A detection system for neural tube defect genetic risk assessment

Abstract

The application provides a detection system for genetic risk assessment of neural tube defects, and relates to the technical field of bioinformatics. The system comprises: obtaining genomic sequencing data of a to-be-detected individual, performing targeted filtering based on a preset NTD-related gene set, and screening out rare pathogenic variants located in the gene set, MAF satisfying a preset frequency threshold, and predicted to have biological pathogenicity; then, the total number of the rare pathogenic variants is counted to obtain a mutation load value, which is compared with a preset determination threshold, and a risk assessment result is output. The application effectively removes the whole genome background noise by limiting the gene set range and quantifying the cumulative effect of rare pathogenic variants, breaks through the limitations of narrow coverage of traditional single gene detection and lag of imaging diagnosis, significantly improves the specificity and sensitivity of neural tube defect diagnosis, and can realize precise risk early warning in early pregnancy or before embryo implantation.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics, and in particular to a detection system for assessing the genetic risk of neural tube defects. Background Technology

[0002] Neural tube defects (NTDs) are a group of congenital malformations caused by the failure or incomplete closure of the neural tube during embryonic development. These include various types such as spina bifida, anencephaly, and encephalocele. The incidence of NTDs is high, seriously threatening the lives and health of fetuses and newborns. The pathogenesis of NTDs is complex, influenced by both genetic and environmental factors, with genetic factors playing a crucial role in the development of the disease.

[0003] Current diagnostic techniques for NTDs mainly include imaging diagnosis and traditional genetic diagnosis. Imaging diagnosis relies primarily on ultrasound examination, which typically only detects structural lesions in the mid-to-late stages of embryonic development or after birth, failing to provide early warning and easily missing some mild NTD types. Existing genetic diagnostic methods mostly focus on individual known pathogenic genes (such as PAX3, VANGL1, etc.), which are insufficient to cover the complex genetic background of NTDs, resulting in low diagnostic accuracy and limited applicability, failing to meet the clinical need for highly sensitive and comprehensive diagnostic methods. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a detection system for assessing the genetic risk of neural tube defects. This invention solves the problems in existing technologies, such as delayed detection by imaging and easy missed diagnoses, narrow coverage and low sensitivity of traditional genetic diagnoses, which make it impossible to achieve early and accurate risk assessment.

[0005] To achieve the above objectives, the present invention provides the following solution: A detection system for assessing the genetic risk of neural tube defects, comprising: The data acquisition module is used to acquire the genome sequencing data of the individual to be tested; The database module is used to store a pre-defined set of genes related to neural tube defects (NTDs). The variant screening module is used to perform quality control and preprocessing on the genome sequencing data, and based on the set of genes related to neural tube defects (NTDs), to detect variants in the preprocessed genome sequencing data, and to screen the detected variants according to preset rare variant frequency thresholds and pathogenicity prediction criteria to obtain rare pathogenic variants. The mutation load analysis module is used to count the total number of rare pathogenic variants carried by the individual under test in the NTDs-related gene set, and obtain the mutation load value; The assessment and determination module is used to compare the mutation load value with a preset determination threshold; when the mutation load value reaches or exceeds the determination threshold, it outputs an assessment result indicating that the individual under test has a high risk of neural tube defects.

[0006] The present invention discloses the following technical effects: This invention provides a detection system for assessing the genetic risk of neural tube defects (NTDs). By constructing a set of NTD-related genes and quantifying the burden characteristics of rare pathogenic variants within them, this invention effectively removes interference from whole-genome background noise. Compared with existing technologies, this system overcomes the limitations of single-gene detection, significantly improving the specificity (over 95%) and sensitivity of the detection. Furthermore, this system does not rely on embryonic morphological characteristics, enabling the diagnostic window to be moved forward to early pregnancy or even before implantation, solving the problem of a narrow intervention window and achieving accurate, early, and comprehensive risk assessment of neural tube defects. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the 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.

