116 results about "Genomic selection" patented technology

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 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.

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 invention relates to methods of identifying and characterizing a Cannabis spp. plant comprising a quantitative trait locus (QTL) or a causal polymorphism associated with a flower density trait, and to Cannabis spp. plants having a flower density trait of interest comprising defined allelic states of polymorphisms defining the QTL or defined allelic states of causal polymorphisms provided herein. Also provided are Cannabis spp. plants with a flower density trait of interest comprising defined allelic states of polymorphisms and plants identified, characterized or produced by the methods described. Further provided are methods of marker assisted selection, genomic selection, marker assisted breeding, and genetic modification, for obtaining plants having a flower density trait of interest.

The invention discloses a construction method of an MD-100K liquid phase chip for genome selection of dairy cattle and dairy-meat cattle. The construction method comprises the following steps: step 1, obtaining multiple varieties of candidate SNP (Single Nucleotide Polymorphism) sites; step 2, performing multiple quality screening on the SNP sites; and step 3, designing and optimizing the probe. The method has the beneficial effects that the method is specially designed for the main dairy cattle variety and the dairy and meat dual-purpose cattle variety in China for the first time, and the problem that imported chips are not suitable for water and soil in local varieties in China is effectively solved.

The invention relates to the technical field of animal genetic breeding, and provides a double-character genome selection platform based on local genetic correlation (LGC). The platform is composed of a phenotype data processing module, a genotype data processing module and a genome selection module (comprising a model selection sub-module and a parameter selection sub-module). The platform performs quality control, data normality test and correction on phenotypic data; performing quality control, sequence alignment, variation detection and genotype filling on the genotype data; and identifying whole genome local genetic correlation, checking the significance of LGC, selecting a model based on a whole genome LGC estimation result of a character pair and a corresponding P value, setting a corresponding model parameter threshold, and realizing section weighted double-character genome selection analysis. According to the method, the genetic evaluation accuracy of complex characters, especially low heritability characters, can be effectively improved, and an efficient and stable genome selection tool is provided for livestock and poultry breeding.

The invention discloses a sample data expansion method and device for whole genome selection, equipment and a medium, and the sample data expansion method for whole genome selection comprises the steps: obtaining real genotype data of a target crop in a historical crop breeding process and phenotype data corresponding to the real genotype data, generating an initial sample set according to the real genotype data and the phenotype data; based on linkage feature information of the real genotype data, performing segment segmentation on initial gene sample data in the initial sample set, and performing segment replacement on segmented gene segments based on auxiliary gene sample data to generate a mixed gene sample; and training a pre-constructed crop prediction model based on the mixed gene sample and the initial sample set. According to the technical scheme, the prediction efficiency of crop character prediction and the reliability of the prediction result are improved.

This invention discloses a method for calculating genome-estimated breeding values ​​based on Manhattan distance marker screening, belonging to the field of crop genetics and breeding technology. The method includes acquiring genotype and phenotypic data, determining whether to execute a Manhattan distance-based marker screening process, calculating the number of target markers to be retained on each chromosome and the number of redundant markers to be removed in each iteration, and calculating the Manhattan distance between the current reference marker and the remaining candidate markers in the current candidate marker set to determine the new reference marker and the new candidate marker set. When the number of markers in the retained marker set reaches the target number, the retained marker sets of all chromosomes are obtained to form an optimized marker subset. The genome selection model is trained based on phenotypic data and the optimized marker subset, and the genome-estimated breeding value is output. This invention solves the problems of limited prediction accuracy and heavy computational load and low efficiency of existing genome selection methods in the context of massive data environments.

The invention discloses a corn genome breeding 45K chip and application thereof, the chip covers 44, 637 SNP loci and 27 InDel loci, and the SNP loci are uniformly distributed on 10 chromosomes and part of scaffold of a corn genome and have rich polymorphism. Compared with an existing corn chip, the SNP marker of the 45K chip is derived from re-sequencing results of 507 corn varieties, has better variety representativeness, and contains 925 key functional gene loci for close planting and high yield and 96 corn variety authenticity identification loci; the method can be more effectively used for functional gene identification, genetic diversity analysis, heterosis group division, whole genome selection analysis and variety authenticity identification.

