Genetic relationship identification method and device, electronic equipment and storage medium
By acquiring and analyzing genetic marker genome sequences, calculating genotype possibilities, and analyzing co-ancestral fragments using presets and learning models, the problem of low accuracy in kinship identification caused by low-deep whole genome sequencing is solved, achieving higher Identification accuracy.
Patent Information
- Application Number
- CN202310953415.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-07-31
- Publication Date
- 2025-02-14
AI Technical Summary
The kinship identification method based on low-deep whole genome sequencing results lead to low accuracy of identification results.
By obtaining the first target genetic marker genome sequence and the second target genetic marker genome sequence, probability calculation is performed to obtain the genotype possibility, the common ancestor fragment and common ancestor coefficient are analyzed based on the preset model, and the preset learning model is analyzed. Retrieve the results of kinship identification.
The accuracy of kinship identification was improved, and more accurate kinship identification results were obtained through the analysis of genotype possibilities and the application of learning models.
Smart Images
Figure CN119446261A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of information technology, and in particular to a method and device for kinship identification, an electronic device, and a storage medium. Background Art
[0002] Currently, for ordinary customers, the price of kinship identification based on high-depth whole genome sequencing greater than 30X is too high. Therefore, a kinship identification method based on low-depth whole genome sequencing has been developed. The depth of low-depth whole genome sequencing can be as low as 1X, making the price lower. However, due to the significant decrease in sequencing depth, the accuracy of genetic marker detection has also decreased, making the accuracy of kinship identification results less accurate. Summary of the invention
[0003] The present disclosure provides a method and device for kinship identification, an electronic device and a storage medium, the main purpose of which is to solve the problem of low accuracy of identification results obtained by kinship identification based on low-depth whole genome sequencing in related technologies.
[0004] According to a first aspect of the present disclosure, a method for kinship identification is provided, comprising:
[0005] Obtaining a first target genetic marker genomic sequence and a second target genetic marker genomic sequence, wherein the genomic site where the first target genetic marker genomic sequence is located in the first identification sample corresponds to the same genomic site where the second target genetic marker genomic sequence is located in the second identification sample;
[0006] Performing probability calculation on the first target genetic marker genome sequence to obtain a first genotype possibility corresponding to the first target genetic marker genome sequence, and performing probability calculation on the second target genetic marker genome sequence to obtain a second genotype possibility corresponding to the second target genetic marker genome sequence;
[0007] Analyze the first genotype possibility, the second genotype possibility and the allele population frequency based on a preset model to obtain a common ancestor segment and a common ancestor coefficient;
[0008] The common ancestor fragment and the common ancestor coefficient are analyzed by a preset learning model to obtain a result of the kinship identification between the first identification sample and the second identification sample.
[0009] Optionally, obtaining the first target genetic marker genome sequence and the second target genetic marker genome sequence includes:
[0010] Obtaining the first identification sample and the second identification sample;
[0011] Sequencing the first identification sample and the second identification sample respectively by a preset genome sequencing program to obtain a first genome sequence corresponding to the first identification sample and a second genome sequence corresponding to the second identification sample;
[0012] The first genome sequence and the second genome sequence are respectively compared with a reference genome data set to obtain the first target genetic marker genome sequence corresponding to the first genome sequence and the second target genetic marker genome sequence corresponding to the second genome sequence, respectively. The reference genome data set is constructed based on the genome sequence of the target population.
[0013] Optionally, the performing probability calculation on the first target genetic marker genome sequence to obtain the first genotype possibility corresponding to the first target genetic marker genome sequence includes:
[0014] Obtaining the first genotype possibility by performing probability calculation based on the bases of the first target genetic marker genome sequence and the sequencing error rate of each base;
[0015] The performing probability calculation on the second target genetic marker genome sequence to obtain the second genotype possibility corresponding to the second target genetic marker genome sequence comprises:
[0016] The second genotype possibility is obtained by performing probability calculation based on the bases of the second target genetic marker genome sequence and the sequencing error rate of each base.
[0017] Optionally, the analyzing the first genotype possibility, the second genotype possibility and the allele population frequency based on a preset model to obtain the common ancestor fragment and the common ancestor coefficient includes:
[0018] inputting the first genotype possibility and the second genotype possibility into the preset model; and
[0019] Inputting the allele population frequencies of the first target genetic marker genomic sequence and the second target genetic marker genomic sequence corresponding to the loci into the preset model;
[0020] Based on the preset model, the first genotype possibility, the second genotype possibility and the allele population frequency are iterated and analyzed for a preset number of times to obtain the common ancestor fragment and the common ancestor coefficient.
[0021] Optionally, the iterative analysis of the first genotype possibility, the second genotype possibility and the allele population frequency based on the preset model for a preset number of times to obtain the common ancestor fragment and the common ancestor coefficient includes:
[0022] Screening candidate parameters of a preset number of gradients;
[0023] The candidate parameters are iteratively analyzed a preset number of times based on the preset model to obtain the common ancestor segment and the common ancestor coefficient.
