An analytical method and system for non-invasive prenatal screening

Abstract

The application discloses an analysis method and system for noninvasive prenatal screening, and the method comprises the following steps: obtaining target high-depth sequencing data of a target sample; performing data preprocessing on the target high-depth sequencing data to obtain depth information and SNP site information of a target region; obtaining a test result through global Z test based on copy number information, obtaining a first analysis result through local microdeletion / microduplication analysis by using a forward-backward search algorithm, and obtaining a classification result through a Bayesian classifier; obtaining a second analysis result through SNP analysis based on the SNP site information; and finally comprehensively judging to obtain a target analysis result. The application can effectively reduce the complexity of experimental operation, reduce the complexity of the diagnosis process, reduce the sample error probability caused by multiple sampling, significantly improve the efficiency of clinical testing, has high practical value and application prospect, and can be widely applied to the technical field of data processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to an analysis method and system for non-invasive prenatal screening. BACKGROUND

[0002] With the continuous development of high-throughput sequencing technology, whole genome high-depth sequencing or targeted sequencing technology based on maternal plasma free DNA is widely used in non-invasive prenatal screening. Initially, non-invasive prenatal screening is mainly used for detection of 21, 18 and 13 trisomy (non-invasive prenatal screening, NIPT), but it has now been extended to detection of other chromosomal aneuploidy and microdeletion / microduplication, as well as non-invasive single gene detection.

[0003] Non-invasive prenatal screening technology based on whole genome low-depth sequencing is mainly used for detection of the risk of fetal chromosomal disease, including aneuploidy and microdeletion / microduplication syndrome, but due to insufficient depth coverage of individual genes, it cannot detect the risk of fetal single gene disease; non-invasive prenatal screening technology based on targeted high-depth sequencing can detect chromosomal diseases within the probe coverage range, and can also be used to detect the risk of fetal single gene disease. The principle of detecting fetal new mutations using maternal peripheral blood is that the mother herself is known to be wild type (genotype represented by 00), if other allele genotypes are detected in the plasma DNA and the mutation frequency is significantly higher than the technical noise, then it can be inferred that the site is paternal or fetal new mutation (fetal genotype represented by 01). SUMMARY

[0004] The main purpose of the embodiment of the present application is to provide an analysis method and system for non-invasive prenatal screening, so as to solve at least one problem of the prior art. The present application can comprehensively and accurately analyze non-invasive prenatal screening.

[0005] To achieve the above-mentioned purpose, one aspect of the embodiment of the present application provides an analysis method for non-invasive prenatal screening, which comprises:

[0006] obtaining targeted high-depth sequencing data of the target sample;

[0007] performing data preprocessing on the targeted high-depth sequencing data to obtain depth information and SNP site information of the targeted region; the depth information of the targeted region is copy number information;

[0008] based on the copy number information, obtaining a test result by global Z test;

[0009] based on the copy number information, performing local microdeletion / microduplication analysis by using a forward-backward search algorithm to obtain a first analysis result;

[0010] obtaining a classification result by a Bayesian classifier based on the copy number information of the target region;

[0011] obtaining a second analysis result by SNP analysis based on the SNP site information;

[0012] comprehensively determining the test result, the first analysis result, the classification result and the second analysis result to obtain a target analysis result.

[0013] In some embodiments, data preprocessing is performed on the targeted high-depth sequencing data to obtain depth information and SNP site information of the target region, including the following steps:

[0014] obtaining the sequencing depth and GC value of each probe based on the targeted high-depth sequencing data;

[0015] normalizing the sequencing depth of the probe to obtain a copy number value;

[0016] establishing a Lowess correction curve according to the GC value and the copy number value of each probe, and then obtaining the copy number information of each probe based on the correction result of the Lowess correction curve;

[0017] obtaining target SNP probe sites based on the targeted high-depth sequencing data, identifying the allele combination and sequencing depth of each target SNP probe site from the targeted high-depth sequencing data to obtain SNP site information.

[0018] In some embodiments, a test result is obtained by global Z-test based on the copy number information, including the following steps:

[0019] taking the targeted region of an aneuploidy or microdeletion / microduplication syndrome as an analysis unit;

[0020] obtaining first copy number information of the analysis unit from the copy number information;

[0021] obtaining a preset number of negative samples as a reference sample library, and calculating the mean and standard deviation of the copy number of each analysis unit in the reference sample library;

[0022] calculating the Z-score value of the analysis unit corresponding to the copy number information based on the first copy number information, in combination with the mean and standard deviation of the copy number;

[0023] determining the test result of the target sample based on the Z-score value.

[0024] In some embodiments, a first analysis result is obtained by using a forward-backward search algorithm for local microdeletion / microduplication analysis based on the copy number information, including the following steps:

[0025] Based on the target region sampled based on the copy number information, a plurality of analysis windows are divided from left to right along each target region based on preset parameters; wherein, the preset parameters include a set value of the window length of the analysis window or a set value of the number of probes in the analysis window;

[0026] Based on the copy number information, the mean value of the copy number of all probes in each analysis window is obtained; the relative position of each analysis window in the entire target region is obtained;

[0027] Based on the preset search seed threshold, the positions with the absolute value of the screening copy number mean greater than the search seed threshold are taken as search seeds along the division analysis window;

[0028] All search seeds are traversed, and when the positions of multiple search seeds are continuous, the multiple search seeds with continuous positions are merged to obtain a seed block;

[0029] The adjacent window of the seed block is searched forward and backward, the mean value of the copy number corresponding to the adjacent window is calculated with all the mean values of the copy number in the seed block to obtain a first average value;

[0030] When the absolute value of the first average value is greater than the preset copy number threshold, the adjacent window is merged into the corresponding seed block;

[0031] All seed blocks are traversed, and when the distance between adjacent seed blocks is less than a preset distance threshold, the mean value of the copy number of the adjacent seed blocks is calculated to obtain a second average value;

[0032] When the absolute value of the second average value is greater than the copy number threshold, the adjacent seed blocks are merged into a new seed block;

[0033] Based on all the remaining seed blocks after merging, the first analysis result of the local microdeletion / microduplication analysis of each target region is determined in combination with the relative position.

