WO2016000267A1 - Method for determining the sequence of a probe and method for detecting genomic structural variation - Google Patents

Abstract

Provided in the present invention is a method for determining the sequence of a probe based on a reference sequence and a method for detecting genomic structural variation. The method for determining the sequence of a probe based on a reference sequence comprises: constructing a first candidate probe set based on a plurality of discrete high-frequency SNP sites, wherein the first candidate probe set is composed of a plurality of candidate probes and each one of a plurality of candidate probes has at least one discrete high-frequency SNP. A plurality of candidate probes of the first candidate probe set is compared with the reference sequence in order to obtain a comparison result. On the basis of the comparison result, the first candidate probe set is firstly screened, so as to obtain a second candidate probe set. The reference sequence is divided into a plurality of windows with a predetermined length, and a plurality of candidate probes of the second candidate probe set are respectively distributed to each of the matching windows, so as to determine its own positional information of a plurality of candidate probes. Based on the positional information and allele frequency of the discrete high-frequency SNP, the second candidate probe set is secondly screened in order to determine the probe sequence.

Description

 Method for determining probe sequence and method for detecting genomic structural variation

Priority information

 No technical field

 The present invention relates to the field of genomics and bioinformatics, and in particular to methods for determining probe sequences and methods for detecting genomic structural variations. Background technique

 DNA copy number variation (CNV) and loss of heterozygosity (LOH) are different types of genomic variation. CNV is a common genomic structural variant with fragments ranging from lkb to a few Mb, mainly characterized by submicroscopic levels of deletion and duplication. LOH refers to a gene deletion on a chromosome on a pair of chromosomes, and the paired chromosomes still exist, showing that only homozygous SNPs appear in a long region of DNA. When the LOH does not change the copy number, that is, only two copies are inherited from one parent, which is called a single parent diomy (UPD). CNV, LOH, and UPD are associated with many common genetic diseases, cancer, and other complex diseases. Establishing an accurate, comprehensive, efficient, fast, simple, and economical method for detecting CNV, LOH, and UPD is of great value for studying chromosomal variation events, identifying the etiology of related diseases, and adopting appropriate treatment options.

 There are several inspection techniques, such as PCR technology, including real-time PCR and multiplex ligature amplification (MLPA). Real-time fluorescent PCR technology analyzes one or several targets per MLPA. - can analyze more than 40 sequences, high sensitivity, detection range is limited by the chromosome and region targeted by the probe; FISH technology, FISH technology is generally used to detect specific chromosomes, unable to detect unknown regions; chip-based technology , including array-based Comparative Genomic Hybridization (aCGH) and SNP-based technology (SNP-array), aCGH can detect CNV within the genome-wide range, and cannot detect polyploids, small fragments The loss detection rate is high; and sequencing technology, based on whole genome sequnecing (WGS) to detect genome-wide structural variation and target region-based sequencing to detect target region variation, there are four main methods for analyzing CNV, including : Paired End Mapping (p Aired-end mapping ), read-depth analysis, split-read strategies, and sequence assembly comparisons.

With the development of sequencing technology, it is necessary to study the methods of discovering genomic structural abnormalities based on sequencing results, especially local region sequencing results, including discovery of chromosome aneuploidy, CNV, insertion-deletion (indel), LOH, UPD. And the means of SNP. Summary of the invention An aspect of the present invention provides a method for determining a probe sequence based on a reference sequence, comprising the steps of: constructing a first candidate probe set based on a plurality of discrete high frequency SNP sites, the first candidate probe set being composed of a plurality of candidates Constructing a probe, and each of the plurality of candidate probes comprises at least one discrete high frequency SNP; comparing the plurality of candidate probes in the first candidate probe set with the reference sequence to obtain a comparison result; Aligning the result, performing a first screening on the first candidate probe set to obtain a second candidate probe set; dividing the reference sequence into a plurality of windows having a predetermined length, and respectively selecting a plurality of candidate probes in the second candidate probe set Pins are assigned to respective matching windows to determine respective positional information of the plurality of candidate probes; second screening of the second set of candidate probes based on said positional information and allele frequencies of discrete high frequency SNPs The probe sequence is determined. Wherein, the discrete high frequency SNP site has an allele frequency greater than 10%, preferably no more than 90%, and the physical distance from any other discrete high frequency SNP site on the reference genome is not less than the candidate probe length, candidate The probe length is 50-250 mer.

 The probe obtained by the method for determining a probe sequence of the present invention is used for hybridization to capture a genome to obtain a plurality of local regions of a genome, and the captured plurality of local regions can represent a whole genome and can reflect the genome-wide variation information for use in discovering the whole The occurrence of structural variations in the gene range.

Another aspect of the present invention provides a method for detecting genomic structural variation suitable for detecting chromosomal aneuploidy, copy number variation, and insertion deletion, comprising the steps of: sequencing a target sample genomic nucleic acid to obtain a genome sequencing result The genome sequencing result is composed of a plurality of reads. Optionally, the sequencing comprises screening by using a probe, wherein the probe is a method for determining a probe sequence based on a reference sequence provided by one aspect of the present invention. acquired. Genomic sequencing results can be obtained by extracting genomic DNA and performing library construction and sequencing on the basis of the existing high-throughput platform instruction manual; genome sequencing results can also be obtained by probe capturing the genome of the target sample and sequencing it. The method for determining a probe sequence based on a reference sequence provided by one aspect of the present invention is obtained; the reference gene component is m regions, and the coverage depth of the target sample genome region i is calculated by using the read sequence falling into the region i in the genome sequencing result. Where m and i are natural numbers, l ≤ i ≤ m, 10 < m _; based on the degree of difference between the coverage depth of the target sample genomic region i and the coverage depth of the region i of the k reference samples, the structural variation of the target sample region i is determined. Occurs, where k is a natural number, k ≥ 2, and the method of obtaining the coverage depth of the region i of each reference sample can refer to the method of obtaining the coverage depth of the target sample region i. By combining regions adjacent to the structural variation, it is further detected whether large structural variations occur in the merged region, or further detecting whether the structural variation occurring in region i spans several regions.

A further aspect of the invention provides a method suitable for detecting loss of heterozygosity of another genomic structural variation, comprising the steps of: obtaining a genome sequencing result of a target sample, optionally, said genome sequencing result is passed The probe captures the genome of the target sample and is obtained by sequencing. The probe is obtained according to the method for determining the probe sequence based on the reference sequence provided by one aspect of the present invention; the gene component is divided into m' regions, and the result is based on the genome sequencing result. The read segment and the population region i data in the region i, obtain the SNP set shared by the target sample genomic region i and the population region i, and calculate the heterozygosity of the segment of each SNP site in the common SNP set of the target sample and the population respectively, and obtain the target. The heterozygosity set of the sample genomic region i and the heterozygosity set u _{0l of the} population region i are compared, and the target sample and the population U _Q1 are compared to determine whether the heterozygosity loss of the target sample region i occurs; wherein, each SNP of the SNP is concentrated, etc. The locus frequency is greater than 0.1, and there is a SNP locus in the SNP set. The segment is bordered by two SNPs upstream and downstream of the SNP, m' and i are natural numbers, m' ≥ i ≥ l, m' ≥ 6. The number of samples taken can truly reflect the population, which can be determined according to the accuracy required for the detection, the statistical method, the distribution of the sample data, etc. The population data is composed of sample data of multiple species, which can be sequenced by whole genome or obtained according to the target sample. The method of data, or obtained from a published database or website, such as thousands of genomic data.

 A further aspect of the present invention provides a computer readable storage medium for storing a program for execution by a computer, and those skilled in the art can understand that when the program is executed, the above-mentioned detection of genomic structural variation can be completed by instructing related hardware. All or part of the steps of the various methods. The storage medium may include: a read only memory, a random memory, a magnetic disk, or an optical disk.

