A genotyping method, system, platform, and storage medium based on an allele core locus consumption model.

Abstract

This invention discloses a genotyping method, system, platform, and storage medium based on an allele core site consumption model. The method involves creating and generating first and second data corresponding to genes, and then generating third data based on the comparison of the first and second data. Based on the third data and variant detection processing, fourth data corresponding to the target gene region is created. A first model corresponding to the gene is constructed. Based on the first model and combined with the fourth data, an allele combination corresponding to the sample is established using a stepwise consumption algorithm based on core defined sites, thus creating a diploid type corresponding to the sample. The system, platform, and storage medium corresponding to the method construct an allele genotyping consumption model based on key defined sites, achieving unified and compatible processing of multi-source sequencing data and high-precision genotyping in complex variant scenarios, improving the accuracy, universality, and interpretability of pharmacogenomics detection.

Description

Technical Field

[0001] This invention belongs to the fields of bioinformatics and pharmacogenomics, specifically relating to a genotyping method, system, platform, and storage medium based on an allele core locus consumption model. Background Technology

[0002] Cytochrome P450 2D6 (CYP2D6) is a key pharmacodynamic gene in pharmacogenomics, exhibiting high polymorphism and involved in the metabolism of approximately 25% of clinically used drugs. Its genotype is included in medication guidelines for codeine, opioids, antidepressants, and tamoxifen. The American Academy of Molecular Pathology (AMP) recommends testing for 10 important Tier 1 alleles. This gene is the P450 enzyme with the most in-depth study of phenotypic transitions.

[0003] The mainstream techniques for CYP2D6 genotyping mainly include mass spectrometry, microarrays, allele-specific PCR, and next-generation sequencing (NGS). Compared with other techniques, NGS has significant advantages in detecting novel variants, structural variants (such as copy number variations, deletions, duplications, and rearrangements), and complex variants.

[0004] Currently, analysis software for CYP2D6 genotyping based on NGS technology is mainly applicable to whole-genome sequencing (WGS), whole-exome sequencing (WES), or targeted sequencing of specific gene sets (panels). However, whole-genome and whole-exome sequencing generate large amounts of data and have high sequencing costs. If the target is only focused on CYP2D6 or some pharmacogenomics-related genes, it can easily lead to a significant waste of data resources and costs. Software developed for targeted sequencing of specific gene sets is usually tightly coupled with commercial kits, which limits its versatility in different panel combinations or self-designed panels, and also results in low CNV detection reliability, i.e., at low sequencing depths, the reliability of detecting copy number variations is low and the results fluctuate greatly. In addition, existing methods have limited ability to handle complex variations (such as multicopy genes and gene rearrangements), making it difficult to accurately distinguish highly homologous allelic variants. Furthermore, most software only outputs a single genotyping result, lacking a quantitative display of the genotyping basis, which is not convenient for manual review and quality control.

[0005] Therefore, in order to address the aforementioned technical shortcomings such as poor universality, low reliability of CNV detection, insufficient typing accuracy, and poor interpretability of results, there is an urgent need to design and develop a genotyping processing method, system, platform, and storage medium based on the allele core locus consumption model. Summary of the Invention

[0006] To overcome the shortcomings and difficulties of the existing technology, the present invention aims to provide a genotyping method, system, platform and storage medium based on the allele core locus consumption model, which makes the genotyping highly compatible, accurate and interpretable.

[0007] The first objective of this invention is to provide a genotyping method based on an allele core locus consumption model; the second objective of this invention is to provide a genotyping system based on an allele core locus consumption model; the third objective of this invention is to provide a genotyping platform based on an allele core locus consumption model; and the fourth objective of this invention is to provide a computer-readable storage medium.

[0008] The first objective of this invention is achieved as follows: the method comprises: First and second data corresponding to the gene are created and generated respectively. Based on the first and second data, a corresponding third data is generated by comparison. The first data is sequencing data, which covers at least the target gene region. The second data is reference genome data. The third data is comparison result data. Based on the third data, combined with mutation detection processing, fourth data corresponding to the target gene region is created and generated; wherein, the fourth data is the mutation information of the sample in the target gene region, and the mutation information includes at least short mutation information and copy number variation information; A first model corresponding to the gene is constructed. Based on the first model and combined with the fourth data, an allele combination corresponding to the sample is established through a stepwise consumption algorithm based on core definition sites, and a diploid type corresponding to the sample is generated. The first model is an allele core site consumption model, which includes preset allele definition information. The definition information is used to define multiple sites of each allele as core definition sites and non-core definition sites.

[0009] Furthermore, after creating and generating fourth data corresponding to the target gene region based on the third data and in conjunction with variant detection processing, the method further includes: Based on the fourth data, corresponding fifth data is generated; and the fifth data is subjected to quality filtering; wherein the fifth data is short variation data; the quality filtering includes filtering based on allele frequency; for heterozygous genotypes, the allele frequency threshold range is set to 0.25 to 0.75.

[0010] Furthermore, after creating and generating fourth data corresponding to the target gene region based on the third data and in conjunction with variant detection processing, the method further includes: A dynamically updated control sample set is constructed, and the sequencing depth of the samples is normalized based on the control sample set; wherein the control sample set is continuously optimized and updated by automatically screening samples without copy number variations from the analyzed samples.

[0011] Furthermore, the construction of the first model corresponding to the gene, based on the first model and combined with the fourth data, using a stepwise depletion algorithm based on core defined sites to establish allele combinations corresponding to the sample, and to create a diploid type corresponding to the sample, further includes: Based on the first model, dynamic weights are applied to each core defined site; wherein, the dynamic weights are pre-calculated and set based on the discriminative power of the corresponding site in distinguishing different alleles; When inferring alleles, the detection status of core definition sites is weighted and evaluated based on dynamic weights.

[0012] Furthermore, the construction of the first model corresponding to the gene, based on the first model and combined with the fourth data, using a stepwise depletion algorithm based on core defined sites to establish allele combinations corresponding to the sample, and to create a diploid type corresponding to the sample, further includes: Based on the fourth data, a genotype corresponding to each mutation site is generated, and combined with copy number variation information, a corresponding initial copy number consumption unit is allocated to each site; Based on the detected variant sites and allele definition information, corresponding sixth data is generated, wherein the sixth data consists of all potential alleles; Based on the sixth data, corresponding seventh data is generated; wherein, the seventh data consists of alleles that have been completely detected at the core definition site; Based on the seventh data, at least one allele is generated. Based on the allele and its core defined site, one copy number consumption unit is consumed to process the corresponding site, and the copy number consumption units for processing the remaining sites are updated. Based on the updated information, the previous steps are repeated, and the next allele is inferred until all copy number consumption units are allocated or no new valid allele can be inferred.