[0024] Optionally, before analyzing the common ancestor segment and the common ancestor coefficient by a preset learning model to obtain a result of the kinship identification between the first identification sample and the second identification sample, the method includes:
[0025] Obtain training samples with known kinship;
[0026] The preset learning model is trained using the training samples to obtain the trained preset learning model.
[0027] According to a second aspect of the present disclosure, there is provided a device for kinship identification, comprising:
[0028] A first acquisition unit is used to acquire a first target genetic marker genome sequence and a second target genetic marker genome sequence, wherein the first target genetic marker genome sequence corresponds to the same genome site at which the first identification sample is located as the second target genetic marker genome sequence corresponds to the same genome site at which the second identification sample is located;
[0029] a calculation unit, configured to perform probability calculation on the first target genetic marker genome sequence to obtain a first genotype possibility corresponding to the first target genetic marker genome sequence, and to perform probability calculation on the second target genetic marker genome sequence to obtain a second genotype possibility corresponding to the second target genetic marker genome sequence;
[0030] A first analysis unit is used to analyze the first genotype possibility, the second genotype possibility and the allele population frequency based on a preset model to obtain a common ancestor segment and a common ancestor coefficient;
[0031] The second analysis unit is used to analyze the common ancestor fragment and the common ancestor coefficient through a preset learning model to obtain a kinship identification result between the first identification sample and the second identification sample.
[0032] Optionally, the first acquiring unit further includes:
[0033] An acquisition module, used for acquiring the first identification sample and the second identification sample;
[0034] A sequencing module, used to sequence the first identification sample and the second identification sample respectively through a preset genome sequencing program to obtain a first genome sequence corresponding to the first identification sample and a second genome sequence corresponding to the second identification sample;
[0035] A comparison module is used to compare the first genome sequence and the second genome sequence with a reference genome data set, respectively, to obtain the first target genetic marker genome sequence corresponding to the first genome sequence and the second target genetic marker genome sequence corresponding to the second genome sequence, respectively, and the reference genome data set is constructed based on the target population genome sequence.
[0036] Optionally, the computing unit is further used for:
[0037] Obtaining the first genotype possibility by performing probability calculation based on the bases of the first target genetic marker genome sequence and the sequencing error rate of each base;
[0038] The computing unit is also used for:
[0039] The second genotype possibility is obtained by performing probability calculation based on the bases of the second target genetic marker genome sequence and the sequencing error rate of each base.
[0040] Optionally, the first analysis unit includes:
[0041] A first input module, configured to input the first genotype possibility and the second genotype possibility into the preset model; and
[0042] A second input module, used to input the allele population frequencies of the first target genetic marker genome sequence and the second target genetic marker genome sequence corresponding sites into the preset model;
[0043] An analysis module is used to perform a preset number of iterative analyses on the first genotype possibility, the second genotype possibility and the allele population frequency based on the preset model to obtain the common ancestor fragment and the common ancestor coefficient.
[0044] Optionally, the analysis module is further used for:
[0045] Screening candidate parameters of a preset number of gradients;
[0046] The candidate parameters are iteratively analyzed a preset number of times based on the preset model to obtain the common ancestor segment and the common ancestor coefficient.
[0047] Optionally, the device further comprises:
[0048] A second acquisition unit is used to acquire training samples with known kinship;
[0049] The training unit is used to train the preset learning model through the training samples to obtain the trained preset learning model.
[0050] According to a third aspect of the present disclosure, there is provided an electronic device, including:
[0051] at least one processor; and
[0052] a memory communicatively connected to the at least one processor; wherein,
[0053] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.
[0054] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.
[0055] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.
[0056] The method and device, electronic device and storage medium provided by the present invention have the following main technical solutions: obtaining a first target genetic marker genome sequence and a second target genetic marker genome sequence, wherein the genome site where the first target genetic marker genome sequence is located in the first identification sample is the same as the genome site where the second target genetic marker genome sequence is located in the second identification sample; performing probability calculation on the first target genetic marker genome sequence to obtain a first genotype possibility corresponding to the first target genetic marker genome sequence, and performing probability calculation on the second target genetic marker genome sequence to obtain a second genotype possibility corresponding to the second target genetic marker genome sequence; analyzing the first genotype possibility, the second genotype possibility and the allele population frequency based on a preset model to obtain a common ancestor segment and a common ancestor coefficient; analyzing the common ancestor segment and the common ancestor coefficient by a preset learning model to obtain a kinship identification result between the first identification sample and the second identification sample. Compared with the related art, based on the probability calculation of the first target genetic marker genome sequence and the second target genetic marker genome sequence respectively, the first genotype possibility corresponding to the first target genetic marker genome sequence and the second genotype possibility corresponding to the second genetic marker are obtained, and the first genotype possibility, the second genotype possibility and the allele population frequency are analyzed based on a preset model to obtain the common ancestor fragment and the common ancestor coefficient corresponding to the second target genetic marker genome sequence and the first target genetic marker genome sequence; the common ancestor fragment and the common ancestor coefficient are analyzed by a preset learning model to obtain the identification result of the kinship between the sample corresponding to the first target genetic marker genome sequence and the sample corresponding to the second target genetic marker genome sequence, the common ancestor fragment and the common ancestor coefficient are obtained based on the genotype possibility, and then the common ancestor fragment and the common ancestor coefficient are analyzed based on the preset learning model to obtain the kinship identification result, thereby improving the accuracy of kinship identification.
