Non-invasive prenatal fetal chromosomal detection device

Abstract

The application provides a non-invasive prenatal fetal chromosome detection device, which enriches fetal free DNA in the plasma of a pregnant woman to be detected through a magnetic bead purification method, obtains a free DNA sample to be detected, improves the fetal free DNA concentration proportion in the sample to be detected, can realize automatic detection of the free DNA sample to be detected, reduces the occurrence of detection failure or false negative results, performs chromosome detection based on a negative Z value and a positive Z value corresponding to the chromosome to be detected, can effectively improve the accuracy and comprehensiveness of fetal chromosome detection, covers non-euploid abnormality detection of 23 pairs of chromosomes, improves the sensitivity and specificity of chromosome detection, provides stronger technical support for prenatal screening, and can be widely applied to the technical field of chromosome detection.

Description

Technical Field

[0001] This application relates to the field of chromosome detection technology, and in particular to a non-invasive prenatal fetal chromosome detection device. Background Technology

[0002] With the continuous advancement of next-generation sequencing (NGS) technology and the gradual reduction in cost, non-invasive prenatal testing (NIPT) technology has been widely applied in clinical practice, becoming the preferred method for screening the three most common chromosomal aneuploidies (T13, T18, and T21). Currently, non-invasive prenatal screening (NIPT) for the three common chromosomal aneuploidies (T13, T18, and T21) is relatively mature and has high accuracy. However, it still has certain technical limitations, as follows:

[0003] 1) Insufficient fetal DNA concentration in maternal plasma. The percentage of cell-free fetal DNA in the plasma of different pregnant women varies greatly, usually ranging from 3% to 30%. Samples with a low concentration of fetal DNA often lead to test failure or false negative results.

[0004] 2) Current publicly available sequencing data preprocessing and noise reduction methods fail to adequately consider the interference of positive chromosomes on the copy number calculation of other chromosomes, as well as the errors introduced by repetitive sequences in repetitive regions. Chromosomal copy number is the ratio of the target chromosome's readings to the total autosomal readings. If a chromosome exhibits aneuploidy, its readings will be significantly higher than the expected value for negative samples, causing a significant deviation in the total autosomal readings and affecting the copy number calculation of other chromosomes. Traditional picard-based methods for removing repetitive sequences fail to effectively remove repetitive regions in the genome, introducing a large number of false positive results.

[0005] 3) Currently, conventional mutation detection algorithms are usually based on the Z-test method calculated from the negative sample set. This method has a single test dimension. The mean and variance of the Z-value calculation formula are based on the negative sample set data. It only considers the distribution of negative samples and ignores the distribution of test samples in the positive sample set. It cannot simultaneously take into account both sensitivity and specificity.

[0006] 4) NIPT testing commonly suffers from insufficient positive predictive value (PPV). Multiple prospective clinical studies of NIPT have shown that the PPV is approximately 10-40% for T13, 60-90% for T18, and 80-90% for T21. Furthermore, false-positive results can lead to unnecessary amniocentesis, causing psychological stress for pregnant women. Since fetal DNA in maternal plasma primarily originates from placental trophoblast cells rather than fetal cells, inconsistencies between the karyotypes of placental trophoblast cells and fetal cells can lead to false-positive NIPT results. The most common type is restricted placental mosaicism, where placental trophoblast cells are abnormal while the fetus is normal. Patents 202210825534.2 and 202311069138.2 propose methods for estimating placental chimerism ratio using fetal concentration and copy number, and for reducing false positives in NIPT detection using the chimerism ratio. However, such methods have significant technical limitations, specifically: First, the chimerism ratio calculation depends on fetal DNA concentration, and the accuracy of current predictions of fetal concentration in female fetuses still has a large error, affecting the accuracy of the chimerism ratio calculation; Second, placental chimerism includes multiple classifications, such as fetal chimerism 4 (TFM4) and fetal chimerism 6 (TFM6), in which fetal cells are abnormal; The above methods can reduce the false positive rate to some extent by simply correcting samples with positive placental chimerism signals to negative, but this introduces a significant risk of missed detection.

[0007] 5) Research on rare chromosomal aneuploidy (RAT) other than chromosomes 13, 18, and 21 is insufficient. Positive samples from rare chromosomes are scarce, and the lack of sufficient samples for reference set establishment, algorithm modeling, and evaluation affects the reliability and accuracy of detection results.

[0008] 6) Non-invasive prenatal screening (NIPT) can detect fetal sex chromosome aneuploidy (SCA) abnormalities, including monosomy X (45,X), Klinefelter syndrome (47,XXY), super-female syndrome (47,XXX), and super-male syndrome (47,XYY), with a combined prevalence of 1:500, making it more common than trisomy syndromes. Currently, NIPT primarily targets autosomal abnormalities, while rare sex chromosome aneuploidies, such as XOXY and XXXY, are usually not included in detection models due to their low clinical incidence, leading to missed or false positives.

[0009] Due to the aforementioned technical limitations, the effectiveness and accuracy of non-invasive prenatal fetal chromosomal aneuploidy testing have decreased. Summary of the Invention

[0010] The main objective of this application is to provide a non-invasive prenatal fetal chromosome testing device that can improve the efficiency and accuracy of non-invasive prenatal fetal chromosome testing.

[0011] On the one hand, this application provides a non-invasive prenatal fetal chromosome testing method, which includes the following steps:

[0012] Based on a magnetic bead purification and enrichment kit for fetal DNA and a library construction system, a plasma cell-free DNA sequencing library corresponding to maternal peripheral blood was constructed.

[0013] Obtain genomic sequencing data from the plasma-free DNA sequencing library;

[0014] The genome sequencing data is aligned to a human reference genome. Based on a preset unit window width, the human reference genome is divided into multiple unit windows, and the number of sequencing sequences aligned to each unit window is calculated.

[0015] Based on the number of sequencing sequences corresponding to each unit window, several target candidate windows are selected from the multiple unit windows, wherein the number of sequencing sequences corresponding to the target candidate windows exceeds a given average level threshold;

[0016] Based on each of the target candidate windows, several genomic repetitive regions in the genome sequencing data are determined. The genomic repetitive regions are genomic regions that appear consecutively in the target candidate windows and whose occurrence frequency is greater than a given threshold.

[0017] Multiple cell-free plasma DNA samples to be tested are obtained from the current testing batch; the cell-free plasma DNA samples to be tested are obtained by enriching fetal cell-free DNA in the plasma of the pregnant women to be tested using a magnetic bead purification method;

[0018] For each chromosome to be tested in each of the cell-free DNA plasma samples to be tested, the chromosome to be tested is screened according to the genome sequencing data and each of the genome repetitive regions to determine the number of gene sequences corresponding to the chromosome to be tested;

[0019] For each of the cell-free DNA plasma samples to be tested, copy number statistics are performed based on the number of gene sequences corresponding to each of the chromosomes to be tested to determine the copy number of each chromosome to be tested;

[0020] For each of the cell-free plasma DNA samples to be tested, a negative sample dataset corresponding to each of the chromosomes to be tested is obtained. Based on the copy number of each chromosome to be tested and the negative sample dataset, the negative Z value corresponding to each chromosome to be tested is determined.

[0021] For each of the cell-free plasma DNA samples to be tested, the copy number of each chromosome to be tested is dynamically corrected based on the negative Z-value corresponding to each chromosome to be tested and a given quantity index threshold, thereby determining the copy number correction value corresponding to each chromosome to be tested;

[0022] Based on the copy number correction value corresponding to each chromosome to be tested in each of the cell-free DNA plasma samples to be tested, the chromosome copy number error correction of the current testing batch is performed, and the batch correction value of the copy number corresponding to each chromosome to be tested in each of the cell-free DNA plasma samples to be tested is determined.

[0023] For each of the cell-free plasma DNA samples to be tested, a corresponding positive sample dataset is obtained. Based on the negative sample dataset and the positive sample dataset, negative Z-values ​​and positive Z-values ​​are determined. Chromosome detection is performed based on the copy number batch correction value, negative Z-value, and positive Z-value corresponding to each chromosome to be tested in each of the cell-free plasma DNA samples to be tested, to determine the chromosome detection results of each of the cell-free plasma DNA samples to be tested. The positive sample dataset is simulated and established based on the negative sample dataset.

[0024] In some embodiments, the step of performing region screening on each chromosome to be tested in each of the cell-free DNA plasma samples to be tested, based on the genome sequencing data and each of the genome repetitive regions, to determine the number of gene sequences corresponding to the chromosome to be tested, specifically includes:

[0025] Based on the genome sequencing data, multiple gene sequencing regions in the chromosome to be tested are determined, along with the number of regions and the length of each gene sequencing region. The length of a region is the sequence length corresponding to the gene sequencing region, and the number of regions is the frequency of occurrence of the gene sequencing region in the chromosome to be tested.

[0026] Based on the length of the region corresponding to each gene sequencing region, multiple gene sequencing groups are divided, and each gene sequencing group includes multiple gene sequencing regions with the same length.

[0027] Based on each of the said genomic repetitive regions, chromosomal repetitive regions corresponding to each of the said genomic repetitive regions are screened from multiple gene sequencing regions, and the target gene sequencing group containing the chromosomal repetitive regions is determined from multiple gene sequencing groups;

[0028] For each of the said chromosomal repetitive regions, the number of regions corresponding to the chromosomal repetitive region is updated to the average number of regions corresponding to the target gene sequencing group, wherein the average number of regions is the average of the number of regions of multiple gene sequencing regions in the gene sequencing group;

[0029] The number of gene sequences corresponding to the chromosome to be detected is determined based on the number of regions corresponding to each gene sequencing region other than the multiple chromosomal repetitive regions, and the updated number of regions for each chromosomal repetitive region.

[0030] In some embodiments, determining the copy number of each of the cell-free DNA plasma samples to be tested, based on the number of gene sequences corresponding to each chromosome to be tested, specifically includes:

[0031] The total number of gene sequences corresponding to the cell-free DNA plasma sample to be tested is determined based on the number of gene sequences corresponding to each chromosome to be tested.

[0032] The copy number of each chromosome to be tested is determined based on the number of gene sequences corresponding to each chromosome and the total number of gene sequences. The copy number is calculated using the following formula:

[0033] ;

[0034] ;

[0035] in, For the first The first of the plasma free DNA samples to be tested The copy number of each chromosome to be tested. For the first The first of the plasma free DNA samples to be tested The number of gene sequences corresponding to the chromosomes to be tested. For the first The total number of gene sequences corresponding to each cell-free DNA plasma sample to be tested. For the first The number of chromosomes to be tested in a single cell-free plasma DNA sample.

[0036] In some embodiments, the step of obtaining a negative sample dataset corresponding to each of the cell-free plasma DNA samples to be tested, and determining the negative Z-value corresponding to each of the chromosomes to be tested based on the copy number of each chromosome to be tested and the negative sample dataset, specifically includes:

[0037] Based on the negative sample dataset corresponding to each chromosome to be tested, determine the mean copy number and standard deviation of the negative sample copy number;

[0038] The negative Z-value for the chromosome to be tested is determined based on the copy number corresponding to the chromosome to be tested, the mean copy number of the negative samples, and the standard deviation of the copy number of the negative samples. The negative Z-value is calculated using the following formula:

[0039] ;

[0040] in, For the first The first of the plasma free DNA samples to be tested The negative Z-value corresponding to each chromosome to be tested For the first The first of the plasma free DNA samples to be tested The copy number of each chromosome to be tested. For the first The mean copy number of the chromosome to be tested in the negative sample dataset. For the first The standard deviation of the negative sample copy number of the chromosome to be tested in the negative sample dataset.

[0041] In some embodiments, the step of dynamically correcting the copy number of each of the cell-free plasma DNA samples to be tested based on the negative Z-value corresponding to each of the chromosomes to be tested and a given quantity index threshold, and determining the copy number correction value corresponding to each chromosome to be tested, specifically includes:

[0042] Based on the negative Z value corresponding to each of the chromosomes to be detected and the number index threshold, a number of first abnormal chromosomes are determined from a plurality of chromosomes to be detected, wherein the negative Z value corresponding to the first abnormal chromosome exceeds the number index threshold.

