[go: up one dir, main page]

WO2025222351A1 - Procédé d'analyse d'aneuploïdie chromosomique et utilisation - Google Patents

Procédé d'analyse d'aneuploïdie chromosomique et utilisation

Info

Publication number
WO2025222351A1
WO2025222351A1 PCT/CN2024/089201 CN2024089201W WO2025222351A1 WO 2025222351 A1 WO2025222351 A1 WO 2025222351A1 CN 2024089201 W CN2024089201 W CN 2024089201W WO 2025222351 A1 WO2025222351 A1 WO 2025222351A1
Authority
WO
WIPO (PCT)
Prior art keywords
sample
window
correction
reference set
baseline
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/089201
Other languages
English (en)
Chinese (zh)
Inventor
彭继光
李婧柔
王伟凤
向嘉乐
宋立洁
彭智宇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
BGI Genomics Co Ltd
Original Assignee
BGI Genomics Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by BGI Genomics Co Ltd filed Critical BGI Genomics Co Ltd
Priority to PCT/CN2024/089201 priority Critical patent/WO2025222351A1/fr
Publication of WO2025222351A1 publication Critical patent/WO2025222351A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/10—Ploidy or copy number detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • This application relates to the field of chromosomal aneuploidy detection technology, and in particular to an analytical method and application for chromosomal aneuploidy.
  • Chromosomal aneuploidy refers to abnormal changes in the ploidy of chromosomes in cells. Common chromosomal aneuploidy disorders include Down syndrome (trisomy 21), Edwards syndrome (trisomy 18), and Patau syndrome (trisomy 13), all of which lead to severe developmental abnormalities in children. Research has found that cell-free fetal DNA exists in maternal peripheral blood, which is an important material for non-invasive prenatal genetic testing (NIPT). Quantitative detection of cfDNA in maternal peripheral blood can detect whether there are structural or numerical abnormalities in the fetus.
  • NIPT non-invasive prenatal genetic testing
  • some methods employ thresholding for the proportion of URs on the chromosome to be tested, eliminating the influence of different sequencing preferences in different segments; some use information from a fixed additional reference sample set for data correction; some use samples from the same batch as a reference, calculating the chromosome baseline for various GC content ranges within that batch using the reference set, and then performing data correction for each sample in that batch; some use intra-batch correction methods; and some use the Manhattan distance of the window features between the test samples in the entire batch and the samples in the reference database to screen a dynamic reference database for that batch of samples, using a weighted linear fitting method to correct the chromosome baseline data.
  • the purpose of this application is to provide an improved method for analyzing chromosomal aneuploidy and its application.
  • the first aspect of this application discloses a method for analyzing chromosomal aneuploidy, including using sequencing data of the sample to be tested and its alignment results to calculate the window depth of the sample to be tested within a set window; calculating the average relative depth of samples in a selected reference set within the set window as a correction baseline; correcting the window depth of the sample to be tested using the correction baseline; calculating the Z-value of the sample to be tested based on the corrected window depth; determining whether the fetal chromosome of the sample to be tested has aneuploidy abnormality based on the Z-value; and selecting the reference set as a set of samples with the lowest difference from the sample to be tested, obtained by screening from a complete reference set consisting of samples with known fetal chromosome information and corresponding maternal peripheral blood cfDNA sequencing data.
  • the Z-score is used to determine whether aneuploidy occurs in the fetal chromosomes.
  • the threshold used is determined by combining theoretical probability and the distribution of actual experimental results.
  • the sequencing data in this application generally refers to the sequencing data of cfDNA from the peripheral blood of pregnant women, thereby achieving non-invasive prenatal genetic testing.
  • the complete reference set consists of all samples used to generate the selection reference set, specifically clinical samples from BGI Genomics that reported no abnormalities between 2020 and 2021. The proportions of various types are kept roughly the same, and as many batches of experimental reagents as possible are included. The specific number of samples constructed is approximately 15,000.
  • the chromosome aneuploidy analysis method of this application creatively selects the samples with the lowest difference from the sample to be tested from past sample data as a selection reference set.
  • the selection reference set is used to calculate the correction baseline and correct the sample to be tested. In the absence of samples from the same batch as a reference, it can more stably and accurately detect chromosome aneuploidy abnormalities in the sample to be tested and reduce batch fluctuations. It is especially suitable for single-sample NIPT detection, which can improve the accuracy of single-sample NIPT detection, has a good distinction between true positive and false positive samples, and reduces false positives.
  • the method for analyzing chromosomal aneuploidy further includes calculating the degree of chimerism of the sample to be tested based on the corrected window depth, and determining whether the fetal chromosomes of the sample to be tested have aneuploidy abnormalities based on the degree of chimerism and the Z-value.
  • the degree of chimerism is the ratio of abnormal fetal cells to all fetal cells.
  • the method for calculating the degree of chimerism is as follows:
  • q ⁇ sub>i ⁇ /sub> represents the degree of chimerism
  • FF ⁇ sub>i ⁇ /sub> represents the relative fetal concentration of chromosome i
  • FF represents the fetal concentration. This represents the average depth after chromosome i correction. The average depth of all corrected autosomes is represented by the value of i, which ranges from 1 to 22.
  • the window is set as several windows divided according to the human reference genome, and there is no overlap between adjacent windows.
  • the window depth is the number of unique alignment sequences of the sample sequencing data within the set window that are aligned to the human reference genome.
  • the relative depth is the quotient of the sample's window depth in a set window divided by the average window depth of each autosome of the sample in the corresponding set window.
  • the method for selecting the reference set includes: calculating the relative depth of the test sample and each sample in the complete reference set within a set window; using the complete reference set to learn and obtain a dimensionality reduction model to reduce the relative depth; using the dimensionality reduction features to calculate the difference between the test sample and each sample in the complete reference set; and selecting several samples with the lowest difference as the selection reference set for the test sample.
  • the dimensionality reduction model is a PCA model obtained by PCA learning using a complete reference set; the dimensionality reduction features are the dimensionality reduction features output after the relative depth is input into the PCA model.
  • the dissimilarity is the Euclidean distance between the first n principal components, where n is a positive integer.
  • the first n principal components cover more than 70% of the variance, preferably 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% of the variance.
  • the dissimilarity is the Euclidean distance of the first ten principal components, and the formula for calculating the dissimilarity is as follows.
  • diversity reference_j represents the difference
  • PC_i sample represents the feature of the sample to be tested after relative depth dimensionality reduction
  • PC_i reference_j represents the feature of the sample in the complete reference set after relative depth dimensionality reduction.
  • PC only represents the feature after relative depth dimensionality reduction and does not specifically refer to the feature after dimensionality reduction by a particular dimensionality reduction model.
  • At least 50 samples with the lowest difference are selected as the reference set for the selection of samples to be tested.
  • the relative depth of the set window for autosomes other than chromosomes 13, 18, and 21 is used to select the reference set.
  • the correction baseline consists of an autosomal correction baseline and an X chromosome correction baseline.
  • the autosomal correction baseline is used for window depth correction of autosomes or female fetuses' X chromosomes
  • the X chromosome correction baseline is used for window depth correction of male fetuses' X chromosomes.
  • the autosomal correction baseline is the average of the relative depths of the autosomes in the set window of all samples in the selected reference set.
  • the X chromosome correction baseline is the average of the relative depths of the X chromosomes in the set window of all female fetuses in the selected reference set.
  • a correction baseline is used to correct the window depth of the sample to be tested. Specifically, this includes (1) correcting autosomal chromosomes or the X chromosome of female fetuses using the following formula,
  • UR ⁇ sub> n ⁇ /sub> is the window depth after baseline correction
  • UR ⁇ sub> a ⁇ /sub> is the window depth before baseline correction
  • baseline is usually the autosomal correction baseline
  • baseline X is the X chromosome correction baseline
  • FF is the fetal concentration.
  • the Y chromosome of the male fetus does not need to be corrected.
  • the autosomal correction baseline is,
  • the baseline for X chromosome correction is:
  • baseline is usually the autosomal correction baseline
  • baseline X is the X chromosome correction baseline
