Genetic locus positioning method, device, electronic device, and storage medium

Abstract

The embodiments of the present application disclose a method, device, electronic device, and storage medium for locating a gene genetic site, which belongs to the field of gene sequencing technology, including: determining a genetic weight matrix according to an average cis-inheritance rate, an average cis-co-inheritance rate, a first ratio, and a second ratio; determining a genetic variance matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, and genetic genetic estimation data of genetic genetic markers in sample tissues, first type cells, and second type cells; determining target genetic estimation data according to the genetic weight matrix, the genetic variance matrix, and genetic genetic estimation data of genetic genetic markers in sample tissues, first type cells, and second type cells; calculating a target test score according to the target genetic estimation data; if the target test score is greater than a preset score threshold, the genetic genetic marker is used as the genetic genetic site, thereby improving the accuracy of locating the genetic genetic site.

Description

Technical Field

[0001] The present application relates to the field of gene sequencing technology, and in particular to a gene genetic site positioning method, device, electronic device, and storage medium. Background Art

[0002] Genetic mapping is used to determine the location of specific genetic genes on chromosomes to guide drug development, assist in the formulation of medical strategies, etc. In related technologies, gene genetic loci are located through tissue-level RNA sequencing (Bulk Ribonucleic Acid sequencing) technology or single-cell RNA sequencing (single-cell Ribonucleic Acid sequencing) technology. However, some gene genetic loci are cell-type specific and can only be detected in specific types of cells. Tissue-based RNA sequencing technology cannot accurately locate gene genetic loci at the cell level. In addition, due to the limitations of single-cell RNA sequencing technology, it is often difficult to locate gene genetic loci with low gene expression levels. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to provide a gene genetic locus positioning method, device, electronic device, and storage medium, aiming to improve the accuracy of genetic locus positioning.

[0004] To achieve the above-mentioned purpose, the first aspect of the embodiment of the present application proposes a method for locating a gene genetic locus, the method comprising:

[0005] Obtain the average cis-inheritance rate and average cis-co-inheritance rate of genetic markers in sample tissues;

[0006] Obtaining genetic estimation data of genetic markers in the sample tissue, genetic estimation data of genetic markers in the first type of cells, and genetic estimation data of genetic markers in the second type of cells, and estimating a first ratio of the first type of cells and a second ratio of the second type of cells in the sample tissue;

[0007] Determine a genetic weight matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio;

[0008] Determining a genetic variance matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the genetic inheritance estimation data of the genetic markers in the sample tissue, the genetic inheritance estimation data of the genetic markers in the first type of cells, and the genetic inheritance estimation data of the genetic markers in the second type of cells;

[0009] Determining target genetic estimation data according to the genetic weight matrix, the genetic variance matrix, the genetic estimation data of the genetic markers in the sample tissue, the genetic estimation data of the genetic markers in the first type of cells, and the genetic estimation data of the genetic markers in the second type of cells;

[0010] A target test score is calculated based on the target genetic estimation data, and if the target test score is greater than a preset score threshold, the genetic marker is used as a genetic locus.

[0011] In some embodiments, obtaining the average cis-inheritance rate and average cis-co-inheritance rate of genetic markers in the sample tissue comprises:

[0012] Obtain linkage disequilibrium scores and expected test scores of genetic markers;

[0013] Linear regression is performed on the linkage disequilibrium score and the expected test score to obtain the average cis-inheritance rate and the average cis-co-inheritance rate.

[0014] In some embodiments, the expected test score includes a first expected test score of the genetic marker in the first type of cells, a second expected test score of the genetic marker in the second type of cells, and a third expected test score of the genetic marker between the first type of cells and the second type of cells, and performing linear regression on the linkage disequilibrium score and the expected test score to obtain the average cis-inheritance rate and the average cis-co-inheritance rate includes:

[0015] Performing linear regression on the linkage disequilibrium score and the first expected test score to obtain a first sub-average cis-heritability;

[0016] Performing linear regression on the linkage disequilibrium score and the second expected test score to obtain the second sub-average cis-heritability;

[0017] Determining the average cis-heritability according to the first average cis-heritability and the second average cis-heritability;

[0018] The linkage disequilibrium score and the third expected test score are subjected to linear regression to obtain the average cis co-inheritance rate.

[0019] In some embodiments, determining a genetic weight matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio comprises:

[0020] Determining a first genetic matrix element according to the average cis-inheritance rate and the average cis-co-inheritance rate;

[0021] Determine a second genetic matrix element according to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio;

[0022] The genetic weight matrix is ​​determined based on the first genetic matrix elements and the second genetic matrix elements.

[0023] In some embodiments, determining a genetic variance matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the genetic inheritance estimation data of genetic markers in the sample tissue, the genetic inheritance estimation data of genetic markers in the first type of cells, and the genetic inheritance estimation data of genetic markers in the second type of cells comprises:

[0024] Determining a candidate genetic matrix based on the genetic inheritance estimation data of the genetic markers in the sample tissue, the genetic inheritance estimation data of the genetic markers in the first type of cells, and the genetic inheritance estimation data of the genetic markers in the second type of cells;

[0025] The genetic variance matrix is ​​determined according to the average cis-inheritance rate, the average cis-co-inheritance rate and the candidate genetic matrix.

[0026] In some embodiments, determining target genetic estimation data according to the genetic weight matrix, the genetic variance matrix, the genetic estimation data of genetic markers in the sample tissue, the genetic estimation data of genetic markers in the first type of cells, and the genetic estimation data of genetic markers in the second type of cells comprises:

[0027] Determining a target genetic estimate based on the genetic weight matrix, the genetic variance matrix, the genetic genetic estimate data of the genetic genetic markers in the sample tissue, the genetic genetic estimate data of the genetic genetic markers in the first type of cells, and the genetic genetic estimate data of the genetic genetic markers in the second type of cells;

[0028] Determining a target genetic variance according to the genetic weight matrix and the genetic variance matrix;

[0029] The target genetic estimate data is determined based on the target genetic estimate and the target genetic variance.

[0030] In some embodiments, the target genetic estimate data includes a target genetic estimate and a target genetic variance, and the step of calculating a target test score based on the target genetic estimate data includes:

[0031] Obtaining the standard deviation of the target genetic variance to obtain a target genetic standard deviation;

[0032] The ratio of the target genetic estimate to the target genetic standard deviation is taken as the target test score.

