A method, system and kit for inferring the age of an individual from semen or a semen stain

Abstract

The present application relates to a method and system for inferring the age of an individual from semen or a semen stain, and a methylation detection kit, which comprises the following steps: firstly, extracting the genomic DNA of the semen or semen stain of the individual; secondly, performing bisulfite treatment on the genomic DNA; thirdly, selecting a plurality of sites in the CpG sites in the genomic DNA and amplifying the selected CpG sites to obtain the methylation rate of the selected CpG sites; and finally, performing regression analysis on the methylation rate of the selected CpG sites and the age of the individual to construct a regression model for inferring the age of an unknown individual from semen or a semen stain. The average absolute error of the present application reaches 1.68 to 4.44 years.

Description

A method, system, and kit for inferring an individual's age from semen or semen stains. Technical Field

[0001] This invention relates to the field of biotechnology, and more specifically to a method, system, and methylation detection kit for inferring an individual's age from semen or semen stains. Background Technology

[0002] For nearly two decades, scholars have been searching for molecular markers that can accurately measure age changes. DNA methylation refers to the modification process in which methyl groups (-CH3) are added to certain bases in a DNA sequence, catalyzed by DNA methyltransferases, using S-adenosylmethionine as a methyl donor. In the human genome, the main form of DNA methylation is the addition of a methyl group to the 5th carbon atom of the cytosine residue in a CpG dinucleotide, forming 5-methylcytosine (5mC). These CpG dinucleotides are also known as CpG sites. Previous studies have shown that compared with markers such as advanced glycosylation end products, racemic aspartate, mitochondrial DNA fragment deletions, telomere repeat sequences, signal-binding T cell receptor deletion loops, messenger RNA, and circular RNA, DNA methylation markers have advantages such as high inference accuracy, accurate quantification, good reproducibility, high chemical stability, and wide applicability of samples, and are now recognized as the most promising molecular markers for age estimation.

[0003] Current technologies primarily rely on age-related CpG (AR-CpG) markers to construct regression models for age estimation. To date, numerous age estimation models have been developed in the field of forensic medicine, based on several or even a dozen AR-CpG markers scattered across the genome, various methylation quantification methods (pyrosequencing, matrix-assisted laser desorption / ionization time-of-flight mass spectrometry, methylation SNaPshot, droplet digital PCR, massively parallel sequencing, methylation-specific PCR, methylation-sensitive high-resolution melting curve analysis, methylation-based quantitative PCR, etc.), and different algorithms (multivariate linear regression, multivariate quantile regression, support vector regression, artificial neural networks, random forest regression, etc.). These models can achieve relatively high accuracy in estimating the age of healthy individuals—the mean absolute error (MAE) varies between 2.0 and 8.0 years depending on the AR-CpG markers, sample size, and age range. In terms of sample type, these models are mostly designed for somatic tissues such as blood, saliva, or oral swabs, with very few targeting semen or sperm stains.

[0004] Mounting evidence suggests that DNA methylation exhibits tissue-specific variability, rendering age estimation models based on somatic tissue analysis unsuitable for semen or sperm stains. In 2013, Horvath developed a multi-tissue age estimation model with a median error of 2.9 years, applicable to 51 different tissue or cell types, based on 39 Illumina DNA methylation array datasets. However, this model failed to apply to sperm samples, estimating an age significantly lower than the individual's actual age. Similarly, in 2015, Lee et al., using Illumina HumanMethylation450 array data from 12 blood, 12 saliva, and 12 semen samples, examined the accuracy of Horvath's and Weidner's models, finding MAE values ​​of 13.3 and 37.3 years respectively for semen samples, indicating that neither model was suitable for estimating individual age from semen samples. Therefore, it is necessary to develop age estimation methods and systems specifically for semen or sperm stains.

[0005] Outside the field of forensic medicine, Jenkins et al. (2018) constructed a generalized linear model to infer an individual's age using the average methylation rate of 51 genomic regions based on Illumina HumanMethylation450 array data from 329 semen samples. These genomic regions contain 3 to 15 CpG sites. The Jenkins model achieved MAEs of 2.04 years and 2.37 years on the training and test sets, respectively, and accurately predicted the actual age of the sperm donor regardless of fertility status. However, to use this model, we must obtain the methylation rate of 267 CpG sites located in the 51 regions using either the Illumina HumanMethylation450 or MethylationEPIC array. However, methylation array analysis is expensive and has high requirements for DNA templates (MethylationEPIC > 250 ng, HumanMethylation450 > 500 ng), which does not meet the application standards of public security practice. More importantly, Nwanaji-Enwerem et al. (2020) found that the Jenkins model performed poorly on another set of methylation array data: the MAE was 33.8 years, indicating that the accuracy of the Jenkins model is still questionable.

[0006] Developing a high-precision, low-cost, low-sample-requirement, rapid, and user-friendly age estimation method is the starting point for forensic individual age estimation research. Previous development of methods and systems for age estimation using semen or semen stains generally followed a process of AR-CpG marker identification, AR-CpG marker validation, and regression model construction. In 2015, Lee et al. developed the first age estimation method and system applicable to semen or semen stains. This research group first identified 106 AR-CpG markers using Illumina HumanMethylation450 array data from 12 semen samples taken from healthy Koreans (29–59 years old). 2AR-CpG markers with a value >0.7 were identified, and 24 of these markers were validated in 31 semen samples (aged 24–67 years) using SNaPshot microsequencing. A multiple linear regression model incorporating three AR-CpG markers (cg06304190-TTC7B; cg06979108-NOX4 and cg12837463) was then established. The model achieved a mean age estimation (MAE) of 5.4 years in an independent test set (n=37, aged 20–73 years). In 2018, Lee et al. further evaluated the model's predictive accuracy (MAE = 4.8 years) in another independent test set (n=12), the sensitivity of the methylated SNaPshot method (>5 ng bisulfite-converted DNA), and the applicability of the age estimation method in real-world forensic cases. They concluded that the previously developed method and system can be used to accurately estimate the age of the individuals who provided the semen or sperm stain samples. In 2019, Li et al. discovered that cg06304190 and cg12837463 were also applicable to age estimation from semen or sperm stain samples of Han Chinese men. This research group constructed a multiple linear regression model using pyrosequencing data from 38 semen samples (21–54 years old). The MAE values ​​for 17 semen samples (21–46 years old), 17 fresh sperm stains (21–46 years old), 17 old sperm stains (stored at room temperature for one month, 21–46 years old), and 17 mixed semen-vaginal secretion samples were 4.129 years, 4.158 years, 4.390 years, and 3.880 years, respectively. In 2021, to discover high-performance semen AR-CpG markers, the VISAGE consortium identified new semen AR-CpG markers using Infinium MethylationEPIC chip data from 40 semen samples from healthy men aged 24–58 years. They validated 10 high-performance markers and 3 AR-CpG markers reported by Lee et al. (2015) using targeted massively parallel sequencing technology. The team ultimately established a multiple linear regression model, including 5 AR-CpG markers located in 4 novel genes (SH2B2, EXOC3, IFITM2, and GALR2) and cg06979108 in the NOX4 gene. Even so, the MAE of the VISAGE model in the independent test set (n=54, 26–57 years old) was still greater than 5 years (MAE=5.1 years). In conclusion, the accuracy of existing methods and systems for age estimation based on semen or sperm stains in the forensic field is low and not comparable to those based on blood or saliva.