The invention relates to the technical field of bioinformatics and crop breeding, and particularly discloses a method, a device and equipment for predicting the close planting yield of brassica napus by using hypocotyl length based on a whole genome selection technology. The method is characterized by comprising the following steps: acquiring whole genome sequencing data of brassica napus germplasm; inputting the whole genome sequencing data into a target model; outputting hypocotyl length data of the brassica napus under a close planting condition through the target model; and according to the hypocotyl length data of the brassica napus under the close planting condition, obtaining a brassica napus germplasm yield potential sorting result. According to the method, whole genome selection and key morphological characters (hypocotyl length) under specific stress (close planting conditions) are combined for the first time, high-yield varieties suitable for close planting cultivation can be efficiently and accurately screened out without on-site close planting tests in the early stage of rape breeding, and the breeding efficiency is remarkably improved.

The invention belongs to the technical field of aquatic genome selective breeding, and particularly relates to an edwardsiella tarda-resistant genome selective breeding liquid-phase chip for oplegnathus punctatus and application of the liquid-phase chip. According to the method, 500 core SNP loci highly associated with the Edwardsiella tarda resistance character are screened out from 2415773 high-quality SNPs by obtaining the whole genome SNP marker of the oplegnathus punctatus and combining the three methods of MME-rank, GWAS and PVE, and the low-density liquid phase chip prepared by taking the 500 core SNP loci as the core shows extremely high prediction accuracy in a reference group and an independent verification group and can be used for detecting the Edwardsiella tarda resistance character of the Edwardsiella tarda resistance character of the Edwardsiella tarda resistance character of the Edwardsiella tarda resistance character of the Edwardsiella tarda resistance character. The method is obviously superior to a whole genome set and a uniformly distributed marker set; family verification shows that the family GEBV calculated based on the chip is significantly related to the actual survival rate of the offspring. According to the method, high-precision disease resistance performance prediction is realized at extremely low cost, and a key technical tool and a solution are provided for rapid breeding of disease-resistant improved varieties of oplegnathus punctatus.

The invention provides a cold-region low-temperature-resistant germination rice gene chip and application thereof, the cold-region low-temperature-resistant germination rice gene chip comprises 1016 SNP sites distributed in 12 chromosomes, is derived from large-scale GWAS analysis of 216 cold-region germplasms, and has the characteristics of high polymorphism, low deletion rate and low heterozygosis rate. The preparation method comprises the following steps: extracting DNA (Deoxyribonucleic Acid), sequencing, comparing and screening associated sites, and designing a specific probe based on the associated sites to prepare a liquid phase chip. The chip shows high precision (genotype consistency is greater than or equal to 98%) and high efficiency in application, and is mainly used for rapid screening, whole genome selective breeding and character correlation analysis of germplasm resources of the low-temperature-resistant rice in the cold region. By means of the chip, the breeding cycle can be shortened from traditional 6-8 generations to 3-4 generations, the screening efficiency is improved by 4-5 times compared with traditional phenotype identification, and a powerful molecular tool and a complete solution are provided for accurate breeding of low-temperature-resistant varieties of the cold-region rice.

The invention discloses a detection method and application of a molecular marker related to cattle cold adaptability. On the basis of large-batch whole genome re-sequencing data of northern cattle and southern cattle, a genome selection signal analysis method is adopted, and multi-omics verification such as transcriptome and epime is combined, so that the molecular marker related to the cold environment adaptability is screened out. The molecular marker can be used for cold adaptive molecular breeding of cattle population, and provides scientific basis and technical support for genetic improvement and resource utilization of cattle species adaptive to cold environment.

