Method and device for identifying maternal source pollution and detecting copy number abnormity by using peak value

Abstract

The invention relates to a method for identifying maternal source pollution and detecting copy number abnormity by using peak values, which comprises the following steps: S10: detecting SNP site information of a detected sample, and extracting LRR feature data, BAF feature data and genotype feature data of each autosome of the detected sample based on an SNP site genome position sequence; s20, according to the BAF characteristic data, obtaining the peak number and the peak value of each autosome, and according to the peak number, the peak value and the LRR characteristic data, judging the chromosome state of each autosome; s30, judging the sample state of the detected sample according to the chromosome state with the highest proportion in all chromosome states; and S40: according to the LRR feature data, the BAF feature data and the genotype feature data, obtaining a CNV result and an ROH result of the detected sample, and / or judging whether the detected sample is whole genome ROH. According to the method, whether the sample has maternal pollution and copy number abnormity or not is judged by utilizing the detection data of the abortion substances, and additional experiments and maternal samples are not needed.

Description

Technical Field

[0001] The present invention relates to the field of genetic detection technology, and in particular to a method and a device for identifying maternal contamination and detecting copy number abnormalities using peak values. Background Art

[0002] Chromosomal disease is a large class of serious genetic diseases caused by abnormal chromosome number or structure, including clinical manifestations such as multiple malformations, mental retardation and growth retardation. It is also an important cause of developmental abnormalities and male and female infertility. Embryonic chromosomal abnormalities are one of the important causes of early spontaneous abortion. Clinically, about 10% to 15% of pregnant women suffer spontaneous abortion, of which 50% to 60% are related to embryonic chromosomal abnormalities. Among spontaneous abortions caused by chromosomal abnormalities, about 96% are due to numerical abnormalities (i.e. copy number abnormalities), and structural abnormalities are only 3% to 5%.

[0003] Genome copy number variation (CNV) usually refers to the decrease or increase in the copy number of large genomic fragments with a length of more than 1kb, which is mainly manifested as duplication and deletion at the submicroscopic level. It is an important part of genomic structural variation. Copy number variation detection (CNV detection) of miscarriage products can help doctors identify the cause of miscarriage, provide a scientific basis for subsequent treatment or intervention measures, provide guidance for the next pregnancy, and avoid unnecessary testing and erroneous treatment.

[0004] Due to the limitations of sampling technology, the abortion product tissues currently collected are often mixed with maternal peripheral blood, uterine decidua tissue, etc., resulting in different degrees of maternal cell contamination (MCC) in the tested samples, affecting the accuracy of the final CNV test results. Therefore, before performing CNV testing, it is usually necessary to first identify the maternal contamination in the abortion product samples through short tandem repeat (STR) testing, but this method requires additional maternal blood samples to be drawn and additional experimental steps to be performed, which not only increases the testing cost but also prolongs the testing cycle. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide a method for identifying maternal contamination and detecting copy number abnormalities using peaks, which can use the SNP site information of the aborted product to determine whether the sample has maternal contamination and copy number abnormalities, without the need for additional experiments and additional maternal peripheral blood samples.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for identifying maternal contamination and detecting copy number abnormalities using peaks comprises the following steps:

[0008] Step S10: Detecting the SNP site information of the sample under test, and extracting the LRR feature data, BAF feature data and genotype feature data based on the genomic position sequence of the SNP site of each autosome of the sample under test according to the SNP site information;

[0009] Step S20: according to the BAF characteristic data, the number of peaks and the peak value on each autosome are obtained, and the chromosome state of each autosome is determined according to the number of peaks, the peak value and the LRR characteristic data;

[0010] Step S30: judging whether the sample state of the sample under test is that there is maternal contamination or no maternal contamination according to the chromosome state with the highest proportion among all chromosome states; if the sample state is that there is no maternal contamination, further judging whether the sample state is haploid, triploid or other, and proceeding to step S40;

[0011] Step S40: Obtain the CNV result of the sample under test according to the LRR characteristic data and the BAF characteristic data; determine whether the sample under test is whole genome ROH and / or obtain the ROH result of the sample under test according to the LRR characteristic data and the genotype characteristic data.

[0012] Compared with the existing technology, the present invention can not only quickly determine the maternal contamination of the sample by comprehensively evaluating the peak value of the BAF data on the autosomes, but also quickly determine whether the sample belongs to special types of samples such as haploid, triploid, whole genome ROH, etc., which can improve detection efficiency, improve detection comprehensiveness, and reduce detection time and cost.

[0013] Further, in step S20, the number of peaks and the peak value on each autosome are obtained using a mixed Gaussian model and / or a sliding window; wherein, obtaining the number of peaks and the peak value on each autosome using a mixed Gaussian model includes:

[0014] A mixed Gaussian model was used to fit the distribution of BAF characteristic data on each autosome, and the Bayesian information content (BIC) of the mixed Gaussian model corresponding to 1 Gaussian distribution to 10 Gaussian distributions was calculated. The number and mean of the Gaussian distributions corresponding to the mixed Gaussian model with the lowest BIC were the number and peak value of peaks on each autosome.

[0015] Furthermore, the step S20 also includes obtaining the number and peak value of the common peaks of the mixed Gaussian model and the sliding window on each autosome, including: obtaining the intersection area between the peaks of the mixed Gaussian model and the peaks of the sliding window, if the intersection area ratio is greater than a first set threshold, then the union area of ​​the peaks of the mixed Gaussian model and the peaks of the sliding window that form the intersection area is the common peak area; using the sliding window to detect the peak value of the common peak area to obtain the peak value and number of the common peak.

[0016] Further, judging the chromosome state of each autosome according to the peak number and peak value in step S20 includes:

[0017] When the number of peaks is 0, and the BAF characteristic data are mainly distributed around 0 and 1, if the mean value of the LRR characteristic data is less than -0.2, the chromosome state of the autosome is the whole chromosome missing, otherwise, the chromosome state is unknown;

[0018] When the peak number is 1, if the peak value is in the interval [0.45, 0.55], and its peak ratio is greater than the second set threshold, the chromosome status of the autosome is normal, otherwise, the chromosome status is maternal contamination;

[0019] When the number of peaks is 2, if the two peaks are in the interval [0.3, 0.4] and [0.6, 0.7] respectively, the chromosome state of the autosome is chromosome duplication, otherwise, the chromosome state is unknown;

[0020] If the number of peaks is greater than or equal to 3, the chromosome status of the autosome is that there is maternal contamination;

[0021] The calculation formula of the peak value ratio is:

[0022]

[0023] Furthermore, obtaining the CNV results of the tested samples in step S40 includes the following steps: obtaining the fragment CNV results on each autosome in the tested samples whose sample status is other based on the LRR feature data, BAF feature data and HMM model, merging adjacent fragment CNV results, and filtering the fragment CNV results based on the length of the CNV and the SNP data contained in the CNV to obtain the CNV results.