[0013] Furthermore, the step of constructing and generating genotypes corresponding to each mutation site based on the fourth data, and allocating corresponding initial copy number consumption units to each site in conjunction with copy number variation information, further includes: Based on copy number variation information, a copy number state and its confidence level corresponding to the region to which each site belongs are generated; Based on the confidence level, one or more probabilistic copy number consumption units are assigned to each site; wherein the copy number consumption units are used to perform probabilistic consumption and inference in the stepwise consumption algorithm.

[0014] The second objective of this invention is achieved as follows: the system is used to implement the genotyping method based on the allele core locus consumption model, the system comprising: The data comparison and generation unit is used to create and generate first data and second data corresponding to the gene, respectively, and to generate corresponding third data based on the comparison of the first data and the second data; wherein, the first data is sequencing data, and the sequencing data covers at least the target gene region; the second data is reference genome data; and the third data is comparison result data; The data processing and generation unit is used to create and generate fourth data corresponding to the target gene region based on the third data and in combination with variant detection processing; wherein the fourth data is the variant information of the sample in the target gene region, and the variant information includes at least short variant information and copy number variant information. A data creation and generation unit is used to construct a first model corresponding to a gene. Based on the first model and combined with the fourth data, an allele combination corresponding to the sample is established through a stepwise consumption algorithm based on core definition sites, and a diploid type corresponding to the sample is created and generated. The first model is an allele core site consumption model, which includes preset allele definition information. The definition information is used to define multiple sites of each allele as core definition sites and non-core definition sites.

[0015] Furthermore, the data processing and generation unit further includes: The first generation module is used to detect and generate corresponding fifth data based on the fourth data; and to perform quality filtering on the fifth data; wherein the fifth data is short variation data; the quality filtering includes filtering based on allele frequency; for heterozygous genotypes, the allele frequency threshold range is set to 0.25 to 0.75; The first processing module is used to construct and generate a dynamically updated control sample set, and to normalize the sequencing depth of the samples based on the control sample set; wherein the control sample set is continuously optimized and updated by automatically screening samples without copy number variations from the analyzed samples. And / or, the data creation and generation unit further includes: The second processing module is used to dynamically weight each core defined site based on the first model; wherein the dynamic weight is pre-calculated and set based on the discrimination of the corresponding site in distinguishing different alleles; The weighted evaluation module is used to perform a weighted evaluation of the detection status of core defined sites based on dynamic weights when inferring the processing of alleles. The allocation processing module is used to construct and generate the genotype corresponding to each mutation site based on the fourth data, and to allocate the corresponding initial copy number consumption unit to each site in combination with copy number variation information. The matching generation module is used to generate corresponding sixth data based on the detected variant sites and allele definition information, wherein the sixth data consists of all potential alleles; The second generation module is used to filter and create corresponding seventh data based on the sixth data; wherein the seventh data consists of alleles whose core definition sites are completely detected. The third processing module is used to construct and generate at least one allele based on the seventh data, consume one copy number consumption unit for processing the corresponding site based on the allele and its core definition site, and update the copy number consumption units for processing the remaining sites; wherein, based on the updated information, the previous steps are repeated and the next allele is inferred until all copy number consumption units are allocated or no new valid allele can be inferred. And / or, the allocation processing module further includes: The third generation module is used to establish the copy number status and its confidence level corresponding to the region to which each site belongs, based on copy number variation information; The fourth processing module is used to allocate one or more probabilistic copy number consumption units to each site based on the confidence level; wherein the copy number consumption unit is used to perform probabilistic consumption and inference in the stepwise consumption algorithm.

[0016] The third objective of this invention is achieved as follows: it includes a processor, a memory, and a genotyping platform control program based on an allele core locus consumption model; wherein the processor executes the genotyping platform control program based on the allele core locus consumption model, the genotyping platform control program based on the allele core locus consumption model is stored in the memory, and the genotyping platform control program based on the allele core locus consumption model implements the genotyping method based on the allele core locus consumption model.

[0017] The fourth objective of this invention is achieved as follows: the computer-readable storage medium stores a genotyping platform control program based on the allele core locus consumption model, and the genotyping platform control program based on the allele core locus consumption model implements the genotyping method based on the allele core locus consumption model.

[0018] This invention generates first and second data corresponding to genes, respectively, and generates corresponding third data based on the comparison of the first and second data. The first data is sequencing data, covering at least the target gene region; the second data is reference genome data; and the third data is alignment result data. Based on the third data, and combined with variant detection processing, a fourth data corresponding to the target gene region is generated. The fourth data is variant information of the sample in the target gene region, including at least short variant information and copy number variant information. A first model corresponding to the gene is constructed, and based on the first model, combined with the third... The four data sets, through a stepwise consumption algorithm based on core defined sites, establish allele combinations corresponding to the samples and generate diploid types corresponding to the samples. The first model is an allele core site consumption model, containing preset allele definition information. This definition information is used to define multiple sites of each allele as core defined sites and non-core defined sites, as well as the corresponding system, platform, and storage medium. By constructing an allele genotyping consumption model based on key defined sites, unified and compatible processing of multi-source sequencing data and high-precision genotyping under complex variation scenarios are achieved, improving the accuracy, universality, and interpretability of pharmacogenomics detection.

[0019] In other words, the technical solution of this invention constructs an allele genotyping consumption model based on key defined sites, combined with experimentally validated and optimized heterozygous genotype allele frequency thresholds (0.25-0.75) and a probabilistic copy number variation linkage mechanism, to achieve unified and compatible processing of targeted sequencing, whole exome sequencing, and whole genome sequencing data. This improves the genotyping accuracy, result interpretability, and analytical process versatility of highly polymorphic genes such as CYP2D6 in complex variation scenarios. Furthermore, through multi-dimensional quantitative sorting output and rich site annotation information, it provides a standardized solution for clinical pharmacogenomics testing that combines high precision, strong compatibility, and good traceability. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the process steps of a genotyping method based on an allele core locus consumption model according to the present invention. Figure 2This is a schematic flowchart of an embodiment of the genotyping method based on the allele core locus consumption model of the present invention; Figure 3 This is a schematic diagram of the genotyping system architecture based on the allele core site consumption model of the present invention. Figure 4 This is a schematic diagram of the genotyping platform architecture based on the allele core site consumption model of the present invention. Figure 5 This is a schematic diagram of a computer-readable storage medium architecture in one embodiment of the present invention. Detailed Implementation

[0022] To facilitate a clearer understanding of the objectives, technical solutions, and advantages of this invention, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of this invention from the content disclosed in this specification.