[0034] In some embodiments, based on the copy number information of the target region, a classification result is obtained by a Bayesian classifier, including the following steps:

[0035] The SEA region corresponding to the alpha gene is taken as an analysis unit; the prior probability of the target sample corresponding to the heavy thalassemia and the DNA concentration of the target individual are obtained;

[0036] Based on all the copy number information in the SEA region, the Bayesian classifier model is established in combination with the prior probability and the DNA concentration, and then the classification result is obtained according to the Bayesian classifier model.

[0037] In some embodiments, based on the SNP site information, a second analysis result is obtained by SNP analysis, including the following steps:

[0038] based on the copy number information, a plurality of analysis windows are divided along each targeted region from left to right based on preset parameters; wherein, the preset parameters include a set value of the window length of the analysis window or a set value of the number of probes in the analysis window;

[0039] based on the copy number information, the maternal genotype proportion and the maternal heterozygous genotype proportion in each analysis window are counted, and the mean value of the copy number of all probes in each analysis window is counted; the relative position of each analysis window in the entire targeted region is obtained;

[0040] based on the maternal genotype proportion and the maternal heterozygous genotype proportion in each analysis window, forward-backward search algorithm is used for maternal chromosome deletion analysis and maternal copy number deletion analysis respectively, and the second analysis result is obtained.

[0041] In some embodiments, the test result, the first analysis result, the classification result and the second analysis result are comprehensively judged to obtain the target analysis result, including the following steps:

[0042] based on the test result to determine the first result;

[0043] when the test result is positive, and the first analysis result and the second analysis result are both negative, the start and end positions of the targeted region corresponding to the positive result are taken as the breakpoint positions of the copy number variation, and then the second result is determined;

[0044] or, when the test result is positive, the first analysis result or the second analysis result is positive, the first analysis result or the second analysis result is used to correct the breakpoint position of the copy number variation, and then the second result is determined;

[0045] or, when the test result is negative, the positive result in the first analysis result or the second analysis result and the breakpoint position are taken as the second result;

[0046] the classification result is subjected to edema fetus detection, and the third result is determined according to the result of edema fetus detection; the result of edema fetus detection includes high risk of severe thalassemia or low risk of severe thalassemia;

[0047] the target analysis result is obtained according to the first result, the second result and the third result.

[0048] In some embodiments, the method for obtaining the targeted high-depth sequencing data of the target sample includes the following steps:

[0049] free DNA is extracted from the peripheral blood sample of the pregnant woman;

[0050] end repair, adapter ligation and PCR amplification of the target fragment are performed on the free DNA to construct a sequencing library;

[0051] The DNA of the targeted region in the library is captured by a hybrid capture technology, high-depth sequencing is performed, and targeted high-depth sequencing data is obtained.

[0052] In some embodiments, the average sequencing depth in the high-depth sequencing is >500X.

[0053] To achieve the above object, another aspect of the embodiment of the present application proposes an analysis system for non-invasive prenatal screening, which comprises:

[0054] A data acquisition module is configured to acquire targeted high-depth sequencing data of a target sample.

[0055] A data preprocessing module is configured to perform data preprocessing on the targeted high-depth sequencing data to obtain depth information and SNP site information of the targeted region; the depth information of the targeted region is copy number information.

[0056] An inspection module is configured to obtain an inspection result by global Z inspection based on the copy number information.

[0057] A first analysis module is configured to obtain a first analysis result by local microdeletion / microduplication analysis using a forward-backward search algorithm based on the copy number information.

[0058] A classification module is configured to obtain a classification result by a Bayesian classifier based on the copy number information of the target region.

[0059] A second analysis module is configured to obtain a second analysis result by SNP analysis based on the SNP site information.

[0060] A comprehensive judgment module is configured to comprehensively judge the inspection result, the first analysis result, the classification result, and the second analysis result to obtain a target analysis result.

[0061] To achieve the above object, another aspect of the embodiment of the present application proposes an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the above method when executing the computer program.

[0062] To achieve the above object, another aspect of the embodiment of the present application proposes a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above method.

[0063] The embodiments of the present application at least have the following beneficial effects: the present application provides an analysis method and system for non-invasive prenatal screening, the scheme obtains target high-depth sequencing data of a target sample; the target high-depth sequencing data is preprocessed to obtain depth information and SNP site information of a target region; the depth information of the target region is copy number information; based on the copy number information, a test result is obtained through global Z test; based on the copy number information, a first analysis result is obtained through local microdeletion / microduplication analysis using a forward-backward search algorithm; based on the copy number information of the target region, a classification result is obtained through a Bayesian classifier; based on the SNP site information, a second analysis result is obtained through SNP analysis; the test result, the first analysis result, the classification result and the second analysis result are comprehensively judged to obtain a target analysis result. The embodiments of the present application can realize non-invasive prenatal screening of various common genetic diseases of fetuses. The embodiments of the present application can effectively reduce the complexity of experimental operation, reduce the complexity of the diagnosis process, reduce the probability of sample errors caused by multiple sampling, significantly improve the efficiency of clinical testing, and have high practical value and application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 is a flowchart of the analysis method for non-invasive prenatal screening provided by the embodiments of the present application;

[0065] Figure 2 is a specific technical route of the analysis method for non-invasive prenatal screening provided by the embodiments of the present application;

[0066] Figure 3 is an optimization experiment of reads fragment number;

[0067] Figure 4 is a distribution diagram of copy number and SNP statistical value of Wolf-Hirschhorn syndrome positive cases;

[0068] Figure 5 is a distribution diagram of copy number and SNP statistical value of Prader-Willi syndrome positive cases;

[0069] Figure 6 is a distribution diagram of copy number and SNP statistical value of Angelman syndrome positive cases.

[0070] The copy number plot is the copy number value distribution plot of the probe corresponding to the target region for detecting Angelman syndrome (AS) and Prader-Willi syndrome (PWS), and each gray box is the result of a fetal concentration enriched data subset, and it can be seen that as the proportion of short fragment reads increases, the fetal concentration increases, and the copy number value of the region gradually decreases, which is consistent with the characteristics of deletion; the SNP statistical value is the statistical value of all SNP probes, and each gray box corresponds to a detected target region. Each point represents the snp.ratio value of a SNP probe; the purple line is the maternal genotype proportion maternal.ratio of the probe in each analysis window of each SNP analysis window i ; the yellow line is the maternal heterozygous genotype proportion maternal.het.ratio of each analysis window i . In case 20, the purple line is close to 1, indicating that the proportion of the mother's genotype is 100%, and the mother does not carry the paternal chromosome, i.e., the paternal deletion; and in case 21, the purple line and the red line both fluctuate within the error range, and no abnormalities are detected. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementation described in the following exemplary examples does not represent all the implementations consistent with the embodiments of the present application, but is only an example of systems and methods consistent with some aspects of the embodiments of the present application as described in detail in the appended claims.