[0024] Further, the step S40 of determining whether the sample under test is whole genome ROH includes: calculating the total heterozygosity ratio of all autosomes of the sample under test whose sample status is other, if the heterozygosity ratio is less than the third set threshold, the sample status is updated to whole genome ROH, otherwise, the sample status is other; wherein the calculation formula of the heterozygosity ratio is as follows:

[0025]

[0026] Further, the ROH result in step S40 is the ROH result of the sample whose state is haploid, triploid or other tested samples, and the ROH acquisition method includes the following steps: n adjacent SNPs are divided into bins according to the position of the SNP site on the genome, if the homozygous ratio of the SNP in the bin is greater than 95%, and the mean value of the LRR characteristic data is near 0, then the bin is a potential ROH region; merge adjacent potential ROH regions to form a new potential ROH region, if the homozygous rate of the SNP in the new potential ROH region is greater than 99%, then the new potential ROH region is the ROH region, and all ROH regions are summarized to obtain the ROH result; wherein, the calculation formula of the homozygous rate is as follows:

[0027]

[0028] The present invention also provides a device for identifying maternal contamination and detecting copy number abnormalities using peak values, comprising:

[0029] A data extraction unit, extracting the LRR characteristic data, BAF characteristic data and genotype characteristic data based on the genomic position sequence of the SNP site of each autosome of the tested sample according to the SNP site information of the tested sample in the database;

[0030] A chromosome state acquisition unit acquires the number of peaks and peak value on each autosome according to the BAF characteristic data, and determines the chromosome state of each autosome according to the number of peaks, peak value and LRR characteristic data;

[0031] A first sample status determination unit, preliminarily determines whether the sample status of the sample under test is that there is maternal contamination or no maternal contamination according to the chromosome status with the highest proportion among all chromosome statuses, and if the sample status is that there is no maternal contamination, further determines whether the sample status of the sample under test is haploid, triploid or others according to the chromosome status with the highest proportion among all chromosome statuses;

[0032] The second sample status determination unit obtains the sample CNV result of the tested sample according to the LRR characteristic data and the BAF characteristic data if the sample status does not indicate maternal contamination; and determines whether the tested sample is whole genome ROH and / or obtains the sample ROH result of the tested sample according to the LRR characteristic data and the genotype characteristic data.

[0033] The present invention also provides a computer device, the computer device comprising a processor and a memory:

[0034] The memory is used to store a computer program and transmit the computer program to the processor;

[0035] The processor is used to execute the method of using peaks to identify maternal contamination and detect copy number abnormalities according to the instructions in the computer program.

[0036] The present invention also provides a computer-readable storage medium, which is used to store a computer program. When the computer program is executed by a processor, the processor executes the method of using peaks to identify maternal contamination and detect copy number abnormalities.

[0037] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a method for identifying maternal contamination and detecting copy number abnormalities using peak values ​​of the present invention;

[0039] Figure 2 A schematic diagram of a process for identifying maternal contamination and detecting copy number abnormalities using peak values ​​provided by the present invention;

[0040] Figure 3 A schematic diagram of the structure of a device for identifying maternal contamination and detecting copy number abnormalities using peak values ​​provided by the present invention;

[0041] Figure 4 The LRR result and BAF result of the maternal contaminated sample in Example 1 are shown;

[0042] Figure 5 This is the peak diagram on the autosome of the maternal contamination sample in Example 1;

[0043] Figure 6 The LRR result and BAF result of the slightly maternal contaminated sample in Example 2 are shown;

[0044] Figure 7 This is the peak diagram on the autosome of the slightly maternally contaminated sample in Example 2;

[0045] Figure 8 It is the peak diagram of the triploid sample in Example 3;

[0046] Fig. 9 is the peak map of the whole genome ROH sample in Example 4;

[0047] Fig.10 This is a CNV result diagram of chromosome 6 duplication of the whole genome ROH sample in Example 4;

[0048] Fig.11 This is a CNV result diagram of chromosome 9 duplication of the whole genome ROH sample in Example 4;

[0049] Fig.12 It is the peak diagram of the sample with missing fragments in Example 5;

[0050] Fig.13 This is the CNV result diagram of the sample with fragment deletion in Example 5. DETAILED DESCRIPTION

[0051] In view of the problem that the existing technology requires collecting maternal peripheral blood samples for maternal contamination detection before performing CNV detection, and confirming that the samples are free of maternal contamination before conducting subsequent CNV detection related experiments, the present invention aims to provide a method that can use the data obtained from the convection product sample detection to judge the maternal contamination of the sample and perform a comprehensive test on the sample, without the need for additional maternal samples and additional experiments.

[0052] See also Figure 1 The present invention provides a method for identifying maternal contamination and detecting copy number abnormalities using peak values, which specifically includes the following steps:

[0053] Step S10: Detecting the SNP site information of the sample under test, and extracting the LRR feature data, BAF feature data and genotype feature data based on the genomic position sequence of the SNP site of each autosome of the sample under test according to the SNP site information;

[0054] Step S20: according to the BAF characteristic data, the number of peaks and the peak value on each autosome are obtained, and the chromosome state of each autosome is determined according to the number of peaks, the peak value and the LRR characteristic data;

[0055] Step S30: judging whether the sample state of the sample under test is that there is maternal contamination or no maternal contamination according to the chromosome state with the highest proportion among all chromosome states; if the sample state is that there is no maternal contamination, further judging whether the sample state is haploid, triploid or other, and proceeding to step S40;

[0056] Step S40: Obtain the sample CNV result of the tested sample according to the LRR feature data and the BAF feature data; determine whether the tested sample is whole genome ROH and / or obtain the sample ROH result of the tested sample according to the LRR feature data and the genotype feature data.

[0057] In order to more clearly understand the technical solution provided by the embodiments of the present application, some key terms involved in the embodiments of the present application are first introduced here:

[0058] SNP (single nucleotide polymorphism) refers to a polymorphism caused by a change in a single nucleotide at a certain site in the chromosome DNA sequence. The frequency of SNP in a population is generally >1%. On average, there is one SNP every 300-1000bp in the human genome. Currently, SNP databases can be obtained from multiple public databases, including, for example, http: / / cgap.ncbi.nih.gov / GAI; http: / / www.ncbi.nlm.nih.gov / SNP; Human SNP database http: / / hgbas.cgr.ki.sei or http: / / hgbase.interactiva.de / .

[0059] Chromosome microarray analysis (CMA) is a molecular karyotype analysis technology for high-throughput detection of genomic DNA copy number variation. It includes two technologies: microarray comparative genomic hybridization chip (array-CGH) and single nucleotide polymorphism array (SNParray). Among them, single nucleotide polymorphism chip technology can diagnose genetic syndromes and chromosomal diseases caused by genomic deletions or duplications based on CNV, and can also diagnose homozygous regions, unbalanced translocations, etc. through SNP analysis.

