Method, computing device, medium and program product for detecting chromosomal state of fetus

Abstract

The invention relates to a method, a computing device, a medium and a program product for detecting a chromosome state of a fetus. The method comprises the following steps: comparing obtained sequencing data of a to-be-detected pregnant maternal peripheral blood sample to a human reference genome so as to obtain initial data of which two ends are compared to the human reference genome; filtering the DNA fragment length in the initial data so as to obtain filtered data of which the DNA fragment length is within a preset length range; calculating a Z value about the target region based on the initial data and the filtered data, respectively, so as to obtain a Z value about the target region in the initial data and a Z value about the target region in the filtered data; and determining the chromosome state of the fetus based on the obtained Z value related to the target region in the initial data and the Z value related to the target region in the filtered data. According to the method, the accuracy of the detection result of the fetal chromosome state can be remarkably improved.

Description

Technical Field

[0001] The present invention relates generally to biological information processing, and in particular, to methods, computing devices, computer storage media, and computer program products for detecting the chromosome status of a fetus. Background Art

[0002] At present, non-invasive prenatal genetic testing (NIPT) for chromosomal aneuploidy is a non-invasive prenatal screening technology that uses high-throughput sequencing (Next-generation Sequencing, NGS) to detect cell-free fetal DNA (cffDNA) fragments in maternal peripheral blood during pregnancy. It is mainly used to detect fetal chromosomal aneuploidy and subchromosomal abnormalities.

[0003] In the traditional method for detecting the chromosomal status of the fetus, the maternal peripheral blood is sequenced during pregnancy, the DNA fragments mapped to different genomes are detected and counted, and the fetal chromosomal status is calculated based on the count. Since there are both fetal DNA fragments and maternal DNA fragments in maternal peripheral blood, and the concentration (or proportion) of fetal DNA fragments is relatively low, the concentration (or proportion) of fetal DNA in maternal peripheral blood has a greater impact on the accuracy of the test results. In addition, if the maternal chromosomes are abnormal, the test results for the fetal chromosomes will also be inaccurate. Furthermore, sex chromosomes are more complex, so it is easy to cause a high proportion of false positives for fetal chromosomal abnormalities.

[0004] In summary, the traditional scheme for detecting the chromosome status of the fetus has the following shortcomings: the detection results of the chromosome status of the fetus are inaccurate due to the low concentration of fetal DNA fragments and the influence of maternal chromosome abnormalities. Summary of the invention

[0005] The present invention provides a method, a computing device, a computer storage medium and a program product for detecting the chromosome status of a fetus, which can significantly improve the accuracy of the detection results of the chromosome status of the fetus.

[0006] According to the first aspect of the present invention, a method for detecting the chromosome status of a fetus is provided. The method comprises: comparing the obtained sequencing data for the maternal peripheral blood sample to be tested to the human reference genome, so as to obtain the initial data of the double-end comparison to the human reference genome; filtering the DNA fragments in the initial data, so as to obtain the filtered data whose DNA fragment length is within the predetermined length range; calculating the Z value of the target region based on the initial data and the filtered data, respectively, so as to obtain the Z value of the target region in the initial data and the Z value of the target region in the filtered data; and determining the chromosome status of the fetus of the maternal peripheral blood sample to be tested based on the obtained Z value of the target region in the initial data and the Z value of the target region in the filtered data.

[0007] In some embodiments, the method for detecting the chromosomal status of a fetus also includes: calculating abnormal content ratio characterization data for the initial data and the filtered data, so as to obtain abnormal content ratio characterization data about the DNA fragments in the initial data and abnormal content ratio characterization data about the DNA fragments in the filtered data; and comparing the abnormal content ratio characterization data about the DNA fragments in the initial data and the abnormal content ratio characterization data about the DNA fragments in the filtered data, so as to determine the source of the chromosomal abnormality based on the comparison result.

[0008] In some embodiments, filtering the length of DNA fragments in the initial data to obtain filtered data with DNA fragment lengths within a predetermined length range includes: obtaining the starting position and the ending position of the DNA fragments in the initial data that are double-ended to the human reference genome; calculating the length of each DNA fragment in the initial data based on the obtained starting position and the ending position; determining whether the calculated length of each DNA fragment in the initial data is greater than a first predetermined size threshold and less than or equal to a second predetermined size threshold; in response to determining that the calculated length of the current DNA fragment in the initial data is greater than the first predetermined size threshold and less than or equal to the second predetermined size threshold, determining that the current DNA fragment in the initial data is filtered data within the predetermined size range; and in response to determining that the calculated length of the current DNA fragment in the initial data is less than or equal to the first predetermined size threshold, or greater than the second predetermined size threshold, filtering out the current DNA fragment in the initial data.

[0009] In some embodiments, calculating the Z value for the target region includes: dividing the number of inserts in the target region by the total number of inserts in the autosomes for the initial data or the filtered data, respectively, so as to obtain the genomic characterization data of the genomic target region, respectively; calculating the mean and standard deviation of the reference sample set genomic characterization data of the genomic target region of the reference sample set; and based on the genomic characterization data of the genomic target region and the mean and standard deviation of the genomic characterization data of the reference sample set, respectively calculating the Z value of the target region in the initial data and the Z value of the target region in the filtered data.

[0010] In some embodiments, determining the fetal chromosome status of the maternal peripheral blood sample to be tested includes: determining whether the Z value of the target region in the filtered data falls within a predetermined threshold range; and in response to determining that the Z value of the target region in the filtered data falls within the predetermined threshold range, determining that the fetal chromosome status of the maternal peripheral blood sample to be tested is abnormal.

[0011] In some embodiments, the predetermined threshold range is greater than or equal to 3, or less than or equal to -3.

[0012] In some embodiments, determining the source of the chromosomal abnormality based on the comparison result includes: comparing the abnormal content ratio characterization data regarding the DNA fragments in the filtered data with the abnormal content ratio characterization data regarding the DNA fragments in the initial data; in response to determining that the abnormal content ratio characterization data regarding the DNA fragments in the filtered data is greater than the abnormal content ratio characterization data regarding the DNA fragments in the initial data, determining that the chromosomal abnormality originates from the fetus; and in response to determining that the abnormal content ratio characterization data regarding the DNA fragments in the filtered data is equal to the abnormal content ratio characterization data regarding the DNA fragments in the initial data, determining that the chromosomal abnormality originates from the mother and the fetus.

[0013] In some embodiments, determining the source of the chromosomal abnormality based on the comparison result includes: in response to confirming that the Y chromosome ratio of the DNA fragments in the filtered data is greater than a predetermined Y chromosome fetal DNA fragment concentration threshold, determining that the fetal DNA of the maternal peripheral blood sample to be tested is derived from a male fetus; constructing a relationship graph between the abnormal X chromosome content ratio characterization data and the Y chromosome ratio of the maternal peripheral blood sample to be tested; in response to determining that the relationship graph is within a range between a first threshold line and a second threshold line, determining that the maternal peripheral blood sample to be tested is a sample with normal sex chromosomes; and in response to determining that the relationship graph is outside the range between the first threshold line and the second threshold line, determining that the maternal peripheral blood sample to be tested is a sample with abnormal sex chromosomes.