[0023] This invention can also be implemented or applied through other different specific examples, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of this invention.

[0024] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0025] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Secondly, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0026] Preferably, the genotyping method based on the allele core locus consumption model of the present invention is applied in one or more terminals or servers. The terminal is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0027] The terminal can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal can interact with the customer via a keyboard, mouse, remote control, touchpad, or voice control device.

[0028] This invention provides a genotyping method, system, platform, and storage medium based on an allele core locus consumption model.

[0029] like Figure 1 The diagram shown is a flowchart of a genotyping method based on an allele core locus consumption model provided in an embodiment of the present invention.

[0030] In this embodiment, the genotyping method based on the allele core locus consumption model can be applied to terminals or fixed terminals with display functions. The terminals are not limited to personal computers, smartphones, tablets, desktop computers or all-in-one computers with cameras, etc.

[0031] The genotyping method based on the allele core locus consumption model can also be applied to a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes, but is not limited to, a wide area network (WAN), a metropolitan area network (MAN), or a local area network (LAN). The genotyping method based on the allele core locus consumption model in this embodiment can be executed by the server, by the terminal, or by both the server and the terminal.

[0032] For example, for terminals requiring genotyping based on the allele core locus consumption model, the genotyping function based on the allele core locus consumption model provided by the method of this invention can be directly integrated into the terminal, or a client for implementing the method of this invention can be installed. Alternatively, the method provided by this invention can also run on servers or other devices in the form of a Software Development Kit (SDK), providing an interface for the genotyping function based on the allele core locus consumption model. Terminals or other devices can then implement the genotyping function based on the allele core locus consumption model through the provided interface. The invention will be further described below with reference to the accompanying drawings.

[0033] like Figures 1-2 As shown, this invention provides a genotyping method based on an allele core locus consumption model, the method comprising the following steps: S01. Create and generate first data and second data corresponding to the gene respectively, and generate corresponding third data based on the comparison of the first data and the second data; wherein, the first data is sequencing data, and the sequencing data covers at least the target gene region; the second data is reference genome data; and the third data is the comparison result data; S02. Based on the third data, and combined with mutation detection processing, a fourth data corresponding to the target gene region is created; wherein, the fourth data is the mutation information of the sample in the target gene region, and the mutation information includes at least short mutation information and copy number variation information; The copy number variation information further includes the detailed analysis results of copy number variation breakpoints; the allele definition information includes the allele type defined by a specific copy number variation breakpoint; wherein, in the genotyping inference step, when a copy number variation matching the preset breakpoint feature is detected, the corresponding allele type is directly determined; S03. Construct a first model corresponding to the gene. Based on the first model and combined with the fourth data, establish an allele combination corresponding to the sample through a stepwise consumption algorithm based on core definition sites, and create a diploid type corresponding to the sample. The first model is an allele core site consumption model, which includes preset allele definition information. The definition information is used to define multiple sites of each allele as core definition sites and non-core definition sites.

[0034] After generating fourth data corresponding to the target gene region based on the third data and in conjunction with mutation detection processing, the process further includes: S021. Based on the fourth data, a corresponding fifth data is generated; and the fifth data is subjected to quality filtering; wherein the fifth data is short variation data; the quality filtering includes filtering based on allele frequency; for heterozygous genotypes, the allele frequency threshold range is set to 0.25 to 0.75.

[0035] After generating fourth data corresponding to the target gene region based on the third data and in conjunction with mutation detection processing, the process further includes: S022. Construct and generate a dynamically updated control sample set, and normalize the sequencing depth of the samples based on the control sample set; wherein the control sample set is continuously optimized and updated by automatically screening samples without copy number variations from the analyzed samples.

[0036] The construction of the first model corresponding to the gene, based on the first model and combined with the fourth data, using a stepwise depletion algorithm based on core defined sites, establishes allele combinations corresponding to the sample and creates a diploid type corresponding to the sample, further includes: S031. Based on the first model, each core defined site is processed with dynamic weights; wherein, the dynamic weights are pre-calculated and set based on the discriminative power of the corresponding site in distinguishing different alleles; S032. When inferring alleles, the detection status of core definition sites is weighted and evaluated according to dynamic weights.

[0037] The construction of the first model corresponding to the gene, based on the first model and combined with the fourth data, using a stepwise depletion algorithm based on core defined sites, establishes allele combinations corresponding to the sample and creates a diploid type corresponding to the sample, further includes: S033. Based on the fourth data, construct and generate the genotype corresponding to each mutation site, and combine the copy number variation information to allocate the corresponding initial copy number consumption unit to each site. S034. Based on the detected variant sites and allele definition information, generate corresponding sixth data, wherein the sixth data consists of all potential alleles; S035. Based on the sixth data, filter and create corresponding seventh data; wherein, the seventh data consists of alleles for which the core definition sites are completely detected; S036. Based on the seventh data, at least one allele is constructed and generated. Based on the allele and its core definition site, one copy number consumption unit is consumed to process the corresponding site, and the copy number consumption units for processing the remaining sites are updated. Based on the updated information, the previous steps are repeated, and the next allele is inferred until all copy number consumption units are allocated or no new effective allele can be inferred.

[0038] The step of constructing and generating genotypes corresponding to each mutation site based on the fourth data, and allocating corresponding initial copy number consumption units to each site in conjunction with copy number variation information, also includes: S0331. Based on copy number variation information, establish and generate the copy number status and its confidence level corresponding to the region to which each site belongs; S0332. Based on the confidence level, assign one or more probabilistic copy number consumption units to each site; wherein the copy number consumption unit is used to perform probabilistic consumption and inference in the stepwise consumption algorithm.