[0008] Figure 1 This is a schematic diagram of a detection system for assessing the genetic risk of neural tube defects, provided as an embodiment of the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0011] like Figure 1 As shown, the present invention provides a detection system for assessing the genetic risk of neural tube defects, comprising: The data acquisition module is used to acquire the genome sequencing data of the individual to be tested; The database module is used to store a pre-defined set of genes related to neural tube defects (NTDs). The variant screening module is used to perform quality control and preprocessing on the genome sequencing data. The quality control and preprocessing include: using FastQC to assess sequencing quality (Q30 ≥ 90%), using Trimmomatic to remove adapters and low-quality sequences, using BWA software to align the sequencing sequences to the human reference genome, and using Picard to remove PCR repetitive sequences. Based on the set of neural tube defects (NTDs) related genes, variant detection is performed on the preprocessed genome sequencing data, and the detected variants are screened according to preset rare variant frequency thresholds and pathogenicity prediction criteria to obtain rare pathogenic variants. The mutation load analysis module is used to count the total number of rare pathogenic variants carried by the individual under test in the NTDs-related gene set, and obtain the mutation load value; The assessment and judgment module compares the mutation load value with a preset judgment threshold. A control reference system is constructed using a large-scale public database (such as the 1000 Genomes Project) to build a mutation load distribution model of a healthy control population as a judgment benchmark. Dynamic threshold matching judgment automatically matches the preset judgment threshold based on the gene set type selected during screening. When the mutation load value reaches or exceeds the judgment threshold, an assessment result indicating a high risk of neural tube defects in the tested individual is output.

[0012] Furthermore, sample data: used to obtain the genome sequencing data of the individual to be tested, the sequencing data may be: Whole exome sequencing data; whole genome sequencing data; targeted sequencing data that can cover the target gene region.

[0013] Sources for NTD-related gene sets may include: publicly available NTD-related gene databases; genes that have been reported in the literature as being clearly or potentially related to NTDs; and candidate genes obtained through bioinformatics analysis.

[0014] Furthermore, the rare mutation frequency threshold is 0.001.

[0015] Furthermore, pathogenicity prediction criteria include: The mutation is a nonsense mutation, a frameshift mutation, or a classic splice site mutation; The variants were missense mutations, meeting the criteria of a CADD score greater than 20, and were determined to be pathogenic by MetaSVM or AlphaMissense. Variant functional annotation: Using ANNOVAR or similar bioinformatics tools, the original variant sites were functionally annotated by comparing them with the human reference genome.

[0016] Using a character matching algorithm, sites labeled as nonsense mutations, frameshift mutations, or canonical splicing mutations are directly extracted.

[0017] Such variants are directly identified as having biological pathogenicity and are included in subsequent burden statistics.

[0018] The multidimensional scoring process for missense mutations involves using pre-calculated scores from three pathogenicity prediction databases—CADD, MetaSVM, and AlphaMissense—for sites labeled as missense mutations. Logical decision operators are set to filter sites that simultaneously meet the following conditions: a CADD (Combined Annotation Dependent Depletion) score > 20 to ensure high evolutionary conservation and potential harmfulness; and a MetaSVM score of "D" (pathogenic) or an AlphaMissense score of "Pathogenic." Multiple algorithms are used for cross-validation to reduce false positives.

[0019] Merging of screening results: The identified variants are merged to obtain a set of rare pathogenic variants with high signal-to-noise ratio.

[0020] Furthermore, the sample of the individual to be tested is derived from the peripheral blood of the test subject, the cell-free fetal DNA in the peripheral blood of the pregnant woman, or the cell-free DNA before embryo implantation.

[0021] Specifically, the workflow corresponding to this embodiment is as follows: Sample types: peripheral blood of the subject (2-5 mL, EDTA anticoagulated), cell-free fetal DNA from peripheral blood of the pregnant woman (10 mL, EDTA anticoagulated), cell-free DNA before embryo implantation; DNA was extracted from the sample for genome sequencing. Sequencing methods included, but were not limited to: Whole exome sequencing (WES), whole genome sequencing (WGS), and targeted capture sequencing covering NTD-related gene regions were performed. The average sequencing depth of the target region was ≥100×, providing reliable genetic information for subsequent mutation detection and analysis.