[0033] To achieve the above-mentioned purpose, a second aspect of the embodiment of the present application provides a gene genetic site positioning device, the device comprising:

[0034] The first acquisition module is used to obtain the average cis-inheritance rate and average cis-co-inheritance rate of gene genetic markers in the sample tissue;

[0035] A second acquisition module is used to acquire genetic estimation data of genetic markers in the sample tissue, genetic estimation data of genetic markers in the first type of cells, and genetic estimation data of genetic markers in the second type of cells, and estimate a first ratio of the first type of cells and a second ratio of the second type of cells in the sample tissue;

[0036] A first determination module is used to determine a genetic weight matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio;

[0037] A second determination module is used to determine a genetic variance matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the genetic inheritance estimation data of the genetic markers in the sample tissue, the genetic inheritance estimation data of the genetic markers in the first type of cells, and the genetic inheritance estimation data of the genetic markers in the second type of cells;

[0038] A third determination module is used to determine target genetic estimation data based on the genetic weight matrix, the genetic variance matrix, the genetic estimation data of the genetic markers in the sample tissue, the genetic estimation data of the genetic markers in the first type of cells, and the genetic estimation data of the genetic markers in the second type of cells;

[0039] A positioning module is used to calculate a target test score based on the target genetic estimation data, and if the target test score is greater than a preset score threshold, the genetic marker is used as a genetic locus.

[0040] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the gene genetic site positioning method described in the first aspect is implemented.

[0041] To achieve the above objectives, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the gene genetic site positioning method described in the first aspect.

[0042] The method, device, electronic device and storage medium for locating the genetic loci proposed in the present application are to obtain the average cis-inheritance rate and the average cis-co-inheritance rate of the genetic markers in the sample tissue, so as to identify the genetic markers that affect the gene expression level based on the average cis-inheritance rate and the average cis-co-inheritance rate. The difficulty of obtaining the real genetic data of the genetic markers is relatively large, which increases the difficulty of locating the genetic loci. In order to reduce the difficulty of locating the genetic loci, the genetic genetic estimation data is used to locate the genetic markers. The genetic genetic estimation data of the genetic markers in the sample tissue, the genetic genetic estimation data of the genetic markers in the first type of cells and the genetic genetic estimation data of the genetic markers in the second type of cells are obtained, and the combination of the RNA sequencing technology at the tissue level and the single-cell RNA sequencing technology is realized by combining the estimated amount at the tissue level with the estimated amount at the cell level, so as to accurately express the genetic effect of the genetic markers at the tissue level and the cell level based on the three estimated amounts of the sample tissue, the first type of cells and the second type of cells, thereby improving the accuracy of the genetic loci location. The sample tissue includes a plurality of cells. In order to solve the problem of low gene expression level in a single cell, the first ratio of the first type of cells and the second ratio of the second type of cells in the sample tissue are estimated. According to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio, the genetic weight matrix is ​​determined to estimate the real genetic data based on the genetic weight matrix. According to the average cis-inheritance rate, the average cis-co-inheritance rate, the genetic genetic estimation data of the genetic genetic markers in the sample tissue, the genetic genetic estimation data of the genetic genetic markers in the first type of cells and the genetic genetic estimation data of the genetic genetic markers in the second type of cells, the genetic variance matrix is ​​determined to jointly estimate the real genetic data based on the genetic weight matrix and the genetic variance matrix. In order to obtain the best unbiased estimate of the real genetic data, according to the genetic weight matrix, the genetic variance matrix, the genetic genetic estimation data of the genetic genetic markers in the sample tissue, the genetic genetic estimation data of the genetic genetic markers in the first type of cells and the genetic genetic estimation data of the genetic genetic markers in the second type of cells, the target genetic estimation data is determined. In order to judge whether the genetic genetic marker is a genetic genetic locus, the target test score is calculated according to the best unbiased estimate of the real genetic data, that is, the target genetic estimation data, to ensure the accuracy of the target test score calculation. If the target test score is greater than the preset score threshold, the genetic genetic marker is used as the genetic genetic locus, thereby improving the accuracy of the genetic genetic locus positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of the method for locating a gene genetic site provided in the embodiment of the present application;

[0044] Figure 2 yes Figure 1Flow chart of step S110 in FIG.

[0045] Figure 3 yes Figure 2 Flow chart of step S220 in FIG.

[0046] Figure 4 yes Figure 1 Flow chart of step S130 in FIG.

[0047] Figure 5 yes Figure 1 Flow chart of step S140 in FIG.

[0048] Figure 6 yes Figure 1 Flow chart of step S150 in FIG.

[0049] Figure 7 yes Figure 1 Flow chart of step S160 in FIG.

[0050] Figure 8 It is a schematic diagram of the structure of a gene genetic site positioning device provided in an embodiment of the present application;

[0051] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0053] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0055] Expression Quantitative Trait Loci (eQTLs): refers to specific regions on chromosomes that can regulate the expression levels of messenger RNA (mRNA) and proteins. The expression levels of messenger RNA and protein are proportional to quantitative traits.

[0056] Genome-Wide Association Study (GWAS): refers to finding the existing sequence variations in the whole genome, namely single nucleotide polymorphisms (SNPs), and screening out SNPs related to diseases.

[0057] Bulk RNA sequencing (bulk RNA-seq) is a sequencing technology that provides a gene expression profile of the entire sample by measuring the total RNA level from all cells in the sample. It cannot distinguish the expression characteristics of different cell types.

[0058] Single-cell RNA sequencing (scRNA-seq) is a more sophisticated sequencing technology that can measure gene expression in single cells, thereby revealing cellular heterogeneity and cell type-specific gene expression patterns.

[0059] Genetic mapping is used to determine the location of specific genetic genes on chromosomes to guide drug development, assist in the formulation of medical strategies, etc. In related technologies, gene genetic loci are located through tissue-level RNA sequencing technology or single-cell RNA sequencing technology. However, some gene genetic loci are cell-type specific and can only be detected in specific types of cells. Tissue-based RNA sequencing technology cannot accurately locate gene genetic loci at the cell level. In addition, due to the limitations of single-cell RNA sequencing technology, it is often difficult to locate gene genetic loci with low gene expression levels.