[0043] Select any of the first abnormal chromosomes as the currently corrected chromosome;

[0044] Obtain the total number of gene sequences corresponding to the cell-free DNA sample of plasma to be tested and the average copy number of the negative sample corresponding to the currently corrected chromosome;

[0045] Based on the total number of gene sequences corresponding to the cell-free DNA sample of plasma to be tested and the average copy number of the negative sample corresponding to the currently corrected chromosome, the number of gene sequences corresponding to the currently corrected chromosome is updated. The updated number of gene sequences is calculated using the following formula: ;

[0046] in, For the first The first of the plasma free DNA samples to be tested The number of updated gene sequences corresponding to the chromosomes to be tested. For the first The total number of gene sequences corresponding to each cell-free DNA plasma sample to be tested. For the first Mean copy number of negative samples corresponding to each chromosome to be tested;

[0047] The total number of gene sequences is updated based on the number of updated gene sequences corresponding to the currently corrected chromosome and the number of gene sequences corresponding to the remaining chromosomes to be detected.

[0048] Based on the updated total number of gene sequences, the number of gene sequences of the remaining chromosomes to be tested, and the number of updated gene sequences corresponding to the currently corrected chromosome, the chromosome copy number is recalculated to determine the copy number correction value corresponding to the currently corrected chromosome, and the copy number correction value corresponding to the remaining chromosomes to be tested.

[0049] When any of the first abnormal chromosomes has not updated the number of gene sequences, the first abnormal chromosome that has not updated the number of gene sequences is obtained as the current corrected chromosome. Then, the process returns to obtain the total number of gene sequences corresponding to the cell-free DNA sample to be tested and the average copy number of the negative sample corresponding to the current corrected chromosome, until all the first abnormal chromosomes have updated the number of gene sequences.

[0050] In some embodiments, the step of correcting the chromosome copy number error of the current testing batch based on the copy number correction value corresponding to each chromosome to be tested in each of the cell-free plasma DNA samples to be tested, and determining the batch correction value of the copy number corresponding to each chromosome to be tested in each of the cell-free plasma DNA samples to be tested, specifically includes:

[0051] Take any of the cell-free plasma DNA samples to be tested as the target detection sample, take any of the chromosomes to be tested in the target detection sample as the current target detection chromosome, and take the copy number correction value corresponding to the current target detection chromosome as the current copy number;

[0052] Obtain the mean copy number of the negative sample corresponding to the current target chromosome, the standard deviation of the negative sample copy number, and the mean copy number of the chromosome in the current batch, wherein the mean copy number of the chromosome in the current batch is the mean copy number of the chromosome corresponding to the current target chromosome in all the cell-free plasma DNA samples to be tested;

[0053] Based on the current copy number, the average copy number of the negative samples, and the average copy number of the chromosomes in the current batch, the normalized copy number corresponding to the current target chromosome is determined;

[0054] The negative Z-value corresponding to the current target chromosome is updated based on the normalized copy number, the mean copy number of the negative samples, and the standard deviation of the copy number of the negative samples.

[0055] Based on the updated negative Z-value corresponding to the current target detection chromosome and the quantity index threshold, it is determined whether the current target detection chromosome is the second abnormal chromosome;

[0056] When the updated negative Z-value corresponding to the current target detection chromosome is greater than the quantity index threshold, the current target detection chromosome is determined to be the second abnormal chromosome. The mean copy number of the negative samples is determined as the current copy number. Based on the current copy number, the mean copy number of the chromosomes in the current batch is re-determined. Then, the normalized copy number corresponding to the current target detection chromosome is determined based on the current copy number, the mean copy number of the negative samples, and the mean copy number of the chromosomes in the current batch, until the updated negative Z-value corresponding to the current target detection chromosome is less than the quantity index threshold.

[0057] When the updated negative Z value corresponding to the current target detection chromosome is less than the quantity exponential threshold, the normalized copy number is determined as the batch correction value of the copy number corresponding to the current target detection chromosome.

[0058] When any chromosome to be detected in the target detection sample has not undergone chromosome copy number error correction, any other chromosome to be detected in the target detection sample that has not undergone chromosome copy number error correction is obtained as the current target detection chromosome. The copy number correction value corresponding to the current target detection chromosome is obtained as the current copy number. Then, the mean copy number of the negative sample corresponding to the current target detection chromosome, the standard deviation of the negative sample copy number, and the mean copy number of the chromosome in the current batch are obtained until all chromosomes to be detected in the target detection sample have undergone chromosome copy number error correction.

[0059] The normalized copy number is calculated using the following formula:

[0060] = ;

[0061] Among them, the chromosome currently being detected is the number The first of the plasma free DNA samples to be tested One chromosome to be tested To detect the normalized copy number of the chromosome corresponding to the current target, This is the copy number correction value for the chromosome corresponding to the current target detection. This represents the average copy number of the target chromosome in the current testing batch. This represents the mean copy number of the negative samples corresponding to the currently targeted chromosome.

[0062] In some embodiments, the step of obtaining a corresponding positive sample dataset for each of the cell-free plasma DNA samples to be tested, determining negative Z-values ​​and positive Z-values ​​based on the negative sample dataset and the positive sample dataset, and performing chromosome detection based on the copy number batch correction value, the negative Z-value, and the positive Z-value corresponding to each of the cell-free plasma DNA samples to be tested, to determine the chromosome detection results for each of the cell-free plasma DNA samples to be tested, specifically includes:

[0063] Obtain the negative sample dataset corresponding to the chromosome to be detected;

[0064] Based on the fetal concentration distribution of the negative sample dataset, the expected copy number of the positive sample dataset is determined, and the positive sample dataset is generated based on the expected copy number and random error.

[0065] The copy number distribution of the negative sample dataset is statistically analyzed to determine the negative Z-value judgment threshold;

[0066] The negative Z-value corresponding to the chromosome to be tested is updated based on the batch correction value of the copy number corresponding to the chromosome to be tested, the mean copy number of the negative samples, and the standard deviation of the copy number of the negative samples.

[0067] When the chromosome to be detected is an autosome, obtain the positive sample dataset corresponding to the chromosome to be detected, perform copy number distribution statistics on the positive sample dataset, and determine the positive Z-value judgment threshold, the mean copy number of positive samples, and the standard deviation of the copy number of positive samples.

[0068] Based on the batch correction value of the copy number corresponding to the chromosome to be tested, the mean copy number of the positive samples, and the standard deviation of the copy number of the positive samples, the positive Z-value corresponding to the chromosome to be tested is determined, and the positive Z-value is calculated by the following formula:

[0069] ;

[0070] in, For the first The first of the plasma free DNA samples to be tested The positive Z-value corresponding to each chromosome to be tested For the first The mean copy number of each chromosome to be tested in the positive sample dataset. For the first The first of the plasma free DNA samples to be tested Batch correction values ​​for the copy number of the chromosome to be tested. For the first Standard deviation of the sample copy number of each chromosome to be tested in the positive sample dataset;

[0071] The chromosome detection result is determined by performing a joint test based on the negative Z-value judgment threshold, the negative Z-value, the positive Z-value judgment threshold, and the positive Z-value.

[0072] When the negative Z-value is greater than the negative Z-value judgment threshold and the positive Z-value is greater than the positive Z-value judgment threshold, the chromosome detection result is determined to be positive for the chromosome to be detected in the cell-free DNA sample of plasma to be tested;

[0073] When the negative Z-value is less than or equal to the negative Z-value judgment threshold, and the positive Z-value is less than or equal to the positive Z-value judgment threshold, the chromosome detection result is determined to be negative for the chromosome to be detected in the cell-free DNA sample of plasma to be tested;

[0074] When the chromosome to be detected is a sex chromosome, a sex chromosome detection decision tree model is obtained, and the chromosome detection result is determined based on the negative Z-value and the negative Z-value using the sex chromosome detection decision tree model.

[0075] In some embodiments, when the chromosome to be detected is a sex chromosome, obtaining a sex chromosome detection decision tree model, and using the sex chromosome detection decision tree model to determine the chromosome detection result based on the negative Z-value and the negative Z-value, specifically includes:

[0076] Obtain the first negative Z-value corresponding to the X chromosome and the second negative Z-value and Z-value judgment threshold corresponding to the Y chromosome;

[0077] The first negative Z-value, the second negative Z-value, and the Z-value judgment threshold are input into the sex chromosome detection decision tree model, and the sex chromosome detection decision tree model is used to output the fetal sex and the corresponding sex chromosome detection results.

[0078] In some embodiments, the fetal DNA purification and enrichment kit based on magnetic beads and the library construction system, which constructs a plasma cell-free DNA sequencing library corresponding to maternal peripheral blood, specifically includes:

[0079] Cell-free DNA was extracted from the plasma of pregnant women.

[0080] The cell-free DNA in the plasma was subjected to fragment screening to obtain cell-free DNA in the plasma after fragment screening;

[0081] The cell-free plasma DNA after fragment screening was padded with A and phosphorylated to obtain the corresponding end repair products;

[0082] The end repair product is connected to a connector to obtain a corresponding connector product.

[0083] The adapter ligation product was purified by magnetic beads, and the purified adapter ligation product was subjected to library PCR amplification to obtain the corresponding PCR product.

[0084] The PCR product was purified by magnetic beads, and the purified PCR product was subjected to library quantification and sequencing to generate the plasma free DNA sequencing library.

[0085] On the other hand, embodiments of this application propose a non-invasive prenatal fetal chromosome testing device, the device comprising:

[0086] The data preprocessing module is used to construct a plasma cell-free DNA sequencing library corresponding to maternal peripheral blood based on a magnetic bead purification and enrichment kit for fetal DNA and a library construction system; acquire genomic sequencing data from the plasma cell-free DNA sequencing library; align the genomic sequencing data to a human reference genome, divide the human reference genome into multiple unit windows according to a preset unit window width, and calculate the number of sequencing sequences corresponding to each unit window; based on the number of sequencing sequences corresponding to each unit window, select several target candidate windows from the multiple unit windows, wherein the number of sequencing sequences corresponding to the target candidate windows exceeds a given average level threshold; and determine several genomic repetitive regions in the genomic sequencing data based on each target candidate window, wherein the genomic repetitive regions are genomic regions in which the target candidate windows appear consecutively and the number of occurrences exceeds a given threshold.

[0087] A chromosome copy number dynamic correction module is used to acquire multiple cell-free plasma DNA samples to be tested in the current testing batch. The cell-free plasma DNA samples are obtained by enriching fetal cell-free DNA in the plasma of pregnant women using a magnetic bead purification method. For each chromosome to be tested in each cell-free plasma DNA sample, based on the genome sequencing data and each genome repetitive region, the number of gene sequences corresponding to the chromosome to be tested is determined. The module is also used to perform copy number statistics on each cell-free plasma DNA sample based on the number of gene sequences corresponding to each chromosome to determine the copy number of each chromosome to be tested. Finally, the module is used to acquire each cell-free plasma DNA sample to be tested. The negative sample dataset corresponding to the chromosome to be tested is used to determine the negative Z-value corresponding to each chromosome to be tested based on the copy number of each chromosome to be tested and the negative sample dataset. For each cell-free DNA plasma sample to be tested, the copy number of each chromosome to be tested is dynamically corrected based on the negative Z-value of each chromosome to be tested and a given quantity index threshold, thereby determining the copy number correction value of each chromosome to be tested. Based on the copy number correction value of each chromosome to be tested in each cell-free DNA plasma sample to be tested, the chromosome copy number error of the current testing batch is corrected, thereby determining the batch correction value of the copy number of each chromosome to be tested in each cell-free DNA plasma sample to be tested.

[0088] The chromosome detection module is used to acquire a corresponding positive sample dataset for each of the cell-free plasma DNA samples to be tested, determine the negative Z-value and the positive Z-value based on the negative sample dataset and the positive sample dataset, perform chromosome detection based on the copy number batch correction value, the negative Z-value and the positive Z-value corresponding to each of the cell-free plasma DNA samples to be tested, and determine the chromosome detection result of each of the cell-free plasma DNA samples to be tested, wherein the positive sample dataset is simulated and established based on the negative sample dataset.