[0022] Construction and limitation of NTD-related gene set: Gene set construction principles: Construct a gene set related to NTDs for analysis, which must meet at least one of the following conditions: Clear association with NTDs has been reported in literature or databases; Involved in neural tube development, folic acid metabolism, or related signaling pathways; Bioinformatics analysis revealed functional associations with known NTD pathogenic genes.

[0023] The form of gene sets: The NTDs-related gene set can be: a high-confidence gene set; an NTDs-related gene set expanded from the high-confidence genes; or a set of multiple sub-genes divided according to pathways or functional modules.

[0024] By limiting the set of genes related to NTDs, subsequent mutation analysis can be separated from the "noisy environment of the whole genome," laying the foundation for the discovery of enrichment of disease-specific mutations.

[0025] Variance detection, screening, and functional annotation: Raw data processing and alignment: Quality control and preprocessing of sequencing data, including: quality assessment using FastQC (Q30 ≥ 90%); removal of adapters and low-quality sequences using Trimmomatic; alignment of reads to the human reference genome using BWA software; and removal of PCR repetitive sequences using Picard.

[0026] Mutation detection: The GATK software was used to detect single nucleotide variants (SNVs) and small insertion / deletion variants (InDels) to obtain a set of individual variant sites.

[0027] Mutation annotation and filtering: Annotate the detected variants using ANNOVAR or similar tools, and filter for mutations that meet the following criteria: 1) Located within the NTDs-related gene set; 2) The minor allele frequency (MAF) in a public database (such as gnomAD) is ≤ 0.001; 3) Predict pathogenic mutations, including: nonsense mutations, frameshift mutations; classical splice site mutations; missense mutations with CADD>20 and identified as pathogenic by MetaSVM or AlphaMissense; Through the above screening, neutral or benign variants are filtered out, and only mutations in NTD disease-related genes that are low-frequency and predicted to have biological functional effects are retained, providing high signal-to-noise ratio data for subsequent enrichment analysis.

[0028] Enrichment analysis of rare pathogenic mutations: Mutation load calculation: For a single sample to be tested, the number of rare pathogenic mutation sites carried in the NTDs-related gene set is counted and recorded as the "NTDs-related rare pathogenic mutation burden value" of the sample.

[0029] Reference system: The mutation burden values ​​mentioned above were compared with the corresponding statistical indicators in a healthy control population, which was obtained from a large-scale public database.

[0030] Enrichment determination logic: When the mutation burden of a test sample is significantly higher than that of a healthy control group, reaching a preset threshold, the sample is considered to have an abnormal enrichment of rare pathogenic mutations in the NTDs-related gene set. Using "disease-related gene limitation" + "rare pathogenic mutation burden" as a quantitative indicator for diagnosis does not detect "the presence of a specific mutation," but rather quantifies the cumulative effect of rare pathogenic mutations in the relevant gene set. This overcomes the limitations of existing single-gene or whole-genome mutation statistical methods, thereby significantly improving diagnostic specificity and sensitivity, and enabling early and specific NTDs risk assessment and diagnosis.

[0031] Diagnostic findings and risk assessment output: Threshold setting: Based on the sample size and application scenario, set a threshold for determining mutation load, for example: Under the condition of a high-confidence related gene set, the threshold can be set to ≥4 rare pathogenic mutations; Under the condition of an expanded gene set, the threshold can be set to ≥5 or higher.

[0032] Diagnosis or risk assessment: When a sample to be tested meets or exceeds the threshold, it can be identified as an individual at high risk of NTDs; it serves as an auxiliary diagnostic basis for NTDs; risk assessment is used for clinical decision support.

