Male family gene comparison method and device, computer device and storage medium

Abstract

Embodiments of the present application disclose a male family gene comparison method and device, computer equipment and a storage medium. The method comprises: obtaining male family information; performing Y-STR-based comparison on the male family information to obtain a comparison result; determining an investigation object and range according to the comparison result; and outputting the investigation object and range to a terminal. The method of the embodiments of the present application can quantize the Y-STR comparison result and intelligently recommend the investigation object and range.

Description

Technical Field

[0001] This invention relates to data analysis methods, and more specifically to methods, apparatus, computer equipment, and storage media for comparing male family gene sequences. Background Technology

[0002] Analysis of male pedigree information can be used to analyze male genetic diseases and family information of male criminals of the same kind. The analysis of male pedigree information is based on the Y-STR comparison method. This method usually involves hundreds of families, and investigators need to spend a lot of time to further screen them. It cannot intelligently recommend the targets and scope of investigation.

[0003] Therefore, it is necessary to design a new method to quantify Y-STR alignment results and intelligently recommend investigation targets and scope. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, apparatus, computer equipment and storage medium for comparing male family gene sequences.

[0005] To achieve the above objectives, the present invention employs the following technical solution: a male family gene comparison method, comprising:

[0006] Obtain male family history information;

[0007] The male pedigree information was compared based on Y-STR to obtain the comparison results;

[0008] The investigation targets and scope are determined based on the comparison results;

[0009] Output the investigation target and scope to the terminal.

[0010] The further technical solution is as follows: the comparison of the male pedigree information based on Y-STR to obtain the comparison result includes:

[0011] The association between each variant and quantitative trait in the male pedigree information was examined to obtain the p-value for the single mutation test;

[0012] The Fisher method, combined with the p-value method, was used to enhance the p-value of individual mutation tests in order to obtain the alignment results.

[0013] The further technical solution is as follows: The method of enhancing the P-value of a single mutation test using Fisher's combined P-value method to obtain the alignment results includes:

[0014] The Fisher method, combined with the p-value, is used to add weights to the p-value of a single mutation test to obtain the alignment results.

[0015] The further technical solution is as follows: The examination of the association between each variation and quantitative trait in the male pedigree information to obtain the P-value for a single mutation test includes:

[0016] Given a gene with M mutations, electrophoretic sequencing is performed on n individuals for each of the M mutations. For the i-th individual, y i x represents the quantitative trait value. im ∈{0,1,2} represents the genotype integral of the m-th mutation, z i1 ~z ik There are k covariates, i = 1 to n, m = 1 to M; for the m-th mutation, a linear regression model is applied: y i =a0+a T z i +b m x im +ε i Where a0 is the intercept term, and a = (a1, a2, ..., a0) k ) T It is a covariate, z i =(z1,z2,…,z) k ) T The regression coefficient; b m It is the genotype integral x im The regression coefficients; ε1~ε n They are independent and identically distributed, and ε i =N(0,σ 2 Let Y = (y1, y2, ..., y k ) T Z i =(1,ε T ) T , i=1~n, Z=(Z1,Z2,…,Z k ) T X m =(x 1m ,x 2m ,…,x km ) T The association test between each mutation and the trait is equivalent to testing the null hypothesis H0: b m =0, according to the theorem, the score test statistic is: S m =(1 / σ 2 )U m V m U m ;where σ 2 Under the null hypothesis, σ 2 The maximum likelihood estimate, i.e., σ 2 = (1 / n)Y T (I n -Q) YI n Let Q be an n-order identity matrix, then Q = Z(Z T Z)- 1 Z T U m =X m T (I n -Q)Y,V m =X m T (I n -Q)X m When n meets the requirements, S m It approximately follows a chi-square distribution with 1 degree of freedom, according to S m The p-values ​​for individual mutation tests are denoted as p1 to p2, respectively. M .

[0017] The further technical solution is as follows: The Fisher combined with P-value method adds weights to the P-value of a single mutation test to obtain the comparison results, including:

[0018] Use y i and covariate z i1 ~z ik Perform linear regression to obtain y i The residuals; the weights are: ω i =Beta(q) m ;1,25)|Cov(Y,X m )|;where q m Let m be the frequency of the second allele of the m-th mutation, where m = 1 to M.

