15 results about "Heritability" patented technology

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.

This invention discloses a selection method for meat geese that comprehensively considers feed conversion efficiency and subcutaneous fat properties, relating to the field of poultry genetics and breeding. The method includes the following steps: S1 determining feed conversion efficiency of meat geese; S2 determining subcutaneous fat properties of meat geese; S3 calculating the family mean values ​​for each trait; S4 analyzing the correlation and heritability of each trait; S5 constructing a comprehensive selection index for feed conversion efficiency and subcutaneous fat properties of meat geese; and S6 selecting breeding geese with high comprehensive selection indices. This invention utilizes individual measurements and single-cage determination of feed conversion efficiency in meat geese, establishes a three-point method for accurately measuring subcutaneous fat thickness, and constructs a comprehensive selection index for residual feed intake, subcutaneous fat percentage, and subcutaneous fat thickness. It provides a selection method that comprehensively considers feed conversion efficiency and subcutaneous fat properties in geese, simultaneously improving feed efficiency and reducing subcutaneous fat properties, achieving simultaneous selection and synergistic improvement of multiple traits, and significantly enhancing breeding efficiency.

The present invention belongs to the field of genetic risk prediction technology and discloses a method for constructing a genetic risk prediction model integrating functional annotation information and its application, comprising: calculating the heritability of the specific annotations of each cell type corresponding to each tissue of the sample to be tested based on the marginal chi-square statistic of each SNP, and then obtaining the heritability of the j-th SNP in the current tissue f and estimating the joint effect size b of M SNPs in tissue f. f , and based on b f The covariance between the phenotype vector y of the n samples to be tested is b f The optimal linear unbiased estimate of the genetic risk prediction model is then obtained. Furthermore, the genetic risk scores corresponding to each tissue are integrated, and the functional annotation information of each tissue is incorporated into the genetic risk prediction. A genetic risk prediction method integrating functional annotation information is also provided. The present invention can improve the accuracy of genetic risk prediction.

The invention relates to the technical field of biological information, and particularly discloses a multi-omics genome combined genetic assessment method and device. According to the method, multi-source data such as a genome, a transcriptome, a proteome, a metabolome, a microbiome and a high-throughput phenotype are integrated by constructing a multi-omics fusion similar matrix, an adaptive dichotomy is adopted to optimize omics weights, a grid search and cross validation system is introduced, and global optimal estimation of parameters is achieved. Verification of the method on livestock and poultry breeding and plant breeding data shows that the prediction accuracy of complex characters can be remarkably improved, and particularly, the prediction precision in low heritability characters is remarkably improved. The invention further provides a corresponding device which comprises a data acquisition module, a matrix construction module, a parameter optimization module and a breeding value calculation module, comprehensive utilization of multi-omics information can be efficiently achieved, and the accuracy and calculation efficiency of breeding value estimation are improved.

The invention discloses an SNP (Single Nucleotide Polymorphism) molecular marker related to a local chicken body type character, the SNP molecular marker is located on a chicken chromosome 4, the number of the SNP molecular marker is Chr475876885, and the genome version is GRCg6a; the nucleotide sequence of the SNP molecular marker is shown as SEQ ID NO.1, the SNP molecular marker is located at the 401bp of the nucleotide sequence of the SEQ ID NO.1 and has C / T polymorphism, and the genotype of the site of the polymorphism is that TT corresponds to a large-size individual, and the genotype of CC corresponds to a small-size individual. According to the SNP molecular marker related to the body type character of the local chicken, individuals are directly bred on the genome level, phenotypic information is not depended on, the selection efficiency can be remarkably improved for characters which are low in heritability and difficult to measure, and the breeding process can be accelerated.

The invention discloses an efficient SNP marker automatic screening system for dairy cow breeding, relates to the technical field of dairy cow breeding screening, and aims to solve the problems that the scientificity and stability of a screening result are reduced, and the error between genetic improvement efficiency and marker utilization rate is increased. The method comprises the following steps: extracting dairy cow breeding character information and gene site data from a genotype-phenotype database, calculating heritability of each character, screening target characters with high genetic contribution degree, calculating character action characteristics and effect directions of gene sites on the basis of an association relationship between the target characters and SNP sites, quantitatively describing regulation intensity of the gene sites, and screening out the target characters with high genetic contribution degree. Generating a character guiding coefficient according to the character guiding coefficient, screening significant candidate sites in combination with a threshold value, performing cross-character frequency statistics on the candidate sites, calculating a breeding efficiency index in combination with a character correlation weight, and performing hierarchical marking according to the index, so as to realize multi-character comprehensive genetic effect evaluation and automatic screening. The breeding data processing efficiency and the gene selection scientificity are improved.