[0014] In some embodiments, determining that the maternal peripheral blood sample to be tested is a sex chromosome abnormal sample includes: in response to determining that the relationship graph is outside the range of the first threshold line and the second threshold line and is higher than the first threshold point, determining that the maternal peripheral blood sample to be tested is a 47XXY or 47XYY abnormal karyotype; and in response to determining that the relationship graph is outside the range of the first threshold line and the second threshold line and is lower than the second threshold point, determining that the maternal peripheral blood sample to be tested is a 46XY / 45X or 46XY / 45Y abnormal karyotype. The second threshold point is lower than the first threshold point.

[0015] According to a second aspect of the present invention, a computing device is also provided, comprising: a memory configured to store one or more computer programs; and a processor coupled to the memory and configured to execute the one or more programs so that the device performs the method of the first aspect of the present invention.

[0016] According to a third aspect of the present invention, a non-transitory computer-readable storage medium is further provided, wherein machine-executable instructions are stored on the non-transitory computer-readable storage medium, and when the machine-executable instructions are executed, the machine executes the method of the first aspect of the present invention.

[0017] According to a fourth aspect of the present invention, a computer program product is further provided, wherein the computer program product comprises a computer program, and when the computer program is executed by a machine, the method according to the first aspect of the present invention is performed.

[0018] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the invention, nor is it intended to limit the scope of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a system for detecting the chromosome status of a fetus according to an embodiment of the present invention is shown.

[0020] Figure 2 A flow chart of a method for detecting the chromosome status of a fetus according to an embodiment of the present invention is shown.

[0021] Figure 3 The frequency curve of fetal DNA fragments at different DNA fragment lengths is shown.

[0022] Figure 4 A flow chart of a method for determining a source of a chromosome abnormality based on a comparison result according to some embodiments of the present invention is shown.

[0023] Figure 5A flowchart of a method for determining a source of a chromosome abnormality based on a comparison result according to other embodiments of the present invention is shown.

[0024] Figure 6 A flow chart of a method for determining a karyotype of a sex chromosome abnormality according to an embodiment of the present invention is shown.

[0025] Figure 7 A flow chart of a method for determining a karyotype of a sex chromosome abnormality according to an embodiment of the present invention is shown.

[0026] Figure 8 The relationship between the abnormal content ratio characteristic data of X chromosome and the Y chromosome ratio is schematically shown.

[0027] Fig. 9 The relationship between the fetal ratio of chromosome Y of the DNA fragments in the initial data and the fetal ratio of chromosome Y of the DNA fragments in the initial data is shown.

[0028] Fig.10 The block diagram of an electronic device suitable for implementing the embodiments of the present invention is schematically shown.

[0029] In the various drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0030] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0031] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects.

[0032] As described above, the conventional scheme for detecting the chromosomal status of the fetus has the disadvantage that the detection result of the chromosomal status of the fetus is inaccurate due to the low concentration of fetal DNA fragments and the influence of maternal chromosomal abnormalities.

[0033] Research has found that the size of fetal DNA fragments has certain characteristics, such as a high proportion of small fragments. In maternal DNA fragments, small fragments account for a low proportion. Therefore, the abnormalities of the fetal chromosome status can be analyzed based on small fragments in the DNA fragments. Figure 1 A schematic diagram of a system 100 for detecting a fetal chromosome state according to an embodiment of the present invention is shown.

[0034] Regarding the computing device 110, it is used, for example, to compare the obtained sequencing data for the maternal peripheral blood sample during the pregnancy to be tested to the human reference genome, so as to obtain the initial data of the double-end comparison to the human reference genome; filter the DNA fragment length in the initial data, so as to obtain the filtered data with the DNA fragment length within a predetermined length range. The computing device 110 is also used, for example, to calculate the proportion of the inserted fragments in the target region based on the initial data and the filtered data, respectively, so as to obtain the Z value of the target region in the initial data and the Z value of the target region in the filtered data; and determine the fetal chromosome status of the maternal peripheral blood sample during the pregnancy to be tested based on the obtained Z value of the target region in the initial data and the Z value of the target region in the filtered data.

[0035] In some embodiments, the computing device 110 may have one or more processing units, including dedicated processing units such as GPU, FPGA and ASIC, and general processing units such as CPU. In addition, one or more virtual machines may also be running on each computing device. The computing device 110 includes, for example: an initial data acquisition unit 112, a filtered data acquisition unit 114, a Z value acquisition unit 116 for the target area in the initial data and the filtered data, and a fetal chromosome state determination unit 118. The initial data acquisition unit 112, the filtered data acquisition unit 114, the Z value acquisition unit 116 for the target area in the initial data and the filtered data, and the fetal chromosome state determination unit 118 may be configured on one or more computing devices 110.

[0036] The initial data acquisition unit 112 is used to compare the acquired sequencing data of the maternal peripheral blood sample during the pregnancy to be tested to the human reference genome, so as to obtain initial data of the double-end comparison to the human reference genome.

[0037] The filtered data acquisition unit 114 is used to filter the DNA fragment length in the initial data so as to acquire filtered data with the DNA fragment length within a predetermined length range.

[0038] The Z value acquiring unit 116 for the target area in the initial data and the filtered data is used to calculate the Z value for the target area based on the initial data and the filtered data, respectively, so as to obtain the Z value for the target area in the initial data and the Z value for the target area in the filtered data.

[0039] The fetal chromosome status determination unit 118 is used to determine the fetal chromosome status of the maternal peripheral blood sample to be tested based on the obtained Z value of the target region in the initial data and the Z value of the target region in the filtered data.

[0040] The following will be combined Figure 2 , Figure 3 and Fig. 9 A method for detecting the chromosome status of a fetus according to an embodiment of the present invention is described. Figure 2 A flow chart of a method 200 for detecting the chromosome status of a fetus according to an embodiment of the present invention is shown. Figure 3 The frequency curve of fetal DNA fragments at different DNA fragment lengths is shown. Fig. 9 The relationship between the fetal proportion of chromosome Y of the DNA fragments in the initial data and the fetal proportion of chromosome Y of the DNA fragments in the initial data is shown. It should be understood that the method 200 can be used, for example, Fig.10 The electronic device 1000 described above is executed. Figure 1 The method 200 is executed at the described computing device 110. It should be understood that the method 200 may also include additional actions not shown and / or may omit the actions shown, and the scope of the present invention is not limited in this respect.

[0041] At step 202 , the computing device 110 compares the acquired sequencing data for the maternal peripheral blood sample during the pregnancy to be tested to the human reference genome, so as to obtain initial data of the paired-end comparison to the human reference genome.

[0042] For example, the computing device 110 compares the sequencing data of the maternal peripheral blood sample during the pregnancy to be tested to the human reference genome via the BWA software, so as to obtain initial data of the paired-end comparison to the human reference genome.

[0043] The sequencing data of the maternal peripheral blood sample to be tested during pregnancy (or simply "the sample to be tested") is, for example but not limited to, low-depth whole genome sequencing data, for example, sequencing depth <= 3X.