[0019] The present invention also provides a male family gene comparison device, comprising:

[0020] Information acquisition unit, used to acquire male family lineage information;

[0021] The comparison unit is used to perform Y-STR-based comparison on the male pedigree information to obtain the comparison results;

[0022] A determining unit is used to determine the investigation target and scope based on the comparison results;

[0023] The output unit is used to output the investigation object and scope to the terminal.

[0024] The further technical solution is as follows: the comparison unit includes:

[0025] The correlation verification subunit is used to examine the correlation between each variation and quantitative trait in the male pedigree information and obtain the P-value of a single mutation test.

[0026] An enhancement subunit is used to enhance the p-value of a single mutation test according to the Fisher-based p-value method to obtain the first alignment result.

[0027] The further technical solution is as follows: the enhancement subunit is used to add weights to the P-value of a single mutation test using the Fisher combined P-value method to obtain the alignment result.

[0028] The present invention also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the above-described method.

[0029] The present invention also provides a storage medium storing a computer program that, when executed by a processor, can implement the above-described method.

[0030] The beneficial effects of this invention compared with the prior art are as follows: This invention determines the gene mutation rate by comparing male family information based on Y-STR, and determines the screening targets and scope according to the comparison results, thereby quantifying the Y-STR comparison results and intelligently recommending screening targets and scope.

[0031] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram illustrating an application scenario of the male family gene comparison method provided in this embodiment of the invention.

[0034] Figure 2 This is a flowchart illustrating the male family gene comparison method provided in an embodiment of the present invention.

[0035] Figure 3 This is a schematic diagram of a sub-process of the male family gene comparison method provided in an embodiment of the present invention;

[0036] Figure 4 This is a schematic block diagram of a male family gene comparison device provided in an embodiment of the present invention;

[0037] Figure 5 A schematic block diagram of the comparison unit of the male family gene comparison device provided in an embodiment of the present invention;

[0038] Figure 6 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0041] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0042] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0043] Please see Figure 1 and Figure 2 , Figure 1 This is a schematic diagram illustrating an application scenario of the male family gene comparison method provided in this embodiment of the invention. Figure 2 This is a schematic flowchart illustrating the male family gene comparison method provided in an embodiment of the present invention. This male family gene comparison method is applied in a server. The server interacts with the terminal, utilizing the mutation patterns of Y-STR loci to conduct in-depth research on Y-STR comparison algorithms, further improving the analytical and judgment capabilities of the Y-STR database, and providing important means for public security organs to effectively and accurately combat crime and improve the prevention and control of regional occupational crimes.

[0044] Figure 2 This is a schematic flowchart of the male family gene comparison method provided in an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S140.

[0045] S110. Obtain male family lineage information.

[0046] In this embodiment, male pedigree information refers to the pedigree information corresponding to a male, including information for each generation: gender, number of people, etc.

[0047] Users can enter and view family information.

[0048] S120. Perform a Y-STR-based comparison on the male pedigree information to obtain the comparison results.

[0049] In this embodiment, the comparison result refers to the correlation between gene mutations and quantitative traits within male pedigree information, and the enhanced P-value is used. A comparison algorithm based on Y-STR gene mutation rate is established to quantify Y-STR comparison results, intelligently recommend investigation targets and scope, and provide scientific reference for investigative departments.

[0050] In one embodiment, please refer to Figure 3 The above-mentioned step S120 may include steps S121 to S122.

[0051] S121. Examine the association between each variation and quantitative trait in the male pedigree information to obtain the P-value for a single mutation test;

[0052] S122. Enhance the P-value of single mutation testing according to Fisher's method combined with the P-value to obtain the alignment results.

[0053] In this embodiment, assuming there are M mutations in a given gene, electrophoretic sequencing is performed on n individuals for the M mutations. For the i-th individual, y i x represents the quantitative trait value. im ∈{0,1,2} represents the genotype integral of the m-th mutation, z i1 ~z ik There are k covariates, i = 1 to n, m = 1 to M.