[0057] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0058] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.
[0059] Figure 1 A schematic diagram of a flow chart of a method for kinship identification provided by an embodiment of the present disclosure;
[0060] Figure 2A schematic diagram of a process for obtaining a genetic marker provided by an embodiment of the present disclosure;
[0061] Figure 3 A schematic diagram of determining and identifying the haplotype of a sample based on a reference genome dataset provided in an embodiment of the present disclosure;
[0062] Figure 4 A schematic diagram of obtaining a common ancestor fragment based on haplotype comparison provided in this embodiment;
[0063] Figure 5 A schematic diagram of a computer program input and output provided by an embodiment of the present disclosure;
[0064] Figure 6 A schematic diagram of the identification effect of the method for kinship identification provided in an embodiment of the present disclosure;
[0065] Figure 7 A schematic diagram of the structure of a kinship identification device provided in an embodiment of the present disclosure;
[0066] Figure 8 A schematic diagram of another kinship identification device provided in an embodiment of the present disclosure
[0067] Fig. 9 A schematic block diagram of an exemplary electronic device 400 provided for an embodiment of the present disclosure. DETAILED DESCRIPTION
[0068] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.
[0069] The following describes the method and apparatus for kinship identification, electronic device, and storage medium according to embodiments of the present disclosure with reference to the accompanying drawings.
[0070] Figure 1 A schematic diagram of a flow chart of a method for kinship identification provided in an embodiment of the present disclosure.
[0071] like Figure 1 As shown, the method comprises the following steps:
[0072] Step 101, obtaining a first target genetic marker genomic sequence and a second target genetic marker genomic sequence, wherein the genomic site of the first target genetic marker genomic sequence at the first identification sample corresponds to the same genomic site of the second target genetic marker genomic sequence at the second identification sample.
[0073] As a refinement of the above step 101, the genome sequence of the first target genetic marker is a partial genome sequence corresponding to the first target genetic marker in the first identification sample. The genome sequence of the second target genetic marker is a partial genome sequence corresponding to the second target genetic marker in the second identification sample; the first target genetic marker and the second target genetic marker are essentially the same, that is, some specific sites selected on the genome sequence, and the genome site where the first target genetic marker is located in the first identification sample corresponds to the genome site where the second target genetic marker is located in the second identification sample. Specifically, the genetic marker sample is the nucleotides of some sites on the chromosome used as markers in bioinformatics analysis, which have two basic characteristics, namely, heritability and recognizability. The heritability means that the target genetic marker can be inherited from the previous generation to the next generation, and the recognizability means that it can be identified based on different shapes. For example, the gene sequences corresponding to single eyelids and double eyelids can be used as genetic markers. In actual scenarios, the selection of the genetic marker is not limited to the above description, and it does not constitute a limitation on the embodiments of the present disclosure.
[0074] Step 102, perform probability calculation on the first target genetic marker genome sequence to obtain the first genotype possibility corresponding to the first target genetic marker genome sequence, and perform probability calculation on the second target genetic marker genome sequence to obtain the second genotype possibility corresponding to the second target genetic marker genome sequence.
[0075] As a refinement of the above step 102, the first genotype possibility is the probability that each base site in the first target genetic marker genome sequence is adenine, guanine, thymine, and cytosine, and the second genotype possibility is the probability that each base site in the second target genetic marker genome sequence is adenine, guanine, thymine, and cytosine. The first genotype possibility is obtained by performing probability calculation on the first target genetic marker genome sequence; and the second genotype possibility is obtained by performing probability calculation on the second target genetic marker genome sequence. It should be understood that the genotype is all combinations of alleles at a specific site on the genome sequence (for example: if the alleles of a site are A and G, then the genotype is AA, AG, and GG). The first target genetic marker genome sequence includes multiple specific sites, and each specific site also has multiple genotypes; the second target genetic marker genome sequence includes multiple specific sites, and each specific site also has multiple genotypes.
[0076] Step 103, analyzing the first genotype possibility, the second genotype possibility and the allele population frequency based on a preset model to obtain a common ancestor segment and a common ancestor coefficient.
[0077] As a refinement of the above step 103, the preset model is an algorithm model, for example, it can be a maximum likelihood estimation, a hidden Markov model, etc. The present disclosure does not limit the selection of the preset model, and the first genotype possibility, the second genotype possibility and the allele group frequency are used as inputs of the preset model, and the common ancestor fragment and the common ancestor coefficient are finally calculated and analyzed. The allele group frequency is the percentage of the allele in a specific locus, and the allele is the allele on the locus corresponding to the first target genetic marker genome sequence and the second target genetic marker genome sequence, and the population is the population in which the first identification sample and the second target identification sample are located.