[0007] On the other hand, Chinese invention patent CN104357561A discloses "A Method and System for Obtaining the Age of Female Individuals in the Chinese Population." This method extracts DNA from female individuals, obtains the methylation rate of 11 CpG sites, and performs regression analysis on the methylation rate of these 11 CpG sites with age to construct a regression model for inferring the age of female individuals in the Chinese population. Chinese invention patent CN109593862A discloses "A Method and System for Obtaining the Age of Male Individuals in the Chinese Population." This method extracts DNA from male individuals, obtains the methylation rate of 9 CpG sites, and performs regression analysis on the methylation rate of these 9 CpG sites with age to construct a regression model for inferring the age of male individuals in the Chinese population. Chinese invention patent CN110257494A discloses a method, system, and amplification detection system for obtaining the age of an individual in the Chinese population. This method extracts DNA from individuals in the Chinese population, obtains the methylation rate of a set of CpG sites, and performs regression analysis on the methylation rate of the corresponding CpG sites against age to construct a regression model for inferring the age of individuals in the Chinese population. However, these invention patents are not applicable to inferring the age of individuals from semen or sperm stain samples.

[0008] In summary, there is currently a lack of a method and system that can accurately infer an individual's age from semen or semen stains and is applicable to forensic medicine. Summary of the Invention

[0009] This invention addresses the technical problems existing in the prior art by providing a method, system, and methylation detection kit for inferring an individual's age from semen or semen stains. The method involves extracting genomic DNA from an individual's semen or semen stains, selecting multiple CpG sites within the genomic DNA, amplifying the selected CpG sites, obtaining the methylation rate of the selected CpG sites, performing regression analysis on the methylation rate of the CpG sites and the individual's age to construct a regression model, and then using the regression model to infer the age of an individual of unknown origin from semen or semen stains, thus achieving the goal of inferring an individual's age using semen or semen stain samples.

[0010] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0011] In a first aspect, embodiments of the present invention provide a method for inferring an individual's age from semen or semen stains, comprising the following steps:

[0012] S1, Extract genomic DNA from individual semen or semen stains;

[0013] S2, the genomic DNA is treated with bisulfite;

[0014] S3, select multiple CpG sites in the genomic DNA and amplify the selected CpG sites to obtain the methylation rate of the selected CpG sites. The CpG sites include cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, cg03634854, ...18037145, cg19983027, cg g03030301, cg04119405, cg25715498, cg06304190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357 and cg24812634;

[0015] S4. Regression analysis is performed on the methylation rate of the selected CpG sites and the age of the individual to construct a regression model for inferring the age of an unknown originator from semen or semen stains.

[0016] Furthermore, step S3 also includes: after obtaining the transformed DNA template treated with bisulfite, amplifying it using amplification primers corresponding to the CpG site to obtain amplification products.

[0017] Furthermore, step S3 also includes: after obtaining the amplification product, performing SNaPshot microsequencing on the amplification product using single-base extension primers corresponding to the CpG site to obtain the methylation rate of the CpG site.

[0018] Furthermore, the multiple sites selected in step S3 include: cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, cg03634854, cg03030301, cg04119405, and cg25715498, a total of 11 CpG sites, which constitute combination one; the amplification product is the nucleotide sequence of SEQ ID NO:1 to SEQ ID NO:22 in the sequence listing, and the single base extension primer is the nucleotide sequence of SEQ ID NO:45 to SEQ ID NO:55 in the sequence listing.

[0019] Furthermore, the regression model corresponding to combination one is as follows:

[0020] Age = -14.969 - 20.699 × β cg01789162 -32.257×β cg03030301 +3.134×βcg03634854 +24.141×β cg04119405 +31.017×β cg11262154 +42.534×β cg18037145 +13.292×β cg19983027 +47.382×β cg19998819 -38.220×β cg25715498 +8.003×β cg27111970 -16.215×β cg27231587 ;

[0021] Where Age represents the estimated age of the unknown originator, β cg01789162 β cg03030301 β cg03634854 β cg04119405 β cg11262154 β cg18037145 β cg19983027 β cg19998819 β cg25715498 β cg27111970 and β cg27231587 These represent the methylation rates at the corresponding CpG sites.

[0022] Furthermore, the multiple sites selected in step S3 include: cg06304190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357, and cg24812634, a total of 11 CpG sites, which constitute combination two; the amplification product is the nucleotide sequence of SEQ ID NO:23 to SEQ ID NO:44 in the sequence listing, and the single base extension primer is the nucleotide sequence of SEQ ID NO:56 to SEQ ID NO:66 in the sequence listing.

[0023] Furthermore, the regression model corresponding to the second combination is as follows:

[0024] Age = 8.254 + 14.037 × β cg04123357 -14.876×β cg06304190 +7.248×β cg06979108 -3.911×β cg12277678 -10.694×β cg12837463 -19.653×β cg13872326 +17.186×β cg20602007 -1.635×β cg20828122 +60.258×β cg21843517 +21.996×β cg24812634 -3.883×βcg25187042 ;

[0025] Where Age represents the estimated age of the unknown originator, β cg04123357 β cg06304190 β cg06979108 β cg12277678 β cg12837463 β cg13872326 β cg20602007 β cg20828122 β cg21843517 β cg24812634 and β cg25187042 These represent the methylation rates at the corresponding CpG sites.

[0026] Furthermore, the multiple sites selected in step S3 include: cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, cg03634854, cg03030301, cg04119405, cg25715498, and cg0630419. The amplification product consists of 22 CpG sites: cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357, and cg24812634. These 22 CpG sites constitute combination three. The amplification product is the nucleotide sequence of SEQ ID NO:1 to SEQ ID NO:44 in the sequence listing, and the single-base extension primer is the nucleotide sequence of SEQ ID NO:45 to SEQ ID NO:66 in the sequence listing.

[0027] Furthermore, the regression model corresponding to combination three is as follows:

[0028] Age = 17.7808 - 2.7234 × β cg01789162 -15.3408×β cg03030301 +18.9289×β cg03634854 +24.1326×β cg04119405 +3.0025×β cg04123357 -12.4580×β cg06304190 +4.7711×β cg06979108 +17.8788×β cg11262154 -0.5972×β cg12277678 -11.9590×β cg12837463 -21.8391×β cg13872326 +18.7880×β cg18037145+10.1366×β cg19983027 +10.7987×β cg19998819 -14.5981×β cg20602007 +4.2982×β cg20828122 +35.6638×β cg21843517 +2.4494×β cg24812634 -2.8791×β cg25187042 -31.9334×β cg25715498 -1.8743×β cg27111970 -10.2882×β cg27231587 ;

[0029] Where Age represents the estimated age of the unknown originator, β cg01789162 β cg03030301 β cg03634854 β cg04119405 β cg04123357 β cg06304190 β cg06979108 β cg11262154 β cg12277678 β cg12837463 β cg13872326 β cg18037145 β cg19983027 β cg19998819 β cg20602007 β cg20828122 β cg21843517 β cg24812634 β cg25187042 β cg25715498 β cg27111970 and β cg27231587 These represent the methylation rates at the corresponding CpG sites.