[0060] Region of homozygosity (ROH) is a phenomenon of loss of heterozygosity that occurs continuously within a certain range in a genomic region. Most diploid cells, such as human somatic cells, have two copies of the genome, one from the father and the other from the mother. At a certain allele locus, if the bases from the father and the mother are different, the locus is heterozygous. If a certain mechanism (such as distant or close relative marriage or gene conversion) causes continuous allele sequences within a certain range to be homozygous and without heterozygotes (the number of copies is still 2), then this region is a genomic homozygous region ROH. The generation of ROH involves identity by descent (IBD) or uniparental disomy (UPD) reasons.

[0061] Copy number variation (CNV) refers to a change in the copy number of at least a portion of a nucleic acid molecule in a test sample compared to the corresponding nucleic acid sequence in a normal sample, wherein the portion has a length greater than 1 kb. The conditions and causes of copy number variation may include: deletions, such as microdeletions; insertions, such as microinsertions, microduplications, duplications; inversions, transpositions, and complex multi-site variations.

[0062] The signal logarithm ratio, i.e., the log2R ratio (LRR, i.e., the log2 transformed value of the standardized SNP intensity), reflects the copy number change of the tested sample relative to the normal sample at the tested SNP site. BAF, i.e., the B allele frequency (i.e., the ratio of the B allele signal intensity to the total SNP signal intensity), reflects the occurrence of different alleles at the tested SNP site in the tested sample. In normal diploid samples, BAF values ​​are distributed around 0, 0.5, and 1 (i.e., for each SNP, there are three possible forms of AA, AB, and BB on the genome), and LRR values ​​are distributed around 0. Abnormal conditions of the tested samples (including sample contamination and copy number abnormalities) will cause an increase or decrease in the total SNP signal intensity and / or the B allele frequency, resulting in BAF and / or LRR values ​​that deviate from the above values. For example, when a chromosome region has copy number loss, the BAF in that region will only be 0 and 1 (genotypes AA and BB), and there will be no value near 0.5 (genotype AB), and the LRR value in that region will decrease to a value less than 0; and when a chromosome region has copy number gain, the BAF value in that region will be around 0, 0.33, 0.67, and 1.0 (genotypes AAA, AAB, ABB, and BBB), and the LRR value in that region will increase to a value greater than 0. Based on this change in BAF and LRR values, in principle, it is possible to estimate the abnormal copy number variation in a sample.

[0063] A Gaussian mixture model is a probabilistic model that assumes that the observed data is a mixture of multiple Gaussian distributions, each of which represents a latent class or group.

[0064] Bayesian information criterion (BIC) is an evaluation index of the reliability of the result obtained by using the Bayesian formula to correct the probability of occurrence of partially unknown states using subjective probability estimates under incomplete intelligence.

[0065] Sliding Window is a commonly used technique in data processing and analysis to perform local operations on sequence data or continuous data. It divides the input data into fixed-size windows and calculates the corresponding results each time the window is moved. The sliding step defines the step distance of the window each time it moves. It determines the degree of overlap between windows and their positional relationship. For example, if the size of the sliding window is 10 and the sliding step is 5, the window will slide 5 units to the right each time it moves.

[0066] A Hidden Markov Model (HMM) is a statistical model used to describe a Markov process in which a system has hidden states. In this model, the states of the system are not directly visible (they are "hidden"), but the changes in the states can be inferred from a series of observed events or signals.

[0067] In the above method, the detection of SNP site information in step S10 includes detecting the nucleic acid of the sample under test using a chromosome microarray chip, and obtaining information of the SNP site corresponding to the chip by analyzing the fluorescence signal after hybridization. Specifically, the chromosome microarray chip includes the Infinium Asian Screening Array (ASA) chip of Illumina Company, etc.

[0068] The other steps of the above method can be performed by a device for identifying maternal contamination and detecting copy number abnormalities. The following is a detailed description of the peak method for identifying maternal contamination and detecting copy number abnormalities provided in an embodiment of the present invention in combination with the device for identifying maternal contamination and detecting copy number abnormalities.

[0069] See also Figure 2 and Figure 3 , Figure 2 This is a flow chart of the method for identifying maternal contamination and detecting copy number abnormalities in this embodiment. Figure 3 The schematic diagram of the structure of the device for identifying maternal contamination and detecting copy number abnormality of this embodiment is shown in FIG. The device for identifying maternal contamination and detecting copy number abnormality of the embodiment of the present invention is provided with a database, and the SNP site information of the sample under test is stored in the database. The device also includes a data extraction unit 10, a chromosome state acquisition unit 20, a sample state determination unit 30, a second sample state determination unit 40 and a result output unit 50.

[0070] The data extraction unit 10 of the device for identifying maternal contamination and detecting copy number abnormalities is used to perform step S10: extracting LRR feature data, BAF feature data and genotype feature data based on the genomic position sequence of the SNP site of each autosome of the tested sample according to the SNP site information of the tested sample. Specifically, the data preprocessing unit 10 includes a BAF feature data extraction module 11a, an LRR feature data extraction module 11b and a genotype feature data extraction module 11c.

[0071] The BAF feature data extraction module 11a is used to execute step S11a: obtaining the BAF feature data of each autosome based on the genomic position sequence of the SNP sites.

[0072] Specifically, the BAF data corresponding to the SNP site is extracted from the raw data of the chip through the annotation file corresponding to the chip, and the following sites in the BAF data are filtered out: 1) sites whose chromosomes are not autosomes or sex chromosomes; 2) sites with incorrect and repeated coordinate information; 3) sites whose genotypes are not AA, AB, and BB; then the filtered BAF data are sorted according to chromosomes and coordinates to obtain BAF feature data based on the genomic position order of the SNP site for each autosome. In this embodiment, BeadArrayFiles software is used to extract data from the Illumina chip.

[0073] The LRR feature data extraction module 11b is used to execute step S11b: obtaining LRR feature data based on the genomic position sequence of SNP sites for each autosome.

[0074] Specifically, the LRR data corresponding to the SNP site is extracted from the raw chip data through the annotation file corresponding to the chip, and the following sites in the LRR data are filtered out: 1) sites whose chromosomes are not autosomes or sex chromosomes; 2) sites with erroneous and repeated coordinate information; 3) sites whose genotypes are not AA, AB, and BB; then the filtered LRR data are sorted according to chromosomes and coordinates to obtain the LRR feature data of each autosome based on the genomic position order of the SNP sites.

[0075] The genotype feature data extraction module 11c is used to execute step S11c: extracting the genotype feature data corresponding to the SNP site from the chip raw data through the annotation file corresponding to the chip.

