IBD fragment identification method, system, electronic device and storage medium
Patent Information
- Application Number
- CN202410791789.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-19
- Publication Date
- 2026-09-22
- Estimated Expiration
- 2044-06-19
AI Technical Summary
目前多数算法是基于定相的方法，然而此类方法主要针对的是高质量的DNA样本，对于疑难DNA样本IBD片段识别结果的假阴性率高，无法有效识别此类样本之间的IBD片段
[0039]综上所述，与现有技术相比，本申请提供的技术方案带来的有效效果至少包括：
Smart Images
Figure CN118692560B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of biometric identification technology, and in particular to an IBD fragment identification method, system, electronic device and storage medium. Background Technology
[0002] IBD fragments refer to identical DNA sequences shared by related individuals from a common ancestor, i.e., identity by descent (IBD). IBD fragment analysis is widely used in forensic pedigree analysis, population evolutionary history studies, mutation and recombination rate estimation, and disease gene association analysis. However, IBD represents a theoretical state of DNA homology and cannot be directly detected. In practice, the presence of IBD fragments can only be inferred based on obtained DNA typing (such as SNP typing). Furthermore, due to mutations in the DNA itself and errors generated during sequencing, sequences that should be IBD may differ, making IBD identification difficult. Based on the characteristics of the typing data, IBD fragment identification methods can be divided into two types: phase-based and non-phase-based. Currently, most algorithms are based on phase-based methods; however, these methods are mainly for high-quality DNA samples and have a high false negative rate for identifying IBD fragments in difficult-to-identify DNA samples, failing to effectively identify IBD fragments between such samples. Summary of the Invention
[0003] The main objective of this application is to overcome the shortcomings and deficiencies of the prior art and provide an IBD fragment identification method, system, electronic device and storage medium. By setting a corresponding threshold based on the difference in the heterozygous ratio distribution between IBD fragments and non-IBD fragments, stable and efficient identification of IBD fragments can be achieved.
[0004] To achieve the above objectives, this application adopts the following technical solution:
[0005] In a first aspect, this application provides a method for IBD fragment identification, comprising the following steps:
[0006] The obtained SNP sites are preprocessed;
[0007] Based on the position of the preprocessed SNP sites on the chromosome, the chromosome is divided into windows with a fixed number of SNP sites, and the proportion of heterohomozygotes in each window is calculated.
[0008] The nuclear density is estimated based on the calculated proportion of heterohomozygotes, and a threshold is set based on the nuclear density estimation results.
[0009] Based on the threshold, IBD segments and non-IBD segments are determined;
[0010] The IBD segment is gap-filled to obtain a new IBD segment, and the start and end positions of the new IBD segment are repositioned.
[0011] As a preferred technical solution, the preprocessing includes:
[0012] SNP sites with a minor allele frequency of less than 0.05 and SNP sites with a genotyping rate of less than 0.5 were removed.
[0013] As a preferred technical solution, the formula for kernel density estimation is:
[0014]
[0015] Where K is the kernel density function; h is the set window width; n is the number of windows; X represents a specific location point, Xn... i This represents the observed proportion of heterohomozygotes.
[0016] The results of the nuclear density estimation include both bimodal and unimodal heterozygous proportion distributions.
[0017] As a preferred technical solution, the step of setting a threshold based on the kernel density estimation result includes:
[0018] For the bimodal distribution of the proportion of heterohomozygotes, identify the trough between the two peaks and set the proportion of heterohomozygotes corresponding to the trough as the first threshold.
[0019] For a unimodal heterohomozygous proportion distribution, several SNP site samples are randomly selected, and then a window is divided to estimate the 15th percentile of the heterohomozygous proportion. The second threshold is then calculated based on the 15th percentile of the heterohomozygous proportion.
[0020] As a preferred technical solution, the step of determining IBD fragments and non-IBD fragments based on the threshold includes:
[0021] Determine whether the proportion of heterohomozygotes in multiple consecutive windows is less than the first threshold or the second threshold. If the proportion of heterohomozygotes in multiple consecutive windows is less than the first threshold or the second threshold, then the region corresponding to the multiple consecutive windows is an IBD segment; otherwise, it is a non-IBD segment.