[0044] For example, computing device 110 uses BWA or similar public software to align sequencing fastq data to a human reference genome sequence (e.g., hg19 or hg38) to generate alignment result data; and based on the alignment result data, retains unique aligned inserts; calculates the position of each insert in the reference genome (position information includes, for example, chromosome number and genome coordinates) and the number of times each position has been sequenced.

[0045] At step 204 , the computing device 110 filters the DNA fragment lengths in the initial data to obtain filtered data with DNA fragment lengths within a predetermined length range.

[0046] Regarding the method for obtaining filtered data, it includes, for example: the computing device 110 obtains the starting position and the ending position of the DNA fragment in the initial data that is double-ended to the human reference genome; based on the obtained starting position and the ending position, the length of each DNA fragment in the initial data is calculated; it is determined whether the calculated length of each DNA fragment in the initial data is greater than a first predetermined size threshold and less than or equal to a second predetermined size threshold; in response to determining that the calculated length of the current DNA fragment in the initial data is greater than the first predetermined size threshold and less than or equal to the second predetermined size threshold, it is determined that the current DNA fragment in the initial data is filtered data that belongs to a predetermined size range; and in response to determining that the calculated length of the current DNA fragment in the initial data is less than or equal to the first predetermined size threshold, or greater than the second predetermined size threshold, the current DNA fragment in the initial data is filtered out. The following will be combined with Figure 4 The method for obtaining the filtering data of DNA fragment lengths within a predetermined length range is specifically described, and will not be repeated here.

[0047] Regarding the predetermined size range, it is, for example, but not limited to, greater than or equal to a first predetermined size threshold and less than or equal to a second predetermined size threshold. Regarding the first predetermined size threshold, it is, for example, 170 bp. In some embodiments, the first predetermined size threshold is, for example, 160 bp. Regarding the second predetermined size threshold, it is, for example, 30 bp.

[0048] like Figure 3 As shown, label 310 indicates a first predetermined size threshold. Label 312 indicates a second predetermined size threshold. The first predetermined size threshold is, for example, 30 bp. The second predetermined size threshold is, for example, 166 bp. Label 320 indicates a distribution curve of DNA fragment sizes of high-concentration fetal DNA in a maternal peripheral blood sample during pregnancy. Label 322 indicates a distribution curve of DNA fragment sizes of low-concentration fetal DNA in a maternal peripheral blood sample during pregnancy. Figure 3As shown, in small-sized DNA fragments, for example, DNA fragments with sizes between the first predetermined size threshold and the second predetermined size threshold, there are more high-concentration fetal DNA. In large-sized DNA fragments, for example, DNA fragments with sizes greater than the second predetermined size threshold, there are more low-concentration fetal DNA. Therefore, it is necessary to screen out small-sized (or "small fragments") DNA fragments with sizes between the first predetermined size threshold and the second predetermined size threshold, so as to perform analysis based on the screened small-sized DNA fragments.

[0049] like Fig. 9 As shown, Fig. 9 The horizontal axis is the fetal proportion of chromosome Y of the DNA fragments in the initial data, and the vertical axis is the fetal proportion of chromosome Y of the DNA fragments in the initial data. Line 910 indicates the regression function (y = 1.56 x +5.41). The dotted line 912 indicates the function y=x.

[0050] At step 206 , the computing device 110 calculates a Z value regarding the target area based on the initial data and the filtered data, respectively, so as to obtain a Z value regarding the target area in the initial data and a Z value regarding the target area in the filtered data.

[0051] Regarding the method for calculating the Z value of the target region, for example, it includes: the computing device 110 divides the number of inserted fragments in the target region by the total number of inserted fragments of the autosome for the initial data or the filtered data, respectively, so as to obtain the genomic characterization data of the genomic target region; calculates the mean and standard deviation of the reference sample set genomic characterization data of the target region of the reference sample set; and based on the genomic characterization data of the genomic target region and the mean and standard deviation of the reference sample set genomic characterization data, respectively calculates the Z value of the target region in the initial data and the Z value of the target region in the filtered data. Specifically, the computing device 110 divides the number of inserted fragments of the target region of the DNA fragment in the initial data by the total number of inserted fragments of the autosome based on the DNA fragment data in the initial data, so as to obtain the genomic characterization data of the genomic target region; calculates the mean and standard deviation of the reference sample set genomic characterization data of the target region of the reference sample set; and calculates the Z value of the target region in the initial data based on the genomic characterization data of the genomic target region and the mean and standard deviation of the reference sample set genomic characterization data. In addition, the computing device 110 also divides the number of inserted fragments in the target region of the DNA fragments in the filtered data by the total number of inserted fragments in the autosomes based on the DNA fragment data in the filtered data, so as to obtain the genomic characterization data of the genomic target region; calculates the mean and standard deviation of the reference sample set genomic characterization data of the target region of the reference sample set; and calculates the Z value of the target region in the filtered data based on the genomic characterization data of the genomic target region and the mean and standard deviation of the genomic characterization data of the reference sample set.

[0052] The following schematically illustrates an algorithm for calculating the Z value of the target area in conjunction with formula (1).

[0053]

[0054] In the above formula (1), GR represents the genomic characterization data of the genomic target region in the maternal peripheral blood sample to be tested during pregnancy. The genomic characterization data is obtained based on the number of inserted fragments in the target region divided by the total number of inserted fragments in the autosome. represents the mean of the genomic representation data for the genomic target region in the reference sample, Represents the standard deviation of the genomic representation data for the genomic target region in the reference sample. Z score Represents the Z value of the target area.

[0055] In some embodiments, the present invention can also be combined with formula (2) to calculate the Z value of the inserted fragment with respect to the target region.

[0056]

[0057] In the above formula (2), ΔF represents the difference in the proportion of small-sized DNA fragments between the target region of the genome and the reference chromosome (reference chromosome refers to all autosomes except chromosomes 21, 18 and 13). Represents the mean difference in the proportion of small-sized DNA fragments between the target region of the genome and the reference chromosome. Represents the standard deviation of the difference in the proportion of short DNA fragments between the target region of the genome and the reference chromosome. Size Z score represents the Z value calculated for the target region based on some embodiments. The following schematically illustrates an algorithm for calculating the difference in the proportion of small-sized DNA fragments (ie, ΔF) between the target region of the genome and the reference chromosome in combination with formula (3).

[0058]

[0059] In the above formula (3), P(≤150) represents the proportion of sequencing fragments originating from the target region and having a size less than or equal to 150 bp. Represents the proportion of sequenced fragments that are derived from the reference chromosome and are less than or equal to 150 bp in size. ΔF represents the difference in the proportion of small-sized DNA fragments between the genomic target region and the reference chromosome.

[0060] At step 208 , the computing device 110 determines the fetal chromosome status of the maternal peripheral blood sample of the pregnancy to be tested based on the obtained Z value of the target region in the initial data and the Z value of the target region in the filtered data.