  • reference is the selection of all samples in the reference set
  • female-reference is the selection of all female fetuses in the reference set
  • n reference is the number of windows for selecting all samples in the reference set
  • n female-reference is the number of windows for selecting all female fetuses in the reference set
  • d is the relative depth of the window.
  • the method for analyzing chromosomal aneuploidy further includes GC correction of the window depth and relative depth calculation using the corrected window depth.
  • a GC correction step is performed before correcting the window depth of the sample to be tested in the baseline correction step.
  • GC correction includes: using the number of unique alignment sequences of the sample sequencing data within a set window to the human reference genome as the initial window depth, labeled as UR o ; calculating the GC content of the human reference genome within the set window, labeled as GC r ; and correcting UR o based on the fitting values of UR o and GC r and the average value of UR o across all windows of the chromosome.
  • GC correction further includes selecting all defined windows on the chromosome and using their UR o and GC r to perform cubic spline interpolation fitting to obtain the relationship: Obtain the fitted values for each set window of the chromosome. Calculate the average UR o for all defined windows of the chromosome, and label it as... The initial window depth is corrected according to the following formula to obtain the GC-corrected window depth UR a ;
  • UR a is the window depth after GC correction, which is the window depth before baseline correction.
  • the Z-value calculation method includes: for each chromosome i to be detected, calculating the Z-value for the window UR of that chromosome and the UR of other autosomal windows of the same chromosome, sorting the obtained Z-values, removing the maximum and minimum statistical values (i.e., removing one maximum statistical value and one minimum statistical value), and calculating the mean of the remaining Z-values to obtain the final Z-value, as detailed below.
  • the second aspect of this application discloses a baseline correction method, which includes calculating the average relative depth of samples in a selected reference set within a set window as a correction baseline, and using the correction baseline to correct the window depth of the sample to be tested; wherein, the selected reference set is a number of samples with the lowest difference from the sample to be tested, selected from a complete reference set consisting of samples with known fetal chromosomal information and corresponding maternal peripheral blood cfDNA sequencing data.
  • the baseline correction method further includes performing GC correction on the window depth and using the corrected window depth for relative depth calculation.
  • the baseline correction method in this application is actually the same as the correction baseline used in the analysis method for chromosomal aneuploidy in this application, which corrects the window depth of the sample to be tested. Therefore, the baseline correction method in this application, including setting the window, window depth, relative depth, selection of the reference set, dimensionality reduction model, difference degree and its calculation method, autosomal correction baseline, X chromosome correction baseline, correction method, GC correction, etc., all refer to the analysis method for chromosomal aneuploidy in the first aspect of this application, and will not be repeated here.
  • the baseline correction method provided in this application can also be applied to CNV detection. Before the specific location of the CNV is determined, it can be used to correct the UR and eliminate the influence of batch sequencing.
  • the third aspect of this application discloses a method for selecting a reference set for screening test samples.
  • This reference set is used for chromosomal aneuploidy analysis.
  • the method for selecting the reference set for test samples includes: calculating the relative depth of the test sample and each sample in the complete reference set within a set window; using the complete reference set to learn and obtain a dimensionality reduction model to reduce the relative depth; using the dimensionality reduction features to calculate the difference between the test sample and each sample in the complete reference set; and selecting several samples with the lowest difference as the selection reference set for the test sample.
  • the complete reference set consists of several samples with known fetal chromosomal information and their corresponding maternal peripheral blood cfDNA sequencing data.
  • the method for selecting the reference set from the test samples in this application is actually the specific screening method for selecting the reference set from the complete reference set in the analysis method for chromosomal aneuploidy in this application. Therefore, the specific settings of the window, relative depth, window depth, difference degree and its calculation method, GC correction, etc. in the method for selecting the reference set from the test samples all refer to the first aspect of this application regarding chromosomal aneuploidy. The analytical methods will not be elaborated here.
  • the fourth aspect of this application discloses an analytical apparatus for chromosome aneuploidy, comprising:
  • the window depth calculation module is used to calculate the window depth of the sample within a set window using the sequencing data and alignment results of the sample to be tested.
  • the baseline correction module is used to calculate the average relative depth of the samples in the selected reference set within a set window, which is used as the correction baseline.
  • the window depth of the sample to be tested is corrected using the correction baseline.
  • the selected reference set consists of several samples with the lowest difference from the sample to be tested, which are selected from a complete reference set composed of samples with known fetal chromosome information and corresponding maternal peripheral blood cfDNA sequencing data.
  • the Z-value calculation module is used to calculate the Z-value of the sample under test based on the corrected window depth.
  • the result judgment module is used to determine whether the fetal chromosomes of the sample under test have aneuploidy based on the Z value.
  • the analysis device for chromosome aneuploidy in this application is actually implemented by combining various modules to realize the analysis method for chromosome aneuploidy in this application. Therefore, the limitations and specific implementation methods of each module, such as setting the window, window depth, relative depth, selection method of the reference set, dimensionality reduction model, difference degree and its calculation method, autosomal correction baseline, X chromosome correction baseline, specific correction method, GC correction, etc., all refer to the analysis method for chromosome aneuploidy in the first aspect of this application, and will not be elaborated here.
  • the key to this application lies in selecting a reference set from the complete reference set.
  • a reference set selection process is required for each new sample to be tested.
  • the complete reference set it can be continuously supplemented by samples that have been tested, thereby continuously improving the complete reference set.
  • the fifth aspect of this application discloses a baseline correction apparatus, the apparatus comprising:
  • the baseline correction module is used to calculate the average relative depth of the samples in the selected reference set within a set window, which is used as the correction baseline.
  • the window depth of the sample to be tested is corrected using the correction baseline.
  • the selected reference set consists of several samples with the lowest difference from the sample to be tested, which are selected from a complete reference set composed of samples with known fetal chromosome information and corresponding maternal peripheral blood cfDNA sequencing data.
  • the baseline correction apparatus of this application is actually the baseline correction method of this application implemented through the baseline correction module; therefore, the limitations and specific implementation of the baseline correction module, such as setting the window, window depth, relative depth, selection method of the reference set, dimensionality reduction model, difference degree and its calculation method, autosomal baseline correction, X chromosome baseline correction, specific correction method, GC correction, etc., can all refer to the baseline correction method of the second aspect of this application, and will not be elaborated here.
  • the sixth aspect of this application discloses an apparatus for selecting a reference set by screening samples to be tested, the apparatus comprising:
  • the reference set selection module is used to calculate the relative depth of the test sample and each sample in the complete reference set within the window; a dimensionality reduction model is obtained by learning from the complete reference set to reduce the dimensionality of the relative depth; the difference between the test sample and each sample in the complete reference set is calculated using the dimensionality-reduced features, and several samples with the lowest difference are selected as the reference set for the test sample; the complete reference set consists of several samples with known fetal chromosome information and their corresponding maternal peripheral blood cfDNA sequencing data.
  • the apparatus for selecting a reference set from test samples in this application is actually the method for selecting a reference set from test samples implemented by the reference set selection module; therefore, the selection...
  • the limitations and specific implementation methods of the reference set selection module such as setting the window, relative depth, window depth, difference degree and its calculation method, GC correction, etc., can refer to the method of selecting reference sets for screening test samples in the third aspect of this application, and will not be elaborated here.
