CN107937571B - Nucleic acid mass spectrum paternity identification method based on information SNP set and primers thereof - Google Patents
Nucleic acid mass spectrum paternity identification method based on information SNP set and primers thereof Download PDFInfo
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Abstract
The invention discloses a nucleic acid mass spectrum paternity test method based on an information SNP set and primers thereof, wherein the information SNP set used in the method is based on data analysis of 40 crowds and has higher heterozygosity rate>0.4, population genetic index Fst<0.06, and lower mutation frequency<0.01%, random population matching rate<10‑15The method is suitable for individual identity identification and paternity test, wherein the nucleotide sequences of the upstream primers of 30 information SNP markers are sequentially shown as SEQ ID No. 1-30, the nucleotide sequences of the downstream primers are sequentially shown as SEQ ID No. 31-60, and the nucleotide sequences of the single-base extension primers are sequentially shown as SEQ ID No. 61-90. The identification result is matched with the actual genetic relationship through flight mass spectrum detection information SNP polymorphism and paternity inference test verification; the information SNP set provided by the invention is used for human paternity test, has accurate result, can quickly, highly-flux and simply perform genotyping by preferably utilizing a mass spectrometry method, thereby calculating the paternity index and performing paternity judgment, and has the advantages of high accuracy, low cost and the like.
Description
Technical Field
The invention relates to a detection system for human paternity test by using a nucleic acid mass spectrometry detection information SNP marker, in particular to a 30 information SNP marker combination, a primer sequence and a paternity test method.
Background
Paternity Testing (fractional Testing) is the identification of genetic relationships between individuals through the detection of human genetic markers based on genetic rule analysis. The modern paternity test method adopts DNA analysis, currently PCR-STR multiplex fluorescence amplification detection mainly based on Capillary Electrophoresis (CE) technology, which applies second generation genetic marker short tandem repeat Sequence (STR) typing technology, namely, the purpose of calculating paternity probability is achieved by detecting, typing and counting 15-20 STRs of DNA samples. STR loci are considered to be the most versatile genetic markers in forensic DNA identification due to the advantages of high polymorphism, large amount of information, wide distribution, and following Mendelian co-dominant inheritance. However, with the widespread use of STR loci in forensic identification, its drawbacks are becoming more and more prominent, such as: the high mutation rate of STR loci brings trouble to the interpretation of the results of paternity test; the PCR amplification is not easy to realize the typing of the degraded DNA; the limitation of the number of STRs is detrimental to the identification of complex relationships. In addition, because the sequences of different alleles of STR are similar in length, the judgment error rate can reach 1% -5%, which directly influences the judgment of the paternity test result.
SNP (Single Nucleotide Polymorphism) is increasingly concerned by people as a third-generation genetic marker following STR, the current SNP typing technology is applied to various aspects such as molecular diagnosis, clinical examination, forensic medicine, genetic disease research, guidance of individualized medication, new drug research and development and the like, STR is gradually replaced, and the SNP typing technology becomes a preferred genetic marker, and the SNP typing technology has the incomparable advantages of wide distribution, abundant quantity, stable heredity, high flux, rapid and accurate detection and the like, and particularly has better identification rate for degraded samples than STR, so the SNP typing technology becomes an emerging popular identification method in the field of paternity testing.
As sequencing technology develops and the size of the population to be examined increases, the resolution of SNP in the genome becomes higher and higher. The research shows that some information SNPs (also called label SNPs) exist in the genome and can represent other SNPs, so that the genotypes of other sites in an individual can be distinguished by only identifying the information SNP sites without performing genetic analysis on all the SNP sites in the genome, and the sites are called information SNP sites or label SNPs. The research finds that: multiple SNPs that are adjacent tend to be inherited in a stable pattern to offspring, while the pattern of combinations of multiple SNP sites located in a region of the same chromosome is defined as a haplotype. Theoretically, the haplotype has informative SNP and a large number of non-informative SNP, and the genotypes of other common SNPs on the haplotype can be known only by identifying the loci of the informative SNP. Therefore, it is not necessary to type all SNP sites when genotyping. Genetic information carried by ten million common SNPs in the whole genome can be replaced by a few informative SNPs. Therefore, the selected information SNP site set can contain more SNP information, the problem of insufficient information amount due to small quantity of SNP is solved, the length of an amplification product can be controlled within 200bp, and the multiple PCR amplification is easy to realize. In selecting the SNPs, the screening principle is as follows: higher heterozygosity and lower population variation. Population genetics index Fst serves as a measure of the allelic variation of a population. The final average heterozygosity of SNPs is selected to be >0.45, Fst is less than 0.06, and the average distance between two SNPs exceeds 37.5Mb, so that the design of primers for later-stage nucleic acid mass spectrum detection is facilitated.
