CN113564266B - SNP typing genetic marker combination, detection kit and application - Google Patents
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Abstract
The invention discloses a SNP typing genetic marker combination, which comprises 56 SNP loci. The invention also discloses a probe set and a kit for detecting the genetic marker combination, application thereof, and a method for carrying out sample pairing and sample pollution analysis on a high-throughput sequencing sample by using the genetic marker combination. The invention selects 56 SNP loci evenly distributed on each chromosome through SNP locus screening to form SNP typing genetic marker combination, and the identity and pollution condition of a high-throughput sequencing sample can be effectively identified by utilizing the genetic marker combination.
Description
Technical Field
The invention relates to the field of bioinformatics, in particular to identification of genetic information, and particularly relates to pollution identification and identity identification of high-throughput sequencing samples.
Background
A Single Nucleotide Polymorphism (SNP) refers to a DNA sequence polymorphism caused by a single nucleotide variation at the genomic level. SNPs may be either two-allele polymorphisms or 3 or 4-allele polymorphisms, the latter two being very rare and almost negligible. Thus, the so-called SNP site has only two alleles. SNPs have the characteristics of high density and high conservation in a genome, each 1000 nucleotides in the human genome has one SNP, and the total number of SNPs is more than 300 ten thousand in 30 hundred million bases of human. Most SNPs are located in non-coding regions of the genome, and some SNPs located in coding regions of the genome cause changes in the coding sequence that do not affect the post-translational amino acid sequence, and such SNPs are non-influential to the phenotype of the individual. However, some SNPs are localized in gene promoters, which cause an increase or decrease in gene transcription activity, resulting in an increase or decrease in the expression level of the protein, and further affect the biological activity thereof. Some SNPs located in protein coding regions may affect the amino acid sequence of post-translationally critical functional groups, thereby affecting the function of the protein, ultimately resulting in response sensitivity to a particular environment or etiology. In recent years, SNP screening has been applied to the study of genetic diseases, the study of pharmaceutical applications, and the study of tumors. Meanwhile, the SNP can be used as a genetic marker for identification of individuals. A plurality of specific SNPs are selected on the whole genome, so that the identity of one person can be accurately identified, and the error rate of identification is reduced and the accuracy is improved along with the increase of the number of the SNPs. The identity number of the individual genetic information is represented by the combination data of the SNP loci, and the combination of the selected gene loci has uniqueness.
The high-throughput sequencing (NGS) can detect a large number of target genes and variant sites thereof at one time, has high detection sensitivity and specificity, has qualitative and quantitative detection, has lower detection cost compared with the detection of the same number of genes and sites, and shows very wide clinical and scientific research application prospects in the fields of noninvasive prenatal screening, tumor gene mutation, genetic diseases, embryo implantation and the like. One of the characteristics of the high-throughput sequencing technology is that the operation steps are multiple, the procedure is complex, and the method comprises sample pretreatment, nucleic acid extraction and fragmentation, library establishment, amplification, target sequence enrichment, sample mixing, preparation before sequencing and sequencing in a laboratory, namely a wet-table experiment process; the method also comprises bioinformatics analysis processes such as data quality analysis, comparison, variation identification, annotation, result report and interpretation after sequencing. Any one of the above links has a problem, and the detection result is affected.
The high-throughput sequencing tumor gene detection technology is a system engineering, the quality control is very important, and three main quality control points are provided. The first is the quality control of the tumor sample, such as tumor type, total number and proportion of cancer cells. The second is library construction, which is an intermediate link, namely whether the sample is prepared properly before the machine is operated, mainly relating to whether the concentration and the fragment size meet the requirements and the like. And the third is whether the data, namely the data of the machine off after sequencing meets the requirements. Since the first two steps may not be problematic, but the final sequencing data reflects the overall quality. The tumor sample-derived pollution can be reflected only by off-line data analysis. At present, a plurality of products for tumor gene detection in the market are available, and the products contain different gene data. Particularly, for products with less gene data, the quality control of sample contamination is difficult to realize according to the information containing genes.
