Method, device, medium and equipment for identifying the consistency between a transplanted embryo and a pregnancy embryo

Abstract

The present invention discloses a method, device, medium and equipment for identifying the consistency between a transplanted embryo and a pregnancy embryo, belonging to the technical field of genetic detection. The method of the present invention includes the following steps: obtaining the whole-genome DNA of a transplanted embryo sample and a pregnancy embryo sample; constructing a sequencing library; performing on-machine sequencing on the sequencing library to obtain sequencing data with a data volume ≥ 80M, and then performing SNP analysis to obtain an SNP data set; performing a genetic relationship analysis on the SNP data sets of the transplanted embryo sample and the pregnancy embryo sample, and judging whether the transplanted embryo and the pregnancy embryo are consistent according to the value of the genetic relationship coefficient. This method can be used as a method for quality control of assisted reproductive embryo transplantation to judge whether the target embryo has been accurately transplanted; it can also be used to identify the source of the pregnancy embryo in sequential transplantation, and to retrospectively analyze possible reasons for abnormal development or fetal arrest of the pregnancy fetus after PGT.

Description

Technical Field

[0001] The present invention relates to the technical field of genetic testing, and in particular, to a method, device, medium, and equipment for identifying the consistency between pre-implantation embryos and pregnancy embryos. Background Art

[0002] The information disclosed in the background art of the present invention is only intended to enhance the overall understanding of the present invention and is not necessarily regarded as an admission or an indication in any form that this information constitutes the prior art already known to those of ordinary skill in the art.

[0003] With the rapid development of human assisted reproductive technology (ART), more and more infertile couples have achieved pregnancy through ART. However, due to reasons such as advanced age and genetic factors, the risks of embryonic chromosomal abnormalities and genetic diseases have increased, resulting in still relatively low pregnancy and live birth rates of ART. To solve this problem, preimplantation genetic testing (PGT) technology has emerged, which provides the possibility for couples to screen out healthy embryos before embryo implantation. Currently, the detection techniques of PGT mainly include: fluorescence in situ hybridization (FISH), array comparative genomic hybridization (aCGH), multiplex ligation-dependent probe amplification (MLPA), and next-generation sequencing (NGS), etc. These techniques are applicable to pre-implantation chromosomal aneuploidy testing (PGT-A), pre-implantation single-gene genetic disease testing (PGT-M), pre-implantation chromosomal structural abnormality testing (PGT-SR), or various combined screenings.

[0004] The healthy embryos preferably screened by PGT are implanted into the mother's body. When the embryos develop to a certain stage, prenatal diagnosis or detection of abortion tissues will be further carried out, that is, the pregnancy embryos are detected. The genetic testing techniques of pregnancy embryos mainly include: quantitative fluorescence polymerase chain reaction (QF-PCR), fluorescence in situ hybridization technology (FISH), non-invasive prenatal DNA testing technology (NIPT), gene chip technology, microarray comparative genomic hybridization technology (array-CGH), and single nucleotide polymorphism array technology (SNP array), etc.

[0005] By performing consistency identification on pregnancy embryos and transplanted embryos, it is possible to determine whether the target embryo has been accurately transplanted, thereby assisting in the quality control of reproductive embryo transplantation; it can also be used to identify the source of pregnancy embryos in sequential transplantation to clarify the optimal embryo transplantation window period; and to retrospectively analyze possible causes such as abnormal development and fetal arrest of pregnancy fetuses after PGT. However, there is currently no set of economical and effective methods to simultaneously detect pregnancy embryos and blastocysts screened by PGT. The main reasons are as follows: First, from a technical perspective, most current chromosome abnormality detection methods have low sequencing depth and are only applicable to detecting copy number variations (CNVs) of chromosome segments, and cannot accurately detect the base information of single nucleotide polymorphism (SNP) sites of chromosome segments; Second, from a clinical perspective, the current PGT and prenatal screening scopes include PGT-A, PGT-M, and PGT-SR. For the genetic detection of most aneuploidies and chromosomal structural variations, the screening requirements can be met without obtaining SNP sites; for the screening of single-gene diseases, the detection methods generally only target the acquisition of SNP sites in specific regions, do not cover the entire genome, and the number of effective SNP sites is limited; Third, from an application perspective, so far there is no method actually used to detect whether pregnancy embryos and transplanted embryos are consistent.