[0030] It should be understood that when selecting CpG sites in the genomic DNA, a regression model can be constructed according to the combination selected in this invention to perform regression analysis and infer the age of the unknown originator. Alternatively, different numbers and combinations of sites can be selected for the above analysis according to the actual situation, i.e., not limited to 11 sites or 22 sites.

[0031] It should also be understood that the regression model used in the embodiments of the present invention is a multiple linear regression model. However, those skilled in the art can choose algorithms such as multiple quantile regression, support vector regression, artificial neural network, and random forest regression to construct a regression model according to actual needs, provided that the MAE is within the target range.

[0032] According to a specific embodiment of the present invention, the above regression model was used to infer the age of semen samples from 253 Han Chinese male individuals (aged 22.00–67.19 years).

[0033] For CpG site combination one, the R-value of the regression model after 5 10-fold cross-validations is... 2 The mean age ranged from 0.6044 to 0.9247 (mean 0.8133, standard deviation 0.0674), the average age ranged from 2.4415 years to 5.7313 years (mean 4.2420 years, standard deviation 0.7315 years), and the mean age ranged from 1.9560 years to 4.2866 years (mean 3.1947 years, standard deviation 0.5578 years).

[0034] For CpG site combination two, the R-value of the regression model after 5 10-fold cross-validation is... 2 The mean age ranged from 0.6795 to 0.9526 (mean 0.8338, standard deviation 0.0542), the average age ranged from 2.9452 years to 5.4641 years (mean 4.0377 years, standard deviation 0.6564 years), and the mean age ranged from 2.3755 years to 4.4424 years (mean 3.2197 years, standard deviation 0.5396 years).

[0035] For CpG site combination three, the R-value of the regression model after 5 10-fold cross-validation is... 2 The mean ranged from 0.6802 to 0.9770 (mean 0.8823, standard deviation 0.0567), the average age ranged from 1.9640 years to 4.8460 years (mean 3.3113 years, standard deviation 0.6308 years), and the mean age ranged from 1.6818 years to 3.5953 years (mean 2.6742 years, standard deviation 0.4501 years).

[0036] Where R 2The coefficient of determination (COD) measures the goodness of fit of the model; a higher value indicates a better fit. RMSE (Root Mean Square Error) represents the square root of the ratio of the sum of squares of the differences between inferred and actual ages in the sample set to the sample size. It measures the deviation between inferred and actual ages; a lower value indicates higher accuracy and less susceptibility to outliers compared to MAE. MAE (Mean Absolute Error) represents the average of the absolute differences between inferred and actual ages in the sample set. It measures the deviation between inferred and actual ages; a lower value indicates higher accuracy. Compared to a single sample split, k-fold crossover... Cross-validation can obtain more accurate model evaluation parameters. Specifically, 10 (k=10) fold cross-validation includes the following steps: First, the total sample is stratified by age into 10 folds (or sample sets) with similar sample sizes. Then, the sample from the i-th (i=1,2,3,…,10) fold is taken as the test sample, and the remaining 9 fold samples are used as training samples to build the model. Next, the model is validated and various parameters are calculated using the test samples. Finally, the average of the parameters of the 10 models is used as the final parameter. After 5 rounds of 10-fold cross-validation, the mean ± standard deviation can be calculated using 50 parameters, thus better measuring the accuracy of the model.

[0037] According to a specific embodiment of the present invention, the above-described regression model can accurately infer the actual age of an individual of unknown origin from semen or semen stains. Specifically, the regression model can infer the age of individuals aged 22.00 to 67.19 years, with an average age of 1.68 to 4.44 years.

[0038] In step S1, the extraction of genomic DNA is a routine technical operation in the art. Those skilled in the art can choose a suitable DNA extraction method or a commercial kit to complete the extraction of genomic DNA from the semen or sperm stain, as long as the genomic DNA can meet the requirements of subsequent methylation analysis. According to a specific embodiment of the present invention, it is best to use differential lysis method to extract sperm-derived genomic DNA.

[0039] In step S2, the bisulfite treatment is a routine technical operation in the art. Those skilled in the art can choose a suitable bisulfite treatment method or a commercial kit to complete the transformation of the genomic DNA, as long as the transformed DNA can meet the requirements of subsequent methylation analysis.

[0040] The SNaPshot microsequencing method is a conventional method in the art for obtaining the methylation rate of CpG sites. Its usage is known in the art, and it is feasible for those skilled in the art to implement this method.

[0041] In a second aspect, embodiments of the present invention provide a system for inferring an individual's age from semen or semen stains, comprising:

[0042] DNA extraction system for extracting genomic DNA from individual semen or sperm stains;

[0043] A bisulfite treatment system for treating the genomic DNA with bisulfite;

[0044] An amplification detection system is used to select multiple CpG sites in the genomic DNA and amplify the selected CpG sites to obtain the methylation rate of the selected CpG sites. The CpG sites include cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, and cg0363485. 4. cg03030301, cg04119405, cg25715498, cg06304190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357 and cg24812634;

[0045] The data acquisition system uses software to perform regression analysis on the methylation rate of selected CpG sites and the age of the individuals, and constructs a regression model to infer the age of individuals of unknown origin from semen or semen stains.

[0046] The software described can be any conventional software used for regression model construction in the art, and its usage method is known in the art. Implementing this method is feasible for those skilled in the art. Those skilled in the art can use this software to construct other regression models as needed, as long as the constructed regression model can meet the accuracy requirements for age inference.

[0047] Furthermore, the amplification detection system uses amplification primers corresponding to the CpG site to amplify the CpG site to obtain amplification products.

[0048] Furthermore, in the amplification detection system, the amplification product is subjected to SNaPshot microsequencing using single-base extension primers corresponding to the CpG site to obtain the methylation rate of the CpG site.

[0049] The amplification primers consist of 22 pairs of primers for amplifying the 22 CpG sites, and the amplification primers are the nucleotide sequences of SEQ ID NO:1 to SEQ ID NO:44 in the sequence listing;

[0050] The single-base extension primers consist of 22 primers used to obtain the methylation rate of the 22 CpG sites by SNaPshot sequencing, and the single-base extension primers are the nucleotide sequences of SEQ ID NO:45 to SEQ ID NO:66 in the sequence listing.

[0051] Thirdly, embodiments of the present invention provide a methylation detection kit, which is used to amplify and detect CpG sites in genomic DNA to obtain the methylation rate of the corresponding CpG sites; the genomic DNA is genomic DNA extracted from individual semen or sperm stains, and the CpG sites include cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, and cg19983027. cg27111970, cg03634854, cg03030301, cg04119405, cg25715498, cg06304190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357 and cg24812634.