[0039] The method further includes the following steps: S040. When multiple possible allele combinations are inferred through the stepwise consumption algorithm, sort and output all possible combinations according to the total number of core definition sites detected in each combination, the detection rate of core definition sites, or the total probability of the combination. S050, Output the identifier information indicating whether the optimal combination is a unique combination.

[0040] Furthermore, the target gene in the method is a cytochrome P450 family gene, preferably the CYP2D6, CYP2C9, or CYP2C19 gene.

[0041] Specifically, in this embodiment of the invention, the scheme requires that the minimum coverage area for targeted capture is the whole exon region of CYP2D6, and any kit whose target region covers the whole exon of CYP2D6 or more is applicable.

[0042] FASTQ (raw sequencing data) quality control was performed using FastQC and MultiQC to assess whether the data met the analysis standards. Key standards included: sequencing quality index Q30 > 75%; data volume determined based on panel requirements; and for panel sequencing, average sequencing depth exceeding 200X. FASTQ data was filtered using FastP parameters, with a quality threshold of 15 for acceptable bases, a maximum allowed percentage of unacceptable bases in a single read of 40%, a minimum read length of 15 bases, enabling sliding window quality trimming, a sliding window size of 4 bases, an average quality threshold of 20 for the sliding window, and base correction for overlapping regions in paired-end data. The following sequencing quality indicators were statistically analyzed: number of raw sequencing reads and bases, number of reads meeting Q20 and Q30, GC content, repetition rate, number of filtered reads and bases, and average read length.

[0043] FASTQ data was aligned to the human reference genome (hg38 version) to generate a BAM file, and an index was built. The following alignment quality metrics were calculated: alignment rate, target coverage rate, target region size, target region coverage, target region coverage meeting 4X / 10X / 30X / 50X / 100X / 200X requirements, average sequencing depth, region coverage at a sequencing depth greater than 20% of the average depth (20% sequencing uniformity), and region coverage at a sequencing depth greater than 50% of the average depth. Subsequently, the BAM file was recalibrated using the GATK tool using Base Quality Score Recalibration (BQSR) to improve the accuracy of the alignment results.

[0044] Use the GATK tool to perform variant calling on the BAM file after BQSR correction, and annotate the rs numbers in the NCBI dbSNP database. Subsequently, split the variant data into two files, SNP and INDEL, according to the type, and filter them using different hard filter parameters. The filter parameters for SNP are: the quality-depth ratio (QD) threshold is <2, the significant value of the Fisher exact test for strand bias (FS) is >60, and the mapping quality threshold (MQ) is <40; the filter parameters for INDEL are: QD is <2 and FS is >200. Merge the SNP and INDEL variant files that have undergone the above quality annotation and preliminary filtering, and filter according to these annotation tags (the FILTER field is PASS), only retaining high-quality variant entries. For the variants that pass the quality filter, further filter them according to their genotypes (homozygous or heterozygous) and allele frequencies (VAF). The VAF threshold for heterozygous genotypes is 0.25 < VAF < 0.75, and the VAF threshold for homozygous genotypes is set to VAF > 0.9. Usually, the theoretical VAF value of heterozygous mutations should be close to 0.5, so its filter threshold range is usually set more strictly (e.g., 0.3 - 0.7); however, based on the validation experimental data of this protocol, relaxing the heterozygous VAF threshold to 0.25 - 0.75 can effectively retain true multi-copy variants while tolerating a certain degree of sample-to-sample contamination.

[0045] Use the CoNVaDING tool to perform copy number variation detection on the BAM file obtained in step 2, and the judgment criteria are as follows: deletion (DEL): the mutation type is DEL and the average target segment normalization value < 0.65; duplication (DUP): the mutation type is DUP and the average target segment normalization value > 1.4. For the CYP2D6 gene, multiply the average target segment normalization value by 2 and round it to obtain the predicted copy number of the sample. This step requires pre-establishing a control sample set using at least 30 samples confirmed to have no copy number variation for depth normalization of all autosomal targets and all targets on each gene.

[0046] Use the short variant results (SNP and INDEL) and copy number (CNV) results obtained in the above steps for CYP2D6 gene typing.

[0047] A long-format genotyping and locus comparison database was constructed based on the CYP2D6 allele definition table in ClinPGx (integrated with PharmGKB, see https: / / www.clinpgx.org / page / cyp2d6RefMaterials). Details of small deletion duplication variants (INDELs) were referenced from the VCF file in the PharmVAR database (see https: / / www.pharmvar.org / gene / CYP2D6). If the genotype of a genotyping locus is defined by non-degenerate bases (ATGC), then that locus is identified and marked as a core genotyping locus (CORE). For some genotypes, the genotype of some loci is represented by degenerate bases in the form of 'reference base + substitution base' (e.g., g.42129819G>K). This means that the locus conforms to the genotype definition regardless of whether this specific variant occurs (i.e., genotype is G or T). When processing such loci, their base information is uniformly and forcibly converted to the corresponding substitution base (in this example, converted to T) to standardize genotyping judgment. The final reference table is partially shown below:

[0048] Note: #CHROM: Chromosome number; POS: Variant location; REF: Reference base; ALT: Substituted base; ALLELE: Genotyping; CORE_SNP: Whether it is a core SNP site for genotyping; The number of core sites corresponding to the genotyping is generated in the JSON file, part of which is as follows: {2":2,"3":1,"4":1,"6":1,"7":1,"8":4,"9":1,"10":3,...}; Note: The key represents the genotype, and the value represents the number of core sites for that genotype. Specifically, the genotyping and site detection rate analysis process is as follows: a) Parse the VCF file to obtain all variant sites of the CYP2D6 gene located on chromosome chr22, and extract the genotype (homozygous 1 / 1 or heterozygous 0 / 1) for each variant site based on the detailed information in the VCF record. The initial copy number used for "genotyping consumption" calculation is set according to the genotype: 2 for homozygous sites and 1 for heterozygous sites.

[0049] b) Read in the predefined genotyping site reference table and the dictionary of the number of core genotyping sites.

[0050] c) Using chromosome (CHROM), position (POS), reference base (REF), and substitution base (ALT) as key fields, merge the VCF variant data parsed in step a with the genotyping site reference table in step b. The merged result retains only the sites actually detected in the VCF file.

[0051] d) Result Judgment and Processing: If no matching results are found after the merging operation, both allele 1 (Allele1, i.e., the preferred allele typing) and allele 2 (Allele2, i.e., the secondary allele typing) will be set to the default typing CYP2D6*1, and an empty sample typing site statistics table will be output.