[0033] Results validation and clinical correlation analysis: Technical verification: Sanger sequencing was performed on highly pathogenic mutation sites to verify the accuracy of the sequencing and analysis results.

[0034] Clinical consistency analysis: The analysis conclusions are comprehensively verified by combining family history, imaging examinations, or clinical diagnostic results.

[0035] Furthermore, the following experimental examples are provided: Example 1: Comparative analysis of case and control groups based on the number of rare pathogenic mutations in NTD-related genes: Sample source and grouping: Sample size: 994 individuals diagnosed with neural tube defects; Sequencing method: whole exome sequencing or whole genome sequencing; Control group (Ctrl group): 2504 healthy individuals from the 1000 Genomes Project; The control group sample was not screened for racial origin, and included individuals from diverse backgrounds. NTD-related gene sets and variant screening methods: Construction of NTDs-related gene set: An NTDs-related gene set was constructed based on published literature, databases, and neural tube development-related pathways.

[0036] Variant screening criteria: Variants with a MAF ≤ 0.001 in public population databases (rare variants); The variant type is a variant that is predicted to have pathogenic potential, including missense mutations (CADD>20 or MetaSVM="D"), nonsense mutations, splice site mutations, or frameshift mutations.

[0037] Only variations located within the NTD-related gene set are retained; Statistical indicator: The number of rare pathogenic mutation sites carried by each sample in the NTDs-related gene set was used as the analysis indicator.

[0038] Statistical analysis method: The number of mutation sites in the NTDs group and the control group was counted. Compare the differences in mutation number distribution between the two groups; Calculate the average number of mutations in the two groups of samples; The diagnostic effectiveness was evaluated under different mutation number thresholds.

[0039] Experimental results: The results showed that in the NTDs-related gene cluster, the number of rare pathogenic mutations in the control group samples was mainly concentrated at 0–1 sites; The overall mutation number distribution of the NTDs group samples shifted significantly towards the high value range.

[0040] Comparison of average mutation count: The average number of rare pathogenic mutations in the control group samples was 0.67; The mean number of rare pathogenic mutations in the NTDs group was 1.57; The difference between the two groups was statistically significant (p=2.55×10). -81 ).

[0041] Diagnostic judgment analysis based on mutation number threshold: When using the presence of ≥4 rare pathogenic mutation sites in a single sample within the NTDs-related gene set as the criterion: The number of samples that met this condition in the NTDs group was 76. The number of samples that met this condition in the control group was 14. The proportion of NTDs in the sample is approximately 84% (76 / (76+14)).

[0042] Example Conclusion: In a control population with diverse backgrounds, NTD cases showed significant enrichment of rare pathogenic mutations within the NTD-related gene set. Even without distinguishing between ethnic origins of the population, the method of the present invention can still effectively distinguish between NTD cases and healthy controls, indicating that the diagnostic method of the present invention has good robustness, and can further improve diagnostic accuracy after being optimized by combining population origin information.

[0043] Example 2: Comparative analysis of the number of rare pathogenic mutations in the case group and the control group based on the NTDs extended gene set: Case group (NTDs group): 994 individuals diagnosed with neural tube defects; Control group (Ctrl group): 2504 healthy individuals from the 1000 Genomes Project; Construction of an extended NTDs-related gene list: Based on the known NTDs-related gene list, and combined with gene function association network, signaling pathway or interaction information, an extended NTDs-related gene list (NTDsPropagateGeneList) is constructed. The extended gene set includes some candidate genes that have not yet been clearly reported as pathogenic genes of NTDs. While expanding the coverage of disease-related signals, it may introduce some genes with low association with NTDs to verify the robustness of the method of the present invention under the extended gene set condition.

[0044] Variant screening and statistical methods: Variance detection was performed on sequencing data from the case group and the control group; Only variations located within the NTD-related extended gene set are retained; Screening for rare functional variants that are predicted to have pathogenic potential; The number of rare pathogenic mutation sites carried by each sample in the NTD-related extended gene set was counted. The distribution of mutation numbers was compared between the case group and the control group.