 According to a final aspect of the present invention, there is provided apparatus for detecting genomic structural variation, comprising: a data input unit for inputting data; a data output unit for outputting data; a storage unit for storing data, including an executable program; a processor, coupled to the data input unit, the data output unit, and the storage unit, for executing an executable program stored in the storage unit, wherein the executing of the program includes completing all or part of the steps of the foregoing methods for detecting genomic structural variation. .

 The probe obtained by the method for determining a probe sequence based on a reference sequence of the present invention can perform target region capture sequencing using a probe or a solid phase/liquid phase chip containing the probe, and can realize low sequencing cost in a genome-wide range. Structural variability was detected, including CNV, LOH, and UPD for 23 pairs of chromosomes covered by humans, and the detection resolution was adjusted by adjusting the average spacing distribution of the probes, ie increasing/decreasing SNP sites, as needed. Utilizing the target region capture sequencing of the present invention in combination with bioinformatics analysis methods, high-resolution, high-accuracy, high-throughput, low-cost CNV, LOH, and UPD detection across the genome is achieved, while genomic structural variation of the present invention The detection method is also applicable to the detection of chromosomal aneuploidy variation, SNP and Indel, and is suitable for structural variation analysis based on whole-genome sequencing data.

 The additional aspects and advantages of the invention will be set forth in part in the description which follows. DRAWINGS

 The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from

 1 is a schematic diagram showing the characteristics of a SeTR probe on a whole genome in one embodiment of the present invention, (A) a length distribution map of a SeTR probe sequence; (B) a physical distance distribution map of two probes in a SETR probe .

 Fig. 2 is a graph showing the results of the test of the SeTR probe in one embodiment of the present invention, (A) coverage depth distribution map of the target region (B) supporting the reads distribution map of the ref base type and the non-ref base type.

 Fig. 3 is a flow chart showing the detection of CNV, LOH and UPD in one embodiment of the present invention.

 Fig. 4 is a diagram showing a reference line in an embodiment of the present invention.

Figure 5 is a schematic diagram showing the genomic structural variation of a sample (GM50275) detected in an embodiment of the present invention, the ring is from the outside to the inside, followed by I) chromosome information, II)! ^Change in value (wavy line); III) R _het Corresponding P value change, IV) R _het value change (point) Detailed description of the invention

 Embodiments of the present invention are described in detail below. The embodiments described below with reference to the drawings are intended to be illustrative of the invention and are not to be construed as limiting.

 It should be noted that the terms "first" and "second" are used for descriptive purposes only, and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may explicitly or implicitly include one or more of the features. Further, in the description of the present invention, "multiple" means two or more unless otherwise stated.

 According to an embodiment of the present invention, there is provided a method for determining a probe sequence based on a reference sequence, comprising the steps of:

 Step 1: Construct the first candidate probe set

 Constructing a first candidate probe set using discrete high frequency SP sites distributed in the genome, each candidate probe in the first candidate probe set comprising at least one discrete high frequency SP site, the discrete high frequency SP site being equipotential The gene frequency is greater than 10%, and the physical distance from any other discrete high frequency SP site on the reference genome is not less than the candidate probe length, and the candidate probe length is 50-250 mer.

 In a specific embodiment of the present invention, the discrete high frequency SP is obtained by thousands of human genome data, and the discrete high frequency SP bits having an allele frequency less than 90% can be further selected from other published genomic data. Point to determine the candidate probe length is 100 mer.

 In one embodiment of the invention, each candidate probe comprises a discrete high frequency S P site and the discrete high frequency S P site is located in the middle of said candidate probe. Thus each candidate probe contains only one high frequency S P site, and there may or may not overlap between adjacent candidate probes. The "middle section" here is relative to the "front section" and the "post-section". It can be understood as usual, such as a sequence. The upper and lower 1/3 are defined as "front" and "back" respectively. 1/3 is "middle"; further, the discrete high frequency SNP site is located at the midpoint of the candidate probe, where the "midpoint" position, such as a sequence containing 2n + 1 nucleotides, The point is the position of the n+1th nucleotide, and when a sequence contains 2n nucleotides, the midpoint of the sequence is the position of the nth or n+1th nucleotide, which enhances the probe to the target. Capture efficiency of discrete high frequency SP sites.

 In a specific embodiment of the present invention, the first candidate probe set is pre-screened based on the GC content and/or single base repeat of the candidate probe sequence in the first candidate probe set, and the first candidate probe is retained. Candidate probes with a concentrated GC content of 35%-65% and/or a single base weight of less than 7. Single base repeatability refers to the number of consecutive occurrences of a base type in a sequence. For example, in TGAAAAAAAAGC, A is consecutively 8 times, and the sequence A has a base repeatability of 8. High or low sequence GC content, high heterozygosity may easily affect the PCR or hybridization capture process of the sequence, bring GC bias, etc., so that the capture specificity is reduced, and the first candidate for retention by this pre-screening The needle set will not hybridize to these sequences, thereby eliminating the effects of GC bias or low specific capture on the results.

 Step 2: Align the first candidate probe set with the reference sequence to obtain the comparison result

Comparing the first candidate probe set with the reference sequence to obtain a comparison result, obtaining the first candidate probe set in the reference sequence Location information on the column. The reference sequence used is a known sequence, and may be any reference template in the biological category to which the target sample belongs in advance. For example, the target sample is human, and the reference sequence can select HG18 or HG19 provided by the National Center for Biotechnology Information (NCBI). Further, a resource library containing more reference sequences can be pre-configured, and the sequence is compared before the sequence comparison. The gender, ethnicity, and geographic factors of the target sample select a closer reference sequence, which is beneficial to obtain a more targeted probe sequence.

 Step 3: Perform a first screening on the first candidate probe set to obtain a second candidate probe set

 In a specific embodiment of the present invention, the candidate probe retained by the first screening needs to satisfy any one of the following two conditions: 1) candidate probes in the first candidate probe set that are aligned to the unique position of the reference genome 2) Aligning the first candidate probe set to multiple positions of the reference sequence and having a mismatch ratio of at least two of the plurality of positions of the reference sequence is less than 10%; for example, the candidate probe length lOOmer, 10 The base mismatch is 10% mismatch ratio, and the mismatch ratio is low. When used for hybridization, it can be completely complementary to the target region, and the capture effect is good and the specificity is high.

 Step 4: Divide the reference sequence into multiple windows, assign the second candidate probe set to the respective matching window, divide the reference sequence into a plurality of windows having a predetermined length, and use the comparison to concentrate the second candidate probe A plurality of candidate probes are assigned to the matching window to obtain position information of each candidate probe on the respective window.

 The lengths of the windows of the plurality of predetermined lengths may be inconsistent, and may overlap without overlapping. In a specific embodiment of the present invention, the reference sequence is a reference genome, and the reference genome is divided into a plurality of windows of consistent length, and the window length is 10Kb, and two adjacent windows are connected but do not overlap.

 Step 5: Perform a second screening of the second candidate probe set based on the position information and the allele frequency of the discrete high frequency S P to determine the probe sequence

 In a specific embodiment of the present invention, performing the second screening comprises two steps, (a) if there are multiple candidate probes located in the same window, determining the candidate probe with the highest allele frequency of the discrete high frequency SP (b) If there is only one candidate probe with the highest allele frequency of the discrete high frequency SP, select the candidate probe with the highest allele frequency of the discrete high frequency SP as the probe, if there are multiple discrete heights The candidate probe having the highest allele frequency of the frequency SP selects the candidate probe closest to the center of the window among the candidate probes having the highest allele frequency of the plurality of discrete high-frequency SPs as the probe. The distance of the candidate probe from the center of the window may be the distance between the midpoint of the candidate probe and the center of the window. The target position is as close as possible to the center of the probe sequence, which helps to improve the capture efficiency.