[0006] Therefore, it is necessary to provide a method that can be simultaneously applied to the detection and consistency identification of PGT transplanted embryos and pregnancy embryos to fill the gap in existing genetic detection technologies. Summary of the Invention

[0007] In view of this, the present invention provides a method, device, medium, and equipment for identifying the consistency between transplanted embryos and pregnancy embryos. The present invention can achieve the consistency judgment of transplanted embryos and pregnancy embryos after PGT screening, and can also accurately identify the source of blastocyst samples of pregnancy embryos, having certain clinical application value.

[0008] In the first aspect, the present invention provides a method for identifying the consistency between transplanted embryos and pregnancy embryos, including the following steps:

[0009] Obtain the whole-genome DNA of the transplanted embryo sample and the pregnancy embryo sample;

[0010] Based on the whole-genome DNA, construct sequencing libraries for the transplanted embryo sample and the pregnancy embryo sample respectively;

[0011] Sequence the sequencing libraries on a machine to obtain sequencing data with a data volume ≥ 80M, and then perform SNP analysis to obtain an SNP data set;

[0012] Perform kinship analysis on the SNP data sets of the transplanted embryo sample and the pregnancy embryo sample, and judge whether the transplanted embryo and the pregnancy embryo are consistent according to the kinship coefficient value.

[0013] Preferably, the whole-genome DNA of the transplanted embryo sample is obtained by whole-genome amplification of the trophoblast cells of the blastocyst.

[0014] Preferably, the pregnancy embryo sample is derived from one or more of aborted tissues, amniotic fluid, chorionic villi, cord blood, and peripheral blood.

[0015] Preferably, the method for constructing the sequencing library is as follows: The whole-genome DNA of the transplanted embryo sample and the pregnancy embryo sample are respectively fragmented into a number of short DNA molecules, the target fragment DNA is obtained through a magnetic bead screening system, and then the sequencing library is obtained through PCR amplification and magnetic bead purification.

[0016] Preferably, after obtaining sequencing data with a data volume ≥ 80M, it further includes performing CNV analysis on the sequencing data. The specific method of the CNV analysis is: After using BEDTools to divide the window, use the CNV-plus software for GC correction and copy number calculation, and determine the CNV fragment through the LogRR value and t statistic to complete the CNV analysis.

[0017] Preferably, the process for obtaining the SNP dataset is specifically as follows: Use the BWA algorithm of the Sentieon software to align the sequencing data with the hg19 genome, perform base quality correction and SNP detection, and then correct the SNP data in combination with a public database; then use the VCFtools software to perform quality control on the SNP data, and perform data format conversion through the PLINK2 / PLINK software to obtain it.

[0018] Preferably, the software used for the kinship analysis is the King software.

[0019] Furthermore, the specific method for judging whether the transplanted embryo and the pregnancy embryo are consistent according to the kinship coefficient value is as follows: When the kinship coefficient value > 0.354, it indicates that the transplanted embryo and the pregnancy embryo are consistent; when the kinship coefficient value ≤ 0.354, it indicates that the transplanted embryo and the pregnancy embryo are not consistent.

[0020] In a second aspect, the present invention provides a device for identifying the consistency between a transplanted embryo and a pregnancy embryo, including:

[0021] A whole-genome DNA data acquisition module for acquiring the whole-genome DNA of the transplanted embryo sample and the pregnancy embryo sample;

[0022] A sequencing library construction module for constructing sequencing libraries of the transplanted embryo sample and the pregnancy embryo sample based on the whole-genome DNA;

[0023] The SNP dataset acquisition module is used to sequence the sequencing library on a machine to obtain sequencing data with a data volume ≥ 80M, and then perform SNP analysis to obtain an SNP dataset;

[0024] The analysis module is used to perform a genetic relationship analysis on the SNP datasets of the transplanted embryo samples and the pregnant embryo samples, and determine whether the transplanted embryo and the pregnant embryo are consistent according to the genetic relationship coefficient value.

[0025] In a third aspect, the present invention provides a computer storage medium storing computer program instructions, which control the device where the computer storage medium is located to execute the steps in the method described in the first aspect above when the computer program instructions are running.

[0026] In a fourth aspect, the present invention provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is running, it executes the steps in the method described in the first aspect above.

[0027] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0028] (1) By limiting the data volume of the sequencing data, the present invention ensures that the average number of effective SNP sites ≥ 200,000 and the average sequencing depth of 10X ≥ 4%, effectively guaranteeing the detection accuracy and laying an important foundation for realizing the identification of the consistency between the pregnant embryo and the transplanted embryo.

