CN114774557B

CN114774557B - Combined markers for estimating individual age in the Chinese Han population and their application

Info

Publication number: CN114774557B
Application number: CN202210204938.XA
Authority: CN
Inventors: 黄代新; 韩雪丽; 肖超; 易少华
Original assignee: Huazhong University of Science and Technology
Current assignee: Huazhong University of Science and Technology
Priority date: 2022-03-02
Filing date: 2022-03-02
Publication date: 2024-11-26
Anticipated expiration: 2042-03-02
Also published as: CN114774557A

Abstract

The present application discloses a combination of markers for inferring the age of individuals in the Chinese Han population and its application. The combination of markers includes 8 CpG sites: chr6:11044628, cg06639320, cg14361627, chr1:207823723, cg19283806, cg17740900, cg07553761 and cg26947034. The 8 CpG sites disclosed in the present application all have strong age correlation, and the age inference model constructed by combining these 8 CpGs has high accuracy and sensitivity for inferring the age of individuals in the Chinese Han population. In actual case applications, the age inference method constructed by the age inference model can accurately infer the age of unknown samples, and the age inference error is 0.27 years.

Description

Combined marker for deducing individual ages of Chinese Han population and application thereof

Technical Field

The application relates to the technical field of forensic age inference, in particular to a combined marker for inferring individual ages of Chinese Han people and application thereof.

Background

With the rapid development of molecular biology, a few biomolecular markers associated with human development and aging processes have been discovered, bringing us new research perspectives and directions. Related researches show that in the aspect of deducing the age of an individual, DNA methylation is obviously superior to other biological molecular markers, such as mRNA, a signal binding T cell receptor deletion ring, racemization of aspartic acid, telomere length and the like, and the biological molecular marker has the advantages of good stability, high accuracy, high sensitivity and the like, and is the currently accepted biological molecular marker with the most application prospect.

DNA methylation (CpG) levels at specific sites of the human genome have a significant correlation with age, known as the "epigenetic clock". Currently, several studies have developed age inference models for blood, saliva, buccal swab, semen or more extensive tissue based on different methylation analysis platforms, confirming that DNA methylation is the most promising biomarker in forensic individual age inference. Studies have demonstrated that methylation of CpG markers has population differences.

Moreover, the prior art generally divides the process of developing forensic age inference models into three phases: screening of CPGs (age-related CPGs), verification of candidate CPGs, and training and verification of models. In the CPGs screening stage, a small percentage of students directly use large sample DNAm chip data to construct an age inference model, such as the classical "HoRvath clock". However, these models often contain more CpGs (HoRvath clocks: 353 or 110) and DNAm data are based on a chip platform, thus requiring the use of corresponding DNAm chips to detect the methylation status of these sites. However, the chip technology is not only high in cost, but also involves complex bioinformatics analysis, and more importantly, the chip technology needs more high-quality DNA templates (450K: >500ng;850K: >250 ng), so that the chip technology cannot be applied to legal medical expert cases well at present.

Huang et al "Huang Y,Yan J,Hou J,et al.Developing a DNA methylation assay FoR human agepRediction in blood and bloodstain[J].FoRensic Sci Int Genet.2015, 17:129-136." picked CpGs at 6 candidate loci (ASPA, ITGA2B, NPTX2, TOM1L1, ZDHC 22 and ZIC 4) and analyzed the methylation level of a total of 38 CpG sites within 6 loci in 89 blood samples collected from individuals of the Han nationality 9-75 years old using pyrosequencing technology, and an age estimation model was constructed based thereon, however the average absolute error of the estimated age from the actual age was 7.870 years, with low accuracy.

Disclosure of Invention

In view of this, the present application aims to screen CpGs strongly correlated with age and to build an age estimation model based on this for application in forensic individual age estimation.

In a first aspect, embodiments of the present application disclose a combination marker for inferring the age of individuals in the chinese han population, the combination marker comprising 8 CpG sites: chr6:11044628, cg06639320, cg14361627, chr1: 207823723, cg19283806, cg17740900, cg07553761 and cg26947034.

In a second aspect, the present examples disclose a primer composition comprising a first primer combination of nucleotide sequences shown as SEQ ID NOS.1-16 and a second primer combination shown as SEQ ID NOS.17-32; the first primer combination is used for carrying out multiplex amplification on 8 CpG sites in the same reaction system, wherein the 8 CpG sites comprise chr6:11044628, cg06639320, cg14361627, chr1:207823723, cg19283806, cg17740900, cg07553761 and cg26947034; the second primer combination is used for single base extension reaction of the products of the multiplex amplification.

