A kit for screening high altitude pulmonary edema susceptible population based on 39 SNP loci

Abstract

The present invention discloses a kit for screening high altitude pulmonary edema susceptible population based on 39 SNP sites. The present invention provides the application of 39 SNPs as detection targets in developing products; product functions: screening high altitude pulmonary edema susceptible individuals; evaluating the risk of high altitude pulmonary edema in subjects. The rs numbers of the 39 SNPs are: 12733147, 2941757, 56240659, 28843785, 4836498, 79522289, 59140633, 12569857, 241970, 11620394, 12148912, 740873, 919197, 6009184, 938355, 9932581, 6194, 9592406, 229 7441, 4762, 2397218, 12462380, 699, 11896604, 12655827, 130293, 2486736, 41417552, 5768002, 40305, 11962222, 2009196, 2148582, 1008438, 10929728, 820336, 6888553, 6010621 and 6882447. The present invention can be used to avoid or reduce the occurrence of high altitude pulmonary edema.

Description

Technical Field

[0001] The invention belongs to the field of gene detection and relates to a kit for screening a population susceptible to high altitude pulmonary edema based on 39 SNP sites. Background Art

[0002] High altitude pulmonary edema (HAPE) refers to the symptoms of dyspnea at rest, chest tightness, chest tightness, cough, white or pink foamy sputum, general fatigue or reduced mobility in patients who have recently arrived at a plateau (generally above 3,000 meters above sea level, but also below 3,000 meters). HAPE is a high altitude idiopathic disease, which is more common in high altitude areas above 3,000 meters. It is characterized by rapid onset, great harm and high mortality rate.

[0003] In recent years, a series of studies have been conducted on the occurrence of HAPE at home and abroad. The disease has a clear genetic susceptibility tendency and is the result of the combined effects of genetics and the environment. The probability of developing the disease in a hypoxic environment is closely related to the individual's susceptibility genes. Therefore, susceptibility genes may serve as an important molecular marker to prevent and diagnose the disease, assess the severity of the disease, and provide effective treatment methods. Summary of the invention

[0004] The purpose of the present invention is to provide a kit for screening a population susceptible to high altitude pulmonary edema based on 39 SNP sites.

[0005] The present invention provides the application of 39 SNPs as detection targets in developing products.

[0006] The present invention also provides a product comprising a substance for detecting a subject's genotype based on 39 SNPs.

[0007] Exemplarily, the substance used to detect the genotype of 39 SNPs of a subject is a primer set; the primer set consists of 117 primers, as shown in sequence 1 to sequence 117 in the sequence listing.

[0008] The primer is a single-stranded DNA molecule.

[0009] The product also includes a carrier having a prediction model recorded thereon.

[0010] The product also includes a carrier recording a method for establishing a prediction model and a method for applying the prediction model.

[0011] The product also includes a carrier recording a method for establishing a prediction model, a method for evaluating a prediction model, and a method for applying a prediction model.

[0012] The present invention also provides the use of a substance for detecting a subject's genotype based on 39 SNPs in preparing a product.

[0013] Exemplarily, the substance used to detect the genotype of 39 SNPs of a subject is a primer set; the primer set consists of 117 primers, as shown in sequence 1 to sequence 117 in the sequence listing.

[0014] The primer is a single-stranded DNA molecule.

[0015] The functions of any of the above products are as follows (a) or (b):

[0016] (a) Screening for individuals susceptible to HAPE;

[0017] (b) Assess the risk of subjects developing high altitude pulmonary edema.

[0018] Any of the above 39 SNPs are as follows: rs12733147, rs2941757, rs56240659, rs28843785, rs4836498, rs79522289, rs59140633, rs12569857, rs241970, rs11620394, rs12148912, rs740873, rs919197, rs6009184, rs938355, rs9932581, rs6194, rs9592406, rs22 97441, rs4762, rs2397218, rs12462380, rs699, rs11896604, rs12655827, rs130293, rs2486736, rs41417552, rs5768002, rs40305, rs11962222, rs2009196, rs2148582, rs1008438, rs10929728, rs820336, rs6888553, rs6010621, and rs6882447.

[0019] Any of the above prediction models is a prediction model glm(PHENOTYPE~PS).

[0020] The method for establishing the prediction model glm(PHENOTYPE~PS) is as follows:

[0021] (1) Prepare two matrices, one containing the genotype information of the 39 SNPs of each subject in the training set, and the other containing the effect value information of the 39 SNPs; multiply the two matrices, calculate the PS value of each subject according to the calculation formula of the polygenic risk score, and save it as an additional data frame, which includes the phenotypic value of each subject (the phenotypic value refers to patients with HAPE or normal people) and the PS value.

[0022] (2) Establish a formula: phenotype~PS; perform logistic regression on the formula, select the function glm, and use the data frame obtained in step (1) as the input data for the regression; obtain the prediction model glm(PHENOTYPE~PS);

[0023] The method for establishing the prediction model specifically includes the following steps.