[0060] Based on this, the embodiments of the present application provide a gene genetic site positioning method, a gene genetic site positioning device, an electronic device and a computer-readable storage medium, aiming to improve the accuracy of gene genetic site positioning.

[0061] The gene genetic site positioning method, gene genetic site positioning device, electronic device and computer-readable storage medium provided in the embodiments of the present application are specifically illustrated by the following embodiments. First, the gene genetic site positioning method in the embodiments of the present application is described.

[0062] The method for locating a genetic locus provided in the embodiment of the present application relates to the field of gene sequencing technology. The method for locating a genetic locus provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the method for locating a genetic locus, etc., but is not limited to the above forms.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] Figure 1 is an optional flow chart of the method for locating a genetic locus provided in the embodiment of the present application, Figure 1 The method may include but is not limited to steps S110 to S160.

[0065] Step S110, obtaining the average cis-inheritance rate and the average cis-co-inheritance rate of the genetic markers in the sample tissue;

[0066] Step S120, obtaining genetic estimation data of genetic markers in the sample tissue, genetic estimation data of genetic markers in the first type of cells, and genetic estimation data of genetic markers in the second type of cells, and estimating a first ratio of the first type of cells and a second ratio of the second type of cells in the sample tissue;

[0067] Step S130, determining a genetic weight matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio;

[0068] Step S140, determining a genetic variance matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the genetic inheritance estimation data of the genetic markers in the sample tissue, the genetic inheritance estimation data of the genetic markers in the first type of cells, and the genetic inheritance estimation data of the genetic markers in the second type of cells;

[0069] Step S150, determining target genetic estimation data based on the genetic weight matrix, the genetic variance matrix, the genetic estimation data of the genetic markers in the sample tissue, the genetic estimation data of the genetic markers in the first type of cells, and the genetic estimation data of the genetic markers in the second type of cells;

[0070] Step S160, calculating the target test score based on the target genetic estimation data, and if the target test score is greater than a preset score threshold, the genetic marker is used as the genetic locus.

[0071] Among the related technologies, data acquisition and processing technologies represented by DNA sequencing, gene chips, bioinformatics algorithms, etc. are used to analyze biomedical data to study complex diseases. GWAS uses a high-throughput method to test the significance of the association between a large number of genetic variations on the genome and complex diseases, so as to find risk genetic variants and susceptible genes at the population level. Although GWAS can identify genomic regions associated with complex traits and diseases to a certain extent, the biological functional interpretation of GWAS results is still challenging because most of the related genetic variations are located in the non-coding regions of the genome. In many studies on the functional annotation of genetic variations, expression quantitative trait loci localization estimates the association between genetic variations and the expression levels of nearby genes, connecting variations with potential target genes regulated by them, which helps to identify genes that may be involved in the pathogenesis of diseases. Studies have shown that genetic variations associated with complex traits are more likely to be eQTLs that have been identified. Compared with other functional annotations such as gene regions, regulatory features, conservation metrics, histone modification patterns, chromatin states, etc., eQTLs have the highest correlation with GWAS results. Because eQTLs research is of great significance for explaining GWAS results, it is necessary to accumulate a large amount of gene expression and genotype data for eQTLs research.

[0072] Most eQTLs studies are based on tissue-level RNA sequencing, which measures the average gene expression levels from hundreds to millions of cells in a tissue. This method has accumulated a large number of samples and data due to the maturity and low cost of RNA sequencing technology. One of the eQTLs studies is the Genotype-Tissue Expression (GTEx) project, which analyzed 15,201 RNA sequencing samples in 49 tissues and identified at least one tissue-related eQTLs in 94.7% of protein-coding genes. Although the use of tissue-level eQTLs results can infer the functions of pathogenic genes and variants, it only explains a very small part of the GWAS signal, and only 5-40% of the GWAS signals co-localize with eQTLs. Transcriptional regulatory mechanisms are dynamic and highly dependent on the current cell type or state. Some eQTLs are cell-type specific and can only be detected in specific cell types. In addition, eQTLs identified based on specific cell types have a higher degree of correlation with GWAS results, and cell-type-specific eQTLs (ct-eQTLs) are crucial for interpreting and understanding GWAS results. Therefore, expanding from tissue-based eQTLs analysis to cell-type-based eQTLs analysis has important scientific significance for expanding the research scope of eQTLs and revealing the cell types and genetic basis associated with complex diseases.

[0073] Single-cell RNA sequencing technology can locate ct-eQTLs. It determines the cell type of each cell and its corresponding average gene expression level by obtaining the RNA expression profile of each cell separately. This method can extend the positioning of eQTLs to a finer-grained cell type resolution. However, the current positioning of ct-eQTLs based on single-cell RNA sequencing has the following disadvantages: single-cell RNA sequencing requires the sorting of single cells and the individual measurement of the gene expression of each cell in the tissue, which makes the cost of single-cell RNA sequencing high, and the sample size used for single-cell RNA sequencing is very limited; due to the limitations of sequencing technology, lower levels of gene expression are often difficult to detect accurately, and signal loss will occur, resulting in very sparse data and high noise. These problems have led to many potential ct-eQTLs being unable to be discovered, and how to improve the positioning accuracy of ct-eQTLs has become an urgent problem to be solved.

[0074] Current ct-eQTLs positioning methods can be divided into two categories: ct-eQTLs positioning methods based on traditional tissue-level RNA sequencing data and ct-eQTLs positioning methods based on single-cell RNA sequencing data.

