CN113637741B - Early-onset white hair genetic risk gene detection kit, early-onset white hair genetic risk assessment system and early-onset white hair genetic risk assessment method - Google Patents
Early-onset white hair genetic risk gene detection kit, early-onset white hair genetic risk assessment system and early-onset white hair genetic risk assessment method Download PDFInfo
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- G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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- C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
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Abstract
The invention provides a kit for detecting early-onset white hair genetic risk genes, and a system and a method for evaluating early-onset white hair genetic risk. The kit can simultaneously carry out SNP typing on gene loci of 16 susceptibility genes related to early-onset white hair, and the 16 gene loci are as follows: rs12563397, rs16886165, rs10461617, rs3127159, rs7775323, rs7803075, rs16888276, rs1881129, rs644383, rs676856, rs17335506, rs57406257, rs9651934, rs2058343, rs117283943, rs150271955. The invention deep excavates the related genetic information of the premature grey hair from the gene level to distinguish the people with high, medium and low risk of the premature grey hair.
Description
Technical Field
The invention belongs to the field of early-onset white hair genetic risk assessment, and particularly relates to an early-onset white hair genetic risk gene detection kit, an early-onset white hair genetic risk assessment system and an early-onset white hair genetic risk assessment method.
Background
Early-onset white hair (PHG, premature Graying of Hair), i.e., premature white hair, is a highly inherited disease, mostly defined as the occurrence of white hair by the age of 20 years of individuals, and its severity is defined by the proportion of white hair to total hair. According to research data, about 5% -30% of Chinese people have premature white hair with different degrees, and the proportion of Chinese people is in an ascending trend.
Due to the particularities of the onset age of premature grey hair, it can have a number of negative effects on daily life, mental health and social contact during the development of teenagers. There is no clear treatment regimen for premature grey hair, and therefore, most patients with premature grey hair continue to use hair dye during the developmental stage, which may have adverse consequences for their development and even for later health. In addition to the influence of teenagers, premature grey hair also causes a certain psychological and economic burden to the family.
In addition to genetic factors, the onset of premature grey hair is regulated by a number of environmental factors, such as smoking, obesity, oxidative stress, psychological disorders (emotional stress, anxiety, depression), alcohol intake, etc. In view of the characteristics that the premature grey hair has a great influence on teenagers and families thereof after the premature grey hair is sick, and no effective scheme and difficult cure exist after the onset of the grey hair, a method capable of discovering the premature grey hair risk of individual teenagers early is urgently needed, so that the early effective intervention is performed to reduce the incidence of the grey hair.
A whole genome association study (GWAS) can determine the association between a single nucleotide polymorphism (Single Nucleotide Polymorphism, SNP) and a phenotypic trait (e.g., premature grey hair in the present invention). The GWAS method is commonly used in the field of disease health, and many common complex disease-associated genetic variations have been identified. Most genetic variations often contribute little to disease risk and are not sufficient to directly predict disease states alone, but their cumulative effects have a more discriminatory and assessment effect on disease risk.
Multigenic risk scoring (Polygenic Risk Score, PRS) is a statistical method that allows to evaluate the genetic risk of a disease or trait, i.e. the cumulative effect of multiple risk sites, on the basis of the genotype profile of an individual. The ability to calculate PRS predictions for disease states using a large number of SNPs is better, but detection of a large number of genetic loci makes it expensive to detect, and is not suitable for large-scale risk screening, so how to find the best predicted PRS for complex disease risk has become the focus of current research. The selection of more appropriate candidate gene loci and PRS construction methods to integrate these genetic variations to construct a more predictive early-onset white hair polygenic genetic risk score would be helpful in developing early-onset white hair early risk predictions.
At present, no mechanism adopts a gene detection method to detect susceptibility of premature grey hair. Common methods of genotyping are: taqman probe method, SNaPshot method, time-of-flight mass spectrometry (MALDI-TOF MS) typing, HRM (high resolution melting curve) typing and other technical platforms. The accuracy of SNP detection based on time-of-flight mass spectrometry (MALDI-TOF) can reach 99.9%, and besides the advantages of high accuracy, strong flexibility, large flux, short detection period and the like, the SNP detection is attractive and has cost performance. The time-of-flight mass spectrometry (MALDI-TOF) is an internationally universal research platform for Single Nucleotide Polymorphisms (SNPs) of genes, and the method has become a new standard in the field by virtue of the scientificity and accuracy thereof. The following Table 1 shows the advantages and disadvantages of some SNP typing methods.
TABLE 1
Disclosure of Invention
The invention aims to provide an early-onset white hair genetic risk gene detection kit, and an early-onset white hair genetic risk assessment system and method, so as to solve the problem that the prior art lacks an early-onset white hair early risk prediction method.
In order to solve the problems, the invention adopts the following technical scheme:
according to a first aspect of the present invention there is provided a kit for genotyping a susceptibility site for premature grey hair and use in assessing whether there is a genetic risk of developing premature grey hair and in assessing the severity of premature grey hair. The kit can simultaneously carry out SNP typing on 16 gene loci related to early-onset white hair susceptibility risk, and comprises 16 pairs of PCR amplification primer pairs for amplifying the 16 gene loci. The 16 gene loci are shown below: rs12563397, rs16886165, rs10461617, rs3127159, rs7775323, rs7803075, rs16888276, rs1881129, rs644383, rs676856, rs17335506, rs57406257, rs9651934, rs2058343, rs117283943, rs150271955.