The development of next-generation sequencing technology and the reduction of cost provide more people with the selection of disease prevention and genetic disease screening through gene detection, and particularly, the NGS-based cancer panel detection enables the field to be rapidly developed. At present, various domestic products are rich, and the products with different sizes of 66 genes, 45 genes and 642 genes exist in the aspect of prostate cancer; the stone burning medicine has products of 8 genes, 68 genes and 168 genes in the aspect of lung cancer; the genes have 26 genes, 48 genes, 50 genes and 452 genes in the aspects of breast cancer and gynecological tumors. Each family has products of different sizes of panel in different cancer species, and how to find a proper method for matching samples and identifying sample pollution has certain difficulty. According to the general technical guide principle of second-generation sequencing, whether a sample is polluted is a necessary quality control condition in a product, but some products with less gene data have less SNP quantity, and some products with less genes can be sequenced by using a single sample, so that the SNP contained in the gene of the product cannot be effectively utilized to pair and identify the pollution of the sample. There are also companies that add positive and negative quality control substances to each batch for sample matching and contamination identification, but the reference substances are costly and cannot accurately identify sample contamination caused during slicing, extraction, etc. Therefore, a simple, efficient and low-cost method for sample matching and pollution identification is urgently needed.
The national meta-code gene develops an individual identification genetic marker combination based on 71 SNP loci, 25 STR loci and Y chromosome polymorphic loci for enhancing the marker combination, wherein 16 SNPs from mitochondria and 55 SNPs from autosomes are in the 71 SNP loci. The method has the defects that many related sites need to customize more PCR primers, multiple PCRs are needed for different types of polymorphic sites, and then mixed library building sequencing is carried out, so that the method is complicated, the analysis period is long, and the cost is high.
The method comprises the steps of selecting SNP loci which are shared by 1000Genome Project and HapMap Project, designing probes, removing points covered by probe sequences with homologous sequences, and finally selecting 3664 SNP loci which are uniformly distributed in Panel for detecting sample pollution. The disadvantages of this method are the numerous sites involved, the need for excessive capture probes, high cost and poor applicability.
Disclosure of Invention
One of the technical problems to be solved by the invention is to provide an SNP typing genetic marker combination which can accurately identify the identity of a sample and judge the pollution condition of the sample.
In order to solve the above technical problems, the SNP genotyping genetic marker set of the present invention comprises 56 SNP sites shown in Table 1.
The second technical problem to be solved by the invention is to provide a probe set for capturing the 56 SNP typing genetic marker combinations, wherein the probe set has sequences shown as SEQ ID NO. 1-56.
The invention also provides a kit for detecting the 56 SNP typing genetic marker combinations. The kit comprises a detection reagent of the 56 SNP typing genetic marker combination. The detection reagent may comprise a capture probe set of the above 56 SNP typing genetic marker combinations.
The kit can further comprise a detection reagent for the mutation genotypes of the 56 SNP sites and/or a high-throughput sequencing reagent.
The fourth technical problem to be solved by the invention is to provide the application of the SNP genotyping genetic marker combination. The 56 SNP typing genetic marker combinations can be used for preparing reagents for sample identity identification, sample pairing judgment and sample pollution condition judgment.
The fifth technical problem to be solved by the present invention is to provide the use of the probe set, wherein the probe set can be used for preparing the detection reagent of the 56 SNP typing genetic marker combination.
The sixth technical problem to be solved by the invention is to provide the application of the kit. The kit can be used for sample identity identification, sample pairing judgment and sample pollution condition judgment.
The seventh technical problem to be solved by the present invention is to provide a sample pairing determination method. The method comprises the following steps:
1) extracting sample DNA, amplifying and establishing a library;
2) capturing and enriching the SNP typing genetic marker combination of claim 1;
3) high-throughput sequencing;
4) splitting sequencing data, performing quality inspection, and removing a joint sequence, a primer and a low-quality base fragment introduced in the library building process;
5) aligning the sequences to a human reference genome;
6) calculating mutation frequencies of 56 SNPs of each sample, and judging mutation types of SNP sites;
7) and calculating the proportion of the same mutation types of the 56 SNPs of the two samples, and judging whether the two samples are matched.
And 7) judging that the two samples are matched when the mutation types are identical and the proportion of the mutation types is more than 95%.