The invention discloses a probe for detecting a penaeus japonicus SNP molecular marker combination, a penaeus japonicus 31K liquid phase breeding chip and application, and belongs to the technical field of molecular marker breeding. The penaeus japonicus SNP molecular marker combination is composed of 31097 SNP molecular markers. The 31097 SNP molecular markers are as shown in the specification table 1. The invention also provides application of the penaeus japonicus liquid phase breeding chip in penaeus japonicus breeding, sample detection, genetic resource genetic diversity analysis, genetic and evolutionary analysis, genetic relationship identification, whole genome association analysis and penaeus japonicus genome selection. The penaeus japonicus 31K liquid phase breeding chip provided by the invention has the characteristics of good site representativeness, strong specificity, high polymorphism and the like, is widely applicable to detection of different varieties of penaeus japonicus materials, can establish a modern breeding system, and promotes the development of the penaeus japonicus molecular breeding industry.

The invention provides an SNP (Single Nucleotide Polymorphism) marker primer pair related to pig multirib traits and application of the SNP marker primer pair. The SNP site is a nucleotide site of chromosome 7 rs701714758 of a reference sequence of an international pig genome version 11.1, has G / A polymorphism, is located in a protein coding region of an ABCD4 gene, and is remarkably associated with rib number characters of Duroc pigs, landrace pigs, large white pigs and Pietrain pigs through verification of whole genome association analysis and meta analysis. The invention further provides a primer pair for detecting the SNP marker, an upstream primer of the primer pair is SEQ ID NO: 2, and a downstream primer of the primer pair is SEQ ID NO: 3. The SNP marker can be applied to marker-assisted selection and whole genome selection of rib number characters of multiple varieties of pigs, can accurately screen a multi-rib number pig group, improves character prediction accuracy, increases pork and rib row yield, meets consumption demands, and has both social and economic benefits.

This invention discloses a computer apparatus and method for predicting phenotypes or performing genome-wide selection using a multi-model ensemble strategy. The invention integrates 16 common GS prediction models and 13 evaluation indicators, and develops a method for integrating multi-model results into a unified framework, ensuring the convenience and flexibility of GS model comparison and selection. Using the method of this invention, efficient model comparison and selection can be performed, enabling genome-wide selection breeding on different crops and for different traits.

The present application relates to the technical field of bioinformatics, and particularly relates to a genome selection method and application thereof in breeding of homologous polyploids. The method comprises the following steps: constructing an additive genomic kinship matrix; constructing a genomic selection model based on the additive genomic kinship matrix; and estimating breeding values of individuals of a species according to the genomic selection model. The genotype matrix used in the additive genomic kinship matrix of the homologous polyploids is a polyploid genotype matrix, and the value of the genotype in the polyploid genotype matrix is determined by the copy number of any one allele. The present application proposes a genome selection method based on the homologous polyploid genomic kinship matrix for the species with the characteristics of homologous polyploids, which can accurately predict the genomic breeding values of individuals of the homologous polyploid species and improve the accuracy of genome selection, and has important significance in the field of genome selection breeding.

The application belongs to the technical field of genomic breeding, and provides a machine learning genomic selection system and method, which includes the whole process of data import, model selection, hyperparameter tuning, model training, evaluation and prediction. The platform integrates various machine learning algorithms, including kernel ridge regression, support vector regression, random forest, k-neighbor algorithm, and provides automatic hyperparameter optimization, model selection and result visualization functions. The platform also provides rich data visualization functions to help users better understand data and model results. The machine learning genomic selection platform of the application has the characteristics of high stability, simple operation and high integration, significantly reduces the technical threshold of genomic selection, enables breeding researchers without programming background to efficiently perform genomic data analysis, and promotes the wide application of machine learning technology in animal breeding.

The invention belongs to the field of molecular breeding of aquatic animals, and particularly relates to a fish genome selective breeding method based on machine learning, which comprises the following steps: acquiring genome sequencing data and target character phenotype data of a target fish individual; carrying out whole genome association analysis to screen out candidate variation sites significantly related to characters; dimensionality reduction is performed on the candidate sites through linkage imbalance analysis, and representative sites are selected to form a feature set; screening the model by using the feature set as an input feature; training the model through the test set to obtain a genome prediction model; and finally, carrying out genome breeding value prediction on individuals with unknown phenotypes by utilizing the trained genome prediction model, and carrying out breeding selection according to a prediction result. According to the method, the prediction accuracy and breeding selection efficiency of complex characters such as fish disease resistance are remarkably improved, and the method has important application value.