[0089] On the other hand, embodiments of this application propose a detection kit for constructing the plasma cell-free DNA sequencing library described above.

[0090] The embodiments of this application include at least the following beneficial effects: The non-invasive prenatal fetal chromosome detection device provided in this application can construct a DNA sequencing library with enriched fetal DNA concentration, effectively increasing the proportion of fetal DNA in the DNA sequencing library, thereby reducing the probability of detection failure and false negatives caused by low fetal DNA concentration, significantly improving the reliability of chromosome detection. The DNA sequencing library is constructed using a kit, improving the stability of the library construction results. It achieves genomic repetitive region elimination and dynamic chromosome copy number correction based on a sliding window, effectively identifying abnormal readings caused by repetitive sequences, reducing the impact of aneuploid abnormal chromosomes on the copy number calculation of other chromosomes, thereby improving the accuracy of chromosome copy number calculation. Based on the negative and positive Z-values ​​of the chromosome to be detected, combined with the Z-values ​​in the negative and positive reference sets, joint analysis is performed to achieve chromosome detection, which better meets the clinically expected requirements for detection sensitivity and specificity. Simultaneously, based on the negative sample dataset, a simulated positive sample dataset is established, effectively solving the problem of insufficient positive samples and difficulty in obtaining positive sample datasets for rare chromosomal aneuploidy abnormalities, and has strong application value. In summary, this application can effectively improve the accuracy and comprehensiveness of fetal chromosome testing, covering the detection of aneuploidy abnormalities in 23 pairs of chromosomes, and improving the sensitivity and specificity of chromosome testing, thus providing stronger technical support for prenatal screening. Attached Figure Description

[0091] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0092] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0093] Figure 1 This is a flowchart of a non-invasive prenatal fetal chromosome testing method provided in an embodiment of this application;

[0094] Figure 2 This is a schematic diagram comparing the chromosome copy number (CV) values ​​of the conventional picard deduplication method and the sliding window-based duplicate region elimination method in the embodiments of this application.

[0095] Figure 3 This is a schematic diagram of the structure of a non-invasive prenatal fetal chromosome testing device provided in an embodiment of this application;

[0096] Figure 4This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0097] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0098] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0099] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0100] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0101] Reference Figure 1 , Figure 1 This is an optional flowchart of a non-invasive prenatal fetal chromosome detection method provided in the embodiments of this application. The method may include, but is not limited to, steps S102 to S112: Step S101, constructing a plasma cell-free DNA sequencing library corresponding to the peripheral blood of the pregnant woman based on a magnetic bead purification and enrichment kit for fetal DNA and a library construction system.

[0102] Step S102: Obtain genomic sequencing data from the cell-free DNA sequencing library in plasma;

[0103] Step S103: Align the genome sequencing data to the human reference genome. Divide the human reference genome into multiple unit windows according to the preset unit window width, and calculate the number of sequencing sequences corresponding to each unit window.

[0104] Step S104: Based on the number of sequencing sequences corresponding to each unit window, select several target candidate windows from multiple unit windows, where the number of sequencing sequences corresponding to the target candidate windows exceeds a given average level threshold.

[0105] Step S105: Based on each target candidate window, determine several genomic repetitive regions in the genome sequencing data. Genomic repetitive regions are genomic regions that appear consecutively in the target candidate windows and whose occurrence frequency is greater than a given threshold.

[0106] Step S106: Obtain multiple cell-free plasma DNA samples to be tested from the current testing batch; the cell-free plasma DNA samples to be tested are obtained by enriching fetal cell-free DNA in the plasma of the pregnant women to be tested using a magnetic bead purification method;

[0107] Step S107: For each chromosome to be tested in each cell-free plasma DNA sample to be tested, based on the genome sequencing data and the repetitive regions of each genome, the chromosome to be tested is screened to determine the number of gene sequences corresponding to the chromosome to be tested.

[0108] Step S108: For each cell-free DNA plasma sample to be tested, the copy number is counted based on the number of gene sequences corresponding to each chromosome to be tested, and the copy number of each chromosome to be tested is determined.

[0109] Step S109: For each cell-free plasma DNA sample to be tested, obtain the negative sample dataset corresponding to each chromosome to be tested, and determine the negative Z value corresponding to each chromosome to be tested based on the copy number corresponding to each chromosome to be tested and the negative sample dataset.

[0110] Step S110: For each cell-free plasma DNA sample to be tested, based on the negative Z-value corresponding to each chromosome to be tested and the given quantity index threshold, the copy number of each chromosome to be tested is dynamically corrected to determine the copy number correction value corresponding to each chromosome to be tested.

[0111] Step S111: Based on the copy number correction value corresponding to each chromosome to be tested in each cell-free plasma DNA sample to be tested, perform chromosome copy number error correction for the current testing batch, and determine the batch correction value of the copy number corresponding to each chromosome to be tested in each cell-free plasma DNA sample to be tested.

[0112] Step S112: For each cell-free plasma DNA sample to be tested, obtain the corresponding positive sample dataset. Based on the negative sample dataset and the positive sample dataset, determine the negative Z-value and the positive Z-value. Based on the copy number batch correction value, negative Z-value, and positive Z-value of each chromosome to be tested in each cell-free plasma DNA sample to be tested, perform chromosome detection and determine the chromosome detection results of each cell-free plasma DNA sample to be tested. The positive sample dataset is simulated and established based on the negative sample dataset.

[0113] In some embodiments, step S101 may include, but is not limited to, steps S201 to S206:

[0114] Step S201: Extract cell-free DNA from the pregnant woman's plasma;

[0115] Step S202: Fragment screening is performed on the cell-free DNA in the plasma to obtain the cell-free DNA in the plasma after fragment screening;

[0116] Step S203: The cell-free plasma DNA after fragment screening is padded with A and phosphorylated to obtain the corresponding end repair products;

[0117] Step S204: Connect the end repair product with a joint to obtain the corresponding joint connection product;

[0118] Step S205: Purify the adapter ligation product with magnetic beads, and amplify the purified adapter ligation product with library PCR to obtain the corresponding PCR product.

[0119] Step S206: Purify the PCR product with magnetic beads, and then perform library quantification and sequencing on the purified PCR product to generate a plasma free DNA sequencing library.

[0120] In some embodiments, steps S201 to S206 specifically include the following steps:

[0121] The first step is to take a certain amount (e.g., 400 μL) of pregnant woman's plasma, extract plasma DNA using a nucleic acid extraction or purification kit, and then elute the DNA with the corresponding amount (e.g., 50 μL) of elution buffer.

[0122] The second step, fragment filtration and purification: Fragment filtration refers to selecting fragments from the extracted cell-free plasma DNA to increase the concentration of fetal DNA. The specific steps are as follows:

[0123] a) Take the extracted plasma free DNA and add 1.3-1.7 times the volume of purification magnetic beads (the purification operation is selected from solid-phase reversible immobilization magnetic bead enrichment, magnetic beads include but are not limited to Beckman's Agencourt AMPure XP magnetic beads, TransGen's Magnetic DNA beads, etc.), mix thoroughly and let stand for 5 minutes, then place on a magnetic rack until clear;

[0124] b) Aspirate the supernatant into 1-2 times its volume of magnetic beads, mix the supernatant and magnetic beads thoroughly, let stand for 5 minutes, and place on a magnetic rack until clear;

[0125] c) Aspirate the supernatant, wash twice with 75%~80% ethanol, then dissolve and elute with 16.5μL TE solution to obtain the screened cell-free plasma DNA;

[0126] The third step is the addition of A and phosphorylation to cell-free plasma DNA: Cell-free DNA from the sieving slides was used to prepare a reaction mixture according to Table 1. This mixture was then subjected to PCR using a PCR instrument to obtain end-repair products. The PCR instrument settings included: 105℃ hot, 30℃ for 30 min, 72℃ for 30 min, and 4℃ hold, as shown in Table 1 below.

[0127] Table 1

[0128]

[0129] Step 4, Adapter ligation: Using the end-repair products, prepare the reaction mixture shown in Table 2. Perform PCR on this reaction mixture using a PCR instrument to obtain the adapter ligation products. The PCR instrument settings include: hot cap off, 20℃ for 15 min, and 4℃ hold, as shown in Table 2 below:

[0130] Table 2

[0131]

[0132] Step 5, magnetic bead purification: The adapter ligation product was purified using 30 μL of purification magnetic beads and finally reconstituted with 19.5 μL of LTE buffer.

[0133] Step 6, Library PCR Amplification: Using the adapter ligation products purified by magnetic beads, prepare the reaction mixture shown in Table 3. Perform PCR reaction on the mixture using a PCR instrument to obtain PCR products. The PCR instrument settings include: heated lid 105℃, 98℃ for 45 sec, (98℃ for 15 sec, 60℃ for 30 sec, 72℃ for 30 sec) * 11, 72℃ for 1 min, 4℃ hold, as shown in Table 3 below:

[0134] Table 3

[0135]

[0136] Step 7, PCR product purification: Purify the PCR product using 44 μL of purification magnetic beads, and finally reconstitute with 25 μL of TE buffer.

[0137] Step 8, Library Quantification: Use Qubit to determine the concentration of PCR products purified by magnetic beads and identify the corresponding library concentration;

[0138] Step 9, Library Sequencing: Sequencing of the PCR products purified from magnetic beads was performed using an Illumina platform sequencer, with each library containing 5M of data.

[0139] In some embodiments, the above-mentioned method for constructing a plasma cell-free DNA sequencing library first uses magnetic bead purification to enrich short fragments of cell-free DNA, thereby increasing the concentration of fetal cell-free DNA in the sample; at the same time, by optimizing the operating parameters of experimental steps such as end repair, adapter ligation, and PCR, the complexity of library construction and experimental time are reduced, and the success rate of library construction is improved.

[0140] For example, plasma samples from six pregnant women (L1-L6) were collected for DNA sequencing library construction and sequencing. Two DNA samples were extracted from each sample. One sample was processed using the method for constructing a cell-free DNA library from pregnant woman plasma that increases fetal DNA concentration proposed in this invention. The other sample was used as a control, in which the fragment screening step was omitted during library construction. This was used to compare the library construction effect without fetal concentration enrichment. The specific steps for DNA sequencing library construction are as follows:

[0141] 1) Plasma DNA extraction: Take 400 μL of pregnant woman's plasma and extract plasma DNA using the above kit. Extract two DNA samples from each sample and mix DNA samples with the same number together for later use.

[0142] 2) Divide the extracted DNA into two equal parts. The first DNA sample serves as a control group and is not subjected to fragment screening. The second DNA sample undergoes fragment filtration and purification to increase the concentration of fetal DNA. The specific steps are as follows:

[0143] a) Take the extracted plasma free DNA and add 1.3-1.7 times the volume of purification magnetic beads (the purification operation is selected from solid-phase reversible immobilization magnetic bead enrichment, magnetic beads include but are not limited to Beckman's Agencourt AMPure XP magnetic beads, TransGen's Magnetic DNA beads, etc.), mix thoroughly and let stand for 5 minutes, then place on a magnetic rack until clear;

[0144] b) Transfer the supernatant to 1-2 times its volume of magnetic beads, mix the supernatant and magnetic beads thoroughly, let stand for 5 minutes, and place on a magnetic rack until clear;

[0145] c) Aspirate the supernatant, wash twice with 75%~80% ethanol, then dissolve and elute with 16.5μL TE solution to obtain the screened cell-free plasma DNA;

[0146] 3) Completion of cell-free plasma DNA, addition of A, and phosphorylation: The cell-free DNA after sieving was used to prepare the reaction mixture according to Table 1, as shown in Table 4 below:

[0147] Table 4

[0148]

[0149] PCR reaction on the instrument: heated to 105℃; 30℃ for 30 min, 72℃ for 30 min, 4℃ hold.

[0150] 4) Connector connection: Prepare the reaction mixture in Table 5, as detailed in Table 5 below:

[0151] Table 5

[0152]

[0153] PCR reaction on the instrument: heat off; 20℃ for 15 min, 4℃ hold;

[0154] 5) Magnetic bead purification: The adapter ligation product was purified using 30 μL of purification magnetic beads and finally reconstituted with 19.5 μL of TE buffer.