The eighth technical problem to be solved by the present invention is to provide a sample contamination analysis method. The method comprises the following steps:
1) extracting sample DNA, amplifying and establishing a library;
2) capturing and enriching the SNP typing genetic marker combination of claim 1;
3) high-throughput sequencing;
4) splitting sequencing data, performing quality inspection, and removing a joint sequence, a primer and a low-quality base fragment introduced in the library building process;
5) aligning the sequences to a human reference genome;
6) calculating mutation frequencies of 56 SNPs of each sample, and judging mutation types and possible pollution sites of the SNP sites;
7) counting the number of the wild type and the homozygote, and if the sum of the number of the wild type and the number of the homozygote is less than or equal to 10, judging that the sample has pollution; and if the sum of the number of the wild type and the homozygous is more than 10, estimating the possible pollution ratio of the sample according to the average value of the possible pollution sites and the mutation frequency.
In the sample pairing judgment method and the sample pollution analysis method, the judgment standard of the SNP site mutation type is as follows:
the mutation frequency of the SNP locus is 40-60%, and the locus is judged to be a heterozygous mutation genotype;
the mutation frequency of the SNP locus is 100 percent, and the locus is judged to be a homozygous mutation genotype;
the mutation frequency of the SNP locus is 0, and the locus is judged to be a wild type;
the mutation frequency of the SNP locus is more than or equal to 85 percent but less than 100 percent, and the site is judged to be a possible pollution site.
The invention screens 56 SNP typing genetic marker combinations, and the genetic marker combinations can be used for carrying out pairing judgment and pollution judgment on different products and different tumor samples, and compared with the existing sample identity identification and pollution judgment analysis method, the method has the following advantages and beneficial effects:
1.56 SNP loci are uniformly distributed on each chromosome, all related loci can be obtained by a probe capture technology at one time, high-throughput sequencing analysis is carried out, individual identity recognition is carried out, the experimental operation and data analysis method is simple and convenient, the cost can be reduced, and the analysis efficiency can be improved;
2.56 SNP loci contain loci on X/Y chromosome, which can effectively distinguish male and female;
the ratio of wild types to homozygotes of 3.56 SNP loci is 1/2-2/3, and according to the genotype analysis results of the loci, whether different samples come from the same individual can be judged, if not, the approximate pollution ratio can be calculated, and the further quality control of sequencing data is realized; the error rate of the calculation of the pollution ratio is very low (the expected error rate is 1.91 e-09), the problems that a part of samples existing in clinical detection can be mixed or polluted due to human reasons are perfectly solved (for example, a tumor sample and a control sample come from different individuals and are wrongly marked as coming from the same patient, or a patient sends a sample without a mark at different time, so that whether the detection is performed in the laboratory or not is unclear, or other human samples are mixed in the sample sequencing analysis process), and the pollution ratio judgment of the samples is also solved.
Drawings
FIG. 1 shows the pairing values of 56 SNPs in 40 pairs of paired samples.
FIG. 2 shows the pairing values of 56 SNPs in 20 pairs of unpaired samples.
FIG. 3 is a graph showing the paired values of 56 SNPs in 25 tumor samples.
FIG. 4 is a graph of the sum of wild-type and homozygous numbers in 60 clinical tumor samples.
FIG. 5 is a schematic diagram of the sample pairing and contamination analysis process of the present invention.
Detailed Description
In order to more specifically understand the technical content, characteristics and effects of the present invention, the technical solution of the present invention will be further described in detail with reference to the accompanying drawings.
Example 1 SNP site screening and Probe design Synthesis
Based on the accumulation of clinical samples and public databases within the applicant company, 56 SNP sites were selected as shown in Table 1.
For the 56 SNP sites in Table 1, the sequences of probes capturing these sites (as shown in Table 2) were designed and synthesized.
Example 256 SNP site Capture and high throughput sequencing
DNA amplification and library construction
1) And extracting sample DNA, and performing enzyme digestion interruption.
2) After the disruption is complete, the DNA is end-repaired. The end repair reaction system comprises: after the cutting, 50 mul of DNA, 7 mul of buffer solution A for end repair and 3 mul of enzyme mixture A for end repair are added, and the total volume is 60 mul. The end repairing reaction condition is 30min at 65 ℃.