The invention belongs to the field of soybean breeding, and relates to soybean lodging research. The invention provides an SNP (Single Nucleotide Polymorphism) molecular marker set remarkably related to lodging resistance of soybeans and application of the SNP molecular marker set. The marker set can be directly used for genome selection modeling and molecular marker-assisted selection of the lodging resistance of the soybeans. More importantly, the gene is taken as a fixed effect to be incorporated into a prediction model, so that the prediction performance of a soybean lodging genome selection model is remarkably improved, and lodging-resistant strains can be selected and predicted in early-stage individuals, so that the genetic improvement generation interval is effectively shortened, the selection accuracy and the breeding efficiency are improved, and the breeding cost is reduced. Finally, the lodging-resistant breeding process of the soybeans is promoted, and the breeding cost is reduced.

The invention provides a genotype-environment interaction prediction method based on multi-modal attention and mixed experts and application of the genotype-environment interaction prediction method, and belongs to the technical field of genome selection. In order to solve the problems of low precision and weak generalization ability caused by rough feature extraction and single interactive modeling in multi-environment prediction in the prior art, the invention constructs a multi-modal deep learning model. According to the method, independent effects and synergistic effects of genotypes and environmental characteristics are explicitly separated and dynamically fused through a double-path attention module; utilizing a Token bidirectional cross-modal attention mechanism to realize fine-grained interaction between the gene and an environment semantic unit; and introducing a hybrid expert layer, adaptively processing a heterogeneous interaction mode through a gating network, and outputting a predicted value. According to the method, the prediction precision and stability of the crop complex characters in multiple environments are remarkably improved, and the technical problem that accurate modeling of the genotype-environment interaction effect is difficult is effectively solved.

The invention discloses a Chinese cherry whole genome liquid phase chip and application thereof, and belongs to the technical field of biology. The invention provides 6279 SNP (Single Nucleotide Polymorphism) loci and molecular markers thereof, and a liquid phase chip constructed based on the molecular markers. The SNP loci comprise loci related to fruit quality such as abiotic stress of Chinese cherry plants, fruit color, fruit size and fruit hardness and phenological characteristics such as tree flowering phase. The Chinese cherry whole genome liquid chip has the characteristics of low typing cost, strong cherry specificity, uniform site coverage, rich functional markers and the like, can be used for Chinese cherry variety identification and genetic relationship analysis, whole genome selective breeding and genetic resource genetic evaluation, and can significantly improve the cross breeding efficiency and selection accuracy.

The invention provides a DeepFLPS genome selection system based on CNN and multi-scale feature extraction, and belongs to the field of genome selection. The inherent limitations of single feature extraction scale and insufficient sparse feature utilization of the current deep convolutional neural network structure are overcome; the system comprises a deep feature learning genome selection DeepFLPS model, the model comprises an input layer, a multi-scale feature extraction module of a binding site interaction rule, a full connection layer and an output layer, and the multi-scale feature extraction module of the binding site interaction rule adopts a parallel convolution channel structure with a plurality of sliding windows increasing progressively. A hierarchical association relationship in genotype data is captured through a progressive expansion convolution kernel, and a hierarchical double-activation function mechanism is set; the novel CNN architecture provided by the invention not only can adaptively capture the interaction effect of different SNPs interval lengths, but also can more effectively retain sparse feature information in genotype data.

This application provides a method and storage medium for shrimp selection breeding with high genotype deletion rates. The shrimp selection breeding method includes: Step S1: acquiring genotype data of shrimp to be tested, wherein the genotype data of shrimp to be tested is low-coverage shrimp genotype data; Step S2: performing predictive processing on the shrimp genotype data based on a genome selection prediction model, outputting shrimp breeding values ​​for multiple shrimp to be screened; Step S3: screening the shrimp to be screened according to the breeding screening threshold and the shrimp breeding values, determining the breeding shrimp, and using the breeding shrimp as the target breeding population. This application realizes the digitalization and standardization of shrimp selection standards by setting breeding screening thresholds. It can not only significantly improve the genetic gain of target traits in offspring, but also effectively avoid the risk of inbreeding depression caused by insufficient selection through sex-specific threshold control.