The nucleic acid mass spectrometry system can be used for quickly analyzing nucleic acid samples, the flux of the nucleic acid mass spectrometry system can meet the requirement of flexible detection amount, the result can be quickly obtained, and the capability of detecting hundreds of gene mutations at low cost is realized. The system is widely applied to SNP genotype analysis and DNA methylation analysis after being introduced.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to establish a set of nucleic acid mass spectrum detection information SNP set and a multiplex PCR primer set thereof for human paternity test aiming at the advantages of high throughput and large information quantity of the information SNP set, and establish a simple, convenient and accurate paternity test method based on the nucleic acid mass spectrum detection information SNP set.
In order to achieve the above objects, the present invention provides a nucleic acid mass spectrometric detection information SNP set for human paternity test, which comprises 30 information SNP markers, wherein the 30 information SNP sets of the present invention are based on data analysis of 40 populations, and have a high heterozygosity rate >0.4, a population genetic index Fst <0.06, a low mutation frequency < 0.01%, and a random population matching rate < 10-15And the average distance between two SNPs exceeds 37.5Mb, so that the method is suitable for individual identification and paternity test. The 30 informative SNP markers, positions, Fst indices and their average heterozygosity rates proposed by the present invention are shown in Table 1.
TABLE 130 informative SNP site information
| SNP | Chromosome | Chromosome zone | Position of | Fst index | Average heterozygosity rate |
| rs560681 | 1 | q23.3 | 157，599，743 | 0.035 | 0.434 |
| rs7520386 | 1 | p36 | 13，900，708 | 0.045 | 0.477 |
| rs12997453 | 2 | q31.3 | 182，238，765 | 0.048 | 0.475 |
| rs6444724 | 3 | q29 | 194，690，082 | 0.045 | 0.468 |
| rs279844 | 4 | p12 | 46，170，583 | 0.03 | 0.485 |
| rs6811238 | 4 | q32.3 | 170，038，345 | 0.031 | 0.485 |
| rs13134862 | 4 | q21.1 | 76，783，075 | 0.054 | 0.471 |
| rs13182883 | 5 | q31 | 136，661，237 | 0.033 | 0.471 |
| rs338882 | 5 | qter | 178，623，331 | 0.056 | 0.454 |
| rs315791 | 5 | q35 | 169，668，498 | 0.058 | 0.449 |
| rs1358856 | 6 | q22 | 123，936，677 | 0.042 | 0.473 |
| rs1336071 | 6 | q16.1 | 94，593，976 | 0.045 | 0.472 |
| rs13218440 | 6 | p24.1 | 12，167，940 | 0.047 | 0.468 |
| rs2272998 | 6 | q24.3 | 148，803，149 | 0.047 | 0.445 |
| rs214955 | 6 | q25 | 152，789，820 | 0.049 | 0.456 |
| rs2503107 | 6 | q22.3 | 127，505，069 | 0.058 | 0.471 |
| rs1019029 | 7 | p22 | 13，667，516 | 0.045 | 0.472 |
| rs10092491 | 8 | p21 | 28，466，991 | 0.039 | 0.456 |
| rs740598 | 10 | q26 | 118，496，889 | 0.04 | 0.463 |
| rs1410059 | 10 | q24.3 | 97，162，585 | 0.054 | 0.467 |
| rs10488710 | 11 | q23.2 | 114，712，386 | 0.025 | 0.441 |
| rs6591147 | 11 | q23 | 105，418，194 | 0.059 | 0.468 |
| rs1058083 | 13 | q32.3 | 98，836，234 | 0.032 | 0.464 |
| rs1821380 | 15 | q13 | 37，100，694 | 0.042 | 0.464 |
| rs7229946 | 18 | q11.1 | 20，992，999 | 0.043 | 0.464 |
| rs9951171 | 18 | p11.3 | 9，739，879 | 0.044 | 0.474 |
| rs985492 | 18 | q11.2 | 27，565，032 | 0.059 | 0.465 |
| rs445251 | 20 | p12.1 | 15，072，933 | 0.041 | 0.463 |
| rs1523537 | 20 | q13.1 | 50，729，569 | 0.042 | 0.472 |
| rs2567608 | 20 | p11.1 | 22，965，082 | 0.044 | 0.475 |
In the 30 information SNP labeled multiplex primer sets, the nucleotide sequences of the upstream primers are sequentially shown as SEQ ID No. 1-30, the nucleotide sequences of the downstream primers are sequentially shown as SEQ ID No. 31-60, and the nucleotide sequences of the single-base extension primers are sequentially shown as SEQ ID No. 61-90. The sequences of the primer sets for the 30 informative SNP markers are shown in Table 2.