[0078] Among them, the common ancestor segment described in the above step 103 is the probability of haplotype matching between the corresponding sites of the first identification sample and the second identification sample. Here, a diploid organism is taken as an example. For example, if the two haplotypes corresponding to the first target genetic marker genome sequence are different from the two haplotypes corresponding to the second target genetic marker genome sequence, it is not matched, and its matching situation is recorded as IBD0. Similarly, if the two haplotypes corresponding to the first target genetic marker genome sequence and the two haplotypes corresponding to the second target genetic marker genome sequence have a pair of haplotypes that are the same, their matching situation is recorded as IBD1, and the two haplotypes corresponding to the first target genetic marker genome sequence are the same as the two haplotypes corresponding to the second target genetic marker genome sequence, and their matching situation is recorded as IBD2. Among them, the ratio of the number of matching situations IBD0, IBD1, and IBD2 to the total number of matching situations is the common ancestor segment, and the common ancestor coefficient is the expected value of the closeness of the kinship.
[0079] Step 104: Analyze the common ancestor fragment and the common ancestor coefficient through a preset learning model to obtain a result of the kinship identification between the first identification sample and the second identification sample.
[0080] As a refinement of the above step 104, the preset learning model is a neural network model, which is the application of machine learning in the embodiment of the present disclosure. The common ancestor fragment and the common ancestor coefficient are taken as inputs and input into the preset learning model. The above parameters are analyzed based on the preset learning model to obtain the identification result of the kinship, and its accuracy is improved compared with the traditional analysis method.
[0081] The method for kinship identification provided by the present disclosure mainly comprises the following technical solutions: obtaining a first target genetic marker genome sequence and a second target genetic marker genome sequence, wherein the genome site where the first target genetic marker genome sequence is located in the first identification sample is the same as the genome site where the second target genetic marker genome sequence is located in the second identification sample; performing probability calculation on the first target genetic marker genome sequence to obtain a first genotype possibility corresponding to the first target genetic marker genome sequence, and performing probability calculation on the second target genetic marker genome sequence to obtain a second genotype possibility corresponding to the second target genetic marker genome sequence; analyzing the first genotype possibility, the second genotype possibility and the allele population frequency based on a preset model to obtain a common ancestor segment and a common ancestor coefficient; and analyzing the common ancestor segment and the common ancestor coefficient by a preset learning model to obtain a kinship identification result between the first identification sample and the second identification sample. Compared with the related art, based on the probability calculation of the first target genetic marker genome sequence and the second target genetic marker genome sequence respectively, the first genotype possibility corresponding to the first target genetic marker genome sequence and the second genotype possibility corresponding to the second genetic marker are obtained, and the first genotype possibility, the second genotype possibility and the allele population frequency are analyzed based on a preset model to obtain the common ancestor fragment and the common ancestor coefficient corresponding to the second target genetic marker genome sequence and the first target genetic marker genome sequence; the common ancestor fragment and the common ancestor coefficient are analyzed by a preset learning model to obtain the identification result of the kinship between the sample corresponding to the first target genetic marker genome sequence and the sample corresponding to the second target genetic marker genome sequence, the common ancestor fragment and the common ancestor coefficient are obtained based on the genotype possibility, and then the common ancestor fragment and the common ancestor coefficient are analyzed based on the preset learning model to obtain the kinship identification result, thereby improving the accuracy of kinship identification.
[0082] Before performing the identification of the kinship in the above embodiment, the method further includes a method of obtaining the first target genetic marker genome sequence and the second target genetic marker genome sequence, Figure 2 A schematic diagram of a method for obtaining a genetic marker provided by an embodiment of the present disclosure is shown in FIG. Figure 2 As shown:
[0083] Step 201: Obtain the first identification sample and the second identification sample.
[0084] As a refinement of the above step 201, the first identification sample is a material with genetic material carried by an organism of the same population, and the second identification sample is a material with genetic material carried by another organism of the same population. For example, the first identification sample can be saliva, hair, etc. of one organism, and the second identification sample can be saliva, etc. of another organism. The present disclosure does not limit the selection of the above first identification sample and the second identification sample.
[0085] Step 202, sequencing the first identification sample and the second identification sample respectively by a preset genome sequencing program to obtain a first genome sequence corresponding to the first identification sample and a second genome sequence corresponding to the second identification sample;
[0086] As a refinement of the above step 202, the preset genome sequencing program can be low-depth whole genome sequencing or high-depth whole genome sequencing. The embodiment of the present disclosure does not limit the selection of the preset genome sequencing program. The first identification sample and the second identification sample are sequenced based on the preset genome sequencing program to obtain a first genome sequence corresponding to the first identification sample and a second genome sequence corresponding to the second identification sample. The first genome sequence is the offline data of the first identification sample after sequencing, which includes the base sequence of the first identification sample and parameters such as the quality score of each base site detection. The specific content is not limited in this embodiment. The second genome sequence is the offline data of the second identification sample after sequencing, similar to the above-mentioned first genome sequence, the second genome sequence includes the base sequence of the second identification sample and parameters such as the quality score of each base site detection in the sequence. Similarly, the above description of the second genome sequence does not constitute a limitation of the present disclosure.