[0052] The basic information of the 22 CpG sites is shown in Table 1:

[0053] Table 1. 22 CpG loci used to infer an individual's age from semen or sperm stains.

[0054] CpG locus genome version chromosome number location gene cg01789162GRCh37 / hg191167170158PPP1CAcg11262154GRCh37 / hg19124644363C12orf4cg19998819GRCh37 / hg192129829458—cg27231587GRCh37 / hg198115399619—cg18037145GRCh37 / hg191333420376LINC00423cg1 9983027GRCh37 / hg191918897875COMPcg27111970GRCh37 / hg195139492957PURAcg03634854GRCh37 / hg191918721531— cg03030301GRCh37 / hg19855015043LYPLA1cg04119405GRCh37 / hg191184096211DLG2cg25715498GRCh37 / hg1911604929 91SLAMF6cg06304190GRCh37 / hg191491283606TTC7Bcg06979108GRCh37 / hg191189322851NOX4cg12837463GRCh37 / hg1 9735300228—cg20828122GRCh37 / hg19171172964BHLHA9cg12277678GRCh37 / hg19104426258LINC00703cg13872326GRCh 37 / hg191727901067GIT1cg20602007GRCh37 / hg192040808637PTPRTcg25187042GRCh37 / hg191665867976—cg21843517 GRCh37 / hg1912123950174SNRNP35cg04123357GRCh37 / hg192146929500COL18A1cg24812634GRCh37 / hg1912114919142— surface

[0055] The 22 pairs of amplification primers and 22 single-base extension primers described in this invention were all designed using PyroMark Assay Design 2.0 software. The sequences of the amplification primers and single-base extension primers and their corresponding CpG sites are shown in Table 2, where F represents the forward primer, R represents the reverse primer, and S represents the single-base extension primer.

[0056] Table 2. Amplification primers and single-base extension primers for 22 CpG sites.

[0057]

[0058]

[0059] The beneficial effects of this invention are: 1. Compared with the prior art, it can infer the actual age of an unknown person from semen or semen stain samples with higher accuracy, and has the characteristics of being user-friendly, low-cost, simple and fast, making it suitable for widespread application.

[0060] 2. Of the 22 CpG sites involved in this invention, apart from the previously reported cg06304190, cg06979108, and cg12837463, the remaining 19 CpG sites were obtained by the inventors based on the methylation rate of approximately 853,307 CpG sites from 90 sperm DNA samples (the subjects' ages ranged from 22 to 51 years). These sites are sperm-specific and, compared to existing technologies that rely on selecting and verifying age-related CpG markers based on semen sample DNA methylation data, are not affected by DNA methylation of somatic cells (such as leukocytes) in semen. This allows for a more accurate estimation of the actual age, which is the fundamental reason why the method and system can infer the actual age of the subject from semen or sperm stain samples with higher precision.

[0061] 3. The regression model constructed from the CpG site combination, with R² obtained from 5 rounds of 10-fold cross-validation. 2 The mean age ranged from 0.6044 to 0.9247 (mean 0.8133, standard deviation 0.0674), the mean age ranged from 2.4415 years to 5.7313 years (mean 4.2420 years, standard deviation 0.7315 years), and the mean age ranged from 1.9560 years to 4.2866 years (mean 3.1947 years, standard deviation 0.5578 years). This indicates that the estimated age is very close to the actual age of the individuals, further demonstrating that the method and system provided by this invention can accurately infer the actual age of the source from semen or semen stains.

[0062] 4. The regression model constructed from the CpG site combination two, with R² obtained from 5 rounds of 10-fold cross-validation. 2The mean age ranged from 0.6795 to 0.9526 (mean 0.8338, standard deviation 0.0542), the mean age ranged from 2.9452 years to 5.4641 years (mean 4.0377 years, standard deviation 0.6564 years), and the mean age ranged from 2.3755 years to 4.4424 years (mean 3.2197 years, standard deviation 0.5396 years). This indicates that the estimated age is very close to the actual age of the individuals, further demonstrating that the method and system provided by this invention can accurately infer the actual age of the source from semen or semen stains.

[0063] 5. The regression model constructed from the aforementioned CpG site combination three, with R² obtained through five 10-fold cross-validation trials. 2 The mean values ​​ranged from 0.6802 to 0.9770 (mean 0.8823, standard deviation 0.0567), the RMSE ranged from 1.9640 years to 4.8460 years (mean 3.3113 years, standard deviation 0.6308 years), and the MAE ranged from 1.6818 years to 3.5953 years (mean 2.6742 years, standard deviation 0.4501 years). This indicates that the estimated age is very close to the actual age of the individuals, further demonstrating that the method and system provided by this invention can accurately infer the actual age of the source from semen or semen stains.

[0064] 6. All three regression model schemes can accurately infer the actual age of the source from semen or semen stains. Specifically, they can infer the age of individuals aged 22.00 to 67.19 years, with an MAE of 1.68 to 4.44 years. Attached Figure Description

[0065] Figure 1 is a schematic flowchart of a method for inferring an individual's age from semen or semen stains according to an embodiment of the present invention;

[0066] Figure 2 is a schematic diagram of a system structure for inferring an individual's age from semen or semen stains according to an embodiment of the present invention;

[0067] Figure 3 is a heatmap for somatic cell contamination assessment;

[0068] Figure 4 is a scatter plot of the model applicability assessment;

[0069] Figure 5 shows a representative map of SNaPshot microsequencing. Detailed Implementation

[0070] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0071] In the description of this application, "a plurality of" means two or more, unless otherwise expressly and specifically defined. In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0072] This invention provides a method for inferring an individual's age from semen or semen stains, as shown in Figure 1, including the following steps:

[0073] S1, Extract genomic DNA from individual semen or semen stains;

[0074] S2, the genomic DNA is treated with bisulfite;

[0075] S3, select multiple CpG sites in the genomic DNA and amplify the selected CpG sites to obtain the methylation rate of the selected CpG sites. The CpG sites include cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, cg03634854, ...18037145, cg19983027, cg g03030301, cg04119405, cg25715498, cg06304190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357 and cg24812634;

[0076] S4. Regression analysis is performed on the methylation rate of the selected CpG sites and the age of the individual to construct a regression model for inferring the age of an unknown originator from semen or semen stains.

[0077] Preferably, after obtaining the transformed DNA template treated with bisulfite, it is amplified using amplification primers corresponding to the CpG sites to obtain amplification products. After obtaining the amplification products, SNaPshot microsequencing is performed on the amplification products using single-base extension primers corresponding to the CpG sites to obtain the methylation rate of the CpG sites.