  • the seventh aspect of this application discloses an analytical apparatus for chromosome aneuploidy, the apparatus comprising a memory and a processor; the memory for storing programs; and the processor for executing the programs stored in the memory to implement the chromosome aneuploidy analytical method, the baseline correction method, or the method for screening test samples and selecting a reference set of this application.
  • the apparatus of this application executes the program stored in memory to implement the method of selecting a reference set for screening test samples
  • the apparatus of this application is actually a device for selecting and constructing the reference set.
  • the selection reference set constructed by this device can be used for non-invasive prenatal chromosomal aneuploidy analysis of single samples, improving the stability and accuracy of single-sample chromosomal aneuploidy detection and reducing batch fluctuations in detection.
  • the apparatus of this application executes the program stored in memory to implement the baseline correction method of this application, the apparatus of this application is actually a baseline acquisition and correction device, which can also improve the stability and accuracy of single-sample chromosomal aneuploidy detection.
  • the eighth aspect of this application discloses a computer-readable storage medium storing a program that can be executed by a processor to implement the method for analyzing chromosomal aneuploidy, the method for baseline correction, or the method for selecting a reference set of samples to be tested.
  • the computer-readable storage medium of this application is actually a computer-readable storage medium for screening the test sample selection reference set.
  • This computer-readable storage medium can be directly used to implement the screening and construction of the selection reference set.
  • the selection reference set obtained thereby can be used to detect fetal chromosomal aneuploidy abnormalities according to the method of this application.
  • the storage medium of this application is actually a storage medium for baseline acquisition and correction to obtain the corresponding corrected baseline.
  • the method and apparatus for analyzing chromosomal aneuploidy in this application utilize a selected reference set to calculate a corrected baseline, which is then used to correct the sample to be tested. This allows for more stable and accurate detection of chromosomal aneuploidy abnormalities in the sample even without samples from the same batch as a reference, reducing batch-to-batch fluctuations.
  • the method and apparatus of this application improve the accuracy of single-sample NIPT detection, exhibiting excellent differentiation between true positive and false positive samples, and reducing false positives.
  • Figure 1 is a flowchart of the chromosome aneuploidy analysis method in the embodiments of this application;
  • Figure 2 is a structural block diagram of the chromosome aneuploidy analysis device in an embodiment of this application;
  • Figure 3 shows the statistical results of the positive rate and gray area rate of different detection methods in the embodiments of this application.
  • This application creatively utilizes existing known databases to screen and select a reference set, and uses the selected reference set to calculate a correction baseline for the test sample to correct it, thereby improving the accuracy and stability of single-sample chromosomal aneuploidy abnormality detection and reducing batch fluctuations in detection.
  • this application proposes a method for analyzing chromosomal aneuploidy, including using sequencing data of the sample to be tested and its alignment results to calculate the window depth of the sample to be tested within a set window; calculating the average relative depth of the selected reference set samples within the set window as a correction baseline; using the correction baseline to correct the window depth of the sample to be tested; calculating the Z-value of the sample to be tested based on the corrected window depth; and determining whether the fetal chromosomes of the sample to be tested have aneuploidy abnormalities based on the Z-value.
  • the selection reference set consists of several samples with the lowest difference from the test sample, selected from a complete reference set composed of samples with known fetal chromosomal information and corresponding maternal peripheral blood cfDNA sequencing data. Specifically, for example, the relative depth between the test sample and each sample in the complete reference set within a defined window is calculated; a PCA model is obtained through PCA learning using the complete reference set; the relative depth is then reduced in dimensionality using the PCA model obtained through PCA learning, with the relative depth as input and the reduced features as output; the difference between the test sample and each sample in the complete reference set is calculated using the reduced features, and the samples with the lowest difference are selected as the selection reference set for the test sample.
  • the defined window consists of several windows divided according to the human reference genome, with no overlap between adjacent windows; the window depth is the number of unique alignment sequences of the sample sequencing data to the human reference genome within the defined window; the relative depth is the quotient of the sample's window depth within the defined window divided by the average window depth of each autosome of the sample within the corresponding defined window; and the chimerism is the ratio of abnormal fetal cells to all fetal cells.
  • the method for analyzing chromosomal aneuploidy specifically includes a sequence alignment step 11, a window depth calculation and GC correction step 12, a reference set selection and screening step 13, a fetal concentration calculation step 14, a baseline correction step 15, a Z-value calculation step 16, a chimerism calculation step 17, and a result judgment step 18.
  • the sequence alignment step 11 includes aligning the peripheral blood cfDNA sequencing data of the pregnant woman to be tested to a human reference genome, obtaining the coordinates of each sequence on the human reference genome, filtering out sequences with poor alignment quality and sequences with multiple alignments, and retaining uniquely aligned sequences.
  • the sequence information contained in the fastq format file generated by the sequencer is aligned to the human reference genome using alignment software BWA, such as GRCh37/hg19.
  • the coordinates and other information of each uniquely aligned sequence are stored in a bam format file. Specifically, for sequences with poor alignment quality...
  • a sequence is a sequence containing mismatched bases, meaning only sequences without mismatched bases are retained.
  • Step 12 which involves calculating the window depth and GC correction, includes dividing the human reference genome into several windows with no overlap between adjacent windows, calculating the number of unique aligned sequences in each window as the initial window depth, labeled as UR o , counting the GC content of the human reference genome in each window, labeled as GC r , selecting all windows on the chromosome, and correcting UR o based on the fitting values of UR o and GC r and the average value of UR o in all windows of the chromosome.
  • UR a is the window depth after GC correction.
  • Step 13 of the reference set selection process includes calculating the relative depth of the test sample and each sample in the complete reference set within the window; using the complete reference set for PCA learning to obtain a PCA model, and performing dimensionality reduction on the relative depth, i.e., using the PCA model obtained through PCA learning, inputting the relative depth, and outputting the dimensionality-reduced features; using the dimensionality-reduced features to calculate the difference between the test sample and each sample in the complete reference set, and selecting several samples with the lowest difference as the selection reference set for the test sample; the complete reference set consists of several samples with known fetal chromosomal conditions and their corresponding maternal peripheral blood cfDNA sequencing data, wherein the several fetal chromosomal conditions are preferably several cases without fetal chromosomal aneuploidy abnormalities; the relative depth is the quotient of the sample's window depth in the window divided by the average window depth of each autosome in the window of that sample.
  • the selection of the reference set is one of the key aspects of this application.
  • a complete reference set containing a large number of samples is established, and a method for selecting a specific reference set from the complete reference set for each sample to be tested is established.
  • the complete reference set contains samples from as many experimental conditions and batches as possible. For example, in one implementation of this application, clinical samples from BGI Genomics that did not report fetal chromosomal aneuploidy abnormalities between 2020 and 2021 are selected, the proportion of various peripheral blood sampling tubes is controlled to be roughly the same, and the experimental reagents from as many batches as possible are included. Specifically, a complete reference set with approximately 15,000 samples is constructed.
  • the relative depth of the windows of autosomes other than chromosomes 13, 18, and 21 is selected and labeled as d, as a feature for selecting the reference set.
  • the relative depth is the quotient of the window depth of the sample in the set window divided by the average window depth of each autosome in the corresponding set window.
  • the window depth after GC correction is divided by the average window depth of each autosome in the corresponding set window, which is used as the result after the window depth is standardized.