[0022] As a preferred technical solution, the process of filling the gaps in the IBD fragment to obtain a new IBD fragment includes:
[0023] The gap types include intermediate gaps and flank gaps; the intermediate gap is located between two IBD segments; the flank gap is located on one side of an IBD segment;
[0024] The two IBD segments and the middle gap are merged; if the merged IBD segment has a flank gap, and the proportion of heterohomozygotes in multiple consecutive windows around the flank gap is less than the first threshold or the second threshold, then the merged IBD segment and the flank gap are merged again to obtain a new IBD segment.
[0025] As a preferred technical solution, the step of repositioning the start and end positions of the new IBD segment includes:
[0026] Using the SNP site in the middle of the first window as the endpoint, the starting point is selected step by step forward using the dichotomy method until the proportion of heterohomozygotes is less than the first threshold or the second threshold. Then, the SNP site whose proportion of heterohomozygotes is just less than the first threshold or the second threshold is taken as the final starting point of the new IBD fragment.
[0027] Starting from the SNP site in the middle of the last window, the endpoint is selected step by step using a dichotomy method until the proportion of heterohomozygotes is just less than the first threshold or the second threshold. The SNP site whose proportion of heterohomozygotes is just less than the first threshold or the second threshold is taken as the final endpoint of the new IBD fragment.
[0028] Secondly, this application provides an IBD fragment recognition system, applied to the aforementioned IBD fragment recognition method, including a preprocessing module, an isohomozygosity ratio module, a threshold setting module, an IBD fragment judgment module, and a start and end position positioning module;
[0029] The preprocessing module is used to preprocess the acquired SNP sites;
[0030] The heterohomozygosity ratio module is used to divide the chromosome into windows with a fixed number of SNP sites according to the position of the preprocessed SNP sites on the chromosome, and calculate the heterohomozygosity ratio in each window.
[0031] The threshold setting module is used to estimate the nuclear density based on the calculated proportion of heterohomozygotes and to set a threshold based on the nuclear density estimation result.
[0032] The IBD segment determination module is used to determine whether an IBD segment or a non-IBD segment is obtained based on the threshold.
[0033] The positioning start and end position module is used to fill the gaps in the IBD segment to obtain a new IBD segment, and to reposition the start and end positions of the new IBD segment.
[0034] Thirdly, this application provides an electronic device, the electronic device comprising:
[0035] At least one processor; and,
[0036] A memory communicatively connected to the at least one processor; wherein,
[0037] The memory stores computer program instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the IBD fragment recognition method.
[0038] Fourthly, this application provides a computer-readable storage medium storing a program that, when executed by a processor, implements the aforementioned IBD fragment recognition method.
[0039] In summary, compared with the prior art, the effective effects of the technical solution provided in this application include at least the following:
[0040] This application proposes an IBD fragment identification method. By setting a corresponding threshold based on the difference in the heterozygous ratio distribution of IBD fragments and non-IBD fragments, the false negative rate can be effectively reduced, achieving stable and efficient identification of IBD fragments in difficult DNA samples. The set threshold can be dynamically changed according to the different genotyping error rates of the samples, making it suitable for genotyping data of various qualities. In addition, adjacent IBD fragments are further merged by gap filling, reducing fragment breakage caused by sequencing errors or mutations. At the same time, the start and end positions of new IBD fragments are repositioned, making the boundaries of IBD fragments more precise and improving the accuracy of IBD fragment identification. Attached Figure Description
[0041] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0042] Figure 1 This diagram illustrates two strategies for IBD fragment identification: phase-based and non-phase-based.