[0155] 6) Library PCR amplification: Prepare the reaction mixtures shown in Table 6, as shown in Table 6 below:

[0156] Table 6

[0157]

[0158] The reaction was performed on the PCR instrument as follows: heated to 105°C; 98°C for 45 seconds, (98°C for 15 seconds, 60°C for 30 seconds, 72°C for 30 seconds) * 11; 72°C for 1 minute; 4°C hold;

[0159] 7) PCR product purification: Purify using 44 μL of purification magnetic beads, and finally reconstitute with 25 μL of TE buffer;

[0160] 8) Library detection: Qubit was used to determine library concentration. Agilent 2100 was used to detect library fragment sizes.

[0161] 9) Library sequencing: Sequencing was performed using an Illumina platform-based sequencer to obtain the sequencing reads of cell-free DNA from the pregnant woman's plasma.

[0162] 10) Determining the impact of fragment screening purification on fetal DNA concentration: To compare the impact of fragment screening on fetal DNA concentration, we first determined whether short cell-free DNA fragments <158bp were successfully enriched based on sequencing read length; secondly, we used the Y chromosome Z-score to determine the fold increase in fetal DNA concentration. According to Table 7, compared to the traditional non-fragmentation method, the fragment screening purification method reduced the DNA sequencing library concentration to 1 / 3, shortened the DNA length by approximately 30bp, and increased the fetal concentration by 2.12 times compared to the non-fragmentation method. This indicates that the fragment screening purification method can effectively enrich short cell-free fetal DNA fragments and increase fetal concentration. Table 7 details are shown below:

[0163] Table 7. Comparison of results between the sieve-based purification method and the non-sieve-based method.

[0164]

[0165] In some embodiments, alignment software (including but not limited to BWA, Bowtie, etc.) is used to align the genome sequencing data (reads) corresponding to the plasma cell-free DNA sequencing library to the human reference genome, and to remove repetitive sequences and low-quality alignment results. For example, repetitive sequences can be removed by using, but not limited to, the Picard MarkDuplicates tool, combined with the alignment software's alignment flag and a threshold set for alignment quality values.

[0166] In steps S102 to S105 of some embodiments, a sliding window-based repetitive region removal method (SW-RR-Removal) is proposed, which aims to effectively identify and remove repetitive regions in the genome, especially those with normal repetition rates at individual sites but with a large number of repetitive sequences in continuous segments, causing abnormal readings.

[0167] The sliding window-based duplicate region removal method (SW-RR-Removal) is as follows:

[0168] First, the human reference genome region is divided into multiple unit windows with a preset unit window width (the window size can be 10bp, 20bp, etc.). The number of sequencing sequences that the genome sequencing data is aligned to each window is calculated (represented by the reads number, hereinafter referred to as the readings). Several target candidate windows are selected from multiple unit windows whose window readings are significantly higher than the average level threshold (the significance level is selected but not limited to 1‰).

[0169] Then, the clustering of each target candidate window in the genome is analyzed, and for each target candidate window, the genomic region in the genome sequencing data where the target candidate window appears consecutively above a given threshold is set as a genomic repetitive region.

[0170] Optionally, refer to Figure 2 , Figure 2 This is an optional comparative diagram showing the chromosome copy number (CV) values ​​of the conventional picard deduplication method and the sliding window-based genomic duplication removal method in the embodiments of this application. To verify the effectiveness of the sliding window-based genomic duplication removal method (SW-RR-Removal), sequencing results from plasma samples of 5 negative-positive pregnant women were selected. The differences between the conventional picard deduplication method and the sliding window-based genomic duplication removal method (SW-RR-Removal) were compared, with the CV value of chromosome copy number being the largest. A smaller CV value indicates a better noise reduction effect. Based on the sequencing results of the 5 negative-positive pregnant women's plasma samples, it is fully demonstrated that the sliding window-based genomic duplication removal method (SW-RR-Removal) can more effectively remove duplication regions, reduce data noise, and improve the accuracy of chromosome copy number analysis compared to the conventional picard deduplication method.

[0171] In some embodiments, all plasma cell-free DNA samples to be tested in the current testing batch are from the plasma of the same pregnant woman. Optionally, short fragments of cell-free DNA from multiple tubes of plasma of the same pregnant woman can be enriched using a magnetic bead purification method to obtain multiple corresponding plasma cell-free DNA samples to be tested.

[0172] In some embodiments, step S107 may include, but is not limited to, steps S301 to S305:

[0173] Step S301: Based on the genome sequencing data, determine the multiple gene sequencing regions in the chromosome to be tested, as well as the number of regions and the length of each gene sequencing region. The length of a region is the sequence length corresponding to the gene sequencing region, and the number of regions is the frequency of occurrence of the gene sequencing region in the chromosome to be tested.

[0174] Step S302: Divide the gene sequencing groups into multiple gene sequencing groups according to the region length corresponding to each gene sequencing region. Each gene sequencing group includes multiple gene sequencing regions with the same region length.

[0175] Step S303: Based on the repetitive regions of each genome, screen the chromosomal repetitive regions corresponding to the repetitive regions of each genome from multiple gene sequencing regions, and determine the target gene sequencing group where the chromosomal repetitive regions are located from multiple gene sequencing groups;

[0176] Step S304: For each chromosome repeating region, update the number of regions corresponding to the chromosome repeating region to the average number of regions corresponding to the target gene sequencing group. The average number of regions is the average number of regions of multiple gene sequencing regions in the gene sequencing group.

[0177] Step S305: Determine the number of gene sequences corresponding to the chromosome to be detected based on the number of regions corresponding to each gene sequencing region (excluding multiple chromosomal repetitive regions) and the updated number of regions for each chromosomal repetitive region.

[0178] In some embodiments, multiple gene sequencing groups are divided according to the length of the sequencing regions of each gene in the chromosome to be tested. For example, all gene sequencing regions with a length of 20kb in the chromosome to be tested are assigned to the same gene sequencing group, and all gene sequencing regions with a length of 30kb in the chromosome to be tested are assigned to another gene testing group, and so on.

[0179] In some embodiments, when calculating the number of gene sequences on the chromosome to be tested, if there are genomic repetitive regions, the number of regions of the genomic repetitive regions on the chromosome is replaced with an expected value (such as the average number of regions mentioned above). For example, if a genomic repetitive region with a length of 20kb is detected in the chromosome to be tested, and the number of regions of this genomic repetitive region is 2000, and the average number of regions on the chromosome to be tested corresponding to all gene sequencing regions with a length of 20kb is 100, then 100 is used instead of 2000 to update the number of regions of this genomic repetitive region on the chromosome to be tested. This effectively reduces the bias introduced by the large number of repetitive sequencing results in genomic repetitive regions on the statistical count of gene sequences on chromosomes. Finally, based on the number of regions corresponding to each gene sequencing region other than multiple chromosomal repetitive regions, and the updated number of regions for each chromosomal repetitive region, the number of gene sequences corresponding to the chromosome to be tested is re-determined.

[0180] In some embodiments, step S108 may include, but is not limited to, steps S401 to S402:

[0181] Step S401: Determine the total number of gene sequences corresponding to the cell-free DNA sample of plasma to be tested based on the number of gene sequences corresponding to each chromosome to be tested.

[0182] Step S402: Based on the number of gene sequences corresponding to each chromosome to be tested and the total number of gene sequences, determine the copy number corresponding to each chromosome to be tested. The copy number is calculated using the following formula:

[0183] ;

[0184] ;

[0185] in, For the first The first of the plasma free DNA samples to be tested The copy number of each chromosome to be tested. For the first The first of the plasma free DNA samples to be tested The number of gene sequences corresponding to the chromosomes to be tested. For the first The total number of gene sequences corresponding to each cell-free DNA plasma sample to be tested. For the first The number of chromosomes to be tested in a single cell-free plasma DNA sample.

[0186] In some embodiments, the cell-free DNA samples of plasma to be tested are statistically analyzed. Chromosomes to be tested Number of gene sequences First, calculate the cell-free DNA sample of the plasma to be tested. Total number of gene sequences on all chromosomes Finally, the cell-free DNA samples from the plasma to be tested were statistically analyzed. Chromosomes to be tested Copy value .

[0187] In some embodiments, step S109 may include, but is not limited to, steps S501 to S502:

[0188] Step S501: Based on the negative sample dataset corresponding to each chromosome to be tested, determine the mean copy number and standard deviation of the negative sample copy number.

[0189] Step S502: Based on the copy number of the chromosome to be tested, the mean copy number of negative samples, and the standard deviation of the copy number of negative samples, determine the negative Z-value corresponding to the chromosome to be tested. The negative Z-value is calculated using the following formula:

[0190] ;

[0191] in, For the first The first of the plasma free DNA samples to be tested The negative Z-value corresponding to each chromosome to be tested For the first The first of the plasma free DNA samples to be tested The copy number of each chromosome to be tested. For the first The mean copy number of the chromosome to be tested in the negative sample dataset. For the first The standard deviation of the negative sample copy number of the chromosome to be tested in the negative sample dataset.

[0192] In some embodiments, step S110 may include, but is not limited to, steps S601 to S607:

[0193] Step S601: Based on the negative Z value and the number index threshold corresponding to each chromosome to be detected, determine a number of first abnormal chromosomes from multiple chromosomes to be detected, wherein the negative Z value corresponding to the first abnormal chromosome exceeds the number index threshold.

[0194] Step S602: Obtain any first abnormal chromosome as the current correction chromosome;

[0195] Step S603: Obtain the total number of gene sequences corresponding to the cell-free DNA sample of plasma to be tested and the average copy number of the negative sample corresponding to the currently corrected chromosome;

[0196] Step S604: Based on the total number of gene sequences corresponding to the cell-free DNA sample to be tested and the average copy number of the negative sample corresponding to the currently corrected chromosome, update the number of gene sequences corresponding to the currently corrected chromosome. The updated number of gene sequences is calculated using the following formula: ;

[0197] in, For the first The first of the plasma free DNA samples to be tested The number of updated gene sequences corresponding to the chromosomes to be tested. For the first The total number of gene sequences corresponding to each cell-free DNA plasma sample to be tested. For the first Mean copy number of negative samples corresponding to each chromosome to be tested;

[0198] Step S605: Update the total number of gene sequences based on the number of updated gene sequences corresponding to the currently corrected chromosome and the number of gene sequences corresponding to the remaining chromosomes to be tested;

[0199] Step S606: Based on the total number of updated gene sequences, the number of gene sequences of the remaining chromosomes to be tested, and the number of updated gene sequences corresponding to the currently corrected chromosome, recalculate the chromosome copy number to determine the copy number correction value corresponding to the currently corrected chromosome, as well as the copy number correction values ​​corresponding to the remaining chromosomes to be tested.

[0200] Step S607: When there is a number of unupdated gene sequences of any first abnormal chromosome, obtain the first abnormal chromosome with any number of unupdated gene sequences as the current corrected chromosome, and then return to obtain the total number of gene sequences corresponding to the cell-free DNA sample of the plasma to be tested and the average copy number of the negative sample corresponding to the current corrected chromosome, until the number of updated gene sequences of all first abnormal chromosomes is obtained.

[0201] In some embodiments, step S111 may include, but is not limited to, steps S701 to S708:

[0202] Step S701: Obtain any cell-free plasma DNA sample to be tested as the target detection sample, obtain any chromosome to be tested in the target detection sample as the current target detection chromosome, and obtain the copy number correction value corresponding to the current target detection chromosome as the current copy number;

[0203] Step S702: Obtain the mean copy number of negative samples, the standard deviation of negative sample copy number, and the mean copy number of chromosomes in the current batch corresponding to the current target chromosome. The mean copy number of chromosomes in the current batch is the mean copy number of the current target chromosome in all cell-free plasma DNA samples to be tested.

[0204] Step S703: Determine the normalized copy number corresponding to the target chromosome based on the current copy number, the average copy number of negative samples, and the average copy number of chromosomes in the current batch.