  • d represents the relative depth
  • UR a represents the window depth after GC correction. For example The average window depth of each autosome in the corresponding window.
  • PCA is a commonly used unsupervised machine learning dimensionality reduction method.
  • This invention preferably uses a PCA model for dimensionality reduction.
  • the PCA model is obtained by learning using the complete reference set.
  • the relative depth information of the sample's window is output to the PCA model, thus obtaining the dimensionality-reduced features (PC) of that sample.
  • the dimensionality-reduced features are used to calculate the difference between the test sample and each sample in the complete reference set. The difference is defined as the Euclidean distance of the first ten principal components.
  • the 50 samples with the lowest difference from the test sample are selected as the specific reference set for subsequent analysis.
  • diversity reference_j is the difference
  • PC_i sample is the feature of the sample to be tested after relative depth dimensionality reduction
  • PC_i reference_j is the feature of the sample in the complete reference set after relative depth dimensionality reduction.
  • PC in PC_i sample and PC_i reference_j only represents the features after relative depth dimensionality reduction, and does not specifically refer to any particular dimensionality reduction model.
  • Fetal concentration calculation step 14 includes determining the fetal concentration of male fetuses based on the proportion of the Y chromosome, or estimating the fetal concentration of female fetuses by establishing a high-dimensional regression model based on the non-uniform distribution of fetal cell-free DNA on the genome.
  • the fetal concentration of male fetuses is determined by the proportion of the Y chromosome.
  • the average Uro value of the Y chromosome window is divided by the average Uro value of the autosomes, and then multiplied by 2 to obtain the fetal concentration FF of the male fetus. Therefore, the fetal DNA concentration of male fetuses is calculated as follows:
  • the concentration of fetal DNA in female fetuses was estimated using a high-dimensional regression model based on the non-uniform distribution of fetal cell-free DNA across the genome.
  • the underlying assumption was that the distribution characteristics of fetal cfDNA and maternal cfDNA across the genome differed between male and female fetuses. Therefore, the fetal concentration estimated using the Y chromosome method for male fetuses was used as input to train the model.
  • a regression model was constructed using neural network machine learning, as detailed below:
  • l is the layer number of the network
  • the first layer is the input layer
  • the last layer is the output layer (with only one neuron)
  • the middle layers are hidden layers.
  • the connection weights are the connection weights from the k-th neuron in layer (l-1) to the j-th neuron in layer l. This represents the input bias of the j-th neuron in the l-th layer.
  • w and b are obtained during model training.
  • the baseline correction step 15 includes calculating the average relative depth of all samples in the selected reference set within the window as the correction baseline, and using the correction baseline to correct the window depth of the sample to be tested.
  • the mean relative depth of the selected reference set samples within the window is the correction baseline.
  • the autosomal correction baseline is the average relative depth of the autosomes of all samples in the selected reference set within the set window
  • the X chromosome correction baseline is the average relative depth of the X chromosome of all female fetal samples in the selected reference set within the set window.
  • the window depth UR of the sample to be tested is divided by the correction baseline of that window to obtain the corrected window depth, denoted as UR ⁇ sub> n ⁇ /sub> .
  • the UR of the X chromosome of male fetal samples needs to be further corrected for fetal concentration.
  • the correction baseline includes the autosomal correction baseline and the X chromosome correction baseline.
  • the autosomal correction baseline is used for window depth correction of autosomes or female fetal X chromosomes
  • the X chromosome correction baseline is used for window depth correction of male fetal X chromosomes.
  • the autosomal correction baseline is the average relative depth of the autosomes of all samples in the selected reference set within the set window
  • the X chromosome correction baseline is the average relative depth of the X chromosome of all female fetal samples in the selected reference set within the set window.
  • the baseline is usually the autosomal correction baseline
  • baseline X is the X chromosome correction baseline
  • reference is the selection of all samples in the reference set
  • female-reference is the selection of all female fetuses in the reference set
  • n reference is the number of windows for selecting all samples in the reference set
  • n female-reference is the number of windows for selecting all female fetuses in the reference set
  • d is the relative depth of the window.
  • the window depth of the sample to be tested is corrected using a correction baseline, as follows:
  • URn is the window depth after baseline correction
  • URa is the window depth after GC correction and before baseline correction
  • baseline is usually the autosomal correction baseline
  • baseline X is the X chromosome correction baseline
  • FF is the fetal concentration.
  • Z-value calculation step 16 includes calculating the Z-value of the sample to be tested based on the window depth after baseline correction.
  • Autosomal chromosomal URs follow a Poisson distribution, and with a large number of windows, they follow a normal distribution. For normal samples, the distribution of each window is indistinguishable, while for abnormal samples, there are slight differences, influenced by fetal concentration; the higher the fetal concentration, the greater the difference.
  • the Z-test can be used to determine fetal chromosomal aneuploidy at a certain fetal concentration. For each chromosome i to be tested, the Z-value is calculated by comparing the UR of that chromosome's window with the URs of other autosomal windows in the same sample. The calculation method is as follows: The obtained Z-values are sorted, and the largest and smallest statistical values are removed. The mean of the remaining Z-values is the final Z-value. Details are as follows:
  • the above formula compares the 22 autosomes within the same sample. This is based on the assumption that the vast majority of chromosomes in a sample should be normal diploid. Therefore, the target chromosome is compared 21 times with the remaining 21 chromosomes. If the target chromosome is normal diploid, the vast majority of the 21 Z-test values should be close to 0, and averaging them yields a negative Z-value. Conversely, if the target chromosome is trisomic, the vast majority of the 21 Z-test values will be much greater than 0, and averaging them yields a positive Z-value.
  • Step 17 which calculates the degree of chimerism, includes calculating the relative fetal concentration of each chromosome in the sample to be tested based on the window depth after baseline correction, and calculating the degree of chimerism of each chromosome based on the relative fetal concentration and the fetal DNA concentration.
  • the relative fetal concentration of a chromosome is defined as the fetal concentration estimated from that chromosome assuming trisomy in the fetus. The calculation method is as follows:
  • FF ⁇ sub>i ⁇ /sub> represents the relative fetal concentration of chromosome i
  • FF represents the fetal concentration. This represents the average depth after chromosome i correction.
  • the value of i is the average depth after correction for all autosomes. The range is from 1 to 22.
  • the result judgment step 18 includes determining whether the fetal chromosomes of the sample to be tested have aneuploidy based on the Z-value and the degree of mosaicism.
  • the final detection result for each chromosome is a comprehensive judgment based on the Z-score and the degree of mosaicism.
  • Different thresholds for mosaicism and Z-scores are set for different chromosomes, and the final detection result is obtained by comparing the detection value with the threshold.
  • An example of trisomy detection on chromosome 21 is shown in Table 1.
  • Z1 , Z2, and Z3 are the threshold values for the Z-value, which are normal distribution thresholds with special significance according to probability theory. For example, ⁇ 1.96 represents a p-value of 0.05, and 3 represents a p-value of approximately 0.01. These thresholds were selected based on the distribution of actual results. The gray area represents samples for which a definitive conclusion cannot be obtained at this time.
  • this application proposes a baseline correction method, which includes calculating the average relative depth of samples in a selected reference set within a set window as the correction baseline, and using the correction baseline to correct the window depth of the sample to be tested; the selected reference set consists of several samples with the lowest difference from the sample to be tested, obtained by screening from a complete reference set composed of samples with known fetal chromosomal information and their corresponding maternal peripheral blood cfDNA sequencing data.
  • this application proposes a method for selecting a reference set for screening test samples.
  • This reference set is used for single-sample non-invasive prenatal chromosomal aneuploidy abnormality analysis.