[0052] If a matching result exists after the merging operation, the detection status of each potential subtype is statistically analyzed, including: all potential subtypes detected, the number of sites in the sample that detected for that subtype, the total number of core sites defined by that subtype, and the subtype site detection rate (number of detected sites / total number of core sites). Subtypes whose "number of detected core sites" is not equal to the "theoretical total number of core sites" (i.e., subtypes that were not fully detected) are filtered out.

[0053] e) Allelic typing inference: Traverse the filtered table and denote the current row's framing as Allele1.

[0054] Then, the number of site copies consumed by Allele1 is subtracted from the total number of copies at all sites (1 less for each relevant site) from the total number of copies at all sites.

[0055] Only entries with a copy number greater than 0 are retained for subsequent Allele2 inference.

[0056] Using the remaining site entries, infer Allele2 using the same logic and process as inferring Allele1.

[0057] If a valid Allele2 cannot be deduced, it is set to the default fractal CYP2D6*1.

[0058] f) Results integration and output: The combined detection statistics of Allele1 and Allele2 sites.

[0059] Calculate and add comprehensive indicators: total number of genotyping sites detected (the sum of Allele1 and Allele2 detections) and total genotyping core site detection rate.

[0060] All possible combinations of Allele1 and Allele2 were deduplicated. All entries were then sorted in descending order based on indicators such as the detection rate of the core genotyping site and the total number of genotyping sites detected.

[0061] In other words, the specific process is as follows: Parse the VCF file → Extract the chr22-CYP2D6 variant site and genotype → Set the initial copy number: homozygous = 2, heterozygous = 1 → Read in the genotyping reference table → Merge the VCF data with the reference table → Determine if there are any matching results? If not, Allele1 = *1, Allele2 = *1. If so, then count the genotyping detection and calculate the detection rate → filter out genotypes that are not fully detected → infer Allele1 → deduct the copy number consumed by Allele1 → retain sites with copy number > 0 → infer Allele2 → determine if Allele2 is valid? If not, Allele2 = *1; if so, retain Allele2. Finally, merge the results and calculate the comprehensive index → ​​remove duplicates and sort → output the final result. The sorted statistical table is the final output result, in the format shown below:

[0062] Note: ALLELE1: Allele 1; ALLELE2: Allele 2; PRESENT_RATIO: Total genotyping site detection rate, which is: ALLELE1_PRESENT_RATIO+ALLELE2_PRESENT_RATIO PRESENT_COUNT: Total number of genotyping sites detected. ALLELE1_PRESENT_COUNT+ALLELE2_PRESENT_COUNT ALLELE1_PRESENT_COUNT: The number of sites detected for allele 1, including degenerate bases; the value may be greater than ALLELE1_PHARMGKB_COUNT. ALLELE1_PHARMGKB_COUNT: The number of core sites for allele 1 in PharmGKB; ALLELE2_PRESENT_COUNT: The number of sites detected for allele 2; ALLELE2_PHARMGKB_COUNT: The number of core sites for allele 2 in PharmGKB; ALLELE1_PRESENT_RATIO: Detection rate of the core locus of allele 1, which is: ALLELE1_PRESENT_COUNT / ALLELE1_PHARMGKB_COUNT; ALLELE2_PRESENT_RATIO: Detection rate of core loci of allele 2; Output a combined table of all VCF loci, allele information, and transcript variant annotations for subsequent manual verification. Partial content is shown below:

[0063] Note: ALLELE: Allele; ID: NCBI dbSNP rs number; VARIANT: Nucleic acid and amino acid variation results of CYP2D6 transcript NM000106.6; #CHROM,POS,REF,ALT: Same as above; GENOTYPE: Genotype, 0 / 1 is heterozygous variation, 1 / 1 is homozygous variation; CORE_SNP: Whether it is a core SNP site for genotyping.

[0064] From all possible allele combinations, the optimal genotyping combination is selected, and its uniqueness is indicated (i.e., whether it is the only possible optimal genotyping combination for this sample). The obtained copy number variation (CNV) detection results are integrated to generate the final allele genotyping information table. Based on the above allele genotyping information table, the final genotyping of the sample is determined: If a deletion (DEL) copy number variation is found in the test results, the presence of the CYP2D6*5 allele is first determined, and then it is combined with the inferred Allele1 to form the final diploid type result.

[0065] If no copy number variation is found, Allele1 and Allele2 are used as the final diploid type results. Using the final determined diploid type results, the CYP2D6 metabolic phenotype of the sample is queried and annotated according to the CYP2D6 Diplotype-Phenotype Table in ClinPGx (see: https: / / www.clinpgx.org / page / cyp2d6RefMaterials). If no matching metabolic phenotype is found in the table, it is marked as 'UNKNOWN'.

[0066] The final result for a single sample is as follows:

[0067] Note: DIPLOTYPE: Diploidy; PHENOTYPE: Metabolic phenotype, generally including ultrarapid, normal, intermediate, and weak metabolizers; CNV_TYPE: Copy number mutation type. WT is wild-type, DEL is deletion, and DUP is duplication; EXON_NUMBER: Number of target regions with copy number variations. If the basic unit of the target region is an exon, this result is the number of exons with copy number variations; CNV_RATIO: Average normalized value of the target region; ALLELE1, ALLELE2: Same as above; TOP_UNIQUE: Whether the optimal allele combination is unique.

[0068] The above method is implemented by combining the Python programming language with the Snakemake workflow management system. This implementation method has the following advantages: high ease of use, easy cross-server distribution and deployment, high scalability, strong system stability, and can efficiently utilize server computing resources through multi-threading and multi-task queue mechanisms, significantly improving analysis efficiency.

[0069] Example 1: Sequencing was performed on 14 samples using the CYP2D6 PGx Panel. These samples included 6 samples from a College of American Pathologists (CAP PT) program (2024-PGX-04 / 5 / 6, 2025-PGX-01 / 2 / 3), 4 commercially available standards, and 4 ethically compliant samples remaining from previous testing using the Thermo Fisher Ion AmpliSeq Pharmacogenomics Research Panel. The genotyping results obtained from this protocol were completely consistent with the known reference genotyping results.