3) After the end repair is completed, ligation reaction is performed. The ligation reaction conditions were 20 ℃ for 15 min. The connection reaction system comprises: 60 mul of end repair product, 5 mul of nuclease-free water, 30 mul of ligation buffer, 10 mul of DNA ligase, 5 mul of MGI UDI linker, and 110 mul of total volume.
4) Purification was performed by adding 0.8x purified magnetic beads.
5) A PCR reaction system shown in Table 3 was prepared, vortexed, mixed and centrifuged briefly, and then PCR was performed according to the procedure shown in Table 4.
6) PCR reaction products were purified using 1x purified magnetic beads.
2. Library hybrid capture and target gene enrichment
1) The reagents shown in Table 5 were added to a 0.2ml low adsorption centrifuge tube and the tube was then aspirated at 47 ℃ and the aspirated sample was allowed to continue hybridization or allowed to stand overnight at room temperature.
2) Adding the reagents shown in the table 6 into a drained centrifugal tube, standing for 5-10 min at room temperature, placing on a PCR instrument, performing hybridization incubation for 4-16 hours at 95 ℃ for 30s and 65 ℃, and setting the temperature of a hot cover to be 100 ℃.
3) And after washing the hybridization capture product, performing PCR amplification and enrichment. The post-capture amplification reaction system is shown in Table 7. The post-capture amplification reaction procedure is shown in table 8. The PCR reaction product was purified using 1.5X purified magnetic beads.
DNA library sequencing and data resolution
The capture library was quantitatively diluted and mixed and paired-end sequenced on a MGISEQ-2000 high-throughput sequencer. And after sequencing is finished, carrying out data splitting by using split Barcode v2.0.0 software according to the barcode information of the sample. After splitting, the original fastq data, Q30 ≧ 85%, is defined as qualified offboard data (see step S1 in FIG. 5).
4. Data pre-processing
Referring to FIG. 5, step S2, the adaptor sequence, primers and low-quality base fragment introduced during the library construction are removed using data preprocessing software (fastp).
5. Data comparison
Referring to fig. 5, step S3, base sequences in the fastq file are aligned to hg19 (GRCh37) human reference genome using sequence alignment software (based on BWA v0.7.17-r1188 and gattk v3.7 software) to generate bam files, and the bam files are sorted according to genome coordinates and then optimized for sequence alignment of the genomic complex regions.
6. Data quality control
Referring to step S4 in fig. 5, the bam file is subjected to arithmetic processing using the mpieup command of the sampools software to obtain an mpieup file, and the frequency of A, C, G, T horizontal sites of 56 SNPs is calculated according to the mpieup file. When the mutation Frequency (Allele Reads Frequency) of the SNP is 40-60%, judging that the site is a heterozygous mutant genotype and the SNP exists; when the mutation frequency of the SNP is 100%, judging that the locus is a homozygous mutant genotype; when the mutation frequency of the SNP is 0%, judging that the site is a wild type; when the mutation frequency of SNP is more than or equal to 85% but less than 100%, the site is judged as a possible contamination site. Referring to step S5 of fig. 5, the number of wild type, homozygous mutation, heterozygous mutation and possible contamination sites were counted. Referring to step S62 in FIG. 5, judging the contamination of the sample according to the sum of the wild type and homozygous mutant genotypes in the 56 SNPs sites, if the sum of the wild type and homozygous mutant genotypes is less than or equal to 10, judging the contamination of the sample, and giving a warning; if the sum of the number of the wild type and the homozygous mutant genotype is more than 10, calculating the possible pollution ratio of the sample according to the average value of the possible pollution sites and the mutation frequency, and giving a pollution ratio result.
The parameters of Q30 base ratio, sequence alignment to reference genome ratio, average effective sequencing depth of target region, capture efficiency, capture uniformity, 1 Xcoverage, contamination ratio, insert length and the like of each sample are calculated by using data quality control software. If the ratio of the base of Q30 is more than or equal to 85 percent, the sequence is compared until the ratio of the reference genome is more than or equal to 95 percent, the average effective sequencing depth is more than or equal to 500x, the capture efficiency is more than or equal to 20 percent, the capture uniformity is more than or equal to 90 percent, the 1x coverage is more than or equal to 98 percent, the pollution ratio is less than 1 percent, and the length of the insert is between 100 and 250bp, the quality control of sample data is passed; otherwise, the sample data quality control fails. And if the data quality control fails, judging that the experiment fails and needing to be performed again.