In a third aspect, embodiments of the present application disclose a kit comprising reagents for detecting a combination marker of the first aspect; optionally, the kit further comprises the primer composition.

In a fourth aspect, the embodiment of the application discloses the application of the combined marker of the first aspect or the primer composition of the second aspect in deducing the individual ages of Chinese Han population.

In a fifth aspect, the embodiment of the application discloses a method for deducing the individual ages of Chinese Han people, which comprises the following steps:

Obtaining methylation rate data of 8 CpG sites of a blood sample of a Chinese Han healthy individual, and forming a training set according to the methylation rate data;

constructing an age inference model according to the training set, wherein the age inference model is a regression model obtained by carrying out regression analysis and training on methylation rate data of 8 CpG sites in the training set and the actual age of the Chinese Han healthy individual;

inputting methylation rates of 8 CpG sites of the Chinese Han nationality individuals to be detected into the age inference model to obtain inferred ages of the individuals;

wherein the 8 CpG sites chr6:11044628, cg06639320, cg14361627, chr1: 207823723, cg19283806, cg17740900, cg07553761 and cg26947034.

In an embodiment of the present application, the step of obtaining methylation rate data of 8 CpG sites of a blood sample of a healthy individual in chinese han nationality includes:

obtaining a DNA sample of the transformed individuals of the Chinese Han population;

Performing multiplex PCR amplification of 8 CpG sites on the DNA sample, wherein the 8 CpG sites are chr6: 11044628, cg06639320, cg14361627, chr1:207823723, cg19283806, cg17740900, cg07553761 and cg26947034;

carrying out single base extension reaction by taking the product of the composite PCR amplification as a template;

Purifying and denaturing the single-base extension product obtained by the single-base extension reaction to obtain a denatured product;

ABI sequencing is carried out on the denatured product to determine the color and the position of each peak in a capillary electrophoresis chart corresponding to the denatured product, and corresponding CpG sites are sequentially determined;

And calculating the methylation rate of the determined CpG sites according to the color and the position of each peak in the capillary electrophoresis chart.

In the embodiment of the application, the primer for multiplex PCR amplification comprises nucleotide sequences shown as SEQ ID NO. 1-16, and the primer for single base extension reaction comprises nucleotide sequences shown as SEQ ID NO. 17-32.

In the embodiment of the application, the regression model is a Multiple Linear Regression (MLR) model, and the regression equation of the MLR model is as follows: age of individual ＝22.333+39.068×chr6:11044628+13.626×cg06639320–12.096×chr1:207823723–12.417× cg19283806+145.103×cg14361627–0.131×cg17740900+25.946×cg07553761–34.205×cg2 6947034.

In a sixth aspect, the embodiment of the present application further discloses a system for deducing the age of individuals in chinese han nationality population, comprising:

The data acquisition device is used for acquiring methylation rate data of 8 CpG sites of a blood sample of a Chinese Han healthy individual and forming a training set according to the methylation rate data;

The model device is connected with the data acquisition device and is used for constructing an age inference model according to the training set;

The age deducing device is connected with the model device and is used for inputting methylation rate data of 8 CpG sites of the Chinese Han nationality individual to be tested into the age deducing model and obtaining the deduced age of the individual.

In an embodiment of the present application, the data obtaining apparatus includes:

the methylation treatment system is used for carrying out conversion treatment on blood DNA of the individuals of the Chinese Han population, so that unmethylated cytosine is converted into uracil, and a DNA sample subjected to conversion treatment is obtained;

a PCR amplification system for performing a multiplex PCR and a single base extension reaction on the converted DNA sample to obtain a denatured product;

The sequencing device is connected with the conversion processing device, and the sequencing device performs ABI sequencing on the denatured product to determine the color and the position of each peak in the capillary electrophoresis chart corresponding to the denatured product, and sequentially determines the corresponding CpG sites;

The computing device is connected with the sequencing device and is used for computing the methylation rate of the determined CpG sites according to the colors and the positions of the peaks in the capillary electrophoresis chart and constructing a training set according to the methylation rate, wherein the training set comprises DNA methylation data of training group samples of Chinese Han population for training, the Chinese Han population comprises Chinese Han adult males and Chinese Han adult females, and the actual ages are distributed between 2 and 82 years.

Compared with the prior art, the application has at least one of the following beneficial effects:

The 8 CpG sites disclosed by the application have higher age correlation (0.8 < r < 1.0), and the inferred accuracy of the MLR model successfully constructed by the methylation rate data of the 8 CpG sites in the range of the inferred age and the actual age within +/-5 years reaches 79.35%.