[0076] Specifically, the genotype data corresponding to the SNP site is extracted from the raw chip data through the annotation file corresponding to the chip, and the following sites in the genotype data are filtered out: 1) sites whose chromosomes are not autosomes or sex chromosomes; 2) sites with erroneous and repeated coordinate information; 3) sites whose genotypes are not AA, AB, and BB; then the filtered genotype data are sorted according to chromosomes and coordinates to obtain the genotype characteristic data of each autosome based on the genomic position order of the SNP sites.

[0077] Further, the chromosome state acquisition unit 20 is used to perform step S20: according to the BAF characteristic data, the number of peaks and the peak value on each autosome are obtained, and the chromosome state of each autosome is determined according to the number of peaks, the peak value and the LRR characteristic data. Specifically, the chromosome state acquisition 20 includes a peak acquisition module 21 and a chromosome state determination module 22.

[0078] The peak acquisition module 21 is used to perform step S21: using a mixed Gaussian model and / or a sliding window to obtain the number of peaks and peak values ​​on each autosome. In this embodiment, the peak acquisition module 21 includes a mixed Gaussian model module 21a, a sliding window module 21b, and a common peak acquisition module 21c.

[0079] The mixed Gaussian model module 21a is used to execute step S21a: use the mixed Gaussian model to fit the distribution of BAF feature data on each autosome, and calculate the Bayesian information content BIC of the mixed Gaussian model corresponding to 1 Gaussian distribution to 10 Gaussian distributions, wherein the number and mean of the Gaussian distribution corresponding to the mixed Gaussian model with the lowest BIC are the number and peak value of peak F1;

[0080] Wherein, the mixed Gaussian model is:

[0081] Where x is the observed data point, π k is the mixture weight of k Gaussian distributions, N(x|μ k ,∑ k ) indicates that the mean is μ k And the covariance matrix is ​​∑ k The probability density function of the kth Gaussian distribution;

[0082] The calculation formula of the Bayesian information BIC is:

[0083] BIC=kln(n)-2ln(L); where k is the Gaussian distribution number, n is the number of BAF feature data, and L is the likelihood function.

[0084] In this embodiment, it is assumed that there is only 1 Gaussian distribution to a maximum of 10 Gaussian distributions in the current chromosome BAF feature data. Each time the assumption is made, the BAF feature data is used to perform model training and calculate the BIC value. 10 assumptions generate 10 BIC values, and the model with the lowest BIC value is taken as the optimal model; the corresponding Gaussian distribution number of the optimal model is the peak number of peak F1, and the mean of each distribution of the optimal model is the peak value of each peak F1.

[0085] The sliding window module 21b is used to perform step S21b: use the sliding window to fit the distribution of BAF feature data on each autosome, obtain BAF distribution data, and use the peak detection method to obtain the peak value and distribution of peak F2 in the BAF distribution data, specifically including the following steps:

[0086] Select an appropriate window size and step size to process the BAF feature data, move the window from 0 to 1 by one step size, and repeat this process until all regional windows are divided; and obtain the BAF distribution data by calculating the number of SNPs in each window. In this embodiment, the step size is less than or equal to the window size.

[0087] Then, the peak detection method is used to identify significant peaks, i.e., local maxima in the BAF distribution data, from the BAF distribution data, and the peaks in which the number of SNPs is lower than the threshold and may be caused by random noise are deleted to obtain the peak value and distribution of peak F2.

[0088] The common peak acquisition module 21c is used to execute step S21c: obtain the intersection area between all peaks in the peak F1 identified by the mixed Gaussian model and the peak F2 identified by the sliding window, and if the intersection area ratio between the peak F1 and the peak F2 is greater than the first set threshold, the union area of ​​the peak F1 and the peak F2 forming the intersection area is used as the common peak area; use the sliding window to detect the peak value of the common peak area to obtain the peak value and peak number of the final common peak F;

[0089] The calculation formula of the intersection area ratio is as follows:

[0090] Wherein, F1 refers to the peak range of peak F1 identified by the mixed Gaussian model, and F2 refers to the peak range of peak F2 identified by the sliding window.

[0091] In this embodiment, the first set threshold is set to 60%.

[0092] The chromosome state type judgment module 22 is used to execute step S22: determine the chromosome state type of each autosome according to the peak number and peak value of the common peak F obtained in step S21c and the LRR characteristic data obtained in step S11b.

[0093] (1) If the peak number of the common peak F is 0, and the BAF characteristic data is mainly distributed around 0 and 1. Further, if the mean value of the LRR characteristic data is less than -0.2, the chromosome state of the autosome is a complete chromosome loss, otherwise, the chromosome state of the autosome is unknown.

[0094] (2) When the peak number of the common peak F is 1, if the peak value of the common peak F is around 0.5, that is, in the interval [0.45, 0.55], and the peak value ratio of the common peak F is greater than the second set threshold, then the chromosome state of the autosome is normal. Otherwise, the chromosome state of the autosome is slightly contaminated, that is, the chromosome has maternal contamination and the contamination ratio is greater than 5%. The calculation formula of the peak ratio is:

[0095] In this embodiment, the second set threshold is 0.2;

[0096] (3) When the number of peaks of the common peak F is 2, if the peak values ​​of the double peaks satisfy that one peak is near 0.33, i.e., in the interval [0.3, 0.4], and the other peak is near 0.67, i.e., in the interval [0.6, 0.7], then the chromosome state of the autosome is chromosome duplication; otherwise, the chromosome state of the autosome is unknown;

[0097] (4) If the number of peaks of the common peak F is greater than or equal to 3, the chromosome status of the autosome is that there is maternal contamination, and the contamination ratio is >10%.

[0098] Further, the first sample state determination unit 30 is used to perform step S30: judging whether the sample state of the sample under test is maternal contamination or no maternal contamination according to the chromosome state with the highest proportion among all chromosome states. If the sample state is no maternal contamination, further judging whether the sample state is haploid, triploid or others.

[0099] Specifically, the chromosome status of each chromosome is counted, and the chromosome status type with the largest number is extracted and recorded as the autosomal status; the sample status is preliminarily determined based on the autosomal status, if the autosomal status is that there is maternal contamination of chromosomes, then the sample status is that there is maternal contamination, otherwise the sample status is that there is no maternal contamination; if the sample status is that there is no maternal contamination, further judgment is made, 1) if the autosomal status is chromosome deletion, then the sample status is updated to haploid; 2) if the autosomal status is chromosome duplication, then the sample status is updated to triploid; 3) if the autosomal status is other types such as normal chromosomes, unknown, etc., then the sample status is updated to other.