The invention relates to the technical field of biological information, in particular to a genetic rate estimation method and system for sequencing data, a computer device and a computer program product. And representing phenotypic data of the concerned character as y = X beta + e, calculating a matrix trace of a genetic relationship matrix by adopting a Girard-Hutchinson random estimation method, and estimating the genetic rate according to a formula when judging that the sequencing data are from the same data source. The invention provides an optimized heredity estimation method, through a controllable random algorithm, on the premise of balancing calculation precision and calculation amount, the heredity can be solved under sample amount increase, especially under a million-level sample scale, and especially under a moment estimation framework, full-sample calculation can be used, large-scale data can be efficiently processed, sample loss is avoided, and the calculation efficiency is improved. And calculation precision is maintained.

The application belongs to the technical field of animal genetics and breeding, and provides a genomic selection analysis method considering trait local genetic correlation significance, comprising a phenotype data processing module, a genotype data processing module and a genomic selection module; the application performs quality control on the phenotype data; performs quality control, sequence alignment and variation detection on the genotype data; performs whole genome correlation analysis to generate summary statistical data; identifies whole genome local genetic correlation, tests the significance of LGC, and realizes double-trait genomic selection analysis with segment weighting; and the application can effectively improve the genetic evaluation accuracy of complex traits, especially low heritability traits, and provides an efficient and robust genomic selection tool for livestock breeding.

The invention discloses an efficient corn drought-resistant breeding method integrating molecular marker development and a whole genome prediction model. The efficient corn drought-resistant breeding method comprises the steps of hybridization matching and DH line induction, multi-environment phenotype and genotype analysis, three-level marker set screening, optimal prediction system construction, excellent selfing line screening and the like. The method has the remarkable effects that a whole genome selection technology is applied to identification and screening of the saline-alkali-tolerant and drought-tolerant corn materials, a whole genome breeding process for selecting the saline-alkali-tolerant and drought-tolerant materials is established, and generation intervals are shortened through early selection, so that the breeding value estimation accuracy is improved, the genetic progress is accelerated, and the breeding efficiency is improved. And the method has a good prediction effect on low-heritability and difficult-to-measure complex characters, and truly realizes the breeding practice guided by a genome technology.

The invention discloses a molecular marker for analyzing reproductive performance of a large white pig and application, the molecular marker is located at five positions on chromosomes 2, 17 and 18, and the polymorphism of the molecular marker is G > A, C > T, T > A, T > C and T > C. The method has the advantages that the breeding efficiency and the selection accuracy can be improved, and meanwhile, the breeding process of high-yield and high-reproductive-performance maternal line pig breeds can be accelerated with the efficiency of selecting genomes with low heritability complexity characters such as the reproductive performance of pigs, especially the litter size, the live litter size and the healthy litter size. Based on the genetic marker provided by the invention, a more accurate and more efficient molecular marker assisted selection (MAS) or genome selection (GS) tool can be developed. By conducting genotype screening in the early stage, the breeding cycle can be remarkably shortened, the genetic improvement process of excellent boars is accelerated, and huge economic benefits are brought to the pig industry.

The invention discloses a breed selection method for comprehensively selecting meat goose feed conversion efficiency and sebum performance, and relates to the field of poultry genetic breeding, and the method comprises the following steps: S1, determining the meat goose feed conversion efficiency; s2, determining the sebum performance of the meat geese; s3, calculating a mean value of each character family; s4, analyzing the correlation and heritability of each character; s5, constructing a meat goose feed conversion efficiency and sebum performance comprehensive selection index; and S6, selecting and remaining breeding goose families with high comprehensive selection indexes. According to the method, the feed conversion efficiency of the meat geese is measured by using an individual measurement single cage, the sebum thickness of the meat geese is accurately measured by establishing a three-point method, a comprehensive selection index of the residual feed intake, the sebum rate and the sebum thickness of the meat geese is established, and a seed selection method for comprehensively selecting the feed conversion efficiency and the sebum performance of the geese is provided. The feed reward of the meat geese can be synchronously improved, the sebum performance is reduced, synchronous selection and collaborative improvement of multiple characters are realized, and the breeding efficiency is remarkably improved.