  • the method for selecting the reference set for test samples includes: calculating the relative depth of the test sample and each sample in the complete reference set within a set window; using the complete reference set to learn and obtain a model, and then reducing the dimensionality of the relative depth, i.e., using the learned dimensionality-reduced model, inputting the relative depth, and outputting the dimensionality-reduced features; using the dimensionality-reduced features to calculate the difference between the test sample and each sample in the complete reference set, and selecting several samples with the lowest difference as the selection reference set for the test sample; the complete reference set consists of several samples with known fetal chromosomal information and their corresponding maternal peripheral blood cfDNA sequencing data.
  • the window is defined as several windows divided according to the human reference genome, with no overlap between adjacent windows.
  • the relative depth is the quotient of the sample's window depth within the set window divided by the average window depth of each autosome within the set window; the window depth is the number of unique alignment sequences of the sample sequencing data within the set window that are compared to the human reference genome.
  • the difference calculation and GC correction are the same as the analysis method for chromosomal aneuploidy in this application, and will not be repeated here.
  • the program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to achieve the above functions.
  • the program can be stored in the memory of a device, and when the program in the memory is executed by the processor, all or part of the above functions can be achieved.
  • the program can also be stored in storage media such as a server, another computer, disk, optical disk, flash drive, or portable hard drive, and can be downloaded or copied to the memory of a local device, or the system of the local device can be updated.
  • storage media such as a server, another computer, disk, optical disk, flash drive, or portable hard drive
  • the program in the memory is executed by the processor, all or part of the functions of the above methods can be achieved.
  • this application proposes an analytical device for chromosomal aneuploidy, comprising: a window depth calculation module, used to calculate the window depth of the sample to be tested within a set window using the sequencing data of the sample to be tested and its alignment results; a baseline correction module, used to calculate the average relative depth of samples in the selected reference set within the set window as the correction baseline, and to correct the window depth of the sample to be tested using the correction baseline; the selected reference set is a set of samples with the lowest difference from the sample to be tested, obtained by screening samples with known fetal chromosomal conditions and their corresponding maternal peripheral blood cfDNA sequencing data from a complete reference set; a Z-value calculation module, used to calculate the Z-value of the sample to be tested based on the corrected window depth; and a result judgment module, used to judge whether the fetal chromosomes of the sample to be tested have aneuploidy abnormalities based on the Z
  • the specific analysis device for chromosome aneuploidy includes a sequence alignment module 21, a window depth calculation and GC correction module 22, a reference set selection and screening module 23, a fetal concentration calculation module 24, a baseline correction module 25, a Z-value calculation module 26, a chimerism calculation module 27, and a result judgment module 28.
  • the sequence alignment module 21 includes components for aligning the peripheral blood cfDNA sequencing data of the pregnant woman to be tested onto the human reference genome, obtaining the coordinates of each sequence on the human reference genome, filtering out sequences with poor alignment quality and sequences with multiple alignments, and retaining uniquely aligned sequences.
  • the sequence information contained in the fastq format file generated by the sequencer is aligned to the human reference genome GRCh37/hg19 using the alignment software BWA, leaving uniquely aligned sequences, and storing the coordinates and other information of each uniquely aligned sequence in a bam format file.
  • the relationship can be obtained by using cubic spline interpolation fitting of UR o and GC r : The fitted value for each window of the chromosome is obtained from this relationship. Calculate the average UR o for all windows of the chromosome, labeled as The UR o is corrected according to the following formula to obtain the window depth after GC correction;
  • UR a is the window depth after GC correction.
  • the reference set selection module 23 includes methods for calculating the relative depth of the test sample and each sample in the complete reference set within a window; using the complete reference set for PCA learning to obtain a PCA model, and then reducing the dimensionality of the relative depth, i.e., using the PCA model obtained through PCA learning, inputting the relative depth, and outputting the dimensionality-reduced features; using the dimensionality-reduced features to calculate the difference between the test sample and each sample in the complete reference set, and selecting several samples with the lowest difference as the reference set for the test sample; the complete reference set consists of several samples with known fetal chromosomal information and their corresponding maternal peripheral blood cfDNA sequencing data; the relative depth is the quotient of the sample's window depth divided by the average window depth of each autosome in the sample within the window.
  • the calculation methods for relative depth and difference are the same as those used in the single-sample non-invasive prenatal chromosomal aneuploidy analysis of this application.
  • the fetal DNA concentration calculation module 24 includes methods for determining the fetal DNA concentration of male fetuses based on the proportion of the Y chromosome, or for estimating the fetal DNA concentration of female fetuses by establishing a high-dimensional regression model based on the non-uniform distribution of cell-free fetal DNA on the genome.
  • the specific formulas for calculating the fetal DNA concentration of male fetuses and the methods for calculating the fetal DNA concentration of female fetuses are the same as the analytical methods for chromosomal aneuploidy in this application.
  • the autosomal correction baseline is the average relative depth of the autosomes within a set window for all samples in the selected reference set;
  • the X-chromosome correction baseline is the average relative depth of the X chromosome within a set window for all female fetuses in the selected reference set.
  • the specific calculation formula and the method for correcting the window depth of the sample to be tested using the correction baseline are the same as the method for single-sample non-invasive prenatal chromosomal aneuploidy analysis in this application.
  • Z-value calculation module 26 includes a method for calculating the Z-value of the sample to be tested based on the baseline-corrected window depth.
  • the Z-value calculation formula is the same as the method for analyzing chromosomal aneuploidy in this application.
  • the chimerism calculation module 27 includes methods for calculating the relative fetal concentration of each chromosome in the sample based on the baseline-corrected window depth, and for calculating the chimerism of each chromosome based on the relative fetal concentration and fetal DNA concentration.
  • the calculation of relative fetal concentration, fetal DNA concentration, and chimerism is the same as the method for single-sample non-invasive prenatal chromosomal aneuploidy analysis in this application.
  • the result determination module 28 includes a method for determining whether the fetal chromosomes in the test sample have aneuploidy based on the Z-score and mosaicism.
  • the specific determination method is the same as the method for single-sample non-invasive prenatal chromosomal aneuploidy analysis in this application.
  • an analysis device for chromosomal aneuploidy including a memory and a processor; the memory includes a program for storing programs; the processor includes a program for executing the program stored in the memory to implement the following method: using sequencing data of the sample to be tested and its alignment results, calculating the window depth of the sample to be tested within a set window; calculating the average relative depth of samples in a selected reference set within the set window as a correction baseline; correcting the window depth of the sample to be tested using the correction baseline; calculating the Z-value of the sample to be tested based on the corrected window depth; and determining whether the fetal chromosomes of the sample to be tested have aneuploidy abnormalities based on the Z-value.
  • the device includes a memory and a processor; the memory includes a program for storing a program; the processor includes a program for executing the program stored in the memory to implement the following method: including calculating the average relative depth of samples in a selected reference set at a set window as a correction baseline, correcting the window depth of the sample to be tested using the correction baseline; the selected reference set is a set of samples with the lowest difference from the sample to be tested, obtained by screening from a complete reference set consisting of samples with known fetal chromosomal information and their corresponding maternal peripheral blood cfDNA sequencing data.
  • the device includes a memory and a processor; the memory includes a program for storing a program; the processor includes a program for executing the program stored in the memory to implement the following method: including calculating the relative depth of the test sample and each sample in the complete reference set within a set window; learning from the complete reference set to obtain a dimensionality reduction model, reducing the relative depth dimensionality, i.e., using the learned dimensionality reduction model, inputting the relative depth, and outputting the dimensionality reduction features; using the dimensionality reduction features to calculate the difference between the test sample and each sample in the complete reference set, and selecting several samples with the lowest difference as the selection reference set for the test sample.