The age inference model constructed based on 8 CpG site methylation rate data provided by the application can detect 1ng of transformation DNA at the lowest time of age inference. The age estimation method provided by the application has the age estimation error of 0.27 years in the practical case application. Therefore, the age inference method provided by the embodiment of the application has high accuracy and sensitivity and good practical application value.

Drawings

FIG. 1 is a capillary electrophoresis methylation level detection chart of 8 CpG sites provided by the embodiment of the application.

FIG. 2 is a graph showing the correlation between methylation levels of 8 CpGs in a blood sample and age, according to an embodiment of the present application.

Fig. 3 is an MLR model constructed based on a training set according to an embodiment of the present application, where the X axis: sample actual age, Y axis: sample inferred age, R: pearson correlation coefficient, MAE: average absolute error, RMSE: root mean square error.

FIG. 4 is a graph showing the verification result of the constructed MLR on the verification set according to the embodiment of the application, wherein the X axis is as follows: sample actual age, Y axis: sample inferred age, R: pearson correlation coefficient, MAE: average absolute error, RMSE: root mean square error.

Fig. 5 shows the results of an age estimation sensitivity test of three volunteers using the age estimation method according to the embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application 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 application. The reagents not specifically and individually described in the present application are all conventional reagents and are commercially available; methods which are not specifically described in detail are all routine experimental methods and are known from the prior art.

1. Materials and methods

1. Sample collection

Blood samples of 529 chinese han nationality unrelated healthy individuals were taken, 240 females (2.33-82 years old) and 289 males (2.17-79 years old). Simultaneously, 529 blood samples were randomly divided into a training group with 374 samples (male 203, female 171) and a verification group with 155 samples (male 86, female 69) at a ratio of 7:3. The time of the sample collection date from the date of birth recorded on the identification card, birth certificate or household book is calculated as the actual age. The study was approved by the medical ethics committee of the university of science and technology, college of medicine, and all volunteers signed written informed consent.

2. DNA extraction and transformation

200. Mu.L of peripheral Blood genomic DNA was extracted using DNeasy Blood & Tissue Kit (Qiagen, hilden, germany) and eluted with 50. Mu.L of Buffer AE. 2. Mu.L of genomic DNA was taken and quantified using a Nanodrop 2000 ultramicro spectrophotometer. Subsequently, 1. Mu.L of genomic DNA was subjected to quality control by agarose gel electrophoresis. All qualified blood sample genomic DNA was bisulphite converted by quality inspection. 600ng of genomic DNA was transformed using EPITECT FAST DNA Bisulfite Kit (Qiagen, hilden, germany) to convert unmethylated cytosines to uracil; then, the DNA template required for the subsequent amplification was obtained by eluting with 15. Mu.L Buffer EB.

3. Primer design

The embodiment of the application provides a combined marker for deducing the individual ages of Chinese Han population, wherein the combined marker comprises 8 CpG sites: chr6:11044628, cg06639320, cg14361627, chr1:207823723, cg19283806, cg17740900, cg07553761 and cg26947034.

The embodiment of the application also discloses a primer composition which comprises a first primer combination of nucleotide sequences shown as SEQ ID NO. 1-16 and a second primer combination shown as SEQ ID NO. 17-32 by amplifying the converted DNA to obtain methylation data of 8 CpGs in a DNA template; the first primer combination is used for carrying out multiplex amplification on 8 CpG sites in the same reaction system; the second primer combination is used for single base extension reaction of the products of the multiplex amplification. All primers were synthesized by the biological engineering (Shanghai) Co., ltd using ULTRAPAGE purification method.

The primer sequences of the first primer combinations are shown in Table 1.

First primer combination primer and fragment size of 18 CpG sites in Table

Note that: y, R denotes the degenerate base C/T, G/A.

The sequences of the second primer combinations are shown in Table 2, and single base extension primers with different lengths are designed in a mode of adding (GACT) n at the 5' end. All primers were synthesized by HPLC purification from the biological engineering (Shanghai) Co., ltd.

Table 28 second primer combination sequences and sizes of CpGs

Note that: y, R denotes the degenerate base C/T, G/A.

4. Construction of a Complex PCR System

The 8 CpG sites were first subjected to a single-site PCR reaction, and the specificity of each site PCR primer was detected by agarose gel electrophoresis to see if a nonspecific band occurred. And placing all CpG sites in a reaction system for multiplex amplification, and exploring the most suitable circulation condition and primer concentration of the system by using polyacrylamide gel electrophoresis, wherein the construction results are shown in tables 3 and 4.