The 30 information SNP markers are obtained by analyzing data based on 40 crowds and screening through strict tests, and the finally selected information SNP set has higher heterozygosity rate>0.4 population genetic index Fst<0.06, lower mutation frequency<0.01% and random population match rate<10-15The screening principle of the information SNPs is met, and the experiment that the whole set of information SNP markers carry out paternity inference verification in an actual population proves that the 30 information SNP marker combinations can accurately verify and infer the human paternity relationship.
The invention finally provides an paternity test method for detecting an information SNP set based on a nucleic acid mass spectrum, which is characterized by comprising the following steps: (1) and (3) extracting a genome: comprises extracting anticoagulation DNA and extracting genome DNA in oral epithelial cells; (2) performing multiplex PCR using the SNP set of human paternity test nucleic acid mass spectrometry of claim 1 or 2, and genotyping the nucleic acid mass spectrometry; (3) and calculating the paternity index and judging paternity.
Compared with the prior art, the invention has the beneficial effects that: the paternity test method for detecting the information SNP set based on the nucleic acid mass spectrum, disclosed by the invention, has the advantages that the information SNP set used in the method is based on data analysis of 40 crowds, and has higher heterozygosity rate>0.4, population genetic index Fst<0.06, and lower mutation frequency<0.01%, random population matching rate<10-15Is suitable for individual identification and paternity test; the identification result is matched with the actual genetic relationship through flight mass spectrum detection information SNP polymorphism and paternity inference test verification; the paternity test method based on the nucleic acid mass spectrometry detection information SNP set has the advantages of high accuracy, high throughput, low cost and the like.
Detailed Description
The following examples are presented to illustrate certain embodiments of the invention in particular and should not be construed as limiting the scope of the invention. The present disclosure may be modified from materials, methods, and reaction conditions at the same time, and all such modifications are intended to be within the spirit and scope of the present invention. Specifically, the reagents used in the embodiments of the present invention are all commercially available products, and the databases used in the embodiments of the present invention are all public online databases.
Example 1: selection of 30 informative SNP markers
The selection of 30 information SNP markers for the paternity testing system is based on the data analysis of 2100 individuals mainly from the population of Africa, Europe, America and Asia, and is carried out by three processes:
1) initially screening out the hybridization rate of >0.45 from the SNPs of the crowd data, wherein the quantity of the SNPs with Fst <0.01 is 2723;
2) on the basis of selecting high heterozygosis rate and low Fst index, SNPs with the position distance less than 1Mb are not selected, and sites on X or Y chromosomes are not selected at the same time, so that 195 primarily screened SNPs are obtained;
3) SNPs with a heterozygosity rate of >0.4, a population genetic index Fst of <0.06 and a mutation frequency of < 0.01% were selected among 195 primary-screened SNPs, thereby selecting 30 optimal SNPs as shown in Table 1.
Example 2: paternity test based on nucleic acid mass spectrometry detection information SNP set
1. Genome extraction
In this embodiment, the magnetic bead method is used to extract genomic DNA, the sample is saliva, 2ml of saliva is collected and stored in saliva stabilizer, and the steps of extracting saliva genomic DNA are as follows:
1) mu.l of lysis solution V, 450. mu.l of sample (a mixture of saliva and saliva stock solution), 220. mu.l of isopropanol, 20. mu.l of proteinase K and 10. mu.l of nucleic acid precipitant were added to a 1.5ml centrifuge tube, vortexed, mixed, and then the tube was subjected to a 56 ℃ water bath for 10min while being inverted 6 to 8 times.