The sequence is specifically shown below, the sequence directions of the amplification primer and the extension primer are from 5 'end to 3' end, the SNP position is referred to hg19, and the rs number is from db151. Wherein, the sequences of the 16 gene loci and the 16 pairs of PCR amplification primers are specifically shown as follows:
1 amplification primer pair No.1 (1:214756124 site) G- - - > A (representing wild type G, mutant A, the same applies below)
SEQ ID NO.1 (upstream primer, hereinafter referred to as "CCCATCCCTGTTTTAACTAGT")
SEQ ID NO.2 (downstream primer, supra) GCTAATAATAATAATCTTTTG
2 amplification primer pair No.2 (5:56023083 site) T- - - > G (representing wild type as T, mutant as G, the same applies hereinafter)
SEQ ID NO.3 (upstream primer, hereinafter referred to as "ATATAGTTGGTGCTCAGTAAA")
SEQ ID NO.4 (downstream primer, supra) CATTTTGGTTACTTGAGGTCA
3 amplification primer pair 3 (5:56104308 site) G- - - - > A (representing wild type G, mutant A, the same applies hereinafter)
SEQ ID NO.5 (upstream primer, hereinafter referred to as "GGGCCTTCAAATGTCAAAAAG")
SEQ ID NO.6 (downstream primer, supra) ATCCGCTAACTTGTCAAATAT
4 amplification primer pair 4 (6:1597934 site) C- - - > G (representing wild type C, mutant G, the same applies hereinafter)
SEQ ID NO.7 (upstream primer, hereinafter referred to as "AAGTACAGTTTAGTGGGCAGG")
SEQ ID NO.8 (downstream primer, supra) CAGAGGAGTTGCTGGTCTGGC
5 amplification primer pair 5 (6:22722969 site) C- - - > T (representing wild type C, mutant T, supra)
SEQ ID NO.9 (upstream primer, hereinafter referred to as "TGAGACCCTGTCTCAAAAATA")
SEQ ID NO.10 (downstream primer, supra) TGTGGCATTTGGGGTAAGGTC
6 amplification primer pair 6 (7:130742066 site) A- - - > G (representing wild type A, mutant G, the same applies hereinafter)
SEQ ID NO.11 (upstream primer, hereinafter referred to as "TTTGATCTCCTTAGACTCTCT")
SEQ ID NO.12 (downstream primer, supra) TTTCTGCTCAAGCTTAGTTCC
7 amplification primer pair 7 (8:117306506 site) T- - - > G (representing wild type T, mutant G, the same applies hereinafter)
SEQ ID NO.13 (upstream primer, hereinafter referred to as "CCTTTCTCCGACCTCAAACAT")
SEQ ID NO.14 (downstream primer, supra) AATGAATTAGAGCTACCTGCA
8 amplification primer pair 8 (8:117334573 site) A- - - - > G (representing wild type A, mutant G, the same applies hereinafter)
SEQ ID NO.15 (upstream primer, hereinafter referred to as "ACCTCAAGCTCAAGCTGGTTG")
SEQ ID NO.16 (downstream primer, supra) TGCAAAGTCACCCTTCCTTTT
9 amplification primer pair 9 (9:208665. RTM.) G- - - - > A (representing wild type G, mutant A, supra)
SEQ ID NO.17 (upstream primer, hereinafter referred to as "TGGGAAGAATATGATTTTTTC")
SEQ ID NO.18 (downstream primer, supra) AAGCATAGAACAGAGCCCAAC
10 amplification primer pair 10 (11:69329784 site) C- - - > A (representing wild type C, mutant A, supra)
SEQ ID NO.19 (upstream primer, hereinafter referred to as "ACTTGCCTAGGAACACACAGC")
SEQ ID NO.20 (downstream primer, supra) CACATGGAGACAGGGAAACGT
11 amplification primer pair 11 (12:88519346 site) T- - > C (representing wild type as T, mutant as C, supra)
SEQ ID NO.21 (upstream primer, hereinafter referred to as "TTACCATTTTGTACACTTCAA")
SEQ ID NO.22 (downstream primer, hereinafter referred to as TGTTTATGGCTTCTATACTTC)
12 amplification primer pair 12 (12:89239190 site) G- - - > A (representing the wild type G, mutant A, the same applies below)
SEQ ID NO.23 (upstream primer, hereinafter referred to as "GCAAGCTTTGGATTAGAGCAA")
SEQ ID NO.24 (downstream primer, hereinafter referred to as "TAATGTGTAAATTCATTTCTT")
13 amplification primer pair 13 (12:89355709 site) C- - - > A (representing wild type C, mutant A, supra)
SEQ ID NO.25 (upstream primer, hereinafter referred to as "TCCTAATGTACCACAAAAGCA")
SEQ ID NO.26 (downstream primer, supra) CTCTGGAGTCTCTATTTGCAC
14 amplification primer pair 14 (12:89366763 site) G- - - > A (representing wild type G, mutant A, the same applies hereinafter)
SEQ ID NO.27 (upstream primer, hereinafter referred to as "GTGGGCTCACAAGCAATTTAT")
SEQ ID NO.28 (downstream primer, supra) TTGCACATAAGGAAGACTTAA
15 amplification primer pair 15 (22:31150167 sites) C- - - > T (representing wild type C, mutant T, supra)
SEQ ID NO.29 (upstream primer, hereinafter referred to as "CTTGGGGAAAAAATAGATTTT")
SEQ ID NO.30 (downstream primer, supra) CTCCCTCTGTTTATAGTTCCT
16 amplification primer pair 16 (22:31458617 site) G- - > A (representing wild type G, mutant A, the same applies hereinafter)
SEQ ID NO.31 (upstream primer, hereinafter referred to as "AGGGTATTTTGCAAATAAGTA")
SEQ ID NO.32 (downstream primer, hereinafter referred to as "GACACAGGAGACCAGAACAGC").
According to a preferred embodiment of the invention, the kit further comprises 16 extension primers for identifying 16 gene locus mutations. The 16 extension primer sequences are specifically shown as follows:
SEQ ID NO.33:CCCTGCCTCCAGAGTCGAGTCCTACG
SEQ ID NO.34:AAAGAAACTTGCCCAAGGCCAGCTTT
SEQ ID NO.35:TGATGTTGGAAACAACACCACTTTGA
SEQ ID NO.36:TTGGCTGGGGAATGCAATCACAGGAC
SEQ ID NO.37:TCTCATTCTCAGCTATTCCAATTGTC
SEQ ID NO.38:TGAACTGCGCTCCTGGATCTTTTACA
SEQ ID NO.39:CGGTAAGTATATGTTTTCTTTGATTT
SEQ ID NO.40:AATTCAGAGAAACTTCTGGGCTCCTA
SEQ ID NO.41:GAAGGAAATTAATTTTTTTCCATAGA
SEQ ID NO.42:GACACTGAGTCTCACAACCCCACAGC
SEQ ID NO.43:CTGCTGTTACTCCAATACTGAAAGAT
SEQ ID NO.44:AGCAATATAAGCTGAGGTGGATATAG
SEQ ID NO.45:TCTAGTGAGATTAAAACTGAATGCAC
SEQ ID NO.46:TCATCTGTAAGATGGGAATCCTAAAA
SEQ ID NO.47:CAGCCAAAAATTGGGCAGACTTTTCC
SEQ ID NO.48:CTCCTTGAAAAATCGTAAAAGAACTG
according to the present invention, preferably, the reaction system for PCR amplification of the kit is as follows:
according to the present invention, preferably, the kit further comprises an SAP reaction system as follows:
SAP buffer 0.17 mu.L
SAP enzyme 0.5U
ddH 2 O was made up to 2. Mu.L.
The kit adopts a time-of-flight mass spectrometer for detection. The target sequence is amplified simultaneously in a system by multiplex PCR, and then SNP sequence specific extension primers are added to extend 1 base at SNP sites. And (3) after co-crystallizing the prepared sample analyte and a chip matrix, exciting the sample analyte and the chip matrix by using strong laser in a vacuum tube of a mass spectrometer, desorbing the nucleic acid molecules into single charge ions, wherein the flight time of the ions in an electric field is inversely proportional to the mass of the ions, and detecting the flight time of the nucleic acid molecules in the vacuum tube to obtain the accurate molecular weight of the sample analyte, thereby detecting SNP site information.
According to the kit provided by the invention, the related gene locus is a gene molecular locus generated based on the research of the early-onset white hair queue of large-scale Chinese Han population, and does not relate to any existing literature and research; a group of SNPs with the earliest predictability of the white hair is selected as the gene molecular markers according to the collected related data. The specific method of selecting the locus comes from the construction method of the early-onset white hair genetic risk prediction model, and SNP contained in the characteristic set of the selected optimal model is taken as a molecule to be detected of the kit; the SNP locus sets related to the kit are common variation loci with the minimum allele frequency not less than one thousandth, so that the wide crowd applicability of the kit is ensured; the corresponding primer group with high specificity is designed, so that the early-onset white hair related risk genes can be subjected to accurate genotyping. The method using the time-of-flight mass spectrometer ensures the accuracy and sensitivity of the detection result, and is simple and feasible.