[0024] (1) Prepare two matrices, one containing the genotype information of the 39 SNPs of each subject in the training set, and the other containing the effect value information of the 39 SNPs; multiply the two matrices, calculate the PS value of each subject according to the calculation formula of the polygenic risk score, and save it as an additional data frame, which includes the phenotypic value of each subject (the phenotypic value refers to patients with HAPE or normal people) and the PS value.

[0025] (2) Establish a formula: phenotype~PS; perform logistic regression on the formula, select the function glm, and the regression input data is the data frame obtained in step (1); obtain the prediction model glm(PHENOTYPE~PS).

[0026] The evaluation method of the prediction model is as follows: the genotype information of the 39 SNPs of each subject in the validation set is input into the prediction model glm(PHENOTYPE~PS), the predict function is selected, and the prediction result of the subject is obtained (the prediction result is a patient with high-altitude pulmonary edema or a normal person).

[0027] The application method of the prediction model is as follows: input the genotype information of the 39 SNPs of the subject to be tested into the prediction model glm (PHENOTYPE~PS), select the predict function, and obtain the prediction result of the subject to be tested (the prediction result is a patient with high altitude pulmonary edema or a normal person).

[0028] The calculation formula of polygenic risk score (PS) is as follows:

[0029]

[0030] In the calculation formula, Gi is the number of effect alleles in the subject's genotype based on a certain SNP (the value is 0, 1, or 2), βi is the effect value of a certain SNP in the association analysis, and n is the number of SNPs included in the calculation (the number of SNPs is 39, and the information of 39 SNPs is shown in Table 1). The effect alleles and effect values ​​of each SNP are shown in Table 3. In Table 3, BETA is the effect value (βi).

[0031] Any of the above training sets consists of statistically significant high altitude pulmonary edema patients and normal subjects.

[0032] Any of the above validation sets consists of statistically significant high altitude pulmonary edema patients and normal subjects.

[0033] Any of the above products can be a kit.

[0034] Any of the above subjects may be Chinese.

[0035] Any of the above-mentioned subjects may be Chinese Han people.

[0036] Any of the above-mentioned subjects may be Chinese Han males.

[0037] Any of the above subjects may be Chinese living in plain areas.

[0038] Any of the above-mentioned subjects may be Chinese Han people living in plain areas.

[0039] Any of the above-mentioned subjects may be Chinese Han males living in plain areas.

[0040] Any of the above subjects may be Chinese who have arrived in plateau areas from plain areas.

[0041] Any of the above-mentioned subjects may be Chinese Han people who move from plain areas to plateau areas.

[0042] Any of the above-mentioned subjects may be Chinese Han males who move from plain areas to plateau areas.

[0043] Any of the above subjects may be Chinese who plan to go to high-altitude areas from plain areas.

[0044] Any of the above-mentioned subjects may be Chinese Han people who are expected to move from a plain area to a plateau area.

[0045] Any of the above-mentioned subjects may be Chinese Han males who are expected to move from plain areas to plateau areas.

[0046] Any of the above-mentioned plateau areas may be the Qinghai-Tibet Plateau.

[0047] Any of the plateau areas mentioned above may be the Lhasa area of ​​the Qinghai-Tibet Plateau.

[0048] The material used to detect the genotype of the subject based on 39 SNPs can be a material for detecting the genotype of the subject based on 39 SNPs based on any of the following technologies: DNA sequencing, restriction fragment length polymorphism, single strand conformation polymorphism, denaturing high performance liquid chromatography, SNP chip, etc. The SNP chip can include a chip based on nucleic acid hybridization reaction, a chip based on single base extension reaction, a chip based on allele-specific primer extension reaction, a chip based on "one-step" reaction, a chip based on primer ligation reaction, a chip based on restriction endonuclease reaction, a chip based on protein DNA binding reaction, a chip based on fluorescent molecule DNA binding reaction, etc.

[0049] Any of the above-mentioned substances for detecting the genotype of a subject based on 39 SNPs includes a primer set. The primer set is composed of all primers for detecting 39 SNPs (for example, the primer set for detecting each SNP is composed of 3 primers, so the primer set for detecting 39 SNPs is composed of 117 primers). The primer set for detecting each SNP is composed of three primers, one of which is a single base extension primer, and the other two are used to amplify a DNA fragment including the SNP. There are no special requirements for the sequence of the two amplification primers, as long as the genomic DNA fragment including the SNP can be amplified. The single base extension primer can be designed according to the upstream or downstream of the SNP in the human genome (excluding the SNP site), and the nucleotide extended by the single base extension primer corresponds to the SNP in the human genome, that is, the 3' terminal nucleotide of the single base extension primer corresponds to the adjacent position nucleotide of the SNP in the human genome (that is, the previous nucleotide or the next nucleotide of the SNP).

[0050] Any of the above-mentioned substances for detecting the genotype of a subject based on a certain SNP may also include PCR reagents, DNA sequencing reagents, DNA sequencers, and the like.