[0070] Example 2: This method is also applicable to the genotyping analysis of other genes in the CYP family. The table below shows the detection results of CYP2C9 and CYP2C19. The data source is the same as in Example 1, and the CAP PT samples used contain CYP2C9 and CYP2C19 standard substances. It should be noted that the default genotyping for CYP2C19 is *38 instead of *1; *36 indicates a complete gene deletion, and *37 indicates a partial exon deletion; the CYP2C9 genotyping does not include copy number variations. CYP2C19 Results:

[0071] CYP2C9 results:

[0072] Example 3: No valid results were obtained using the PGx genotyping software Aldy and Cyrius. This was because Aldy's PGx mode was incompatible with the current panel, which did not include the CYP2D8 gene (Aldy's default copy number calculation baseline region), and its WGS mode was also unsuitable for the current small-region targeted experimental protocol. Cyrius, as a WGS CYP2D6 analysis software, was incompatible with the data types generated by this experimental protocol.

[0073] To achieve the above objectives, the present invention also provides a genotyping system based on an allele core locus consumption model, such as... Figure 3 As shown, the system is applied to the genotyping method based on the allele core locus consumption model, and the system includes: The data comparison and generation unit is used to create and generate first data and second data corresponding to the gene, respectively, and to generate corresponding third data based on the comparison of the first data and the second data; wherein, the first data is sequencing data, and the sequencing data covers at least the target gene region; the second data is reference genome data; and the third data is comparison result data; The data processing and generation unit is used to create and generate fourth data corresponding to the target gene region based on the third data and in combination with variant detection processing; wherein the fourth data is the variant information of the sample in the target gene region, and the variant information includes at least short variant information and copy number variant information. A data creation and generation unit is used to construct a first model corresponding to a gene. Based on the first model and combined with the fourth data, an allele combination corresponding to the sample is established through a stepwise consumption algorithm based on core definition sites, and a diploid type corresponding to the sample is created and generated. The first model is an allele core site consumption model, which includes preset allele definition information. The definition information is used to define multiple sites of each allele as core definition sites and non-core definition sites.

[0074] The data processing and generation unit further includes: The first generation module is used to detect and generate corresponding fifth data based on the fourth data; and to perform quality filtering on the fifth data; wherein the fifth data is short variation data; the quality filtering includes filtering based on allele frequency; for heterozygous genotypes, the allele frequency threshold range is set to 0.25 to 0.75; The first processing module is used to construct and generate a dynamically updated control sample set, and to normalize the sequencing depth of the samples based on the control sample set; wherein the control sample set is continuously optimized and updated by automatically screening samples without copy number variations from the analyzed samples. And / or, the data creation and generation unit further includes: The second processing module is used to dynamically weight each core defined site based on the first model; wherein the dynamic weight is pre-calculated and set based on the discrimination of the corresponding site in distinguishing different alleles; The weighted evaluation module is used to perform a weighted evaluation of the detection status of core defined sites based on dynamic weights when inferring the processing of alleles. The allocation processing module is used to construct and generate the genotype corresponding to each mutation site based on the fourth data, and to allocate the corresponding initial copy number consumption unit to each site in combination with copy number variation information. The matching generation module is used to generate corresponding sixth data based on the detected variant sites and allele definition information, wherein the sixth data consists of all potential alleles; The second generation module is used to filter and create corresponding seventh data based on the sixth data; wherein the seventh data consists of alleles whose core definition sites are completely detected. The third processing module is used to construct and generate at least one allele based on the seventh data, consume one copy number consumption unit for processing the corresponding site based on the allele and its core definition site, and update the copy number consumption units for processing the remaining sites; wherein, based on the updated information, the previous steps are repeated and the next allele is inferred until all copy number consumption units are allocated or no new valid allele can be inferred. And / or, the allocation processing module further includes: The third generation module is used to establish the copy number status and its confidence level corresponding to the region to which each site belongs, based on copy number variation information; The fourth processing module is used to allocate one or more probabilistic copy number consumption units to each site based on the confidence level; wherein the copy number consumption unit is used to perform probabilistic consumption and inference in the stepwise consumption algorithm.

[0075] The system further includes: a fifth processing module, used to sort and output all possible combinations based on the total number of core definition sites detected in each combination, the detection rate of core definition sites, or the total probability of the combination when multiple possible allele combinations are inferred through the stepwise consumption algorithm; and an output generation module, used to output identification information as to whether the optimal combination is a unique combination.

[0076] Furthermore, the target gene in the method is a cytochrome P450 family gene, preferably the CYP2D6, CYP2C9, or CYP2C19 gene.

[0077] In the system embodiment of the present invention, the specific details of the method steps involved in the genotyping process based on the allele core site consumption model have been described above. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, which will not be repeated here.

[0078] To achieve the above objectives, the present invention also provides a genotyping platform based on an allele core locus consumption model, such as... Figure 4 As shown, the system includes a processor, a memory, and a genotyping platform control program based on an allele core locus consumption model. The processor executes the genotyping platform control program based on the allele core locus consumption model, which is stored in the memory. This control program implements the steps of the genotyping method based on the allele core locus consumption model, for example: S01. Create and generate first data and second data corresponding to the gene respectively, and generate corresponding third data based on the comparison of the first data and the second data; wherein, the first data is sequencing data, and the sequencing data covers at least the target gene region; the second data is reference genome data; and the third data is the comparison result data; S02. Based on the third data, and combined with mutation detection processing, a fourth data corresponding to the target gene region is created; wherein, the fourth data is the mutation information of the sample in the target gene region, and the mutation information includes at least short mutation information and copy number variation information; S03. Construct a first model corresponding to the gene. Based on the first model and combined with the fourth data, establish an allele combination corresponding to the sample through a stepwise consumption algorithm based on core definition sites, and create a diploid type corresponding to the sample. The first model is an allele core site consumption model, which includes preset allele definition information. The definition information is used to define multiple sites of each allele as core definition sites and non-core definition sites.

[0079] The specific details of the steps have been explained above and will not be repeated here.