[0061] Regarding the proportion of fetal DNA fragments, it is the proportion of free fetal DNA fragments (Cell-free fetal DNA, referred to as "cffDNA") in maternal plasma. It should be understood that in non-invasive fetal testing, the proportion of fetal cffDNA in maternal plasma is linearly correlated with the abnormal proportion of variation. Fetal free DNA concentration (fetal fraction, FF) is the percentage of cffDNA in the total amount of maternal free DNA (Cell-free DNA, referred to as "cfDNA"). It should be understood that FF is the main bioinformatics analysis quality control factor for non-invasive prenatal screening sequencing results. Low FF may lead to false negative results. Therefore, it is very important to accurately estimate the proportion of fetal cffDNA in maternal plasma. There are many methods to calculate the proportion of fetal cffDNA in maternal plasma.

[0062] The present invention can calculate the proportion of fetal DNA fragments (ie, the proportion of fetal cffDNA in maternal plasma) based on the Y chromosome, DNA fragment size and SeqFF model, thereby more accurately calculating the proportion of fetal cffDNA in maternal plasma.

[0063] For example, for a female fetus, the present invention can use a linear regression model to combine the calculation results of the DNA fragment size-based method and the calculation results based on the SeqFF model to calculate the bound fetal free DNA concentration. For example, the following formula (4) illustrates an algorithm for calculating the bound fetal free DNA concentration.

[0064]

[0065] In the above formula (4), BFF Represents the concentration of bound free fetal DNA. SeqFF Representative based on SeqFF Free fetal DNA concentration calculated by the model. Size represents the concentration of free fetal DNA calculated from the data after DNA fragment size filtering. SeqFF The model is used to directly calculate the proportion of fetal DNA from routine deep sequencing data for non-invasive prenatal screening. SeqFF This high-dimensional model requires large-scale sample data during construction, so when FF is lower than 5%, its performance is significantly reduced. Therefore, the present invention can significantly improve FF through DNA fragment size filtering.

[0066] For male fetuses, whose sex chromosomes are not consistent with those of their mothers, the study showed that the correlation coefficient between the bound free fetal DNA concentration BFF and the Y chromosome-based fetal DNA ratio was 0.94.

[0067] Regarding the method for determining the fetal chromosome status of the maternal peripheral blood sample to be tested during pregnancy, it may include multiple methods. In some embodiments, the method for determining the fetal chromosome status includes: the computing device 110 determines whether the Z value of the target area in the filtered data belongs to a predetermined threshold range; in response to determining that the Z value of the target area in the filtered data belongs to the predetermined threshold range, determining that the fetal chromosome status of the maternal peripheral blood sample to be tested during pregnancy is abnormal. Regarding the predetermined threshold range, it is, for example, greater than or equal to 3, or less than or equal to -3.

[0068] In some embodiments, the method for determining the chromosome status of a fetus further includes: the computing device 110 compares the abnormal content ratio characterization data of the DNA fragments in the initial data and the abnormal content ratio characterization data of the DNA fragments in the filtered data to determine the source of the chromosome abnormality based on the comparison result.

[0069] For example, the present invention obtains 78 maternal peripheral blood samples to be tested during pregnancy, each of which contains a male fetus. For the sequencing data of each sample, the initialization data of the DNA fragments in the initial data of the human reference genome are obtained, that is, the DNA fragment data in the initial data. The DNA fragment data in the initial data of the 78 maternal peripheral blood samples to be tested during pregnancy constitute the initial data (or "Tag" for short). The DNA fragment length in the initial data is filtered, for example, 30-170bp fragments are selected to form a new sample data set, that is, filtered data (or "Filter Tag" for short). Then, for each sample data, the fetal DNA concentration (or "Filter Tag FF" for short) of the DNA fragments in the filtered data and the fetal DNA concentration (or "Tag FF" for short) of the DNA fragments in the initial data are calculated according to the Y chromosome. Experimental data show that Filter Tag FF is on average twice that of Tag FF. For the case where the fetal DNA concentration in the maternal peripheral blood sample to be tested is low, Filter Tag FF is even increased to 3 times that of Tag FF.

[0070] In the above scheme, by obtaining the initial data of the double-end comparison to the human reference genome; filtering the DNA fragment length in the initial data to obtain the filtered data with the DNA fragment length within the predetermined length range; the present invention can obtain the data of small-sized DNA fragments with the DNA fragment length within the predetermined length range, thereby effectively increasing the concentration of fetal free DNA in the maternal peripheral blood sample to be tested during pregnancy. In addition, by calculating the Z value of the target area based on the initial data and the filtered data, respectively, so as to obtain the Z value of the target area in the initial data and the Z value of the target area in the filtered data; and based on the obtained Z value of the target area in the initial data and the Z value of the target area in the filtered data, the fetal chromosome state of the maternal peripheral blood sample to be tested during pregnancy is determined, and the present invention can significantly reduce the inaccurate detection results for the fetal chromosome state caused by the low concentration of fetal DNA fragments and the influence of maternal chromosome abnormalities. Therefore, the present invention can significantly improve the accuracy of the detection results for the fetal chromosome state.

[0071] In some embodiments, the method 200 further includes: the computing device 110 calculates abnormal content ratio characterization data for the initial data and the filtered data.

[0072] The following will be combined Figure 4 A method 400 for acquiring DNA fragment data in filtered data having DNA fragment lengths within a predetermined length range according to an embodiment of the present invention is described. Figure 4FIG. 4 is a flowchart of a method 400 for obtaining DNA fragment data in filtered data whose DNA fragment lengths are within a predetermined length range according to an embodiment of the present invention. It should be understood that the method 400 may be used, for example, in Fig.10 The electronic device 1000 described above may also be used. Figure 1 The method 400 is executed at the described computing device 110. It should be understood that the method 400 may also include additional actions not shown and / or may omit the actions shown, and the scope of the present invention is not limited in this respect.

[0073] At step 402 , computing device 110 obtains the starting position and the ending position of the DNA fragment in the initial data of the paired-end comparison to the human reference genome.

[0074] For example, the computing device 110 obtains the starting position and the ending position of the DNA fragment in each of the initial data and compares them to the DNA fragment of the human reference genome.

[0075] At step 404 , the computing device 110 calculates the length of each DNA fragment in the initial data based on the acquired starting position and ending position.

[0076] The computing device 110 calculates the length of each DNA fragment in the initial data based on the difference between the starting position and the ending position of each DNA fragment in the initial data.

[0077] At step 406 , the computing device 110 determines whether the calculated length of each DNA fragment in the initial data is greater than a first predetermined size threshold and less than or equal to a second predetermined size threshold.

[0078] Regarding the first predetermined size threshold, in some embodiments, it is, for example but not limited to, 40 bp.

[0079] Regarding the second predetermined size threshold, in some embodiments, it is, for example but not limited to, 156 bp.

[0080] At step 408, if the computing device 110 determines that the calculated length of the current DNA fragment in the initial data is greater than the first predetermined size threshold and less than or equal to the second predetermined size threshold, the current DNA fragment in the initial data is determined to be filtered data within the predetermined size range.

[0081] At step 410, if the computing device 110 determines that the calculated length of the current DNA fragment in the initial data is less than or equal to the first predetermined size threshold, or greater than the second predetermined size threshold, the current DNA fragment in the initial data is filtered out.