The application belongs to the technical field of aquatic genetics, and particularly relates to application of a group of SNP sites in detection of anti-NNV traits of Hippocampus kuda. The application provides a group of molecular marker combinations containing 680 core SNP sites, and sequences of the SNP sites are shown as SEQ ID NO. 1 to SEQ ID NO. 680. The SNP sites are obtained by whole genome resequencing of a reference population of H. kuda, and are screened by using MLE-rank algorithm to be significantly associated with anti-NNV traits. Based on the SNP combinations, the application further provides corresponding liquid phase chips, genomic selection breeding methods and kits. The technology can realize early and accurate evaluation of anti-NNV traits of H. kuda, significantly improve breeding efficiency, reduce detection cost, and provide an effective molecular breeding tool for breeding of H. kuda disease-resistant fine varieties.

The invention discloses a domestic rabbit whole genome multifunctional chip and application, and belongs to the technical field of molecular biology. The invention relates to a rabbit whole genome 10K liquid phase breeding chip based on a target area genome sequence liquid phase capture accurate positioning sequencing typing technology. The chip comprises 10331 SNP (Single Nucleotide Polymorphism) loci. The chip can be applied to rabbit genetic diversity evaluation, germplasm resource and genetic relationship identification, important economic character whole genome association analysis and whole genome selective breeding, has the advantages of high throughput, high interval coverage rate, high locus detection rate, high flexibility and the like, and provides powerful tool support for rabbit molecular breeding and variety identification.

The present application relates to the technical field of corn whole genome selection breeding, and discloses a data fusion analysis method and system for corn whole genome selection breeding, which comprises the following steps: collecting multi-dimensional data and constructing a multi-source data fusion network; generating a dynamic breeding data set through noise filtering and format unification processing; obtaining characteristic correlation data through spatiotemporal correlation processing; establishing a data-heredity response relationship model and generating heredity correlation dynamic data; generating core selection range data through selection efficiency correlation processing; constructing a statistical prediction model and optimizing breeding parameters. The system comprises data acquisition, data processing, correlation modeling, efficiency evaluation and selection analysis modules. The present application realizes efficient integration and dynamic analysis of multi-source data, improves the accuracy and efficiency of breeding selection, and is suitable for corn breeding optimization under complex environment.

The invention relates to the technical field of gene chips, in particular to a sorghum whole genome liquid phase chip as well as a construction method and application thereof. A detection target of the sorghum whole genome liquid phase chip disclosed by the invention is composed of 5446 SNP molecular markers and 3 InDel molecular markers; the positions of the 5446 SNP molecular markers and the 3 InDel molecular markers, which are positioned on a sorghum reference genome Sorghumbicolv3.1. 1, are as shown in a table 1; the sorghum whole genome liquid chip can be used for sorghum variety identification, genetic relationship identification, genetic diversity analysis, molecular genetic map construction, gene positioning and cloning, molecular marker-assisted selection and whole genome selective breeding, and provides an important molecular basis for important agronomic trait analysis and biological breeding of sorghum.

The invention belongs to the technical field of molecular biology, and particularly relates to a universal SNP molecular marker for predicting multiple phenotypic characters of corn, and a screening method and a prediction system thereof. According to the method, through vectorization conversion of genotype data, mutual information screening, LD filtering, LASSO and random forest fusion screening three-time dimensionality reduction is carried out on the converted genotype data, invalid and redundant markers are removed layer by layer, a universal marker suitable for predicting multiple phenotypic characters of corn is developed, and compared with feature screening of traditional whole genome selection, the method has the advantages that the method is simple and convenient to operate, and the efficiency is high. While the cost is reduced, the breadth of screening traits is improved. According to the method, an integrated model of the self-attention neural network, the LASSO and the random forest is constructed, the advantages of the three models are fused through advantage complementation and weight optimization of different models, the limitation of a single model is made up, accurate and efficient prediction of multiple corn phenotypic characters is finally achieved, and the breeding period is shortened.