  • Another implementation of this application also provides a computer-readable storage medium, which includes a program that can be executed by a processor to implement the following method: using sequencing data of the sample to be tested and its alignment results, calculating the window depth of the sample to be tested within a set window; calculating the average relative depth of samples in a selected reference set within the set window as a correction baseline; correcting the window depth of the sample to be tested using the correction baseline; calculating the Z-value of the sample to be tested based on the corrected window depth; and determining whether the fetal chromosome of the sample to be tested has aneuploidy based on the Z-value.
  • the storage medium may include a program that can be executed by a processor to implement the following method: including calculating the average relative depth of samples in a selected reference set at a set window as a correction baseline, using the correction baseline to correct the window depth of the sample to be tested; the selected reference set is a set of samples with the lowest difference from the sample to be tested, obtained by screening from a complete reference set consisting of samples with known fetal chromosomal information and their corresponding maternal peripheral blood cfDNA sequencing data.
  • the storage medium may include a program that can be executed by a processor to implement the following method: calculating the relative depth of the test sample and each sample in the complete reference set within a set window; learning from the complete reference set to obtain a dimensionality reduction model, and reducing the relative depth by using the learned dimensionality reduction model, inputting the relative depth, and outputting the dimensionality-reduced features; using the dimensionality-reduced features to calculate the difference between the test sample and each sample in the complete reference set, and selecting several samples with the lowest difference as the selection reference set for the test sample.
  • This application creatively selects samples with the highest variability from samples under different experimental conditions and batches. Low-quality samples are used as a selection reference set. A corrected baseline is calculated using this reference set and applied to the test sample.
  • This method enables more stable and accurate detection of chromosomal aneuploidy abnormalities in the test sample when no samples from the same batch are available or sufficient as a reference, reducing batch-specific fluctuations. This method improves the accuracy of single-sample NIPT detection, exhibits excellent differentiation between true positive and false positive samples, and reduces false positives.
  • Sequence alignment steps The sequence information of the sequencing data of 973 samples was compared to the human reference genome GRCh37/hg19 using BWA, and the coordinates and other information of the first unique aligned sequence were stored in a bam format file.
  • Window depth calculation and GC correction steps Divide the human reference genome into windows, each approximately 60kb in size, with no overlap between adjacent windows. Calculate the number of unique aligned sequences (URo ) and the GC content (GCo ) of each sequence within each window. Calculate the GC content (GCr) of the human reference genome in each window. Select all windows on the chromosome and use cubic spline interpolation to fit the relationship between URo and GCr to obtain the following formula: The fitted value for each window of the chromosome is obtained from this relationship. Calculate the average UR o for all windows of the chromosome, labeled as The UR o is corrected according to the following formula to obtain the window depth after GC correction.
  • URo is the initial window depth
  • URa is the window depth after GC correction
  • the relative depth of the windows of autosomes other than chromosomes 13, 18, and 21 is selected and labeled as d. This is used as a feature for selecting the reference set.
  • the relative depth is the quotient of the window depth of the sample in the set window divided by the average window depth of each autosome in the corresponding set window.
  • the window depth UR after GC correction is divided by the average window depth of each autosome in the sample, which is used as the result after standardization of the window depth.
  • PCA model is obtained by learning using a complete reference set.
  • the relative depth information of the window is output into the PCA model to obtain the dimensionality-reduced features of the sample.
  • the dimensionality-reduced features are then used to calculate...
  • the difference between the test sample and each sample in the complete reference set is defined as the Euclidean distance of the first ten principal components.
  • the 50 samples with the lowest difference from the test sample are selected as a sample-specific selection reference set for subsequent analysis.
  • diversity reference_j is the difference
  • PC_i sample is the feature of the sample to be tested after relative depth dimensionality reduction
  • PC_i reference_j is the feature of the sample in the complete reference set after relative depth dimensionality reduction.
  • Fetal concentration calculation steps The fetal concentration of a male fetus is determined by the proportion of the Y chromosome. The mean UR value of the Y chromosome window is divided by the mean UR value of the autosomes, and then multiplied by 2 to obtain the fetal concentration FF of the male fetus. Therefore, the fetal DNA concentration of a male fetus is calculated as follows:
  • l is the layer number of the network
  • the first layer is the input layer
  • the last layer is the output layer (with only one neuron)
  • the middle layers are hidden layers.
  • the connection weights are the connection weights from the k-th neuron in layer (l-1) to the j-th neuron in layer l. This represents the input bias of the j-th neuron in the l-th layer.
  • w and b are obtained during model training.
  • baseline is usually the autosomal corrected baseline
  • baseline X is the X chromosome corrected baseline
  • reference is all samples selected from the reference set
  • female-reference is all female fetal samples selected from the reference set.
  • n reference is the number of windows used to select all samples in the reference set
  • n female-reference is the number of windows used to select all female fetuses in the reference set
  • d is the relative depth of the window.
  • Z-score calculation steps For each chromosome i to be tested, calculate the Z-score for the window UR of that chromosome and the UR of other autosomal windows of the same sample. The calculation method is as follows: Sort the obtained Z-scores, remove the largest and smallest statistical values, and calculate the mean of the remaining Z-scores to obtain the final Z-score. Details are as follows:
  • FF ⁇ sub> i ⁇ /sub> represents the relative fetal concentration of chromosome i. This represents the average depth after chromosome i correction. The average depth of all autosomes after correction is given, where i ranges from 1 to 22.
  • the intra-batch correction method is as follows: In the baseline correction step, the baseline is calculated using samples from the same batch as the test sample with similar total GC content (GC content difference less than 0.02). The only difference between the intra-batch correction method test and the scheme of this embodiment is that steps 3) and 5) are omitted, and the GC correction method in step 2) is replaced by the intra-batch correction method.
  • the method using a fixed reference set is as follows: In the baseline correction step, a fixed baseline data is used for all test samples. All samples (approximately 1000 cases) in a fixed complete reference set are used to calculate the baseline data, and there is no step of selecting a reference set. The difference between the fixed reference set correction experiment and the scheme in this embodiment is only that steps 3) and 5) are omitted, and the GC correction method in step 2) is replaced by the fixed reference set correction method.
  • the correction window size was specifically chosen to be 60k.
  • the threshold selections in the Z-value method are shown in Table 3.
  • Table 2 show that the method using a selected reference set provided correct detection results for 973 samples, with a significantly lower number of false negatives compared to the method using a fixed reference set. Therefore, the algorithm using a selected reference set has better sensitivity and is superior to the method using a fixed reference set in the detection of sex chromosomes.
  • This example uses the established model to detect continuous samples from the production line.
  • the karyotype of a sample does not reflect the true distribution characteristics of the population in terms of data distribution, and it cannot be used for evaluation.
  • a series of tests were conducted using clinical samples from the production line to evaluate its performance in practical applications, including anomaly reporting rate, retest rate, and gray area rate.
  • Several complete batches from our production line between June and December 2021 were used as the test set.
  • the reporting rates of various anomalies were statistically analyzed in the first experiment using the reference set selection method established in this invention for samples that passed quality control and were not GC outliers. These results were compared with those obtained through in-batch correction and fixed reference set methods. A total of 5572 samples were analyzed, and the results are shown in Table 4 and Figure 3.
  • the in-batch correction method and the fixed reference set method were the same as in Example 1.