[0029] (2) According to the method provided by the present invention for identifying the consistency between the transplanted embryo and the pregnant embryo, it can determine whether the pregnant embryo has been accurately transplanted and identify which transplanted embryo (D5 blastocyst or D6 blastocyst) the pregnant embryo in sequential transplantation comes from, filling the gap in the prior art. It can be used as a method for quality control of assisted reproductive embryo transplantation to determine whether the target embryo has been accurately transplanted; it can also be used to identify the source of the pregnant embryo in sequential transplantation to clarify the optimal embryo transplantation window period; and retrospectively analyze possible reasons for abnormal development or fetal arrest of the pregnant fetus after PGT. Detailed implementation manners

[0030] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0031] The present invention provides a method for identifying the consistency between a transplanted embryo and a pregnant embryo, including the following steps:

[0032] Obtain the whole-genome DNA of the transplanted embryo sample and the pregnant embryo sample;

[0033] Construct sequencing libraries for the transplanted embryo samples and the pregnant embryo samples respectively based on the whole-genome DNA.

[0034] Sequence the sequencing libraries on a machine to obtain sequencing data with a data volume ≥ 80M, and then perform SNP analysis to obtain an SNP data set.

[0035] Perform a genetic relationship analysis on the SNP data sets of the transplanted embryo samples and the pregnant embryo samples, and judge whether the transplanted embryo and the pregnant embryo are consistent according to the value of the genetic relationship coefficient.

[0036] Based on the system screening of the whole-genome sequencing data volume, the present invention has developed an economical and effective new method suitable for the consistency identification of pregnant embryos and transplanted embryos - Embryo Consistency Identification Testing (ECIT). This method can not only judge whether the target embryo has been accurately transplanted, that is, identify whether the transplanted embryo after PGT and the maternal pregnant embryo are the same embryo; at the same time, for women with repeated implantation failures, sometimes a sequential transplantation strategy (transplanting D5 blastocysts and D6 blastocysts continuously for two days) is adopted, and the method of the present invention can also identify the origin of the pregnant embryo in sequential transplantation (D5 blastocyst or D6 blastocyst). The method of the present invention effectively fills the gap in the current technology and has certain clinical application value.

[0037] Since the number of trophectoderm (TE) cells in blastocysts is limited, the whole-genome DNA (gDNA) of the transplanted embryo samples in the present invention is obtained by whole-genome amplification of the trophectoderm cells (i.e., TE cells) of the blastocysts. The present invention does not impose special restrictions on the method of the whole-genome amplification, and the whole-genome amplification method commonly used in the art can be adopted to obtain it. For example, methods such as Sureplex, MDA or Malbac can be selected.

[0038] In the present invention, the pregnant embryo samples are derived from one or more of miscarriage tissues, amniotic fluid, chorionic villi, cord blood, and peripheral blood.

[0039] In the present invention, the method for constructing the sequencing library is as follows: Fragment the whole-genome DNA of the transplanted embryo samples and the pregnant embryo samples into several short DNA molecules, obtain the target fragment DNA through a magnetic bead screening system, and then obtain the sequencing library through PCR amplification and magnetic bead purification. The present invention does not impose special restrictions on the specific methods of fragmentation, magnetic bead screening, PCR amplification, and magnetic bead purification, and the commonly used methods in the art can be adopted.

[0040] The present invention discovers that the size of the sequencing data volume affects the number of effective SNP sites and the sequencing depth. When the data volume reaches more than 80M, the average number of effective SNP sites ≥ 200,000, and the average sequencing depth of 10X ≥ 4%, effectively ensuring the detection accuracy and laying an important foundation for realizing the consistency identification of pregnancy embryos and transplanted embryos.

[0041] In the present invention, after obtaining sequencing data with a data volume ≥ 80M, it further includes performing CNV analysis on the sequencing data. The specific method of the CNV analysis is as follows: after using BEDTools to divide windows, use the CNV-plus software for GC correction and copy number calculation, and determine CNV segments through the LogRR value and t statistic to complete the CNV analysis. The present invention can realize the simultaneous detection of CNV and SNP in PGT.