TABLE 3 Complex PCR reaction System

Component (A)	Dosage of	Final concentration
			2×Multiplex PCR Mix	10μL	1×
Primer mixture	4.5μL	See Table 1
			Post-transformation genomic DNA	10ng
RNase-Free Water	Variable
			Total volume of	20μL

TABLE 4 Complex PCR reaction procedure

5. Single base extension reaction

(1) Complex PCR product purification

Excess primer sequences and dNTPs in the complex PCR reaction product need to be removed. First rSAP (1U/. Mu.L) and Exonuclease I (5U/. Mu.L) were mixed in a 1:1 ratio, then 2. Mu.L of the mixture and 3. Mu.L of the complex PCR product were sequentially added to a new 200. Mu.L PCR tube, mixed well and centrifuged, and finally the PCR tube was put on a thermal cycler with cycle parameters set to 60min at 37℃and 20min at 80℃to obtain purified amplification products.

(2) Single base extension reaction (SBE)

And (3) taking the amplified product purified in the step (1) as a template, mixing the second primer combinations with all sequencing sites shown in the table 2 according to the experimental requirement according to a certain concentration, and performing single-base extension reaction by using SNapshot TM Multiplex Kit, wherein the reaction system and the reaction procedure are shown in tables 5 and 6.

TABLE 5 Single base extension reaction System

TABLE 6 Single base extension reaction procedure

(3) Purification and denaturation of products of single base extension reactions

Taking out the product of single base extension reaction, centrifuging briefly to remove water drops on the tube cover, adding 1 mu L rSAP into the SBE product, covering the PCR tube cover, mixing, centrifuging, placing on a thermal cycler, setting the circulation parameters at 37 ℃ for 60min and 80 ℃ for 20min, and completing the product purification. Fully mixing deionized Formamide (Hi-Di TM Formamide) and GENESCANTM120, 120LIZ TM dye Size Standard according to the ratio of 36:1, adding 9.0 mu L of the mixed Formamide-120 LIZ into a centrifuge tube, adding 1 mu L of purified SBE product, fully shaking and uniformly mixing by a vortex mixer, centrifuging briefly, placing on a thermal cycler, setting the denaturation condition at 95 ℃ for 5min, and immediately placing on ice after the denaturation is finished.

6. Genetic sequencer detection

The whole denatured product was added to a 96-well plate, the ABI sequencer was turned on, sample data was input, and electrophoresis conditions were set to 15kv for 30min. After detection by ABI sequencer, useAnd (3) analyzing data by using the ID Software v3.2, judging CpG sites corresponding to the extension products according to the colors and positions of the peaks in the capillary electrophoresis chart, and regulating the concentration of the extension primers according to the peak value of each site until the peak chart of each site is clear and the heights of all peaks are relatively balanced, and determining the final concentration of the SBE composite reaction primers.

7. Methylation level analysis of 8 CpGs

And (3) determining amplification conditions such as optimal primer concentration, annealing temperature, cycle times, template quantity and the like of CpG site composite PCR and SBE extension reaction through a pre-experiment, and finally detecting and analyzing methylation levels of selected CpG sites of all samples under the optimal conditions by using a 3130 gene genetic analyzer. The methylation level of each site was calculated from the color and peak height of the product peak at that site in the capillary electrophoresis chart.

SBE primer design is divided into a forward primer and a reverse primer, cpG site product peaks of the forward primer are represented as immediately adjacent red and black peaks, and represent methylated cytosine (C) and unmethylated thymine (T) respectively, and methylation level calculation formulas are as follows: methylation ratio = C peak height/(C peak height + T peak height) x 100%. The CpG site product peaks of the reverse primer appear as immediately adjacent blue and green, representing methylated guanine (G) and unmethylated adenine (A), respectively, and their methylation levels are calculated as: methylation ratio = G peak height/(G peak height + a peak height) ×100%.

When only a single T/A peak appears in the product peaks, it is indicated as a completely unmethylated site, with a methylation level of 0; when only a single C/G peak appears in the product peaks, this indicates a complete methylation site with a methylation level of 1. Methylation level calculations of CpG sites were performed according to the above formula and methylation data for all samples were obtained.

And further establishing an age inference model applicable to blood samples of Chinese Han population by analyzing age correlation of 8 CpGs. All samples were statistically analyzed using the R software package and correlation charts were made using GRAPHPAD PRISM software.