 In a specific embodiment of the present invention, after the second screening of the second candidate probe set, when two candidate probes in the second candidate probe set fall into adjacent two windows respectively in the reference genome When the upper distance is greater than the length of any of the adjacent two windows, optionally, a short tandem repeat sequence or a portion of the short tandem repeat sequence between the adjacent two candidate probes on the reference genome is further added. The second candidate probe set after the second screening is combined to form a probe sequence. Thus, the capture of the whole genome by the probe sequences obtained by these designs enables a relatively uniform distribution of the captured regions, enabling the capture of the identified regions to better reflect the entire genome information.

 According to another embodiment of the present invention, there is provided a method for detecting a genomic structural variation, the genomic structural variation comprising at least one of chromosomal aneuploidy, copy number variation, and insertion deletion, comprising the steps of:

(i) sequencing the target sample genomic nucleic acid to obtain genome sequencing results, said genomic measurement The sequence results are composed of multiple reads. Genomic sequencing results can be obtained by whole-genome sequencing, such as by extracting genomic DNA, according to existing high-throughput platform guidelines, such as Illumina Hiseq2000/2500, Roche 454, Life technologies Ion Torrent a single molecule or nanopore sequencing platform or the like for library construction and sequencing on the machine to obtain a read; or capture the genome of the target sample by a probe and perform sequencing, and the probe can be provided by an aspect of the present invention. The method of determining the needle is determined by design, and then synthesized or prepared according to the existing method.

(2) The reference gene component is m regions, and the coverage depth TD _{1 of the} target sample genomic region i is calculated by using the read segment falling into the region i in the readout in the sequencing result, where m and i are natural numbers, and i is the region number , l ≤ i ≤ m, 10 < m.

In a specific embodiment of the present invention, the calculation formula of the coverage depth of the region i is _ the number of reads falling into the region i ^ ^ _ the total number of bases included in the read of the region i. Length ^, the length of the area i, eight dry, ¹ number of the clothing area. The position of the reading paragraph to the genome can be determined by sequence alignment, and various alignment software such as SOAP (Short Oligonucleotide Analysis Package), bwa (Burrows-Wheeler Aligner), samtools, GATK (Genome Analysis Toolkit) and the like can be used for the alignment.

 (3) Based on the difference between the coverage depth of the target sample genomic region i and the coverage depth of the region i of the k reference samples, the occurrence of structural variation of the target sample region i is determined, wherein k is a natural number, k ≥ 2.

In a specific embodiment of the present invention, the degree of difference between the coverage depth of the target sample genomic region i and the coverage depth of the region i of the k reference samples is compared by comparing the coverage depth of the genomic region i of the target sample and the reference sample. The coefficient, the determination of the coverage depth coefficient of the target sample genomic region i includes the following steps: (a) performing the first correction to obtain the TD^ the first correction is by covering the 2n consecutive regions including the region i The depth value is implemented by linear regression, where η is a natural number, 10<n≤m/2, and in a specific embodiment of the present invention, ^{11) 31 =} ( ” ^{TDj )/n} obtained by the first corrected linear regression Where TD" represents the coverage depth of the jth region in n consecutive regions, j is a natural number, l ≤ j ≤ n; (b) after obtaining the first corrected coverage depth TD _ai of region i, further to 10 ₃₁ is homogenized to obtain ^{1 Α} ^, and further κ ^ ^^ ¹¹ ^ is obtained, in a specific implementation of the present invention

TD _a = V^TD, in the /n mode, ^ai ^ ^J obtained by homogenizing the first corrected coverage depth TD _M of the region i. In a specific embodiment of the present invention, after the target sample is obtained, the pair further includes

n,

y is a natural number indicating a reference sample number, and R _y is a reference depth coefficient of the reference sample y genomic region i.

In another embodiment of the present invention, after obtaining the target sample, further comprising performing a second correction to obtain, ^R -, wherein, ! ^The depth of coverage of the genomic region i for k reference samples and one target sample R - ^y=1

The average of the degree coefficients, ^{ai k+} l , y is a natural number indicating the reference sample number, and _y is the coverage depth coefficient of the reference sample y genomic region i.

 In the above process of calculating the coverage depth coefficient of the target sample genomic region i, the correction, homogenization, and the like of the intermediate values can reduce errors caused by fluctuations in experimental conditions, differences between samples, and the like, so that the final n It can be truly reflected and the fluctuation amplitude ratio around 1 is small, and the plurality of samples conform to the normal distribution; in the above embodiment, the first correction is performed, and then the first corrected value is normalized, which is equivalent to two requests. The process of averaging, that is, before the mean depth of coverage of the n consecutive regions including the region i is used to represent the coverage depth of the region i, the calculation of the coverage depth value of each of the n regions is performed by using the region as the first The coverage depths of the n consecutive regions of the regions are represented by the mean value, which is equivalent to correcting the TD^ by using the coverage depth values of the 2n consecutive regions including the target region i to stabilize the coverage depth of the continuous regions. It should be noted that other correction or averaging processing can be used to stabilize the coverage depth values of adjacent regions, for example, the average coverage depth of several regions spaced from the target region to correct the target region. The depth of coverage is a concept of the present invention. Referring to the calculation process of the coverage depth coefficient of the sample genomic region i, reference may be made to the calculation process of the coverage depth coefficient of the target sample genomic region i, and the reference sample data may be pre-computed and processed for backup, or may be synchronized with the calculation process of the target sample. obtain.

 In a specific embodiment of the present invention, the degree of difference between the coverage depth of the target sample genomic region i and the coverage depth of the region i of the k reference samples is determined by t test whether the difference in coverage depth coefficients between the two is significant. Realized. In a specific embodiment of the invention, the calculation of the t-test statistic of the target sample genomic region i

The formula is Y ^k , where ¹ ^ represents the average of ^ of the k reference samples, ¹ ^ is the reference sample y genomic region

The second corrected coverage depth factor for domain i, ' ^R ,

, S is the standard deviation of _k reference samples,

. Based on the value of the target sample genomic region 1, a significant level I is obtained. When Ρ 05 .05 , it is determined that the region i has a structural variation; otherwise, it is determined that the region i does not undergo structural variation. In another embodiment of the present invention, the theoretical value t _{l0 is} obtained based on the value of the target sample genomic region i and the predetermined significant level P _lQ , and when ≥ 3⁄4, it is determined that the region i undergoes structural variation, and vice versa. It is determined that the region i does not undergo structural variation, and the predetermined P _{1 ()} ≤ 0.05. According to the t-value table of the t-test, the corresponding 1⁄2 can be found after the predetermined P ₁₍₎ .

In one embodiment of the present invention, in order to detect a larger CNV or an insertion defect, after performing step (3), the W regions in the same direction and consecutively are merged to obtain a first-level merged region, and the two primary merges are merged. When the two primary merged areas are in the same direction and the span does not exceed L areas, the secondary merged area is obtained, and the secondary merge is detected. The structural variation of the region; wherein, the same direction region refers to the region where the t-statistic of the coverage depth of the region is greater than 0 or both are less than 0, and W and L are both natural numbers, W≥2, LW≤l. To further detect larger structural variations, and so on, such as further merging the eligible secondary merged regions, the merged conditions can be similarly the same in the same direction of the two secondary merged regions and the distance between the reference genomes does not exceed L areas or L secondary merge areas.

 In a specific embodiment of the present invention, detecting a structural variation of the secondary merged region is based on a difference in coverage depth of the secondary merged region of the target sample genome and a coverage depth of a corresponding region on the plurality of reference sample genomes. To determine whether the secondary merged region has structural variation, or to determine whether the structural variation occurring in region i spans W regions. Refer to the acquisition of the coverage depth of the corresponding secondary merged region on the sample genome, the calculation of the t-statistic of the coverage depth of the secondary merged region on the target sample genome, and the structural variation judgment process. See the structural variation of the previously relatively small region i. Calculate the judgment process.