[0054] To examine the association between a set of mutations and quantitative traits within a Y-STR gene, we first examine the association between each variant and the quantitative trait, obtaining the p-value for each individual mutation. For the m-th mutation, we apply a linear regression model: y i =a0+a T z i +b m x im +ε i Where a0 is the intercept term, and a = (a1, a2, ..., a...). k ) T It is a covariate, z i =(z1,z2,…,z) k ) T The regression coefficient; b mIt is the genotype integral x im The regression coefficients; ε1~ε n They are independent and identically distributed, and ε i =N(0,σ 2 Let Y = (y1, y2, ..., y k ) T Z i =(1,ε T ) T , i=1~n, Z=(Z1,Z2,…,Z k ) T X m =(x 1m ,x 2m ,…,x km ) T The association test between each mutation and the trait is equivalent to the test of the null hypothesis H0: b m =0, according to the theorem, the score test statistic is: S m =(1 / σ 2 )U m V m U m ;where σ 2 Under the null hypothesis, σ 2 The maximum likelihood estimate, i.e., σ 2 = (1 / n)Y T (I n -Q) Y I n Let Q be an n-order identity matrix, then Q = Z(Z T Z)- 1 Z T U m =X m T (I n -Q)Y,V m =X m T (I n -Q)X m .

[0055] When n is relatively large, S m The distribution approximately follows a chi-square distribution with 1 degree of freedom, thus yielding the p-values ​​for individual mutation tests, denoted as p1 to p2. MBecause the frequency of minor alleles in mutations is very low, the power of single mutation testing is not high. To enhance the association signal of mutations, the Fisher statistic is combined with the p-value of single mutation testing, following the Fisher-P-value combination method. The Fisher statistic is defined as follows: Suppose there are K mutations in a locus, and let pi be the p-value of the i-th mutation test, called the Fisher statistic. When identifying rare mutations, it is generally believed that the rarer the mutation, the more likely it is to be a functional mutation. Therefore, a weight is added to the Fisher statistic to make the weight of rarer mutations greater. To eliminate the influence of covariates, only y is used. i and covariate z i1 ~z ik Perform linear regression to obtain y i The residuals. In addition to considering the frequency of minor alleles of rare mutations, the correlation between each mutation and the trait is also considered, resulting in a weight of ω. i =Beta(q) m ;1,25)|Cov(Y,X m )|;where q m Let m be the frequency of the minor allele of the m-th mutation, where m = 1 to M. The weights satisfy the following conditions: the rarer the variant, the larger its value; the stronger the correlation between the variant and the trait, the larger its value.

[0056] S130. Determine the investigation targets and scope based on the comparison results;

[0057] In this embodiment, the scope of objects that meet the requirements is determined by comparing the results. By utilizing the different mutation rates of high mutation sites, medium mutation sites, and low mutation sites in Y-STR, a new probability algorithm system for comparison is established. The comparison results of Y-STR database are comprehensively scored, and priority objects and scopes for investigation are intelligently recommended. This can effectively solve the application defects of the current Y-STR DNA database, which has too many false positive comparison results and insufficient recognition ability. It can also effectively prevent false negative results, which may lead to detection errors.

[0058] S140. Output the investigation object and scope to the terminal.

[0059] The application of Y-STR information is extended from case analysis to the analysis and control of high-risk groups. The regional and kinship characteristics of regional occupational crimes are organically combined with Y-STR information to identify and assess regional high-risk groups. Furthermore, a crime early warning model is built to enhance the ability to identify and control key personnel. The results of Y-STR comparisons are quantified, and intelligent recommendations are made for investigation targets and scope, providing scientific references for investigative departments.

[0060] The method described in this embodiment can be applied to police case investigation, often solving cases in a short time and improving efficiency. It improves the accuracy of Y-STR applications; secondly, it innovatively integrates with geographic systems to explore the depth of Y-STR application in case analysis; and thirdly, it innovatively applies to regionally specific high-risk groups, expanding the scale of Y-STR applications and enhancing the analysis, early warning, and control capabilities for high-risk groups.

[0061] The aforementioned male family gene comparison method determines the gene mutation rate by comparing male family information based on Y-STR, and determines the screening targets and scope based on the comparison results, thereby quantifying the Y-STR comparison results and intelligently recommending screening targets and scope.

[0062] Figure 4 This is a schematic block diagram of a male family gene comparison device 300 provided in an embodiment of the present invention. Figure 4 As shown, corresponding to the above-described male family gene comparison method, the present invention also provides a male family gene comparison device 300. This male family gene comparison device 300 includes a unit for performing the above-described male family gene comparison method, and the device can be configured in a server. Specifically, please refer to... Figure 4 The male family gene comparison device 300 includes an information acquisition unit 301, a comparison unit 302, a determination unit 303, and an output unit 304.

[0063] Information acquisition unit 301 is used to acquire male pedigree information; comparison unit 302 is used to perform Y-STR-based comparison on the male pedigree information to obtain comparison results; determination unit 303 is used to determine the investigation object and scope based on the comparison results; output unit 304 is used to output the investigation object and scope to the terminal.