8. Establishment of age inference model and method for obtaining inferred age

Based on the steps, the embodiment of the application discloses a method for deducing the individual ages of Chinese Han people, which comprises the following steps:

S1, obtaining methylation rate data of 8 CpG sites of a blood sample of a Chinese Han healthy individual, and forming a training set according to the methylation rate data; the 8 CpG sites chr6:11044628, cg06639320, cg14361627, chr1:207823723, cg19283806, cg17740900, cg07553761 and cg26947034.

S2, constructing an age inference model according to the training set, wherein the age inference model is a regression model obtained by carrying out regression analysis and training on methylation rate data of 8 CpG sites carried out on the training set and the real age of the Chinese Han healthy individual;

S3, inputting the methylation rate of 8 CpG sites of the Chinese Han nationality individual to be detected into the age inference model to obtain the inferred age of the individual.

In step S2, the step of establishing an age estimation model provided in a specific embodiment includes:

S21, performing Pearson (Pearson) correlation analysis on the actual ages of the individual samples in the training group and the verification group by using a corr () function in R.4.0.2 software to obtain Pearson correlation coefficients of the 8 CpG sites. The methylation data of the individual samples of the training group form a training set, and the methylation data of the individual samples of the verification group form a verification set.

S22, constructing an MLR age inference model in R.4.0.2 software by using lm () function based on the Pearson correlation analysis result and utilizing training set data;

s23, calculating parameters such as a correlation coefficient (r), an average absolute error (MAE), a Root Mean Square Error (RMSE) and the like of the constructed MLR age estimation model by utilizing DMwR software package, and taking the parameters as indexes for measuring the error between the estimated age and the real age obtained according to the MLR age estimation model.

In a specific embodiment, step S3 further includes: the data of the verification set are respectively input into a constructed MLR age estimation model, and the age estimation efficiency of the MLR age estimation model constructed based on the training set data is estimated by taking a correlation coefficient (r), an average absolute error (MAE), a Root Mean Square Error (RMSE) and the like as measurement standards.

The specific verification process is as follows:

S31, importing data of an authentication set by using an R.4.0.2 prediction () function into an MLR model, DMwR packages r, MAE and RMSE of the authentication set by using a regr.eval () function, finally obtaining inferred age data of individual samples in the authentication group by using a data.frame () function, and dividing the authentication group samples into six age groups in Office 2019 after the inferred age data are exported: 2-19 years old; 20-29 years old; 30-39 years old; 40-49 years old; 50-59 years old; and on the premise that the inferred age is within +/-5 years old from the actual age by 60-82 years old, calculating the sample percentage of the inferred age of each age group within +/-5 years old from the actual age, namely the inferred accuracy.

S32, simultaneously obtaining MAE values of all age groups by using SUMPRODUCT algorithm, so as to comprehensively evaluate the model.

S33, finally, performing t-test and Wilcoxon-Matt-WHITNEY TEST rank sum test on all samples in R4.0.2 to detect whether the determined CpGs have sex differences. All data analysis processes were performed in R4.0.2, part of the statistics were done in Office 2019, and the plots were done in GRAPHPAD PRISM.

9. Sensitivity test

The sensitivity of the method to infer age of an individual is assessed by continuously reducing the amount of post-conversion DNA used in the multiplex PCR reaction to detect methylation levels of selected CpG sites, without compromising the accuracy of the method described above to obtain inferred age.

Three volunteers of 21 years, 40 years and 60 years, respectively, were randomly selected, and the amount of post-transformation genomic DNA used for the multiplex PCR reaction was 20ng, 10ng, 5ng, 2ng, 1ng, respectively, while 4 replicates were performed per group, and Standard Deviation (SD), mean (Mean) and Mean Absolute Error (MAE) of each group was calculated from all methylation data obtained. Based on the parameters between the estimated age and the actual age obtained by the DNA input of 20ng, it is assumed that the estimated result is correct, meaning that the error range between the estimated age and the actual age is + -2 years old, and the sensitivity of the above age estimation model is evaluated using SD+MAE of 4 repeated experiments to estimate the age of less than or equal to 5 years old as a supplementary condition.

10. Practical application

100Ng of DNA solution of blood trace detection material 2021-503BN-A left on site in rape homicide is taken, DNA sulfite conversion is carried out by EPITECT FAST DNA BisulFite ConveRsion Kit (Kaiji biotechnology Co., germany), composite PCR amplification is carried out on the converted DNA by using an individual age inference methylation amplification Kit (developed in the laboratory), composite analysis is carried out on the amplified product by using SNaPshot ^TM Multiplex Kit (applied biosystems Co., USA), and capillary electrophoresis and typing are carried out by using an ABI-3130 type genetic analyzer (applied biosystems Co., USA). Based on methylation level datSup>A of the sample 2021-503BN-A at all detection markers, the deduced ages were calculated according to the MLR model constructed as described above and the age deducing method.