Example 3 sample pairing analysis
1. Determining sample pairing decision criteria
Randomly selecting 40 pairs of verified matched samples and 20 pairs of verified unpaired samples, capturing 56 SNPs sites in each sample by using the capture probe of the embodiment 1 according to the method of the embodiment 2, and performing library sequencing to obtain sequencing data of passing quality control.
Counting the mutation types of 56 SNPs in each sample, and obtaining results shown in FIGS. 1 and 2, respectively, wherein 40 pairs of matched samples have the same mutation type ratio of 56 SNPs in each pair of samples of more than 95%; the mutation types of the 56 SNPs in 20 pairs of unpaired samples in each group of samples have the same ratio of 30-50%.
According to the detection result, the judgment standard for determining the sample pairing is as follows: for any two samples, if the mutation types of over 95% of the 56 SNPs are the same, the two samples are the same sample, and the samples are determined to be paired and belong to the same person.
2. Tumor sample pairing analysis
And randomly selecting 25 pairs of samples, capturing 56 SNPs loci in each sample by using the capture probe in the embodiment 1 according to the method in the embodiment 2, and establishing a library for sequencing to obtain sequencing data of quality control passing.
According to the method of example 2, mutation types of 56 SNPs in each sample were counted (see steps S1 to S5 in FIG. 5), and whether the samples were paired or not was judged based on the above-determined sample pairing judgment criteria (see step S61 in FIG. 5).
The results are shown in FIG. 3, the mutation types of the 56 SNPs of 20 pairs of samples are all more than 95% in the same proportion, and the samples are determined to be paired and belong to the same person; the mutation types of 56 SNPs of 5 pairs of samples have the same proportion of 30-50%, and the samples are judged to be unpaired and not belong to the same person.
Example 4 tumor sample contamination analysis
Selecting 60 clinical samples at random, capturing 56 SNPs sites in each sample by using the capture probe of example 1 according to the method of example 2, establishing a library for sequencing, calculating mutation frequencies of the 56 SNPs in each sample, and counting the number of wild types and homozygotes. The results are shown in FIG. 4, the sum of the wild type and the homozygote in the 60 clinical samples is in the range of 15-30, which indicates that the sample has low possibility of contamination.
Example 5 verification of tumor sample contamination ratio calculation method
Because there are few clinical samples contaminated, this example uses a method of mixing two samples to simulate sample contamination.
Selecting two cell strain samples HG00556 and HG00119, randomly extracting the sequencing depth of the sample HG00119 to 495X, 490X, 475X and 400X, then randomly extracting the sequencing depth of the sample HG00556 to 5X, 10X, 25X, 50X and 100X, mixing the sequencing depth with the randomly extracted samples HG00119, and simulating the conditions of the sequencing depth of 500X, the pollution proportion of 1%, 2%, 5%, 10% and 20%.
The proportion of contamination of the sample was calculated by the method of example 2 using the capture probe of example 1, and the detected contamination proportion substantially coincided with the expected contamination proportion as shown in table 9. Therefore, 56 SNPs not only can sensitively detect sample pollution, but also can calculate the proportion of sample pollution by averaging the number of possible pollution sites and mutation frequency according to the proportion of wild-type and homozygous pollution.
The above-mentioned embodiments are merely possible and preferred embodiments of the present invention, which are intended to illustrate the present invention and not to limit the scope of the claims of the present invention, therefore, all equivalent changes and modifications made in the claims of the present invention should fall within the scope of the claims of the present invention.