[0100] Further, the second sample status determination unit 40 is used to perform step S40: according to the LRR feature data, the BAF feature data and the genotype feature data, obtain the CNV result and the ROH result on each autosome, and determine whether the sample under test is whole genome ROH according to the heterozygous ratio of the sample under test. The second sample status determination unit 40 includes a sample CNV analysis module 40A, a whole genome ROH analysis module 40B and a sample ROH analysis module 40C.

[0101] The sample CNV analysis module 40A is used to perform step S40A: obtaining the CNV results of the sample with the sample status of other according to the LRR feature data and the BAF feature data, and the method for obtaining the sample CNV results includes: using the PennCNV software, the BAF feature data of the sample, the LRR feature data of the sample and the pre-trained HMM model, obtaining the fragment CNV results on each chromosome in the sample, merging the adjacent small fragment CNVs, and filtering the fragment CNV results according to the length of the CNV and the SNP data contained in the CNV. The pre-trained HMM model described in this embodiment is an existing publicly available HMM model trained based on more than 100 research and development negative samples as a baseline.

[0102] The whole genome ROH analysis module 40B is used to perform step S40B: judging whether the sample status of the sample under test whose sample status is other is whole genome ROH according to the genotype feature data, specifically including: calculating the heterozygosity ratio of all autosomes of the sample under test whose sample status is other, if the heterozygosity ratio is less than the third set threshold, the sample status is updated to whole genome ROH; otherwise, the sample status is other; wherein the calculation formula of the heterozygosity ratio is as follows:

[0103] In this embodiment, the third set threshold is set to 0.1.

[0104] The sample ROH analysis module 40C is used to perform step S40C: according to the LRR characteristic data and the genotype characteristic data, the ROH result of the sample under test is obtained, and the sample state is haploid, triploid or other. The method for obtaining the sample ROH result includes the following steps: according to the position of the SNP on the genome, n adjacent SNPs are divided into bins. If the homozygosity rate of the SNP in the bin is greater than 95%, and the mean value of the LRR characteristic data is near 0, then the bin is a potential ROH region; merge adjacent potential ROH regions to form a new potential ROH region. If the homozygosity rate of the SNP in the new potential ROH region is greater than 99%, then the new potential ROH region is a ROH region. All ROH regions are aggregated, and regions with a small number of SNPs and a short length are filtered to obtain the final sample ROH result.

[0105] The calculation formula of the homozygosity rate of the SNP is as follows:

[0106] Wherein, k is the number of homozygous SNP sites in the bin, and n is the number of all SNP sites in the bin.

[0107] Furthermore, the result output unit 50 is used to execute step S50: output the final sample status of the tested sample and the CNV result and / or ROH result of the tested sample:

[0108] 1) If the sample status obtained in step S30 is maternal contamination, it is indicated that the sample has maternal contamination;

[0109] 2) If the sample status obtained in step S40B is whole genome ROH, it is indicated that the sample status is whole genome ROH, and at the same time, if CNV exists in the sample, the CNV result is output;

[0110] 3) If the sample state obtained in step S30 is haploid or triploid, the sample state is prompted to be haploid or triploid, and if the sample has a ROH region, the ROH result is output;

[0111] 4) If the sample status obtained in step S40B is other, it is prompted that the sample status is other. At the same time, if the sample has CNV and / or ROH regions, the CNV result and / or ROH result are output.

[0112] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method of using peaks to identify maternal contamination and detect copy number abnormalities.

[0113] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method of using peaks to identify maternal contamination and detect copy number abnormalities are implemented.

[0114] For better understanding and implementation, the present invention is described in detail below with reference to specific embodiments and drawings.

[0115] Example 1

[0116] The gDNA of sample RD22112301 was detected using the Infinium Asian Screening Array chip of Illumina, and the information of the chip corresponding to the SNP site was obtained by analyzing the fluorescence signal after hybridization, and then the sample status and CNV results and ROH results of the sample were obtained by using the method of using peaks to identify maternal contamination and detect copy number abnormalities provided by the present invention. The sample used in this example is a research and development test sample, and the specific steps of the method are as follows:

[0117] Step S11a: Use Illumina's BeadArrayFiles software to extract BAF data corresponding to all SNP sites from the chromosome microarray data; filter out SNP data whose chromosomes are not autosomes and sex chromosomes, whose coordinate information is incorrect or repeated, and whose genotype information is not AA, AB, or BB; finally, sort the SNP sites according to their positions on the genome to obtain BAF feature data based on the genomic position order of the SNP sites for each autosome.

[0118] Step S11b: Use Illumina's BeadArrayFiles software to extract the LRR data corresponding to all SNP sites from the chromosome microarray data; filter out the SNP data whose chromosomes are not autosomes and sex chromosomes, whose coordinate information is incorrect or repeated, and whose genotype information is not AA, AB, or BB; finally, sort the SNP sites according to their positions on the genome to obtain the LRR characteristic data of each autosome based on the genomic position order of the SNP sites.

[0119] Step S11c: Use Illumina's BeadArrayFiles software to extract the genotype data corresponding to all SNP sites from the chromosome microarray data, and filter out the SNP data whose chromosomes are not autosomes and sex chromosomes, whose coordinate information is incorrect or repeated, and whose genotype information is not AA, AB, or BB. Finally, sort the SNP sites according to their positions on the genome to obtain the genotype characteristic data.

[0120] Step S21a: Use a mixed Gaussian model to perform model training on the BAF feature data one by one from 1 distribution to 10 distributions, and calculate the BIC values ​​corresponding to the K distributions, and take the model with the lowest BIC value as the optimal model; the number of distributions of the optimal model is the number of peaks of peak F1, and the mean of each distribution is the peak value of each peak F1.

[0121] Step S21b: Use a window of size 0.025 to slide in steps of 0.005, count the number of SNPs in each window one by one, and find all the peaks in the BAF feature data through the peak detection method, remove the peaks with SNP numbers less than 100, and obtain the peak number and peak value of peak F2.

[0122] Step S21c: The union area of ​​peak F1 and peak F2 whose intersection area ratio is greater than 60% between peak F1 identified by the mixed Gaussian model and peak F2 identified by the sliding window method is taken as the common peak area, and peak detection is performed in the common peak area with a 0.025 sliding window and a step size of 0.005 to determine the peak value and number of peaks of the final common peak F.

[0123] Referring to Table 1, in this embodiment, the peak detection result of sample RD22112301 shows that the peak number of the common peak F of chromosome 1 is 5, and the peak values ​​are 0.16, 0.36, 0.48, 0.62 and 0.88 respectively.

[0124] Table 1: Peak detection results of chromosome 1 of sample RD22112301

[0125]

[0126] Step S22: Determine the chromosome state type of each chromosome according to the peak number and peak value of the common peak F obtained in step S21c and the LRR feature data obtained in step S11b.