[0087] Step 203, respectively aligning the first genome sequence and the second genome sequence with a reference genome data set to obtain the first target genetic marker genome sequence corresponding to the first genome sequence and the second target genetic marker genome sequence corresponding to the second genome sequence, respectively, wherein the reference genome data set is constructed based on the target population genome sequence.
[0088] As a refinement of the above step 203, it is necessary to understand that the first genome sequence and the second genome sequence obtained through the above step 202 are genome sequence fragments with disordered order. In order to obtain the genome sequence fragments in order and to obtain the first target genetic marker genome sequence in the first genome sequence and the second target genetic marker genome sequence in the second gene sequence, the first genome sequence is compared with the reference genome data set to obtain the first genome sequence in order and the first target genetic marker genome sequence in the first genome sequence is determined based on the reference genome data set; the second genome sequence is compared with the reference genome data set to obtain the second genome sequence in order and the second target genetic marker genome sequence in the second genome sequence is determined based on the reference genome data set. In the description of the above embodiment, the meanings of the first target genetic marker genome sequence and the second target genetic marker genome sequence have been explained, and for the sake of clarity and simplicity, this embodiment will not be repeated. The first target genetic marker genome sequence and the second target genetic marker genome sequence are sites on chromosomes with allele population frequencies greater than 5%. It should be understood that this embodiment does not limit the size of the above allele population frequency.
[0089] As a refinement of the above embodiment, when performing the probability calculation of the first target genetic marker genome sequence in step 102 to obtain the first genotype possibility corresponding to the first target genetic marker genome sequence, the method includes: performing the probability calculation according to the bases of the first target genetic marker genome sequence and the sequencing error rate of each base to obtain the first genotype possibility. And
[0090] When performing the probability calculation on the second target genetic marker genome sequence as described in step 102 to obtain the second genotype possibility corresponding to the second target genetic marker genome sequence, the method includes: performing a probability calculation based on the bases of the second target genetic marker genome sequence and the sequencing error rate of each base to obtain the second genotype possibility.
[0091] The calculation of the first genotype possibility is obtained by probability calculation based on the base type in the first target genetic marker genome sequence and the sequencing error rate of each base; the calculation of the second genotype possibility is obtained by probability calculation based on the base type in the second target genetic marker genome sequence and the sequencing error rate of each base.
[0092] As a refinement of the above embodiment, when executing step 103 to analyze the first genotype possibility, the second genotype possibility and the allele group frequency based on the preset model to obtain the common ancestor segment and the common ancestor coefficient, the following implementation methods can be used but are not limited to, for example: inputting the first genotype possibility and the second genotype possibility into the preset model; and inputting the allele group frequencies of the corresponding sites of the first target genetic marker genome sequence and the second target genetic marker genome sequence into the preset model; based on the preset model, performing an iterative analysis of the first genotype possibility, the second genotype possibility and the allele group frequency for a preset number of times to obtain the common ancestor segment and the common ancestor coefficient.
[0093] Exemplarily, the first genotype possibility and the second genotype possibility and the allele population frequency are used as input parameters of the hidden Markov model, that is, as input parameters of the preset model. The hidden Markov model iterates a preset number of times based on the above parameters to finally obtain the optimal common ancestor fragment parameters and the common ancestor coefficient.
[0094] In order to facilitate a clear understanding of the calculation of the above-mentioned common ancestor fragment and common ancestor coefficient, the present disclosure provides a principled embodiment, including:
[0095] For diploid identification samples, two identification samples have three states R = (k0, k1, k2) at the same genetic marker site (or IBD). k0 represents the probability that there is no identical genetic marker (e.g., AA and aa) at the site, k1 represents the probability that there is an identical genetic marker (e.g., AA and Aa) at the site, and k2 represents the probability that there are two identical genetic markers (e.g., AA and AA) at the site. Therefore, the probabilities of the three state transitions can be obtained as follows:
[0096]
[0097] Among them, α represents the probability of state change, which controls the speed of state change. At the same time, the state transition probability between k0 and k2 is set to 0.
[0098] Assume that the true genotypes of the two samples whose kinship needs to be identified are: and The genotypes obtained from sequencing data are: and The corresponding allele population frequency (assuming 2 alleles): The state of IBD is: X=(X1,…,X L), where i and j represent two samples, and L represents the total number of genetic markers. Therefore, the IBD state of the kinship between the two samples (i and j) is a hidden Markov chain. From this, it can be deduced that:
[0099]
[0100] in:
[0101]
[0102] Table 1 shows the emission probabilities See the table below for details:
[0103] Table 1
[0104]
[0105] in: Represents the population frequency of genotype a at position l.
[0106] Thus, the above calculation is implemented through the program to obtain the state R=(k0, k1, k2) of each genetic marker, that is, the common ancestor segment is obtained, and then the common ancestor coefficient is calculated based on the common ancestor segment.