[0205] Step S704: Update the negative Z-value corresponding to the current target chromosome based on the normalized copy number, the mean copy number of negative samples, and the standard deviation of the copy number of negative samples.

[0206] Step S705: Based on the updated negative Z-value and quantity index threshold corresponding to the current target chromosome, determine whether the current target chromosome is the second abnormal chromosome;

[0207] Step S706: When the updated negative Z-value corresponding to the current target chromosome is greater than the number index threshold, the current target chromosome is determined to be the second abnormal chromosome. The mean copy number of negative samples is determined as the current copy number. Based on the current copy number, the mean copy number of chromosomes in the current batch is re-determined. Then, the normalized copy number corresponding to the current target chromosome is determined based on the current copy number, the mean copy number of negative samples, and the mean copy number of chromosomes in the current batch, until the updated negative Z-value corresponding to the current target chromosome is less than the number index threshold.

[0208] Step S707: When the updated negative Z value corresponding to the current target chromosome is less than the number index threshold, the normalized copy number is determined as the batch correction value of the copy number corresponding to the current target chromosome.

[0209] Step S708: When there is any chromosome to be detected in the target detection sample that has not undergone chromosome copy number error correction, obtain any other chromosome to be detected in the target detection sample that has not undergone chromosome copy number error correction as the current target detection chromosome, obtain the copy number correction value corresponding to the current target detection chromosome as the current copy number, and then return to obtain the mean copy number of negative samples, the standard deviation of negative sample copy number, and the mean copy number of chromosomes in the current batch corresponding to the current target detection chromosome, until all chromosomes to be detected in the target detection sample have undergone chromosome copy number error correction.

[0210] In some embodiments, the above = -( - );

[0211] Among them, the chromosome currently being detected is the number The first of the plasma free DNA samples to be tested One chromosome to be tested To detect the normalized copy number of the chromosome corresponding to the current target, This is the copy number correction value for the chromosome corresponding to the current target detection. This represents the average copy number of the target chromosome in the current testing batch. This represents the mean copy number of the negative samples corresponding to the currently targeted chromosome.

[0212] In some embodiments, the chromosome copy number is the ratio of the target chromosome's reading to the total chromosome reading. If a chromosome exhibits aneuploidy, its reading will be significantly higher than the expected value for a negative sample, causing a significant deviation in the total chromosome reading and affecting the copy number calculation of other chromosomes. To mitigate this impact, a dynamic chromosome copy number correction method is proposed. In each iteration, the copy number of the target chromosome and the negative Z-value are calculated to identify chromosomes that may have aneuploidy. The method dynamically corrects the interference of these abnormal chromosomes on the overall copy number calculation, improving the accuracy of chromosome copy number quantification. The dynamic chromosome copy number correction method includes copy number correction within the current sample and copy number correction within the current batch. The specific details of the dynamic chromosome copy number correction method are as follows:

[0213] The first step is to correct the copy number within the current sample. The specific steps are as follows:

[0214] 1) Obtain cell-free DNA samples from the plasma to be tested. Chromosomes to be tested Copy value ;

[0215] 2) Using the chromosome to be tested The corresponding negative sample dataset is used to calculate the chromosome to be detected. Mean copy number of negative samples and the standard deviation of negative sample copy number Calculate the chromosome to be tested Negative Z-value ,in, ;

[0216] 3) Set the chromosome to be tested The number index threshold is used to determine the chromosome to be detected. Whether the chromosome to be tested contains aneuploidy or other chromosomal abnormalities. of If the value is 6, exceeding the set quantity index threshold, then the quantity index threshold will be marked as the first abnormal chromosome.

[0217] 4) Assuming the chromosome to be tested The first abnormal chromosome was identified, and the cell-free DNA sample from the plasma to be tested was examined. Chromosomes to be tested Given the currently corrected chromosome, calculate the expected number of gene sequences on the first abnormal chromosome: The chromosome to be tested Replace the number of gene sequences with the expected number of gene sequences. The gene sequence count was updated, and the cell-free DNA samples from the plasma to be tested were recalculated. The number of gene sequences for the remaining chromosomes to be tested was determined, and the cell-free DNA sample from the plasma to be tested was updated. The total number of gene sequences, the updated total number of gene sequences is ;

[0218] 5) Based on the updated total number of gene sequences Recalculate the copy number correction values ​​for the remaining chromosomes to be tested, as well as the chromosomes to be tested. Copy number correction value: Then, obtain the next first abnormal chromosome as the current correction chromosome, and return to step 4). Finally, iterate through steps 4) to 5) for a total of Next, stop the iteration and output the plasma free DNA sample to be tested. Copy number correction values ​​of each chromosome to be tested after multiple rounds of iterative correction ,in, Cell-free DNA sample from plasma to be tested The number of chromosomes with the first abnormality;

[0219] The second step is to perform copy number correction within the current batch. The specific steps are as follows:

[0220] 1) Obtain cell-free DNA samples from the plasma to be tested. To obtain the target sample, obtain the cell-free DNA sample from the plasma to be tested. Chromosomes to be tested To detect chromosomes for the current target, obtain cell-free DNA samples from the plasma to be tested. Chromosomes to be tested The copy number correction value obtained after in-sample copy number correction This is the current number of copies.

[0221] 2) Calculate the chromosomes to be tested in all cell-free plasma DNA samples within the current testing batch. mean copy number ;

[0222] 3) Obtain the chromosome to be tested Mean copy number of corresponding negative samples and the standard deviation of negative sample copy number Perform batch error correction to obtain cell-free DNA samples from the plasma to be tested. Chromosomes to be tested Normalized copy value ,in, = -( - );

[0223] 4) Update the chromosome to be tested based on the normalized copy number, the mean copy number of the negative samples, and the standard deviation of the copy number of the negative samples. The negative Z-value, the updated negative Z-value ;

[0224] 5) Based on the chromosome to be tested The number index threshold is used to determine the chromosome to be detected. Is it a second abnormal chromosome that may have chromosomal aneuploidy?

[0225] 6) Assume the chromosome to be tested For the second abnormal chromosome, the batch-corrected copy number value of the second abnormal chromosome was determined as the mean copy number of the negative samples. Recalculate the chromosomes to be tested in all cell-free plasma samples within the current testing batch. mean copy number Repeat steps 3) to 6) until the chromosome to be tested is reached. The negative Z-value is less than the quantity index threshold;

[0226] 7) When the chromosome to be tested The negative Z-value is less than the quantity exponential threshold, so the normalized copy number is... The chromosome to be tested was identified. Batch correction value for copy number;

[0227] 8) Identify the cell-free DNA sample in the plasma to be tested. If any of the target chromosomes that have not undergone copy number error correction still exist in the target sample, and if so, then any other target chromosome in the target sample that has not undergone copy number error correction is selected as the current target chromosome. The copy number correction value corresponding to the current target chromosome is then obtained as the current copy number. This process is repeated from step 3) to step 8) until the target plasma cell-free DNA sample is obtained. All chromosomes to be detected in the test have undergone chromosome copy number error correction;

[0228] 9) When the plasma cell-free DNA sample to be tested All chromosomes to be tested in the process are corrected for chromosome copy number error, and the resulting plasma cell-free DNA sample is output. Batch correction values ​​for the copy number of each chromosome to be tested.

[0229] In some embodiments, for example, the sequencing results of 18 plasma samples from aneuploid positive pregnant women (including 2 T13 samples (numbered S1-S2), 5 T18 samples (numbered S3-S7), and 11 T21 samples (numbered S8-S18)) were selected, and the performance difference between the conventional method without dynamic copy number correction and the above-mentioned dynamic copy number correction method was compared. Table 8 shows the Z-values ​​of chromosomes 13, 18, and 21 for each plasma sample from aneuploid positive pregnant women, as detailed below:

[0230] Table 8. Chromosomal Z-score results of plasma samples from aneuploid positive pregnant women.

[0231]

[0232] The results in Table 8 show that the dynamic copy number correction method can effectively improve the detection value of positive fetal samples compared with the conventional method without dynamic copy number correction, referring to the Z value of chromosome 13 in T13 sample, the Z value of chromosome 18 in T18 sample, and the Z value of chromosome 21 in T21 sample, without causing abnormal values ​​in other chromosomes.

[0233] In some embodiments, step S112 may include, but is not limited to, steps S801 to S810:

[0234] Step S801: Obtain the negative sample dataset corresponding to the chromosome to be tested;

[0235] Step S802: Based on the fetal concentration distribution of the negative sample dataset, determine the expected copy number of the positive sample dataset; and generate the positive sample dataset based on the expected copy number and random error.

[0236] Step S803: Perform copy number distribution statistics on the negative sample dataset to determine the negative Z-value judgment threshold;

[0237] Step S804: Update the negative Z-value corresponding to the chromosome to be tested based on the batch correction value of the copy number corresponding to the chromosome to be tested, the mean copy number of negative samples, and the standard deviation of the copy number of negative samples.

[0238] Step S805: When the chromosome to be detected is an autosome, obtain the positive sample dataset corresponding to the chromosome to be detected, perform copy number distribution statistics on the positive sample dataset, and determine the positive Z-value judgment threshold, the mean copy number of positive samples, and the standard deviation of the copy number of positive samples.

[0239] Step S806: Based on the batch correction value of the copy number corresponding to the chromosome to be tested, the mean copy number of positive samples, and the standard deviation of the copy number of positive samples, determine the positive Z-value corresponding to the chromosome to be tested. The positive Z-value is calculated using the following formula:

[0240] ;

[0241] in, For the first The first of the plasma free DNA samples to be tested The positive Z-value corresponding to each chromosome to be tested For the first The mean copy number of each chromosome to be tested in the positive sample dataset. For the first The first of the plasma free DNA samples to be tested Batch correction values ​​for the copy number of the chromosome to be tested. For the first Standard deviation of the sample copy number of each chromosome to be tested in the positive sample dataset;

[0242] Step S807: Based on the negative Z-value judgment threshold, the negative Z-value, the positive Z-value judgment threshold, and the positive Z-value, a joint test is performed to determine the chromosome detection result;

[0243] Step S808: When the negative Z-value is greater than the negative Z-value judgment threshold and the positive Z-value is greater than the positive Z-value judgment threshold, the chromosome detection result is determined to be positive for the chromosome to be detected in the cell-free DNA sample of the plasma to be tested.

[0244] Step S809: When the negative Z-value is less than or equal to the negative Z-value judgment threshold, and the positive Z-value is less than or equal to the positive Z-value judgment threshold, the chromosome detection result is determined to be negative for the chromosome to be detected in the cell-free DNA sample of the plasma to be tested.

[0245] Step S810: When the chromosome to be detected is a sex chromosome, obtain the sex chromosome detection decision tree model, and use the sex chromosome detection decision tree model to determine the threshold and negative Z value based on the negative Z value to determine the chromosome detection result.

[0246] In some embodiments, a negative sample dataset corresponding to autosomes is established: the negative sample dataset consists of sequencing data of cell-free DNA from the plasma of more than 10,000 negative pregnant women. Using the negative sample dataset, the copy number distribution of negative samples on the 22 autosomes can be obtained, and the chromosomes to be tested can be statistically analyzed. Mean copy number in negative samples ( ) and standard deviation ( ).

[0247] When the chromosome to be tested is an autosome, a positive sample dataset corresponding to the autosome is established. The data source for the positive sample dataset includes real positive plasma samples that have been collected and diagnosed as fetal aneuploidy. Using the positive sample dataset, the copy number distribution of positive samples on chromosomes can be obtained, and the chromosome to be tested can be statistically analyzed. The mean of the positive sample dataset ( ) and standard deviation ( ).