According to a second aspect of the present invention, there is provided a premature grey hair genetic risk assessment system comprising: the acquisition module is used for acquiring a DNA sample of a tester; a detection module comprising an early-onset white hair genetic risk gene detection kit as described above for genotyping the DNA sample; the prediction module comprises an early-onset white hair genetic risk prediction model and is used for scoring the early-onset white hair genetic risk of a tester; and the evaluation module comprises an early-onset white hair genetic risk classification system which is used for determining the early-onset white hair genetic risk level of a tester.
According to a preferred scheme of the invention, the early-onset white hair genetic risk prediction model utilizes whole genome association analysis and machine learning algorithm to integrate risk gene locus information to predict and quantitatively score the early-onset white hair genetic risk.
The main use scene of the premature grey hair genetic risk prediction model is to score the risk of the premature grey hair genetic risk by combining the sex information with genotyping data measured by the premature grey hair genetic risk gene detection kit. The model may be used to score risk when the whole genome data of the subject is available and gender-aware, and other scenarios involving the genetic loci involved in the model characteristics are included.
The early-onset white hair genetic risk prediction model performs performance evaluation in independent groups with sufficient sample size, the AUC value is 0.68, and in the current polygene prediction field, the current AUC value is at an excellent level; according to the 100 quantile grouping mode, the ratio (OR) of case/control between the highest percentile and the middle quantile is 11.66, and the relative Risk Ratio (RR) is 2.37; when the classification threshold was selected to be 0.5, the Sensitivity (Sensitivity) was 0.89, and the positive correct rate (PPV) was 0.67; overall Accuracy (Accuracy): 0.66. The detection sensitivity and the positive accuracy of the premature gray hair population can be improved by improving the classification threshold line.
The genetic risk prediction model for premature grey hair also has prediction and differentiation capability for people with severity of premature grey hair (AUC of 0.65 for people with slightly grey hair, AUC of 0.69 for people with nearly normal grey hair and AUC of 0.69 for people with most of the hair whitened). The early-onset white hair proportion in each multi-gene grading score is estimated to be basically consistent with the actual distribution based on the bivariate normal distribution, so that objective early-onset white hair absolute risk can be provided.
The feature ID and model weight and risk allele information contained in the genetic risk prediction model of premature gray hair of the present invention are shown in table 2 below (SNP position reference hg19, rs number from db151 is referred to).
TABLE 2
According to the premature grey hair genetic risk prediction model provided by the invention, 17 total characteristics including gender and gene loci and an intercept item-1.25867205 with a weight of 1 are used, the gender weight value is-0.60813851, and all the characteristics are subjected to weighted summation of risk allele doses to serve as the premature grey hair genetic risk score. When the model is used, the coding is strictly performed according to the dosage number of the risk allele in genotype (for example, rs12563397, the risk allele is A, the genotype GA is coded to be 1, the genotype is AA coded to be 2, and the genotype is GG coded to be 0.
According to the early-onset white hair genetic risk prediction model provided by the invention, the used characteristics and weights are obtained by training based on big data and a machine learning and genomics related method, the data is used as a drive, the high predictability is used as a target, and the deviation caused by directly using some characteristics and weights of the existing research is avoided; the polygenic score in combination with gender, age, or other environmental factors is the primary direction of investigation for the current risk score; and (3) carrying out strict performance evaluation in an independent crowd queue, and verifying the distinguishing capability and the application range of the model in risk evaluation.
According to a preferred scheme of the invention, the adopted early-onset white hair genetic risk classification system is used for cutting and grouping risk scores obtained by an early-onset white hair genetic risk prediction model, and the risk degree is prompted according to the early-onset white hair prevalence rate of the collected data set.
The partition interval is as follows: grade 1: [ 0-0.201297176117716): low risk; grade 2: [ 0.201297176117716-0.281098927835626): lower risk; grade 3: [ 0.281098927835626-0.363050852087241): general risk; grade 4: [ 0.363050852087241-0.453887538258589): higher risk; grade 5: [ 0.453887538258589-0.566654292179563): high risk; grade 6: [ 0.566654292179563-1): very high risks. The risk level is 6 levels from low to high.
The system provides a global early-onset white hair risk level distribution in the general population and a proportion of the population at a particular risk level that has actually suffered from early-onset white hair. The method comprises the following steps: class 1:10.9%; class 2:18.3%; grade 3:23.8%; grade 4:22.7%; grade 5:17%; grade 6:7.3%.
The actual distribution of premature grey hair for the population at each particular risk level is shown in table 3 below.
TABLE 3 Table 3
According to the early-onset white hair genetic risk classification system provided by the invention, the early-onset white hair genetic risk score obtained by the model is used for classifying individual risk classes and degrees, so that the overall risk distribution in Chinese people under a macroscopic view and the proportion of people under the same risk class that the early-onset white hair actually occurs are provided. The system not only provides a qualitative risk degree early warning result, but also can provide more digital quantitative distribution results obtained based on actual data, so that a detected person can better feel own risk.
According to a third aspect of the present invention, there is provided a method for evaluating genetic risk of premature grey hair, as shown in fig. 1, comprising the steps of: s1: obtaining a DNA sample of a tester; s2: genotyping the DNA sample using the early-onset white hair gene detection kit as described above;
S3: constructing an early-onset white hair genetic risk prediction model, and obtaining an early-onset white hair genetic risk score of a tester by using the early-onset white hair genetic risk prediction model; s4: and determining the personal risk level, the overall risk distribution of the crowd or the actual early-onset white hair distribution in a specific risk level group according to the difference of testers by using an early-onset white hair risk level classification system.
In step S3, the construction of the genetic risk prediction model for premature grey hair includes the following steps: s31: acquiring premature gray hair phenotype data of volunteers in a premature gray hair questionnaire mode; s32: obtaining genetic data for volunteers taking part in an early-onset white hair questionnaire; s33: preprocessing and classifying the collected questionnaires and gene data, and classifying a training set, a verification set and a test set according to a machine learning processing mode; s34: carrying out whole genome association analysis on the training set to obtain early-onset white hair GWAS statistical data, wherein the GWAS statistical data comprises site numbers and significance P values associated with the early-onset white hair; s35: according to the GWAS statistical data, combining every two based on the site linkage correlation r2 and the significance P value, and filtering a site set; s36: based on each filtered locus set, adaptively selecting features by using a feature selection algorithm in machine learning, using the features filtered by the feature selection algorithm, building an early-onset white hair genetic risk prediction model by using a logistic regression algorithm in a training set, and performing prediction and performance evaluation on an independent verification set; s37: selecting a model and an optimal genetic locus that perform best on the validation set according to the AUC values based on the predicted performance on the validation set; s38: and (3) applying the optimal model to an independent test set for prediction to obtain the prediction performance, risk division capability and crowd risk distribution of the model in general population.
Preferably, step S32 includes: genotyping was performed on a high-throughput gene chip based on an Axiom precision medical research array using a GeneTitan multichannel instrument platform, enabling the acquisition of the gene data.