[0107] As a refinement of the above embodiment, when performing the iterative analysis of the first genotype possibility, the second genotype possibility and the allele population frequency based on the preset model for a preset number of times to obtain the common ancestral fragment and the common ancestral coefficient, the following implementation methods can be adopted but are not limited to, for example: screening candidate parameters with a preset number gradient; performing an iterative analysis of the candidate parameters for a preset number of times based on the preset model to obtain the common ancestral fragment and the common ancestral coefficient.
[0108] For example, two to three gradient candidate parameters are selected, and the candidate parameters are iteratively analyzed for a preset number of times based on the preset model to obtain the optimal common ancestor segment and then calculate the common ancestor coefficient. The selection of candidate parameters based on the preset number of gradients greatly reduces the amount of data analysis for each iteration, thereby saving analysis time.
[0109] As a refinement of the above embodiment, before executing step 104 to analyze the common ancestor fragment and the common ancestor coefficient through the preset learning model to obtain the kinship identification result between the first identification sample and the second identification sample, the method also includes but is not limited to the following implementation methods such as: obtaining training samples with known kinship; training the preset learning model through the training samples to obtain the trained preset learning model.
[0110] Exemplarily, the preset learning model is trained using training samples with known kinship, and finally the preset learning model with an identification accuracy that meets a preset value is obtained, and the identification of kinship between identification samples is completed based on the preset learning model.
[0111] In some embodiments, the method for kinship identification further comprises: obtaining haplotype sequences of the first identification sample and the second identification sample based on the reference genome dataset. In order to more intuitively demonstrate the process of obtaining haplotype sequences of the identification samples based on the reference genome dataset, Figure 3 A schematic diagram of determining and identifying the haplotype of a sample based on a reference genome dataset is provided in an embodiment of the present disclosure.
[0112] In some embodiments, the acquisition of the common ancestral fragment can be based on comparing the above haplotypes. Figure 4 A schematic diagram of obtaining common ancestral fragments based on haplotype comparison provided in this embodiment determines the closeness of kinship by comparing the proportions of common ancestral fragments, wherein the diplotypes of parents and children are taken as examples.
[0113] In some embodiments, the common ancestor coefficient is calculated based on the common ancestor fragment to determine the distance of the relationship. In order to more intuitively display the common ancestor coefficient Table 2 shows the relationship between the common ancestry coefficient and the distance of kinship. As to the relationship between the two, it should be understood that the contents shown in Table 2 are merely exemplary and do not constitute a limitation of the present disclosure.
[0114] Table 2
[0115]
[0116] In some embodiments, the first genotype possibility and the second genotype possibility can be obtained by preset software, that is, the first genotype possibility and the second genotype possibility are obtained by analyzing the first genome sequence and the second genome sequence by preset software.
[0117] Corresponding to the above-mentioned method for identifying kinship, the present invention also provides a computer program for calculating the common ancestor segment and the common ancestor coefficient according to the first genotype possibility and the second genotype possibility, Figure 5 A schematic diagram of a computer program input and output provided in an embodiment of the present disclosure; Figure 5 As shown, the specific working process of the computer program includes:
[0118] Obtain the first genotype possibility file in beagle format and the second genotype possibility file in beagle format, and the allele population frequency file, analyze based on the computer program, obtain the common ancestor fragment and the common ancestor coefficient, and input the common ancestor fragment and the common ancestor coefficient into the preset learning model to obtain the kinship result. The first genotype possibility file and the second genotype possibility file are independently input, which can prevent too much data in a single file from exceeding the maximum range of the file array, thereby making the program run more stable; the computer program selects 2-3 gradient candidate parameters for iterative analysis, which greatly shortens the analysis time. The preset learning model can use the k-nearest neighbor method to preset the kinship result.
[0119] Figure 6 A schematic diagram of the identification effect of the method for identifying kinship provided in the embodiment of the present disclosure, such as Figure 6 As shown:
[0120] Among them, 1stC refers to cousins, great-grandfather and great-grandson; FS refers to brothers and sisters; GAH refers to grandparents and children, grandparents and nephews, aunts, uncles and nephews, half brothers and sisters; MZ refers to identical twins; PC refers to parents and children; UN refers to a more distant relationship than 1stC.
[0121] In summary, this embodiment can achieve the following effects:
[0122] 1. The common ancestor fragment and the common ancestor coefficient are obtained based on the genotype possibility, and then the common ancestor fragment and the common ancestor coefficient are analyzed based on the preset learning model to obtain the kinship identification result, thereby improving the accuracy of kinship identification.
[0123] 2. Based on the genotype possibility, the common ancestor fragment is directly obtained, and the common ancestor coefficient is calculated based on this, which ensures the accuracy of the data results to the greatest extent.
[0124] 3. By presetting candidate parameters of quantitative gradients, the analysis time is greatly reduced.
[0125] 4. Suitable for analysis of whole genome data with mixed high and low depth.
[0126] Corresponding to the above-mentioned method for kinship identification, the present invention also provides a device for kinship identification. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be described in detail in this embodiment.