[0080] In this embodiment of the invention, the built-in processor of the genotyping platform based on the allele core locus consumption model can be composed of integrated circuits. For example, it can be composed of a single packaged integrated circuit, or multiple integrated circuits packaged with the same or different functions. This includes combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor connects to various components using various interfaces and lines, and executes programs or units stored in memory, as well as calling data stored in memory, to perform various functions of genotyping based on the allele core locus consumption model and process data. The memory is used to store program code and various data. It is installed in the genotyping processing platform based on the allele core locus consumption model and enables high-speed, automatic access to programs or data during operation. The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0081] To achieve the above objectives, the present invention also provides a computer-readable storage medium, such as... Figure 5 As shown, the computer-readable storage medium stores a genotyping platform control program based on the allele core locus consumption model. This genotyping platform control program implements the steps of the genotyping method based on the allele core locus consumption model; for example: S01. Create and generate first data and second data corresponding to the gene respectively, and generate corresponding third data based on the comparison of the first data and the second data; wherein, the first data is sequencing data, and the sequencing data covers at least the target gene region; the second data is reference genome data; and the third data is the comparison result data; S02. Based on the third data, and combined with mutation detection processing, a fourth data corresponding to the target gene region is created; wherein, the fourth data is the mutation information of the sample in the target gene region, and the mutation information includes at least short mutation information and copy number variation information; S03. Construct a first model corresponding to the gene. Based on the first model and combined with the fourth data, establish an allele combination corresponding to the sample through a stepwise consumption algorithm based on core definition sites, and create a diploid type corresponding to the sample. The first model is an allele core site consumption model, which includes preset allele definition information. The definition information is used to define multiple sites of each allele as core definition sites and non-core definition sites.

[0082] The specific details of the steps have been explained above and will not be repeated here.

[0083] In the description of embodiments of the present invention, it should be noted that any process or method description in the flowcharts or otherwise described herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the present invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0084] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, a “computer-readable medium” can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0085] This invention generates first and second data corresponding to genes, respectively, and generates corresponding third data based on the comparison of the first and second data. The first data is sequencing data, covering at least the target gene region; the second data is reference genome data; and the third data is alignment result data. Based on the third data, and combined with variant detection processing, a fourth data corresponding to the target gene region is generated. The fourth data is variant information of the sample in the target gene region, including at least short variant information and copy number variant information. A first model corresponding to the gene is constructed, and based on the first model, combined with the third... The four data sets, through a stepwise consumption algorithm based on core defined sites, establish allele combinations corresponding to the samples and generate diploid types corresponding to the samples. The first model is an allele core site consumption model, containing preset allele definition information. This definition information is used to define multiple sites of each allele as core defined sites and non-core defined sites, as well as the corresponding system, platform, and storage medium. By constructing an allele genotyping consumption model based on key defined sites, unified and compatible processing of multi-source sequencing data and high-precision genotyping under complex variation scenarios are achieved, improving the accuracy, universality, and interpretability of pharmacogenomics detection.

[0086] That is to say, the technical solution of the present invention realizes unified and compatible processing of targeted sequencing, whole exome sequencing, and whole genome sequencing data by constructing an allele typing consumption model based on key definition sites, combining experimentally verified and optimized heterozygous genotype allele frequency thresholds (0.25 - 0.75) and a probabilistic copy number variation linkage mechanism, improving the typing accuracy, result interpretability, and analysis process universality of highly polymorphic genes such as CYP2D6 in complex variation scenarios. Through multi-dimensional quantitative ranking output and rich site annotation information, a standardized solution with high precision, strong compatibility, and good traceability is provided for clinical pharmacogenomics testing.

[0087] In other words, the present invention proposes a new method for CYP2D6 gene typing with strong compatibility and high accuracy. The method is based on NGS technology and can uniformly process data from targeted sequencing (Panel) of a specific gene set, whole exome sequencing (WES), and whole genome sequencing (WGS), solving the problem of standardizing the typing process under multiple sequencing strategies. By setting a genotype judgment threshold that has been verified and can effectively balance multi-copy variations and sample contamination. The VAF threshold for heterozygous genotypes is set as 0.25 < VAF < 0.75, and the homozygous genotype threshold is set as VAF > 0.9. This standard effectively improves the typing accuracy in complex variation scenarios. And by introducing the concept and determination criteria of the "CYP2D6 typing core sites", and by distinguishing core and non-core sites, a clear basis is provided for the determination of allele typing, enhancing the reliability and interpretability of the typing algorithm.

[0088] Furthermore, this invention constructs an automated diploid type inference process based on the "genotyping consumption" algorithm. This process uses each genotyping locus as a basic computational unit, combining its copy number attribute to progressively calculate and allocate alleles, thereby accurately inferring the preferred allele (Allele1) and secondary allele (Allele2). A multi-dimensional genotyping ranking and screening mechanism based on the number and detection rate of genotyping loci is also established. This mechanism automatically outputs a list of possible genotyping combinations sorted by confidence level, providing clear and quantitative data support for result verification and final determination, improving analytical efficiency and result reliability. This invention also provides rich and multi-dimensional locus annotation information for result verification. The output results include key information such as chromosome location, transcript nucleotide and amino acid variations, and dbSNP rs numbers, facilitating manual verification and validation of genotyping results and ensuring the rigor and transparency of the analytical process. For all genes in ClinPGx that are similar to CYP2D6 and have allele definition tables (such as CYP2C19, CYP2C9, etc.), the same standardized analysis method can be used to construct their genotyping and phenotype inference process.

[0089] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A genotyping method based on an allele core locus consumption model, characterized in that, The method includes: First and second data corresponding to the gene are created and generated respectively. Based on the first and second data, a corresponding third data is generated by comparison. The first data is sequencing data, which covers at least the target gene region. The second data is reference genome data. The third data is comparison result data. Based on the third data, combined with mutation detection processing, fourth data corresponding to the target gene region is created and generated; wherein, the fourth data is the mutation information of the sample in the target gene region, and the mutation information includes at least short mutation information and copy number variation information; A first model corresponding to the gene is constructed. Based on the first model and combined with the fourth data, an allele combination corresponding to the sample is established through a stepwise consumption algorithm based on core definition sites, and a diploid type corresponding to the sample is generated. The first model is an allele core site consumption model, which includes preset allele definition information. The definition information is used to define multiple sites of each allele as core definition sites and non-core definition sites.

2. The genotyping method based on the allele core locus consumption model according to claim 1, characterized in that, After generating fourth data corresponding to the target gene region based on the third data and in conjunction with mutation detection processing, the process further includes: Based on the fourth data, corresponding fifth data is generated; and the fifth data is subjected to quality filtering; wherein the fifth data is short variation data; the quality filtering includes filtering based on allele frequency; for heterozygous genotypes, the allele frequency threshold range is set to 0.25 to 0.