According to a fourth aspect of the present invention there is also provided the use of an early-onset white hair genetic risk assessment system for assessing the level of early-onset white hair genetic risk.
According to the invention, the early-onset white hair gene detection kit is provided, the problem that the prior art lacks an early-onset white hair gene detection method is solved, and the kit has the advantages of high detection efficiency, large detection flux and low detection cost, so that early-onset white hair genetic risk prediction is realized; secondly, constructing and selecting an optimal genetic risk model by using a machine learning Method (ML) through a large-scale genome-wide association analysis (GWAS) of early-onset white hair queue crowd, and providing a genetic risk level assessment method specially aiming at the early-onset white hair of Chinese crowd; in order to make the model exert practical value better, the invention provides a kit gene detection scheme matched with the model, overcomes the defects of mismatching, incompatibility and the like between genetic risk assessment and genotyping, has the characteristics of large detection flux and low detection cost, and is used for solving the common screening of the genetic risk level of the premature grey hair of large-scale people, and achieving the early discovery, early prevention and early treatment of the premature grey hair. Genetic information related to the premature grey hair is deeply mined from the gene level to distinguish the people with high, medium and low risk of the premature grey hair.
In summary, the early-onset white hair gene detection kit, the early-onset white hair genetic risk assessment system and the early-onset white hair genetic risk assessment method provided by the invention have the following remarkable advantages compared with the prior art:
1) By the invention, the prior art has not provided a model for predicting and dividing genetic risk of premature grey hair and a kit for detecting genes of the premature grey hair. According to the genetic research results of large-scale early-onset white hair (more than 2.5 thousands of people participate, so far, the largest-scale Chinese crowd queues early-onset white hair research) of Chinese queue people, the found susceptibility sites and site sets used by the model are more suitable for Chinese crowd; all data driven, not related to and based on public data sets and others have been studied. The new combinations of sites we present are not mentioned in any document, and are obtained by extensive raw data analysis;
2) And a machine learning algorithm is used for constructing a risk prediction model by implementing feature selection and optimal model selection algorithms in training and verifying two independent sets, and compared with a traditional PRS model construction method, the risk prediction model has higher prediction performance and fewer model sites. Model overall risk prediction performance and risk distribution are demonstrated in a large sample-size set of queues. The system is matched with an early-onset white hair genetic risk division system and a large sample amount queue set, and objective early-onset white hair absolute risk scale, relative risk and other risks in different dimensions are provided. Early-onset white hair in the least significant bit array in the set accounts for only 3%; early-onset white hair occupies up to 83% in the highest-ranking array; the overall trend is that the higher the quantile the higher the premature white hair occupancy in the population. The absolute risk given by the large sample size of the cohort set is substantially consistent with estimating the absolute risk within each polygenic scoring score based on a bivariate normal distribution. The model not only has good predictability for the people suffering from premature grey hair, but also has higher predictability for the severity of the premature grey hair;
3) The existing research is unclear in the genetic mechanism of early-onset white hair and the mechanisms such as aging, metabolism, immune correlation and the like behind white hair, and SNP loci related to the invention and linkage areas, genes and gene paths corresponding to loci provide scientific research value for the genetic correlation of the early-onset white hair and the research of highly-correlated diseases behind the early-onset white hair;
4) Compared with the technologies of fluorescence quantitative PCR, gene chip, sanger sequencing, NGS and the like, the time-of-flight mass spectrometry detection method adopted by the invention has the characteristics of rapidness, high sensitivity, low cost, simplicity and easiness, and is suitable for multi-gene and multi-site detection. The early-onset white hair genetic risk profile was predicted using only saliva sample testing. The SNP typing accuracy is high, the detection rate can reach more than 95 percent, and the accuracy is more than 98 percent.
Drawings
FIG. 1 shows a logic diagram of a method for evaluating genetic risk of premature grey hair according to the present invention;
FIG. 2 shows a detailed technical schematic diagram of the construction flow of the genetic risk prediction model of premature grey hair and the design flow of the gene detection kit, wherein the source and the method of the early grey hair susceptibility gene locus set are specifically defined;
FIG. 3 shows a logic diagram of an early-onset white hair study questionnaire;
FIG. 4 illustrates a GWAS quality control flow diagram;
a and b in fig. 5 show the manhattan and Q-Q diagrams, respectively, of GWAS statistics;
FIG. 6 shows a box plot of the genetic risk score distribution for case (premature white hair, right gray) and control (non-premature white hair, left black) populations in a separate test set consisting of a population of premature white hair and non-premature white hair, with the vertical axis being the genetic risk score, and the small dots in the two groupings in the plot representing the average of the set of genetic risk scores;
FIG. 7 shows a distribution diagram of the 50 quantile of the predictive score and the early-onset white hair duty cycle in different sexes in a set consisting of early-onset white hair and non-early-onset white hair samples aged 20-60 years;
FIG. 8 shows a plot of 50 quantiles of the predictive score of the model versus early-onset white hair duty cycle in the set consisting of early-onset white hair and non-early-onset white hair samples, aged 20-60 years;
FIG. 9 shows a graph of the actual absolute risk at a score of 50 given by a set of early-onset white hair and non-early-onset white hair samples, aged 20-60 years, versus the absolute risk within 50 quantiles estimated based on a bivariate normal distribution;
FIG. 10 shows a schematic diagram of primer design of the kit of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1: construction of early-onset white hair genetic risk prediction model
This example provides a predictive model for assessing genetic risk of premature grey hair, which covers the polymorphic sites of the premature grey hair gene of the chinese han population, including 16 SNP sites and sex characteristics of human genomic DNA. The construction steps of the early-onset white hair genetic risk assessment model are shown in fig. 2.
1) Early-onset white hair phenotype data is obtained. A questionnaire was designed, pushed and issued, and study volunteers voluntarily filled out the questionnaire, and early-onset white hair phenotype data were collected.
2) Genetic data was obtained for participants taking part in the questionnaire. The locus in the whole genome range and the genotype information thereof are obtained through SNP chips or sequencing by a biological information analysis means.
3) The collected questionnaire and gene data are preprocessed and integrated. And (3) performing quality control according to questionnaire filling data and design logic of the questionnaire, and taking premature grey hair as a control group and premature grey hair as a case group by adopting an experimental paradigm of 'case control group'. The training set, the validation set and the test set are divided according to a machine learning processing mode. The three sets are independent of each other and do not contain the same individual.
4) And carrying out whole genome association analysis on the training set to obtain early-onset white hair GWAS statistical data, wherein the GWAS statistical data comprises site numbers and significance P values associated with the early-onset white hair.
5) Based on GWAS statistics, a pairwise combination (p_t strategy) of the site linkage correlation r2 and the significance P value is performed for site set filtering.
6) Feature selection (including SNPs and geners) is performed adaptively based on each filtered set of sites using a feature selection algorithm in machine learning. And using the characteristics filtered by the characteristic selection algorithm, establishing a risk prediction model of the premature grey hair by applying a logistic regression algorithm in a training set, and performing prediction and performance evaluation on an independent verification set.
7) Based on the predicted performance on the validation set, the model that performs best on the validation set and the optimal gene locus are selected according to the AUC values.
8) And (3) applying the optimal model to an independent test set for prediction to obtain the prediction performance, risk division capability and crowd risk distribution of the model in general population.