 According to still another embodiment of the present invention, there is provided a method suitable for detecting loss of heterozygosity in genomic structural variation, comprising the steps of:

 (1) sequencing the target sample genomic nucleic acid to obtain the result of genome sequencing, wherein the genome sequencing result is composed of multiple reads, and the genome sequencing result can be obtained by whole-genome sequencing, for example, by extracting genomic DNA, according to the existing high Guidance manual for the flux platform, such as using Illumina Hiseq2000/2500, Roche 454, Life technologies Ion Torrent, single molecule or nanopore sequencing platform for library construction and sequencing on the machine to obtain reads; or by probe capture The genome of the target sample is obtained and sequenced, and the probe can be designed and determined by the method for determining the probe provided by one aspect of the present invention, and then synthesized or prepared according to the existing method.

(2) The reference gene components are divided into m' regions, and based on the read segment information and the population region i data falling in the reference genome region i in the sequencing result, the SP sets shared by the target sample genome region i and the population region i are obtained, respectively, The heterozygosity of the fragment of each SP site in the consensus SP set of the target sample and the population, the heterozygosity set 1^ of the target sample genomic region i, and the heterozygosity set U _{Ql of the} population region i, the comparison target sample U and the population U _Ql is determined whether the loss of heterozygosity in the target sample region i occurs; wherein the allele frequency of each SP in the shared SP set is greater than 0.1, and the segment of the SP site in the shared SP set is The two SPs upstream and downstream of the SP are boundary points, m' and i are natural numbers, m'> i > l , m' ≥ 6.

In a specific embodiment of the present invention, the heterozygosity of the fragment in which an SP site is located is represented by a sub-allelic frequency coefficient of the SP site, and the sub-allelic frequency coefficient of the SP site is R _het = MAF / ( 1-MAF), MAF is the sub-allelic frequency of the high frequency SP.

In a specific embodiment of the present invention, the target sample U and the population U _Q1 are compared to determine whether the loss of heterozygosity of the target sample region i occurs, including whether the variance of U and the variance of U _Ql are significantly different by using the F test. If the variance of U^PU _Q1 is significant, it is determined that there is a loss of heterozygosity in the target sample region i, and conversely, it is determined that there is no loss of heterozygosity in the target sample region i.

In a specific embodiment of the present invention, the F test includes separately calculating the variance of the U^PU _lQ , and using the variance of the obtained target sample U and the variance of the population U _lQ to obtain two statistics of the reciprocal statistic F _up ^ and utilization. The statistic of the reciprocal of each other obtains a significant level p _F , comparing the magnitude of p _F with a predetermined significance level p _FQ , p _F ≤p _F0 indicating the two inclusion calculation formulas,

_; Wherein, _v is the target sample genomic region i and groups region i consensus SP concentrated SP number, q is the number of the target sample genomic region i and groups region i consensus SP concentrate the SP, R ^ AV for the target sample genome zone i The sub-allelic frequency coefficient of the ^Vth SNP in the SP set is the average of the sub-allelic frequency coefficients of the q SNPs in the consensus SP set of the target sample genomic region i, ^Rte , ^lQ , ^v population sample genomic region SP shared allele frequency coefficients times the concentration of the first V i th SNP's, ^{Rte, lQ} SP sample is total genomic region population set of q i, the average of the SNP, Pupper Punder times and allele frequency coefficients, respectively Obtained from F _upper and F _under , p _F Q ≤ 0.05 p _F Q can be set to a value that is normally set, or adjusted according to known information that is known, requirements for detection accuracy, and the like.

 In one embodiment of the present invention, in order to detect a larger LOH, after step (2), W's heterozygous loss and continuous regions are merged to obtain a three-level merged region, and two tertiary merges are merged. When the span between the two three-level merged regions does not exceed L' regions, a four-level merged region is obtained, and the heterozygosity set of the four-level merged region of the target sample and the heterozygosity set of the same region of the same are respectively obtained. Two heterozygosity sets are used to determine whether the four-level merged region of the target sample has a loss of heterozygosity, where W' and L' are both natural numbers, W' > 2, W' /2 ≥ L '. In a specific embodiment of the invention, W' ≥ 4. To detect the LOH occurring in a larger area, and so on, for example, to further merge the eligible four-level merged region, the merge condition can be similarly that the distance between the two four-level merged regions on the reference genome does not exceed L' Zone or L' three-level merged zone.

 According to still another embodiment of the present invention, there is provided a method for detecting a diploid of a single parent, wherein when there is loss of heterozygosity in a genomic region of a target sample, the copy number of the region is calculated, and when the copy number of the region is the normal genome of the same species When the copy number of the region is the same, it is determined that there is UPD in the genomic region of the target sample; whether or not the LOH is present in the genomic region can be performed by the LOH detection method of the aspect disclosed in the present invention.

 A person skilled in the art may understand that all or part of the steps of the various methods in the above embodiments may be completed by a program instruction related hardware, and the program may be stored in a computer readable storage medium, and the storage medium may include: a read only memory, Random access memory, disk or CD, etc.

According to a last embodiment of the present invention, there is provided an apparatus for detecting a genomic structure variation, comprising: a data input unit for inputting data; a data output unit for outputting data; and a storage unit for storing data, including Executable program; processor, connected with the above data input unit, data output unit and storage unit data The executable program for executing the storage in the storage unit, the execution of the program includes all or part of the steps of the various methods in the above embodiments. The specific probe design method and the structural mutation detection method according to the present invention will be described in detail below in conjunction with specific target individuals. The name definitions or specific parameter settings involved in the following process are selected as:

 1. Design the probes as Seleted Target Region Primers (SeTR);

 2. The "cover depth", "sequencing depth" and "depth" below can be used interchangeably; the "region" and "target area" below can be used interchangeably;

 3. Library construction and sequencing were performed according to the small fragment library construction instructions provided by the Hiseq 2000 platform and the sequencing instructions. The library size was 300bp-350bp, pair-end sequencing, and the read length was 91bp. Type is PE91+8+91);

 4. The reference genome or reference sequence selected for comparison is the human reference genome (hgl9, Build 37).

 In the examples, the specific techniques or conditions are not indicated, according to the techniques or conditions described in the literature in the field (for example, refer to J. Sambrook et al., Huang Peitang et al., Molecular Cloning Experimental Guide, Third Edition, Science Press) Or follow the product manual. The reagents or instruments used are not specified by the manufacturer, and are conventional products that are commercially available, for example, from Illumina.

 Example 1 : Chip Design, Preparation, Testing

 In general, high (>60%) or low (<35%) GC content and high heterozygosity tend to adversely affect the DNA fragment during PCR or probe capture. To avoid this phenomenon, we designed The special probe, we call it SeTR. When designing the SeTR probe, the following principles are followed: a) The uniqueness and stability of the probe sequence is high, requiring low heterozygosity and moderate GC (35%~60%) content, b) contains discrete high frequency SP markers (SNP markers), allele frequencies of each SNP (allele frequency, 0.9>AF>0.1) for better detection of genome-wide LOH, c) The final target area exhibits a relatively uniform distribution.

 The selection process of the SeTR probe design or target area is as follows:

 1) Based on the thousand human gene database (ftp: ftp ftp.ncbi.nih.gov/1000genomes/ftp/release), select a candidate SNP set with an allele frequency (AF) of 10% to 90%, and then Then, in the SP set, an SP with a physical distance less than lOOpb between the two SPs is removed, thereby forming a SP makerl set.