[0248] Optionally, for aneuploidy types with fewer than 100 positive plasma samples, a theoretical distribution of the aneuploidy reference set derived from fetal DNA concentration distribution was established. Taking the construction of negative and positive sample datasets for T21 as an example, the specific steps are as follows:

[0249] 1) Establishing the T21 negative sample dataset: The negative sample dataset consists of sequencing data from the plasma cell-free DNA of over 10,000 negative pregnant women. Using this dataset, the copy number distribution of negative samples across the 22 autosomes can be obtained, and chromosomal analysis can be performed. Mean copy number in negative samples ( ) and standard deviation ( )

[0250] 2) Establish a T21 positive sample dataset, specifically including:

[0251] a) Collect fetal concentration values ​​from 10,000 negative samples;

[0252] b) Calculate the expected copy number of 10,000 positive samples: Assume the fetal concentration of cell-free DNA in plasma samples from 10,000 negative samples is [sample value missing]. The chromosome to be tested in this sample When the three-body anomaly occurs, the expected copy number is [value missing]. = ;

[0253] c) The expected copy number is added to the random error to generate the copy number distribution of a dataset of 10,000 positive samples. Specifically, the mean of the negative samples is used. ) and standard deviation ( The mean is generated using the R language's rnorm function. The standard deviation is 10,000 random errors and random error and expected copy number The copies are added together to obtain the copy number distribution values ​​of the simulated positive sample dataset. The calculation formula is: .

[0254] The copy number distribution of positive samples on chromosome j to be tested was obtained using the positive sample dataset, and chromosome statistics were performed. The mean of the positive sample dataset ( ) and standard deviation ( ).

[0255] In some embodiments, optionally, the percentile value (1 - sensitivity) of the positive sample dataset is determined based on the sensitivity specified by the kit. Value, take it as Judgment threshold basis (As mentioned above, the negative Z-value threshold) For example, if the kit's intended sensitivity is 99%, the 1st percentile of a positive sample corresponds to... Positive samples conform to Z Based on the specificity specified by the kit, find the percentile value (specificity) corresponding to the negative reference set. As Judgment threshold basis Assuming the kit's intended specificity is no less than 99.95%, the 99.95th percentile of negative samples corresponds to... Positive samples meet The above two conditions and Z All conditions must be met; a threshold for determining a negative Z-value must be set. .

[0256] In some embodiments, based on the sensitivity specified by the kit, the percentile value (1 - sensitivity) corresponding to the positive sample dataset is found. Value, take it as Judgment threshold basis (As mentioned above, the positive Z-value threshold). For example, if the kit's intended sensitivity is 99%, the 1st percentile of a positive sample corresponds to... Positive samples conform to Z Based on the specificity specified by the kit, find the percentile value (specificity) corresponding to the negative reference set. As Judgment threshold basis Assuming the kit's intended specificity is no less than 99.95%, the 99.95th percentile of negative samples corresponds to... Positive samples meet The above Z and Both conditions must be met to set a positive Z-value threshold. .

[0257] In some embodiments, it is assumed that the negative Z-value of the chromosome to be detected is... Positive Z-value The threshold for judging a negative Z-value is The threshold for judging positive Z-values ​​is The joint testing rules are shown in Table 9 below:

[0258] Table 9 Joint Inspection Rules

[0259]

[0260] 1) Wherein, if and If the chromosome to be tested is positive, then... and If a sample does not meet any of the above conditions, the result is unclear and a retest is required. The results are determined based on the T21 negative and positive samples. and The percentile values ​​and the sensitivity specified by the reagent kit were used to determine... and T21 negative and positive samples and The percentile values ​​are shown in Table 10, which is as follows:

[0261] Table 10 T21 Negative and Positive Samples and Percentile index value table

[0262]

[0263] 2) Based on the results in Table 10, select... Judgment threshold Assuming the kit's intended sensitivity is no less than 99%, that corresponds to a positive sample percentile of 0.10%. 4.79, assuming the kit's intended specificity is no less than 99.95%, i.e., the percentile for negative samples = 99.95%. Both of the above conditions must be met; therefore, it is determined that... 4.79;

[0264] 3) Based on the results in Table 10, select... Judgment threshold Assuming the kit's intended sensitivity is no less than 99%, that corresponds to a positive sample percentile of 0.10%. -2.48, assuming the kit's intended specificity is no less than 99.95%, which corresponds to a negative sample percentile of 99.95%. Both of the above conditions must be met; therefore, it is determined that... -2.63;

[0265] 4) Determine the joint test rule for the Z-value, and assume... The threshold for judgment is , The threshold for judgment is The joint test rules for Z-values ​​are shown in Table 9 above.

[0266] 5) Calculate the two-dimensional Z-value of chromosome 21 in the cell-free DNA plasma sample to be tested. and ): Calculate the negative Z-value ( ): Calculate the positive Z-score ( ): ;

[0267] 6) Based on Table 9, calculate the two-dimensional Z-value ( and Combined tests are used to determine whether the sample to be tested has a T21 anomaly: (e.g.) and If it is judged as positive; and If the above conditions are not met, the result is unclear and a retest is required.

[0268] In some embodiments, a test dataset was established, including 1012 positive samples from the gold standard T21 and 98988 negative samples from follow-up, totaling 100,000 samples for testing. To verify the effectiveness of the above-mentioned combined Z-score test method, the conventional Z-score test method, the conventional Z-score test + placental mosaicism method, and the above-mentioned combined Z-score test method were used to test each sample in the test dataset. The specific results are as follows:

[0269] The Z-score joint test method described above was used to test 1012 positive samples in the test dataset. The results are shown in Table 11. Table 11 is detailed below:

[0270] Table 11 Positive Sample Test Results

[0271]

[0272] According to Table 11, 1012 T21 positive samples were obtained in the test dataset, 14 of which were judged as uncertain, with a retest rate of 1.38%; among the 998 samples that were successfully tested, 988 were judged as positive and 10 were judged as negative, and the detection sensitivity of the samples that were successfully controlled was 98.99%.

[0273] The Z-score joint test method described above was used to test 98,988 negative samples in the test dataset. The results are shown in Table 12. Table 12 is detailed below:

[0274]

[0275] According to Table 12, among the 98,988 T21 negative samples in the test dataset, 83 were judged as uncertain, with a retest rate of 0.08‰; among the 98,895 samples that were successfully tested, 96 were judged as positive and 98,809 were judged as negative. The specificity of the samples that were successfully tested was 99.91%, which is close to the sensitivity level intended for the kit.

[0276] In summary, the above-mentioned Z-score joint test method was used to test 100,000 samples in the test dataset. The final retest rate was 0.1% (97 / 100,000), the sensitivity was 98.99% (988 / 998), the specificity was 99.91% (98809 / 98895), and the positive predictive value was 91.14% (988 / 1084).

[0277] In contrast, conventional Z-score tests, such as NIPT, use the following methods: As a single-dimensional detection threshold, NIPT was used to test each sample in the test dataset. The test results were as follows: 1007 out of 1012 T21 positive samples were judged as positive and 5 as negative, with a sensitivity of 99.5% (1007 / 1012); 98988 T21 negative samples were judged as negative and 450 as positive, with a specificity of 99.5% (98538 / 98988); the positive predictive value was 69.11% (1007 / 1457).

[0278] The conventional Z-score test combined with the placental mosaicism method was adopted. To improve the accuracy of detecting false positives in placental mosaicism by combining fetal DNA concentration with fetal DNA concentration, the expected copy number of positive samples can be calculated. (Assuming sample...) The fetal concentration was in the sample. The chromosome of this sample When the three-body anomaly occurs, the expected copy number is [value missing]. = So, the placental mosaic ratio The following calculations can be performed based on the measured copy values ​​and the expected values: = The rule for judging a positive sample is as follows: and Other samples that do not meet the positive sample rule are judged as negative. For comparison, the test dataset is tested using the conventional Z-score test combined with the placental mosaicism method. The specific test results are as follows:

[0279] 1) Test 1: The 152 samples with less than 80% were labeled as placental mosaic samples. The results of T21 positivity assessment based on placental mosaicism information were as follows: 969 out of 1012 T21-positive samples were judged as positive, and 43 were judged as negative, with a sensitivity of 95.75% (969 / 1012); among 98988 T21-negative samples, 98954 were judged as negative, and 34 were judged as positive, with a specificity of 99.96% (98954 / 98988); the positive predictive value was 96.61% (969 / 1058).

[0280] 2) Test 2: Less than 60% of the 136 samples were labeled as placental mosaic samples. The results of T21 positivity assessment based on placental mosaicism information were as follows: Of 1012 T21-positive samples, 981 were judged as positive and 31 as negative, with a sensitivity of 96.93% (981 / 1012); Of 98988 T21-negative samples, 98912 were judged as negative and 76 as positive, with a specificity of 99.92% (98538 / 98988); the positive predictive value was 92.80% (871 / 1321).

[0281] 3) Test 3: Less than 30% of the 89 samples were labeled as placental mosaic samples. The results of T21 positivity determination based on placental mosaicism information were as follows: 988 out of 1012 T21-positive samples were judged as positive, and 25 were judged as negative, with a sensitivity of 97.62% (981 / 1012); among 98988 T21-negative samples, 98899 were judged as negative and 89 were judged as positive, with a specificity of 99.91% (98538 / 98988); the positive predictive value was 91.73% (871 / 1368).

[0282] Finally, the detection performance of the three methods is summarized and compared, as shown in Table 13. Table 13 is as follows:

[0283] Table 13 Comparison of Detection Performance of Three Methods

[0284]

[0285] According to Table 13, the combined Z-score test method exhibits the best overall performance, achieving a specificity of 99.91% and a combined PPV of 91.14% while maintaining a sensitivity of 98.99%. The conventional Z-score test method has a sensitivity of 99.50%, a specificity of 99.50%, and a PPV of 69.11%. The performance of the conventional Z-score test combined with placental chimerism is related to the set chimerism ratio. When the chimerism ratio is set to 30%, the sensitivity is only 97.62%, while the specificity (99.91%) and PPV (91.73%) are close to those of the combined Z-score test method. Therefore, the combined Z-score test method has the best overall performance, simultaneously meeting the requirements of sensitivity and specificity, with a sensitivity close to 99%, a specificity of 99.9%, and a PPV > 90%. The conventional Z-score test method has the highest sensitivity, but its specificity and PPV are the lowest among all methods. The conventional Z-score test combined with placental chimerism has insufficient sensitivity and a high false negative rate.

[0286] In some embodiments, step S809 may include, but is not limited to, steps S901 to S902:

[0287] Step S901: Obtain the first negative Z value corresponding to the X chromosome, the second negative Z value corresponding to the Y chromosome, and the Z value judgment threshold;

[0288] Step S902: Input the first negative Z-value, the second negative Z-value, and the Z-value judgment threshold into the sex chromosome detection decision tree model, and use the sex chromosome detection decision tree model to output the fetal sex and the corresponding sex chromosome detection results.