[0042] In the present invention, the process of obtaining the SNP data set is specifically as follows: use the BWA algorithm of the Sentieon software to align the sequencing data with the hg19 genome, perform base quality correction and SNP detection, and then combine with a public database to correct the SNP data; then use the VCFtools software to perform quality control on the SNP data and perform data format conversion through the PLINK2 / PLINK software to obtain it.

[0043] In the present invention, the software used for the kinship analysis is the King software. Further, the specific method for judging whether the transplanted embryo and the pregnancy embryo are consistent according to the kinship coefficient value is as follows: when the kinship coefficient value (kinship value) > 0.354, it indicates that the transplanted embryo and the pregnancy embryo are consistent; when the kinship coefficient value (kinship value) ≤ 0.354, it indicates that the transplanted embryo and the pregnancy embryo are not consistent.

[0044] The present invention also provides a device for identifying the consistency of transplanted embryos and pregnancy embryos, including:

[0045] A whole-genome DNA data acquisition module for acquiring the whole-genome DNA of the transplanted embryo sample and the pregnancy embryo sample;

[0046] A sequencing library construction module for constructing sequencing libraries of the transplanted embryo sample and the pregnancy embryo sample respectively based on the whole-genome DNA;

[0047] An SNP data set acquisition module for performing on-machine sequencing on the sequencing library to obtain sequencing data with a data volume ≥ 80M and Q30 ≥ 80%, and then performing SNP analysis to obtain an SNP data set;

[0048] An analysis module for performing a genetic relationship analysis on the SNP data sets of the transplanted embryo samples and the pregnant embryo samples, and determining whether the transplanted embryo and the pregnant embryo are consistent according to the genetic relationship coefficient value.

[0049] The present invention also provides a computer storage medium storing computer program instructions that control a device where the computer storage medium is located to execute the above method for identifying the consistency between a transplanted embryo and a pregnant embryo when the computer program instructions are running.

[0050] The present invention also provides a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program executes the above method for identifying the consistency between a transplanted embryo and a pregnant embryo when running.

[0051] The technical solution of the present invention will be further described below in conjunction with specific embodiments.

[0052] Example 1

[0053] This example provides an Embryo Consistency Identification Test (ECIT) method, including the following steps:

[0054] (1) Extract the gDNA of the pregnant embryo (aborted tissue or amniotic fluid), extract the gDNA of the trophectoderm (TE) cells of the transplanted embryo (D5 blastocyst), and obtain a gDNA amplification product through MDA whole genome amplification (WGA) technology.

[0055] (2) Fragment the gDNA of the pregnant embryo (aborted tissue or amniotic fluid) and the gDNA amplification product of the transplanted embryo (blastocyst) into a number of short DNA molecules, obtain the target fragment DNA through a magnetic bead screening system, and obtain the final sequencing library through PCR amplification and magnetic bead purification.

[0056] (3) Sequence the constructed sequencing library on a DA500 sequencing platform (Suzhou Beikang Medical Devices Co., Ltd.) to obtain test data with a total data volume of 50M, 80M, and 100M.

[0057] (4)Performing CNV and SNP analysis using bioinformatics tools: Using the BWA algorithm of the Sentieon software to align the data with the hg19 genome, performing base quality correction and SNP detection, and then combining with a public database to correct SNPs; after using BEDTools to divide windows, using the CNV-plus software for GC correction and copy number calculation, determining CNV segments through LogRR values and t-statistics to complete CNV analysis; using the VCFtools software to perform quality control on SNP data, converting the data format through PLINK2 / PLINK software, and using the King software to identify the same origin of pregnancy embryo samples and transplanted embryo samples. When the kinship coefficient value > 0.354, it indicates that the two samples are of the same origin, and thus the identification of whether the pregnancy embryo and the transplanted embryo are the same embryo can be achieved.

[0058] Statistical analysis was performed on the effective SNP locus numbers and 10X sequencing depths of different test data volumes of three types of clinical samples (i.e., abortion tissues, amniotic fluid, and blastocysts), and the results are shown in Table 1.

[0059] Table 1 Effective SNP locus numbers and 10X sequencing depths of three types of clinical samples

[0060]

[0061] As can be seen from Table 1, when the sequencing data volume is 50M, the effective SNP locus numbers of the three types of clinical samples are less than 220,000, and the 10X sequencing depth is less than 4%; when the sequencing data volume is 80M, the effective SNP locus numbers of the three types of clinical samples are all relatively large, and with the increase in data volume, the effective locus numbers do not increase significantly. At the same time, the 10X sequencing depth is all ≥ 4%, which effectively guarantees the acquisition and detection accuracy of SNPs. Therefore, 80M is the selected optimal data volume for subsequent application examples.