 2) Take each S P of the SNP makerl set as the midpoint, and intercept the reference genome sequence 50pb upstream and downstream to form a theoretical probe sequence set of lOObp, and then compare the probe sequence set back to the reference sequence. If there is no mismatch in the optimal alignment of a certain probe sequence, and the next best alignment is less than 5% mismatch, then the corresponding S P is preserved, thus forming the SNP maker 2 set.

 3) Based on the SNP maker2 set, we selected the S P maker that is uniform in the physical location of the reference genome as the final S P maker set. In our study, we chose a SNP maker set with a physical distance of approximately lOKbp.

4) If in the final SNP maker set, there is a distance between two adjacent SPs greater than lOKbp, use a short tandem repeat (STR) between them to fill the uniformity. After designing the SeTR probe, we commissioned Roche to produce the SeTR liquid phase chip. The SeTR liquid phase chip contains 278,800 probes with a total size of 41,795,106 bp covering an effective whole genome (2.89G) 1.45% region. The average length of the SeTR probe was 149 bp, and the average physical distance between adjacent probes was 10.6 kbp, as shown in Table 1 and Figure 1. Table 1 Distribution of SeTR probes on each chromosome

Use 3 quality-qualified DNA samples, YH (Yanhuang sample, Chinese genomic DNA), HG00537 (a sample from the Thousand Human Genome Project) Wo B GM50275 (obtained from the Couriel Institute for Research) Medical Research's human fibroblast sample) was used to test the availability of the SeTR chip to ensure that the probe chip could be used for subsequent detection studies. All three samples were sequenced using SeTR capture to obtain sequencing sequences ( _rea ds). First, we remove the contamination that is contaminated by the adapter and the lower quality, such as the average quality value below 20, the remaining reads are clean reads (clean reads), the clean reads are compared to the reference sequence hgl9, The reads of 98.13%~99.29 were compared to the reference genome, and the target area reached 67.43%~67.87%. In addition, the target area of 99.73%~99.95 was covered by at least one read, and over 99%. The area was covered at least 10 times, as shown in Table 2, which performed better than the same type of exome capture chip, such as the exome liquid chip produced by Roche Nimblegen. In addition, the depth distribution of the target region is similar to the Poisson distribution as shown in Fig. 4A, and the non-reference sequence base type of the most heterozygous sites in the target region is shown in Fig. 4B (the non The number of reads supported by -reference allele is almost the same as the number of reads supported by the reference allele, that is, the number of positive and negative reads supported by a high heterozygous site is comparable (positive and negative reads are respectively sourced from two). A homologous chromosome), which shows that this probe has no obvious haplotype (commonly referred to as the base sequence, ie ref type). The bias of capture is better, and the capture uniformity of the target region is better.

 Table 2 Comparison results of three samples

Target covered >=30X, % )

Ratio of the portion with the sequencing depth >= 40X in the target region (Fraction of 80.03 79.01 77.81 target covered >= 40X, %) Example 2: Target region library construction, sequencing

 1. Test materials, reagents, instruments

 Samples: 15 target gDNA samples (human genomic DNA, sample numbers are shown in Table 3 below, "GM" begins with human fibroblasts), and 24 reference DNA samples.

 Main reagent instruments: PCR instrument, pipette, centrifuge, comfort thermomixer, DNA interrupter, vortex shaker, magnetic stand, electrophoresis instrument, Hiseq2000 sequencer, Nanodrop UV spectrophotometer, etc. Or if the instrument does not indicate the manufacturer, it is a regular product that can be obtained through the market.

 Probe Design and Synthesis: By the first example, a target area of approximately 41M was selected from the human genome, and a NimbleGen SeqCap EZ liquid probe was customized from Roche. The probe set was able to capture the corresponding The target area of the design.

 2, library construction

 1) Genomic DNA extraction

Use the QIAGEN DNA Extraction Kit (DNA Mini Kit) and follow the kit instructions to extract genomic DNA from the target sample for approximately 3-5 μ _§ for subsequent experiments. The extracted DNA (30-100 ng) was run for electrophoresis to determine whether it was intact and the degree of degradation.

 2) Genomic DNA disruption and purification

Interrupt the genomic DNA using the covaris E210 instrument (refer to the instrument instructions). The DNA was interrupted to 200-250 bp. Using the QIAquick PCR Purification kit (250) kit according to the kit instructions, the DNA fragment was purified interrupted, electrophoresis main band size meets the requirements, i.e., whether the size of the main belt 200-250bp _o

 3) end repair, end plus A, add linker, preamplification

According to the requirements of database construction, the DNA fragment of the above-mentioned fragmentation was subjected to end repair and purification according to the steps of the construction of the double-end tag library and the reagents and reaction conditions, and the base A was added and purified by terminal repair. At both ends of the latter DNA fragment, the purified end is added with the A product; the sequencing linker is ligated at both ends of the terminal A product, and the DNA fragment with the linker is purified using magnetic beads capable of complementary binding to the sequencing linker. The PCR reaction system is prepared, the DNA fragment with the linker is amplified, the PCR product is purified by magnetic beads, and the main band size of the amplified product is 300-350 bp by electrophoresis; the amount of DNA is detected by Nanodrop ultraviolet spectrophotometer, and the total amount needs to be greater than 1.0 μ _§ .

 4) SeTR probe hybridization and elution, amplification

Purchasing or configuring the hybridization and elution reagents in the kit instructions according to the instructions of the commercially available NimbleGen SeqCap EZ Hybrid Elution Kit. A 1.5 mL centrifuge tube was prepared, Cot-1 DNA was added, the universal blocking sequence (Block), the tagged sequence (index N Block) and the DNA sample after step 3). Then centrifuge for lmin, 60 ° C vacuum Concentrated and dried, then added to the hybridization buffer, etc., shaken and centrifuged, placed in a metal dry bath at 95 ° C for 10 min, shaken and centrifuged at high speed. A 4.5 ul probe was added to the centrifuge tube and hybridized on a PCR machine (47 ° C, 64-72 hours). Elution is performed after completion of the hybridization. Then, according to the final amplification step of the library construction specification, PCR is carried out, and the PCR reaction system is prepared as required, and the DNA obtained by hybridization elution, polymerase, substrate, PCR reaction buffer, Flowcell primer (based on the sequencer of the sequencing chip flowcell) The primers with a fixed sequence design are homogeneously mixed. The PCR program was predenatured at 94 °C for 2 min, denatured at 94 °C for 15 s, annealed at 58 °C for 30 s, extended at 72 °C for 30 s, reacted for 15 cycles, and extended at 72 °C for 5 min. After the completion of the PCR, the PCR product was taken out, centrifuged, and magnetic beads were purified to obtain a target region library. The concentration of the library was measured using a Nanodrop UV spectrophotometer and prepared for sequencing on the machine.

 3. Hiseq2000 high-throughput sequencing

 A quality-qualified DNA library was sequenced according to the Hiseq2000 operating instructions. The amount of data per sample is approximately

At 4.5G, the average sequencing depth reaches 100X, but it is difficult to reach 100% due to the efficiency of the capture chip. Through analysis, the effective sequencing depth of the final target region is 30X~45X. Example 3: Detection of CNV, LOH and UPD

 See Figure 3 for the overall process. After the sequencing is completed, the offline data is in the fastq file format. Then, the high-quality reads and reference genomes (Hgl9, Build 37) are filtered to obtain the SAM format comparison file by BWA software, and then the SAM comparison file format is converted into a binary BAM file by using samtools software, and The results are deduplicated and sorted. Next, the samtools software will be used again to convert the BAM format to the PILEUP format. See the Bioinformatics Strategy section for details.

 First, sequencing data filtering, comparison

 The sequencing data of the above-mentioned embodiment illumina Hiseq2000 is first subjected to simple data filtering, and the reads which are contaminated by the adapter, containing N ratio higher than 5% and average mass value lower than Q20 are removed. The bwa alignment software is then used to compare the filtered data to the human reference genome (hgl9, Build 37), and the output sequence alignment result is a SAM (sequence alignment/map) format comparison file (referred to as SAM file), and then The SAM files were converted to binary BAM files using the Samtools software, PCR duplicates were removed and sorted, and the results were compared and recalibrated using GATK software.