[0127] See Table 1 and Figure 4-5 , Figure 4 The BAF graph showing severe contamination from the mother source has significant data separation. Figure 5 It shows that multiple peaks appear on each chromosome, that is, in the sample RD22112301 of this embodiment, the number of peaks of chromosomes 1 to 22 is greater than 3, that is, the chromosome status of each chromosome is that there is maternal contamination, and the contamination ratio is >10%.

[0128] Step S30: Summarize the chromosome status types of all autosomes obtained in step S22, and determine whether the sample status of the sample is with maternal contamination or without maternal contamination. If the sample status is without maternal contamination, further determine whether the sample status is haploid, triploid or other.

[0129] In this embodiment, by summarizing the status of 22 autosomal chromosomes of sample RD22112301, it was found that the status of the autosomal chromosomes was that there was maternal contamination of chromosomes, that is, the status of the sample was that there was maternal contamination, and it was severe maternal contamination, and the contamination ratio was greater than 10%.

[0130] Step S50: Output the sample status and / or CNV result and / or ROH result: indicating that the sample has maternal contamination.

[0131] Example 2

[0132] The gDNA of sample RD23120601 was detected using the Infinium Asian Screening Array chip of Illumina. The information of the SNP site corresponding to the chip was obtained by analyzing the fluorescence signal after hybridization. Then, the sample status, CNV results, and ROH results of the sample were obtained using the following steps:

[0133] Step S11a: Obtain BAF feature data based on the genomic position sequence of SNP sites for each autosome.

[0134] Step S11b: Obtain LRR feature data based on the genomic position sequence of SNP sites for each autosome.

[0135] Step S11c: Obtain genotype characteristic data of each autosome based on the genomic position sequence of the SNP sites.

[0136] Step S21a: Use the mixed Gaussian model to obtain the peak value and peak number of peak F1 on each autosome.

[0137] Step S21b: Use a sliding window to obtain the peak value and peak number of peak F2 on each autosome.

[0138] Step S21c: Obtain the peak value and peak number of the common peak F of peak F1 and peak F2 on each autosome.

[0139] Step S22: Determine the chromosome status of each autosome based on the peak number, peak value and LRR characteristic data of the common peak F.

[0140] See also Figure 6-7 ,In this embodiment, in the sample RD23120601, the peak numbers of chromosomes 1 to 22 are all 1, and the peak ratio is 0.158, which is less than the threshold of 0.2, that the chromosome state of each chromosome is slight chromosome contamination.

[0141] Step S30: Summarize the chromosome status types of all autosomes obtained in step S22 to determine whether the sample has maternal contamination or no maternal contamination. If the sample status is no maternal contamination, further determine whether the sample status is haploid, triploid or other.

[0142] In this embodiment, by summarizing the status of 22 autosomal chromosomes of sample RD22112301, it was found that the status of the autosomal chromosomes was slight maternal contamination, that is, the status of the sample was maternal contamination.

[0143] Step S50: Output the sample status and / or CNV result and / or ROH result: indicating that the sample has maternal contamination.

[0144] Example 3

[0145] The gDNA of sample RD24041801 was detected using the Infinium Asian Screening Array chip of Illumina. The information of the SNP site corresponding to the chip was obtained by analyzing the fluorescence signal after hybridization. Then, the sample status, CNV result, and ROH result of the sample were obtained by the following steps:

[0146] Step S11a: Obtain BAF feature data based on the genomic position sequence of SNP sites for each autosome.

[0147] Step S11b: Obtain LRR feature data based on the genomic position sequence of SNP sites for each autosome.

[0148] Step S11c: Obtain genotype characteristic data of each autosome based on the genomic position sequence of the SNP sites.

[0149] Step S21a: Use the mixed Gaussian model to obtain the peak value and peak number of peak F1 on each autosome.

[0150] Step S21b: Use a sliding window to obtain the peak value and peak number of peak F2 on each autosome.

[0151] Step S21c: Obtain the peak value and peak number of the common peak F of peak F1 and peak F2 on each autosome.

[0152] Step S22: Determine the chromosome status of each autosome based on the peak number, peak value and LRR characteristic data of the common peak F.

[0153] See also Figure 8 In this embodiment, in sample RD24041801, chromosomes 1 to 22 are all double peaks, and the peak value of one peak is in the interval [0.3, 0.4], and the peak value of the other peak is in the interval [0.6, 0.7], that is, the chromosome state is chromosome duplication.

[0154] Step S30: Summarize the chromosome status types of all autosomes obtained in step S22 to determine whether the sample has maternal contamination or no maternal contamination. If the sample status is no maternal contamination, further determine whether the sample status is haploid, triploid or other.

[0155] In this embodiment, by summarizing the status of 22 autosomal chromosomes, it was found that there was no maternal contamination of chromosomes in the autosomal state, that is, the sample state was no maternal contamination; further judgment showed that the autosomal state was all chromosome duplication, that is, the sample state was triploid.

[0156] S40C: Obtain the ROH results of the tested samples whose sample status is haploid, triploid or other: According to the position of SNP on the genome, 1000 adjacent SNPs are divided into a bin. When the homozygosity rate of SNP in the bin is >95% and the LRR of the region is near 0, the bin is considered to be a potential ROH region; a new potential ROH region is formed by merging adjacent potential ROH regions. If the homozygosity rate of SNP in the new potential ROH region is >99%, the region is considered to be a ROH region. Filter out the regions with less than 500 SNPs and less than 1M in length to obtain the final sample ROH result.

[0157] In this embodiment, it is found through analysis that the sample does not have a ROH region.

[0158] S50: Output sample status and / or CNV result and / or ROH result: indicating that the sample is triploid.

[0159] Example 4

[0160] The gDNA of sample RD23103101 was detected using the Infinium Asian Screening Array chip of Illumina. The information of the SNP site corresponding to the chip was obtained by analyzing the fluorescence signal after hybridization. Then, the sample status, CNV result, and ROH result of the sample were obtained by the following steps:

[0161] Step S11a: Obtain BAF feature data based on the genomic position sequence of SNP sites for each autosome.

[0162] Step S11b: Obtain LRR feature data based on the genomic position sequence of SNP sites for each autosome.

[0163] Step S11c: Obtain genotype characteristic data of each autosome based on the genomic position sequence of the SNP sites.

[0164] Step S21a: Use the mixed Gaussian model to obtain the peak value and peak number of peak F1 on each autosome.

[0165] Step S21b: Use a sliding window to obtain the peak value and peak number of peak F2 on each autosome.

[0166] Step S21c: Obtain the peak value and peak number of the common peak F of peak F1 and peak F2 on each autosome.

[0167] Step S22: Determine the chromosome status of each autosome based on the peak number, peak value and LRR characteristic data of the common peak F.

[0168] See also Fig. 9In this embodiment, in sample RD23103101, chromosomes 6 and 9 showed chromosome duplication, chromosomes 11, 16 and 21 showed unknown, and the remaining chromosomes showed normal chromosomes.

