%, and technical support is provided for intelligent breeding of grapes.

The application provides a genetic sexing strain of the ayu based on whole genome association analysis and an application thereof. A GWAS analysis is performed on 426 ayu groups by using a GCTA and GEMMA double algorithm, and a core SNP marker verified by statistics is screened out. The marker can be used for precise selection of seeds at the fry stage, shortens the breeding and screening period, and promotes the industrialization of high-efficiency molecular design breeding of the ayu.

The present invention belongs to the field of biotechnology and specifically discloses a genome-wide association analysis method for milk production-related traits in dairy goats and its application. 330 ewes were randomly selected from two Xinong Saanen dairy goat populations as experimental subjects for genome-wide association analysis. Traits such as milk production, milk composition, and udder phenotype were collected. Blood samples were collected during lactation, and genomic DNA was extracted. The extracted genomic DNA was tested for integrity, purity, and concentration. 298 qualified samples were resequenced. Genome-wide association analysis was then performed on seven indicators of production-related traits in Xinong Saanen dairy goats to screen candidate genes, providing theoretical support for the future selection and breeding of new high-quality dairy goat varieties.

The application provides a snowflake black cattle breeding method and system based on whole genome association analysis, relates to the technical field of black cattle breeding, and comprises the following steps: extracting PacBio long read HiFi sequencing data and second-generation whole genome resequencing data in a blood sample, and extracting Hi-C chromosome conformation capture sequencing data and multi-tissue full-length transcriptome sequencing data in a tissue sample; performing genome assembly to obtain a chromosome-level reference genome sequence; performing gene structure annotation on the reference genome sequence; detecting a whole genome SNP marker data set of a breeding population; screening whole genome SNP marker data meeting a preset significance threshold; performing functional annotation to obtain molecular markers for snowflake black cattle breeding. The application provides reliable and practically functional molecular marker support for the directional breeding of snowflake black cattle meat quality-related key economic traits.