[0064] In one embodiment, such as Figure 5 As shown, the comparison unit 302 includes an association verification subunit 3021 and an enhancement subunit 3022.

[0065] The correlation verification subunit 3021 is used to verify the correlation between each variation and quantitative trait in the male pedigree information to obtain the P-value of the single mutation test; the enhancement subunit 3022 is used to enhance the P-value of the single mutation test according to the Fisher combined P-value method to obtain the alignment result.

[0066] In one embodiment, the enhancement subunit 3022 is used to add weights to the p-value of a single mutation test using the Fisher-P-value method to obtain the alignment result.

[0067] In one embodiment, the enhancement subunit 3022 is used to use y i and covariate zi1 ~z ik Perform linear regression to obtain y i The residuals; the weights are: ω i =Beta(q) m ;1,25)|Cov(Y,X m )|;where q m Let m be the frequency of the second allele of the m-th mutation, where m = 1 to M.

[0068] In one embodiment, the correlation verification subunit 3021 uses the correlation setting that a given gene has M mutations, and performs electrophoretic sequencing on the M mutations in n individuals. For the i-th individual, y i x represents the quantitative trait value. im ∈{0,1,2} represents the genotype integral of the m-th mutation, z i1 ~z ik There are k covariates, i = 1 to n, m = 1 to M; for the m-th mutation, a linear regression model is applied: y i =a0+a T z i +b m x im +ε i Where a0 is the intercept term, and a = (a1, a2, ..., a0) k ) T It is a covariate, z i =(z1,z2,…,z) k ) T The regression coefficient; b m It is the genotype integral x im The regression coefficients; ε1~ε n They are independent and identically distributed, and ε i =N(0,σ 2 Let Y = (y1, y2, ..., y k ) T Z i =(1,ε T ) T , i=1~n, Z=(Z1,Z2,…,Z k ) T X m =(x 1m ,x 2m ,…,x km ) T The association test between each mutation and the trait is equivalent to testing the null hypothesis H0: b m =0, according to the theorem, the score test statistic is: S m =(1 / σ 2 )U m V mU m ;where σ 2 Under the null hypothesis, σ 2 The maximum likelihood estimate, i.e., σ 2 = (1 / n)Y T (I n -Q) Y I n Let Q be an n-order identity matrix, then Q = Z(Z T Z)- 1 Z T U m =X m T (I n -Q)Y,V m =X m T (I n -Q)X m When n meets the requirements, S m It approximately follows a chi-square distribution with 1 degree of freedom, according to S m The p-values ​​for individual mutation tests are denoted as p1 to p2, respectively. M .

[0069] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned male family gene comparison device 300 and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0070] The aforementioned male family gene comparison device 300 can be implemented as a computer program, which can perform tasks such as... Figure 6 It runs on the computer device shown.

[0071] Please see Figure 6 , Figure 6 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a server, wherein the server can be a standalone server or a server cluster composed of multiple servers.

[0072] See Figure 6 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0073] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a male pedigree gene comparison method.

[0074] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0075] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can perform a male family gene comparison method.

[0076] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0077] The processor 502 is used to run a computer program 5032 stored in the memory to perform the following steps:

[0078] Obtain male pedigree information; perform Y-STR-based comparison on the male pedigree information to obtain comparison results; determine the investigation targets and scope based on the comparison results; output the investigation targets and scope to the terminal.

[0079] In one embodiment, when the processor 502 performs the step of comparing the male pedigree information based on Y-STR to obtain the comparison result, it specifically implements the following steps:

[0080] The association between each variant and quantitative trait in the male pedigree information was examined to obtain the p-value for the single mutation test; the p-value for the single mutation test was enhanced according to the Fisher combined p-value method to obtain the comparison results.

[0081] In one embodiment, when processor 502 implements the step of enhancing the p-value of a single mutation test according to the Fisher-based p-value method to obtain the alignment result, it specifically implements the following steps:

[0082] The Fisher method, combined with the p-value, is used to add weights to the p-value of a single mutation test to obtain the alignment results.