[0072] It can be understood that the terms "first", "second", and the like used in the present application can be used herein to describe various concepts, but unless specifically stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "when" or "in response to determining".

[0073] The terms "at least one", "multiple", "each", "any" and the like used in the present application include one, two or more than two, multiple includes two or more than two, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application.

[0075] The non-invasive prenatal screening analysis method provided by the embodiments of the present application relates to the technical field of data processing. The non-invasive prenatal screening analysis method provided by the embodiments of the present application can be applied to a terminal, can be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, and the like, but is not limited thereto; the server end can be configured as a stand-alone physical server, can be configured as a server cluster or a distributed system composed of multiple physical servers, can also be configured as a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and basic cloud computing services such as big data and artificial intelligence platforms, and the server can also be a node server in a blockchain network; the software can be an application that implements the non-invasive prenatal screening analysis method, and the like, but is not limited to the above forms.

[0076] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as a program module. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0077] Figure 1 is an optional flowchart of the non-invasive prenatal screening analysis method provided by the embodiments of the present application, Figure 1 The method in can include but is not limited to including steps S100 to S700, Figure 2 is a specific technical route of the embodiments of the present application;

[0078] S100, obtaining target high-depth sequencing data of a target sample;

[0079] 1, sample collection

[0080] 50 cases of prenatal diagnosis of fetal karyotype or genotype results of pregnant women peripheral blood samples, including 23 cases of positive samples and 27 cases of negative samples. All plasma samples were separated within 4 hours after collection. Sample information is shown in the following table:

[0081] Table 1

[0082]

[0083]

[0084] 2, sample DNA extraction and library construction

[0085] Targeted high-depth library construction and sequencing: collect a tube of maternal peripheral blood, after plasma separation and DNA extraction; library construction, pooling the same batch of hybridization library; then hybridization, capture the DNA of the target region in the library, library quality control after PCR, library quality control passes high-depth sequencing, the average sequencing depth > 500X.

[0086] 1) sample DNA extraction: the above sample type each selected as an example, using nucleic acid extraction or purification reagent (Boao Wood Gene Technology Co., Ltd., item number: S10020). Using the method of extracting cfDNA in large volume, optimizing the extraction process to adapt to the detection of this project, one-time single-tube single-sample extraction of 2-3 ml plasma, so that the amount of DNA obtained by sample extraction reaches the required volume for single library construction, without subsequent concentration or purification.

[0087] 2) end repair and adapter ligation: use the Ion Xpress Plus Fragment Library Kit kit of Thermofisher Scientific company to carry out end repair and sequencing adapter ligation on DNA fragments, and the product is purified by Agencourt AMPure XP Kit. The reaction system is as follows:

[0088] Table 2 end repair reaction system

[0089]

[0090] Table 3 sequencing adapter ligation reaction system

[0091]

[0092]

[0093] The adapter used in this step is an adapter primer containing A and P adapters of the UID sequence designed by the inventors of the present application (for specific sequences, see patent document with application number CN201910565206.1); the introduction of the UID can better remove point mutation errors introduced during PCR in sequencing, and can remove errors caused by the polymerase itself; in addition, it can also be used to distinguish between identical gene sequences under natural conditions (false repetitive sequences) and true repetitive sequences caused by PCR.

[0094] 3) Pre-hybridization amplification: the purified product of the previous step is subjected to PCR amplification enrichment, and the PCR system is as follows:

[0095] Table 4 PCR amplification reaction system

[0096]

[0097] The primer sequences are different for different sequencing platforms. The primers for the proton platform in this embodiment are as follows:

[0098] F: CCACTACGCCTCCGCTTTCCTCTCTATG; R: CCATCTCATCCCTGCGTGTC;

[0099] The PCR amplification product is purified using Agencourt AMPure XP Kit to obtain a DNA library for hybridization capture.

[0100] The plasma DNA in this round of operation is quantified using Qubit, and the DNA library is quantified using qPCR. The results of some samples are shown in the following table:

[0101] Table 5

[0102]

[0103] 3. Probe capture and sequencing

[0104] 1) Probe capture: using SureSelect TE Reagent Kit, PTN, 16 (Agilent) kit, hybridize the DNA library that has been mixed in equal amounts according to the instructions, which includes pre-hybridization blocking of the library, preparation of buffer working solution, preparation of the probe mixture working solution designed by the present patent, and addition of the probe to the blocked DNA mixture library for hybridization capture at 67.5°C for 22 hours.

[0105] The regions targeted by the probes are as follows:

[0106] Table 6

[0107]

[0108] 2) Enrichment of target region of library after capture: Dynabeads MyOne Streptavidin Tl (Invitrogen) was used to bind the library after capture for 2-3h, and then the Tl magnetic beads bound library was eluted.

[0109] The washing steps are shown as follows:

[0110] Table 7

[0111]

[0112] Table 8

[0113]

[0114] 3) Post-PCR amplification: the eluted enriched Tl magnetic beads were mixed with 36.5 μΐ of nuclease-free water, and PCR amplification was performed as follows:

[0115] Table 9

[0116]

[0117] The PCR primer sequences are shown in Table 4, and the PCR products were purified using Agencourt AMPure XP Kit to obtain the sequencing products.

[0118] 4) Sequencing: the sequencing products were sequenced using the Ion Torrent platform, and the sequencing results were analyzed using bioinformatics. The targeted high-depth sequencing technology uses probes to capture free DNA in the target region and performs high-depth sequencing, and the average depth of each site is usually not less than 100X. For example, in some embodiments, fetal multi-concentration gradient data can be first established, and a gradient fetal concentration enrichment dataset is generated: using the feature that the length of fetal free DNA is significantly lower than that of maternal free DNA, the off-machine data of each sample can be set to different length screening thresholds (such as <168bp), and screening short fragment reads can generate fetal multi-concentration gradient data, which can effectively reduce the interference of maternal background. In the examples, six data sets with step-by-step increase in fetal concentration were established, and the threshold values of reads screening were >178bp, all reads, <178bp, <168bp, <158bp, and <148bp, respectively. Generating fetal concentration step-by-step increase data sets and analyzing whether the copy number of the chromosome or chromosome interval to be tested is consistent with the trend of fetal concentration change can help to confirm true positive sites.