[0062] The clinical samples of aborted tissues from three families were detected and analyzed using the CNV-seq technique. The specific process is as follows: Genomic DNA (gDNA) was extracted from the clinical samples (aborted tissues), and a gDNA library was constructed using a genomic copy number aberration detection kit (Suzhou Beikang Medical Devices Co., Ltd.). Then, the samples were sequenced on a DA500 sequencer (Suzhou Beikang Medical Devices Co., Ltd.). After sequencing, bioinformatics data analysis was performed. The analysis process is as follows: The sequences were aligned with the hg19 genome using the TMAP software, and duplicate and low-quality data were removed using multiple software such as BamDuplicates, SAMtools, and BEDTools. The CHD_NT_UR software was used to divide the chromosomes into non-overlapping windows of 20 kb to calculate the number of matching sequences, and GC correction was performed using the CNV-seq software to eliminate GC bias in sequencing. The CNV-seq software was used for sliding window merging and calculating the LogRR value to evaluate the CNV situation, and the CBS algorithm was used to determine the exact breakpoints of CNV segments.

[0063] The clinical samples of aborted tissues from the same three families were detected and analyzed using the ECIT method of this embodiment, and the data obtained by the above CNV-seq technique were compared, as shown in Table 2.

[0064] Table 2 Comparison of sequencing data of clinical samples of aborted tissues by two methods

[0065]

[0066] Note: In Table 2, the Q30 base ratio refers to the proportion of bases with an error rate of less than one-thousandth in the sequencing data. The same applies hereinafter.

[0067] As can be seen from Table 2, as the data volume increases, the sequencing depth in specific regions increases, which significantly improves the number of effective SNP sites and the detection depth, enabling the acquisition of more and accurate SNP base information (≥200,000). Thus, it can be seen that the ECIT method of this embodiment compensates for the applicable limitations of the CNV-seq technique to a certain extent, providing a feasible basis and laying an important foundation for the identification of the genetic relationship of pregnancy embryos.

[0068] Application Example 1

[0069] In this application example, the ECIT method provided in Example 1 was used to detect the consistency between the transplanted embryos and the pregnancy embryos in 3 families to determine whether they are the same embryo.

[0070] The specific detection process is as follows:

[0071] (1)Extract the gDNA of the pregnancy embryo abortion tissue and the gDNA of the trophectoderm (TE) cells of the transferred embryo (D5 blastocyst) separately, and obtain the gDNA amplification product through the MDA whole genome amplification (WGA) technology;

[0072] (2)Fragment the gDNA of the pregnancy embryo abortion tissue and the gDNA amplification product of the transferred embryo into several short DNA molecules respectively, obtain the target fragment DNA through the magnetic bead screening system, and obtain the final sequencing library through PCR amplification and magnetic bead purification;

[0073] (3)Sequence the constructed sequencing library on the DA500 sequencing platform (Suzhou Beikang Medical Devices Co., Ltd.) to obtain a total data volume of 80M;

[0074] (4)Use bioinformatics tools for CNV and SNP analysis: Use the BWA algorithm of the Sentieon software to align the data with the hg19 genome, perform base quality correction and SNP detection, and then combine the public database to correct the SNPs; After using BEDTools to divide the windows, use the CNV-plus software for GC correction and copy number calculation, determine the CNV fragments through the LogRR value and t statistic, and complete the CNV analysis; Use the VCFtools software to perform quality control on the SNP data, convert the data format through the PLINK2 / PLINK software, and use the King software to identify the same origin of the two samples. When the kinship value > 0.354, it means that the two samples are from the same origin. The detection results are shown in Table 3.

[0075] Table 3 Data analysis results of three family samples

[0076]

[0077] As can be seen from Table 3, the kinship values of the pregnancy embryos (abortion tissues) and the transferred embryos (blastocyst TE cells) in the 3 families are all greater than 0.354, indicating that the pregnancy embryos and the transferred embryos in the 3 families are all from the same sample respectively, indicating that the pregnancy embryos in the 3 families all had abortions after the target embryos were implanted into the uterus, confirming that the transfer was correct. In summary, the ECIT method provided in Example 1 can be used to determine whether the embryo has been accurately transferred, that is, to detect whether the pregnancy embryo and the transferred embryo are the same embryo.