2. Results

1. Establishing a composite PCR system and a composite SBE system

In order to place all of the selected AR-CpGs in one reaction system, the final concentrations of the first primer combination for multiplex PCR amplification of 8 CpG sites and the second primer combination for single base extension reaction were determined through repeated debugging of multiple electrophoresis (Table 7).

TABLE 7 8 primer final concentrations of CpG sites in multiplex PCR and SBE systems

After the final concentration of the primer in the composite system is optimized, composite PCR and single base extension reaction are carried out on all samples to detect the methylation rate of 8 CpGs in the samples. Wherein the CpG site of the completely unmethylated housekeeping gene β -Actin (ACTB) served as an internal control for examining whether the conversion of the sample bisulfite was complete, as shown in FIG. 1, when ACTB was only able to observe the A peak in the electropherogram, indicating complete conversion.

2. Analysis of age dependence of methylation levels of 8 CpGs

Based on the established composite PCR system and single base extension reaction system, the research detects 529 Chinese Han crowd blood samples and obtains methylation data of 8 CpG sites. In order to evaluate the correlation between the methylation level and the age of 8 CpG sites, pearson correlation analysis is carried out on 529 methylation data and the actual age of a corresponding blood sample, and the detection result shows that all CpG sites have strong age correlation, and the Pearson correlation coefficient range is 0.8363-r-0.9251.

Methylation level versus age-related scatter plots plotted according to Pearson correlation coefficients for each site are shown in fig. 2. As can be seen from the figure, chr6:11044628 (ELOVL 2) has the highest age-related correlation, the correlation coefficient r= 0.9251, and cg06639320 and cg26947034 have age-related correlations as high as 0.9054 and 0.8932, respectively, and 0.8363 is reached even though the methylation level of cg14361627 has the lowest age-related correlation, belonging to the category having a strong correlation with age. The 8 CpG sites can be primarily considered as biological molecular markers with the efficacy of estimating the age of Chinese Han population in blood by combining the pearson correlation coefficients, namely, the specific age can be estimated according to the methylation level change trend of the CpG sites. Meanwhile, 529 total samples were divided into male (289) and female (240) groups, and CPGs were subjected to sex-differential testing using two independent samples, t-test and Wilcoxon-Matt-WHITNEY TEST rank-sum test, and the results showed that there was no significant sex-differential (p > 0.05) in correlation of the 8 CPGs with age.

3. Construction of age inference model

The regression equation of the MLR model constructed by the method is as follows:

The model of individual age ＝22.333+39.068×chr6:11044628+13.626×cg06639320–12.096× chr1:207823723–12.417×cg19283806+145.103×cg14361627–0.131×cg17740900 +25.946×cg07553761–34.205×cg26947034, accounts for 92.33% of age variation (R ² = 0.9233), while the samples infer that there is a significant correlation between age and actual age, the correlation coefficient r=0.9609, mae= 3.5166, rmse= 5.3438.

4. Verification of age inference model

The age inference model constructed by the training set was validated using 155 methylation data (male 86, female 69) from the validation set, and the same statistical indicators r, MAE and RMSE were selected to verify the accuracy of model inference age.

The results are shown in FIG. 4, in the MLR model, the correlation coefficient of inferred age and actual age is 0.9558, and MAE and RMSE are 3.7058 and 5.5937, respectively.

5. Inferred efficacy of age inference model at different age groups

To further analyze the inferred efficacy of the age inference model at different age groups, the validation set samples were divided into six age groups: 2-19 years old; 20-29 years old; 30-39 years old; 40-49 years old; 50-59 years old; 60-82 years old. And calculating the average absolute error MAE of the MLR age inference model constructed based on the training set data for deducing the age and the actual age of each age group and the inference accuracy. Based on the inferred age within + -5 years old from the actual age, each obtained result is interpreted as correct (not exceeding + -5 years old) or incorrect (exceeding + -5 years old), and the percentage of samples of the inferred ages of each age group within + -5 years old from the actual age is the inferred accuracy, and the relevant statistical analysis results are shown in Table 8. All data analysis processes were performed in R4.0.2, part of the statistics were done in Office 2019, and the plots were done in GRAPHPAD PRISM.