[0119] S200, data preprocessing of the targeted high-depth sequencing data to obtain depth information and SNP site information of the target region;

[0120] wherein the depth information of the target region is the copy number information;

[0121] It should be noted that in some embodiments, step S200 can include: obtaining the sequencing depth and GC value of each probe based on the targeted high-depth sequencing data; and performing normalization processing on the sequencing depth of the probe to obtain the copy number value.

[0122] A Lowess correction curve is established based on the GC value and the copy number value of each probe, and then the copy number information of each probe is obtained based on the correction result of the Lowess correction curve; the target SNP probe site is obtained based on the targeted high-depth sequencing data, and the allele combination and the sequencing depth of each target SNP probe site are identified from the targeted high-depth sequencing data to obtain the SNP site information.

[0123] Exemplarily, in some specific embodiments, the data preprocessing can be implemented as follows: (1) using BWA alignment software to align the sequencing reads to the human reference genome (alignment parameters bwamem-t20-B1-O1-L1-T-M reference), and removing repetitive sequences and low-quality alignment results; (2) calculating the depth information of the probe and performing data noise reduction processing: calculating the sequencing depth of each probe and performing normalization processing to remove the influence of the difference in capture efficiency between the probes, and then using the GC value (the molar percentage of guanine and cytosine to all bases in the whole genome DNA) of the probe and the normalized copy number value to establish a lowess correction curve to correct the influence of GC bias, thereby obtaining the copy number value of each probe, and the copy number value of the probe site i is denoted as copy.ratio i ; (3) calculating the genotype information of the bi-allelic SNP (bi-allelic SNP) probe site, using varscan software to identify the allele combination and the sequencing depth of each SNP site, and calculating the frequency of the allele (referred to as genotype A) consistent with the human genome reference sequence (the genotype inconsistent with the human reference sequence is referred to as genotype B), and the allele frequency of the SNP site i is denoted as snp.ratio i .

[0124] S300, obtaining a test result by global Z-test based on the copy number information;

[0125] It should be noted that in some embodiments, step S300 can include: taking the targeted region of aneuploidy or microdeletion / microduplication syndrome as an analysis unit; obtaining first copy number information of the analysis unit from the copy number information; obtaining a preset number of negative samples as a reference sample library, calculating the mean and standard deviation of the copy number of each analysis unit in the reference sample library; calculating the Z-score value of the analysis unit corresponding to the copy number information based on the first copy number information, in combination with the mean and standard deviation of the copy number; and determining the test result of the target sample based on the Z-score value.

[0126] Exemplarily, in some specific embodiments, the global Z test can be implemented as follows:

[0127] Based on the copy number information, chromosomal aneuploidy abnormalities and long fragment deletion / duplication are identified, and the main analysis steps include: (1) the targeted region of aneuploidy or microdeletion / microduplication syndrome is taken as an analysis unit, and the average copy number of all probes in each targeted region is calculated as the copy number value copy.ratio of the analysis unit. i (2) 200 negative samples are collected as a reference sample library, and the mean and standard deviation of the copy number of each analysis unit in the reference sample library are calculated. (3) The Z value (Z-score) of the analysis unit is calculated, and the Z value is used to determine whether the sample has aneuploidy (the analysis unit covers the full length of the chromosome, and Z value greater than 3 is judged as trisomy and less than -3 is judged as monosomy) and long fragment deletion / duplication (the analysis unit covers a local region within the chromosome, and Z value greater than 3 is judged as microduplication and less than -3 is judged as microdeletion). It should be noted that the breakpoint position of the deletion / duplication reported by the global Z test method is not clear, so the starting and ending positions of the probe targeting region are taken as the starting and ending positions of the copy number variation by default. The Z-score calculation formula is:

[0128]

[0129] In the formula, copy.ratio i represents the copy number value of the analysis unit, mean i represents the mean of the copy number, and sd i represents the standard deviation of the copy number.

[0130] S400, based on the copy number information, a forward-backward search algorithm is used for local microdeletion / microduplication analysis to obtain a first analysis result;

[0131] It should be noted that in some embodiments, step S400 can include: based on the target region sampled based on the copy number information, dividing each target region into a plurality of analysis windows from left to right based on a preset parameter; wherein the preset parameter includes a set value of the window length of the analysis window or a set value of the number of probes in the analysis window; based on the copy number information, the average copy number of all probes in each analysis window is obtained; the relative position of each analysis window in the entire target region is obtained; based on a preset search seed threshold, sites with an absolute value of the copy number average greater than the search seed threshold are screened out as search seeds along the divided analysis windows; all search seeds are traversed, and when the positions of a plurality of search seeds are continuous, the plurality of search seeds with continuous positions are merged to obtain a seed block; the adjacent windows of the seed block are searched forward and backward, the average value of the copy number average corresponding to the adjacent windows and all copy number averages in the seed block are calculated to obtain a first average value; when the absolute value of the first average value is greater than a preset copy number threshold, the adjacent windows are merged into the corresponding seed block; all seed blocks are traversed, and when the distance between adjacent seed blocks is less than a preset distance threshold, the average value of the copy number averages of the adjacent seed blocks is calculated to obtain a second average value; when the absolute value of the second average value is greater than the copy number threshold, the adjacent seed blocks are merged into a new seed block; based on all the seed blocks remaining after merging, the first analysis result of the local microdeletion / microduplication analysis of each target region is determined in combination with the relative position.

[0132] Exemplarily, in some specific embodiments, the local microdeletion / microduplication analysis can be implemented as follows:

[0133] Based on the copy number information, the forward-backward search algorithm is used to identify the microdeletion / repeat in the chromosome and the target region. The main analysis steps include: (1) dividing the analysis window in the target region. Each disease corresponding target region is divided into k analysis units from left to right. The division rule can be selected as fixed window length or fixed probe number, and the method of fixed probe number is preferred, so that the copy number error of each window is relatively stable; while the method of fixed window length will significantly increase the data fluctuation in the window with low probe density, which is not conducive to downstream data analysis. (2) Calculate the average copy number of all probes in each analysis window. (3) Use the forward-backward search algorithm to identify the chromosome microdeletion / microduplication region (block).

[0134] Specifically, the logic of the forward-backward search algorithm is described as follows:

[0135] Input data: copy.ratio of all windows in the entire target region and locs corresponding to the relative position.