[0078] Application Example 2

[0079] This application example uses the ECIT method provided in Example 1 to perform consistency detection on the transferred embryos (D5 and D6 blastocysts) and the pregnancy embryos in 3 families to determine which transferred embryo (D5 or D6 blastocyst) the pregnancy embryo comes from.

[0080] The detection method is as follows:

[0081] (1)Extract the gDNA of the amniotic fluid of the pregnant embryo and the gDNA of the TE cells of two compound transferred blastocyst-stage embryos (D5 and D6 blastocysts) respectively, and obtain the gDNA amplification product through the MDA whole-genome amplification technology;

[0082] (2)Fragment the gDNA of the amniotic fluid of the pregnant embryo and the gDNA amplification products of two compound transferred blastocyst-stage embryos into several short DNA molecules respectively, obtain the target fragment DNA through the magnetic bead screening system, and obtain the final sequencing library through PCR amplification and magnetic bead purification;

[0083] (3)Sequence the constructed sequencing library on the machine to obtain a total data volume of more than 80M, and perform CNV and SNP analysis using bioinformatics tools: Use the BWA algorithm of the Sentieon software to align the data with the hg19 genome, and perform base quality correction and SNP detection, and then combine the public database to correct the SNP; After using BEDTools to divide the window, use the CNV-plus software to perform GC correction and copy number calculation, and determine the CNV fragment through the LogRR value and t statistic to complete the CNV analysis; Use the VCFtools software to perform quality control on the SNP data, convert the data format through the PLINK2 / PLINK software, and use the king software to identify the same origin of two samples (D5 blastocyst or D6 blastocyst and the pregnant embryo). When the kinship value > 0.354, it indicates that the two samples are of the same origin. The detection results are shown in Table 4.

[0084] Table 4 Analysis results of three family samples

[0085]

[0086] As can be seen from Table 4, in family 1, the kinship value between the pregnant embryo (amniotic fluid) and the D5 blastocyst is greater than 0.354, while the kinship value with the D6 blastocyst is less than 0.354, indicating that the pregnant embryo (amniotic fluid) comes from the D5 blastocyst; in family 2, the kinship value between the pregnant embryo (amniotic fluid) and the D6 blastocyst is greater than 0.354, while the kinship value with the D5 blastocyst is less than 0.354, indicating that the pregnant embryo (amniotic fluid) comes from the D6 blastocyst; in family 3, the kinship value between the pregnant embryo (amniotic fluid) and the D6 blastocyst is greater than 0.354, while the kinship value with the D5 blastocyst is less than 0.354, indicating that the pregnant embryo (amniotic fluid) also comes from the D6 blastocyst. In summary, the ECIT method provided in Example 1 can identify which transferred embryo the pregnant embryo comes from in sequential transplantation.

[0087] Example 2

[0088] This embodiment provides a device for identifying the consistency between a transplanted embryo and a pregnancy embryo, including:

[0089] A whole-genome DNA data acquisition module for acquiring the whole-genome DNA of the transplanted embryo sample and the pregnancy embryo sample;

[0090] A sequencing library construction module for constructing sequencing libraries of the transplanted embryo sample and the pregnancy embryo sample respectively based on the whole-genome DNA;

[0091] An SNP data set acquisition module for subjecting the sequencing library to on-machine sequencing to obtain sequencing data with a data volume ≥ 80M, and then performing SNP analysis to obtain an SNP data set;

[0092] An analysis module for performing a genetic relationship analysis on the SNP data sets of the transplanted embryo sample and the pregnancy embryo sample, and judging whether the transplanted embryo and the pregnancy embryo are consistent according to the genetic relationship coefficient value.

[0093] Embodiment Three

[0094] This embodiment provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions run, they control the device where the computer storage medium is located to execute the steps in a method for identifying the consistency between a transplanted embryo and a pregnancy embryo as described in Embodiment One above.

[0095] Embodiment Four

[0096] This embodiment provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program runs, it executes the steps in a method for identifying the consistency between a transplanted embryo and a pregnancy embryo as described in Embodiment One above.