[0045] Experimental results: Differences in mutation number distribution: Analysis results show that in the NTD-related extended gene set: The distribution of rare pathogenic mutations in the case group samples shifted significantly towards the high value range overall; The number of mutations in the control group samples was mainly concentrated in the lower range.

[0046] Comparison of average mutation count: The average number of rare pathogenic mutations in the control group samples was 1.41; The mean number of rare pathogenic mutations in the NTDs group was 3.30; The difference between the two groups was statistically highly significant (p=5.36×10). -148 ).

[0047] Diagnostic criteria based on mutation number threshold: When using the presence of ≥5 rare pathogenic mutation sites in a single sample within the NTDs-related extended gene set as the criterion: The number of samples that met this condition in the NTDs group was 263. The number of samples that met this condition in the control group was 56. The proportion of NTDs in the sample is approximately 82.4% (263 / (263+56)).

[0048] Example Conclusion: Under the conditions of an expanded gene set related to NTDs, the method described in this invention can still effectively distinguish between NTDs cases and healthy controls. The method of this invention can be adapted to NTDs-related gene sets of different sizes and compositions, and has good flexibility and application scalability. Gene set expansion significantly increases the number of rare pathogenic mutations detectable in a single sample, thereby enhancing the coverage of disease-related signals; Because the expanded gene set may introduce some genes that are less associated with NTDs, the diagnostic accuracy is slightly lower than that of the core gene set described in Example 1.

[0049] Furthermore, the present invention also provides the following comparative examples: Comparative Example 1: Analysis based on the number of pathogenic mutations across the entire genome: Sample size: 994 cases; Sample type: Individuals diagnosed with neural tube defects; Control group (Ctrl group): 2504 healthy individuals from the 1000 Genomes Project; No population origin screening was performed on the sample, which included individuals from different ancestral backgrounds; Analysis methods: Variance detection was performed on the sequencing data of the case group and the control group; No specific set of disease-related genes was specified; No population structure correction or population source filtering was performed; The total number of variant sites predicted as harmful across the entire genome for each sample was counted. Compare the distribution of the number of harmful mutations carried by each sample in the case group and the control group.

[0050] Experimental results: The results show that, across the entire genome: Each sample carried an average of more than 30 harmful mutation sites; The mean number of harmful mutations in the control group samples was 35.56; The mean number of harmful mutations in the NTDs group was 33.98.

[0051] Statistical analysis: Although there was a statistically significant difference between the two groups (p=1.81×10⁻⁶), -6 However, the main difference is that the number of mutations in the control group samples is slightly higher than that in the NTDs group samples, and the difference is relatively small.

[0052] The impact of population structure on the results: Further analysis revealed that the distribution of mutation numbers in the control group shifted towards the higher value range, which is because the control group contained more individuals of African ancestry.

[0053] Because African populations have higher genetic diversity in their evolutionary history, they carry a significantly higher number of variations across their entire genome than other populations, making this method susceptible to significant population structure bias.

[0054] Conclusion of the comparative example: Without limiting the scope of disease-related genes and without controlling the population structure, the number of pathogenic mutations across the entire genome cannot be used as an effective diagnostic indicator for NTDs. This method is easily affected by significant differences in the genetic background of the population, resulting in the control group showing a higher mutation load; Even if statistical differences exist, it is impossible to establish a clinically meaningful diagnostic threshold; Therefore, analysis methods based on the number of mutations across the entire genome are not suitable for the diagnosis or risk assessment of NTDs.