2) The tube was removed, 20. mu.l of magnetic beads were added, vortexed gently and mixed, and vortexed 1 time at 2min in a 56 ℃ water bath for 20 minutes.
3) Taking out the centrifuge tube, placing on a magnetic frame, magnetically attracting for 2min, and absorbing and discarding the supernatant.
4) Adding 900 μ l inhibitor remover IR, vortex mixing, standing on magnetic frame for magnetic attraction for 2min, and removing supernatant.
5) And (4) repeating the step.
6) Adding 900 μ l of rinsing liquid WB, vortex, mixing for 30s, placing on a magnetic rack, magnetically attracting for 2min, and removing the supernatant.
7) And (6) repeating the step.
8) The tube cap is opened, the tube is dried for 3-5min at room temperature, 70-120 μ l of elution Buffer (water bath at 56 ℃ in advance for 10min) is added, vortex mixing is carried out for 1min, then the tube is placed on a magnetic frame to be magnetically attracted for 2min, and DNA is absorbed and transferred to a new EP tube.
9) The concentration was measured using Nanodrop2000 and qubit3.0 and recorded to ensure genomic DNA concentrations >10 ng/. mu.l, A260/A280 between 1.7 and 2.0, stored at-20 ℃. Unqualified samples need to be re-extracted.
2.384 well multiplex PCR amplification
1) The multiplex PCR process was identical to the ordinary PCR process, and Primer mix, PCR buffer, dNTP mix, MgCl were mixed as shown in Table 32Taq enzyme and water were added to the reaction system and all reagents were added in an additional 38% volume to prevent losses during pipetting.
TABLE 3 multiplex PCR amplification reaction System
2) Mu.l of genomic DNA at a concentration of 10 ng/. mu.l was added to each well of the 384-well plate, 4. mu.l of the above reaction mixture was added to each well of the 384-well plate, gently vortexed, and subjected to flash centrifugation.
3) The 384-well plate was sealed with a sealing plate membrane and placed on a PCR instrument for amplification reaction, with the procedure shown in table 4:
TABLE 4 multiple PCR reaction cycling parameters
3. Digestion with SAP enzyme (shrimp alkaline phosphatase)
The step is to eliminate residual dNTPs in the amplification reaction and prevent the influence on subsequent reactions, and specifically comprises the following steps:
1) SAP enzyme digestion reagents were prepared as per table 5 in 1.5ml centrifuge tubes (all reagents had an additional 38% increase in volume to prevent loss during pipetting).
TABLE 5 SAP enzymatic digestion reaction System
| Reagent | Volume (1 reaction) | Volume (384 reaction) |
| Water (chromatogram pure) | 1.53μl | 810.9μl |
| SAP buffer (10X) | 0.17μl | 90.1μl |
| SAP enzyme (1.7U/ul) | 0.30μl | 159.0μl |
| Total volume | 2.00μl | 1060.0μl |
2) The SAP mixture was vortexed for 5 seconds and mixed.
3) Centrifuging the SAP mixture at 5000rpm for 10 s, and dispensing 2. mu.l of SAP mixture into each well of a 384-well plate using a spotting apparatus;
4) the 384-well plate was sealed with a sealing plate membrane and placed on a PCR instrument for digestion, with the procedure shown in table 6:
TABLE 6 SAP enzymatic digestion reaction cycle parameters
| Temperature of | Time | Number of cycles |
| 37℃ | 40min | 1 |
| 85℃ | 5min | 1 |
| 4℃ | forever | 1 |
4. Single base extension reaction
1) Placing the SAP reaction plate in a centrifuge for 3min at 3000 r/m; the iPLEX Buffer Plus (10X), iPLEX Termination mix, Primer mix, iPLEX enzyme and water reaction systems were added to 1.5ml centrifuge tubes as shown in Table 7, all reagents added 38% more volume to prevent loss during pipetting;
TABLE 7 Single-base extension reaction System
2) Using a spotting instrument to dispense 2 μ l of the reaction mixture into each well of a 384-well plate;
3) the 384-well plate was sealed with a sealing plate membrane and placed on a PCR instrument for single base extension reaction, the procedure is shown in Table 8:
TABLE 8 Single base extension reaction cycle parameters
5. Resin purification and data collection
Adding 16 mul of water into the reaction product for dilution, and adding prepared resin for desalination and purification; and (4) spotting the purified sample on a detection chip, and performing mass spectrum detection on the chip to obtain an SNP typing result.