[0097] The steps or modules involved in Embodiments Two to Four above correspond to those in Embodiment One. For specific implementation manners, reference may be made to the relevant description part of Embodiment One. The term "computer-readable storage medium" should be understood to include a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode, or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0098] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for identifying the consistency of a transplanted embryo and a pregnant embryo, characterized in that: The steps include: Obtaining whole genome DNA of transplanted embryo samples and pregnant embryo samples; whole genome DNA of transplanted embryo samples is obtained from trophectoderm cells of blastocysts by whole genome amplification; constructing sequencing libraries of transplanted embryo samples and pregnant embryo samples based on the whole genome DNA respectively; The sequencing library was sequenced on a machine to obtain sequencing data with a data volume of ≥80M, and then SNP analysis was performed to obtain a SNP data set; the SNP data set acquisition process was as follows: the sequencing data was aligned with the hg19 genome using the BWA algorithm of the Sentieon software, and base quality correction and SNP detection were performed, and then the SNP data were corrected in combination with the public database; then the SNP data was quality controlled using the VCFtools software, and the data format was converted using the PLINK2 / PLINK software to obtain; King software was used to perform kinship analysis on the SNP data sets of transplanted embryo samples and pregnant embryo samples. The consistency of transplanted embryos and pregnant embryos was determined based on the kinship coefficient value. The specific method was as follows: when the kinship coefficient value was >0.354, it indicated that the transplanted embryos were consistent with the pregnant embryos; when the kinship coefficient value was ≤0.354, it indicated that the transplanted embryos were inconsistent with the pregnant embryos. The pregnancy embryo sample is derived from one or more of abortion tissue, amniotic fluid, chorionic villus, umbilical cord blood, and peripheral blood; The method for constructing the sequencing library is as follows: the whole genome DNA of the transplanted embryo sample and the pregnant embryo sample is fragmented into several short DNA molecules, the target fragment DNA is obtained by a magnetic bead screening system, and then the sequencing library is obtained by PCR amplification and magnetic bead purification; After obtaining sequencing data with a data volume ≥ 80M, CNV analysis of the sequencing data is also included.

2. The method according to claim 1, characterized in that The specific method of CNV analysis is: after dividing the window using BEDTools, using CNV-plus software to perform GC correction and copy number calculation, determining the CNV fragment through LogRR value and t statistic, and completing CNV analysis.

3. A device for identifying the consistency of a transplanted embryo with a pregnant embryo, characterized in that: include: A whole genome DNA data acquisition module is used to obtain the whole genome DNA of transplanted embryo samples and pregnant embryo samples; The whole genome DNA of the transferred embryo samples was obtained by whole genome amplification from the trophectoderm cells of the blastocyst; A sequencing library construction module, used to construct sequencing libraries of transplanted embryo samples and pregnant embryo samples based on the whole genome DNA; The SNP data set acquisition module is used to sequence the sequencing library on the machine to obtain sequencing data with a data volume of ≥80M, and then perform SNP analysis to obtain the SNP data set; the SNP data set acquisition process is as follows: the sequencing data is aligned with the hg19 genome using the BWA algorithm of the Sentieon software, and base quality correction and SNP detection are performed, and then the SNP data is corrected in combination with the public database; then the SNP data is quality controlled using the VCFtools software, and the data format is converted through the PLINK2 / PLINK software to obtain; The analysis module is used to perform kinship analysis on the SNP data sets of the transplanted embryo samples and the pregnant embryo samples using King software, and judge whether the transplanted embryo is consistent with the pregnant embryo according to the kinship coefficient value. The specific method is as follows: when the kinship coefficient value is >0.354, it indicates that the transplanted embryo is consistent with the pregnant embryo; when the kinship coefficient value is ≤0.354, it indicates that the transplanted embryo is inconsistent with the pregnant embryo; The pregnancy embryo sample is derived from one or more of abortion tissue, amniotic fluid, chorionic villus, umbilical cord blood, and peripheral blood; The method for constructing the sequencing library is as follows: the whole genome DNA of the transplanted embryo sample and the pregnant embryo sample is fragmented into several short DNA molecules, the target fragment DNA is obtained by a magnetic bead screening system, and then the sequencing library is obtained by PCR amplification and magnetic bead purification; After obtaining sequencing data with a data volume ≥ 80M, CNV analysis of the sequencing data is also included.

4. A computer storage medium, characterized in that: The computer storage medium stores computer program instructions, and when the computer program instructions are executed, the device where the computer storage medium is located is controlled to execute the steps in the method according to claim 1 or 2.

5. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: The computer program executes the steps of the method according to claim 1 or 2 when running.

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