[0075] A large amount of data has been accumulated from eQTLs research based on RNA sequencing at the tissue level. By detecting the interactive effects of SNPs and cell type ratios on gene expression, cell type interaction QTLs (ieQTLs) are determined. ieQTLs belong to ct-eQTLs under the broad definition. This type of method has only some minor differences in model assumptions. First, the proportion of different cell types in each tissue sample is estimated through deconvolution algorithms such as CIBERSORT and xCell. , is the sample size, K is the number of cell types, and R is the real number space. Then the linear mixed model (LMM) was used to detect gene expression. and the genotype-cell type ratio interaction The linear mixed model is expressed as: ,in is the genotype data of the i-th SNP, is the proportion vector of cell type k, is the average genetic effect of the tissue sample, is the genetic effect of the ith SNP in the tissue sample, is the genetic effect of the ith SNP in cell type k, is the cis inheritance rate of cell type k, is the gene expression error of the ith SNP. , it indicates that the current SNP i is an ieQTL related to cell type k. Although this method can be directly applied to existing tissue-level RNA sequencing data, it is heavily dependent on two prerequisites: The proportion of the cell type k represented in the tissue must be significant, otherwise the interaction term will degenerate to a zero vector; The variance of must be large enough, otherwise the interaction term will degenerate to These two strong assumptions make it difficult to satisfy the statistical power of current ieQTLs in real data analysis, especially for the location of rare cell type-specific eQTLs.

[0076] The ct-eQTLs positioning method based on single-cell RNA sequencing directly uses single-cell RNA sequencing data to locate ct-eQTLs. The ct-eQTLs positioning method based on single-cell RNA sequencing annotates each cell in the single-cell RNA sequencing data to a different cell type, merges the gene expression of cells annotated as the same cell type in a given sample to obtain the average gene expression level at cell type resolution, and uses the same classical linear model as the tissue-level eQTLs positioning to locate ct-eQTLs. In related technologies, single-cell RNA sequencing technology was used to analyze 25,000 peripheral blood mononuclear cells (PBMC) single-cell RNA sequencing data, and a total of 379 eQTLs were identified in six cell types, of which 48 were newly discovered ct-eQTLs. In another study on brain transcriptional regulation, single-cell RNA sequencing data from the prefrontal cortex, temporal cortex, and deep white matter were used to find a total of 7,607 eQTLs in eight major brain cell types, of which 46% showed cell type-specific correlations, especially microglia. Through co-localization analysis of GWAS results for brain-related diseases (such as Alzheimer's, schizophrenia, etc.), it was found that the pathogenic risk mediated by genetic variation usually occurs in specific cell types, emphasizing the heterogeneity of transcriptional regulation between different cell types.

[0077] However, compared with ct-eQTLs studies based on tissue-level RNA sequencing, the sample size of single-cell RNA sequencing data is still greatly limited due to cost issues. In addition, due to the current sequencing technology, single-cell RNA sequencing data has high noise and limited data volume, which makes many potential ct-eQTLs undiscoverable.

[0078] Collecting tissue-level RNA sequencing data ,t represents organization, represents the expression level vector of a given gene, represents the genotype matrix, M is the number of cis-SNPs in the gene of a given tissue sample, usually around 200, is the tissue sample size, usually around 1000. Without loss of generality, assume that the gene expression vector And the genotype matrix The columns of have been normalized to column vectors with zero mean and unit variance, and are adjusted for covariates such as sex, age, and the highest principal component. Consider the following linear model relating genotype to gene expression:

[0079] ,

[0080] in, , T represents the transpose operation; is the genetic effect size of cis-SNPs, j represents the jth SNP, is the cis-inheritance rate of a given gene within a tissue. is the genetic effect size of the jth SNP in the sample tissue, which has a mean of 0 and a variance of The normal distribution of . is an independent error term with mean 0 and variance The normal distribution of Is the size of The identity matrix of tissue-level gene expression is a weighted average at the cell type level, although it can be detected by To locate tissue-level eQTLs, it is not possible to determine which cell types they originate from. In contrast, single-cell eQTLs mapping first annotates each cell in the single-cell RNA sequencing data to a different cell type, and then merges the gene expression of cells annotated as the same cell type in a given sample to obtain the average gene expression level at cell type resolution. For the sake of convenience, consider that there are only two different cell types in the tissue, and obtain a cell type-level eQTLs dataset through single-cell RNA sequencing. ,in and is the expression level vector of a given gene in cell types 1 and 2 respectively, is the genotype matrix, is the single-cell eQTL sample size, and M represents the number of genes (cis-SNPs) in a single cell, which is usually less than 200. Therefore, the linear model can be updated to the following Bayesian hierarchical linear model:

[0081] ,

[0082] in, , and , is the genetic effect size of cis-SNPs corresponding to cell types 1 and 2, T represents the transposition operation, and is the cis-inheritance rate of a given gene in cell types 1 and 2, and are independent error terms, and is the ratio vector of cell types 1 and 2 in tissue RNA sequencing samples, represents the element-wise product. It can be seen that tissue-level gene expression Can be broken down into cell type 1 gene expression and cell type 2 gene expression A weighted average, where the weights are the proportions of cell types and However, the heterogeneity of cell type ratios in tissue samples poses a huge challenge to the integrated analysis of the eQTLs localization model based on tissue-level RNA sequencing and the eQTLs localization model based on single-cell RNA sequencing. On the one hand, the need to consider the heterogeneity of each sample separately makes it difficult to apply the integrated analysis to eQTLs data of the summary statistic type. On the other hand, it is also difficult to design an efficient and reliable algorithm to solve the computational difficulties of the simultaneous existence and mutual influence of multiple cell types in the analysis of true ct-eQTLs (cell type specificity). Assuming that the data at the individual level Not accessible, but summary statistics type data can be obtained through simple linear regression , and . Simple linear regression can be expressed as:

[0083] ,

[0084] ,

[0085] in, is the true coefficient of the first linear regression pair in the Bayesian hierarchical linear model The estimate, yes The corresponding standard error; is the true coefficient of the second linear regression pair in the Bayesian hierarchical linear model The estimate, yes The corresponding standard error; is the true coefficient of the third linear regression pair in the Bayesian hierarchical linear model The estimate, yes The corresponding standard error.

[0086] Obviously, the eQTLs and ct-eQTLs obtained by independent analysis are not fully utilized. , and The correlation between the two results leads to suboptimal statistical power of eQTLs. In order to connect the eQTLs data at the tissue level and the cell type level, the true marginal effect size is defined as:

[0087] ,

[0088] in, is the correlation between SNP j and SNP s, i.e., linkage disequilibrium (LD); represents conditional expectation; T represents transposition operation; is the genotype of the jth SNP at the cell type level.