[0082] For example, the computing device 110 extracts DNA fragments in all the initial data whose lengths are greater than or equal to a first predetermined size threshold (e.g., 30 bp) and less than or equal to a second predetermined size threshold (e.g., 170 bp) from the initial data, so as to generate the screened initial data, i.e., the filtered data. The computing device 110 filters out DNA fragments in the initial data that are less than the first predetermined size threshold (e.g., 30 bp) or greater than the second predetermined size threshold (e.g., 170 bp).

[0083] Experimental data show that the concentration of small-sized DNA fragments between the first predetermined size threshold and the second predetermined size threshold is more related to the concentration of fetal free DNA. Therefore, method 400 is conducive to reducing the impact of the detection results of the fetal chromosome status caused by the low concentration of fetal free DNA and the influence of maternal chromosome abnormalities by selecting small-sized DNA fragments.

[0084] The following will be combined Figure 5 A method 500 for determining a source of a chromosome abnormality based on a comparison result according to an embodiment of the present invention is described. Figure 5 FIG. 5 is a flow chart of a method 500 for determining the source of a chromosome abnormality based on a comparison result according to some embodiments of the present invention. It should be understood that the method 500 may be implemented, for example, in Fig.10 The electronic device 1000 described above may also be used. Figure 1 The method 500 is executed at the described computing device 110. It should be understood that the method 500 may also include additional actions not shown and / or may omit the actions shown, and the scope of the present invention is not limited in this respect.

[0085] At step 502 , the computing device 110 calculates abnormal content ratio characterization data for the initial data and the filtered data, so as to obtain abnormal content ratio characterization data about the DNA fragments in the initial data and abnormal content ratio characterization data about the DNA fragments in the filtered data.

[0086] Regarding mosaicism, it is a term used in genetics to refer to individuals with mosaic or mixed manifestations of different genetic traits, and also refers to one type of chromosomal abnormality.

[0087] Regarding the data representing the abnormal content ratio (Aberration-containing fraction, AcF), taking Down syndrome (DS, Down syndrome is also known as 21-trisomy syndrome) positive samples as an example, if it is a non-chimera case, the AcF value is approximately equal to the fetal DNA concentration value.

[0088] Studies have shown that if only the fetus carries the abnormality, only DNA molecules of fetal origin in the maternal peripheral blood during pregnancy will contain abnormalities, and the abnormal content ratio characterization data (i.e., AcF) will be approximately equal to the ratio of fetal DNA fragments in the maternal peripheral blood during pregnancy. Similarly, if only the mother carries the abnormality, only those DNA molecules derived from the mother in the maternal peripheral blood during pregnancy will contain abnormalities, and AcF will be equal to the ratio of maternal DNA fragments in the maternal peripheral blood during pregnancy. On the other hand, if both the mother and the fetus carry abnormal content ratios, all DNA molecules in the maternal peripheral blood during pregnancy will be derived from cells containing abnormalities, and AcF will be 100%. In some embodiments, the abnormal content ratio characterization data can be calculated in combination with formula (5).

[0089]

[0090] In the above formula (5), AcF represents abnormal content ratio characterization data. GR represents genomic characterization data of the genomic target region in the maternal peripheral blood sample to be tested during pregnancy. The genomic characterization data is obtained based on the number of inserted fragments in the target region divided by the total number of inserted fragments in the autosome. Represents the mean of the genomic representation data for the genomic target region in the reference sample.

[0091] At step 504 , the computing device 110 compares the abnormal content ratio characterization data about the DNA fragments in the initial data with the abnormal content ratio characterization data about the DNA fragments in the filtered data to determine the source of the chromosome abnormality based on the comparison result.

[0092] It should be understood that by filtering the DNA fragment length to obtain DNA fragment data in the filtered data with a DNA fragment length within a predetermined length range, the percentage of fetal DNA will theoretically increase, and the percentage of maternal DNA will be reduced. Therefore, the concentration value or percentage of fetal DNA (for example, before filtering, the fetal DNA concentration is 10, after filtering, the fetal DNA concentration is 15, increased to 1.5 times) will increase, and the increase ratio should be consistent with the increase ratio of the AcF value of the positive sample (for example, the AcF value of the positive sample in the case of non-chimeric Down syndrome increases from 10 to 15). It should be understood that if only the fetus carries an abnormality, its AcF value will increase for the filtered data. If only the mother carries an abnormality, its AcF value will decrease for the filtered data. If both the mother and the fetus carry an abnormality, the AcF value of the DNA fragment set after filtering will not change compared with the AcF value of the DNA fragment set before filtering. Thereby, the present invention can use the relationship between the abnormal content ratio characterization data of the DNA fragments in the initial data and the abnormal content ratio characterization data of the DNA fragments in the filtered data to infer the source of the abnormality.

[0093] In some embodiments, a method for determining the source of a chromosomal abnormality based on a comparison result, for example, includes: the computing device 110 compares the abnormal content ratio characterization data regarding the DNA fragments in the filtered data with the abnormal content ratio characterization data regarding the DNA fragments in the initial data; in response to determining that the abnormal content ratio characterization data regarding the DNA fragments in the filtered data is greater than the abnormal content ratio characterization data regarding the DNA fragments in the initial data, determining that the chromosomal abnormality originates from the fetus; and in response to determining that the abnormal content ratio characterization data regarding the DNA fragments in the filtered data is equal to the abnormal content ratio characterization data regarding the DNA fragments in the initial data, determining that the chromosomal abnormality originates from the mother and the fetus.

[0094] By adopting the above-mentioned means, the present invention can accurately determine the source of chromosomal abnormality.

[0095] The following will be combined Figure 6 A method 600 for determining a source of a chromosome abnormality based on a comparison result according to other embodiments of the present invention is described. Figure 6 FIG. 6 is a flowchart of a method 600 for determining the source of a chromosome abnormality based on a comparison result according to some other embodiments of the present invention. It should be understood that the method 600 can be implemented, for example, in Fig.10 The electronic device 1000 described above may also be used. Figure 1 The method 600 is executed at the described computing device 110. It should be understood that the method 600 may also include additional actions not shown and / or may omit the actions shown, and the scope of the present invention is not limited in this respect.

[0096] As mentioned above, by filtering the DNA fragment length to obtain filtered data with DNA fragment lengths within a predetermined length range, the percentage of fetal DNA will theoretically increase and the percentage of maternal DNA will decrease. The following is combined with formula (6) to illustrate the reduction in the abnormal content ratio of DNA fragments in the filtered data when only the mother carries the abnormality.

[0097]

[0098] In the above formula (6), Represents the proportion of fetal DNA fragments. Represents the proportion of maternal DNA fragments. Represents the ratio of the fetal DNA fragments to be filtered. θ represents the increase ratio of the abnormal content ratio of the DNA fragments in the filtered data relative to the abnormal content ratio of the DNA fragments in the initial data when only the fetus carries the abnormality. Represents the proportion of filtered maternal DNA fragments.F The data are represented by the ratio of abnormal content of DNA fragments in the initial data when only the mother carries the abnormality.