[0136] Algorithm parameters: searching seed threshold seed.cutoff, block copy number threshold block.cutoff, minimum seed length seed.block.min.len, minimum block length block.min.len, maximum distance max_distance allowed for merging adjacent blocks.

[0137] A, initialize searching seeds. Use the preset searching seed threshold (seed.cutoff) to screen the sites with absolute copy number greater than the threshold as the starting value of searching, i.e. searching seeds.

[0138] B, extend the searching seeds to form searching seed.blocks. If the searching seed positions are continuous, merge them; meanwhile, traverse each possible starting point (seeds or seed.blocks), and search the adjacent sites forward and backward. If the absolute value of the average copy number after merging the adjacent sites is greater than the block copy number threshold (block.cutoff), then the adjacent sites are included, and the seed.block is updated.

[0139] C, use the iterative forward-backward searching algorithm to find the missing / duplicated blocks:

[0140] a) if there is a candidate region (searching.seed>seed.block.min.len) long enough, search the adjacent seed.blocks forward and backward at the same time;

[0141] b) if the current block is adjacent to the previous block and meets certain conditions (distance < max_distance, and the absolute value of the copy number after merging is > block.cutoff), merge the two blocks to form a new seed.block;

[0142] c) repeat steps a and b until the detection result is unchanged, then add the blocks to the blocks list.

[0143] S500, based on the copy number information of the target region, obtain the classification result through the Bayesian classifier;

[0144] It should be noted that in some embodiments, step S500 can include: taking the SEA region corresponding to the alpha gene as an analysis unit; obtaining the prior probability of heavy thalassemia corresponding to the target sample and the DNA concentration of the target individual; based on all the copy number information in the SEA region, combining the prior probability and the DNA concentration to establish a Bayesian classifier model, and then obtaining the classification result according to the Bayesian classifier model.

[0145] Exemplarily, in some embodiments, the Bayesian classifier can be implemented as follows:

[0146] A Bayesian classifier model is established to calculate the probability of the fetus being a severe thalassemia based on the prior probability of severe thalassemia, the fetal DNA concentration and the multi-dimensional information of the α thalassemia gene region copy number value. The main analysis steps include: (1) taking the SEA region corresponding to the α gene as the analysis unit (referred to as the SEA region), and calculating the average copy number of all probes in the SEA region as the copy number value copy.ratio of the analysis unit sea . (2) Calculate the fetal DNA concentration fc of the sample and the prior probability of severe thalassemia. When the fetus is a SEA deletion carrier (deletion of two α thalassemia genes) and the father and mother are both SEA deletion carriers, there are three genotypes of the fetus: normal, SEA deletion carrier and severe α thalassemia (Hb Bart's hydrops fetus), and their prior probabilities are 25%, 50% and 25%, respectively; (3) A multi-dimensional Bayesian probability model is used to calculate the Bayesian posterior probability P of the fetus being normal, a SEA deletion carrier and a severe α thalassemia (Hb Bart's hydrops fetus) genotype:

[0147]

[0148] Wherein, A, B and C represent normal, SEA deletion carrier and severe α thalassemia genotypes, respectively; P A , P B and P C are the prior probabilities of the three genotypes, which are set to 0.25, 0.5 and 0.25 respectively according to Mendelian inheritance law; P(Ratio i |A, fc), P(Ratio i |B, fc) and P(Ratio i |C, fc) represent the probability of the sample α thalassemia gene copy number value being Ratio i under the condition of known current fetal DNA concentration fc and categories A, B and C; P(C|Ratio i , fc) is the probability of the sample being category C, i.e. severe α thalassemia, under the condition of the current sample α thalassemia gene copy number value being Ratio i and the fetal concentration being fc. A threshold is set to judge the sample as severe thalassemia if the probability of severe thalassemia is greater than 50%.

[0149] S600, based on SNP site information, a second analysis result is obtained through SNP analysis;

[0150] It should be noted that in some embodiments, step S600 can include: based on the targeted region sampled by the copy number information, dividing each targeted region into a plurality of analysis windows from left to right based on a preset parameter; wherein the preset parameter includes a set value of the window length of the analysis window or a set value of the number of probes in the analysis window; based on the copy number information, obtaining the maternal genotype ratio and the maternal heterozygous genotype ratio in each analysis window, and obtaining the average copy number of all probes in each analysis window; obtaining the relative position of each analysis window in the entire targeted region; based on the maternal genotype ratio and the maternal heterozygous genotype ratio in each analysis window, using a forward-backward search algorithm to respectively perform maternal chromosomal deletion analysis and maternal copy number deletion analysis to obtain a second analysis result.

[0151] It should be noted that maternal chromosomal deletion analysis using a forward-backward search algorithm for maternal genotype ratio and maternal copy number deletion analysis using a forward-backward search algorithm for maternal heterozygous genotype ratio, the corresponding determination condition of the search seed and the merging seed block is set to be less than the corresponding preset threshold, the principle of the process is the same, and details are not repeated here.

[0152] Exemplarily, in some specific embodiments, SNP analysis can be implemented as follows:

[0153] Integrating the genotype information of SNPs can further identify the source of copy number variation, such as distinguishing Angelman syndrome (maternal chromosomal fragment deletion) and Prader-Willi syndrome (paternal chromosomal fragment deletion). The main analysis steps include: (1) dividing the analysis window in the targeted region. Along each targeted region corresponding to the disease, it is divided into k analysis units from left to right. The division rule can be selected as fixed window length or fixed SNP probe number, and the method of fixed SNP probe number is preferred, so that the error of the statistical value of each window is relatively stable. (2) Statistics maternal.ratio i and maternal.het.ratio i of the probes in each analysis window. (3) Average copy.ratio i of all probes. (4) Use the forward-backward search algorithm of the principle 4 to identify maternal.ratio i and maternal.het.ratio iThere are abnormal regions, where the proportion of maternal genotype is significantly lower than the normal region, which is the maternal chromosome deletion region; the proportion of maternal heterozygous genotype is lower than the normal region, which is the maternal copy number deletion region.(5) Differentiate Angelman syndrome and Prader-Willi syndrome. When the sample is detected to have 15q11.2-q13 deletion, if the SNP analysis finds that there is paternal deletion in the region, it is judged as Prader-Willi syndrome, otherwise it is judged as Prader-Willi syndrome.