[0051] Any of the above substances for detecting the genotype of a subject based on 39 SNPs includes a primer set. The primer set consists of all primers for detecting 39 SNPs (for example, the primer set for detecting each SNP consists of 2 primers, so the primer set for detecting 39 SNPs consists of 78 primers). The pair for detecting each SNP consists of two primers for amplifying a DNA fragment including the SNP. There are no special requirements for the sequences of the two primers, as long as they can amplify a genomic DNA fragment including the SNP.

[0052] The combination of 39 SNPs provided by the present invention can be used to screen individuals susceptible to high altitude pulmonary edema, predict human susceptibility to high altitude pulmonary edema, screen individuals carrying human high altitude pulmonary edema susceptibility genes, and evaluate the risk of high altitude pulmonary edema.

[0053] The invention can be used to avoid or reduce the occurrence of high altitude pulmonary edema, and has great application and promotion value in plateau areas. DETAILED DESCRIPTION

[0054] The present invention is further described in detail below in conjunction with specific embodiments. The examples given are only for illustrating the present invention, not for limiting the scope of the present invention. The examples provided below can be used as a guide for further improvements by ordinary technicians in the field of this technology, and do not constitute a limitation of the present invention in any way. The experimental methods in the following examples, unless otherwise specified, are all conventional methods, and are carried out according to the techniques or conditions described in the literature in this field or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can be obtained from commercial channels. Ethical statement: Each subject signed an informed consent form, and this study was approved by the Medical Ethics Committee of the Seventh Medical Center of the People's Liberation Army and the General Hospital of the People's Liberation Army of Tibet. All patients with high-altitude pulmonary edema were diagnosed in the hospital. The diagnostic criteria refer to the literature: Nomenclature, classification and diagnostic criteria of high-altitude disease in my country; Journal of Plateau Medicine, Vol. 20, No. 1, 2010; 9-11. The normal person in the present invention refers to a non-high-altitude pulmonary edema patient.

[0055] Example 1: Screening for SNPs associated with the risk of high altitude pulmonary edema

[0056] The matched groups consisted of HAPE patients and corresponding normal subjects.

[0057] Establish multiple paired groups, conduct a large number of whole genome sequencing and sequence comparison analysis, and obtain a large number of differential SNPs. Verify the allele frequency and genotype frequency of each differential SNP in the high altitude pulmonary edema patient group and the corresponding normal group in turn for association analysis. Try a variety of SNP combinations and preliminarily establish models and verify models respectively. Through a large number of performance comparisons, a combination of 39 SNPs was finally screened.

[0058] Information on the 39 SNPs is shown in Table 1. In Table 1, the chromosome locations all refer to GRCh37.p13.

[0059] Table 1

[0060]

[0061] Example 2: Establishment of a method for detecting the genotype of a subject based on each SNP

[0062] The genotypes of the subjects based on each SNP were tested separately (39 SNPs in total, information is shown in Table 1).

[0063] 1. Take peripheral blood from the subjects and extract genomic DNA.

[0064] 2. Using the genomic DNA extracted in step 1 as a template, perform PCR amplification using specific primer pairs.

[0065] The specific primer pair consists of primer F and primer R.

[0066] PCR amplification was performed in a 384-well plate, with one reaction system in each well.

[0067] The composition of the PCR amplification reaction system (5 μL): 0.5 μL 10× PCR buffer, 0.4 μL 25 mM MgCl2 solution, 0.1 μL 25 mM dNTP mix, genomic DNA, HotStart Taq DNA polymerase, primer F, primer R, and the volume is made up with ultrapure water. In the 5 μL system, the content of genomic DNA is 20-50 ng, the content of HotStar Taq enzyme is 0.5 U, the content of primer F is 0.5 pmol, and the content of primer R is 0.5 pmol.

[0068] The reaction program of PCR amplification was as follows: 94°C for 4 minutes; 94°C for 20 seconds, 56°C for 30 seconds, 72°C for 1 minute, 45 cycles; 72°C for 3 minutes; and 4°C hold.

[0069] 3. Perform alkaline phosphatase treatment (to remove free dNTPs in the system).

[0070] Take the 384-well plate that has completed step 2, prepare the reaction system and then carry out the reaction.

[0071] The composition of the reaction system (7 μL): 5 μL of the product solution obtained in step 2, 0.3 μL of SAP, 0.17 μL of 10×SAP buffer, and 1.53 μL of ultrapure water.

[0072] SAP (shrimp alkaline phosphatase): product specification is 1.7U / μL; Agena company, product number is 10002.1. 10×SAP buffer is the matching buffer for SAP.

[0073] Reaction conditions: 37°C for 40 minutes; 85°C for 5 minutes; maintain at 4°C.

[0074] 4. Perform single base extension.

[0075] Take the 384-well plate that has completed step 3, prepare the reaction system, and then perform a single base extension reaction.

[0076] The composition of the single base extension reaction system (10 μL): 7 μL of the product solution obtained in step 3, 0.3 μL of single base extension reaction enzyme, 0.17 μL of 10× single base extension reaction buffer, 1 μL of single base extension primer, and 1.53 μL of ultrapure water.