[0055] Comparative Example 2: Analysis based on the number of pathogenic mutations across the entire genome, after removing African samples: Case group (NTDs group): Sample size: 994 cases; Sample type: Individuals diagnosed with neural tube defects; Control group (Ctrl group): Sample source: 1000 Genomes Project; Samples of African ancestry were included in the analysis after removal. The sample includes individuals from multiple non-African populations; Analysis method: Variance detection was performed on the sequencing data of the case group and the control group; It does not limit the set of genes associated with any disease; No targeted screening of variants at the functional or frequency level; The total number of variant sites predicted as harmful across the entire genome for each sample was counted. Compare the distribution of the number of harmful mutations carried by each sample in the case group and the control group.

[0056] Experimental results: The analysis results show that after removing the African samples: In both the case group and the control group, the average number of harmful mutations carried by each sample was more than 30. The mean number of harmful mutations in the control group samples was 34.3; The mean number of harmful mutations in the NTDs group was 33.98; The difference between the two groups was not statistically significant (p=0.38).

[0057] Mutation number distribution characteristics: The mutation number distributions in the case group and the control group highly overlapped, and no obvious distribution shift or threshold range that could be used for diagnosis could be observed.

[0058] Analysis of the impact of residual population structure; Conclusion of the comparative example: This comparative example clearly demonstrates that: After removing African samples, there was no significant difference in mutational burden levels between NTD cases and healthy controls based solely on the number of unfiltered pathogenic mutations across the entire genome.

[0059] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0060] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A detection system for assessing the genetic risk of neural tube defects, characterized in that, include: The data acquisition module is used to acquire the genome sequencing data of the individual to be tested; The database module is used to store a pre-defined set of genes related to neural tube defects (NTDs). The variant screening module is used to perform quality control and preprocessing on the genome sequencing data, and based on the set of genes related to neural tube defects (NTDs), to detect variants in the preprocessed genome sequencing data, and to screen the detected variants according to preset rare variant frequency thresholds and pathogenicity prediction criteria to obtain rare pathogenic variants. The mutation load analysis module is used to count the total number of rare pathogenic variants carried by the individual under test in the NTDs-related gene set, and obtain the mutation load value; The assessment and determination module is used to compare the mutation load value with a preset determination threshold; when the mutation load value reaches or exceeds the determination threshold, it outputs an assessment result indicating that the individual under test has a high risk of neural tube defects.

2. The detection system for assessing the genetic risk of neural tube defects according to claim 1, characterized in that, The genome sequencing data includes: Whole exome sequencing data, whole genome sequencing data, or targeted sequencing data covering the set of genes associated with the NTDs.

3. The detection system for assessing the genetic risk of neural tube defects according to claim 1, characterized in that, The gene set associated with neural tube defects (NTDs) includes: Publicly available databases of NTD-related genes; Literature reports genes that are clearly or potentially associated with NTDs; Genes involved in neural tube development, folic acid metabolism, or related signaling pathways; Candidate genes that are functionally associated with known NTD pathogenic genes were obtained through bioinformatics analysis.

4. The detection system for assessing the genetic risk of neural tube defects according to claim 1, characterized in that, The rare mutation frequency threshold is 0.

001.

5. The detection system for assessing the genetic risk of neural tube defects according to claim 1, characterized in that, Pathogenicity prediction criteria include: The mutation is a nonsense mutation, a frameshift mutation, or a classic splice site mutation; The mutation is a missense mutation, and it meets the criteria of a CADD score greater than 20 and is determined to be pathogenic by MetaSVM or AlphaMissense.

6. The detection system for assessing the genetic risk of neural tube defects according to claim 1, characterized in that, The decision threshold was set based on the distribution of a large-scale healthy control population; The determination thresholds include: When the set of NTDs-related genes is a high-confidence gene set, the determination threshold is set to ≥4 rare pathogenic variants; When the set of NTDs-related genes is an extended set of genes, the determination threshold is set to ≥5 rare pathogenic variants.

7. The detection system for assessing the genetic risk of neural tube defects according to claim 1, characterized in that, The samples of the individuals to be tested are derived from cell-free fetal DNA in the peripheral blood of the test subjects, cell-free DNA in the peripheral blood of pregnant women, or cell-free DNA from before embryo implantation.

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