[0154]

[0155] Wherein, homo.probes.num i The total number of SNP sites where the mother is homozygous genotype (SNP condition: snp.ratio>0.8 or snp.ratio<0.2); homo.maternal.probes.num i The total number of SNP sites where the mother is homozygous genotype and the paternal genotype is found in the plasma (SNP condition: snp.ratio>0.98 or snp.ratio<0.02). maternal.probes.num i The total number of SNP sites where the mother is homozygous genotype and the paternal genotype is found in the plasma (SNP condition: snp.ratio>0.98 or snp.ratio<0.02). maternal.probes.num i The number of sites in the analysis window where the mother's genotype is heterozygous (SNP condition: snp.ratio≥0.2 or snp.ratio≤0.8); probes.num i The number of SNP probes in the analysis window.

[0156] S700, comprehensively determine the test result, the first analysis result, the classification result and the second analysis result, and obtain a target analysis result;

[0157] It should be noted that in some embodiments, step S700 can include: determining a first result based on the test result; when the test result is positive, and the first analysis result and the second analysis result are both negative, taking the start and end positions of the target region corresponding to the positive result as the breakpoint positions of the copy number variation, and then determining a second result; or, when the test result is positive, and the first analysis result or the second analysis result is positive, correcting the breakpoint positions of the copy number variation using the first analysis result or the second analysis result, and then determining a second result; or, when the test result is negative, taking the positive result in the first analysis result or the second analysis result and the breakpoint positions as the second result; performing edema fetus detection on the classification result, and determining a third result according to the result of the edema fetus detection; the result of the edema fetus detection includes high risk of severe thalassemia or low risk of severe thalassemia; and obtaining a target analysis result according to the first result, the second result and the third result.

[0158] Exemplarily, in some specific embodiments, the comprehensive determination can be implemented as follows:

[0159] The main rules of the integrated analysis are as follows: (1) Chromosome aneuploidy detection: the global Z test result is used as the criterion; (2) Chromosome microdeletion and microduplication detection: when the global Z test detects positive, if neither the microdeletion / microduplication analysis nor the SNP analysis detects positive, a positive result is issued and the start and end positions of the target region are taken as the breakpoint positions of the copy number variation; when the global Z test detects positive, and the microdeletion / microduplication analysis result or the SNP analysis detects positive, the result of the local variation analysis or the SNP analysis is used to correct the breakpoint positions of the copy number variation; when the global Z test detects negative, the positive result of the traversal analysis or the SNP analysis and the breakpoint positions are outputted; (3) Edema fetus detection: the result of the Bayesian detection algorithm module is used, and if the detection result is positive, the fetus is considered to be at high risk of severe thalassemia, otherwise, at low risk.

[0160] The following table shows a comparison of the AOH detection results based on the SNP-mixture heterozygosity and the chip method SNP-array.

[0161] In 50 cases, 23 positive samples all detected the chromosome types marked in the sample information, with a positive coincidence rate of 100%; 27 negative samples all did not detect severe thalassemia, chromosome aneuploidy and microdeletion / microduplication abnormalities, with a negative coincidence rate of 100%.

[0162] In this embodiment, fetal multi-concentration gradient data were established. For each sample, six gradients (>178bp, all reads, <178bp, <168bp, <158bp, <148bp) of fetal concentration enrichment data subsets were generated. Each data subset could be used to run the analysis workflow of the technical route. As shown in the figure below, as the proportion of short reads increases, the fetal concentration gradually increases, and the ratio value of positive samples with copy number duplication gradually increases; while the ratio value of positive samples with copy number deletion decreases step by step. However, as the enrichment coefficient increases, the overall usable data volume decreases, leading to poor data homogeneity. Therefore, this invention preferentially uses the data subset <158bp as the judgment result of the global Z-test and the local deletion module. Figure 3 ).

[0163] The detection results for each positive sample in this embodiment are shown in the figure below. Each sample's result includes the results from the four detection modules and the final comprehensive judgment result. Comparing the comprehensive judgment result with the labeled information, the detection results and the labeled information are 100% consistent.

[0164]

[0165]

[0166] For example, a positive case of Wolf-Hirschhorn syndrome is provided.

[0167] Case 17 is a positive sample for Wolf-Hirschhorn syndrome, with copy number deletion occurring in a localized region within the target area. This sample failed to be detected using a global Z-test; its copy number was -1.949, and the Z-value was -2.081, failing to meet the positive threshold of the global test. However, the local microdeletion and microduplication analysis module automatically generated a result indicating a local deletion in the chr4:185965-4737426 interval. Furthermore, the SNP analysis module indicated a paternal deletion in the chr4:185989-5059668 interval. The results from the local microdeletion and microduplication analysis module and the SNP analysis module were consistent; therefore, Case 17 was ultimately diagnosed as Wolf-Hirschhorn syndrome. Figure 4 ).

[0168] For example, a positive case of Prader-Willi syndrome and Angelman syndrome is provided.

[0169] Case 20 and case 21 are positive samples of Prader-Willi syndrome and Angelman syndrome respectively. The deletion positions of the two syndromes are the same, so the deletion signal of the target region (chr15:22819714-28422634) can be detected by the method of copy number analysis (including global Z test and local microdeletion / microduplication analysis module) (as shown in Figure 5 and Figure 6 Copy number schematic diagram), but it fails to further distinguish that the samples belong to Prader-Willi syndrome and Angelman syndrome. In combination with the results of SNP analysis (case 20 carries paternal deletion and case 21 does not carry paternal deletion, see Figure 5 and Figure 6 SNP statistical value distribution diagram), it can be further judged that case 20 and case 21 are Prader-Willi syndrome and Angelman syndrome respectively.

[0170] The embodiment of the present application also provides an analysis system for non-invasive prenatal screening, which can realize the analysis method for non-invasive prenatal screening. The system comprises:

[0171] A data acquisition module is configured to acquire targeted high-depth sequencing data of a target sample.

[0172] A data preprocessing module is configured to perform data preprocessing on the targeted high-depth sequencing data to obtain depth information and SNP site information of a target region. The depth information of the target region is copy number information.

[0173] A test module is configured to obtain a test result by global Z test based on the copy number information.

[0174] A first analysis module is configured to obtain a first analysis result by local microdeletion / microduplication analysis using a forward-backward search algorithm based on the copy number information.

[0175] A classification module is configured to obtain a classification result by a Bayesian classifier based on the copy number information of the target region.