[0077] Single base extension reaction enzyme: product specification is 1.7U / μL; Agena Company, product number is 1432. 10× single base extension reaction buffer is the matching buffer for single base extension reaction enzyme.

[0078] The reaction procedure for single base extension is as follows:

[0079] ①94℃30 seconds;

[0080] ②94℃5 seconds;

[0081] ③52℃ for 5 seconds, 80℃ for 5 seconds, 5 cycles;

[0082] ④94℃5s, 52℃5s, 80℃5s, 40 cycles;

[0083] ⑤72℃ for 3 minutes;

[0084] ⑥Maintain at 4℃.

[0085] 5. Perform resin purification.

[0086] Take the 384-well plate that has completed step 4, add 16 μl of water to each well, then add Clean Resin resin (Sequenom, USA) to each well, seal the film, rotate vertically at a low speed for 30 minutes to allow the resin to fully contact with the reactants, and then centrifuge to allow the resin to sink to the bottom of the well. The supernatant is the extension product after resin purification.

[0087] 6. Chip spotting.

[0088] The MassARRAY Nanodispenser RS1000 sample dispensing device (SEQUENOM) was started to transfer the extension product after resin purification to a 384-well SpectroCHIP (Sequenom) chip (SEQUENOM).

[0089] 7. Mass spectrometry detection.

[0090] The spotted SpectroCHIP chip was analyzed using MALDI-TOF, and the detection results were typed and output using TYPER 4.0 software (sequenom).

[0091] Primer F, primer R, and single base extension primer for detecting the genotype of the subject based on each SNP are shown in Table 2.

[0092] Table 2

[0093]

[0094]

[0095]

[0096] Example 3: Establishing a risk assessment model

[0097] 1. Calculation formula of polygenic risk score

[0098] The calculation formula of polygenic risk score (PS) is as follows:

[0099]

[0100] In the calculation formula, Gi is the number of effect alleles in the subject's genotype based on a certain SNP (the value is 0, 1, or 2), βi is the effect value of a certain SNP in the association analysis, and n is the number of SNPs included in the calculation (the number of SNPs is 39, and the information of 39 SNPs is shown in Table 1). The effect alleles and effect values ​​of each SNP are shown in Table 3. In Table 3, BETA is the effect value (βi).

[0101] Table 3

[0102]

[0103]

[0104] Taking rs12733147 as an example, its polymorphic form is A / G, and the effect allele is A; if the subject's genotype is AA, then the number of its effect alleles is 2; if the subject's genotype is GG, then the number of its effect alleles is 0; if the subject's genotype is AG, then the number of its effect alleles is 1.

[0105] 2. Establish a prediction model (logistic regression model)

[0106] The subject group consists of several patients with high altitude pulmonary edema and several normal persons. According to the method of Example 2, the genotype of each subject based on each SNP (39 SNPs in total) was detected respectively.

[0107] The subjects were sampled 1000 times. Each time, 80% of the HAPE patients and 80% of the normal subjects in the subjects formed the training set, and the remaining 20% ​​of the HAPE patients and 20% of the normal subjects formed the validation set.

[0108] R language is used to build models, evaluate models and apply models.

[0109] The steps to build the model are as follows:

[0110] (1) Prepare two matrices, one containing the genotype information of the 39 SNPs of each subject in the training set, and the other containing the effect value information of the 39 SNPs; multiply the two matrices, calculate the PS value of each subject according to the above-mentioned polygenic risk score calculation formula, and save it as an additional data frame, which includes the phenotypic value of each subject (the phenotypic value refers to patients with HAPE or normal people) and the PS value.

[0111] (2) Establish a formula: phenotype~PS; perform logistic regression on the formula, select the function glm, and use the data frame obtained in step (1) as the input data for the regression; obtain the prediction model glm(PHENOTYPE~PS);

[0112] The steps of evaluating the model are as follows: input the genotype information of 39 SNPs of each subject in the validation set into the prediction model glm(PHENOTYPE~PS), select the predict function, and obtain the prediction results of the subjects (the prediction results are patients with high-altitude pulmonary edema or normal people).

[0113] The model was evaluated based on the consistency between the predicted results and the actual phenotypes of the subjects in the validation set. After 1000 samplings, the average AUC was above 0.95, indicating that the classification effect of this model was good.

[0114] The steps of applying the model are as follows: input the genotype information of the 39 SNPs of the subject to be tested into the prediction model glm (PHENOTYPE~PS), select the predict function, and obtain the prediction result of the subject to be tested (the prediction result is a patient with high altitude pulmonary edema or a normal person).

[0115] Example 4: Risk assessment of subjects

[0116] Peripheral blood samples were obtained from the Tibet PLA General Hospital. 166 peripheral blood samples were obtained from 166 patients with HAPE, and 142 peripheral blood samples were obtained from 142 normal subjects (non-HAPE patients). The 166 HAPE patients and 142 normal subjects were all unrelated Chinese male Han people who had lived in the plains for a long time (long-term means more than 1 year) and had their peripheral blood drawn after arriving in Lhasa (3658m above sea level). All of them had no history of smoking or drinking, and no autoimmune-related diseases (such as vitiligo, psoriasis, type I diabetes, etc.), and no history of high altitude travel before.