2. Calculating the second corrected coverage depth coefficient r P segment heterozygosity R _het of the target region calculates the n and segment heterozygosity R of each target region according to the information contained in the probe region file obtained after the above filtering ratio _Het value. Based on the value of n, the CNV was predicted by t test, and according to R _Het , the F test was used to predict LOH and UPD.

 Third, the detection of CNV, LOH and P UPD analysis

 1, CNV detection

 1.1 Calculate the depth factor of each target area

Calculate the depth of the target area, and use the representation (as in Equation 1). In order to maintain the stability of several consecutive target areas TD, the method of Equation 2 is used to correct the TD, that is, the depth of the n' areas behind the i-th area is used. The TD is corrected to obtain TD _ai , and then TD _ai is normalized by Equations 3 and 4, at which time the depth coefficient of each target region is obtained. Equation 1: TDi = Tibase I Tden Equation 2: TDa, = C TD / (w '+ 1), w '≥ 9 Equation 3: TDa, = (∑:+" ίλ») I (n '+ 1)

^/ : R = m ₁ / m ₁ , _Tl base: number of bases aligned to the target area i; TJen: length of the target area i.

 1.2 Create a baseline using multiple reference samples (k=24) data, correct

 Due to the fluctuation of each experimental condition and the difference between the samples themselves, there is a certain fluctuation in the efficiency of each capture, and the resulting fluctuations are likely to cause CNV false signals. Therefore, according to multiple fluctuations, create a unified one. Figure 4 is a good example of creating a baseline for this detection, precursor

( preRi)

The distribution of the distribution is very large, and the fluctuation is relatively small. After the correction of the _baseline , _{Γι is} obtained, which has smaller fluctuations and is more sensitive and easier to detect the occurrence of CNV. Theoretically, in the case where CNV does not occur in different samples, the values are theoretically consistent with the Poisson distribution in the same target region, and both are relatively stable around the respective unique values, in order to maintain their unique characteristics. The stability of the value, by investigating the 13⁄4 values of the same region of multiple samples, using the mean (mean instead of this unique value, constructing a unique baseline for each target region. Based on each The target area is the assumption that the mean ly value fluctuates around the value of mean ly. We divide the mean Ri into _Γι , which in turn causes η to fluctuate around a normal distribution.

 1.3 Detection of CNV in the target area

Theoretically, the η values of the same target region from multiple samples should conform to the normal distribution, so when investigating the target region i of a sample, you can compare the n values of this region with multiple samples, using the τ test, t statistics The formula for calculating the quantity is as follows.

The 1 in the subscript of each parameter in the formula represents the target sample, 2 represents multiple reference samples, and ^ represents a sample to be tested! The average of ^ is the average of the _ι of the reference sample, ^ is theoretically all the samples to be tested! ^Average,

^ Theoretically all reference sample r _l2 average, 8 ₁ and S ₂ are the standard deviation of the sample to be tested and the reference sample, respectively, df is the degree of freedom, df ^ + n ^

When the sample to be tested is 1 or = i, the theoretical mean values of the sample to be tested and the reference sample are the same, and the above formula is simplified as:

 Through the above simplified formula, each target region corresponds to a t value of a detectable CNV, and then a P value (confidence) is obtained. When P<0.05 of a certain region, this region is a region where CNV occurs.

 1.4 Detection of large CNV

 Based on the p-value of the single-region t-test, a pseudo-signal value is attached to each region to characterize whether it is considered by the next CNV region connection, and then along the chromosome, the target regions that may have a consistent CNV are connected into blocks to determine The final size and copy number of CNV.

 The marking rule of the pseudo signal value is that when the measured values of at least four consecutive target regions are in the same direction (the t value is greater than or at the same time less than 0), that is, when the corresponding region of the reference sample is deviated, if the P values of the three regions are smaller than the first Threshold (such as 0.05, commonly used significant horizontal threshold), and the fourth does not exceed the second threshold (0.2, four times the first threshold), then the four regions are marked as off-direction (such as the partial mark is +, partial The small mark is -), merged into one block; here the number of consecutive and same direction regions and the first and second threshold values are adjustable. If the distance between one block and another block does not exceed the span of 5 regions, the two blocks are merged into one large block, and so on, and finally the block is obtained; refer to the method formula of 1.3 above, this block The r value is in all the areas it contains! The average value of ^ indicates that the r value of the block domain of the sample to be tested and the reference sample is subjected to t-test to calculate the P value of the block. When P<0.05 of the block, CNV occurs in this block, thereby determining the boundary and size of the block, and obtaining the boundary and size of the large CNV.

 By analyzing the target sample of 15 samples, the CNV results we obtained were highly consistent with the known verification results (S P-array results), and there were no false positives or false negatives, as shown in Table 3. Furthermore, we simulated eight 30X genome-wide data, including five normal samples and three CNV-containing samples. By comparing CNV detection of these eight simulated data, we compared the CNV predictions of the currently reported exome regions. Software CONTRA (Li J, Lupat R, et a/, CONTRA: copy number analysis for targeted resequencing, Bioinformatics. 2012 May 15; 28(10): 1307-13), the results show that our method sensitivity and specificity are both It has reached 100%, and the copy number of each is also accurately detected. The detection accuracy of CNV can reach 500Kb and can be accurately located, while the sensitivity of CONTRA is 88.9%, the specificity is only 66.7%, and the copy number is not given. Out, as shown in Table 4.

 table 3

CONTR 1-5 Normal 88.9% 66.7%

A 6 chr20 15007645 15492763 0.49- ΝΑ

 Chrl9 45003283 45496699 0.49+ ΝΑ

 7 chr20 16009467 17992034 1.98- ΝΑ

 Chrl9 15009149 15993267 0.98+ ΝΑ

 Chrl9 50000342 50998777 1 - ΝΑ

 8 chr20 63704 9990568 9.93- ΝΑ

 Chr20 10007121 12995181 2.99- ΝΑ

 Chr20 19869958 35830028 ΝΑ chr20 42008751 42442770 0.43+ ΝΑ

 Chrl9 3430053 31917721 28.49 ΝΑ

 +

 Chrl9 35004532 35595304 0.59- ΝΑ

 + ―

 Results 8 true positive CNVs, 3 false positive CNVs

 2, LOH detection

 2.1 Detection of heterozygous status in all genomes

In a region of the sample genome to be tested, find the SP site with a gene frequency (AF) of 0.1 0.9 in the data of thousands of people, and calculate the area where these SP sites are among the thousands of people and the sample to be tested according to the following formula. R _Het value. When the region i in the sample to be tested is in an absolute hybrid state, then R _het = l, otherwise, when it is absolutely homozygous, R _het =0.

R _het = MAF l (\ - MAF) ^ _MAF ( _min or allele frequency) is the minor allele frequency. In the sample to be tested, any SP site m in a certain region is taken as the starting point, and n SP sites are successively taken as the heterozygosity set Sm in the region, that is, ^ = H , Rh , , . , R _n ,,, in the same way, in the thousand person database, take the SP position of the same position, constitute the heterozygosity set Pm, ie

P _m = {Rket, pketm , Rket, _{P (m +}

_{P (m + ")}?} F heterozygosity two test sets variances are equal. Specifically, the variance are calculated in the same set of heterozygosity region ² and the variance thousand samples of the sample to be tested heterozygosity set region

^Sp , and the p value of the heterozygosity set Sm of the region of the sample to be tested. S ^s — ~n-

^Sp *J c max = max{ c ^ , Ό c }, Ό c mm = min ^ ^ , c }

Ho '. Gs = Op

F upper― ^"^ ^Χ , dfs― dfp— ΪΙ— \

 S min

F under― ^ ^ ⁿ , dfs― dfp— fl— \

 S max

 P― pupper + (1― p unde )

When ≤0.01, we accept H _A and judge that the heterozygosity set Sm loses the heterozygosity in the population, that is, the LOH occurs in the region where the set Sm is located.