[0089] From the definition of the true marginal effect size, we can see that and Differences in different samples lead to differences in organizational level data and cell type level data , It is difficult to establish a direct connection. The present embodiment uses summary statistics type eQTLs data for integrated analysis to ignore the differences in cell type ratios in tissue samples. The cell type ratio vector of a single given sample in tissue-level RNA sequencing data is The sample mean can be used Approximately. Based on summary statistics at the organizational level , we can deduce that:

[0090] ,

[0091] in, gtj , i is with The corresponding original non-standardized genotype data, i represents the i-th tissue sample, j represents the j-th SNP, and It is the average cell type ratio of samples in tissue-level RNA sequencing data, and const represents a constant.

[0092] In ①, only SNP j is considered. When it is large enough, the law of large numbers can be used to obtain approximation ②. According to ②, ③ can be derived. The marginal effect size can be approximately estimated by the average cell type ratio. Therefore, the Bayesian hierarchical linear model can be updated as follows:

[0093] .

[0094] Organizational level data Can be updated to:

[0095] .

[0096] Thus, a simple but effective linear model was established to transform the organizational level data and cell type level data Integrated.

[0097] See also Figure 2 In some embodiments, step S110 may include but is not limited to steps S210 to S220:

[0098] Step S210, obtaining linkage disequilibrium scores and expected test scores of genetic markers;

[0099] Step S220, performing linear regression on the linkage disequilibrium score and the expected test score to obtain the average cis-inheritance rate and the average cis-co-inheritance rate.

[0100] In step S210 of some embodiments, the genetic marker is a single nucleotide polymorphism SNP. The expected test score is the z score of the genetic marker. The z score is a summary statistic of eQTLs. The correlation between the genetic marker and each other genetic marker is obtained, and all correlations are added to obtain the linkage disequilibrium score, i.e., the LD score. If the genetic marker is represented as SNP j, the linkage disequilibrium score is represented as:

[0101] ,

[0102] in, is the correlation between SNP j and SNP s; S represents the number of genetic marker SNPs; is the linkage disequilibrium score of SNP j.

[0103] In step S220 of some embodiments, when performing integrated analysis of ct-eQTLs, and The following probability structure is introduced:

[0104] ,

[0105] Among them, E represents expectation, Var represents variance, represents the genetic covariance of cis-SNPs in two cell types in the sample tissue. The diagonal elements of the genetic covariance The average cis-inheritance rates of SNPs in cell types 1 and 2, respectively. The off-diagonal elements and Equal, indicating the average cis-co-inheritance rate of SNPs between the two cell types in the sample tissue.

[0106] Through the above steps S210 to S220, the average cis-inheritance rate and the average cis-co-inheritance rate of the genetic marker can be obtained, so as to perform ct-eQTLs integrated analysis based on the average cis-inheritance rate and the average cis-co-inheritance rate, thereby improving the accuracy of ct-eQTLs positioning.

[0107] See also Figure 3 In some embodiments, step S220 may include but is not limited to steps S310 to S340:

[0108] Step S310, performing linear regression on the linkage disequilibrium score and the first expected test score to obtain the first sub-average cis-heritability;

[0109] Step S320, performing linear regression on the linkage disequilibrium score and the second expected test score to obtain the second sub-average cis-heritability;

[0110] Step S330, determining the average cis-heritability according to the first sub-average cis-heritability and the second sub-average cis-heritability;

[0111] Step S340, performing linear regression on the linkage disequilibrium score and the third expected test score to obtain the average cis co-inheritance rate.

[0112] In step S310 of some embodiments, the sample tissue includes a first type of cell and a second type of cell, the first type of cell is a cell of cell type 1, and the second type of cell is a cell of cell type 2. The expected test score includes a first expected test score of the genetic marker in the first type of cell, a second expected test score of the genetic marker in the second type of cell, and a third expected test score of the genetic marker between the first type of cell and the second type of cell. The first expected test score is the z score of the genetic marker SNP in cell type 1, the second expected test score is the z score of the genetic marker SNP in cell type 2, and the third expected test score is the z score of the genetic marker SNP between cell type 1 and cell type 2. The expectation of the square of the z score of the genetic marker SNP is proportional to the linkage disequilibrium score, and this relationship can be accurately given by the following equation:

[0113] ,

[0114] in, represents the expectation of the square of the z-score of the genetic marker SNP j in the cell type eQTL analysis; is the sample size of single-cell eQTL; M is the number of genetic markers in a single cell; is the inflation constant due to confounding bias; is the linkage disequilibrium score of genetic marker SNP j; Represents the heritability of gene genetic marker SNP j.

[0115] Linkage disequilibrium score and the first expected test score (z score of SNP j in cell type 1 eQTL analysis) was input into the above equation for linear regression, and the square of the z score was regressed to the LD score to obtain the estimated cis-heritance , is the cis-inheritance rate of the genetic marker SNP j in cell type 1. and M get The average cis-heritability of the first child is estimated by: .

[0116] In step S320 of some embodiments, the linkage disequilibrium score and the second expected test score (z score of SNP j in cell type 2 eQTL analysis) was input into the above equation for linear regression to obtain the estimated cis-heritance , is the cis-inheritance rate of the genetic marker SNP j in cell type 2. and M get The average cis-heritability of the second child is estimated by: .

[0117] In step S330 of some embodiments, the first sub-average cis-inheritance rate and the second sub-average cis-inheritance rate are used as the average cis-inheritance rate, that is, the average cis-inheritance rate is .

[0118] In step S340 of some embodiments, the linkage disequilibrium score and the third expected test score (Z score of SNPj between cell type 1 and cell type 2) was input into the above formula for linear regression to obtain the estimated cis co-inheritance rate , is the cis-co-inheritance rate of the genetic marker SNP j between cell type 1 and cell type 2. and M get The average cis-co-inheritance rate is obtained by estimating the average cis-co-inheritance rate. The average cis-co-inheritance rate is expressed as: .

[0119] Through the above steps S310 to S340, the unknown parameters can be estimated from the summary statistics according to linear regression to obtain the average cis-inheritance rate of SNPs and the average cis-co-inheritance rate of SNPs between cell types. By combining the cis-co-inheritance of SNPs across cell types, the complex structural relationship between cell types can be considered, revealing the common and specific transcriptional regulation patterns in different cell types, and improving the accuracy of eQTL positioning of SNPs.