Table 8 MLR age inference potency of inference model

Note that: AD represents the absolute value of the deviation between the inferred age and the actual age

As can be seen from table 8, the estimated accuracy of the age group model ± 5 years old, in which the MAE value was the smallest, was also the highest, and the estimated accuracy was the lowest in the age groups 60 to 82, among the estimated results of the constructed MLR age estimation model.

6. Sensitivity of

As shown in FIG. 5 and Table 9, the methylation level at 1ng DNA content can be detected by the age estimation method provided by the embodiment of the application, and the MAE variation amplitude of the age estimated by the model is larger along with the decrease of the DNA input amount of PCR amplification.

As can be seen from Table 9, three samples were examined using the MLR age estimation model and the age estimation method established in this way, and the results showed that when the DNA content was 10ng, the estimated age MAE was minimum, SD+MAE was less than or equal to 5 years old, and three conditions of estimated age and actual age error range between.+ -. 2 years were satisfied simultaneously. The sensitivity test results show that age inferences with reliable accuracy and consistency can be obtained when DNA is converted using > 10ng of bisulfite. Therefore, the MLR age inference model and the age inference method established by the embodiment of the application can detect DNA samples with lower content and have higher sensitivity.

TABLE 9 inferred sensitivity of MLR age inference model

7. Practical application

According to the MLR model and the age estimation method disclosed by the application, the biological age estimation average value of the test material 2021-503BN-A individual is 38.27 years old in the MLR model, the error between the biological age estimation average value and the true age of 38 years old is 0.27 years old and less than 0.3 years old, the accuracy of the MLR model estimation age is further verified, and the possibility of putting the model standardization into practical application in the future is provided.

In conclusion, the 8 CpG sites disclosed by the application have higher age correlation (0.8 < r < 1.0), and the inferred accuracy of the MLR model successfully constructed by the 8 CpG site methylation rate results in the age range of +/-5 years reaches 79.35%. When the age inference model constructed based on the methylation rate of 8 CpG sites provided by the application is used for age inference, 1ng of converted DNA can be detected at the minimum. The age estimation method provided by the application has an age estimation error of 0.27 years in practical case application. Therefore, the age inference method provided by the embodiment of the application has high accuracy and sensitivity and good practical application value.

The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application.