[0176] A second analysis module is configured to obtain a second analysis result by SNP analysis based on the SNP site information.

[0177] A comprehensive determination module is configured to comprehensively determine the test result, the first analysis result, the classification result and the second analysis result to obtain a target analysis result.

[0178] It can be understood that the contents in the above method embodiments are all applicable to the present system embodiments, the present system embodiments specifically implement the functions same as the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0179] The present application also provides an electronic device, which comprises a memory and a processor. The memory stores a computer program, and the processor implements the analysis method for non-invasive prenatal screening when executing the computer program. The electronic device can be any smart terminal, such as a tablet computer or a vehicle-mounted computer.

[0180] It can be understood that the contents in the above method embodiments are all applicable to the present device embodiments, the present device embodiments specifically implement the functions same as the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0181] The present application also provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the analysis method for non-invasive prenatal screening.

[0182] It can be understood that the contents in the above method embodiments are all applicable to the present storage medium embodiments, the present storage medium embodiments specifically implement the functions same as the above method embodiments, and achieve the same beneficial effects as the above method embodiments.

[0183] The memory is a non-transitory computer readable storage medium, which can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0184] The noninvasive prenatal screening analysis method and system provided by the embodiment of the present application can realize noninvasive prenatal screening of multiple common genetic diseases of fetuses, effectively reduce the complexity of experimental operation, reduce the complexity of the diagnosis process, reduce the probability of sample errors caused by multiple sampling, significantly improve the efficiency of clinical testing, and have high practical value and application prospect.

[0185] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. It can be understood by those skilled in the art that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0186] It can be understood by those skilled in the art that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than those shown in the figures, or combine certain steps or different steps.

[0187] It can be understood by those skilled in the art that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than those shown in the figures, or combine certain steps or different steps.

[0188] It can be understood by those skilled in the art that all or some steps in the method disclosed above, the function modules / cells in the system and the device can be implemented as software, firmware, hardware and appropriate combinations thereof.

[0189] The terms "first", "second", "third", "fourth", and the like in the description and in the claims of this application, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of orderly or chronological mundane operation, reverse order operation, based on completion of some desired or other convenient events, or based on other modification that can be wished to those with average skill in the art. Additionally, the terms "comprising", "having", "including", and "containing" are to be construed as open-ended terms (i.e., meaning "including, but not limited to", "comprising, but not limited to", "having, but not limited to", or "including, but not limited to") unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein and each separate value is incorporated into the specification as if it were individually recited herein. The use of any of the following terms "coupled", "connected", or "communicatively coupled", among others, are used herein to describe a relationship between or among two or more elements and / or some other relationship, depen¬ding on the context. For example, the term "coupled" or "connected" can be used to indicate that two elements interact with each other, that one element is coupled to or connected to another element, or that some other relationship exists between or among the elements. The term "communicatively coupled" can be used to indicate that two or more elements are able to exchange information. The use of terms "coupled", "connected", or "communicatively coupled", among others, are not intended to mean that an element is directly connected to another element or directly communicatively coupled to another element.

[0190] It should be understood that, in this application, "at least one", means one or more, "multiple" means two or more. "And / or" is used to describe the relationship between the associated objects, which means that there can be three relationships, for example, "A and / or B" can mean: only A, only B, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c, can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0191] In several embodiments provided by the present application, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described system embodiments are only illustrative, for example, the division of the above-mentioned units is only a logical functional division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed elements can be indirect coupling or communication connection through some interface, system or unit, which can be electrical, mechanical or other forms.

[0192] The units described above as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0193] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0194] When the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in the form of a contribution to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods in the embodiments of the present application. The foregoing storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0195] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not intended to limit the scope of the embodiments of the present application. Any modification, equivalent replacement, and improvement made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. An analytical method for non-invasive prenatal screening, characterized in that, The method includes the following steps: Obtain targeted high-depth sequencing data of the target sample; The targeted high-depth sequencing data is preprocessed to obtain the depth information of the target region and SNP site information; the depth information of the target region is copy number information. Based on the copy number information, the test result is obtained through a global Z-test. Based on the copy number information, a first analysis result is obtained by performing local micro-deletion / micro-duplication analysis using a forward-backward retrieval algorithm; Based on the copy number information of the target region, a classification result is obtained through a Bayesian classifier. Based on the SNP site information, a second analysis result is obtained through SNP analysis; The test results, the first analysis results, the classification results, and the second analysis results are comprehensively evaluated to obtain the target analysis results; Among them, based on copy number information, using forward... The backward retrieval algorithm performs local micro-deletion / micro-duplication analysis to obtain the first analysis result, including the following steps: Based on the target region sampled using copy number information, multiple analysis windows are obtained by dividing each target region from left to right according to preset parameters. These preset parameters include a set value for the window length or a set value for the number of probes within the analysis window. The average copy number of all probes in each analysis window is calculated based on the copy number information. The relative position of each analysis window within the entire target region is obtained. Based on a preset seed threshold, sites along the dividing analysis windows whose absolute value of the average copy number is greater than the seed threshold are used as seed sites. All seed sites are iterated over; when multiple seed sites are in consecutive positions, they are merged to obtain the final seed. The process involves: Searching for neighboring windows of a seed block in a forward-backward direction; averaging the copy number of each neighboring window with the average copy number of all targets in the seed block to obtain a first average; merging neighboring windows into the corresponding seed block when the absolute value of the first average is greater than a preset copy number threshold; traversing all seed blocks, averaging the copy number of adjacent seed blocks when the distance between them is less than a preset distance threshold to obtain a second average; merging adjacent seed blocks into a new seed block when the absolute value of the second average is greater than the copy number threshold; and determining the first analysis result for local micro-deletion / micro-duplication analysis of each target region based on all remaining seed blocks after merging and their relative positions. Based on the SNP site information, a second analysis result is obtained through SNP analysis, including the following steps: Based on the target region sampled using copy number information, multiple analysis windows are obtained by dividing each target region from left to right according to preset parameters. These preset parameters include a set value for the window length or a set value for the number of probes within the analysis window. Based on the copy number information, the proportions of maternal genotypes and maternal heterozygous genotypes in each analysis window are statistically calculated, and the mean copy number of all probes in each analysis window is calculated. The relative position of each analysis window within the entire target region is obtained. Based on the proportions of maternal genotypes and maternal heterozygous genotypes in each analysis window, a forward-backward retrieval algorithm is used to perform maternal chromosome deletion analysis and maternal copy number deletion analysis, respectively, to obtain the second analysis result.