In step 1), an early-onset white hair questionnaire is designed, and a question logic diagram is shown in fig. 3: wherein. The questionnaire can be an off-line questionnaire (the filling form of a roller is changed into a manual filling form), or can be an on-line electronic questionnaire. This embodiment employs a web electronic questionnaire, optionally hosted in an app, named "premature white hair questionnaire" for subject recruitment. The subject needs to complete filling of the questionnaire. A total of 24431 parts of questionnaire was collected. Wherein the ages of men and women are 11561/12870, respectively, and the ages range is 2 years to 121 years. The data may be exported as a txt file. Not limited to all of the problems in fig. 3, but preferably includes the above-described problems.
In step 2), participants in the premature gray hair questionnaire, after depositing 2mL of saliva in the saliva holding tube, will be sent to the laboratory, purify the DNA in the saliva, and genotype on a high throughput gene chip based on Axiom precision medical research array (Affymetrix) using the GeneTitan multichannel instrument platform (Thermo Fisher Scientific). The gene chip covers approximately 90 ten thousand SNPs of 22 autosomes, 2 sex chromosomes and the mitochondrial genome. After pipeline processing is performed through Affy gene data, the data is output in a binary format of plink.
In step 3), pretreatment and quality control of questionnaire data and gene data are performed, respectively. And for questionnaire data, any version of open source statistical software R is used for controlling and processing the quality of the questionnaire result.
Step 3) -1: draw "age" in questionnaire, "do you have premature white hair? "," when you start growing white hair? "these several problem data". Samples 12-70 were filtered. Unreliable answers are excluded and classified according to the following conditions:
excluding premature grey hair after age 30, answer: the question "whether you have premature white hair" is answered in the affirmative, but "when you start growing white hair" selects the sample "after 30 years old".
People older than 30 years old do not know when they start premature grey hair, and are not considered premature grey hair: the question "whether you have premature white hair" is answered in the affirmative, but "when you start growing white hair" selects a person who "remembers unclear" and "age" is 30 years or more, and is considered not premature white hair.
Excluding samples for which the premature grey hair condition itself is not clear: the "unclear" sample was answered in the question "whether you have premature white hair".
In the case that the question "whether you have premature white hair" is answered in the affirmative, as a premature white hair group, i.e. a case group (case), code 1; in the question "whether you have premature white hair" is a negative answer, a control group (control), which is a non-premature white hair population, is coded as 0.
The age of natural white hair and early-onset white hair is distinguished, and white hair appears before 20 years old in Asian population is generally selected as the time distinguishing point of the early-onset white hair, but the invention is based on 1) the data of people with 20 years old and 30 years old as the time distinguishing points, and the genetic mechanism is basically consistent; 2) Selecting 30 years old can expand the case number to some extent, increasing the detection efficacy of GWAS. The present invention is set to identify premature white hair, not natural white hair, no more than 30 years old.
A total of 22477 fills were made after the treatment was completed, with an early-onset white hair rate of 32%.
And 3) -2, dividing the processed premature gray hair phenotype data into data sets, and dividing the data sets into training sets, verification sets and test sets of the phenotype data according to 7&1& 2. When the data sets are divided, a hierarchical sampling method is used, a function createFolds () of a third party extension packet 'caret' of R can be used, layering is carried out according to a field containing early-onset white hair information, so that the case vs control proportion of the three data sets is consistent, and no distribution deviation exists.
And 3) -3, respectively matching the gene data according to the sample numbers of the processed questionnaire data, and filtering the gene data based on the sample information by using a biological information common software Plink-key command. After the division is completed, the training set is 15733 people, the test set is 4496 people, and the verification set is 2248 people. Quality control of the gene training set referring specifically to fig. 4, all autosomal related operations thereof can be accomplished by selecting the corresponding command in the Plink software, and all non-pseudo-very site related operations are accomplished by the command corresponding to the "XWAS" software. Simple includes removing sites with MAF less than 0.001, removing samples and sites with sample deletion rate and site deletion rate greater than 0.02, removing sites which do not meet Hash temperature balance, removing closely related samples, removing samples of non-Chinese Han nationality, and using MDS analysis instead of PCA analysis. After quality control, the total number of autosomal and pseudonormothermic SNPs was 394624. The number of the very false-dyeing SNP is 6945. No inpute was performed and only the original typing data was used for subsequent analysis.
In step 4), performing genome-wide association analysis on the phenotype training set and the gene training set, adopting a case control coding form, and using a logistic algorithm and a gene addition (additive) model to treat the SNP containing 2 normal alleles as the number of risk alleles of the SNP as 0; a SNP containing 1 normal allele is considered to have a number of risk alleles for that SNP of 1; a SNP containing 0 normal alleles is considered to have a number of risk alleles for that SNP of 2. Covariates are the major components of the population, PC1-PC5, and gender, to control false positive associations resulting from stratification of the population and gender. The obtained statistics of early-onset white hair GWAS include the significance P value associated with early-onset white hair. Will be used as a key indicator for the next step of bit-point filtering. Subsequent analysis was performed using only autosomal and pseudonormothermic SNPs. The manhattan plot and Q-Q plot of GWAS statistics are shown in fig. 5.
In step 5), the set of sites is screened based on the P value of the early-onset white hair GWAS statistical data and the linkage r2 between the sites of the training set by using a pruning and threading method, wherein the screening standard is that the P value can be set to 5e-4, 5e-5 and 5e-8, the linkage r2 value can be set to 0.2, 0.5, 0.8 and 0.95, and the total of 12 (3.4) screening standards are combined two by two to screen the SNP set. Each combination results in a set of gene locus sets. The results are shown in Table 4 below.
TABLE 4 Table 4
| Number of sites | Screening criteria combination |
| 264 | 5*10^-4_0.2 |
| 291 | 5*10^-4_0.5 |
| 341 | 5*10^-4_0.8 |
| 370 | 5*10^-4_0.95 |
| 73 | 5*10^-5_0.2 |
| 87 | 5*10^-5_0.5 |
| 110 | 5*10^-5_0.8 |
| 126 | 5*10^-5_0.95 |
| 16 | 5*10^-8_0.2 |
| 20 | 5*10^-8_0.5 |
| 27 | 5*10^-8_0.8 |
| 32 | 5*10^-8_0.95 |
In step 6), it is carried out in 4 steps.
Step 6) -1: combining the phenotype training set and the gene training set according to sample codes, wherein the number of risk alleles of the SNP which contains 2 normal alleles is regarded as 0; a SNP containing 1 normal allele is considered to have a number of risk alleles for that SNP of 1; a SNP containing 0 normal alleles is considered to have a number of risk alleles for that SNP of 2 (ensuring compliance with the genotype encoding scheme in step 4, i.e. ensuring compliance of normal and risk alleles). The premature grey hair code is coded according to a case control paradigm, the case code is 1, and the control code is 0. The read.plink () function in the open source statistics software R and third party software packages snpStats can be used for the reading and encoding of the genetic data.
Step 6) -2: extracting a corresponding subset of the training set according to the SNP locus set filtered by the P_T strategy, adding gender (gender) as a candidate predictive factor, wherein the male code is 1, and the female code is 2.