 2.2 Detecting large LOH

Combined with the results of 2.1, a subset of four consecutive lost heterozygous states is recorded as a minimum unit by means of detecting large CNV step 1.4. If there is no more than 2 subset spans between the two units, the two units are merged into a larger unit, and so on, and finally connected into a block. At this time, according to the sample to be tested and the reference set of thousands The R _Het value is subjected to the F test, and the p value of the block is calculated, when! When ≤0.01, we think that LOH occurs in this block, otherwise it is non-LOH block.

 Alternatively, to more accurately detect the merging conditions can be set more stringent, such as to avoid false positives caused by some random errors, the area defined at least greater than 5M may be a true LOH. On this basis, under the condition that the block fault tolerance is 1 (that is, the p value of one subset in the block is allowed to be greater than 0.01), a continuous subset of p ≤ 0.01 near the subset of ≤ 0.01 in 2.1 and Merger. Finally, an additional F-test is performed on the RHet in the merged region. If the p-value is less than 0.01, the block is considered to be a true LOH.

 3, UPD detection

Combined with the above-mentioned genome-wide CNV and LOH detection results, UPD detection was performed according to the Mendelian inheritance rule. If a DNA region is shown as heterozygous in the thousand-person data, ie, R _Het =l, and in the actual detection, the heterozygous state disappears, that is, R _Het approaches 0, then it is determined that this region is LOH. , if there are CNVs in this area and there are two copies (CN=2), that is, the copy number does not change (the sample in this example is a diploid sample, and the normal diploid sample genome is two regions) A copy), it is determined that a single parent diploid (UPD) has occurred in this region.

In 13 of the 15 samples, 10 LOHs greater than 5M and 4 UPDs greater than 5M were detected. The results are shown in Table 5. The detection of LOH and UPD in the absence of paired samples (usually taking their own lesions) The comparison between the organization and the normal organization is a paired sample, that is, there is a certain associated sample, and the present embodiment detects LOH and UPD by comparing the target sample with a plurality of reference sample sets, the target sample and the reference. The sample set has no correlation, so it is not a paired sample. The LOH detection result of >5M is consistent with the CNV result of CN=1 (the accuracy of the LOH detection result can be verified by the CNV detection result), and the solution of the present invention detects the accuracy of LOH and UPD. High and can reach 5M The accuracy of the level.

 The Circos diagram (Figure 5) shows the CNV, LOH and UPD results of the GM50275 sample.

 table 5

Industrial applicability

 The method for determining a probe sequence based on a reference sequence of the invention can be effectively used for determining a probe sequence, and the obtained probe is used for hybridization to capture a genome to obtain a plurality of local regions of the genome, and the captured plurality of local regions can represent the whole The genome, which reflects the genome-wide variation, is used to discover the occurrence of structural variations across the entire genome. Although specific embodiments of the invention have been described in detail, those skilled in the art will understand. Various modifications and alterations of those details are possible in light of the teachings of the invention. The full scope of the invention is given by the appended claims and any equivalents thereof.

 In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "illustrative embodiment", "example", "specific example", or "some examples", etc. Particular features, structures, materials or features described in the examples or examples are included in at least one embodiment or example of the invention. In the present specification, the schematic representation of the above terms does not necessarily mean the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

Claims

Claim

A method for determining a probe sequence based on a reference sequence, comprising:

 (1) constructing a first candidate probe set based on a plurality of discrete high frequency SP sites, wherein the first candidate probe set is composed of a plurality of candidate probes, wherein, among the plurality of candidate probes Each one contains at least one of said discrete high frequency SP sites;

 (2) aligning the plurality of candidate probes in the first candidate probe set with a reference sequence to obtain a comparison result;

 (3) performing a first screening on the first candidate probe set based on the comparison result to obtain a second candidate probe set composed of a plurality of candidate probes;

 (4) dividing the reference sequence into a plurality of windows each having a predetermined length, respectively assigning a plurality of candidate probes in the second candidate probe set to respective matching windows, so as to determine the plurality of candidate probes The respective position information of the needle;

(5) performing a second screening on the second candidate probe set based on the position information and the allele frequency of the discrete high frequency S P to determine the probe sequence.

 The method according to claim 1, characterized in that the allelic frequency of each of said plurality of discrete high frequency S P sites is at least 10%, preferably not more than 90%, respectively.

 The method according to claim 1, wherein a physical distance of any two adjacent discrete high frequency SP sites of the plurality of discrete high frequency SP sites on the reference sequence is not less than The length of the candidate probe.

 4. The method according to claim 1, wherein the candidate probe has a length of 50 to 250 mer, preferably 100 mer.

 5. The method of claim 1 wherein said candidate probe comprises one of said discrete high frequency S P sites and said discrete high frequency SP sites are located in a middle segment of said candidate sequence.

 The method according to claim 5, wherein the discrete high frequency S P site is located at a midpoint of the candidate probe.

 7. The method of claim 1 wherein the candidate probe is taken from the reference sequence.

8. The method according to claim 1, wherein the first candidate is pre-selected based on at least one of a GC content of the candidate probe and a single base repetition number before the performing the comparison The probe set is pre-screened.

 9. The method of claim 8, wherein the pre-screening comprises retaining candidate probes that satisfy at least one of:

 GC content is 35%-65%;

 The single base is less than 7.

 10. The method of claim 1, wherein the first screening comprises retaining candidate probes that satisfy at least one of the following conditions:

 a candidate probe that is uniquely aligned with the reference sequence;

Aligning to a plurality of locations of the reference sequence, and at least two of the plurality of locations have a mismatch ratio of less than 10% of the candidate probes.

The method according to claim 1, wherein in the step (4), the reference sequence is divided into a plurality of windows each having the same predetermined length.

 The method according to claim 11, wherein the reference sequence is divided into a plurality of windows having a length of 10 Kb.

 13. The method according to claim 1, wherein in the step (5), the probe is determined according to the following steps:

 (a) if there are a plurality of said candidate probes located in the same window, determining a candidate probe having the highest frequency of the allele of the discrete high frequency S P ;

 (b) if there is only one candidate probe having the highest allele frequency of the discrete high frequency SP, selecting the candidate probe having the highest allele frequency of the discrete high frequency SP as the probe, if There are a plurality of candidate probes having the highest allele frequency of the discrete high frequency SP, and selecting the candidate probes having the highest allele frequency of the plurality of the discrete high frequency SPs is closest to the center of the window A candidate probe is used as the probe.

 The method according to claim 1, wherein after determining the probe, the method further comprises: determining a distance between two adjacent probes on the reference sequence;

 If the distance between the adjacent two probes is greater than the maximum length of the two windows in which the adjacent probes are located, further selecting a short serial repeat sequence or a short tandem repeat between the two windows A portion of the sequence acts as a probe.

 15. The method of claim 1, wherein the reference sequence is a reference genome or a portion thereof.

 16. A method of detecting genomic structural variation, the genomic structural variation comprising at least one of chromosomal aneuploidy, copy number variation, and insertion deletion, wherein the method comprises

 (1) sequencing the target sample genomic nucleic acid to obtain a genome sequencing result, the genome sequencing result being composed of a plurality of reads, wherein, optionally, the sequencing comprises screening by using a probe, wherein the probe A needle obtained by the method of any one of claims 1 to 15;

(2) The reference gene component is m regions, and the coverage depth TD ₁ m and i of the region i is calculated as a natural number using the number of the read segments falling into the region i, and i represents the number of the region, lim, 10<m ;

 (3) determining whether the region i has a structural variation based on the degree of difference between the coverage depth of the region i and the coverage depth of the region i of the k reference samples, where k is a natural number, k 2 .