[0117] The method of Example 2 was followed to perform the operation. The genotype of the subject based on the 39 SNPs was detected.

[0118] The PS value of each subject was calculated according to the calculation formula of the polygenic risk score in Example 3. The average PS value of the normal group (142 cases) was -3.85, and the average PS value of the high altitude pulmonary edema patient group (166 cases) was 1.99.

[0119] According to the steps of applying the model in Example 3, the genotype of the subject based on the 39 SNPs is substituted into the prediction model glm (PHENOTYPE-PS), the predict function is selected, and the prediction result of the subject is obtained (the prediction result is a patient with high altitude pulmonary edema or a normal person).

[0120] The prediction results (predicted to be patients with high altitude pulmonary edema or normal people) and the actual phenotype (actual phenotype is patients with high altitude pulmonary edema or normal people) were fitted. The results showed that AUC = 0.95, indicating that the model can achieve excellent predictive ability, with a sensitivity of 85.2%, a specificity of 9.0%, and a positive predictive value and a negative predictive value of 0.88 and 0.88. The model accuracy indicators are shown in Table 2. The confusion matrix is ​​shown in Table 3.

[0121] Table 2 Accuracy of binary prediction model based on 39 SNPs (N=308)

[0122] N AUC Sensitivity Specificity Negative predictive value Positive predictive value 39 0.95 0.852 0.09 0.88 0.88

[0123] Table 3 Confusion matrix for predicting high altitude pulmonary edema based on 39 SNPs (N=308)

[0124]

[0125] The above results show that the risk model composed of 39 HAPE-related susceptibility genes can achieve a good prediction of the disease. It can be used to screen individuals susceptible to HAPE, predict human susceptibility to HAPE, screen individuals carrying HAPE susceptibility genes, and evaluate the risk of HAPE.