[0078] Based on the above methods, embodiments of the present invention also provide a system for inferring an individual's age from semen or semen stains and a methylation detection kit, wherein the system for inferring an individual's age from semen or semen stains, as shown in Figure 2, includes:

[0079] DNA extraction system for extracting genomic DNA from individual semen or sperm stains;

[0080] A bisulfite treatment system for treating the genomic DNA with bisulfite;

[0081] An amplification detection system is used to select multiple CpG sites in the genomic DNA and amplify the selected CpG sites to obtain the methylation rate of the selected CpG sites. The CpG sites include cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, and cg0363485. 4. cg03030301, cg04119405, cg25715498, cg06304190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357 and cg24812634;

[0082] The data acquisition system uses software to perform regression analysis on the methylation rate of selected CpG sites and the age of the individuals, and constructs a regression model to infer the age of individuals of unknown origin from semen or semen stains.

[0083] The software described can be any conventional software used for regression model construction in the art, and its usage method is known in the art. Implementing this method is feasible for those skilled in the art. Those skilled in the art can use this software to construct other regression models as needed, as long as the constructed regression model can meet the accuracy requirements for age inference.

[0084] Furthermore, the amplification detection system uses amplification primers corresponding to the CpG site to amplify the CpG site to obtain amplification products.

[0085] Furthermore, in the amplification detection system, the amplification product is subjected to SNaPshot microsequencing using single-base extension primers corresponding to the CpG site to obtain the methylation rate of the CpG site.

[0086] The amplification primers consist of 22 pairs of primers for amplifying the 22 CpG sites, and the amplification primers are the nucleotide sequences of SEQ ID NO:1 to SEQ ID NO:44 in the sequence listing;

[0087] The single-base extension primers consist of 22 primers used to obtain the methylation rate of the 22 CpG sites by SNaPshot sequencing, and the single-base extension primers are the nucleotide sequences of SEQ ID NO:45 to SEQ ID NO:66 in the sequence listing.

[0088] The methylation detection kit is used to amplify and detect CpG sites in genomic DNA to obtain the methylation rate of the corresponding CpG sites; the genomic DNA is extracted from individual semen or sperm stains, and the CpG sites include cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, ... g03634854, cg03030301, cg04119405, cg25715498, cg06304190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357 and cg24812634.

[0089] The above embodiments will be further explained below with specific examples.

[0090] Example 1 – Identification of sperm-specific age-related CpG markers

[0091] 1 Dataset

[0092] A publicly available dataset (accession number: GSE149318) from the Gene Expression Omnibus database was used to identify sperm-specific age-related CpG (AR-CpG) markers. This dataset contains Illumina Methylation EPIC methylation data from sperm samples and paired whole blood samples from 90 men aged 22–51 years.

[0093] 2. Assessment of somatic cell contamination

[0094] The 14 CpG sites in the DLK1 gene show significant differences in methylation between sperm cells and somatic cells, which can be used to assess somatic cell contamination. Using 90 whole blood samples from dataset GSE149318 and 12 whole blood and 12 semen samples from dataset GSE59509 as controls, the results showed that somatic cell contamination was present in the 12 semen samples, while almost no somatic cell contamination was found in the 90 sperm samples (Figure 5).

[0095] 3. Model Applicability Assessment

[0096] Three models—Horvath Pan-Tissue Clock, Horvath Skin and Blood Clock, and JenkinsGerm Line Age Calculator—were used to infer the actual ages of the subjects from 90 whole blood samples and 90 sperm samples in dataset GSE149318. The results showed that the Horvath Pan-Tissue Clock and Horvath Skin and Blood Clock models, built based on non-sperm or semen samples, could not accurately infer the actual ages of the subjects from sperm samples. While the JenkinsGerm Line Age Calculator model, built based on semen samples, performed slightly better than the other two models, its inference accuracy was still low, indicating that age inference from semen or sperm samples still requires dedicated AR-CpG labeling (Figure 4).

[0097] 4. Identification of sperm-specific AR-CpG markers

[0098] First, the `outyx` function in the R package `wateRmelon` was used to detect outliers in 90 sperm samples. Then, the R package `ChAMP` was used to filter low-quality probes and samples: (i) probes with a detection p-value > 0.01, (ii) non-CpG probes, (iii) probes containing single nucleotide polymorphisms that affect quantification, (iv) multi-target probes, (v) probes located on the X and Y chromosomes, and samples with more than 1% low-quality probes (detection p-value > 0.01). Next, the β values ​​of the remaining probes were normalized using the BMIQ method. Finally, Pearson correlation analysis was performed on the methylation rate of each CpG site and individual age. Sperm-specific AR-CpG markers were obtained using Pearson's R > 0.5 or < -0.5 and Benjamini-Hochberg corrected p-value < 0.05 as thresholds (Table 3). The top 20 CpG sites with the highest absolute correlation coefficients were selected as candidate AR-CpG markers, and AR-CpG sites with β values ​​less than 0.01 were removed for subsequent validation.

[0099] Table 3. Basic information on 31 sperm-specific AR-CpG markers

[0100]

[0101]

[0102] Example 2 – Validation of Candidate AR-CpG Labels

[0103] 1 sample

[0104] In accordance with the principle of informed consent, 2 ml of ejaculate samples were collected from each of 253 healthy male individuals (aged 22.00–67.19 years), and their actual ages were recorded. Actual age was calculated as the number of days between the sample collection date and the birth date recorded on the ID card, divided by 365, and rounded to two decimal places.

[0105] 2-methylation detection

[0106] (1) DNA extraction and quantification: After differential lysis, sperm-derived genomic DNA was extracted from the semen samples using the QIAamp DNA Mini kit (QIAGEN, Germany). The specific steps are as follows: (i) Add 200 μL of semen to a 1.5 mL centrifuge tube; (ii) Add 1 mL of buffer 1 (150 mM NaCl, 10 mM... (ii) Add EDTA (pH=8.0), vortex at full speed for 10 seconds, then centrifuge at 4000 rpm for 5 minutes. Carefully remove the supernatant, leaving approximately 100 μL of precipitate and buffer 1; (iii) Repeat step (ii) once; (iv) Add 190 μL of buffer G2 (QIAGEN) and 20 μL of proteinase K (Tiangen), vortex at full speed for 10 seconds, and incubate at 56°C with shaking at 500 rpm for 90 minutes; (v) Centrifuge at 14000 rpm for 5 minutes, and carefully remove the supernatant; (vi) Add 500 μL of buffer G2, vortex at full speed for 10 seconds, then centrifuge at 14000 rpm for 5 minutes, and carefully remove the supernatant; (vii) Repeat step (vi) twice; (vii) Add 160 μL of buffer G2, 200 μL of buffer solution AL, 20 μL of proteinase K (QIAGEN), and 40 μL of 1M. DTT, vortex for 15 seconds, and incubate at 70°C with shaking at 850 rpm for at least 30 minutes, or first add 160 μL buffer G2, 20 μL proteinase K (QIAGEN) and 40 μL 1M DTT, vortex for 15 seconds, incubate at 56°C for 1 hour, then add 200 μL buffer solution AL, vortex, and incubate at 70°C with shaking at 850 rpm for 10 minutes; (ⅸ) Continue from step 6 of the Tissue Protocol in the QIAamp DNA Mini and Blood MiniHandbook to complete the extraction of genomic DNA.