[0120] In step S120 of some embodiments, the genetic estimation data is a marginal effect estimate. Genetic estimation data of genetic markers in sample tissues are obtained, that is, tissue-level eQTL marginal effect estimates provided by large-scale, high-quality tissue-level RNA sequencing. , the genetic genetic estimation data of genetic markers in the first type of cells, that is, the cell type-resolution eQTL marginal effect estimation provided by small sample size single-cell RNA sequencing of the corresponding cell type , genetic genetic estimates of genetic markers in the second type of cells, i.e., cell-type-resolution eQTL marginal effect estimates provided by single-cell RNA sequencing of other cell types . CIBERSORT, xCell and other methods are used to estimate the first proportion of the first type of cells and the second proportion of the second type of cells in the sample tissue. This type of method first uses a single-cell RNA sequencing data to obtain the reference gene expression spectrum matrix (signature matrix) corresponding to each cell type, and then decomposes the tissue RNA sequencing data into a linear combination of the gene expression spectrum of each cell type to obtain an estimate of the proportion of different cell types in each tissue sample. The first proportion is the proportion of cell type 1 in the sample tissue The second ratio is the ratio of cell type 2 in the sample tissue By directly modeling the summary statistics of eQTLs, that is, converting them into modeling summary statistics data in the model derivation, these summary statistics (summary statistics) can be used as input without using real data, thus providing greater practicality in large-scale data integration analysis. In addition, the differences in the proportion of cell types in tissue samples can be ignored when performing integrated analysis of summary statistic type eQTLs data. This breakthrough helps to solve the problem of technical differences in different data collection methods and provides new ideas for integrating data of different levels and types.

[0121] See also Figure 4 In some embodiments, step S130 may include but is not limited to steps S410 to S430:

[0122] Step S410, determining a first genetic matrix element according to the average cis-inheritance rate and the average cis-co-inheritance rate;

[0123] Step S420, determining a second genetic matrix element according to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio;

[0124] Step S430: determining a genetic weight matrix according to the first genetic matrix element and the second genetic matrix element.

[0125] In step S410 of some embodiments, the conditional mean of the marginal effect size of ct-eQTLs is expressed as:

[0126] ,

[0127] in, The genetic weight matrix for the genetic markers in the first type of cells.

[0128] The average cis-co-inheritance rate of genetic marker SNP j The average cis-inheritance rate of the first subgene of the genetic marker SNP j The ratio of is taken as the first genetic matrix element.

[0129] In step S420 of some embodiments, the mean value of the first ratio is obtained. and the mean of the second proportion , according to the average cis-co-inheritance rate of genetic marker SNP j The average cis-inheritance rate of the first subgene of the genetic marker SNP j Determine the first genetic matrix element and the mean of the second proportion After multiplication with the first genetic matrix element, the mean of the first ratio Add together to obtain the second genetic matrix element.

[0130] In step S430 of some embodiments, 1 is used as the element of the first row of the matrix, the first genetic matrix element is used as the element of the second row of the matrix, and the second genetic matrix element is used as the element of the third row of the matrix to obtain a genetic weight matrix.

[0131] Through the above steps S410 to S430, a genetic weight matrix can be obtained, so as to determine the best unbiased estimator of the marginal effect based on the genetic weight matrix, thereby improving the accuracy of gene genetic positioning.

[0132] See also Figure 5 In some embodiments, step S140 may include but is not limited to steps S510 to S520:

[0133] Step S510, determining a candidate genetic matrix based on the genetic estimation data of genetic markers in the sample tissue, the genetic estimation data of genetic markers in the first type of cells, and the genetic estimation data of genetic markers in the second type of cells;

[0134] Step S520, determining a genetic variance matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate and the candidate genetic matrix.

[0135] In step S510 of some embodiments, summary statistics data at the tissue level and the cell type level are defined. It can be broken down into:

[0136] ,

[0137] in, , and They represent the independent estimation errors of gene genetic marker SNP j in cell type 1, cell type 2 and sample tissue, and their variances are , as well as On this basis, the covariance matrix is ​​derived as follows:

[0138] ,

[0139] Here, cov represents covariance.

[0140] According to the covariance matrix, according to the mean of the first proportion and the mean of the second proportion Determine the matrix A, based on the genetic genetic estimation data of genetic genetic markers in the sample tissue , Genetic inheritance estimation data of genetic markers in type I cells and genetic inheritance estimates of genetic markers in the second type of cells , matrix A, linkage disequilibrium score, and matrix determined by average cis inheritance rate and average cis co-inheritance rate , determine the candidate genetic matrix .

[0141] In step S520 of some embodiments, the conditional variance of the marginal effect size of ct-eQTLs is expressed as:

[0142] ,

[0143] Among them, C is The constant matrix formed by include and , represents the average cis-heritance rate of the first sub-gene of the j-th SNP, represents the average cis-co-inheritance rate of the j-th SNP, ; represents the genetic variance matrix of genetic markers in the first type of cells.

[0144] According to the matrix A, the genetic covariance of the j-th SNP , average cis inheritance rate , average cis-co-inheritance rate and the candidate genetic matrix Calculate the conditional variance and use the inverse matrix of the conditional variance as the genetic variance matrix.

[0145] Through the above steps S510 to S520, a genetic variance matrix can be obtained to jointly determine the best unbiased estimate of the marginal effect based on the genetic weight matrix and the genetic variance matrix.

[0146] See also Figure 6 In some embodiments, step S150 may include but is not limited to steps S610 to S630:

[0147] Step S610, determining a target genetic estimate based on the genetic weight matrix, the genetic variance matrix, the genetic estimate data of the genetic markers in the sample tissue, the genetic estimate data of the genetic markers in the first type of cells, and the genetic estimate data of the genetic markers in the second type of cells;

[0148] Step S620, determining the target genetic variance according to the genetic weight matrix and the genetic variance matrix;

[0149] Step S630, determining target genetic estimate data according to the target genetic estimate and the target genetic variance.

[0150] In step S610 of some embodiments, the first-order moment condition is determined according to the conditional mean of the marginal effect size of ct-eQTLs, and the first-order moment condition is expressed as:

[0151] ,

[0152] Where m is the probability density function of the marginal effect.