[0126] The present invention has been described in detail above. It will be apparent to those skilled in the art that the present invention may be implemented in a wide range under equivalent parameters, concentrations and conditions without departing from the spirit and scope of the present invention and without the need for unnecessary experimentation. Although the present invention provides specific embodiments, it should be understood that further improvements may be made to the present invention. In short, according to the principles of the present invention, this application intends to include any changes, uses or improvements to the present invention, including changes made by conventional techniques known in the art that depart from the scope disclosed in this application. Applications of some of the basic features may be made within the scope of the following appended claims. Sequence Listing <110> Chinese People's Liberation Army General Hospital <120> A kit for screening high altitude pulmonary edema susceptible population based on 39 SNP loci <130> GNCYX220125 <160> 117 <170> SIPOSequenceListing 1.0 <210> 1 <211> 30 <212> DNA <213> Artificial Sequence <400> 1 acgttggatg cgagaccaac ctgatcaata 30 <210> 2 <211> 29 <212> DNA <213> Artificial Sequence <400> 2 acgttggatg actataggca cctgccagc 29 <210> 3 <211> 15 <212> DNA <213> Artificial Sequence <400> 3 atggtgaaac cccat 15 <210> 4 <211> 30 <212> DNA <213> Artificial Sequence <400> 4 acgttggatg tgcagaatgg cctctctttg 30 <210> 5 <211> 30 <212> DNA <213> Artificial Sequence <400> 5 acgttggatg gcaggagaat tgcttgaatc 30 <210> 6 <211> 17 <212> DNA <213> Artificial Sequence <400> 6 ttagacagag tgttgct 17 <210> 7 <211> 30 <212> DNA <213> Artificial Sequence <400> 7 acgttggatg ccattagtac aagctctttc 30 <210> 8 <211> 30 <212> DNA <213> Artificial Sequence <400> 8 acgttggatg cagcacttga gtggtgtttc 30 <210> 9 <211> twenty one <212> DNA <213> Artificial Sequence <400> 9 ggctttttgt acagctaaaa c 21 <210> 10 <211> 30 <212> DNA <213> Artificial Sequence <400> 10 acgttggatg tgcgcctggc ctgtttttac 30 <210> 11 <211> 30 <212> DNA <213> Artificial Sequence <400> 11 acgttggatg atgatcatac tgcccaaaac 30 <210> 12 <211> 19 <212> DNA <213> Artificial Sequence <400> 12 tctgtgaaaa taacattgg 19 <210> 13 <211> 29 <212> DNA <213> Artificial Sequence <400> 13 acgttggatg taggttgaat tttgaagtc 29 <210> 14 <211> 29 <212> DNA <213> Artificial Sequence <400> 14 acgttggatg atacagctat agataggag 29 <210> 15 <211> twenty one <212> DNA <213> Artificial Sequence <400> 15 gtgaattttg aagtcctcta c 21 <210> 16 <211> 30 <212> DNA <213> Artificial Sequence <400> 16 acgttggatgggttatactttacctaaagaag 30 <210> 17 <211> 30 <212> DNA <213> Artificial Sequence <400> 17 acgttggatg cttagtttag cacatgtgta 30 <210> 18 <211> 18 <212> DNA <213> Artificial Sequence <400> 18 aataaactca cccaaaag 18 <210> 19 <211> 30 <212> DNA <213> Artificial Sequence <400> 19 acgttggatg tgctactacg acaatgacgc 30 <210> 20 <211> 30 <212> DNA <213> Artificial Sequence <400> 20 acgttggatg cttccccatg aaaagtatcc 30 <210> twenty one <211> 20 <212> DNA <213> Artificial Sequence <400> twenty one cgcctatgtc aaacgcactg 20 <210> twenty two <211> 30 <212> DNA <213> Artificial Sequence <400> twenty two acgttggatg ttcatttgca tgagcagagg 30 <210> twenty three <211> 30 <212> DNA <213> Artificial Sequence <400> twenty three acgttggatg gcgcagggtg aagacaatta 30 <210> twenty four <211> 20 <212> DNA <213> Artificial Sequence <400> twenty four ggccgaggcc atggatgcag 20 <210> 25 <211> 30 <212> DNA <213> Artificial Sequence <400> 25 acgttggatg aaaaggtggg tgacaatgcg 30 <210> 26 <211> 30 <212> DNA <213> Artificial Sequence <400> 26 acgttggatg gggaaaagct gtcaaggaac 30 <210> 27 <211> 19 <212> DNA <213> Artificial Sequence <400> 27 gtggtgcgtc tgtggtctc 19 <210> 28 <211> 30 <212> DNA <213> Artificial Sequence <400> 28 acgttggatg cagcatctct aggctgtaac 30 <210> 29 <211> 30 <212> DNA <213> Artificial Sequence <400> 29 acgttggatg tcctgaaggt aagccttaac 30 <210> 30 <211> 16 <212> DNA <213> Artificial Sequence <400> 30 gtaacttctg cccgaa 16 <210> 31 <211> 30 <212> DNA <213> Artificial Sequence <400> 31 acgttggatg tccctcaatg aaatgtcttg 30 <210> 32 <211> 30 <212> DNA <213> Artificial Sequence <400> 32 acgttggatg cgtatagagc agtggaattg 30 <210> 33 <211> twenty three <212> DNA <213> Artificial Sequence <400> 33 acctatcaaa aatcaattaa tca 23 <210> 34 <211> 30 <212> DNA <213> Artificial Sequence <400> 34 acgttggatg agagttggtc ccgccgatgt 30 <210> 35 <211> 30 <212> DNA <213> Artificial Sequence <400> 35 acgttggatg tcacagacgg