[0083] In one embodiment, when processor 502 performs the step of examining the association between each variant and quantitative trait in the male pedigree information to obtain the p-value for a single mutation test, it specifically implements the following steps:

[0084] Given a gene with M mutations, electrophoretic sequencing is performed on n individuals for each of the M mutations. For the i-th individual, y i x represents the quantitative trait value. im ∈{0,1,2} represents the genotype integral of the m-th mutation, z i1 ~z ik There are k covariates, i = 1 to n, m = 1 to M; for the m-th mutation, a linear regression model is applied: y i =a0+a T z i +b m x im +ε i Where a0 is the intercept term, and a = (a1, a2, ..., a0) k ) T It is a covariate, z i =(z1,z2,…,z) k ) T The regression coefficient; b m It is the genotype integral x im The regression coefficients; ε1~ε n They are independent and identically distributed, and ε i =N(0,σ 2 Let Y = (y1, y2, ..., y k ) T Z i =(1,ε T ) T , i=1~n, Z=(Z1,Z2,…,Z k ) T X m =(x 1m ,x 2m ,…,x km ) T The association test between each mutation and the trait is equivalent to testing the null hypothesis H0: b m =0, according to the theorem, the score test statistic is: S m =(1 / σ 2 )U m V m U m ;where σ 2 Under the null hypothesis, σ 2 The maximum likelihood estimate, i.e., σ 2 = (1 / n)Y T (I n -Q) Y I n Let Q be an n-order identity matrix, then Q = Z(Z T Z)- 1 Z T Um =X m T (I n -Q)Y,V m =X m T (I n -Q)X m When n meets the requirements, S m It approximately follows a chi-square distribution with 1 degree of freedom, according to S m The p-values ​​for individual mutation tests are denoted as p1 to p2, respectively. M .

[0085] In one embodiment, when processor 502 implements the step of adding weights to the p-value of a single mutation test using the Fisher-P-value method to obtain the alignment result, the following steps are specifically implemented:

[0086] Use y i and covariate z i1 ~z ik Perform linear regression to obtain y i The residuals; the weights are: ω i =Beta(q) m ;1,25)|Cov(Y,X m )|;where q m Let m be the frequency of the second allele of the m-th mutation, where m = 1 to M.

[0087] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0088] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0089] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein when executed by a processor, the computer program causes the processor to perform the following steps:

[0090] Obtain male pedigree information; perform Y-STR-based comparison on the male pedigree information to obtain comparison results; determine the investigation targets and scope based on the comparison results; output the investigation targets and scope to the terminal.

[0091] In one embodiment, when the processor executes the computer program to perform the step of comparing the male pedigree information based on Y-STR to obtain the comparison result, it specifically implements the following steps:

[0092] The association between each variant and quantitative trait in the male pedigree information was examined to obtain the p-value for the single mutation test; the p-value for the single mutation test was enhanced according to the Fisher combined p-value method to obtain the comparison results.

[0093] In one embodiment, when the processor executes the computer program to implement the step of enhancing the p-value of a single mutation test according to the Fisher-based p-value method to obtain the alignment result, it specifically implements the following steps:

[0094] The Fisher method, combined with the p-value, is used to add weights to the p-value of a single mutation test to obtain the alignment results.

[0095] In one embodiment, when the processor executes the computer program to perform the step of examining the association between each variant and quantitative trait in the male pedigree information and obtaining the p-value for a single mutation test, it specifically implements the following steps:

[0096] Given a gene with M mutations, electrophoretic sequencing is performed on n individuals for each of the M mutations. For the i-th individual, y i x represents the quantitative trait value. im ∈{0,1,2} represents the genotype integral of the m-th mutation, z i1 ~z ik There are k covariates, i = 1 to n, m = 1 to M; for the m-th mutation, a linear regression model is applied: y i =a0+a T z i +b m x im +ε i Where a0 is the intercept term, and a = (a1, a2, ..., a0) k ) T It is a covariate, zi =(z1,z2,…,z) k ) T The regression coefficient; b m It is the genotype integral x im The regression coefficients; ε1~ε n They are independent and identically distributed, and ε i =N(0,σ 2 Let Y = (y1, y2, ..., y k ) T Z i =(1,ε T ) T , i=1~n, Z=(Z1,Z2,…,Z k ) T X m =(x 1m ,x 2m ,…,x km ) T The association test between each mutation and the trait is equivalent to testing the null hypothesis H0: b m =0, according to the theorem, the score test statistic is: S m =(1 / σ 2 )U m V m U m ;where σ 2 Under the null hypothesis, σ 2 The maximum likelihood estimate, i.e., σ 2 = (1 / n)Y T (I n -Q) Y I n Let Q be an n-order identity matrix, then Q = Z(Z T Z)- 1 Z T U m =X m T (I n -Q)Y,V m =X m T (I n -Q)X m When n meets the requirements, S m It approximately follows a chi-square distribution with 1 degree of freedom, according to S m The p-values ​​for individual mutation tests are denoted as p1 to p2, respectively. M .