75.

3. A genotyping method based on an allele core locus consumption model according to claim 1 or 2, characterized in that, After generating fourth data corresponding to the target gene region based on the third data and in conjunction with mutation detection processing, the process further includes: A dynamically updated control sample set is constructed, and the sequencing depth of the samples is normalized based on the control sample set; wherein the control sample set is continuously optimized and updated by automatically screening samples without copy number variations from the analyzed samples.

4. The genotyping method based on the allele core locus consumption model according to claim 1, characterized in that, The construction of the first model corresponding to the gene, based on the first model and combined with the fourth data, using a stepwise depletion algorithm based on core defined sites, establishes allele combinations corresponding to the sample and creates a diploid type corresponding to the sample, further includes: Based on the first model, dynamic weights are applied to each core defined site; wherein, the dynamic weights are pre-calculated and set based on the discriminative power of the corresponding site in distinguishing different alleles; When inferring alleles, the detection status of core definition sites is weighted and evaluated based on dynamic weights.

5. A genotyping method based on an allele core locus consumption model according to claim 1 or 4, characterized in that, The construction of the first model corresponding to the gene, based on the first model and combined with the fourth data, using a stepwise depletion algorithm based on core defined sites, establishes allele combinations corresponding to the sample and creates a diploid type corresponding to the sample, further includes: Based on the fourth data, a genotype corresponding to each mutation site is generated, and combined with copy number variation information, a corresponding initial copy number consumption unit is allocated to each site; Based on the detected variant sites and allele definition information, corresponding sixth data is generated, wherein the sixth data consists of all potential alleles; Based on the sixth data, corresponding seventh data is generated; wherein, the seventh data consists of alleles that have been completely detected at the core definition site; Based on the seventh data, at least one allele is generated. Based on the allele and its core defined site, one copy number consumption unit is consumed to process the corresponding site, and the copy number consumption units for processing the remaining sites are updated. Based on the updated information, the previous steps are repeated, and the next allele is inferred until all copy number consumption units are allocated or no new valid allele can be inferred.

6. The genotyping method based on the allele core locus consumption model according to claim 5, characterized in that, The step of constructing and generating genotypes corresponding to each mutation site based on the fourth data, and allocating corresponding initial copy number consumption units to each site in conjunction with copy number variation information, also includes: Based on copy number variation information, a copy number state and its confidence level corresponding to the region to which each site belongs are generated; Based on the confidence level, one or more probabilistic copy number consumption units are assigned to each site; wherein the copy number consumption units are used to perform probabilistic consumption and inference in the stepwise consumption algorithm.

7. A genotyping system based on an allele core locus consumption model, characterized in that, The system is applied to the genotyping method based on the allele core locus consumption model as described in any one of claims 1 to 6, and the system comprises: The data comparison and generation unit is used to create and generate first data and second data corresponding to the gene, respectively, and to generate corresponding third data based on the comparison of the first data and the second data; wherein, the first data is sequencing data, and the sequencing data covers at least the target gene region; the second data is reference genome data; and the third data is comparison result data; The data processing and generation unit is used to create and generate fourth data corresponding to the target gene region based on the third data and in combination with variant detection processing; wherein the fourth data is the variant information of the sample in the target gene region, and the variant information includes at least short variant information and copy number variant information. A data creation and generation unit is used to construct a first model corresponding to a gene. Based on the first model and combined with the fourth data, an allele combination corresponding to the sample is established through a stepwise consumption algorithm based on core definition sites, and a diploid type corresponding to the sample is created and generated. The first model is an allele core site consumption model, which includes preset allele definition information. The definition information is used to define multiple sites of each allele as core definition sites and non-core definition sites.

8. The genotyping system based on the allele core locus consumption model according to claim 7, characterized in that, The data processing and generation unit further includes: The first generation module is used to detect and generate corresponding fifth data based on the fourth data; and to perform quality filtering on the fifth data; wherein the fifth data is short variation data; the quality filtering includes filtering based on allele frequency; for heterozygous genotypes, the allele frequency threshold range is set to 0.25 to 0.75; The first processing module is used to construct and generate a dynamically updated control sample set, and to normalize the sequencing depth of the samples based on the control sample set; wherein the control sample set is continuously optimized and updated by automatically screening samples without copy number variations from the analyzed samples. And / or, the data creation and generation unit further includes: The second processing module is used to dynamically weight each core defined site based on the first model; wherein the dynamic weight is pre-calculated and set based on the discrimination of the corresponding site in distinguishing different alleles; The weighted evaluation module is used to perform a weighted evaluation of the detection status of core defined sites based on dynamic weights when inferring the processing of alleles. The allocation processing module is used to construct and generate the genotype corresponding to each mutation site based on the fourth data, and to allocate the corresponding initial copy number consumption unit to each site in combination with copy number variation information. The matching generation module is used to generate corresponding sixth data based on the detected variant sites and allele definition information, wherein the sixth data consists of all potential alleles; The second generation module is used to filter and create corresponding seventh data based on the sixth data; wherein the seventh data consists of alleles whose core definition sites are completely detected. The third processing module is used to construct and generate at least one allele based on the seventh data, consume one copy number consumption unit for processing the corresponding site based on the allele and its core definition site, and update the copy number consumption units for processing the remaining sites; wherein, based on the updated information, the previous steps are repeated and the next allele is inferred until all copy number consumption units are allocated or no new valid allele can be inferred. And / or, the allocation processing module further includes: The third generation module is used to establish the copy number status and its confidence level corresponding to the region to which each site belongs, based on copy number variation information; The fourth processing module is used to allocate one or more probabilistic copy number consumption units to each site based on the confidence level; wherein the copy number consumption unit is used to perform probabilistic consumption and inference in the stepwise consumption algorithm.

9. A genotyping platform based on an allele core locus consumption model, characterized in that, The system includes a processor, a memory, and a genotyping platform control program based on an allele core locus consumption model. The processor executes the genotyping platform control program based on the allele core locus consumption model, which is stored in the memory. The genotyping platform control program based on the allele core locus consumption model implements the genotyping method based on the allele core locus consumption model as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a genotyping platform control program based on the allele core locus consumption model. The genotyping platform control program based on the allele core locus consumption model implements the genotyping method based on the allele core locus consumption model as described in any one of claims 1 to 6.

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