[0289] In some embodiments, for the detection of fetal sex chromosome aneuploidy, it is not necessary to construct a positive sample dataset; only a negative sample dataset of sex chromosomes needs to be constructed. For the detection of aneuploidy of the other 23 pairs of autosomes in the fetus, both positive and negative sample datasets need to be constructed. Taking the detection of fetal sex chromosome aneuploidy as an example, when applying the above-mentioned non-invasive prenatal fetal chromosome detection method, it should be noted that by constructing a negative reference set for female fetuses and a negative reference set for male fetuses, the Z values ​​of the X and Y chromosomes of the sample to be tested are systematically calculated. and ), and combined with decision tree model and and A combined testing method is used to detect and classify chromosomal aneuploidy abnormalities. The specific steps are as follows:

[0290] I. Construction of Negative Reference Sets for Female and Male Fetuses: Before calculating the Z-score, it is necessary to construct negative reference sets. The negative reference set for female fetuses consists of sequencing data from the plasma cell-free DNA of over 2000 female fetuses identified as female by the gold standard. Using the negative reference set, the copy number distribution of the X and Y chromosomes in negative female fetus samples can be obtained, and the mean copy number of the X chromosome in negative female fetus samples can be calculated. and standard deviation Mean copy number of Y chromosome in negative female fetal samples and standard deviation The male fetus negative reference set consists of sequencing data from the plasma cell-free DNA of over 2000 female fetuses identified by the gold standard. This set is primarily used to construct a reference set for male fetuses in negative male fetus samples. and The linear relationship, therefore, is derived from the male fetal samples. and The values ​​form the information of the reference set;

[0291] II. Calculate the Z-values ​​of the X and Y chromosomes in the sample:

[0292] Calculate the Z-score of the X chromosome ( ):

[0293] ;

[0294] in, The negative Z-value of the X chromosome. This is the batch correction value for the copy number of the X chromosome to be tested;

[0295] Calculate the Z-value of the Y chromosome ( ):

[0296] ;

[0297] in, The negative Z-value of the Y chromosome. This is the batch correction value for the copy number of the Y chromosome to be tested;

[0298] III. Two-dimensional Z-value ( and Conjunctive test: The first negative Z-score of the X chromosome in the fetal sample to be tested is calculated. ) and the second negative Z-score corresponding to the Y chromosome ( Afterwards, these two Z-values ​​are jointly tested to identify various sex chromosome aneuploidy abnormalities, predict the Z-value range of the 99% confidence interval for the Y chromosome of the negative male fetus reference sample, and based on the negative male fetus reference sample... and Establish a linear relationship between the values, combined with the fetal sample to be tested. The value is used to obtain the maximum value of the 99% confidence interval for the fetal sample under negative conditions. and minimum value According to the first reference of the male fetal negative reference set The second reference value and the female fetal negative reference set Value, Establish Value reference threshold range, from The threshold for the Y chromosome Z value used to distinguish between male and female fetuses is determined within the reference threshold range. Optionally, , and The data is input into a trained sex chromosome detection decision tree model. First, the sex chromosome detection decision tree model is used to determine the sex of the fetus, assuming the sample... If the result is positive, it is classified as a male fetus; otherwise, it is classified as a female fetus. Then, for female fetuses, a sex chromosome detection decision tree model is used based on... To determine sex chromosome abnormalities in female fetal samples, if If judged as XXX, < If it is classified as XO, If the range is [-3, 3], it is classified as XX. For male fetal samples, a sex chromosome detection decision tree model is used based on... , , as well as To determine sex chromosome abnormalities in male fetal samples, if Between [-3, 3] and If it is judged as XXY, <-3 and If it is judged as XYY, then... <-3 and If it is judged as XOXY, < and [ Between ], it is judged as XY.

[0299] For example, the process and results of determining sex chromosome copy number variations are shown in Table 14, which is as follows:

[0300] Table 14 Results of Sex Chromosome Copy Number Variation Judgment

[0301]

[0302] This application provides a test kit for non-invasive prenatal screening, which, when combined with a library construction system, performs the above-mentioned step S101 to complete the construction of a plasma cell-free DNA sequencing library. This test kit optimizes steps such as end repair, adapter ligation, PCR library expansion, library quantification, and sequencing to construct a plasma cell-free DNA sequencing library, thereby improving the library construction success rate. Optionally, this test kit is a magnetic bead purification and enrichment kit for fetal DNA. The main components of this test kit are shown in Table 15 below. Table 15 details are as follows:

[0303] Table 15 Main Components of the Detection Kit

[0304]

[0305] Reference Figure 3 , Figure 3 This is an optional structural schematic diagram of a non-invasive prenatal fetal chromosome testing device provided in this application embodiment. The device is used to implement the aforementioned non-invasive prenatal fetal chromosome testing method and may include:

[0306] The data preprocessing module is used to construct a plasma cell-free DNA sequencing library corresponding to maternal peripheral blood based on a magnetic bead purification and enrichment kit for fetal DNA and a library construction system; acquire genomic sequencing data from the plasma cell-free DNA sequencing library; align the genomic sequencing data to a human reference genome, divide the human reference genome into multiple unit windows according to a preset unit window width, and calculate the number of sequencing sequences corresponding to each unit window; based on the number of sequencing sequences corresponding to each unit window, select several target candidate windows from the multiple unit windows, where the number of sequencing sequences corresponding to the target candidate windows exceeds a given average level threshold; and based on each target candidate window, identify several genomic repetitive regions in the genomic sequencing data, where genomic repetitive regions are genomic regions that consecutively appear in the target candidate windows and occur more than a given threshold number of times.

[0307] The chromosome copy number dynamic correction module is used to acquire multiple cell-free plasma DNA samples to be tested in the current testing batch. The cell-free plasma DNA samples are obtained by enriching fetal cell-free DNA in the plasma of the pregnant women being tested using a magnetic bead purification method. For each chromosome to be tested in each cell-free plasma DNA sample, based on genome sequencing data and repetitive regions of each genome, the chromosome is region-selected to determine the number of gene sequences corresponding to the chromosome being tested. For each cell-free plasma DNA sample, copy number statistics are performed based on the number of gene sequences corresponding to each chromosome to determine the copy number of each chromosome being tested. For each cell-free plasma DNA sample... Obtain the negative sample dataset corresponding to each chromosome to be tested. Based on the copy number of each chromosome to be tested and the negative sample dataset, determine the negative Z-value corresponding to each chromosome to be tested. For each cell-free plasma DNA sample to be tested, perform dynamic copy number correction on each chromosome to be tested based on the negative Z-value and a given quantity index threshold, and determine the copy number correction value corresponding to each chromosome to be tested. Based on the copy number correction value corresponding to each chromosome to be tested in each cell-free plasma DNA sample to be tested, perform chromosome copy number error correction for the current testing batch, and determine the batch correction value of the copy number corresponding to each chromosome to be tested in each cell-free plasma DNA sample to be tested.

[0308] The chromosome detection module is used to obtain the corresponding positive sample dataset for each cell-free plasma DNA sample to be tested, determine the negative Z-value and positive Z-value based on the negative sample dataset and the positive sample dataset, and perform chromosome detection based on the copy number batch correction value, negative Z-value and positive Z-value of each chromosome to be tested in each cell-free plasma DNA sample to be tested, and determine the chromosome detection results of each cell-free plasma DNA sample to be tested. The positive sample dataset is simulated and established based on the negative sample dataset.

[0309] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0310] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described non-invasive prenatal fetal chromosome detection method. This electronic device can be any smart terminal, including a tablet computer.

[0311] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0312] Please see Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0313] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0314] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and called and executed by the processor 901 using the non-invasive prenatal fetal chromosome detection method of the embodiments of this application.

[0315] The input / output interface 903 is used to implement information input and output;

[0316] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0317] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0318] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0319] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described non-invasive prenatal fetal chromosome detection method.

[0320] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0321] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0322] This application provides a non-invasive prenatal fetal chromosome detection device that enriches fetal cell-free DNA in the plasma of pregnant women using a magnetic bead purification method to obtain a plasma cell-free DNA sample. This increases the concentration of fetal cell-free DNA in the sample, enabling automatic detection of the plasma cell-free DNA sample and reducing the occurrence of detection failures or false negatives. Based on the negative and positive Z-values ​​corresponding to the chromosomes to be detected, chromosome detection is performed, effectively improving the efficiency and accuracy of non-invasive prenatal fetal chromosome detection.

[0323] This application provides a non-invasive prenatal fetal chromosome detection device, proposing a library construction method that can enrich fetal DNA concentration. By adding a DNA short fragment enrichment step before DNA end repair, the proportion of fetal DNA in the library is effectively increased, thereby reducing the probability of detection failure and false negatives caused by low fetal DNA concentration and significantly improving the reliability of the detection. A plasma cell-free DNA library construction kit that can enrich fetal DNA concentration is also proposed. This kit includes complete library construction steps, including DNA fragment screening, end repair, and ligation PCR amplification. The kit components are optimized at each stage to improve the stability of the library construction results. A sliding window-based repetition region elimination method (SW-RR-Rem) is also proposed. The method of dynamic correction of chromosome copy number (Z-value) effectively identifies abnormal readings caused by repetitive sequences, reduces the impact of aneuploid abnormal chromosomes on the copy number calculation of other chromosomes, and thus improves the accuracy of chromosome copy number calculation. A combined negative and positive Z-value test method is proposed, combining Z-values ​​from both negative and positive reference sets for joint analysis. This combined test considers both the sensitivity and specificity requirements of the kit, while the traditional single Z-value test only considers specificity requirements. This method allows for flexible setting of differentiated negative and positive Z-value threshold combinations for different chromosomes, thus better meeting the clinically expected detection sensitivity and specificity requirements and reducing the false negative rate. The traditional Z-value test plus placental mosaicism identification method has insufficient sensitivity and a high false negative rate. Simultaneously, a negative reference dataset was simulated to effectively address the problem of insufficient positive samples and difficulty in obtaining positive sample datasets for rare chromosomal aneuploidy abnormalities. The copy number distribution of positive samples was derived from the fetal concentration distribution of negative samples, achieving a reasonable expansion of the aneuploidy positive reference set. Furthermore, random error numbers were generated based on the mean and standard deviation of negative samples, making the simulated data closer to the real distribution characteristics and improving the credibility of the positive sample dataset. The established positive sample reference data can be extended to different types of chromosomal copy number variation detection scenarios, such as non-invasive chromosomal microdeletion and microduplication detection, and has strong application value. By constructing a sex chromosome detection decision tree model, combined with the differences between male and female fetal samples, the negative Z-values ​​of the X chromosome and Y chromosome were calculated, and the expected value of the Y chromosome was calculated using regression curves to detect its deviation significance, accurately identifying the type of fetal sex chromosome aneuploidy abnormality.

[0324] In summary, the non-invasive prenatal fetal chromosome testing device provided in this application can effectively improve the accuracy and comprehensiveness of fetal chromosome testing, covering the detection of aneuploidy abnormalities in 23 pairs of chromosomes, improving the sensitivity and specificity of chromosome testing, and providing stronger technical support for prenatal screening.