[0097] In one embodiment, when the processor executes the computer program to implement the step of adding weights to the p-value of a single mutation test using the Fisher-P-value method to obtain the alignment result, it specifically implements the following steps:

[0098] Use y i and covariate z i1 ~z ik Perform linear regression to obtain y i The residuals; the weights are: ω i =Beta(q) m ;1,25)|Cov(Y,X m )|;where q m Let m be the frequency of the second allele of the m-th mutation, where m = 1 to M.

[0099] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0100] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0101] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0102] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0103] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0104] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for comparing male family gene sequences, characterized in that, include: Obtain male family history information; The male pedigree information was compared based on Y-STR to obtain the comparison results; The investigation targets and scope are determined based on the comparison results. Output the investigation target and scope to the terminal; The step of performing a Y-STR-based comparison of the male pedigree information to obtain the comparison results includes: The association between each variant and quantitative trait in the male pedigree information was examined to obtain the p-value for the single mutation test; The Fisher method combined with the p-value is used to add weight to the p-value of a single mutation test to obtain the alignment results; The process of examining the association between each variant and quantitative trait in the male pedigree information to obtain the p-value for a single mutation test includes: Given a gene with M mutations, electrophoretic sequencing is performed on n individuals for each of the M mutations. For the i-th individual, y i x represents the quantitative trait value. im ∈{0,1,2} represents the genotype integral of the m-th mutation, z i1 ~z ik There are k covariates, i = 1 to n, m = 1 to M; for the m-th mutation, a linear regression model is applied: y i =a0+a T z i +b m x im +ε i Where a0 is the intercept term, and a = (a1, a2, ..., a...). k ) T It is a covariate, z i =(z1, z2, …, z k ) T The regression coefficient; b m It is the genotype integral x im The regression coefficients; ε1~ε n They are independent and identically distributed, and ε i =N(0,σ 2 Let Y = (y1, y2, …, y k ) T Z i =(1,ε T ) T , i=1~n, Z=(Z1, Z2,…, Z k ) T X m =(x 1m , x 2m , …,x km ) T The association test between each mutation and the trait is equivalent to testing the null hypothesis H0: b m =0, according to the theorem, the score test statistic is: S m =(1 / σ 2 )U m V m U m ; where σ 2 Under the null hypothesis, σ 2 The maximum likelihood estimate, i.e., σ 2 =(1 / n)Y T (I n -Q) Y I n Let Q be an n-order identity matrix, then Q = Z(Z T Z)- 1 Z T U m =X m T (I n -Q)Y,V m = X m T (I n -Q)X m When n meets the requirements, S m It approximately follows a chi-square distribution with 1 degree of freedom, according to S m The p-values ​​for individual mutation tests are denoted as p1~p1. M .

2. The male family gene comparison method according to claim 1, characterized in that, The method of using Fisher combined with p-value to add weights to the p-value of a single mutation test to obtain the comparison results includes: Use y i and covariate z i1 ~z ik Perform linear regression to obtain y i The residuals; the weights are: ω i =Beta(q m ;1,25)|Cov(Y,X m )|;where q m Let m be the frequency of the second allele of the m-th mutation, where m = 1 to M.

3. A male family gene comparison device, characterized in that, The device uses the male family gene comparison method as described in any one of claims 1 to 2, comprising: Information acquisition unit, used to acquire male family lineage information; The comparison unit is used to perform Y-STR-based comparison on the male pedigree information to obtain the comparison results; A determining unit is used to determine the investigation target and scope based on the comparison results; The output unit is used to output the investigation object and scope to the terminal; The comparison unit includes: The correlation verification subunit is used to examine the correlation between each variation and quantitative trait in the male pedigree information and obtain the P-value of a single mutation test. An enhancement subunit is used to enhance the p-value of a single mutation test according to the Fisher-based p-value method to obtain the first alignment result; The enhancement subunit is used to add weights to the p-value of a single mutation test using the Fisher-P-value method to obtain the alignment result.

4. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1 to 2.

5. A storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, can implement the method as described in any one of claims 1 to 2.

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