[0153] In the framework of generalized moment estimation, the marginal effect of eQTL in cell type 1 was As a parameter to be estimated, and by solving the above first-order moment conditions, we can get A best linear unbiased estimator of :

[0154] ,

[0155] in, is the best linear unbiased estimate of the jth SNP in the first cell type; is the weight parameter; is a vector of tissue-level and cellular-level marginal effect estimates.

[0156] According to the genetic weight matrix , genetic variance matrix , Genetic inheritance estimation data of genetic genetic markers in sample tissues , Genetic inheritance estimation data of genetic markers in type I cells and genetic inheritance estimates of genetic markers in the second type of cells The best linear unbiased estimator is calculated to determine the target genetic estimate of the genetic marker in the first type of cells.

[0157] Similarly, calculate the genetic variance matrix of the genetic markers in the second type of cells , the calculation method is as follows:

[0158] ,

[0159] in, include and , represents the average cis-heritance rate of the second child of the j-th SNP, represents the average cis-co-inheritance rate of the j-th SNP, which is same, ; represents the genetic variance matrix of the genetic markers in the second type of cells.

[0160] The genetic weight matrix of the genetic markers in the second type of cells is calculated as follows:

[0161] ,

[0162] in, A genetic weight matrix for the genetic markers in the second type of cells.

[0163] According to the genetic weight matrix , genetic variance matrix , Genetic inheritance estimation data of genetic genetic markers in sample tissues , Genetic inheritance estimation data of genetic markers in type I cells and genetic inheritance estimates of genetic markers in the second type of cells Calculate the best linear unbiased estimator to determine the target genetic estimate of the genetic marker in the second type of cells, which is the marginal effect of the cell type 2 eQTL Estimated value of .

[0164] The embodiment of the present application achieves efficient calculation by deriving the optimal linear unbiased estimator of the marginal effect of ct-eQTLs, and provides a new method for solving multi-parameter estimation problems related to biomedical big data.

[0165] In step S620 of some embodiments, based on the asymptotic normality of the generalized moment estimate, the target genetic variance is determined according to the genetic weight matrix and the genetic variance matrix, and the target genetic variance is the variance of the target genetic estimate. The target genetic variance is expressed as:

[0166] ,

[0167] ,

[0168] in, , Represent the target genetic variance of genetic markers in the first type of cells and the second type of cells respectively.

[0169] It can be seen from the calculation formulas of target genetic estimation and target genetic variance that the embodiment of the present application allows the calculation time to increase linearly with the increase in the number of SNPs and the number of cell types, and can achieve very efficient eQTL integration analysis.

[0170] In step S630 of some embodiments, the target genetic estimate and the target genetic variance are used as target genetic estimate data.

[0171] Through the above steps S610 to S630, target genetic estimation data can be obtained, so as to realize the positioning of eQTL in different cell types based on the target genetic estimation data.

[0172] See also Figure 7 In some embodiments, step S160 may include but is not limited to steps S710 to S720:

[0173] Step S710, obtaining the standard deviation of the target genetic variance to obtain the target genetic standard deviation;

[0174] Step S720, taking the ratio of the target genetic estimate to the target genetic standard deviation as the target test score.

[0175] In step S710 of some embodiments, the target genetic estimate data includes a target genetic estimate and a target genetic variance. After obtaining the target genetic estimate and the target genetic variance, It is equivalent to an estimator of a new cell type-specific eQTL effect, but the estimator alone is not enough, and it is necessary to determine whether the eQTL effect is significantly different from 0. The embodiment of the present application uses the Wald test to identify statistically significant ct-eQTLs.

[0176] Specifically, the target test score is calculated based on the target genetic estimation data, and a hypothesis test is performed on the target test score. If the target test score is greater than a preset score threshold, the genetic marker is considered to be an eQTL in the cell gene, and the genetic marker is used as a genetic locus. If the target test score is less than or equal to the preset score threshold, the genetic marker is not a genetic locus.

[0177] When calculating the target test score, the standard deviation is calculated based on the target genetic variance to obtain the target genetic standard deviation. The target genetic standard deviation is the square root of the target genetic variance.

[0178] In step S720 of some embodiments, the ratio of the target genetic estimate to the target genetic standard deviation is used as the target test score. It should be noted that the target test score of the genetic marker in the first type of cell can be determined based on the target genetic estimate of the genetic marker in the first type of cell and the target genetic standard deviation on the first type of cell. The target test score of the genetic marker in the second type of cell can be determined based on the target genetic estimate of the genetic marker in the second type of cell and the target genetic standard deviation on the second type of cell. Whether the genetic marker is a genetic locus in the first type of cell or the second type of cell is determined based on the target test scores of the genetic marker in the first type of cell and the second type of cell, respectively.

[0179] Through the above steps S710 to S720, a target test score can be obtained to perform positioning analysis on the genetic markers based on the target test score.

[0180] The embodiment of the present application uses the ct-eQTLs summary statistics of small sample scRNA-seq data, the tissue-level eQTLs summary statistics of large sample bulkRNA-seq data, and the average cell type ratio of tissue samples as model input, and outputs the improved ct-eQTLs summary statistics. By integrating the eQTL data at the tissue level and the cell type level into a unified model, the advantages of each data type can be effectively utilized, the statistical power of ct-eQTLs priority sorting can be enhanced, and the statistical power of ct-eQTLs positioning can be significantly improved. At the same time, a well-controlled type I error is generated, and high computational efficiency is demonstrated, providing a closed-form solution at each step. In addition, the output can be easily applied to downstream analysis, such as co-localization with GWAS risk variants, to more effectively discover gene regulation in disease mechanisms at the cell type level.