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Biophysics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Biotechnology (AREA)
  • Evolutionary Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Molecular Biology (AREA)
  • Genetics & Genomics (AREA)
  • Artificial Intelligence (AREA)
  • Bioethics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Evolutionary Computation (AREA)
  • Public Health (AREA)
  • Software Systems (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

L'invention concerne un procédé d'analyse d'aneuploïdie chromosomique et son utilisation. Le procédé consiste à : utiliser des données de séquençage d'un échantillon à tester et un résultat d'alignement de celui-ci pour calculer la concentration d'ADN fœtal dans ledit échantillon et la profondeur de fenêtre dudit échantillon dans une fenêtre définie ; et calculer la profondeur relative moyenne de tous les échantillons dans un ensemble de référence sélectionné à l'intérieur de la fenêtre définie en tant que ligne de base de correction, utiliser la ligne de base de correction pour corriger la profondeur de fenêtre dudit échantillon, calculer une valeur Z dudit échantillon sur la base de la profondeur de fenêtre corrigée, et sur la base de la valeur Z, déterminer si une anomalie de type aneuploïdie s'est produite sur un chromosome fœtal dudit échantillon. L'utilisation de l'ensemble de référence sélectionné pour calculer la ligne de base de correction afin de corriger ledit échantillon permet une détection plus stable et précise de l'anomalie de type aneuploïdie chromosomique dans ledit échantillon à condition qu'il n'y ait pas d'échantillons du même lot en tant que référence, ce qui permet de réduire la variabilité inter-lot lors du test.
PCT/CN2024/089201 2024-04-22 2024-04-22 Procédé d'analyse d'aneuploïdie chromosomique et utilisation Pending WO2025222351A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2024/089201 WO2025222351A1 (fr) 2024-04-22 2024-04-22 Procédé d'analyse d'aneuploïdie chromosomique et utilisation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2024/089201 WO2025222351A1 (fr) 2024-04-22 2024-04-22 Procédé d'analyse d'aneuploïdie chromosomique et utilisation