[0325] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0326] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0327] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A non-invasive prenatal fetal chromosomal detection device, characterized in that, The device comprises: A data preprocessing module is configured to construct a sequencing library of free DNA in plasma of a pregnant woman based on a fetal DNA kit and a library construction system, obtain genomic sequencing data in the sequencing library of free DNA in plasma, align the genomic sequencing data to a human reference genome, divide the human reference genome into a plurality of unit windows according to a preset unit window width, calculate the number of sequencing sequences in each unit window to which the genomic sequencing data is aligned, select a plurality of target candidate windows from the plurality of unit windows according to the number of sequencing sequences in each unit window, and determine a plurality of genomic repeat regions in the genomic sequencing data according to each target candidate window, wherein the genomic repeat region is a genomic region in which the target candidate window appears continuously and the number of appearances is greater than a given threshold. A chromosome copy number dynamic correction module is configured to obtain a plurality of to-be-detected plasma free DNA samples in a current detection batch, wherein the to-be-detected plasma free DNA samples are obtained by enriching fetal free DNA in plasma of a to-be-detected pregnant woman by a magnetic bead purification method, for each to-be-detected chromosome in each to-be-detected plasma free DNA sample, performing region screening on the to-be-detected chromosome according to the genomic sequencing data and each genomic repeat region, determining the number of gene sequences corresponding to the to-be-detected chromosome, for each to-be-detected plasma free DNA sample, performing copy number statistics according to the number of gene sequences corresponding to each to-be-detected chromosome, determining the copy number of each to-be-detected chromosome, obtaining a negative sample data set corresponding to each to-be-detected chromosome for each to-be-detected plasma free DNA sample, determining a negative Z value corresponding to each to-be-detected chromosome according to the copy number corresponding to each to-be-detected chromosome and the negative sample data set, performing copy number dynamic correction on each to-be-detected chromosome according to the negative Z value corresponding to each to-be-detected chromosome and a given quantity index threshold for each to-be-detected plasma free DNA sample, determining a copy number correction value corresponding to each to-be-detected chromosome, and performing chromosome copy number error correction for the current detection batch according to the copy number correction value corresponding to each to-be-detected chromosome in each to-be-detected plasma free DNA sample, and determining a copy number batch correction value corresponding to each to-be-detected chromosome in each to-be-detected plasma free DNA sample. The chromosome detection module is used for obtaining a corresponding positive sample data set for each of the to-be-detected plasma free DNA samples, determining a negative Z value and a positive Z value according to the negative sample data set and the positive sample data set, performing chromosome detection according to the copy number batch correction value of each of the to-be-detected chromosomes, the negative Z value and the positive Z value in each of the to-be-detected plasma free DNA samples, and determining a chromosome detection result of each of the to-be-detected plasma free DNA samples, wherein the positive sample data set is simulated and established according to the negative sample data set. The method comprises the following steps: According to the copy number and the negative sample data set of each of the to-be-detected chromosomes, the negative Z value corresponding to each of the to-be-detected chromosomes is determined, and specifically comprises: According to the negative sample data set of each of the to-be-detected chromosomes, the negative sample copy number mean value and the negative sample copy number standard deviation are determined; According to the copy number, the negative sample copy number mean value and the negative sample copy number standard deviation of the to-be-detected chromosome, the negative Z value corresponding to the to-be-detected chromosome is determined, and the negative Z value is calculated by the following formula: ； in, For the first The first of the plasma free DNA samples to be tested The negative Z-value corresponding to each chromosome to be tested For the first The first of the plasma free DNA samples to be tested The copy number of each chromosome to be tested. For the first The mean copy number of the chromosome to be tested in the negative sample dataset. For the first Standard deviation of the negative sample copy number of the chromosome to be tested in the negative sample dataset; The method comprises the following steps: The negative sample data set corresponding to the to-be-detected chromosome is obtained; According to the fetal concentration distribution of the negative sample data set, the copy number expected value of the positive sample data set is determined, and the positive sample data set is generated according to the copy number expected value and random error; The copy number distribution of the negative sample data set is counted to determine the negative Z value judgment threshold; According to the copy number batch correction value, the negative sample copy number mean value and the negative sample copy number standard deviation of the to-be-detected chromosome, the negative Z value corresponding to the to-be-detected chromosome is updated; When the to-be-detected chromosome is an autosome, the positive sample data set corresponding to the to-be-detected chromosome is obtained, the copy number distribution of the positive sample data set is counted, and the positive Z value judgment threshold, the positive sample copy number mean value and the positive sample copy number standard deviation are determined; According to the copy number batch correction value, the positive sample copy number mean value and the positive sample copy number standard deviation of the to-be-detected chromosome, the positive Z value corresponding to the to-be-detected chromosome is determined, and the positive Z value is calculated by the following formula: ； in, For the first The first of the plasma free DNA samples to be tested The positive Z-value corresponding to each chromosome to be tested For the first The mean copy number of each chromosome to be tested in the positive sample dataset. For the first The first of the plasma free DNA samples to be tested Batch correction values ​​for the copy number of the chromosome to be tested. For the first Standard deviation of the sample copy number of each chromosome to be tested in the positive sample dataset; According to the negative Z value judgment threshold, the negative Z value, the positive Z value judgment threshold and the positive Z value, joint test is performed to determine the chromosome detection result; determining that the chromosome detection result is positive for the chromosome to be detected in the plasma cell-free DNA sample to be detected when the negative Z value is greater than the negative Z value determination threshold and the positive Z value is greater than the positive Z value determination threshold; determining that the chromosome detection result is negative for the chromosome to be detected in the plasma cell-free DNA sample to be detected when the negative Z value is less than or equal to the negative Z value determination threshold and the positive Z value is less than or equal to the positive Z value determination threshold; when the chromosome to be detected is a sex chromosome, obtaining a sex chromosome detection decision tree model, and determining the chromosome detection result according to the negative Z value determination threshold and the negative Z value by using the sex chromosome detection decision tree model; the when the chromosome to be detected is a sex chromosome, obtaining a sex chromosome detection decision tree model, and determining the chromosome detection result according to the negative Z value determination threshold and the negative Z value by using the sex chromosome detection decision tree model, specifically includes: obtaining a first negative Z value and a second negative Z value corresponding to X chromosome and Y chromosome, and a Z value determination threshold; inputting the first negative Z value, the second negative Z value and the Z value determination threshold into the sex chromosome detection decision tree model, and outputting the fetal sex and the corresponding sex chromosome detection result by using the sex chromosome detection decision tree model.

2. The non-invasive prenatal fetal chromosomal detection device of claim 1, wherein, The method specifically includes: determining a plurality of gene sequencing regions in the chromosome to be detected, and a region quantity and a region length corresponding to each of the gene sequencing regions according to the genomic sequencing data, the region length being a sequence length corresponding to the gene sequencing region, and the region quantity being a frequency of occurrence of the gene sequencing region in the chromosome to be detected; dividing a plurality of gene sequencing groups according to the region length corresponding to each of the gene sequencing regions, each of the gene sequencing groups including a plurality of gene sequencing regions with the same region length; selecting, according to each of the genomic repeat regions, a chromosome repeat region corresponding to each of the genomic repeat regions from a plurality of gene sequencing regions, and determining a target gene sequencing group in which the chromosome repeat region is located from a plurality of gene sequencing groups; for each of the chromosome repeat regions, updating the region quantity corresponding to the chromosome repeat region to an average region quantity corresponding to the target gene sequencing group, the average region quantity being an average value of the region quantities of a plurality of gene sequencing regions in the gene sequencing group; determining the gene sequence quantity corresponding to the chromosome to be detected according to the region quantity corresponding to each of the gene sequencing regions except for the plurality of chromosome repeat regions, and the updated region quantity of each of the chromosome repeat regions.

3. The non-invasive prenatal fetal chromosomal detection device of claim 1, wherein, The copy number of each of the to-be-detected chromosomes is determined according to the number of gene sequences corresponding to each of the to-be-detected chromosomes, and specifically includes: The total number of gene sequences corresponding to the to-be-detected plasma free DNA sample is determined according to the number of gene sequences corresponding to each of the to-be-detected chromosomes. The copy number corresponding to each of the to-be-detected chromosomes is determined according to the number of gene sequences corresponding to each of the to-be-detected chromosomes and the total number of gene sequences, and is calculated by the following formula: ； ； in, For the first The first of the plasma free DNA samples to be tested The copy number of each chromosome to be tested. For the first The first of the plasma free DNA samples to be tested The number of gene sequences corresponding to the chromosomes to be tested. For the first The total number of gene sequences corresponding to each cell-free DNA plasma sample to be tested. For the first The number of chromosomes to be tested in a single cell-free plasma DNA sample.

4. The non-invasive prenatal fetal chromosomal detection device of claim 1, wherein, The copy number of each of the to-be-detected chromosomes is determined according to the negative Z value corresponding to each of the to-be-detected chromosomes and a given number index threshold, and specifically includes: A plurality of first abnormal chromosomes are determined from the plurality of to-be-detected chromosomes according to the negative Z value corresponding to each of the to-be-detected chromosomes and the number index threshold, and the negative Z value corresponding to the first abnormal chromosome exceeds the number index threshold. Any of the first abnormal chromosomes is obtained as a current correction chromosome. The total number of gene sequences corresponding to the to-be-detected plasma free DNA sample and the average negative sample copy number corresponding to the current correction chromosome are obtained. According to the total number of gene sequences corresponding to the plasma free DNA sample to be detected and the mean of the copy number of the negative sample corresponding to the current corrected chromosome, the number of gene sequences corresponding to the current corrected chromosome is updated, and the updated number of gene sequences is calculated by the following formula: ; wherein, is the number of genes corresponding to the i-th chromosome in the j-th sample, is the number of genes corresponding to the i-th chromosome in the j-th sample, is the updated number of genes corresponding to the i-th chromosome in the j-th sample to be detected, is the total number of genes corresponding to the j-th sample to be detected, is the total number of genes corresponding to the j-th sample to be detected, is the mean of the copy number of the negative sample corresponding to the i-th chromosome, is the mean of the copy number of the negative sample corresponding to the i-th chromosome. The total number of gene sequences is updated according to the updated number of gene sequences corresponding to the current correction chromosome and the number of gene sequences corresponding to each of the remaining to-be-detected chromosomes. The copy number of the current correction chromosome and the copy number of the remaining to-be-detected chromosomes are determined according to the updated total number of gene sequences, the number of gene sequences of the remaining to-be-detected chromosomes, and the updated number of gene sequences corresponding to the current correction chromosome. When any of the first abnormal chromosomes has not updated the number of gene sequences, any of the first abnormal chromosomes that has not updated the number of gene sequences is obtained as the current correction chromosome, and then the total number of gene sequences corresponding to the to-be-detected plasma free DNA sample and the average negative sample copy number corresponding to the current correction chromosome are obtained until all the first abnormal chromosomes have updated the number of gene sequences.

5. The non-invasive prenatal fetal chromosomal detection device of claim 1, wherein, The copy number of each of the to-be-detected chromosomes is determined according to the copy number correction value corresponding to each of the to-be-detected chromosomes in each of the to-be-detected plasma free DNA samples, and specifically includes: Any of the to-be-detected plasma free DNA samples is obtained as a target detection sample, any of the to-be-detected chromosomes in the target detection sample is obtained as a current target detection chromosome, and the copy number correction value corresponding to the current target detection chromosome is obtained as a current copy number. obtaining the negative sample copy number mean, the negative sample copy number standard deviation and the current batch chromosome copy number mean corresponding to the current target detection chromosome, wherein the current batch chromosome copy number mean is the copy number mean corresponding to the current target detection chromosome in all the to-be-detected plasma cell-free DNA samples; determining the normalized copy number corresponding to the current target detection chromosome according to the current copy number, the negative sample copy number mean and the current batch chromosome copy number mean; updating the negative Z value corresponding to the current target detection chromosome according to the normalized copy number, the negative sample copy number mean and the negative sample copy number standard deviation; determining whether the current target detection chromosome is a second abnormal chromosome according to the updated negative Z value corresponding to the current target detection chromosome and the quantity index threshold value; when the updated negative Z value corresponding to the current target detection chromosome is greater than the quantity index threshold value, determining that the current target detection chromosome is the second abnormal chromosome, determining the negative sample copy number mean as the current copy number, re-determining the current batch chromosome copy number mean according to the current copy number, and then returning to determine the normalized copy number corresponding to the current target detection chromosome according to the current copy number, the negative sample copy number mean and the current batch chromosome copy number mean until the updated negative Z value corresponding to the current target detection chromosome is less than the quantity index threshold value; when the updated negative Z value corresponding to the current target detection chromosome is less than the quantity index threshold value, determining the normalized copy number as the copy number batch correction value corresponding to the current target detection chromosome; when there is any to-be-detected chromosome in the target detection sample which has not been subjected to chromosome copy number error correction, taking the remaining any to-be-detected chromosome in the target detection sample which has not been subjected to chromosome copy number error correction as the current target detection chromosome, obtaining the copy number correction value corresponding to the current target detection chromosome as the current copy number, and then returning to obtain the negative sample copy number mean, the negative sample copy number standard deviation and the current batch chromosome copy number mean corresponding to the current target detection chromosome until all the to-be-detected chromosomes in the target detection sample are subjected to chromosome copy number error correction; the normalized copy number is calculated by the following formula: = ； wherein the current detection chromosome is the th chromosome to be detected in the th chromosome to be detected, is a normalized copy number corresponding to the current target detection chromosome, is a copy number correction value corresponding to the current target detection chromosome, is a current batch chromosome copy number mean corresponding to the current target detection chromosome in the current detection batch, is a negative sample copy number mean corresponding to the current target detection chromosome.

6. The non-invasive prenatal fetal chromosomal detection device of claim 1, wherein, the fetal DNA enrichment kit based on magnetic beads and the library construction system, which constructs a plasma cell-free DNA sequencing library corresponding to the peripheral blood of a pregnant woman, specifically comprising: extracting plasma cell-free DNA from the plasma of a pregnant woman; performing fragment screening on the plasma cell-free DNA to obtain fragment-screened plasma cell-free DNA; performing end repair on the fragment-screened plasma cell-free DNA to obtain corresponding end repair products; performing adapter ligation on the end repair products to obtain corresponding adapter ligation products; The linker ligation product is subjected to magnetic bead purification, and the linker ligation product after magnetic bead purification is subjected to library PCR amplification to obtain a corresponding PCR product; The PCR product is subjected to magnetic bead purification, and the PCR product after magnetic bead purification is subjected to library quantification and sequencing to generate the plasma cell-free DNA sequencing library.

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