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Suzhou Rendong bioengineering Co., Ltd
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<213> Artificial Sequence (Artificial Sequence)
<400> 14
acattgtcca agcatgactt cagattcttt tccacttctt tttggactga gaacgcaaca 60
ttttgtagca ctctggacgt tttgcttgga cctgatccag gttgtggtat ctgtaggacg 120
<210> 15
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 15
tctccaccgt gtgctcgctg gtgagccgga tagcgttcct ccgctcccgc cagtcccgcg 60
aaggcctgcc ccacgtcggc ttcttctttt ctagaaaatg atggaaacat ttgtgcggtc 120
<210> 16
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 16
cacaggtcgt cttcccgtga cgcccagatc tgtcctgcag gatggagcca gcaccctcag 60
aggttcgact cgccgtccgg gaagccattc atgccctctc gtcttcggag gatggcggcc 120
<210> 17
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 17
tgggcagggt gcagagtcac gcactcctct tgagaaaaat acacagccag atcctcaaag 60
agtattggtc cctgaaacac aagagcctgt gtcagcacaa gagggtggag ggtcagcccc 120
<210> 18
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 18
ccacgccctg tgttgtcagg ccggccacca tcagccagtg ccacagtgat ggtccgagcg 60
tggtcagcca gcacccggta ggccatgtca atcccatcgg catcctcagc accaactttc 120
<210> 19
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 19
gtggcatgca tcaagcacat cgtggactgc atccgggcag agctacagag cattgaagag 60
ggtgtgcaag ggcaacagga tgccctcaac agtgccaagc tgcactcagt tcttttcatg 120
<210> 20
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 20
aaagtcctcg ttgttcctct gggatgcaac atgagagagc agcacactga ggctttatgg 60
gttgccctgc cacaagtgaa caggtcccag catgaaagca gggacaagaa aattgagctc 120
<210> 21
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 21
tttgaccatc ctcatatccc agtggccagt cttcctccct tcctgtctcc ctctctttca 60
ttgacatggg gaatctcata cctactttca tcagcatcta taacatttta gtgcatttct 120
<210> 22
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 22
attacaattt ccttcttatt ctactttgga cataaaggac tgatatccac tgaatctgtc 60
tactaaggta tgtctaaaag cataagcaga cagggttcct aaccaaagag gctccaggag 120
<210> 23
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 23
ggctgttttt aaagtgtgcc ccaaacataa tcccggacta ttccttacct tcaagagatg 60
ggtcatcatc atagattggt tttgctgaac cagaaaagaa gagttcgata ttcttctcga 120
<210> 24
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 24
tgactggaat gggagaagtg gattttctga catttgatcc tatagctaaa atggcaaaaa 60
ctgttaagta cgatgtacaa gctgtagcta tcattgtggt ggtattgaaa ctgctctttc 120
<210> 25
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 25
gctgatattt tagcttttgc ataacttggg gtgtaagaga aggctcttcc aagagctgtt 60
gcactcagta caactgcaga gatcaccctg taaccagaca gacacacagg aagagagcag 120
<210> 26
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 26
gggctcccag ggtctcctct ctcccctttt agcccaggta ttcccactgg accaggtggc 60
cccacatcat gcaaacctta atggggaaaa cagaattaat actatatctt ctcttttctt 120
<210> 27
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 27
gcgatcagga aggaggccgt ttgataattc atccgtggtt tcaaggggtg aacgatggcg 60
agatatctgg tgggggaggg aagccaacag tagtaatgat gaatacgtgg aaacgttagt 120
<210> 28
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 28
ccgtaaagca agagtaactt actgatctgt tctactaact tgagcatcac tcctccaaca 60
tgaaggtctc cagatactct cagtgtgacg tctttctgct gctcttcatt gggatggtca 120
<210> 29
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 29
tcaaagtcca cctcgttgac cgaggcgttc ttctccaaca gcagccgtgt gctagactcg 60
tccccgttct gggctgcaaa gtggagggct gtccactggt cctcatcctt ggcgttgaca 120
<210> 30
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 30
tctgattcag aagctcgtca ggtgggtcgg aaagtgacgt cgccttcgtc ttcatcctct 60
tccagctcct ctgattctga atctgatgat gaggctgacg tttcagaggt cactcctcga 120
<210> 31
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 31
ctgcccaggc ttccgaggat gccccagcca ttccccagct agacagaata ccaccccttg 60
tacagagcac ctaggcctcg aggcccctcc tcctgaacaa ggcttggttc tccttcaggc 120
<210> 32
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 32
ctgtttggca tgaacttgtt cattacaggt acattcactt aacaggctct ctttccaccc 60
ttgtagaaat acaaaaataa gacttaatac agacgatggc atgggcttag taactacccc 120
<210> 33
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 33