[0127] Figure 7A schematic diagram of the structure of a kinship identification device provided in an embodiment of the present disclosure, such as Figure 7 As shown, including:
[0128] A first acquisition unit 31 is used to acquire a first target genetic marker genome sequence and a second target genetic marker genome sequence, wherein the first target genetic marker genome sequence at a genome site of the first identification sample corresponds to the same genome site of the second target genetic marker genome sequence at the second identification sample;
[0129] A calculation unit 32 is used to perform probability calculation on the first target genetic marker genome sequence to obtain a first genotype possibility corresponding to the first target genetic marker genome sequence, and to perform probability calculation on the second target genetic marker genome sequence to obtain a second genotype possibility corresponding to the second target genetic marker genome sequence;
[0130] A first analysis unit 33 is used to analyze the first genotype possibility, the second genotype possibility and the allele population frequency based on a preset model to obtain a common ancestor segment and a common ancestor coefficient;
[0131] The second analysis unit 34 is used to analyze the common ancestor segment and the common ancestor coefficient through a preset learning model to obtain a result of the kinship identification between the first identification sample and the second identification sample.
[0132] The device for kinship identification provided by the present disclosure mainly comprises the following technical solutions: obtaining a first target genetic marker genome sequence and a second target genetic marker genome sequence, wherein the genome site where the first target genetic marker genome sequence is located in the first identification sample is the same as the genome site where the second target genetic marker genome sequence is located in the second identification sample; performing probability calculation on the first target genetic marker genome sequence to obtain a first genotype possibility corresponding to the first target genetic marker genome sequence, and performing probability calculation on the second target genetic marker genome sequence to obtain a second genotype possibility corresponding to the second target genetic marker genome sequence; analyzing the first genotype possibility, the second genotype possibility and the allele population frequency based on a preset model to obtain a common ancestor segment and a common ancestor coefficient; and analyzing the common ancestor segment and the common ancestor coefficient by a preset learning model to obtain a kinship identification result between the first identification sample and the second identification sample. Compared with the related art, based on the probability calculation of the first target genetic marker genome sequence and the second target genetic marker genome sequence respectively, the first genotype possibility corresponding to the first target genetic marker genome sequence and the second genotype possibility corresponding to the second genetic marker are obtained, and the first genotype possibility, the second genotype possibility and the allele population frequency are analyzed based on a preset model to obtain the common ancestor fragment and the common ancestor coefficient corresponding to the second target genetic marker genome sequence and the first target genetic marker genome sequence; the common ancestor fragment and the common ancestor coefficient are analyzed by a preset learning model to obtain the identification result of the kinship between the sample corresponding to the first target genetic marker genome sequence and the sample corresponding to the second target genetic marker genome sequence, the common ancestor fragment and the common ancestor coefficient are obtained based on the genotype possibility, and then the common ancestor fragment and the common ancestor coefficient are analyzed based on the preset learning model to obtain the kinship identification result, thereby improving the accuracy of kinship identification.
[0133] Figure 8 A schematic diagram of the structure of a kinship identification device provided in an embodiment of the present disclosure;
[0134] Furthermore, in a possible implementation of this embodiment, as Figure 8 As shown, the first acquisition unit 31 includes:
[0135] An acquisition module 311 is used to acquire the first identification sample and the second identification sample;
[0136] A sequencing module 312 is used to sequence the first identification sample and the second identification sample respectively through a preset genome sequencing program to obtain a first genome sequence corresponding to the first identification sample and a second genome sequence corresponding to the second identification sample;
[0137] The comparison module 313 is used to compare the first genome sequence and the second genome sequence with a reference genome data set, respectively, to obtain the first target genetic marker genome sequence corresponding to the first genome sequence and the second target genetic marker genome sequence corresponding to the second genome sequence, respectively, and the reference genome data set is constructed based on the target population genome sequence.
[0138] Optionally, the computing unit 32 is further configured to:
[0139] Obtaining the first genotype possibility by performing probability calculation based on the bases of the first target genetic marker genome sequence and the sequencing error rate of each base;
[0140] The calculation unit 32 is also used for:
[0141] The second genotype possibility is obtained by performing probability calculation based on the bases of the second target genetic marker genome sequence and the sequencing error rate of each base.
[0142] Optionally, the first analyzing unit 33 includes:
[0143] A first input module 331 is used to input the first genotype possibility and the second genotype possibility into the preset model; and
[0144] A second input module 332 is used to input the allele population frequencies of the first target genetic marker genome sequence and the second target genetic marker genome sequence corresponding sites into the preset model;
[0145] The analysis module 333 is used to perform a preset number of iterative analyses on the first genotype possibility, the second genotype possibility and the allele population frequency based on the preset model to obtain the common ancestor fragment and the common ancestor coefficient.
[0146] Optionally, the analysis module 333 is further used for:
[0147] Screening candidate parameters of a preset number of gradients;
[0148] The candidate parameters are iteratively analyzed a preset number of times based on the preset model to obtain the common ancestor segment and the common ancestor coefficient.
[0149] Optionally, the device further comprises:
[0150] A second acquisition unit 35 is used to acquire training samples with known kinship;
[0151] The training unit 36 is used to train the preset learning model using the training samples to obtain the trained preset learning model.