[0169] Step S30: Summarize the chromosome status types of all autosomes obtained in step S22, and determine whether the sample status of the tested sample is maternal contamination or no maternal contamination based on the chromosome status with the highest proportion. If the sample status is no maternal contamination, further determine whether the sample status is haploid, triploid or other.

[0170] In this embodiment, by summarizing the status of 22 autochromatic chromosomes, it was found that there was no maternal contamination of chromosomes in the autochromatic state, that is, the sample status was no maternal contamination; further judgment showed that the status of most chromosomes was normal, and the final sample status was others.

[0171] S40A: Obtain CNV results of the tested samples whose sample status is other: Use PennCNV software, BAF feature data of the sample, LRR feature data of the sample and pre-trained HMM model to obtain the CNV results of the fragments on each chromosome in the sample, and filter out CNVs with a CNV region length less than 100k and a SNP number less than 50 in the CNV region.

[0172] See also Figure 10-11 In this embodiment, it was found through analysis that chromosomes 6 and 9 of the sample showed chromosome duplication.

[0173] S40B: Summarize the genotype data of SNPs in all chromosomes of the tested sample whose sample status is other. If the heterozygosity ratio of the tested sample is greater than the second set threshold, the sample status of the tested sample is updated to whole genome ROH.

[0174] In this embodiment, the heterozygous ratio of the RD23103101 sample is 0.0939, which is less than the threshold value of 0.1, that is, the state of the sample is updated to whole genome ROH.

[0175] S50: Output sample status and / or CNV result and / or ROH result: The sample is indicated to be whole genome ROH, and the karyotype is arr[hg19](1-22)×2hmz, (X,N)×1hmz, (6,9)×3.

[0176] Example 5

[0177] The gDNA of sample RD23071601 was detected using the Infinium Asian Screening Array chip of Illumina. The information of the SNP site corresponding to the chip was obtained by analyzing the fluorescence signal after hybridization. Then, the sample status, CNV results, and ROH results of the sample were obtained using the following steps:

[0178] Step S11a: Obtain BAF feature data based on the genomic position sequence of SNP sites for each autosome.

[0179] Step S11b: Obtain LRR feature data based on the genomic position sequence of SNP sites for each autosome.

[0180] Step S11c: Obtain genotype characteristic data of each autosome based on the genomic position sequence of the SNP sites.

[0181] Step S21a: Use the mixed Gaussian model to obtain the peak value and peak number of peak F1 on each autosome.

[0182] Step S21b: Use a sliding window to obtain the peak value and peak number of peak F2 on each autosome.

[0183] Step S21c: Obtain the peak value and peak number of the common peak F of peak F1 and peak F2 on each autosome.

[0184] Step S22: Determine the chromosome status of each autosome based on the peak number, peak value and LRR characteristic data of the common peak F.

[0185] See also Fig.12 In this embodiment, in sample RD23071601, chromosomes 1 to 22 all showed normal chromosomes.

[0186] Step S30: Summarize the chromosome status types of all autosomes obtained in step S22, and determine whether the sample status of the tested sample is maternal contamination or no maternal contamination based on the chromosome status with the highest proportion. If the sample status is no maternal contamination, further determine whether the sample status is haploid, triploid or other.

[0187] In this embodiment, by summarizing the status of 22 autosomal chromosomes, it was found that there was no maternal contamination of chromosomes in the autosomal state, that is, the sample status was no maternal contamination; further judgment showed that the status of the autosomes was normal or unknown, that is, the sample status was other.

[0188] S40A: Obtain CNV results of the tested samples whose sample status is other: Use PennCNV software, BAF feature data of the sample, LRR feature data of the sample and pre-trained HMM model to obtain the CNV results of the fragments on each chromosome in the sample, and filter out CNVs with a CNV region length less than 100k and a SNP number less than 50 in the CNV region.

[0189] See also Fig.13 In this embodiment, analysis revealed that there was a fragment deletion in the region from 62546172 to 68494674 of chromosome 18 of the sample.

[0190] S40B: Summarize the genotype data of SNPs in all chromosomes of the tested sample whose sample status is other. If the heterozygosity ratio of the tested sample is greater than the second set threshold, the sample status of the tested sample is updated to whole genome ROH.

[0191] In this embodiment, the heterozygous ratio of the RD23103101 sample is 0.1785, which is greater than the threshold value of 0.1, that is, the state of the sample remains unchanged and is still other.

[0192] S40C: Obtain the ROH results of the tested samples whose sample status is haploid, triploid or other: According to the position of SNP on the genome, 1000 adjacent SNPs are divided into a bin. When the homozygosity rate of SNP in the bin is >95% and the LRR of the region is near 0, the bin is considered to be a potential ROH region; a new potential ROH region is formed by merging adjacent potential ROH regions. If the homozygosity rate of SNP in the new potential ROH region is >99%, the region is considered to be a ROH region. Filter out the regions with less than 500 SNPs and less than 1M in length to obtain the final sample ROH result.

[0193] In this embodiment, it is found through analysis that the sample does not have a ROH region.

[0194] S50: Output sample status and / or CNV result and / or ROH result: indicating that the sample has a fragment deletion and the karyotype is arr[hg19]18q22.1q22.2(62546172_68494674)×1.

[0195] Comparative Example 1

[0196] This comparative example uses STR detection to obtain the maternal contamination results of samples RD22112301, RD22120801, RD23120601, RD24052801, and RD24050301: the whole blood of the sample to be tested and the mother of the sample is collected, and the traditional STR locus detection is performed to obtain the maternal contamination results of the sample. At the same time, the method provided by the present invention is used to obtain the maternal contamination results of the samples RD22112301, RD22120801, RD23120601, RD24052801, and RD24050301.

[0197] Table 2 Results of maternal contamination of the same sample by the method provided by the present invention and STR loci detection

[0198]

[0199] The test results are shown in Table 2. The method provided by the present invention can detect the maternal contamination results of the samples, which are consistent with the results of traditional STR loci detection. Therefore, it can replace traditional STR loci detection.

[0200] Experimental Example 1

[0201] The heterozygosity ratios of different types of clinical research and development samples were calculated using the method provided by the invention. Referring to Table 3, the heterozygosity ratio of the whole genome ROH was less than 0.1, while the heterozygosity ratios of the remaining samples were all greater than 0.1.

[0202] Table 3 Heterozygous ratio of different types of samples

[0203]

[0204] In summary, compared with the prior art, the method of utilizing peak values ​​provided by the present application has the following advantages:

[0205] (1) The method provided by the present invention does not require additional experiments to determine whether there is maternal contamination in the sample, and does not require additional maternal peripheral blood. It can improve the detection efficiency of copy number abnormalities, improve the comprehensiveness of detection, and reduce the time and cost of detection.