[0043] Figure 2 A flowchart illustrating an IBD fragment recognition method provided in one embodiment of this application;
[0044] Figure 3 A schematic diagram illustrating the partitioning window and calculation of the proportion of heterohomozygotes provided in one embodiment of this application;
[0045] Figure 4 A schematic diagram illustrating the setting of a threshold based on kernel density estimation results, provided for one embodiment of this application;
[0046] Figure 5 A schematic diagram illustrating the identification of IBD segments according to one embodiment of this application;
[0047] Figure 6 A schematic diagram illustrating the filling of gaps and the start and end positions of repositioning, provided for one embodiment of this application;
[0048] Figure 7 A schematic diagram showing a performance comparison between the algorithm used in this application and existing algorithms for IBD segment recognition, provided as an embodiment of this application;
[0049] Figure 8 A schematic diagram showing a comparison of the accuracy of the algorithm used in this application and commonly used existing algorithms in inferring kinship, provided as an embodiment of this application;
[0050] Figure 9 A schematic diagram showing a comparison of the computational efficiency of the algorithm used in this application with commonly used existing algorithms, provided as an embodiment of this application;
[0051] Figure 10 This is a block diagram of an IBD fragment recognition system provided in one embodiment of this application. Detailed Implementation
[0052] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.
[0053] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.
[0054] In genetic research, accurate identification of IBD fragments is crucial for understanding genetic relationships, genetic diseases, and population structure. Based on the characteristics of genotyping data, IBD fragment identification methods can be divided into two categories: phase-based and phase-free. (See [link to relevant documentation]). Figure 1 ( Figure 1In the diagram, 0 represents the reference genotype, and 1 represents the variant genotype; the red area represents the IBD fragment. However, most existing algorithms are based on phasing methods. Since phasing is time-consuming and can introduce additional errors, leading to missed IBD fragment identification, this application uses non-phasing SNP genotyping data for IBD fragment identification. When the genotyping error rate is 0, if two individuals have an IBD fragment in a certain region of the chromosome, they should have completely identical DNA sequences, i.e., the ratio of heterohomozygotes (e.g., 0 / 0 and 1 / 1) is 0, while the ratio of heterohomozygotes for non-IBD fragments is approximately [missing information]. Here, p and q represent the frequencies of the reference genotype and the variant genotype, respectively. When considering DNA mutations and errors generated during sequencing, the proportion of heterozygous individuals for the IBD fragment is no longer zero, but should be less than the proportion of heterozygous individuals for the non-IBD fragment (genotyping error rate less than a certain threshold). Therefore, if both IBD and non-IBD fragments are present in two individuals, the proportion of heterozygous individuals for the relevant fragments may exhibit a bimodal phenomenon, allowing for the identification of a threshold to distinguish between the two. This threshold can dynamically change with the genotyping error rate of the samples and is applicable to genotyping data of varying quality.
[0055] The SNP mentioned in this application refers to single nucleotide polymorphism.
[0056] Please see Figure 2 One embodiment of this application provides an IBD fragment identification method, comprising the following steps:
[0057] S1. Preprocess the obtained SNP sites.
[0058] Furthermore, the preprocessing includes: removing SNPs with a minor allele frequency of less than 0.05, SNPs with a genotyping rate of less than 0.5, and SNPs exhibiting significant linkage disequilibrium (LD); the remaining number of SNPs should be no less than 160,000. By removing SNPs that do not meet specific criteria and samples with low genotyping rates, the accuracy and reliability of subsequent analyses are ensured.
[0059] S2. Based on the position of the preprocessed SNP sites on the chromosome, the chromosome is divided into windows with a fixed number of SNP sites, and the proportion of heterohomozygotes in each window is calculated.
[0060] Furthermore, to reduce the impact of random effects, the number of SNP loci should be no less than 100, and the number of windows should be no less than 1000. Simultaneously, loci without genotyping data in any sample should be removed. Then, the proportion of heterohomozygotes (e.g., 0 / 0 and 1 / 1) within each window should be calculated. Please refer to [link to relevant documentation]. Figure 3 .
[0061] Specifically, heterohomozygotes refer to two samples that have different homozygous genotypes at a specific SNP locus, i.e., one is type 0 / 0 and the other is type 1 / 1.