[0107] 2 μL of genomic DNA was quantified using a Nanodrop 2000 micro-volume spectrophotometer. Subsequently, 0.5 g of agarose powder (Takara), 50 mL of 0.5×TBE buffer (self-prepared), and 5 μL of 4S Red Plus Nucleic AcidStain (Sangon Biotech) were used to prepare a 1% agarose gel. The 1 μL of genomic DNA was then subjected to quality control by agarose gel electrophoresis (1% w / v, 110 v, 30 min).

[0108] (2) Bisulfite treatment: 500 ng of genomic DNA was treated with bisulfite using the EpiTectFast DNABisulfite Kit (QIAGEN, Germany) and eluted with 20 μL BufferEB.

[0109] (3) Primer Design and Synthesis: Amplification primers (F and R) and single-base extension primers (S) were designed using PyroMarkAssay Design 2.0 software (QIAGEN, Germany). All primers were synthesized by Sangon Biotech (Shanghai) Co., Ltd. Amplification primers were purified by ULTRAPAGE, and single-base extension primers were purified by HPLC. The following steps were followed to prepare the stock solution and working solution: Before opening the tube, centrifuge at 4000 rpm for 1 minute; then slowly open the tube cap and add an appropriate amount of enzyme-free water or TE buffer (pH = 8.0, low EDTA) to prepare 100 μM stock solution; then cap the tube again, shake thoroughly to mix, and incubate at room temperature for 1 hour to ensure complete primer dissolution; after brief centrifugation, prepare 10× amplification primer working solution according to the final primer concentrations listed in Table 4, and prepare 5× single-base extension primer working solution according to the final primer concentrations listed in Table 5, for use in multiplex PCR amplification and single-base extension reactions, respectively. The working solutions were aliquoted and stored at -20℃ for later use, with no more than three freeze-thaw cycles. The amplification primers and single-base extension primer sequences of the AR-CpG marker are shown in Table 2. For comparison, three CpG markers (cg06304190, cg06979108 and cg12837463) reported by Lee et al. (2015) were also included for validation.

[0110] Table 4 Final concentrations of primers in the multiplex amplification system

[0111]

[0112]

[0113] Table 5. Final concentrations of single-base extension primers in the SNaPshot microsequencing system

[0114]

[0115] (4) Multiplex PCR amplification: Multiplex PCR amplification was performed using a QIAGEN Multiplex PCR Kit (QIAGEN, Germany) in a 20 μL reaction volume. Two multiplex amplification systems were performed separately. First, the reaction mixture was prepared according to the proportions of the components in Table 6, then aliquoted into PCR tubes or 96-well plates, and finally 1 μL of transforming DNA was added. The PCR tubes were placed on a Thermo Fisher Scientific 2720 gene amplification instrument (USA), and PCR amplification was performed according to the parameters listed in Table 7. A negative control without DNA template was included in each batch of reaction to determine the presence of contamination.

[0116] Table 6. Composite PCR amplification system

[0117]

[0118] Table 7 PCR reaction parameters

[0119]

[0120] (5) Agarose electrophoresis (optional): Take 5 μL of amplification product and perform quality control on the amplification product by agarose electrophoresis (1.5% w / v, 110v, 45 minutes) to ensure that the amplification is successful and there is no contamination.

[0121] (6) Pre-SBE purification: Prepare the reaction mixture according to Table 8, aliquot it into 0.2 mL amplification tubes or 96-well plate wells, and finally add 3 μL of the complex amplification product. Centrifuge. Perform purification on an ABI 2720 thermal cycler according to the parameters listed in Table 9 to remove primers, single-stranded DNA, and single nucleotides from the amplification product. The purified product can be stored overnight at 4°C or long-term at -20°C.

[0122] Table 8 Purification system before single base extension (pre-SBE)

[0123]

[0124] Table 9 Purification reaction parameters

[0125] Serial Number Step Number of Cycles Temperature Time 1 Purification 137℃ 60 min 2 Enzyme Inactivation 180℃ 20 min 3 Hold 14℃ ∞ surface

[0126] (7) Single-base extension: The single-base extension reaction was performed using the SNaPshot Multiplex Kit (Thermo Fisher Scientific, USA). The reaction mixture was prepared according to Table 10, aliquoted into 0.2 mL amplification tubes or 96-well plate wells, and finally 1 μL of purified product was added and centrifuged. Single-base extension was performed using an ABI 2720 thermal cycler according to the parameters listed in Table 11. The single-base extension product can be stored overnight at 4°C or for long-term storage at -20°C.

[0127] Table 10 Single-base extension system

[0128]

[0129] Table 11 Parameters of Single Base Extension Reaction

[0130]

[0131] (8) Post-SBE purification: After single-base extension, add 1 μL of 1 U / μL shrimp alkaline phosphatase (SAP) to each amplification tube or 96-well plate reaction well and centrifuge. Perform post-SBE purification on an ABI 2720 thermal cycler according to the parameters listed in Table 9. The purified product can be stored overnight at 4°C or long-term at -20°C.

[0132] (9) Electrophoresis detection: Take a clean 96-well plate and add 9 μL of a mixture of deionized formamide (Thermo Fisher Scientific, USA) and LIZ-120 (Thermo Fisher Scientific, USA) (1:99) to each well, followed by 1 μL of post-SBE purified product. Perform electrophoresis detection on an Applied Biosystems 3130 Genetic Analyzer according to the operating manual, with the injection time set to 5 seconds. Perform data analysis using GeneMapper Software v3.2 according to the operating manual to obtain electrophoretic patterns.

[0133] 3. Data Analysis

[0134] The methylation rate of each CpG site is defined as the ratio of the peak height of G to the sum of the peak heights of G and A, or the ratio of the peak height of C to the sum of the peak heights of C and T. Bisulfite conversion is equal to 1 minus the methylation level of the BC sites. Samples with a bisulfite conversion rate >0.97 are considered to meet the quality control requirements.

[0135] The Pearson correlation coefficient and p-value between the methylation rate of each CpG site and the actual age of 253 samples were calculated using the `cor.test` function in R software. The absolute values ​​of the correlation coefficients were defined as follows: 0.0–0.2 indicated very weak or no correlation; 0.2–0.4 indicated a weak correlation; 0.4–0.6 indicated a moderate correlation; 0.6–0.8 indicated a strong correlation; and 0.8–1.0 indicated a very strong correlation.

[0136] 4 Results

[0137] A system was successfully constructed for detecting the methylation rate of 22 CpG sites (Figure 5). The methylation rate of the 22 candidate AR-CpG markers was correlated with age, but the strength of the correlation varied (data not shown).

[0138] Example 3 – Construction of a Multiple Linear Regression Model

[0139] 1. Data Analysis

[0140] Based on the methylation data of 22 CpG sites in 253 sperm-derived genomic DNA samples (aged 22.00–67.19 years) obtained in Example 2, the CpG site combination I (i.e., panel I, including cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, cg03634854, cg03030) was used. 301, cg04119405, and cg25715498 (a total of 11 CpG sites), and CpG site combination two (i.e., panel II, including cg06304190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357, and cg24). 812634 contains 11 CpG sites), and CpG site combination three (i.e., panel I + panel II, including cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, cg03634854, cg03030301, cg04119405, cg25715498, cg063) A total of 22 CpG loci (cg04190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357, and cg24812634) were selected as inclusion markers. A multiple linear regression model was constructed using the R package caret, and the model accuracy was evaluated through 5 rounds of 10-fold cross-validation.