[0099] The data are characterized by the ratio of the abnormal content of DNA fragments in the filtered data representing abnormalities (ie, Filter AcF). The ratio of abnormal content in the DNA fragments in the filtered data represents the amount of reduction in the data when only the mother carries the abnormality.

[0100] At step 602 , if the computing device 110 determines that the abnormal content ratio characterizing data about the DNA fragments in the filtered data is greater than the abnormal content ratio characterizing data about the DNA fragments in the initial data, it is determined that the chromosomal abnormality originates from the fetus.

[0101] For example, if the following expression (7) holds true, it is determined that the chromosomal abnormality originates from the fetus.

[0102]

[0103] In the above formula (7), Represents the data about the abnormal content ratio of DNA fragments in the filtered data. represents the abnormal content ratio of DNA fragments in the initial data. θ represents the predetermined multiple.

[0104] In some embodiments, the predetermined multiple is, for example but not limited to, 1.8.

[0105] At step 604 , if the computing device 110 confirms that the absolute value of the abnormal content ratio characterization data of the DNA fragments in the filtered data is less than the absolute value of the abnormal content ratio characterization data of the DNA fragments in the abnormal filtered data, it is determined that the chromosome abnormality originates from the mother.

[0106] For example, if the following expression (8) holds true, it is determined that the chromosomal abnormality originates from the mother.

[0107]

[0108] In the above formula (8), Represents the data about the abnormal content ratio of DNA fragments in the filtered data. The abnormal content ratio of the DNA fragments in the filtered data representing the abnormality characterizes the data.

[0109] At step 606, if the computing device 110 confirms that the absolute value of the data characterizing the abnormal content ratio of DNA fragments in the filtered data is greater than or equal to the absolute value of the data characterizing the abnormal content ratio of DNA fragments in the abnormal filtered data, and is less than or equal to the absolute value of the data characterizing the abnormal content ratio of DNA fragments in the initial data, it is determined that the chromosomal abnormality originates from the fetus and the mother.

[0110] For example, if the following expression (9) holds true, it is determined that the chromosomal abnormality originates from the fetus and the mother.

[0111]

[0112] In the above formula (9), Represents the data about the abnormal content ratio of DNA fragments in the filtered data. The abnormal content ratio of the DNA fragments in the filtered data representing the abnormality characterizes the data. represents the abnormal content ratio of DNA fragments in the initial data. θ represents the predetermined multiple.

[0113] By adopting the above scheme, the present invention can accurately determine the source of chromosome abnormality.

[0114] The present invention also includes a method for determining the karyotype of a sex chromosome abnormality.

[0115] The following will be combined Figure 7 A method 700 for determining the karyotype of a sex chromosome abnormality according to an embodiment of the present invention is described. Figure 7 FIG. 7 is a flowchart of a method 700 for determining a karyotype of a sex chromosome abnormality according to an embodiment of the present invention. It should be understood that the method 700 may be used, for example, in Fig.10 The electronic device 1000 described above may also be used. Figure 1 The method 700 is executed at the described computing device 110. It should be understood that the method 700 may also include additional actions not shown and / or may omit the actions shown, and the scope of the present invention is not limited in this respect.

[0116] It should be understood that sex chromosome abnormalities (SCA) include a variety of abnormal karyotypes, such as 47XXY, 47XYY, 46XY / 45X, and 46XY / 45Y. It should be understood that compared with female fetal DNA data, male fetal DNA data will contain Y chromosome data, and therefore, the present invention can use the concentration of fetal DNA fragments determined based on the Y chromosome (abbreviated as "ChrY%") to determine gender.

[0117] At step 702, if the computing device 110 confirms that the concentration of the DNA fragments in the filtered data is greater than a predetermined Y chromosome fetal DNA fragment concentration threshold, it is determined that the fetal DNA in the maternal peripheral blood sample to be tested is derived from a male fetus.

[0118] Regarding the predetermined Y chromosome fetal DNA fragment concentration threshold, that is, the predetermined ChrY% threshold, it is, for example but not limited to, 2.

[0119] At step 704 , the computing device 110 constructs a relationship graph between the abnormal content ratio representation data of the X chromosome and the Y chromosome ratio of the maternal peripheral blood sample during the pregnancy to be tested.

[0120] Regarding the method of constructing a relationship graph, in some embodiments, the computing device 110 constructs a relationship graph between the abnormal content ratio characterization data of the X chromosome and the Y chromosome ratio of the sample to be tested based on a linear regression function. The following formula (10) schematically shows a linear regression function for constructing a relationship graph between the abnormal content ratio characterization data of the X chromosome and the Y chromosome ratio of the DNA fragment in the initial data. The following formula (11) schematically shows a linear regression function for constructing a relationship graph between the abnormal content ratio characterization data of the X chromosome and the Y chromosome ratio of the DNA fragment in the filtered data.

[0121] y = -0.99x + 0.74 (10) y = -1.03x + 0.90 (11) In the above formulas (10) and (11), x represents the Y chromosome ratio of the sample to be tested, and y represents the abnormal content ratio of the X chromosome of the sample to be tested. Figure 8The relationship graph between the abnormal content ratio characterization data of the X chromosome and the Y chromosome ratio is schematically shown. The relationship graph 810 on the left indicates the relationship between the abnormal content ratio characterization data of the X chromosome and the Y chromosome ratio of the DNA fragment in the initial data. The vertical dotted line 816 indicates the Y chromosome ratio when x = 0.75, that is, y = -0.99x + 0.74 = 0. The horizontal dotted line 818 indicates the quality control standard (cutoff), for example, the quality control standard of the relationship graph 810 is 4% (i.e., cutoff = 4%) for the abnormal content ratio characterization data of the X chromosome of the DNA fragment in the initial data. The relationship graph 820 on the right indicates the relationship between the abnormal content ratio characterization data of the X chromosome and the Y chromosome ratio of the DNA fragment in the filtered data. The vertical dotted line 826 indicates the Y chromosome ratio when x = 0.87, that is, y = -1.03x + 0.90 = 0. The horizontal dotted line 828 indicates the quality control standard (cutoff). For example, the quality control standard indicates that the abnormal content ratio of the X chromosome in the DNA fragment in the filtered data is 6% (ie, cutoff = 6%). It should be understood that the abnormal content ratio characterization data of chromosome X (ie, AcF) and chromosome Y ratio (ie, Y ChrY%) for male fetal samples are linearly correlated because the AcF of chromosome X is the fetal DNA fragment ratio.

[0122] exist Figure 8 In the relationship graph shown, the horizontal axis represents the Y chromosome ratio of the DNA fragments in the initial data or the DNA fragments in the filtered data, and the vertical axis represents the abnormal content ratio of the X chromosome of the DNA fragments in the initial data or the DNA fragments in the filtered data.

[0123] At step 706, if the computing device 110 determines that the relationship graph is within the range between the first threshold line and the second threshold line, it is determined that the maternal peripheral blood sample during the pregnancy to be tested is a sample with normal sex chromosomes.