[0152] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is no longer limited in this embodiment.
[0153] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
[0154] Fig. 9 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.
[0155] like Fig. 9 As shown, the device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 402 or a computer program loaded from a storage unit 408 to a RAM (Random Access Memory) 403. In RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An I / O (Input / Output) interface 405 is also connected to the bus 404.
[0156] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0157] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the method of kinship identification. For example, in some embodiments, the method of kinship identification may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the aforementioned kinship identification method in any other appropriate manner (eg, by means of firmware).
[0158] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a special purpose or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0159] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0160] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0161] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).
[0162] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.
[0163] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.
[0164] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.
[0165] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.
[0166] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.
Claims
1. A method for identifying kinship, characterized in that: include: Obtaining a first target genetic marker genomic sequence and a second target genetic marker genomic sequence, wherein the genomic site where the first target genetic marker genomic sequence is located in the first identification sample corresponds to the same genomic site where the second target genetic marker genomic sequence is located in the second identification sample; Performing probability calculation on the first target genetic marker genome sequence to obtain a first genotype possibility corresponding to the first target genetic marker genome sequence, and performing probability calculation on the second target genetic marker genome sequence to obtain a second genotype possibility corresponding to the second target genetic marker genome sequence; Analyze the first genotype possibility, the second genotype possibility and the allele population frequency based on a preset model to obtain a common ancestor segment and a common ancestor coefficient; The common ancestor fragment and the common ancestor coefficient are analyzed by a preset learning model to obtain a result of the kinship identification between the first identification sample and the second identification sample.
2. The method according to claim 1, characterized in that The obtaining of the first target genetic marker genome sequence and the second target genetic marker genome sequence comprises: Obtaining the first identification sample and the second identification sample; Sequencing the first identification sample and the second identification sample respectively by a preset genome sequencing program to obtain a first genome sequence corresponding to the first identification sample and a second genome sequence corresponding to the second identification sample; The first genome sequence and the second genome sequence are respectively compared with a reference genome data set to obtain the first target genetic marker genome sequence corresponding to the first genome sequence and the second target genetic marker genome sequence corresponding to the second genome sequence, respectively. The reference genome data set is constructed based on the genome sequence of the target population.
3. The method according to claim 1, characterized in that The performing probability calculation on the first target genetic marker genome sequence to obtain the first genotype possibility corresponding to the first target genetic marker genome sequence comprises: Obtaining the first genotype possibility by performing probability calculation based on the bases of the first target genetic marker genome sequence and the sequencing error rate of each base; The performing probability calculation on the second target genetic marker genome sequence to obtain the second genotype possibility corresponding to the second target genetic marker genome sequence comprises: The second genotype possibility is obtained by performing probability calculation based on the bases of the second target genetic marker genome sequence and the sequencing error rate of each base.
4. The method according to claim 1, characterized in that The analysis of the first genotype possibility, the second genotype possibility and the allele population frequency based on the preset model to obtain the common ancestor fragment and the common ancestor coefficient includes: inputting the first genotype possibility and the second genotype possibility into the preset model; and Inputting the allele population frequencies of the first target genetic marker genomic sequence and the second target genetic marker genomic sequence corresponding to the loci into the preset model; Based on the preset model, the first genotype possibility, the second genotype possibility and the allele population frequency are iterated and analyzed for a preset number of times to obtain the common ancestor fragment and the common ancestor coefficient.
5. The method according to claim 4, characterized in that The iterative analysis of the first genotype possibility, the second genotype possibility and the allele population frequency based on the preset model for a preset number of times to obtain the common ancestor fragment and the common ancestor coefficient includes: Screening candidate parameters of a preset number of gradients; The candidate parameters are iteratively analyzed a preset number of times based on the preset model to obtain the common ancestor segment and the common ancestor coefficient.
6. The method according to claim 1, characterized in that Before analyzing the common ancestor segment and the common ancestor coefficient by using a preset learning model to obtain a result of the kinship identification between the first identification sample and the second identification sample, the method includes: Obtain training samples with known kinship; The preset learning model is trained using the training samples to obtain the trained preset learning model.
7. A device for identifying kinship, characterized in that: include: A first acquisition unit is used to acquire a first target genetic marker genome sequence and a second target genetic marker genome sequence, wherein the first target genetic marker genome sequence corresponds to the same genome site at which the first identification sample is located as the second target genetic marker genome sequence corresponds to the same genome site at which the second identification sample is located; a calculation unit, configured to perform probability calculation on the first target genetic marker genome sequence to obtain a first genotype possibility corresponding to the first target genetic marker genome sequence, and to perform probability calculation on the second target genetic marker genome sequence to obtain a second genotype possibility corresponding to the second target genetic marker genome sequence; A first analysis unit is used to analyze the first genotype possibility, the second genotype possibility and the allele population frequency based on a preset model to obtain a common ancestor segment and a common ancestor coefficient; The second analysis unit is used to analyze the common ancestor fragment and the common ancestor coefficient through a preset learning model to obtain a kinship identification result between the first identification sample and the second identification sample.
8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.
9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.
10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.