gacgctgaag 30 <210> 36 <211> 16 <212> DNA <213> Artificial Sequence <400> 36 tcaggtgctg tgtggc 16 <210> 37 <211> 31 <212> DNA <213> Artificial Sequence <400> 37 acgttggatg ctgtgctcac attttgaata a 31 <210> 38 <211> 29 <212> DNA <213> Artificial Sequence <400> 38 acgttggatg agaggaggcg tagctatgt 29 <210> 39 <211> 18 <212> DNA <213> Artificial Sequence <400> 39 aaacataaca aagaggaa 18 <210> 40 <211> 30 <212> DNA <213> Artificial Sequence <400> 40 acgttggatg tgaagccatc gtcaatcagg 30 <210> 41 <211> 29 <212> DNA <213> Artificial Sequence <400> 41 acgttggatg ggttccacca agccatcgg 29 <210> 42 <211> 18 <212> DNA <213> Artificial Sequence <400> 42 cccggcccca gagcctgc 18 <210> 43 <211> 30 <212> DNA <213> Artificial Sequence <400> 43 acgttggatg acagatagtt gcagaagcac 30 <210> 44 <211> 30 <212> DNA <213> Artificial Sequence <400> 44 acgttggatg aatatccagc cactgccttg 30 <210> 45 <211> twenty four <212> DNA <213> Artificial Sequence <400> 45 atatttgcag aagcacagca tgta 24 <210> 46 <211> 30 <212> DNA <213> Artificial Sequence <400> 46 acgttggatg aaacagaaaa acggcggagg 30 <210> 47 <211> 29 <212> DNA <213> Artificial Sequence <400> 47 acgttggatg tggaatggtg gcaggagtg 29 <210> 48 <211> 17 <212> DNA <213> Artificial Sequence <400> 48 acggaggccg gaggcag 17 <210> 49 <211> 30 <212> DNA <213> Artificial Sequence <400> 49 acgttggatg tgcagtaggg tcatttggtc 30 <210> 50 <211> 30 <212> DNA <213> Artificial Sequence <400> 50 acgttggatg gaagagtgtt gcctcatacc 30 <210> 51 <211> twenty three <212> DNA <213> Artificial Sequence <400> 51 cccagggtca tttggtcatc cag 23 <210> 52 <211> 30 <212> DNA <213> Artificial Sequence <400> 52 acgttggatg tggatatgtg tcatgacggg 30 <210> 53 <211> 30 <212> DNA <213> Artificial Sequence <400> 53 acgttggatg tacctgtgac aacagcatcg 30 <210> 54 <211> 18 <212> DNA <213> Artificial Sequence <400> 54 gtgatgccat ttcacaca 18 <210> 55 <211> 30 <212> DNA <213> Artificial Sequence <400> 55 acgttggatg gtgtccactt ttaatcaggg 30 <210> 56 <211> 30 <212> DNA <213> Artificial Sequence <400> 56 acgttggatg aactccctgt ccctcaactc 30 <210> 57 <211> twenty two <212> DNA <213> Artificial Sequence <400> 57 ggggacaggg ctctctaata aa 22 <210> 58 <211> 29 <212> DNA <213> Artificial Sequence <400> 58 acgttggatg tgtacagggc ctgctagtg 29 <210> 59 <211> 30 <212> DNA <213> Artificial Sequence <400> 59 acgttggatg acaaacggct gcttcaggtg 30 <210> 60 <211> 15 <212> DNA <213> Artificial Sequence <400> 60 agctgctgct gtcca 15 <210> 61 <211> 30 <212> DNA <213> Artificial Sequence <400> 61 acgttggatg ccttcctagg catctgctaa 30 <210> 62 <211> 30 <212> DNA <213> Artificial Sequence <400> 62 acgttggatg tcagaggaat ccagataagg 30 <210> 63 <211> 26 <212> DNA <213> Artificial Sequence <400> 63 aaatttatac agataaactc atacag 26 <210> 64 <211> 30 <212> DNA <213> Artificial Sequence <400> 64 acgttggatg taagtgctct tcccaacctc 30 <210> 65 <211> 29 <212> DNA <213> Artificial Sequence <400> 65 acgttggatg ggcacacagg aacactccc 29 <210> 66 <211> twenty three <212> DNA <213> Artificial Sequence <400> 66 ggggtcaacc tctcggatca cac 23 <210> 67 <211> 30 <212> DNA <213> Artificial Sequence <400> 67 acgttggatg acgtaggtgt tgaaagccag 30 <210> 68 <211> 30 <212> DNA <213> Artificial Sequence <400> 68 acgttggatg gattgacagg ttcatgcagg 30 <210> 69 <211> twenty one <212> DNA <213> Artificial Sequence <400> 69 cccccgtcca cactggctcc c 21 <210> 70 <211> 30 <212> DNA <213> Artificial Sequence <400> 70 acgttggatg tactaccaag gacagtgacg 30 <210> 71 <211> 30 <212> DNA <213> Artificial Sequence <400> 71 acgttggatg tcctttgtct ctgacctagc 30 <210> 72 <211> 19 <212> DNA <213> Artificial Sequence <400> 72 tgacgggtct gagatttac 19 <210> 73 <211> 30 <212> DNA <213> Artificial Sequence <400> 73 acgttggatg ggtgcacagc tcattccatc 30 <210> 74 <211> 30 <212> DNA <213> Artificial Sequence <400> 74 acgttggatg gatgtggtct ggtgtcaatc 30 <210> 75 <211> twenty two <212> DNA <213> Artificial Sequence <400> 75 aaccggggca tgtggttcca ag 22 <210> 76 <211> 30 <212> DNA <213> Artificial Sequence <400> 76 acgttggatg taaaccagat caccaagcac 30 <210> 77 <211> 31 <212> DNA <213> Artificial Sequence <400> 77 acgttggatg caagactttt ccctatgcta c 31 <210> 78 <211> twenty four <212> DNA <213> Artificial Sequence <400> 78 ctgaggattc tattagcagg ggac 24 <210> 79 <211> 30 <212> DNA <213> Artificial Sequence <400> 79 acgttggatg gaggttttca ttcacttcgg 30 <210> 80 <211> 30 <212> DNA <213> Artificial Sequence <400> 80 acgttggatg ggccagtctt agcaacttac 30 <210> 81 <211> 16 <212> DNA <213> Artificial Sequence <400> 81 tcacttcggt aggtga 16 <210> 82 <211> 30 <212> DNA <213> Artificial Sequence <400> 82 acgttggatg gatctgcaaa ttccaattcc 30 <210> 83 <211> 30 <212> DNA <213> Artificial Sequence <400> 83 acgttggatg caaggtgctg catatgtgtg 30 <210> 84 <211> 17 <212> DNA <213> Artificial Sequence <400> 84 tccatatgct cctctcc 17 <210> 85 <211> 30 <212> DNA <213> Artificial Sequence <400> 85 acgttggatg gcagcgactg gagacaatag 30 <210> 86 <211> 30 <212> DNA <213> Artificial Sequence <400> 86 acgttggatg tacctgatat gacaagcgag 30 <210> 87 <211> 27 <212> DNA <213> Artificial Sequence <400> 87 acaatagata tattttcttg cttttaa 27 <210> 88 <211> 30 <212> DNA <213> Artificial Sequence <400> 88 acgttggatg aaatgggacc cctgtgtatg 30 <210> 89 <211> 30 <212> DNA <213> Artificial Sequence <400> 89 acgttggatg tgtagccaag aatgacgctg 30 <210> 90 <211> 20 <212> DNA <213> Artificial Sequence <400> 90 tggcacgacc taccatccac 20 <210> 91 <211> 30 <212> DNA <213> Artificial Sequence <400> 91 acgttggatg cgagtgcagc actcagtgtt 30 <210> 92 <211> 30 <212> DNA <213> Artificial Sequence <400> 92 acgttggatgccccatttcgtgatagcaag 30 <210> 93 <211> twenty one <212> DNA <213> Artificial Sequence <400> 93 gaatcactca gtgttgagac c 21 <210> 94 <211> 30 <212> DNA <213> Artificial Sequence <400> 94 acgttggatg tgagaagagc tggatagacc 30 <210> 95 <211> 30 <212> DNA <213> Artificial Sequence <400> 95 acgttggatg gagttatcca ccttaaaggg 30 <210> 96 <211> twenty two <212> DNA <213> Artificial Sequence <400> 96 ctcccgatgt aagaccccca aa 22 <210> 97 <211> 30 <212> DNA <213> Artificial Sequence <400> 97 acgttggatg tctgggtact acagcagaag 30 <210> 98 <211> 30 <212> DNA <213> Artificial Sequence <400> 98 acgttggatg agctcagtta catctgagag 30 <210> 99 <211> 15 <212> DNA <213> Artificial Sequence <400> 99 tcagctcctt ctcgg 15 <210> 100 <211> 30 <212> DNA <213> Artificial Sequence <400> 100 acgttggatg aatcaccgag ctcgatgagg 30 <210> 101 <211> 30 <212> DNA <213> Artificial Sequence <400> 101 acgttggatg tctgattggt ccaaggaagg 30 <210> 102 <211> 18 <212> DNA <213> Artificial Sequence <400> 102 ggcgggaata ttccaggg 18 <210> 103 <211> 30 <212> DNA <213> Artificial Sequence <400> 103 acgttggatg ataactgggtcccctgcaag 30 <210> 104 <211> 30 <212> DNA <213> Artificial Sequence <400> 104 acgttggatg aggtaggaaa acctgagctg 30 <210> 105 <211> 18 <212> DNA <213> Artificial Sequence <400> 105 tcccctgcaa gtttttaa 18 <210> 106 <211> 30 <212> DNA <213> Artificial Sequence <400> 106 acgttggatg cagcccagaa caagctgtta 30 <210> 107 <211> 30 <212> DNA <213> Artificial Sequence <400> 107 acgttggatg ttgatctaag ccccgaagac 30 <210> 108 <211> 20 <212> DNA <213> Artificial Sequence <400> 108 ggaacaagct gttagcagga 20 <210> 109 <211> 30 <212> DNA <213> Artificial Sequence <400> 109 acgttggatg agcttgttca aactgtaggg 30 <210> 110 <211> 31 <212> DNA <213> Artificial Sequence <400> 110 acgttggatg ctgtagttaa attctgaagc c 31 <210> 111 <211> twenty one <212> DNA <213> Artificial Sequence <400> 111 caggcaaata tgccaaattt a 21 <210> 112 <211> 30 <212> DNA <213> Artificial Sequence <400> 112 acgttggatg agcacgagaa cagcaccgag 30 <210> 113 <211> 30 <212> DNA <213> Artificial Sequence <400> 113 acgttggatg tgacaacctc ttgacgaccc 30 <210> 114 <211> twenty four <212> DNA <213> Artificial Sequence <400> 114 ggtgggaaca gcaccgagga aaag 24 <210> 115 <211> 30 <212> DNA <213> Artificial Sequence <400> 115 acgttggatg aatgttagga atgggcctgc 30 <210> 116 <211> 30 <212> DNA <213> Artificial Sequence <400> 116 acgttggatg tctcagaatt cacaggccag 30 <210> 117 <211> twenty four <212> DNA <213> Artificial Sequence <400> 117 agcataatta tttcctcaca tcct 24

Claims

1. Use of a substance for detecting a subject's genotype based on 39 SNPs in the preparation of a product; the function of the product is as follows (a) or (b): (a) Screening for individuals susceptible to HAPE; (b) Assess the risk of subjects developing high-altitude pulmonary edema; The 39 SNPs are as follows: rs12733147, rs2941757, rs56240659, rs28843785, rs4836498, rs79522289, rs59140633, rs12569857, rs241970, rs11620394, rs12148912, rs740873, rs919197, rs6009184, rs938355, rs9932581, rs6194, rs9592406, rs2297441, rs 4762, rs2397218, rs12462380, rs699, rs11896604, rs12655827, rs130293, rs2486736, rs41417552, rs5768002, rs40305, rs11962222, rs2009196, rs2148582, rs1008438, rs10929728, rs820336, rs6888553, rs6010621, and rs6882447.

2. The use according to claim 1, characterized in that: The substance used to detect the genotype of the subject based on 39 SNPs is a primer set; the primer set consists of 117 primers, as shown in sequence 1 to sequence 117 in the sequence table.

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