2. The method according to claim 1, characterized in that, The process of preprocessing the targeted high-depth sequencing data to obtain depth information and SNP site information of the targeted region includes the following steps: Based on the targeted high-depth sequencing data, the sequencing depth and GC value of each probe were obtained; The sequencing depth of the probe is normalized to obtain the copy number; A Lowess correction curve is established based on the GC value and copy number of each probe, and then the copy number information of each probe is obtained based on the correction result of the Lowess correction curve. Based on the targeted high-depth sequencing data, target SNP probe sites are obtained, and the allelic combination and sequencing depth of each target SNP probe site are identified from the targeted high-depth sequencing data to obtain the SNP site information.

3. The method according to claim 1, characterized in that, The process of obtaining the test result based on the copy number information through a global Z-test includes the following steps: The target region of aneuploidy or microdeletion / microduplication syndrome is used as the analysis unit; Obtain the first copy number information of the analysis unit from the copy number information; A predetermined number of negative samples are obtained as a reference sample library, and the mean copy number and standard deviation of copy number of each analysis unit in the reference sample library are calculated. Based on the first copy number information, the Z-score value of the analysis unit corresponding to the copy number information is calculated by combining the copy number mean and the copy number standard deviation; The test result of the target sample is determined based on the Z-score value.

4. The method according to claim 1, characterized in that, The classification result obtained by using a Bayesian classifier based on the copy number information of the target region includes the following steps: The SEA region corresponding to the α gene is used as the analysis unit; the prior probability of severe thalassemia corresponding to the target sample and the DNA concentration of the target individual are obtained; Based on the copy number information of all the SEA regions, a Bayesian classifier model is established by combining the prior probability and the DNA concentration, and then the classification result is obtained according to the Bayesian classifier model.

5. The method according to claim 1, characterized in that, The process of comprehensively judging the test results, the first analysis results, the classification results, and the second analysis results to obtain the target analysis results includes the following steps: The first result is determined based on the test results; When the test result is positive, and neither the first analysis result nor the second analysis result is positive, the start and end positions of the target region corresponding to the positive result are used as the breakpoint positions of copy number variation, thereby determining the second result; Alternatively, if the test result is positive, or the first analysis result or the second analysis result is positive, the breakpoint position of the copy number variation is corrected using the first analysis result or the second analysis result, thereby determining the second result; Alternatively, if the test result is negative, the positive result in the first analysis result or the second analysis result, along with the breakpoint location, shall be taken as the second result; The classification results are subjected to hydrops fetal edema detection, and a third result is determined based on the results of the hydrops fetal edema detection; the results of the hydrops fetal edema detection include high risk of severe thalassemia or low risk of severe thalassemia. The target analysis result is obtained based on the first result, the second result, and the third result.

6. The analytical method according to claim 1, characterized in that, The method for obtaining targeted high-depth sequencing data of the target sample includes the following steps: Cell-free DNA was extracted from peripheral blood samples from pregnant women. End repair, adapter ligation, and PCR amplification of the target fragment were performed on free DNA to construct a sequencing library. DNA from the target region in the library is captured using hybridization capture technology, and then high-depth sequencing is performed to obtain targeted high-depth sequencing data.

7. The analytical method according to claim 6, characterized in that, The average sequencing depth in the high-depth sequencing is >500X.

8. An analytical system for non-invasive prenatal screening, characterized in that, The system includes: The data acquisition module is used to acquire targeted high-depth sequencing data of the target sample; The data preprocessing module is used to preprocess the targeted high-depth sequencing data to obtain the depth information of the target region and SNP site information; the depth information of the target region is copy number information. The verification module is used to obtain the verification result based on the copy number information through a global Z-test; The first analysis module is used to perform local micro-deletion / micro-duplication analysis based on the copy number information using a forward-backward retrieval algorithm to obtain the first analysis result; The classification module is used to obtain the classification result by using a Bayesian classifier based on the copy number information of the target region. The second analysis module is used to obtain a second analysis result based on the SNP site information through SNP analysis; The comprehensive judgment module is used to comprehensively judge the test results, the first analysis results, the classification results, and the second analysis results to obtain the target analysis results; Among them, based on copy number information, using forward... The backward retrieval algorithm performs local micro-deletion / micro-duplication analysis to obtain the first analysis result, including the following steps: Based on the target region sampled using copy number information, multiple analysis windows are obtained by dividing each target region from left to right according to preset parameters. These preset parameters include a set value for the window length or a set value for the number of probes within the analysis window. The average copy number of all probes in each analysis window is calculated based on the copy number information. The relative position of each analysis window within the entire target region is obtained. Based on a preset seed threshold, sites along the dividing analysis windows whose absolute value of the average copy number is greater than the seed threshold are used as seed sites. All seed sites are iterated over; when multiple seed sites are in consecutive positions, they are merged to obtain the final seed. The process involves: Searching for neighboring windows of a seed block in a forward-backward direction; averaging the copy number of each neighboring window with the average copy number of all targets in the seed block to obtain a first average; merging neighboring windows into the corresponding seed block when the absolute value of the first average is greater than a preset copy number threshold; traversing all seed blocks, averaging the copy number of adjacent seed blocks when the distance between them is less than a preset distance threshold to obtain a second average; merging adjacent seed blocks into a new seed block when the absolute value of the second average is greater than the copy number threshold; and determining the first analysis result for local micro-deletion / micro-duplication analysis of each target region based on all remaining seed blocks after merging and their relative positions. Based on the SNP site information, a second analysis result is obtained through SNP analysis, including the following steps: Based on the target region sampled using copy number information, multiple analysis windows are obtained by dividing each target region from left to right according to preset parameters. These preset parameters include a set value for the window length or a set value for the number of probes within the analysis window. Based on the copy number information, the proportions of maternal genotypes and maternal heterozygous genotypes in each analysis window are statistically calculated, and the mean copy number of all probes in each analysis window is calculated. The relative position of each analysis window within the entire target region is obtained. Based on the proportions of maternal genotypes and maternal heterozygous genotypes in each analysis window, a forward-backward retrieval algorithm is used to perform maternal chromosome deletion analysis and maternal copy number deletion analysis, respectively, to obtain the second analysis result.

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