aggtatgtaa cccacctcag ggtgggaatt ctttgcatgg gatcgttcaa agttctgaga 60
aaagcccaat gtaggtcaga cacgactgct ccttggactg gggaagactt tccttggttg 120
<210> 34
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 34
cagcttctag gggtgttcat ttggtttggt gttgatccac ccaacatcat catagactat 60
gatgaacaca agacaatgaa ccctgagcaa gccagagggg ttctcaagtg tgacattaca 120
<210> 35
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 35
tgaatgtgct gttctaccag atttttcttg taggcaacaa tcacagggcg gccaaatgtg 60
tccagatagg tgtagtgcag ctcatctggg gcacggctga tttcataggg actatcaatt 120
<210> 36
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 36
ctttttcttc tttttcttgt ctatttacat gcccttgcat gaaacccctc ttaaactggg 60
atctgtgttc tttttgttct ggaacatacc taaaataaaa acattcttga atatcttcac 120
<210> 37
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 37
cacttcttgc tgcttgtgct catttcacag atggaagctt tctggaaaca gatggcaaat 60
atccagcact ttcttgtgga ccagtttaag tgttccagct ccaaagcccg acagctgatg 120
<210> 38
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 38
aacaatataa tctggcattt aactgccttt ataattaatg aaccttactt aaatcccaat 60
tgtcgacaaa ccacgtatgt attcagctca gtccagccat catcacagac agttccccac 120
<210> 39
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 39
atattttgtt tttgtaagtt tccacatgta tttttcttac cttcatgttg aagcaatatc 60
ctttccacca tactgctaga cgtagctaca tctataggtc gcagaagctc tgtatttttg 120
<210> 40
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 40
ttgcagcaca ctgctgcctc accattgtca tcgcacgatt cggactggaa ggagctcaga 60
gactgcactt cagacaggct ttcccgggca ctgtatggct gggaaggctt ttcctctaga 120
<210> 41
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 41
agtgtattgt gcaggctgaa aggggcgact ttgacagcag taccactgtg ggtttcagaa 60
tcatattcta caggtgagga agcgagcaga gacgtggacg cctgggtctt ttccctagag 120
<210> 42
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 42
tatggggcag aaataagggg cttttccaca ggttttcctt tggaggaaga tttcagtggt 60
gactttagag aatactcaac agtgtctcat cccatagcaa aagaagaaac ggtaatgatg 120
<210> 43
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 43
cgcctctctg tgcacgtgaa gaccaatgag acggcctgca accaaacagc cgtcatcaag 60
cccctcacta aaagttacca aggctctggc aagagcctga ccttttcaga taccagcacc 120
<210> 44
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 44
accctgtgct tggacccagg tcgtacgatc atcttcacaa cccaccacct ggatgaagct 60
gaagcgctga gtgaccgcgt ggccgtcctc cagcatggga ggctcaggtg ctgcggtcct 120
<210> 45
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 45
atatccaggg gttctcctat gtcttttgaa gattctagtc gaatcatccc actcttttat 60
ctttttagct ccttgtttag tcattcacta atttccatac atgataacga attcttcggt 120
<210> 46
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 46
gagaagttgc agccgcctct ttggagtcgg agtgtgtctt acctcggccc tggtcacgac 60
tatgcgaatc aacgtctcct catcggtccc cgcacccttc atcgacttgt acagacgttc 120
<210> 47
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 47
gggactgcgt tccaggcaca ggccacgctg aatatggcat cgttcatgtt gtagcgggca 60
tgggggccct gcagggggac cggagggtta atgaagttcc tcatgaaatc tctccgaatg 120
<210> 48
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 48
ccttctactg tgttcttata ggcaaagatg ctggaaggag atctggtttc aaagatgcta 60
cgagctgttc tgcagtctca taagaatgga gtagcattac cccggctcca aggagagtac 120
<210> 49
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 49
tgcttgacct tcactccctt ggtgaaggtc cgcttcttga ggacaaagtc atagttggcg 60
gtagagttca tggcatagaa gacgttggcg ttttcaggga tcttcagctc tggtggggag 120
<210> 50
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 50
atagctagtc tttgaacaga gtaggtgctc cagaatattc taagtgaagg agagaaagat 60
ttcaaatgtt tgtaagcatg gaagagtaag gcaatgatgg agctactagc aaaggcaaag 120
<210> 51
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 51
tctccaggtt ctgacgacgg gaacctcggc tctgtgtaca tttatgtgct cctaatcgtg 60
ggaacccttg tctgtggcat cgtcctcggc ttcctcttta aaaggtaacc tgtgaaacac 120
<210> 52
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 52
atctctgagt ttcgcattct gggattctct agagccatct tgcgcctctg atcgcgagac 60
cacacgatga atgcgttcat gggtcgcttc actctatcct ggacgttgcc tttactgttt 120
<210> 53