Step 6) -3: the adaptive selection machine learns the feature selection algorithm commonly used to filter the site set again according to the combination of r2 and P values in the p_t strategy. Three common feature selection algorithms are used to reject potentially invalid sites, effector microsites and redundant sites with collinearity. The basic principle of the self-adaption is as follows: the algorithm for the subsequent selection of P at the 5 x 10-5 level is lasso, boruta and stepwise regression Step of random forest class; the algorithm for P selection at the 5 x 10-8 level is lasso and stepwise regression Step, and the algorithm for P selection at the 5 x 10-4 level is only lasso. Wherein lasso uses the cv.glmcet function in the third party software package glmcet in R (parameters: family= "binominal", type.measure= "auc", standard=true, nfold=5), and features with regression coefficients other than 0 are used as output features; wherein Boruta of the random forest class uses lasso for feature selection using Boruta (), tentativeRoughFix () and getSelectedAttributes () functions in the third party software package Boruta in R. Stepwise regression Step was directly selected using a built-in Step (fit, direction= "both") function, fit being a logistic regression model fitted with a glm function for unfiltered sites. And taking the characteristics contained in the final stepwise regression-finalized model as an output characteristic set.
Step 6) -4: and (3) using the features selected by each feature selection algorithm under each (combination of 19 feature selection algorithms and P_T strategies) combination, and using logistic regression to establish a binary logistic regression model of premature grey hair, wherein dependent variables are the premature grey hair state of 0/1 code consisting of case and control, and independent variables are gene training data sets consisting of site sets after feature selection.
In step 7), the 18 models are applied to independent verification sets, and AUC values commonly used in machine learning are taken as classification efficiency of the evaluation model in the verification sets, as shown in table 5 below.
TABLE 5
The model with the optimal classification performance is the model with the highest AUC value in the verification set. Wherein the test set and the verification set perform additional filtering (without any missing values at any sites, 217 and 108 samples are filtered respectively) to ensure the accuracy of verification and testing. The selected p_t combination is: p=5×10-8, r2=0.95, selected feature selection method: step ", the number of selected features is: 17 (16 SNP+gene). The molecular markers and molecular weights involved in the optimal early-onset white hair genetic risk assessment model are shown in the above summary section.
In step 8), in order to verify the predictive and risk differentiating ability of the optimal model in the population of premature grey hair. And applying the optimal model to an independent test set, and evaluating the prediction performance of the optimal model. Its AUC is 0.66-0.68.
In an independent test set consisting of a population of premature white hair and non-premature white hair, a box plot of genetic risk score distributions for the case (premature white hair, right gray) and control (non-premature white hair, left black) populations is shown in fig. 6. The vertical axis is the gene risk score, and the small dots in the two groupings in the figure represent the average of the gene risk scores of the group. It can be seen that the genetic risk of the premature white hair population is overall higher than the score of the non-premature white hair population in the test set.
A 50 quantile plot of the independent test set consisting of early-onset white hair and non-early-onset white hair populations is shown in fig. 7. Wherein, the upper graph emphasizes the distribution condition of the whole and sex suffering from premature grey hair in the relation graph of 50 quantiles of the predictive score of the model and the premature grey hair proportion in the set consisting of premature grey hair and non-premature grey hair samples with ages of 20-60 years. The x-axis is quantile, and the y-axis is early-onset white hair sample proportion; the lower graph is a plot of the quantile of the genetic risk score of premature grey hair versus the early grey hair percentage of the model in separate sets of men and women.
In the collection of early-onset white hair and non-early-onset white hair samples, aged 20-60 years, the relationship between the 20 and 50-bit numbers of the predictive score of the model and the early-onset white hair duty ratio is shown in fig. 8, emphasizing the early-onset white hair duty ratio of the two extreme groups. The x-axis represents quantile and the y-axis represents early-onset white hair sample proportion. Early-onset white hair in the lowest-ranking array only accounts for 3%; early white hair occupies up to 83% of the highest quantile array.
As shown in fig. 9, the actual absolute risk at a score of 50 given by the set of early-onset white hair and non-early-onset white hair samples aged 20-60 years is plotted against the absolute risk within 50 quantiles based on the bivariate normal distribution estimation. The objectivity and accuracy of the model discrimination capability is emphasized. The x-axis is predicted early-onset white prevalence per quantile and the y-axis is predicted actual early-onset white prevalence per quantile in the independent set of the present invention.
In the 100 quantile group, the case/control ratio (OR) between the highest percentile and the middle quantile is 11.66, and the relative Risk Ratio (RR) is 2.37; the case/control ratio (OR) between the highest (top 1%) percentile and the lowest (bottom 1%) percentile was 79.33 and the relative Risk Ratio (RR) was 11.04. When the classification threshold was selected to be 0.5, the Sensitivity (Sensitivity) was 0.89, and the positive correct rate (PPV) was 0.67; overall Accuracy (Accuracy): 0.66. The detection sensitivity and the positive accuracy of the premature gray hair population can be improved by improving the classification threshold line.
To verify the predictive ability of the model to the severity of premature grey hair. We answer in the affirmative the question "whether you have premature grey hair" and according to the question "at worst, which is your premature grey hair closest? "answer option, the crowd with answer of" slightly white hair "," nearly half white hair "," most hair whitened "is coded as case group (case) to be 1 in turn; the control group (control), which is a non-premature white hair population, was coded as 0 in the question "whether you have premature white hair" as a negative answer.
The results showed that: the model also has predictive differentiation ability for people with the severity of premature grey hair: for the "slightly white" population, the AUC was 0.65; AUC for "near half white" population was 0.69; for the "majority of hair whitened" population, the AUC was 0.69.
Example 2: construction of early-onset white hair genetic risk gene detection kit
The related kit site and primer design flow:
an early-onset white hair genetic risk prediction model is obtained according to the embodiment 1, and SNP loci in the early-onset white hair genetic risk prediction model are selected as gene molecular loci of a kit to form a PANEL detection model. And (3) aiming at all SNP loci involved in the PANEL model for detecting the established early-onset white hair genetic risk level, carrying out the design of multiple PCR amplification upstream and downstream primer pairs and single-base extension primer sequences (shown in the summary of the invention), wherein the primer design schematic diagram is shown in figure 10.
Details of the kit
1.1 major Components
PCR reaction mixture, taq Enzyme, amplification primer mixture, SAP Buffer, SAP Enzyme, single base extension reaction mixture, iilex Enzyme, extension primer mixture, ddH 2 O, positive control, desalting resin and mass spectrum chip.
1.2 storage conditions
The product was stored at-20deg.C.
1.3 instruments
A universal PCR instrument; DR massaray.
1.4 sample requirement
The product is suitable for extracting genome DNA from oral mucosa cells, oral sloughed cells, saliva, blood and dried blood slices, and the ratio of DNA A260/A280 is required to be 1.8-2.0. Frozen DNA samples should be below-20℃and avoid repeated freeze thawing.
1.5 test methods
1. PCR reaction
1) A5 uLPCR reaction system was prepared by sequentially adding the relevant reagent components according to Table 6, and split-packed into 96-well PCR plates, 3 uL/well.
TABLE 6
2) The DNA template was removed, melted on ice (4 ℃) and centrifuged briefly after vortexing for 10s, and a quantity of DNA was aspirated to 5 ng/. Mu.L for further use.