6. Data analysis
SNP typing data were analyzed, and Paternity Index (PI) was calculated according to the formula shown in Table 9. When a plurality of genetic markers are used for paternity test, the paternity index of each genetic marker is respectively PI1, PI2, PI3, … PIN, and the multiplication of the paternity indexes of n genetic markers is a Cumulative Paternity Index (CPI), then:
CPI ═ PI1 × PI2 × PI3 × … × PIn (1, 2, 3, n represent PI values of 1, 2, 3, n loci)
TABLE 9 calculation formula of dyad autosomal patency index
Note: p, q, and r represent distribution frequencies of allele P, Q, R
(1) The assumption that the detected male is not the biological father of the child is supported when the cumulative paternity index of the detected male is less than 0.0001. The appraisal opinions can be expressed as: based on the existing data and DNA analysis results, the male to be tested was excluded as the biological father of the child.
(2) When the cumulative paternity index of the detected male is greater than 10000, the hypothesis that the detected male is the biological father of the child is supported. The appraisal opinions can be expressed as: and supporting that the male to be detected is the biological father of the child according to the existing data and the DNA analysis result.
The parent-child relationship may be excluded if the number of SNP sites having a CPI value of 0 and a PI value of 0 is greater than or equal to 3, and the parent-child relationship may not be determined nor excluded if the number of SNP sites having a CPI value of 0 and a PI value of 0 is 1 or 2.
Example 3: application in actual population
In order to verify the feasibility and accuracy of paternity test of the whole set of information SNP sets in an actual family, 20 groups of samples were collected in this example, of which 10 groups were combinations with clear paternal-child relationship, and the remaining 10 groups were pairwise combinations of unrelated unknown males and either child. The 20 groups of samples are respectively numbered KY-1 to KY-20. Parent-child affinity inference was performed using the data results obtained in example 2, and the results are shown in table 10.
Application of table 1030 information SNP set in practical group
| Combination numbering | Cumulative Paternity Index (CPI) | Relative opportunity of father and Right (RCP) | Actual relationship of relationship |
| KY-1 | 856324110 | 1 | Family of life |
| KY-2 | 625341255.97 | 1 | Family of life |
| KY-3 | 965423826.91 | 1 | Family of life |
| KY-4 | 0 | 0 | Is not in person |
| KY-5 | 0 | 0 | Is not in person |
| KY-6 | 3.27E-13 | 3.27E-13 | Is not in person |
| KY-7 | 7.53684515E-14 | 7.53684515E-14 | Is not in person |
| KY-8 | 2357891455 | 99.99999901％ | Family of life |
| KY-9 | 65289712234 | 1 | Family of life |
| KY-10 | 0 | 0 | Is not in person |
| KY-11 | 0 | 0 | Is not in person |
| KY-12 | 0 | 0 | Is not in person |
| KY-13 | 1.91532452E-18 | 1.91532452E-18 | Is not in person |
| KY-14 | 56422522235.65 | 1 | Family of life |
| KY-15 | 0 | 0 | Is not in person |
| KY-16 | 658921355535 | 99.999999902％ | Family of life |
| KY-17 | 489238587464 | 99.999999901％ | Family of life |
| KY-18 | 989835796575 | 1 | Family of life |
| KY-19 | 0 | 0 | Is not in person |
| KY-20 | 896181185228 | 1 | Family of life |
Note: paternal Relative Chance (RCP): because the paternity index is a real number, the chance of your right is not easily seen by the number, the habit converts the paternity index into a conditional probability, namely the relative chance of paternity and the probability of paternity, and you show the degree of confidence that the suspected father is a child.
As can be seen from Table 10, when the 30 information SNP sets and the multiplex PCR primer sets thereof provided by the invention are used for human paternity test, the paternity relationships are obvious, and the actual paternity relationships are completely matched.