[0181] See also Figure 8 The present application also provides a gene genetic site positioning device, which includes:

[0182] The first acquisition module 810 is used to obtain the average cis-inheritance rate and the average cis-co-inheritance rate of the genetic markers in the sample tissue;

[0183] A second acquisition module 820 is used to acquire genetic estimation data of genetic markers in the sample tissue, genetic estimation data of genetic markers in the first type of cells, and genetic estimation data of genetic markers in the second type of cells, and estimate a first ratio of the first type of cells and a second ratio of the second type of cells in the sample tissue;

[0184] A first determination module 830 is used to determine a genetic weight matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio;

[0185] A second determination module 840 is used to determine a genetic variance matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the genetic inheritance estimation data of the genetic markers in the sample tissue, the genetic inheritance estimation data of the genetic markers in the first type of cells, and the genetic inheritance estimation data of the genetic markers in the second type of cells;

[0186] A third determination module 850 is used to determine target genetic estimation data based on the genetic weight matrix, the genetic variance matrix, the genetic estimation data of the genetic markers in the sample tissue, the genetic estimation data of the genetic markers in the first type of cells, and the genetic estimation data of the genetic markers in the second type of cells;

[0187] The positioning module 860 is used to calculate the target test score based on the target genetic estimation data, and if the target test score is greater than a preset score threshold, the genetic marker is used as the genetic locus.

[0188] The specific implementation of the gene genetic site positioning device is basically the same as the above-mentioned specific embodiment, and will not be repeated here.

[0189] The present application also provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned gene genetic site location method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0190] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0191] The processor 910 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0192] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 920, and the processor 910 calls and executes the gene genetic site positioning method of the embodiment of this application;

[0193] Input / output interface 930, used to implement information input and output;

[0194] Communication interface 940, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0195] bus 950 , which transmits information between various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0196] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0197] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned gene genetic site positioning method is implemented.

[0198] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0199] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0200] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0201] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0202] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0203] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0204] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0205] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0206] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0207] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0208] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0209] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for locating a gene locus, characterized in that: The method comprises: Obtaining an average cis-inheritance rate and an average cis-co-inheritance rate of genetic markers in sample tissues, specifically comprising: obtaining a linkage disequilibrium score and an expected test score of the genetic marker; the expected test score comprises a first expected test score of the genetic marker in a first type of cell, a second expected test score of the genetic marker in a second type of cell, and a third expected test score of the genetic marker between the first type of cell and the second type of cell; performing linear regression on the linkage disequilibrium score and the expected test score to obtain the average cis-inheritance rate and the average cis-co-inheritance rate; Obtaining genetic estimation data of genetic markers in the sample tissue, genetic estimation data of genetic markers in the first type of cells, and genetic estimation data of genetic markers in the second type of cells, and estimating a first ratio of the first type of cells and a second ratio of the second type of cells in the sample tissue; Determining a genetic weight matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio specifically includes: determining a first genetic matrix element according to the average cis-inheritance rate and the average cis-co-inheritance rate; determining a second genetic matrix element according to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio; determining the genetic weight matrix according to the first genetic matrix element and the second genetic matrix element; Determining a genetic variance matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the genetic inheritance estimation data of genetic markers in the sample tissue, the genetic inheritance estimation data of genetic markers in the first type of cells, and the genetic inheritance estimation data of genetic markers in the second type of cells, specifically includes: determining a candidate genetic matrix according to the genetic inheritance estimation data of genetic markers in the sample tissue, the genetic inheritance estimation data of genetic markers in the first type of cells, and the genetic inheritance estimation data of genetic markers in the second type of cells; determining the genetic variance matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, and the candidate genetic matrix; Determining target genetic estimation data according to the genetic weight matrix, the genetic variance matrix, genetic estimation data of genetic markers in sample tissue, genetic estimation data of genetic markers in first type of cells and genetic estimation data of genetic markers in second type of cells, specifically comprising: determining target genetic estimation according to the genetic weight matrix, the genetic variance matrix, genetic estimation data of genetic markers in sample tissue, genetic estimation data of genetic markers in first type of cells and genetic estimation data of genetic markers in second type of cells; determining target genetic variance according to the genetic weight matrix and the genetic variance matrix; determining target genetic estimation data according to the target genetic estimation and the target genetic variance; A target test score is calculated based on the target genetic estimation data, and if the target test score is greater than a preset score threshold, the genetic marker is used as a genetic locus.

2. The method according to claim 1, characterized in that The step of performing linear regression on the linkage disequilibrium score and the expected test score to obtain the average cis-inheritance rate and the average cis-co-inheritance rate comprises: Performing linear regression on the linkage disequilibrium score and the first expected test score to obtain a first sub-average cis-heritability; Performing linear regression on the linkage disequilibrium score and the second expected test score to obtain the second sub-average cis-heritability; Determining the average cis-heritability according to the first average cis-heritability and the second average cis-heritability; The linkage disequilibrium score and the third expected test score are subjected to linear regression to obtain the average cis co-inheritance rate.

3. The method according to any one of claims 1 to 2, characterized in that: The target genetic estimation data includes a target genetic estimation and a target genetic variance, and the step of calculating a target test score based on the target genetic estimation data includes: Obtaining the standard deviation of the target genetic variance to obtain a target genetic standard deviation; The ratio of the target genetic estimate to the target genetic standard deviation is taken as the target test score.

4. A gene genetic site positioning device, characterized in that: The device is used to implement the gene genetic site positioning method according to any one of claims 1 to 3, and the device comprises: The first acquisition module is used to obtain the average cis-inheritance rate and average cis-co-inheritance rate of gene genetic markers in the sample tissue; A second acquisition module is used to acquire genetic estimation data of genetic markers in the sample tissue, genetic estimation data of genetic markers in the first type of cells, and genetic estimation data of genetic markers in the second type of cells, and estimate a first ratio of the first type of cells and a second ratio of the second type of cells in the sample tissue; A first determination module is used to determine a genetic weight matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the first ratio and the second ratio; A second determination module is used to determine a genetic variance matrix according to the average cis-inheritance rate, the average cis-co-inheritance rate, the genetic inheritance estimation data of the genetic markers in the sample tissue, the genetic inheritance estimation data of the genetic markers in the first type of cells, and the genetic inheritance estimation data of the genetic markers in the second type of cells; A third determination module is used to determine target genetic estimation data based on the genetic weight matrix, the genetic variance matrix, the genetic estimation data of the genetic markers in the sample tissue, the genetic estimation data of the genetic markers in the first type of cells, and the genetic estimation data of the genetic markers in the second type of cells; A positioning module is used to calculate a target test score based on the target genetic estimation data, and if the target test score is greater than a preset score threshold, the genetic marker is used as a genetic locus.

5. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

Images