[0062] S3. Based on the calculated proportion of heterohomozygotes, perform nuclear density estimation and set a threshold based on the nuclear density estimation results.
[0063] The formula for calculating the kernel density estimate is as follows:
[0064]
[0065] Where K is the kernel density function, h is the set window width, and n is the number of windows; X represents a specific location point, X... i This represents the observed proportion of heterohomozygotes.
[0066] The calculated nuclear density estimates include the phenomenon of bimodal heterohomozygous proportion distribution, i.e., there are both IBD and non-IBD segments between the two individuals; as well as the phenomenon of unimodal heterohomozygous proportion distribution.
[0067] The bimodal heterozygous ratio distribution refers to the presence of two distinct peaks in the data distribution of heterozygous ratios; the peak closer to the y-axis corresponds to IBD segments, while the peak farther from the y-axis corresponds to non-IBD segments. The unimodal heterozygous ratio distribution, on the other hand, exhibits a single distinct peak, indicating that the data distribution between two samples may consist entirely of IBD segments or entirely of non-IBD segments.
[0068] Please see Figure 4 In this embodiment, for the phenomenon of bimodal heterohomozygosity distribution, the peak closer to the Y-axis corresponds to the IBD segment, while the peak farther from the Y-axis corresponds to the non-IBD segment. The heterohomozygosity ratio value corresponding to the trough between the two is set as the first threshold. For the case of heterohomozygosity distribution with a single peak (such as unrelated individuals, parent-child relationships, identical twins, or two SNP genotyping data of the same individual but with genotyping errors, the heterohomozygosity ratio distribution may be much smaller than that of unrelated individuals, so only one peak can be observed), since there is only one obvious peak, a clear threshold cannot be directly obtained from the kernel density estimation results. Therefore, a default second threshold needs to be set. For this case, several SNP loci samples are randomly drawn from the dataset, and then a window is divided to estimate the 15th percentile (He) of the heterohomozygosity ratio. 15 The formula for calculating the second threshold is:
[0069]
[0070] When the phenomenon of a unimodal heterohomozygous proportion distribution is observed, the heterohomozygous proportion value corresponding to the 15th percentile (minQ) of the heterohomozygous proportion is used as the second threshold.
[0071] S4. Based on the threshold, determine whether the segment is IBD or not. Please refer to [link to relevant documentation]. Figure 5 .
[0072] Further steps to determine whether an IBD fragment or a non-IBD fragment is obtained include:
[0073] Theoretically, if the proportion of heterohomozygotes in a window is less than a set first or second threshold, that window may be an IBD fragment. However, due to random effects, a window representing a non-IBD fragment may also be less than the set first or second threshold. This situation is called a false positive (FPR), which incorrectly identifies a non-IBD fragment as an IBD fragment. Therefore, to reduce the impact of false positives in a single window, we consider the case where multiple consecutive windows are less than the set first or second threshold. That is, we determine whether the proportion of heterohomozygotes in multiple consecutive windows is less than the set first or second threshold. If the proportion of heterohomozygotes in multiple consecutive windows is less than the set first or second threshold, then the region corresponding to these multiple consecutive windows is an IBD fragment; otherwise, it is a non-IBD fragment. Specifically: for a non-IBD fragment, if its threshold is a second threshold calculated based on the 15th percentile of the proportion of heterohomozygotes, then the probability of observing a proportion of heterohomozygotes less than the second threshold for n consecutive windows is:
[0074] P = minQ n ,
[0075] Where minQ represents the second threshold; therefore, the probability of identifying a non-IBD segment as an IBD segment, i.e., the false positive rate, is:
[0076] FPR = minQ n ×[N-22×(n-1)];
[0077] Where N represents the total number of windows; since 22×(n-1) is usually much smaller than N, the value of n can be estimated by setting the false positive rate, i.e.:
[0078]
[0079] If n consecutive windows are detected to be smaller than the second threshold, then the region corresponding to these n windows is an IBD segment.