3) mu.L of 5 ng/. Mu.L of DNA template was added to each well of a 96-well plate, covered with a tube cap, vortexed for 10s and centrifuged briefly for later use, and a blank (2. Mu.L ddH) had to be set for each experiment 2 O), negative control (2 μl DNA extraction eluate) and positive control.
4) The 96-well PCR plate was placed into an amplificator, run the program: pcr, procedure was as follows:
after completion, the mixture was kept at 4 ℃.
2. SAP reaction
After the PCR reaction was completed, an SAP mix was prepared in a 1.5mL EP tube. The SAP reaction mixture comprises the following components:
SAP buffer 0.17 mu L CutSmart buffer (manufacturer NEB)
SAP enzyme 0.5U (manufacturer NEB)
ddH 2 O was made up to 2. Mu.L.
1) To each well was added 2 μl of SAP mix (total volume after mix addition: 7 μl).
2) Plates were sealed with membranes (life or other company better quality membranes), vortexed and centrifuged (4000 rpm,5 seconds).
3) The plates were placed on a PCR instrument for the following procedure:
the temperature of the mixture is 37 ℃ for 40 minutes,
the temperature of the mixture is 85 ℃ for 5 minutes,
preserving heat at 4 ℃.
3. Extension reaction
1) The SAP reaction plate was removed and centrifuged at 2000rpm for 1min.
2) An iPLEX extension mix was prepared in a 1.5mL tube according to Table 7 below. The numbers in Table 7 were calculated as a 96 well plate plus 38% excess. Please adjust the number according to the number of actual reactions.
TABLE 7
| Single base extension reaction mixture (buffer+AcyNTPs) | 0.4 mu L (from NEB) |
| iPlex Enzyme | 1U (produced from NEB) |
| Extension primer mixture | 0.94μL |
| Water and its preparation method | Supplement to 2 mu L |
3) Add 2 μl of the iPLEX extension mix to each well and mix well (total volume after addition: 9 μl).
4) The plate was sealed with a membrane, vortexed and centrifuged (4000 rpm for 5 seconds).
5) The 96-well plate was put on a PCR instrument for the following thermal cycles:
4. resin purification
1) Spreading clean Resin (Resin) on 96/15mg pit plate (blank), scraping with scraper repeatedly, leveling Resin, compacting, and making Resin content of each hole uniform until Resin changes from deep yellow to pale yellow.
2) 41. Mu.L of water was added to each well of the sample plate, the membrane was sealed (with a conventional membrane), and then centrifuged.
3) The sample plate is gently reversed in the air and placed on the pit plate with the resin, and the holes of the two plates are in one-to-one correspondence. The crater plate is then inverted with the sample plate (both plates cannot move horizontally during the process) and the resin is allowed to drop into the wells.
4) The plate was sealed with a film (conventional film) and placed on a rotator and shaken upside down for 15 minutes.
5) The plates were centrifuged at 3200g (4000 rpm of standard plate centrifuge) for 5 minutes.
5. Mass spectrometry analysis
Mass spectrometry was performed using DR MassArray.
1) Opening the 'plate management system' software, editing the experimental plan file, including the position of the sample, the sample name and the primers used, and connecting the mass spectrometer with the established experimental plan file.
2) And (3) starting software by a Start All icon, and checking whether each indicator lamp is normal.
3) Clicking the "chip tray enter/exit" button places the chip on the tray and then on the chip deck, and records the position of the chip (1 on the left and 2 on the right). The hand does not touch the chip surface; placing the 96 plates at the position marked with MTP1/2, placing the plates in the direction of the lower left corner of A1, and fixing the plates; when the chip is used for the first time, 75 mu L of correction standard substance is added into the calibration substance sample adding groove, and when the chip is not used for the first time, the correction standard substance is not required to be added. Then click the chip tray enter/exit button again, closing the clamping plate.
4) Clicking on the "Add/maintain resin" button opens the resin tank, adds resin or supplements the autoclaved purified water. A. When the instrument is started for the first time, 28g of resin is added into a resin tank, and 16mL of sterilized purified water is added for uniform mixing. B. In the first use, 9g of resin is poured into a resin tank, 5.2ml of high-pressure sterilized purified water is added, and the mixture is uniformly mixed by a gun head. C. When the resin is not used for the first time, the liquid level needs to be observed, and if the liquid level of the water is lower than the resin surface, a proper amount of high-pressure sterilizing purified water is supplemented so that the liquid level of the water is higher than the resin surface. D. The resin solution should be used as soon as possible after being added to the resin tank and cannot be left for more than 30 days.
5) The program setting parameters are shown in table 8 below.
TABLE 8
6) After the mass spectrum is struck, clicking a button for removing the old chip from the analyzer, returning the chip to a chip deck, clicking a button for entering/exiting the chip tray, taking out the 96-well plate, sealing the film, and storing at-20+/-5 ℃; the chip is put back into the packaging box and is placed into a dehumidifier for preservation (the chip needs to be used as soon as possible after being opened, the preservation time is not longer than 30 days), the correction standard sample is recovered and preserved at the temperature of minus 20+/-5 ℃, and then the chip tray entering/exiting button is clicked, and the clamping plate is closed.
1.6 interpretation of test results
Validity judgment: the standard can detect the corresponding genotype, the blank (ddH) 2 And O) no signal is detected, when the weak positive control can detect the corresponding positive signal, the detection result is valid, otherwise, the detection result is invalid.
Example 3: the application of the genetic risk assessment method for premature grey hair provided by the invention in the aspect of assessing the genetic risk of suffering from premature grey hair and the severity of the premature grey hair
1 early-onset white hair young saliva sample is selected, 3 compound holes are arranged on each sample with the number of mofang-earlygreyhair_01, and SNP typing is carried out on a nucleic acid mass spectrum platform, wherein the process is as follows:
1.1 extraction of DNA
DNA extraction step
1) The 2ml centrifuge tube was removed and labeled with sample number.
2) 10ul FineMag Particles G,30 μl of protease K and 350 μl of Buffer MLD were added to each centrifuge tube.
3) Transfer 500 μl saliva sample to centrifuge tube, mix upside down 3 times, mix for 15sec with high speed vortex. Placing in a constant temperature incubator at 45 ℃ for 10min.
4) The centrifuge tube was placed on a magnetic rack for 30sec and after the beads were fully adsorbed, the liquid was carefully removed.
5) The centrifuge tube was removed from the magnet holder, 600 μl Buffer RBP was added, and the beads were resuspended by vortexing for 15sec.
6) The centrifuge tube was placed on a magnetic rack for 30sec and the liquid was carefully removed with a pipette when the beads were fully adsorbed.
7) Repeating the steps 6) and 7) once.
8) The centrifuge tube was removed from the magnet holder, 600 μl of 80% ethanol was added, and the beads were resuspended by vortexing for 15sec.
9) The centrifuge tube is placed on a magnetic rack for standing for 30sec, and after the magnetic beads are completely adsorbed, a liquid is removed by a liquid-transferring gun.