Publications (1)

Publication Number Publication Date
WO2025222351A1 true WO2025222351A1 (fr) 2025-10-30

Family

ID=97489237

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2024/089201 Pending WO2025222351A1 (fr) 2024-04-22 2024-04-22 Procédé d'analyse d'aneuploïdie chromosomique et utilisation

Country Status (1)

Country Link
WO (1) WO2025222351A1 (fr)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106520940A (zh) * 2016-11-04 2017-03-22 深圳华大基因研究院 一种染色体非整倍体和拷贝数变异检测方法及其应用
CN107133495A (zh) * 2017-05-04 2017-09-05 北京医院 一种非整倍性生物信息的分析方法和分析系统
WO2019051812A1 (fr) * 2017-09-15 2019-03-21 深圳华大智造科技有限公司 Procédé permettant de déterminer une région chromosomique conservée prédéfinie, procédé permettant de déterminer la présence ou l'absence d'une variation de nombre de copies dans un génome échantillon, et système et support lisible par ordinateur
CN111226281A (zh) * 2019-12-31 2020-06-02 深圳华大临床检验中心 确定染色体非整倍性、构建分类模型的方法和装置
CN115223654A (zh) * 2022-07-13 2022-10-21 深圳华大基因股份有限公司 检测胎儿染色体非整倍体异常的方法、装置及存储介质
CN117409858A (zh) * 2022-07-08 2024-01-16 上海明悦医疗科技有限公司 胚胎植入前染色体异常的检测方法和装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106520940A (zh) * 2016-11-04 2017-03-22 深圳华大基因研究院 一种染色体非整倍体和拷贝数变异检测方法及其应用
CN107133495A (zh) * 2017-05-04 2017-09-05 北京医院 一种非整倍性生物信息的分析方法和分析系统
WO2019051812A1 (fr) * 2017-09-15 2019-03-21 深圳华大智造科技有限公司 Procédé permettant de déterminer une région chromosomique conservée prédéfinie, procédé permettant de déterminer la présence ou l'absence d'une variation de nombre de copies dans un génome échantillon, et système et support lisible par ordinateur
CN111226281A (zh) * 2019-12-31 2020-06-02 深圳华大临床检验中心 确定染色体非整倍性、构建分类模型的方法和装置
CN117409858A (zh) * 2022-07-08 2024-01-16 上海明悦医疗科技有限公司 胚胎植入前染色体异常的检测方法和装置
CN115223654A (zh) * 2022-07-13 2022-10-21 深圳华大基因股份有限公司 检测胎儿染色体非整倍体异常的方法、装置及存储介质

Similar Documents

Publication Publication Date Title
US20240194293A1 (en) Noninvasive prenatal screening using dynamic iterative depth optimization
CN110268044B (zh) 一种染色体变异的检测方法及装置
CN113674803A (zh) 一种拷贝数变异的检测方法及其应用
CN111226281B (zh) 确定染色体非整倍性、构建分类模型的方法和装置
WO2019213811A1 (fr) Procédé, appareil et système de détection d'aneuploïdie chromosomique
CN115223654A (zh) 检测胎儿染色体非整倍体异常的方法、装置及存储介质
CN108595912B (zh) 检测染色体非整倍性的方法、装置及系统
CN110191964B (zh) 确定生物样本中预定来源的游离核酸比例的方法及装置
CN116240273B (zh) 一种基于低深度全基因组测序的判断母源污染比例的方法及其应用
WO2025222351A1 (fr) Procédé d'analyse d'aneuploïdie chromosomique et utilisation
WO2024140881A1 (fr) Procédé et dispositif de détermination de la concentration d'adn fœtal
WO2019213810A1 (fr) Procédé, appareil et système pour la détection d'une aneuploïdie chromosomique
US20240203521A1 (en) Evaluation and improvement of genetic screening tests using receiver operating characteristic curves
CN118098345B (zh) 一种染色体非整倍体的检测方法、装置、设备及存储介质
KR102532991B1 (ko) 태아의 염색체 이수성 검출방법
CN118313354B (zh) 细胞亚群自动注释方法、计算机程序和存储介质
CN120260909A (zh) 一种基于大数据的动态生物标志物分析方法及系统
CN120600104A (zh) 基于全外显子组测序队列的单基因水平拷贝数变异识别方法、系统、设备及介质
CN115588464A (zh) 性染色体核型推测方法、工具、计算机设备及存储介质
CN114944195A (zh) 一种拷贝数变异检测前的数据矫正方法
HK40081922A (en) Method, device and storage medium for detecting fetal chromosomal aneuploid abnormalities
HK40057523A (en) A method, device, storage medium for detecting copy number variation and application thereof
CN120727100A (zh) 一种乳腺癌易感基因有害胚系变异评估模型的构建方法及其系统
CN114067909A (zh) 一种矫正同源重组缺陷评分的方法、装置和存储介质
Gentleman et al. Processing Affymetrix Expression Data

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24936273

Country of ref document: EP

Kind code of ref document: A1