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 53
ggcttccgaa gaccttcaga aaagaaccag cacataatga gacaccataa agaagttggt 60
ctgccctaac agtgtgtcta caagcttgta aagatgttgg ccttgaagca gaaaattcat 120
<210> 54
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 54
acttgagaaa ccactttatt tgggatgaag aatccaccca ctattcttta cagagcccag 60
gggactgcta atgcaaacag tgatcaaaat tagtaaagag aaaaattacc tcatagctga 120
<210> 55
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 55
gtctaattta tttttctggt tactctcaga aataatttca gaaatgagtg tgacatcttt 60
ccctgctgac tgaaaccact aactttccac acaagggtaa tatatatata ggacattaca 120
<210> 56
<211> 120
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 56
taaaactttc aggaccctga aatacagaac tgcaaagaaa cggcctaaga tggttgaatg 60
ctctttattt ttctttaatt tagacatgtt caaacgttca atgtcttaca tacttagtta 120
Claims (11)
- An SNP genotyping genetic marker set, comprising 56 SNP sites, wherein rs numbers of the 56 SNP sites in an SNP database are shown as follows:。
- 2. the probe set for capturing the SNP typing genetic marker combination according to claim 1, wherein the probe set has a sequence as shown in SEQ ID NO 1-56.
- 3. A kit comprising a detection reagent comprising the SNP genotyping genetic marker set of claim 1.
- 4. The kit of claim 3, wherein the kit further comprises a reagent for detecting the mutation genotype at the 56 SNP sites according to claim 1.
- 5. The kit according to claim 3 or 4, wherein the kit comprises a capture probe set of the SNP genotyping genetic marker combination according to claim 1, and the sequence of the probe set is shown as SEQ ID NO 1-56.
- 6. Use of a reagent for detecting the combination of SNP-typing genetic markers according to claim 1 in the preparation of a reagent for sample pairing judgment and sample contamination judgment.
- 7. Use of a probe set according to claim 2 for the preparation of a detection reagent for a combination of SNP-typing genetic markers according to claim 1.
- 8. Use of a kit according to any one of claims 3 to 5 for the identification of a sample and the determination of contamination of a sample.
- 9. A sample pairing judgment method, comprising the steps of:1) extracting sample DNA, amplifying and establishing a library;2) capturing and enriching the SNP typing genetic marker combination of claim 1;3) high-throughput sequencing;4) splitting sequencing data, performing quality inspection, and removing a joint sequence, a primer and a low-quality base fragment introduced in the library building process;5) aligning the sequences to a human reference genome;6) calculating mutation frequencies of 56 SNPs of each sample, and judging mutation types of SNP sites;7) and calculating the proportion of the same mutation types of the 56 SNPs of the two samples, and judging whether the two samples are matched.
- 10. The method according to claim 9, wherein in step 7), the ratio of the same mutation types is 95% or more, and the two samples are determined to be paired.
- 11. A method for analyzing contamination of a sample, comprising the steps of:1) extracting sample DNA, amplifying and establishing a library;2) capturing and enriching the SNP typing genetic marker combination of claim 1;3) high-throughput sequencing;4) splitting sequencing data, performing quality inspection, and removing a joint sequence, a primer and a low-quality base fragment introduced in the library building process;5) aligning the sequences to a human reference genome;6) calculating mutation frequencies of 56 SNPs of each sample, and judging mutation types and possible pollution sites of the SNP sites;7) counting the number of the wild type and the homozygote, and if the sum of the number of the wild type and the number of the homozygote is less than or equal to 10, judging that the sample has pollution; and if the sum of the number of the wild type and the homozygous is more than 10, estimating the possible pollution ratio of the sample according to the average value of the possible pollution sites and the mutation frequency.
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