[0080] S5. Fill the gaps in the IBD segment to obtain a new IBD segment, and reposition the start and end positions of the new IBD segment.
[0081] For further details, please refer to Figure 6 Because the proportion of heterohomozygous cells in the windows of the random effects IBD fragments may exceed the set first or second threshold, causing the fragment to be interrupted, gap filling is necessary. There are two types of gaps depending on their location: gaps between two IBD fragments are called intermediate gaps, and gaps on one side of an IBD fragment are called flank gaps. If the distance between two seed fragments is close and the intermediate gap is short, since the probability of two recombinations occurring in the same short region is extremely low, this gap can be considered to actually be an IBD fragment, and therefore the two IBD fragments and the intermediate gap are merged. If the merged IBD fragment has flank gaps, and the proportion of heterohomozygous cells in multiple consecutive windows surrounding the flank gaps is less than the first or second threshold, then the merged IBD fragment and the flank gap are merged again to obtain a new IBD fragment. The above steps are repeated until no new IBD fragments are generated.
[0082] To obtain a new IBD segment, its starting position may be in the first window or the window before it, and its ending position may be in the last window or the window after it. Therefore, it is necessary to reposition the start and end positions of the new IBD segment. The steps include:
[0083] Using the SNP site in the middle of the first window as the endpoint, a binary search method is used to progressively select the starting point until the homozygous ratio is just below the first or second threshold. The SNP site with a homozygous ratio just below the first or second threshold is then taken as the final starting point of the new IBD fragment. Similarly, using the SNP site in the middle of the last window as the starting point, a binary search method is used to progressively select the endpoint until the homozygous ratio is just below the first or second threshold. The SNP site with a homozygous ratio just below the first or second threshold is then taken as the final endpoint of the new IBD fragment. Based on the above determination of the start and end positions of new IBD fragments, complete IBD fragment information can be output. For initially identified IBD fragments, this application further merges adjacent IBD fragments through a gap-filling step, reducing fragment breaks caused by sequencing errors or mutations. Simultaneously, the start and end positions of the new IBD fragments are repositioned, making the boundaries of the IBD fragments more precise and improving the accuracy of IBD fragment identification.
[0084] This application achieves stable and efficient identification of IBD fragments by setting a corresponding threshold based on the difference in the heterozygous ratio distribution between IBD and non-IBD fragments. Furthermore, this threshold can dynamically change with the genotyping error rate of SNP site samples, making it suitable for genotyping data of various qualities. Please refer to... Figure 7 , Figure 8 , Figure 9Compared with existing methods, the clusIBD algorithm (error-aware IBD fragment recognition algorithm) used in this application has a higher tolerance for typing errors and is more stable in IBD fragment recognition. For example, when the SNP typing error rate is as high as 5%, the clusIBD algorithm of this application can still effectively identify IBD fragments, while the performance of existing algorithms in identifying IBD fragments is close to 0. (See [reference]) Figure 7 In terms of kinship inference, when the typing error rate is low, the accuracy of the clusIBD algorithm in this application is comparable to that of existing algorithms; however, when the typing error rate is high (>5%), the clusIBD algorithm in this application significantly outperforms existing algorithms. (See [reference needed]). Figure 8 Furthermore, regarding computational efficiency, the clusIBD algorithm employed in this application uses a window-based IBD recognition strategy, which is computationally simple and significantly more efficient than probability-based methods such as IBDseq. (See [reference]). Figure 9 .
[0085] This application targets the characteristics of DNA samples in fields such as forensic medicine, ecology, and archaeology, and can achieve stable and efficient identification of IBD fragments between such difficult DNA samples. It is expected to promote the in-depth mining of genetic information between difficult DNA samples and has important value for forensic pedigree investigation, wildlife protection, and ancient DNA research.
[0086] It should be noted that, for the sake of simplicity, the aforementioned method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously.