[0141] 2CpG site combination one (panel I)

[0142] Using CpG site combination 1 as the inclusion marker, the multiple linear regression model fitted using the caret package is as follows:

[0143] Age = -14.969 - 20.699 × β cg01789162 -32.257×β cg03030301 +3.134×β cg03634854 +24.141×β cg04119405 +31.017×β cg11262154 +42.534×β cg18037145 +13.292×β cg19983027 +47.382×β cg19998819-38.220×β cg25715498 +8.003×β cg27111970 -16.215×β cg27231587 , where β cg01789162 β cg03030301 β cg03634854 β cg04119405 β cg11262154 β cg18037145 β cg19983027 β cg19998819 β cg25715498 β cg27111970 and β cg27231587 These represent the methylation rates at the corresponding CpG sites;

[0144] The R-squared value of the regression model after 5 10-fold cross-validations is... 2 The mean age ranged from 0.6044 to 0.9247 (mean 0.8133, standard deviation 0.0674), the average age ranged from 2.4415 years to 5.7313 years (mean 4.2420 years, standard deviation 0.7315 years), and the mean age ranged from 1.9560 years to 4.2866 years (mean 3.1947 years, standard deviation 0.5578 years).

[0145] 3CpG site combination two (panel II)

[0146] Using CpG site combination 2 as the inclusion marker, the multiple linear regression model fitted using the caret package is as follows:

[0147] Age = 8.254 + 14.037 × β cg04123357 -14.876×β cg06304190 +7.248×β cg06979108 -3.911×β cg12277678 -10.694×β cg12837463 -19.653×β cg13872326 +17.186×β cg20602007 -1.635×β cg20828122 +60.258×β cg21843517 +21.996×β cg24812634 -3.883×β cg25187042 , where β cg04123357 β cg06304190 β cg06979108 β cg12277678 β cg12837463 β cg13872326 β cg20602007 β cg20828122 β cg21843517 β cg24812634 and β cg25187042 These represent the methylation rates at the corresponding CpG sites;

[0148] The R-squared value of the regression model after 5 10-fold cross-validations is... 2 The mean age ranged from 0.6795 to 0.9526 (mean 0.8338, standard deviation 0.0542), the mean age ranged from 2.9452 years to 5.4641 years (mean 4.0377 years, standard deviation 0.6564 years), and the mean age ranged from 2.3755 years to 4.4424 years (mean 3.2197 years, standard deviation 0.5396 years).

[0149] 4CpG site combination three (panel I + panel II)

[0150] Using CpG site combination three as the inclusion marker, the multiple linear regression model fitted using the caret package is as follows:

[0151] Age = 17.7808 - 2.7234 × β cg01789162 -15.3408×β cg03030301 +18.9289×β cg03634854 +24.1326×β cg04119405 +3.0025×β cg04123357 -12.4580×β cg06304190 +4.7711×β cg06979108 +17.8788×β cg11262154 -0.5972×β cg12277678 -11.9590×β cg12837463 -21.8391×β cg13872326 +18.7880×β cg18037145 +10.1366×β cg19983027 +10.7987×β cg19998819 -14.5981×β cg20602007 +4.2982×β cg20828122 +35.6638×β cg21843517 +2.4494×β cg24812634 -2.8791×β cg25187042 -31.9334×β cg25715498 -1.8743×β cg27111970 -10.2882×β cg27231587 , where β cg01789162 β cg03030301 β cg03634854 β cg04119405 β cg04123357 β cg06304190 β cg06979108 β cg11262154 β cg12277678 β cg12837463 β cg13872326 β cg18037145 βcg19983027 β cg19998819 β cg20602007 β cg20828122 β cg21843517 β cg24812634 β cg25187042 β cg25715498 β cg27111970 and β cg27231587 These represent the methylation rates at the corresponding CpG sites;

[0152] The R-squared value of the regression model after 5 10-fold cross-validations is... 2 The mean ranged from 0.6802 to 0.9770 (mean 0.8823, standard deviation 0.0567), the average age ranged from 1.9640 years to 4.8460 years (mean 3.3113 years, standard deviation 0.6308 years), and the mean age ranged from 1.6818 years to 3.5953 years (mean 2.6742 years, standard deviation 0.4501 years).

[0153] Example 4 – Inferring the actual age of the donor from a semen sample (differential lysis method for extracting genomic DNA from the sperm source)

[0154] 1 sample

[0155] The implementation process of this invention is illustrated using a semen sample from a Han Chinese male individual (numbered HanM). The actual age was calculated using the same method as in Example 2. At the time of semen sample collection, HanM's actual age was 48.61 years.

[0156] 2-methylation detection

[0157] The methylation of 22 CpG sites was detected using the same method as in Example 2. In practical applications, CpG site combination one, CpG site combination two, CpG site combination three, or other validated CpG site combinations can be selected for detection.

[0158] 3-methylation detection results

[0159] In this embodiment, the methylation detection results of sample HanM are shown in Table 12.

[0160] Table 12. Methylation rate of 22 CpG sites in the sperm-derived genomic DNA of semen samples from male individual HanM.

[0161]

[0162]

[0163] 4 Age estimation

[0164] Substituting the methylation rate of semen samples from male individuals (HanM) into the regression model constructed based on CpG site combination:

[0165] Age = -14.969 - 20.699 × β cg01789162 -32.257×β cg03030301 +3.134×β cg03634854 +24.141×β cg04119405 +31.017×β cg11262154 +42.534×β cg18037145 +13.292×β cg19983027 +47.382×β cg19998819 -38.220×β cg25715498 +8.003×β cg27111970 -16.215×β cg27231587 , where β cg01789162 β cg03030301 β cg03634854 β cg04119405 β cg11262154 β cg18037145 β cg19983027 β cg19998819 β cg25715498 β cg27111970 and β cg27231587 The values ​​represent the methylation rates at the corresponding CpG sites, and the estimated age of the male individual HanM is 47.08 years.

[0166] Substituting the methylation rate of semen samples from male individuals (HanM) into the regression model constructed based on CpG site combination two:

[0167] Age = 8.254 + 14.037 × β cg04123357 -14.876×β cg06304190 +7.248×β cg06979108 -3.911×β cg12277678 -10.694×β cg12837463 -19.653×β cg13872326 +17.186×β cg20602007 -1.635×β cg20828122 +60.258×β cg21843517 +21.996×β cg24812634 -3.883×β cg25187042 , where β cg04123357 β cg06304190 β cg06979108 β cg12277678 β cg12837463 β cg13872326 β cg20602007 β cg20828122 β cg21843517 βcg24812634 and β cg25187042 The values ​​represent the methylation rates at the corresponding CpG sites, and the estimated age of the male individual HanM is 45.03 years.