 17. Method according to claim 16, characterized in that the coverage depth of the region i is determined using the following formula:

 Number of reads falling into area i

_The total number of bases contained in the read segment of _T n falling into region i is out. Feng Yi RT + Huaikou

 TDi = "丄 ^ , where 1 is the area of the area

 Length of area 1

18. The method according to claim 16, wherein the target sample genomic region i has a deep coverage The test of the degree of difference between the degree and the coverage depth of the region i of the k reference samples was performed by t-test.

19. The method according to claim 16, wherein the comparison between the coverage depth of the region i and the coverage depth of the region i of the k reference samples is by comparing the genomic regions of the target sample and the reference sample The coverage depth coefficient of i is determined, wherein the determining the coverage depth coefficient of the area i includes the following steps,

(a) performing a first correction to obtain a first corrected coverage depth TD _ai , the first correction being performed by linearly regressing a coverage depth value of 2 n consecutive regions including the region i, where η is Natural number, 10<η = 3⁄4m/2;

(b) TD _ai ® line homogenization to obtain ^TD and obtain ^{Rl = TI TD} _ai .

20. The method according to claim 19, wherein in the step (a), the first corrected coverage depth TD _{ai is} determined based on the following formula _: ^{TDai = (} ∑ j ^TD J ) ^/n , where TDj represents The coverage depth of the j-th region of the n consecutive regions, j is a natural number, 1 η.

21. The method according to claim 20, wherein in step (b), the homogenization of D0 ₃₁ is obtained based on the following formula

.

22. The method according to any one of claims 18 to 21, characterized in that, after obtaining the target sample, further comprising performing a second correction to obtain _Γι ,

, where _Ral is the coverage of the _k reference sample genomic regions i

 k

R ― y ⁼¹

The average value of the depth coefficient of the cover, ^{31 k} , y is a natural number indicating the reference sample number, and R _Y is the coverage depth coefficient of the reference sample y genomic region i.

The method according to any one of claims 18 to 21, wherein after obtaining the target sample, further comprising performing a second correction on the image to obtain ^R, wherein R _AI is k reference samples and one target sample of

R y=i

The average value of the coverage depth coefficient of the genomic region i, ³¹ k+1 , is a natural number indicating the reference sample number, and y is the coverage depth coefficient of the reference sample y genomic region i.

24. The method according to claim 22 or 23, wherein the t-test is performed, the target sample base

Flat

Mean, ^ is the second corrected coverage depth coefficient of the reference sample y genomic region i, ' ^R

, for 1^ reference sample standard deviation, .

 The method according to claim 24, wherein, based on the value of the target sample genomic region i, obtaining a significant level when P^O.05, determining that the region i has a structural variation; otherwise, determining the region i There is no structural variation.

The method according to claim 24, wherein the theoretical value t _{lQ is} obtained based on the value of the target sample genomic region i and the predetermined significant level P _lQ , and when t _l0 , the structural variation of the region i is determined On the contrary, it is determined that there is no structural variation in the region i; the predetermined 13⁄4 0.05.

 The method according to any one of claims 16 to 21, wherein after performing step (3), the W regions in the same direction and consecutively are merged to obtain a first-level merged region, and the two first-level merges are merged. Region when the two primary merged regions are in the same direction and the span between them does not exceed L regions, obtaining a secondary merged region, based on the coverage depth of the secondary merged region of the target sample genome and a plurality of reference samples The degree of difference in the depth of coverage of the corresponding region on the genome to detect the structural variation of the secondary merged region; wherein, the same direction region refers to the region where the t statistic of the region is greater than 0 or both are less than 0, and both W and L are natural numbers. W 2, LW^ lo

 28. A method of detecting loss of heterozygosity, characterized in that

 (1) sequencing the target sample genomic nucleic acid to obtain a genome sequencing result, the genome sequencing result being composed of a plurality of reads, wherein, optionally, the sequencing comprises screening by using a probe, wherein the probe A needle obtained by the method of any one of claims 1 to 15;

(2) The reference gene component is divided into m' regions, and the SNP sites shared by the target sample genomic region i and the population region i are obtained based on the data of the read data falling in the region i and the data of the population region i in the genome sequencing result. A consensus SP set is constructed, and the heterozygosity of each SP spot in the common SP set of the target sample and the population is calculated, and the heterozygosity set 1^ of the target sample genomic region i and the heterozygosity set U _{Ql of the} population region i are obtained. Comparing the target sample and the population U _Q1 to determine whether there is a loss of heterozygosity in the target sample region i; wherein the segment where the SP site is located is bordered by two SNPs upstream and downstream of the SP, m′ and i is a natural number, m' ^ i ^ l , m' 6.

 29. The method of claim 28, wherein the allele frequencies of each of the SNPs in the shared SNP set are greater than 0.1.

30. The method according to claim 28, wherein the heterozygosity of the fragment in which the SP site is located is represented by a sub-allelic frequency coefficient of the SP site, and the sub-equal of the SP site Gene frequency coefficient R _het =MAF/

(1-MAF), MAF is the minor allele frequency of the SNP.

31. The method according to claim 30, wherein the comparing the target sample U and the population U _Q1 to determine whether a heterozygosity loss of the target sample region i occurs, including a variance determined by an F test and a variance of U _Q1 Whether there is a significant difference, if the variance of U^PU _Q1 is significant, it is determined that the target sample region i has a loss of heterozygosity, and conversely, it is determined that the target sample region i has a loss of heterozygosity.

32. The method according to claim 31, wherein the F-test comprises separately calculating a UPU _l0 . Variance, using the variance of the resulting target sample 1^ and the variance of the population U _lQ ^. Calculate two statistics that are reciprocal to each other

F _up ^ and F^to, using the reciprocal statistics to obtain a significant level p _F , comparing p _F with a predetermined significant level p _F0

_,,; Wherein, _v is the target sample genomic region i and i shared frequency region group concentration SNP SP number, q is the number of high-frequency SP target sample concentration SNP genomic region i and i shared area groups, the target The Vth of the common high frequency SP set in the sample genomic region i

The sub-allelic frequency coefficient of SP, ^Rte , i is the average of the sub-allelic frequency coefficients of the q-water SP in the shared high-frequency SP concentration of the target sample genomic region i, R ^ ¹ ^ ¹ ^ population sample genomic region i The total allele frequency coefficient of the Vth SP of the high frequency SP concentration is shared! ^^° is the average of the sub-allelic frequency coefficients of q SPs in the shared high-frequency SP concentration of the population sample genomic region i, p _upP and puncto were obtained according to F _up ^ and F _uncta , respectively, p _F0 0.05.

 33. The method according to any one of claims 28 to 32, characterized in that after step (2), W's heterogeneous loss and continuous regions are merged to obtain a three-level merged region, and two merged When the span between the two tertiary merged regions does not exceed L' regions, a four-level merged region is obtained, and the heterozygosity set of the four-level merged region of the target sample and the heterozygosity of the same region of the group are respectively obtained. Set, compare the two heterozygosity sets to determine whether the heterogeneity loss occurs in the four-level merged region of the target sample, where W' and L' are both natural numbers, W'^2, W'12 L'.

 34. A method for detecting a diploid of a single parent, characterized in that, when detecting loss of heterozygosity in a genomic region of a target sample, calculating a copy number of the genomic region, when the copy number of the genomic region is different from the normal genome of the same species When the copy number of the region is the same, it is determined that the target sample genomic region is a single parent diploid; and the loss of heterozygosity in the target sample genome region is determined by the method according to any one of claims 27 to 32.