[0087] Based on the same idea as the IBD fragment recognition method in the above embodiments, this application also provides an IBD fragment recognition system, which can be used to execute the above-described IBD fragment recognition method. For ease of explanation, the schematic diagram of an embodiment of the IBD fragment recognition system only shows the parts related to the embodiments of this application. Those skilled in the art will understand that the illustrated structure does not constitute a limitation on the system, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0088] Please see Figure 10 In another embodiment of this application, an IBD fragment identification system 100 is provided, which includes a preprocessing module 101, an isohomozygosity ratio module 102, a threshold setting module 103, an IBD fragment identification module 104, and a start and end position positioning module 105.
[0089] The preprocessing module 101 is used to preprocess the acquired SNP sites;
[0090] The heterohomozygous ratio module 102 is used to sort the chromosomes according to the positions of the preprocessed SNP sites on the chromosomes, divide the chromosomes into windows with a fixed number of SNP sites, and calculate the heterohomozygous ratio in each window.
[0091] The threshold setting module 103 is used to estimate the nuclear density based on the calculated proportion of heterohomozygotes and to set a threshold according to the nuclear density estimation result.
[0092] The IBD segment determination module 104 is used to determine IBD segments and non-IBD segments based on the threshold.
[0093] The positioning start and end position module 105 is used to fill the gaps in the IBD segment to obtain a new IBD segment, and to reposition the start and end positions of the new IBD segment.
[0094] It should be noted that the IBD fragment recognition system of this application corresponds one-to-one with an IBD fragment recognition method of this application. The technical features and beneficial effects described in the embodiments of the above-mentioned IBD fragment recognition method are all applicable to the embodiments of the IBD fragment recognition system. For details, please refer to the description in the embodiments of the method of this application, which will not be repeated here.
[0095] Furthermore, in the above embodiment of an IBD fragment recognition system, the logical division of each program module is merely an example. In actual applications, the above functions can be assigned to different program modules as needed, for example, for the sake of corresponding hardware configuration requirements or software implementation convenience. That is, the internal structure of the IBD fragment recognition system can be divided into different program modules to complete all or part of the functions described above.
[0096] In another embodiment, an electronic device for implementing an IBD fragment recognition method is provided, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor; when the processor executes the computer program, it implements an IBD fragment recognition method according to any embodiment of this application.
[0097] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the device.
[0098] The device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The device may include, but is not limited to, a processor and memory.
[0099] The processor can be a Central Processing Unit (CPU), or 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the device, connecting various parts of the device via various interfaces and lines.
[0100] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc. In addition, the memory may include high-speed random access memory and non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
[0101] Accordingly, this application also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute an IBD fragment recognition method as described in any of the above embodiments.
[0102] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.
[0103] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0104] The above embodiments are preferred embodiments of this application, but the implementation of this application is not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of this application shall be considered equivalent substitutions and shall be included within the protection scope of this application.
Claims
1. A method for IBD fragment recognition, characterized in that, Includes the following steps: The obtained SNP sites are preprocessed; Based on the position of the preprocessed SNP sites on the chromosome, the chromosome is divided into windows with a fixed number of SNP sites, and the proportion of heterohomozygotes in each window is calculated. Heterohomozygotes refer to two samples with different homozygotes at a specific SNP site, i.e., one is type 0 / 0 and the other is type 1 / 1. Kernel density estimation is performed based on the calculated heterohomozygosity ratio. A threshold is set based on the kernel density estimation result. Specifically, for a bimodal heterohomozygosity ratio distribution, the trough between the two peaks is identified, and the proportion of heterohomozygosity corresponding to the trough is set as the first threshold. For a unimodal heterohomozygosity ratio distribution, the 15th percentile of the heterohomozygosity ratio is estimated, and the second threshold is calculated based on the 15th percentile of the heterohomozygosity ratio. Based on the threshold, IBD segments and non-IBD segments are determined, specifically including: determining whether the proportion of heterohomozygotes in multiple consecutive windows is less than the first threshold or the second threshold; if the proportion of heterohomozygotes in multiple consecutive windows is less than the first threshold or the second threshold, then the region corresponding to the multiple consecutive windows is an IBD segment; otherwise, it is a non-IBD segment. The IBD segment is gap-filled to obtain a new IBD segment, and the start and end positions of the new IBD segment are repositioned.