[0206] (2) The present invention can not only quickly determine the maternal contamination of the sample by comprehensively evaluating the peak value of the BAF data on the autosomes, but also quickly determine whether the sample belongs to a special type of sample such as haploid, triploid, whole genome ROH, etc.

[0207] (3) The present invention can effectively avoid overfitting and underfitting that may occur in mixed Gaussian model results or sliding window results by using the method of determining the final peak number as the common peak number.

[0208] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, and the present invention is also intended to include these modifications and modifications.

Claims

1. A method for identifying maternal contamination and detecting copy number abnormalities using peaks, characterized in that: include: Step S10: Detecting the SNP site information of the sample under test, and extracting the LRR feature data, BAF feature data and genotype feature data based on the genomic position sequence of the SNP site of each autosome of the sample under test according to the SNP site information; Step S20: according to the BAF characteristic data, the number of peaks and the peak value on each autosome are obtained, and the chromosome state of each autosome is determined according to the number of peaks, the peak value and the LRR characteristic data; Step S30: judging whether the sample state of the sample under test is that there is maternal contamination or no maternal contamination according to the chromosome state with the highest proportion among all chromosome states; if the sample state is that there is no maternal contamination, further judging whether the sample state is haploid, triploid or other, and proceeding to step S40; Step S40: Obtain the CNV result of the sample under test according to the LRR characteristic data and the BAF characteristic data; determine whether the sample under test is whole genome ROH and / or obtain the ROH result of the sample under test according to the LRR characteristic data and the genotype characteristic data.

2. According to claim 1, a method for identifying maternal contamination and detecting copy number abnormalities using peaks, characterized in that: In step S20, the number of peaks and the peak value on each autosome are obtained using a mixed Gaussian model and / or a sliding window; wherein obtaining the number of peaks and the peak value on each autosome using a mixed Gaussian model includes: A mixed Gaussian model was used to fit the distribution of BAF characteristic data on each autosome, and the Bayesian information content (BIC) of the mixed Gaussian model corresponding to 1 Gaussian distribution to 10 Gaussian distributions was calculated. The number and mean of the Gaussian distributions corresponding to the mixed Gaussian model with the lowest BIC were the number and peak value of peaks on each autosome.

3. According to claim 2, a method for identifying maternal contamination and detecting copy number abnormalities using peaks, characterized in that: The step S20 also includes obtaining the number and peak value of the common peaks of the mixed Gaussian model and the sliding window on each autosome, including: obtaining the intersection area between the peaks of the mixed Gaussian model and the peaks of the sliding window method, if the intersection area ratio is greater than a first set threshold, then the union area of ​​the peaks of the mixed Gaussian model and the peaks of the sliding window method that form the intersection area is the common peak area; using the sliding window to detect the peak value of the common peak area to obtain the peak value and number of the common peak.

4. According to claim 1, a method for identifying maternal contamination and detecting copy number abnormalities using peaks, characterized in that: Determining the chromosome status of each autosome according to the peak number and peak value in step S20 includes: When the number of peaks is 0, and the BAF characteristic data are mainly distributed around 0 and 1, if the mean value of the LRR characteristic data is less than -0.2, the chromosome state of the autosome is the whole chromosome missing, otherwise, the chromosome state is unknown; When the peak number is 1, if the peak value is in the interval [0.45, 0.55], and its peak ratio is greater than the second set threshold, the chromosome status of the autosome is normal, otherwise, the chromosome status is maternal contamination; When the number of peaks is 2, if the two peaks are in the interval [0.3, 0.4] and [0.6, 0.7] respectively, the chromosome state of the autosome is chromosome duplication, otherwise, the chromosome state is unknown; If the number of peaks is greater than or equal to 3, the chromosome status of the autosome is that there is maternal contamination; The calculation formula of the peak value ratio is:

5. The method of identifying maternal contamination and detecting copy number abnormalities using peak values ​​according to claim 1, characterized in that: The step S40 of obtaining the CNV result of the tested sample includes the following steps: according to the LRR feature data, the BAF feature data and the HMM model, obtaining the fragment CNV results on each autosome in the tested samples whose sample status is other, merging the adjacent fragment CNV results, and filtering the fragment CNV results according to the length of the CNV and the SNP data contained in the CNV to obtain the CNV result.

6. The method of identifying maternal contamination and detecting copy number abnormalities using peaks according to claim 1, characterized in that: The step S40 of determining whether the sample under test is whole genome ROH includes: calculating the total heterozygosity ratio of all autosomes of the sample under test whose sample status is other, if the heterozygosity ratio is less than the third set threshold, the sample status is updated to whole genome ROH, otherwise, the sample status is other; wherein the calculation formula of the heterozygosity ratio is as follows:

7. The method of identifying maternal contamination and detecting copy number abnormalities using peaks according to claim 1, characterized in that: The ROH result in step S40 is the ROH result of the sample whose state is haploid, triploid or other. The ROH acquisition method includes the following steps: according to the position of the SNP site on the genome, n adjacent SNPs are divided into bins, if the homozygosity rate of the SNP in the bin is greater than 95%, and the mean value of the LRR characteristic data is near 0, then the bin is a potential ROH region; merge adjacent potential ROH regions to form a new potential ROH region, if the homozygosity rate of the SNP in the new potential ROH region is greater than 99%, then the new potential ROH region is the ROH region, and all ROH regions are summarized to obtain the ROH result.

8. A device for identifying maternal contamination and detecting copy number abnormalities using peaks, characterized in that: The device comprises: A data extraction unit, extracting the LRR characteristic data, BAF characteristic data and genotype characteristic data based on the genomic position sequence of the SNP site of each autosome of the tested sample according to the SNP site information of the tested sample in the database; A chromosome state acquisition unit obtains the number of peaks and peak value on each autosome according to the BAF characteristic data, and determines the chromosome state of each autosome according to the number of peaks, peak value and LRR characteristic data; A first sample status determination unit, which determines whether the sample status of the sample under test is that there is maternal contamination or no maternal contamination according to the chromosome status with the highest proportion among all chromosome statuses, and if the sample status is that there is no maternal contamination, further determines whether the sample status of the sample under test is haploid, triploid or others according to the chromosome status with the highest proportion among all chromosome statuses; The second sample status determination unit obtains the sample CNV result of the tested sample according to the LRR characteristic data and the BAF characteristic data if the sample status does not indicate maternal contamination; and determines whether the tested sample is whole genome ROH and / or obtains the ROH result of the tested sample according to the LRR characteristic data and the genotype characteristic data.

9. A computer device, characterized in that: The computer device comprises a processor and a memory: The memory is used to store a computer program and transmit the computer program to the processor; The processor is configured to execute the method according to any one of claims 1 to 7 according to instructions in the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the processor is enabled to perform the method according to any one of claims 1 to 7.

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