10 Remove the tube from the magnetic rack, add 600 μl absolute ethanol, vortex 15sec to resuspend the beads.
11 Placing the centrifuge tube on a magnetic rack for standing for 30sec, and carefully removing liquid by using a pipette when the magnetic beads are completely adsorbed.
12 Placing the centrifuge tube on a magnetic frame, opening a tube cover, and airing at room temperature for 10min.
13 Taking the centrifuge tube off the magnetic frame, adding 50 μl Buffer EB, sucking and beating with a pipetting gun, and incubating at 70deg.C for 5min.
14 Placing the centrifuge tube on a magnetic rack for standing for 1-2min after short centrifugation until the magnetic beads are completely adsorbed. After complete adsorption of the beads, the DNA solution was transferred to a new labeled 0.6ml centrifuge tube.
The extracted DNA was typed using the early-onset white hair gene detection kit and time-of-flight mass spectrometer, type 4.0 software and the detection results were output according to the experimental procedure provided in example 2.
Genotyping results for mofang-hignmyia_01 are shown in Table 9 below:
TABLE 9
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1.2 carrying out SNP genotyping on a subject (male) through a premature grey hair genetic risk detection kit, acquiring sex information of a detection sample, coding according to the number of risk alleles of each SNP locus and a gene detection result of the locus, which are indicated in a model (normal homozygosity is 0, heterozygosity is 1, risk mutation homozygosity is 2), sex male coding is 1, sex female coding is 2, and the coded results are shown in the third column of the table.
The calculation formula is as follows: premature grey hair genetic risk score = snp_1 x coffi_1+snp_2 x coffi_2+ & snp_16 x coffi_16+gender x coffi_gender+intercept 1
As shown in the above disclosure, the feature-encoded values are multiplied by the corresponding weight values in the model, and then summed, plus the intercept term (-1.25867205), to obtain a score that is the genetic risk score for premature grey hair in the subject.
And 1.3, carrying out risk rating on the obtained scores according to a risk division system. By integrating the genetic data and the sex information through the model, the genetic risk score of 0.7761105 can be obtained. An early-onset white hair risk classification system is matched, and risk classification is carried out according to the segmentation intervals given in claim 7.
The dividing result is: the individual early-onset white hair gene risk of mofang-earlygreyhair_01 is the highest "grade 6", the risk level is the "extremely high risk population", and the early-onset white hair prevalence rate (32%) is obviously higher. The population distribution for each risk class is shown in table 10 below.
Table 10
| Risk level | The ratio of the level groups |
| Grade 1 | 10.9％ |
| Class 2 | 18.3％ |
| Grade 3 | 23.8％ |
| Grade 4 | 22.7％ |
| Grade 5 | 17.0％ |
| Grade 6 | 7.3％ |
In the group of level 6, the actual proportion of the early-onset white hair vs non-early-onset white hair group in the level is shown by the data distribution of the independent test set with a large sample size: 67.2% vs 32.8%.
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and various modifications can be made to the above-described embodiment of the present invention. All simple, equivalent changes and modifications made in accordance with the claims and the specification of the present application fall within the scope of the patent claims. The present invention is not described in detail in the conventional art.
SEQUENCE LISTING
<110> Chengdu twenty-three magic cube Biotechnology Co., ltd
<120> kit for detecting genetic risk of premature gray hair, and system and method for evaluating genetic risk of premature gray hair
<160> 48
<170> PatentIn version 3.5
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Claims (2)
1. The kit for detecting the genetic risk genes of the premature grey hair is characterized in that the kit can be used for SNP typing of gene loci of 16 susceptibility genes related to the premature grey hair at the same time, and the 16 gene loci are shown as follows: rs12563397, rs16886165, rs10461617, rs3127159, rs7775323, rs7803075, rs16888276, rs1881129, rs644383, rs676856, rs17335506, rs57406257, rs9651934, rs2058343, rs117283943, rs150271955;
the kit for detecting the early-onset white hair genetic risk gene comprises 16 pairs of amplification primers for amplifying 16 gene fragments, wherein the sequences of the 16 pairs of amplification primers are specifically shown as follows:
；
The kit for detecting the early-onset white hair genetic risk gene also comprises 16 extension primers for sequentially identifying the 16 gene mutation sites, and the sequences of the extension primers are shown in SEQ ID NO. 33-SEQ ID NO. 48.
2. An early-onset white hair genetic risk assessment system, comprising:
the acquisition module is used for acquiring a DNA sample of a tester;
A detection module comprising the early-onset white hair genetic risk gene detection kit of claim 1 for genotyping the DNA sample;
the prediction module comprises an early-onset white hair genetic risk prediction model and is used for scoring the early-onset white hair genetic risk of a tester; and
the evaluation module comprises an early-onset white hair genetic risk grading system and is used for determining the early-onset white hair genetic risk grade of a tester;
the early-onset white hair genetic risk prediction model utilizes whole genome association analysis and a machine learning algorithm to integrate risk gene locus information to predict and quantitatively score the early-onset white hair genetic risk;
the premature grey hair genetic risk prediction model uses 17 features in total including gender and gene locus and an intercept item-1.25867205 with weight of 1, and all the features are subjected to weighted summation of risk allele doses to serve as premature grey hair genetic risk scores, wherein the feature ID and feature weight are as follows:
；
Genotyping results are shown in the following table:
；
carrying out SNP genotyping on a subject by using a premature grey hair genetic risk detection kit, acquiring sex information of a detection sample, coding according to risk alleles of each SNP locus and gene detection results of the locus, which are shown in a model, wherein the number of the risk alleles is 0, the heterozygosity is 1, the risk mutation homozygosity is 2, the sex male code is 1, the sex female code is 2, and the coded results are shown in the third column of the table;
The calculation formula is as follows: premature grey hair genetic risk score = snp_1 x cofpi_1+snp_2 x cofpi_2+ & snp_16 x cofpi_16+gender x cofpi_gender+intercept 1;
as shown in the formula, the value after feature coding is multiplied by the corresponding weight value in the model, and then summation is carried out, and intercept term-1.25867205 is added, so that the obtained score is the premature grey hair genetic risk score of the testee;
the early-onset white hair genetic risk grading system comprises six grades in total, wherein grade 1:0-0.201297176117716, rank 2:0.201297176117716-0.281098927835626, rank 3:0.281098927835626-0.363050852087241, rank 4:0.363050852087241-0.453887538258589, rank 5:0.453887538258589-0.566654292179563, rank 6:0.566654292179563-1, 5 sections of ranks 1 to 5 are left-closed and right-open, and a section of rank 6 is left-closed and right-closed, and from rank 1 to rank 6, the genetic risk of premature white hair gradually increases.
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| JP6596724B1 (en) * | 2019-04-16 | 2019-10-30 | 日本メナード化粧品株式会社 | Methods for determining genetic predisposition to gray hair |
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| CN104673899A (en) * | 2010-05-06 | 2015-06-03 | 赛昆塔公司 | Methods Of Monitoring Conditions By Sequence Analysis |
| CN108427000A (en) * | 2017-02-15 | 2018-08-21 | 广州市锐博生物科技有限公司 | A kind of method and kit of capture nucleic acid binding protein |
| JP6596724B1 (en) * | 2019-04-16 | 2019-10-30 | 日本メナード化粧品株式会社 | Methods for determining genetic predisposition to gray hair |
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