The paternity test method for detecting the information SNP set based on the nucleic acid mass spectrum, disclosed by the invention, has the advantages that the information SNP set used in the method is based on data analysis of 40 crowds, and has higher heterozygosity rate>0.4, population genetic index Fst<0.06, and lower mutation frequency<0.01%, random population matching rate<10-15Is suitable for individual identification and paternity test; verifying and verifying the flight mass spectrum detection information SNP polymorphism and paternity test, wherein the identification result is consistent with the actual genetic relationship; the paternity test method based on the nucleic acid mass spectrometry detection information SNP set has the advantages of high accuracy, low cost and the like.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
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<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 22
acgttggatg aagatagcct tccacctttt 30
<210> 23
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 23
acgttggatg aggttctgga gttctccatg 30
<210> 24
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 24
acgttggatg tatggaacct ctgctgtctt 30
<210> 25
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 25
acgttggatg aaatgaaaga aggaaacatc 30
<210> 26
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 26
acgttggatg ttgttcctct gggatgcaac 30
<210> 27
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 27
acgttggatg cgaggtgagc ccagaggacc 30
<210> 28
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 28
acgttggatg ccactgcacc tggcctacaa 30
<210> 29
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 29
acgttggatg ttcatttctg catgggtggg 30
<210> 30
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 30
acgttggatg ctgcgccaac cccattctct 30
<210> 31
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 31
acgttggatg agtcaggatg caaactcttg 30
<210> 32
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 32
acgttggatg gaagactctg tcccagccac 30
<210> 33
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 33
acgttggatg accatttaac agctctgatg 30
<210> 34
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 34
acgttggatg tcctactccg tagtaaatga 30
<210> 35
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 35
acgttggatg tcctactccg tagtaaatga 30
<210> 36
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 36
acgttggatg aagtacttct atgaaaatga 30
<210> 37
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 37
acgttggatg aggtcttctt ggtatagctc 30
<210> 38
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 38
acgttggatg cgttactttc ttcctgcctt 30
<210> 39
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 39
acgttggatg catttttctc tccttctgtc 30
<210> 40
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 40
acgttggatg cgcacctctt atttgctcct 30
<210> 41
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 41
acgttggatg aaattatctt ataaactgca 30
<210> 42
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 42
acgttggatg atacctgaaa gcatattaaa 30
<210> 43
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 43
acgttggatg tcaagcagcc ccaacccatc 30
<210> 44
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 44
acgttggatg agcgactcct acgagagaag 30
<210> 45
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 45
acgttggatg gattttcatc aactttatcg 30
<210> 46
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 46
acgttggatg ctaggtctcc acaaccattt 30
<210> 47
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 47
acgttggatg aggataagct cagcctactc 30
<210> 48
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 48
acgttggatg accagtatcc ccgcaaacta 30
<210> 49
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 49
acgttggatg cagctttgca gggccagggt 30
<210> 50
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 50
acgttggatg cttggatatc cttctagctt 30
<210> 51
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 51
acgttggatg actgtattag gagttcccac 30
<210> 52
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 52
acgttggatg ctcctcagcc ctggtctgct 30
<210> 53
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 53
acgttggatg ttgttcttct ccatcccatt 30
<210> 54
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 54
acgttggatg gggcacaatg gagccactga 30
<210> 55
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 55
acgttggatg cagacttgga taaagcagtc 30
<210> 56
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 56
acgttggatg tgtccctgct ttcatgctgg 30
<210> 57
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 57
acgttggatg tcctcctcta aaccaaggga 30
<210> 58
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 58
acgttggatg acatgaacaa attatagtct 30
<210> 59
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 59
acgttggatg tgagacaatg cacagaactg 30
<210> 60
<211> 30
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 60
acgttggatg gcaccttcca ggaggcagca 30
<210> 61
<211> 20
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 61
ctcctgtgac ctgagtaaat 20
<210> 62
<211> 17
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 62
tgtttggtga gctgtat 17
<210> 63
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 63
tgcaagaaag gtaggtaaaa act 23
<210> 64
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 64
aaccaaattg ttgaacactg gttact 26
<210> 65
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 65