The application relates to the technical field of genome selection, and provides a multi-omics genome selection method and application thereof in livestock and poultry breeding, which comprises the following steps: obtaining reference population data and target population data and carrying out pretreatment; training an optimal linear unbiased prediction model and an elastic network model by using the reference population data; inputting cis-single nucleotide polymorphism site genotype data of the target population into the trained gene expression prediction model to obtain predicted gene expression of each individual in the target population; estimating the cis-heritability of each gene based on the reference population data, and screening gene prediction expression data greater than a cis-heritability threshold; integrating the screened gene prediction expression data and genome data to construct a multi-omics genome selection model, and obtaining a genome breeding value of each individual based on the multi-omics genome selection model. The application can adapt to genetic regulation structure differences of different genes and obtain high-quality prediction results.

The invention relates to the field of corn breeding, and particularly discloses a corn breeding process design system and an implementation method, the corn breeding process design system comprises a core germplasm resource module used for storing and managing screened and identified high-quality corn core germplasm resources, the core germplasm resources are subjected to gene mining based on extreme traits, and the core germplasm resources are subjected to genetic analysis; the method is obtained through a mixed sample analysis method, and has definite general combining ability (GCA) and special combining ability (SCA) evaluation. The core database module is used for storing genetic composition information of core germplasm resources, and the genetic composition information comprises high-throughput phenotype data, combining ability data and heritability data; according to the method, bad resources are eliminated from phenotypes through multi-environment accurate evaluation, SCA, GCA and heritability evaluation are carried out on selected excellent resources according to genetic design, and a genetic information base of core resources is constructed in the process, so that a breeder can accurately push a target improvement basis according to high GCA and clear SCA rules, and the breeding efficiency is improved. Powerful support is provided for subsequent breeding work.

The invention belongs to the technical field of genetic breeding, and particularly relates to a genome selective breeding method for cashmere thickness traits of cashmere goats, which is carried out by combining an SNP (Single Nucleotide Polymorphism) site data set with the density of 10K and a Bayesian A genome selection model, and comprises the following steps: carrying out genetic typing by adopting the SNP site data set with the density of 10K to construct a reference group; and estimating a genome estimation breeding value of the candidate individual by using a Bayesian A genome selection model so as to realize genome selection breeding of the cashmere thickness character of the cashmere goat. According to the method disclosed by the invention, the influence of a data set based on randomly screened SNP loci with different densities and a genome selection model on the prediction accuracy of the cashmere thickness character genome of the Inner Mongolia cashmere goat is compared, and the heritability, the prediction accuracy and unbiasedness are systematically evaluated; a low SNP site data set with the density of 10K and a Bayesian A genome selection model are found for the first time, and high accuracy of genome selection breeding of the cashmere thickness character of the Inner Mongolia cashmere goat can be ensured while the cost is saved.

The application provides a fine wool sheep genome selection method, relates to the field of animal genetic breeding, and comprises the following steps: assembling a sheep genome reference population, determining main phenotypic traits of the sheep genome reference population; designing a liquid phase chip based on resequencing data of a plurality of sheep breeds and performing fine wool sheep genome selection to obtain GWAS prior information; estimating genetic variance and error variance of different traits based on a RRBLUP model to obtain statistical results of trait heritability; performing genome estimated breeding value prediction analysis based on the statistical results of trait heritability; constructing breeding target traits according to breeding targets, constructing a genome comprehensive breeding value estimation model based on the breeding target traits; calculating comprehensive breeding values of sheep individuals based on the genome comprehensive breeding value estimation model, performing comprehensive breeding value ranking, and selecting and mating sheep according to the comprehensive breeding values; and performing important economic trait genome breeding value prediction analysis based on the GWAS prior information.