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<211> 22

<212> DNA

<213> Artificial Sequence

<400> 6

cccctaatcc caacaaatac at 22

<210> 7

<211> 19

<212> DNA

<213> Artificial Sequence

<400> 7

tgagggaggg gaatgtttg 19

<210> 8

<211> 25

<212> DNA

<213> Artificial Sequence

<400> 8

acccttctct aaaaataact atcct 25

<210> 9

<211> 29

<212> DNA

<213> Artificial Sequence

<400> 9

ggtttttygg ttaagttatg tttaatagt 29

<210> 10

<211> 30

<212> DNA

<213> Artificial Sequence

<400> 10

aaaatttcrc tctattacca ttacctaact 30

<210> 11

<211> 29

<212> DNA

<213> Artificial Sequence

<400> 11

gttaaatttt tttagttttg ggatggtat 29

<210> 12

<211> 28

<212> DNA

<213> Artificial Sequence

<400> 12

cttcttacca aatcttcaat tctataca 28

<210> 13

<211> 21

<212> DNA

<213> Artificial Sequence

<400> 13

tatyggtggt ttgggggaga g 21

<210> 14

<211> 27

<212> DNA

<213> Artificial Sequence

<400> 14

aaaaaacact acrctccaca acataac 27

<210> 15

<211> 21

<212> DNA

<213> Artificial Sequence

<400> 15

gtgatgagtt agtggtttgg t 21

<210> 16

<211> 30

<212> DNA

<213> Artificial Sequence

<400> 16

acatatacaa acctcaaaat taaaataacc 30

<210> 17

<211> 18

<212> DNA

<213> Artificial Sequence

<400> 17

tgtggayttg ggagagga 18

<210> 18

<211> 20

<212> DNA

<213> Artificial Sequence

<400> 18

catcccccaa arttcacaat 20

<210> 19

<211> 19

<212> DNA

<213> Artificial Sequence

<400> 19

ggygatttgt aggtttagt 19

<210> 20

<211> 27

<212> DNA

<213> Artificial Sequence

<400> 20

gactggtttt gggagtatag tagttat 27

<210> 21

<211> 35

<212> DNA

<213> Artificial Sequence

<400> 21

gacgactgac tgatgggatt aaattttgga atatt 35

<210> 22

<211> 42

<212> DNA

<213> Artificial Sequence

<400> 22

gagactgact gactgactgg aggggaatgt ttgtatttat tt 42

<210> 23

<211> 48

<212> DNA

<213> Artificial Sequence

<400> 23

gactgactga ctgactgact ttaatagttt tagaaattat tttgtttt 48

<210> 24

<211> 58

<212> DNA

<213> Artificial Sequence

<400> 24

gactgactga ctgactgact gactgactga ctgactacaa attccttcct aatatccc 58

<210> 25

<211> 64

<212> DNA

<213> Artificial Sequence

<400> 25

gactgactga ctgactgact gactgactga ctgactgact cctcaaaaac crtcraccac 60

crac 64

<210> 26

<211> 70

<212> DNA

<213> Artificial Sequence

<400> 26

gactgactga ctgactgact gactgactga ctgactgact gactgactat tctttaacct 60

cccttataac 70

<210> 27

<211> 75

<212> DNA

<213> Artificial Sequence

<400> 27

gactgactga ctgactgact gactgactga ctgactgact gactgactga ctgactccac 60

cccacttctc tctaa 75

Claims

1. A combination marker for deducing the age of individuals of the chinese han population, characterized in that said combination marker comprises 8 CpG sites: chr6: 11044628, cg06639320, cg14361627, chr1: 207823723, cg19283806, cg17740900, cg07553761 and cg26947034.

2. The primer composition is characterized by comprising a first primer combination with a nucleotide sequence shown as SEQ ID NO. 1-16 and a second primer combination with a nucleotide sequence shown as SEQ ID NO. 17-32;

The first primer combination is used for carrying out multiplex amplification on 8 CpG sites in the same reaction system, wherein the 8 CpG sites comprise chr6: 11044628, cg06639320, cg14361627, chr1: 207823723, cg19283806, cg17740900, cg07553761 and cg26947034;

The second primer combination is used for carrying out single base extension reaction on the complex amplification product.

3. A kit is characterized in that, comprising the primer composition of claim 2.

4. Use of a combination marker according to claim 1 or a primer composition according to claim 2 for inferring the age of individuals of the chinese han population.

5. A method of inferring the age of individuals in a chinese han population, comprising:

Obtaining methylation data of 8 CpG sites in a blood sample of a Chinese Han healthy individual, and forming a training set according to the methylation data;

Constructing an age inference model according to the training set, wherein the age inference model is a regression model obtained by carrying out regression analysis and training on methylation data of 8 CpG sites of the training set and the actual age of the Chinese Han healthy individual;

Wherein the 8 CpG sites chr6: 11044628, cg06639320, cg14361627, chr1: 207823723, cg19283806, cg17740900, cg07553761 and cg26947034.

6. The method of claim 5, wherein the step of obtaining the methylation rate of 8 CpG sites in a blood sample of a healthy individual of chinese han nationality comprises:

Carrying out single base extension reaction by taking the complex PCR amplification product as a template;

ABI sequencing is carried out on the denatured product, and corresponding CpG sites are sequentially determined according to the colors and the positions of the peaks in the capillary electrophoresis chart corresponding to the denatured product;

7. The method of claim 6, wherein the primer for multiplex PCR amplification comprises a nucleotide sequence as set forth in SEQ ID NO. 1-16 and the primer for single base extension reaction comprises a nucleotide sequence as set forth in SEQ ID NO. 17-32.

8. The method of claim 5, wherein the model is an MLR model, and wherein the regression equation for the MLR model is: age of individual =22.333+39.068×chr6:11044628+13.626×cg06639320–12.096×chr1:207823723–12.417×cg19283806+145.103×cg14361627–0.131×cg17740900+25.946×cg07553761–34.205×cg26947034.

9. A system for inferring the age of individuals in the chinese han population, comprising:

the data acquisition device is used for acquiring methylation data of 8 CpG sites of a blood sample of a Chinese Han healthy individual and forming a training set according to the methylation data;

The age deducing device is connected with the model device and is used for inputting the methylation rate of 8 CpG sites of the Chinese Han nationality individual to be tested into the age deducing model and obtaining the deduced age of the individual.

10. The system of claim 9, wherein the data obtaining means comprises:

a PCR amplification system for performing multiplex PCR amplification and single base extension reaction on the converted DNA sample to obtain a denatured product;

The computing device is connected with the sequencing device and is used for computing the methylation rate of the determined CpG sites according to the colors and the positions of the peaks in the capillary electrophoresis chart, and constructing a training set according to the methylation rate, wherein the training set comprises DNA methylation data of training group samples of Chinese Han population used for training, the Chinese Han population comprises Chinese Han adult males and Chinese Han adult females, and the real ages are distributed between 2 and 82 years.