gaagtttaga gagttgtgag t 21
<210> 66
<211> 20
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 66
aagccaggtt tgtttaaagt 20
<210> 67
<211> 29
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 67
gtcagatata tcttagatga agcaatagg 29
<210> 68
<211> 16
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 68
ccccaccctg ttcctt 16
<210> 69
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 69
tcaccttctt tcgtgtgcct gtgca 25
<210> 70
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 70
aaaatgagga aactaatgca taggc 25
<210> 71
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 71
ctaagcatcc tgtgtacata t 21
<210> 72
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 72
aagaatgaat ttgaaaaagc atcag 25
<210> 73
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 73
ctgagattca cctctagtcc ctctg 25
<210> 74
<211> 21
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 74
ggtcgtcagt gtggtcagag g 21
<210> 75
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 75
ggtgaacaaa gactgaaaag gtgat 25
<210> 76
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 76
gacaacttga gaaagttagg tatat 25
<210> 77
<211> 28
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 77
ggtcaacaca agatagaagc agactagg 28
<210> 78
<211> 26
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 78
cttccagata gagctaaaac tgaagt 26
<210> 79
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 79
cattgctcgt ctatggttag tctcg 25
<210> 80
<211> 24
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 80
cgggaaacat ctagcatttt tctt 24
<210> 81
<211> 16
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 81
tctgcaaatg tggcag 16
<210> 82
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 82
agattgtcat aactctggac gtatg 25
<210> 83
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 83
tgggttaatt ttgctcagag tatcc 25
<210> 84
<211> 28
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 84
tcttctgtga ttttatattc ttatttat 28
<210> 85
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 85
ccagcaaaca tgtaaagtgt gagag 25
<210> 86
<211> 27
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 86
ggacatgttc acttgtggca gggcaat 27
<210> 87
<211> 25
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 87
agaaatgccc aaaagacttc aggaa 25
<210> 88
<211> 18
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 88
aaaaactgca agtggttg 18
<210> 89
<211> 19
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 89
aacaagatct tgtagggat 19
<210> 90
<211> 17
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 90
tacaacttcc gccgatt 17
Claims (2)
1. A multiplex PCR primer set for human paternity testing, the multiplex PCR primer set comprising: the primer comprises an upstream primer nucleotide sequence shown as SEQ ID NO. 1-30, a downstream primer nucleotide sequence shown as SEQ ID NO. 31-60 and a single-base extension primer nucleotide sequence shown as SEQ ID NO. 61-90.
2. A method for identifying the paternity of a nucleic acid profile based on an information SNP set for non-diagnostic purposes is characterized by comprising the following steps: (1) and (3) extracting a genome: comprises extracting anticoagulation DNA and extracting genome DNA in oral epithelial cells; (2) performing multiplex PCR using the multiplex PCR primer set for human paternity testing according to claim 1, genotyping by nucleic acid mass spectrometry; (3) and calculating the paternity index and judging paternity.
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| CN201810040431.9A CN107937571B (en) | 2018-01-16 | 2018-01-16 | Nucleic acid mass spectrum paternity identification method based on information SNP set and primers thereof |
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| CN107937571B true CN107937571B (en) | 2021-12-07 |
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| CN109628564B (en) * | 2019-02-25 | 2022-02-01 | 北京市理化分析测试中心 | Primer set for detecting SNP polymorphism and method for detecting SNP polymorphism by using primer set |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103173557A (en) * | 2013-04-08 | 2013-06-26 | 上海邃志生物科技有限公司 | Multiple PCR (polymerase chain reaction) primer combination and detection method used for human paternity test |
| CN106536752A (en) * | 2014-03-14 | 2017-03-22 | 凯尔迪克斯公司 | Methods of monitoring immunosuppressive therapies in transplant recipient |
| CN107217095A (en) * | 2017-06-15 | 2017-09-29 | 广东腾飞基因科技股份有限公司 | The mankind's paternity identification multiple PCR primer group and detection method |
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2018
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103173557A (en) * | 2013-04-08 | 2013-06-26 | 上海邃志生物科技有限公司 | Multiple PCR (polymerase chain reaction) primer combination and detection method used for human paternity test |
| CN106536752A (en) * | 2014-03-14 | 2017-03-22 | 凯尔迪克斯公司 | Methods of monitoring immunosuppressive therapies in transplant recipient |
| CN107217095A (en) * | 2017-06-15 | 2017-09-29 | 广东腾飞基因科技股份有限公司 | The mankind's paternity identification multiple PCR primer group and detection method |
Non-Patent Citations (1)
| Title |
|---|
| Parallel Analysis of 124 Universal SNPs for Human Identification by Targeted Semiconductor Sequencing;Suhua Zhang等;《sci rep》;20151222;第5卷;第18683篇，1-9 * |
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