[0124] like Figure 8 As shown, in the relationship graph 810 about the DNA fragments in the initial data, 812 indicates the first threshold line about the DNA fragments in the initial data, and 814 indicates the second threshold line about the DNA fragments in the initial data. If the correlation graph 810 is within the range between the first threshold line 812 and the second threshold line 814, it is determined that the maternal peripheral blood sample to be tested during pregnancy is a sample with normal sex chromosomes.

[0125] The first threshold line 812 of the DNA fragments in the initial data is constructed based on, for example, the function shown in the following expression (12). The first threshold line 814 of the DNA fragments in the initial data is constructed based on, for example, the function shown in the following expression (13). The range between the first threshold line 812 and the second threshold line 814 is a normal sample. Its confidence interval is 99.5%.

[0126] y = -0.99x + 4.15 (12) y = -0.99x - 2.67 (13) In the above formulas (12) and (13), x represents the Y chromosome ratio of the sample to be tested, and y represents the abnormal content ratio of the X chromosome of the sample to be tested.

[0127] In the relationship graph 820 regarding the DNA fragments in the filtered data, 822 indicates the first threshold line regarding the DNA fragments in the filtered data, and 824 indicates the second threshold line regarding the DNA fragments in the filtered data. If the relationship graph of the sample to be tested is within the range between the first threshold line 822 and the second threshold line 824 regarding the DNA fragments in the filtered data, it is determined that the maternal peripheral blood sample to be tested is a sample with normal sex chromosomes. Regarding the first threshold line 822 of the DNA fragments in the filtered data, it is constructed, for example, based on the function shown in the following expression (14). Regarding the second threshold line 824 of the DNA fragments in the filtered data, it is constructed, for example, based on the function shown in the following expression (15).

[0128] y = -1.03x + 5.37 (14) y = -1.03x - 3.57 (15) In the above formulas (14) to (15), x represents the Y chromosome ratio of the sample to be tested, and y represents the abnormal content ratio characterization data of the X chromosome of the sample to be tested.

[0129] At step 708 , if the computing device 110 determines that the relationship graph is outside the range between the first threshold line and the second threshold line, it is determined that the maternal peripheral blood sample during the pregnancy to be tested is a sample with sex chromosome abnormality.

[0130] At step 710 , if the computing device 110 determines that the relationship graph is outside the range of the first threshold line and the second threshold line and is higher than the first threshold point, it is determined that the maternal peripheral blood sample during the pregnancy to be tested is an abnormal karyotype of 47XXY or 47XYY.

[0131] Regarding the first threshold point, it is, for example, the first threshold point 815 regarding the DNA fragments in the initial data (ie, point A in the relationship graph 810); or the first threshold point 825 regarding the DNA fragments in the filtered data (ie, point A in the relationship graph 820).

[0132] In some embodiments, if ChrY%>1.5*BFF, the sample to be tested is a 47XYY abnormal karyotype. If ChrY%1.5*BFF, the sample to be tested is a 47XXY abnormal karyotype. BFF can be calculated, for example, using the formula (4) mentioned above.

[0133] In some embodiments, the present invention can further distinguish the source of abnormal karyotype 47XXY or 47XYY based on the Like-Mosaic value (or "Like-Mosaic value", LM value). The following formula (16) schematically shows the calculation method of the predetermined ratio (LM value).

[0134] LM=LenAB / LenBC (16) In the above formula (16), LenAB represents the length of the line segment AB in the relationship graph (i.e., the distance between point A and point B), and LenBC represents the length of the line segment BC in the relationship graph (i.e., the distance between point B and point C). LM represents a predetermined ratio, which is equal to the ratio of the length of the line segment AB in the relationship graph to the length of the line segment BC. It should be understood that if the sample is a non-mosaic 47XXY abnormality, then at this concentration, the sample should theoretically be at point C, but the actual situation is at point A. Therefore, line segment AB represents the difference between the concentration calculated by ChrY and the concentration calculated by ChrX. Line segment BC represents the difference between the concentration calculated by ChrY and 0. AB / BC represents the mosaic ratio. If the fetus is normal, the LM value will be approximately equal to "0". If the fetus is 47XXY and is not Mosaic in nature, the LM value will be approximately equal to "1". If the fetus carries an abnormality, the LM value of the DNA fragment in the filtered data will not change relative to the LM value of the DNA fragment in the initial data. If the mother is abnormal, the LM value of the DNA fragment in the filtered data will be reduced relative to the LM value of the DNA fragment in the initial data. Therefore, the present invention can determine the source of the abnormality based on the ratio of the LM value of the DNA fragment in the filtered data to the LM value of the DNA fragment in the initial data. The following formula (17) schematically shows the calculation method of the LM value ratio.

[0135]

[0136] In the above formula (17), RLM represents the ratio of the LM value regarding the DNA fragment in the filtered data to the LM value regarding the DNA fragment in the original data. Filter Tag LM Represents the LM value of DNA fragments in the filtered data. Tags LM Represents the LM value of the DNA fragment in the original data.

[0137] In some embodiments, if the RLM value is determined to be greater than 0.8, the abnormality is determined to be from the fetus.

[0138] At step 712, if the computing device 110 determines that the relationship graph is outside the range of the first threshold line and the second threshold line and is lower than the second threshold point, it is determined that the maternal peripheral blood sample of the pregnancy to be tested is an abnormal karyotype of 46XY / 45X or 46XY / 45Y. The second threshold point is lower than the first threshold point.

[0139] Regarding the second threshold point, it is, for example, the second threshold point 817 regarding the DNA fragments in the initial data (ie, point D in the relationship graph 810); or the second threshold point 827 regarding the DNA fragments in the filtered data (ie, point D in the relationship graph 820).

[0140] In some embodiments, if ChrY%>1.5*BFF, the sample to be tested is a 46XY / 45X abnormal karyotype. If ChrY%1.5*BFF, the sample to be tested is a 46XY / 45Y abnormal karyotype.

[0141] In some embodiments, the present invention can further distinguish the source of abnormal karyotype 46XY / 45X or 46XY / 45Y based on a predetermined ratio. The following formula (18) schematically shows a method for calculating the predetermined ratio (LM value).

[0142] LM=LenDE / LenEF (18) In the above formula (16), LenDE represents the length of the line segment DE in the relationship graph (ie, the distance between point D and point E), LenEF represents the length of the line segment EF in the relationship graph (ie, the distance between point E and point F), and LM represents the predetermined ratio.

[0143] By adopting the above-mentioned means, the present invention can avoid the high false positive rate of fetal chromosomal abnormalities caused by the complexity of sex chromosomes, and can effectively detect the karyotype of sex chromosome abnormalities and infer the origin of the abnormality.

[0144] Fig.10 The block diagram of the electronic device 1000 suitable for implementing the embodiment of the present invention is schematically shown. The electronic device 1000 may be used to implement the execution Figure 2 , Figures 4 to 7 The methods 200, 400 to 700 shown in FIG. Fig.10 As shown, the electronic device 1000 includes a central processing unit (i.e., CPU 1001), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (i.e., ROM 1002) or computer program instructions loaded from a storage unit 1008 to a random access memory (i.e., RAM 1003). In RAM 1003, various programs and data required for the operation of the electronic device 1000 can also be stored. CPU 1001, ROM 1002, and RAM 1003 are connected to each other via a bus 1004. An input / output interface (i.e., I / O interface 1005) is also connected to the bus 1004.

