6 results about "Snp data" patented technology

The invention provides a causal inference method and device based on genetic variation, electronic equipment and a medium. The causal inference method comprises the following steps: acquiring whole genome SNP data and proteome data of a detection sample of a target population from a detection platform; performing whole genome association analysis on the whole genome SNP data to obtain outcome SNP data associated with the target phenotype; performing protein quantitative trait site analysis based on the whole genome SNP data and the proteome data to obtain exposure SNP data associated with the target exposure factor; and performing data preprocessing on the outcome SNP data and the exposed SNP data, and performing Mendel randomization analysis on the preprocessed outcome SNP data and exposed SNP data to obtain a causal relationship between the target exposure factor and the target phenotype. According to the method, the accuracy and reliability of Mendel stochastic analysis results are improved.

The invention discloses an interpretation method and system for mixed DNA diallelic SNP data based on WGA-WGS, and relates to the technical field of forensic genetics, and the specific steps are as follows: obtaining intersection SNP sites based on WGA-WGS typing data of a mixed sample and a target individual sample, and generating an SNP site set; determining the number N of mixed contributors and a single allele typing error rate; calling a preset integrated typing error observation probability model based on the application scene, and calculating a total likelihood ratio based on the number N of mixed contributors and a single allele typing error rate on the basis of the SNP site set; the mixed sample is explained based on the total likelihood ratio, the whole process is operated based on a preset mathematical model and objective parameters, and potential bias caused by manual intervention and subjective judgment is reduced to the maximum extent; the scientific preciseness of evidence explanation is ensured, and the repeatability and verifiability of an analysis result are also ensured.

The application discloses a mixed DNA biallelic SNP data interpretation method and system based on WGA-WGS, and relates to the technical field of forensic genetics, and the specific steps are as follows: obtaining intersection SNP sites based on WGA-WGS typing data of a mixed sample and a target individual sample, and generating a SNP site set; determining the number N of mixed contributors and a single allele typing error rate; calling a preset observation probability model of integrated typing errors based on an application scenario, calculating a total likelihood ratio based on the number N of mixed contributors and the single allele typing error rate on the basis of the SNP site set; and interpreting the mixed sample based on the total likelihood ratio, wherein the whole process in the application is operated based on a preset mathematical model and objective parameters, so that potential bias caused by artificial intervention and subjective judgment is minimized, the scientific rigor of evidence interpretation is ensured, and the repeatability and verifiability of analysis results are also ensured.

The invention discloses a method for selecting single nucleotide polymorphism (SNP) for phenotype prediction based on feature importance, which comprises the following steps of: performing missing filling on SNP data, and dividing a sample into a training set and a test set before phenotype-related screening to avoid data leakage; then, by taking the gene as a unit, fitting phenotypes of the SNPs positioned on the promoter, the exon and the intron by adopting a regression model, calculating correlation coefficients and carrying out multiple inspection correction, and taking a significant correlation gene as a candidate; selecting one SNP (Single Nucleotide Polymorphism) from each gene in the candidate genes on the basis of feature importance to form an Important-SNP set; and encoding the set, and inputting the encoded set into a prediction model to obtain a phenotype prediction result. According to the method, the feature dimension is remarkably reduced while the prediction accuracy is maintained or improved, and the method has relatively high interpretability and engineering availability, is suitable for phenotype prediction of crops, can be used as a core marker for design and optimization of a breeding chip, and provides efficient and interpretable technical support for molecular breeding and genome selection.

The invention relates to a method for detecting nucleoplasm gene loci having an interaction effect with agronomic traits, which comprises the following steps: pairing nuclear genome SNP data and mitochondrial genome SNP data, calculating a genetic linkage imbalance coefficient between the nuclear genome SNP data and the nucleoplasm of each mitochondrial genome SNP, taking each nuclear genome SNP as a core, and calculating the genetic linkage imbalance coefficient between the nuclear genome SNP data and each mitochondrial genome SNP; nuclear genome SNP and mitochondrial genome SNP pairs with abnormal and prominent linkage strength are screened out to serve as candidate cytoplasmic interaction coupling pairs, a mixed linear model is constructed to implement GWAS analysis, and cytoplasmic gene loci with an interaction effect with agronomic traits are screened out. According to the method, the nucleoplasm gene loci with high relevance with the agronomic traits to be detected are screened out, so that the calculation power and time required by analysis can be greatly reduced, and the workload of molecular genetic research on the agronomic traits of the crops is reduced. According to the method, the result is obtained through analysis by software, the analysis steps are simple, the consumed time is short, the nucleoplasm gene loci are accurately detected, and the application range is wide.

The invention discloses a genome selection method based on a two-way class attention mechanism and dynamic gating fusion, and belongs to the technical field of genome selection. The method comprises the following steps: acquiring a 012 coding matrix of a genetic polymorphism site, capturing an independent effect and a synergistic effect of the SNP site by using an independent effect module and a synergistic effect module respectively, combining the independent effect and the synergistic effect through dynamic gating, further performing fitting in a full-connection neural network, and after model training is completed, obtaining a 012 coding matrix of the genetic polymorphism site. And outputting a determination coefficient (R) of the test set, a Pearson's correlation coefficient (PCC) and a weight file of each SNP site. The method is characterized in that the long-range effect of an attention mechanism is introduced, the influence of the genetic variation independent effect and the cooperative relation on the phenotype is analyzed from the global perspective, automation from SNP data to phenotype prediction is achieved, and the model has the advantages of being high in precision and high in interpretability.