2. The IBD fragment recognition method according to claim 1, characterized in that, The preprocessing includes: SNP sites with a minor allele frequency of less than 0.05 and SNP sites with a genotyping rate of less than 0.5 were removed.
3. The IBD fragment recognition method according to claim 1, characterized in that, The formula for the kernel density estimation is as follows: Where K is the kernel density function; h is the set window width; and n is the number of windows; X Indicates a specific location point; X i This represents the observed proportion of heterohomozygotes. The results of the nuclear density estimation include both bimodal and unimodal heterozygous proportion distributions.
4. The IBD fragment identification method according to claim 1, characterized in that, The process of filling the gaps in the IBD fragment to obtain a new IBD fragment includes: The gap types include intermediate gaps and flank gaps; the intermediate gap is located between two IBD segments; the flank gap is located on one side of an IBD segment; The two IBD segments and the middle gap are merged; if the merged IBD segment has a flank gap, and the proportion of heterohomozygotes in multiple consecutive windows around the flank gap is less than the first threshold or the second threshold, then the merged IBD segment and the flank gap are merged again to obtain a new IBD segment.
5. The IBD fragment recognition method according to claim 1, characterized in that, The step of repositioning the start and end positions of the new IBD segment includes: Using the SNP site in the middle of the first window as the endpoint, the starting point is selected step by step forward using the dichotomy method until the proportion of heterohomozygotes is less than the first threshold or the second threshold. Then, the SNP site whose proportion of heterohomozygotes is just less than the first threshold or the second threshold is taken as the final starting point of the new IBD fragment. Starting from the SNP site in the middle of the last window, the endpoint is selected step by step using a dichotomy method until the proportion of heterohomozygotes is just less than the first threshold or the second threshold. The SNP site whose proportion of heterohomozygotes is just less than the first threshold or the second threshold is taken as the final endpoint of the new IBD fragment.
6. An IBD fragment recognition system, characterized in that, An IBD fragment identification method applicable to any one of claims 1-5 includes a preprocessing module, a heterohomozygosity ratio module, a threshold setting module, an IBD fragment judgment module, and a start and end position location module; The preprocessing module is used to preprocess the acquired SNP sites; The heterohomozygote ratio module is used to sort the preprocessed SNP sites on the chromosome, divide the chromosome into windows with a fixed number of SNP sites, and calculate the heterohomozygote ratio in each window. Heterohomozygote refers to two samples having different homozygote types at a specific SNP site, i.e., one is type 0 / 0 and the other is type 1 / 1. The threshold setting module is used to estimate the kernel density based on the calculated homozygous ratio, and set a threshold according to the kernel density estimation result. Specifically, it includes: for a bimodal homozygous ratio distribution, identifying the trough between the two peaks, and setting the proportion of homozygous corresponding to the trough as the first threshold; for a unimodal homozygous ratio distribution, estimating the 15th percentile of its homozygous ratio, and calculating the second threshold based on the 15th percentile of the homozygous ratio. The IBD fragment determination module is used to determine IBD fragments and non-IBD fragments based on the threshold. Specifically, it includes: determining whether the proportion of heterohomozygotes in multiple consecutive windows is less than the first threshold or the second threshold. If the proportion of heterohomozygotes in multiple consecutive windows is less than the first threshold or the second threshold, then the region corresponding to the multiple consecutive windows is an IBD fragment; otherwise, it is a non-IBD fragment. The positioning start and end position module is used to fill the gaps in the IBD segment to obtain a new IBD segment, and to reposition the start and end positions of the new IBD segment.
7. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores computer program instructions that can be executed by the at least one processor to enable the at least one processor to perform an IBD fragment identification method as described in any one of claims 1-5.
8. A computer-readable storage medium storing a program, characterized in that, When the program is executed by the processor, it implements an IBD fragment recognition method according to any one of claims 1-5.