[0145] Multiple components in the electronic device 1000 are connected to the I / O interface 1005, including: an input unit 1006, an output unit 1007, and a storage unit 1008. The CPU 1001 performs the various methods and processes described above, such as performing methods 200, 400 to 700. For example, in some embodiments, methods 200, 400 to 700 may be implemented as a computer software program, which is stored in a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the CPU 1001, one or more operations of the methods 200, 400 to 700 described above may be performed. Alternatively, in other embodiments, the CPU 1001 may be configured to perform one or more actions of the methods 200, 400 to 700 in any other appropriate manner (e.g., by means of firmware).

[0146] It should be further noted that the present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0147] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0148] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0149] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit may execute the computer-readable program instructions, thereby implementing various aspects of the present invention.

[0150] Various aspects of the present invention are described herein with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to an embodiment of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0151] These computer-readable program instructions can be provided to a processor in a voice interaction device, a general-purpose computer, a special-purpose computer, or a processing unit of other programmable data processing devices, thereby producing a machine, so that when these instructions are executed by a processing unit of a computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions enable the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0152] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0153] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the equipment, method and computer program product according to multiple embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of this module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0154] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

[0155] The above are only optional embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for detecting the chromosome status of a fetus, characterized in that: include: Comparing the obtained sequencing data of the maternal peripheral blood sample during the pregnancy to be tested to the human reference genome, so as to obtain the initial data of the paired-end comparison to the human reference genome; Filtering the DNA fragment length in the initial data to obtain filtered data with DNA fragment length within a predetermined length range; Calculating a Z value for the target area based on the initial data and the filtered data, respectively, so as to obtain a Z value for the target area in the initial data and a Z value for the target area in the filtered data; as well as The fetal chromosome status of the maternal peripheral blood sample during the pregnancy to be tested is determined based on the obtained Z value of the target region in the initial data and the Z value of the target region in the filtered data.

2. The method according to claim 1, characterized in that Also includes: Calculating abnormal content ratio characterization data for the initial data and the filtered data to obtain abnormal content ratio characterization data about the DNA fragments in the initial data and abnormal content ratio characterization data about the DNA fragments in the filtered data; and The data characterizing the abnormal content ratio of DNA fragments in the initial data and the data characterizing the abnormal content ratio of DNA fragments in the filtered data are compared to determine the source of the chromosome abnormality based on the comparison result.

3. The method according to claim 1, characterized in that Filtering the DNA fragment length in the initial data to obtain filtered data with DNA fragment length within a predetermined length range includes: Obtain the starting and ending positions of the DNA fragments in the initial data of the paired-end comparison to the human reference genome; Based on the obtained starting position and ending position, the length of each DNA fragment in the initial data is calculated; Determining whether the calculated length of each DNA fragment in the initial data is greater than a first predetermined size threshold and less than or equal to a second predetermined size threshold; In response to determining that the calculated length of the current DNA fragment in the initial data is greater than a first predetermined size threshold and less than or equal to a second predetermined size threshold, determining that the current DNA fragment in the initial data is filtered data belonging to a predetermined size range; and In response to determining that the calculated length of the current DNA fragment in the initial data is less than or equal to a first predetermined size threshold, or greater than a second predetermined size threshold, the current DNA fragment in the initial data is filtered out.

4. The method according to claim 1, characterized in that Calculating the Z value about the target area includes: For the initial data or the filtered data, respectively, the number of inserted fragments in the target region is divided by the total number of inserted fragments in the autosome, so as to obtain the genomic characterization data of the genomic target region respectively; Calculate the mean and standard deviation of the reference set sample genome representation data for the target region of the reference set sample; and Based on the genome characterization data of the genome target region and the mean and standard deviation of the genome characterization data of the reference set samples, the Z value of the target region in the initial data and the Z value of the target region in the filtered data are calculated respectively.

5. The method according to claim 2, characterized in that: Determination of the fetal chromosome status of the maternal peripheral blood sample during the pregnancy to be tested includes: Determining whether the Z value of the target area in the filtered data falls within a predetermined threshold range; and In response to determining that the Z value of the target region in the filtered data belongs to a predetermined threshold range, it is determined that the chromosome status of the fetus of the maternal peripheral blood sample during the pregnancy to be tested is abnormal.

6. The method according to claim 3, characterized in that The predetermined threshold value range is greater than or equal to 3, or less than or equal to -3.

7. The method according to claim 1, characterized in that The sources of chromosomal abnormalities determined based on the comparison results include: comparing the data characterizing the abnormal content ratio of DNA fragments in the filtered data with the data characterizing the abnormal content ratio of DNA fragments in the initial data; In response to determining that the abnormal content ratio characterizing data about the DNA fragments in the filtered data is greater than the abnormal content ratio characterizing data about the DNA fragments in the initial data, determining that the chromosomal abnormality originates from the fetus; and In response to determining that the abnormal content ratio characterizing data regarding the DNA fragments in the filtered data is equal to the abnormal content ratio characterizing data regarding the DNA fragments in the initial data, it is determined that the chromosomal abnormality originates from the mother and the fetus.

8. The method according to claim 1, characterized in that The sources of chromosomal abnormalities determined based on the comparison results include: In response to confirming that the Y chromosome ratio of the DNA fragments in the filtered data is greater than a predetermined Y chromosome fetal DNA fragment concentration threshold, determining that the fetal DNA in the maternal peripheral blood sample to be tested is derived from a male fetus; Constructing a graph of the relationship between the abnormal content ratio representation data of the X chromosome and the Y chromosome ratio in the maternal peripheral blood sample to be tested during pregnancy; In response to determining that the relationship graph is within the range between the first threshold line and the second threshold line, determining that the maternal peripheral blood sample of the pregnancy to be tested is a sample with normal sex chromosomes; and In response to determining that the relationship graph is outside the range between the first threshold line and the second threshold line, the maternal peripheral blood sample during the pregnancy to be tested is determined to be a sample with sex chromosome abnormality.

9. The method according to claim 8, characterized in that The maternal peripheral blood samples to be tested during pregnancy are confirmed to be samples with sex chromosome abnormalities, including: In response to determining that the relationship graph is outside the range of the first threshold line and the second threshold line and is higher than the first threshold point, determining that the maternal peripheral blood sample during the pregnancy to be tested is an abnormal karyotype of 47XXY or 47XYY; and In response to determining that the relationship graph is outside the range of the first threshold line and the second threshold line and is lower than the second threshold point, it is determined that the maternal peripheral blood sample to be tested is an abnormal karyotype of 46XY / 45X or 46XY / 45Y, and the second threshold point is lower than the first threshold point.

10. A computing device, characterized in that include: at least one processing unit; At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the device to perform the steps of the method according to any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that: A computer program is stored on a computer-readable storage medium, and when the computer program is executed by a machine, the method according to any one of claims 1 to 9 is implemented.

12. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a machine, performs the method according to any one of claims 1 to 9.

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