[0168] Substituting the methylation rate of HanM semen samples from male individuals into the regression model constructed based on CpG site combination three:

[0169] Age = 17.7808 - 2.7234 × β cg01789162 -15.3408×β cg03030301 +18.9289×β cg03634854 +24.1326×β cg04119405 +3.0025×β cg04123357 -12.4580×β cg06304190 +4.7711×β cg06979108 +17.8788×β cg11262154 -0.5972×β cg12277678 -11.9590×β cg12837463 -21.8391×β cg13872326 +18.7880×β cg18037145 +10.1366×β cg19983027 +10.7987×β cg19998819 -14.5981×β cg20602007 +4.2982×β cg20828122 +35.6638×β cg21843517 +2.4494×β cg24812634 -2.8791×β cg25187042 -31.9334×β cg25715498 -1.8743×β cg27111970 -10.2882×β cg27231587 , where β cg01789162 β cg03030301 β cg03634854 β cg04119405 β cg04123357 β cg06304190 β cg06979108 β cg11262154 β cg12277678 β cg12837463 β cg13872326 β cg18037145 β cg19983027 β cg19998819 β cg20602007 β cg20828122 β cg21843517 β cg24812634 β cg25187042 β cg25715498 β cg27111970 and β cg27231587The values ​​represent the methylation rates at the corresponding CpG sites, and the estimated age of the male individual HanM is 48.03 years.

[0170] The actual age of male individual HanM at the time of semen collection was known to be 48.61 years, indicating that the estimated age obtained using the method and system provided by this invention is very close to the actual age.

[0171] Example 5 – Inferring the actual age of the donor from a semen sample (direct extraction of genomic DNA from semen)

[0172] 1 sample

[0173] The implementation process of this invention is illustrated using a sample set of 40 semen samples. Actual age was calculated using the same method as in Example 2. At the time of semen sample collection, the actual age of the male individuals in this sample set ranged from 22.65 to 67.19 years.

[0174] 2-methylation detection

[0175] Except for the DNA extraction method, the methylation detection method for the 22 CpG sites was the same as in Example 2. Genomic DNA was directly extracted from the semen sample using the QIAamp DNA Mini kit (QIAGEN, Germany). The specific steps are as follows: (i) Add 100 μL of semen to a 1.5 mL centrifuge tube; (ii) Add 80 μL of buffer G2, 20 μL of proteinase K (QIAGEN), and 20 μL of 1 M DTT, vortex to mix, and incubate at 56 °C for 1 hour; (iii) Add 200 μL of buffer solution AL, vortex to mix, and incubate at 70 °C with shaking at 850 rpm for 10 minutes; (iv) Continue from step 6 of the Tissue Protocol in the QIAamp DNA Mini and Blood Mini Handbook to complete the extraction of genomic DNA.

[0176] In practical applications, you can choose to detect CpG site combination one, CpG site combination two, CpG site combination three, or other validated CpG site combinations.

[0177] Age estimation

[0178] The methylation rates of 40 sperm DNA samples were substituted into a regression model constructed based on a combination of methylation rates and CpG sites from 253 sperm-derived DNA samples:

[0179] Age = -14.969 - 20.699 × β cg01789162 -32.257×β cg03030301 +3.134×β cg03634854 +24.141×β cg04119405 +31.017×β cg11262154+42.534×β cg18037145 +13.292×β cg19983027 +47.382×β cg19998819 -38.220×β cg25715498 +8.003×β cg27111970 -16.215×β cg27231587 , where β cg01789162 β cg03030301 β cg03634854 β cg04119405 β cg11262154 β cg18037145 β cg19983027 β cg19998819 β cg25715498 β cg27111970 and β cg27231587 The values ​​represent the methylation rates at the corresponding CpG sites. The MAE for the semen sample set was 4.6127 years, the RMSE was 6.0681 years, and the R... 2 The Pearson correlation coefficient between the inferred age and the actual age is 0.9259, which is 0.8574.

[0180] The methylation rates of 40 sperm DNA samples were substituted into a regression model constructed based on a combination of methylation rates and CpG sites from 253 sperm-derived DNA samples:

[0181] Age = 8.254 + 14.037 × β cg04123357 -14.876×β cg06304190 +7.248×β cg06979108 -3.911×β cg12277678 -10.694×β cg12837463 -19.653×β cg13872326 +17.186×β cg20602007 -1.635×β cg20828122 +60.258×β cg21843517 +21.996×β cg24812634 -3.883×β cg25187042 , where β cg04123357 β cg06304190 β cg06979108 β cg12277678 β cg12837463 β cg13872326 β cg20602007 β cg20828122 β cg21843517 β cg24812634 and β cg25187042 The values ​​represent the methylation rates at the corresponding CpG sites. The MAE for the semen sample set was 4.7364 years, the RMSE was 5.6787 years, and the R... 2The Pearson correlation coefficient between the inferred age and the actual age is 0.8961, and the correlation coefficient between the inferred age and the actual age is 0.9466.

[0182] The methylation rates of 40 sperm DNA samples were substituted into a regression model constructed based on a combination of methylation rates and CpG sites from 253 sperm-derived DNA samples:

[0183] Age = 17.7808 - 2.7234 × β cg01789162 -15.3408×β cg03030301 +18.9289×β cg03634854 +24.1326×β cg04119405 +3.0025×β cg04123357 -12.4580×β cg06304190 +4.7711×β cg06979108 +17.8788×β cg11262154 -0.5972×β cg12277678 -11.9590×β cg12837463 -21.8391×β cg13872326 +18.7880×β cg18037145 +10.1366×β cg19983027 +10.7987×β cg19998819 -14.5981×β cg20602007 +4.2982×β cg20828122 +35.6638×β cg21843517 +2.4494×β cg24812634 -2.8791×β cg25187042 -31.9334×β cg25715498 -1.8743×β cg27111970 -10.2882×β cg27231587 , where β cg01789162 β cg03030301 β cg03634854 β cg04119405 β cg04123357 β cg06304190 β cg06979108 β cg11262154 β cg12277678 β cg12837463 β cg13872326 β cg18037145 β cg19983027 β cg19998819 β cg20602007 β cg20828122 β cg21843517 β cg24812634 β cg25187042 β cg25715498 β cg27111970 and β cg27231587The values ​​represent the methylation rates at the corresponding CpG sites. The MAE for the semen sample set was 4.2667 years, the RMSE was 5.2560 years, and the R... 2 The Pearson correlation coefficient between the inferred age and the actual age is 0.9366, and the correlation coefficient between the inferred age and the actual age is 0.9678.

[0184] The above results show that the method and system provided by the present invention can be used to infer the individual age of sperm DNA, but its accuracy is lower than that of sperm-derived DNA in Example 3.

[0185] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0186] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for inferring an individual's age from semen or semen stains, characterized in that, Includes the following steps: S1, Extract genomic DNA from individual semen or sperm stains; S2, Treat the genomic DNA with bisulfite; S3, Select 22 CpG sites from the genomic DNA and amplify the selected CpG sites to obtain the methylation rate of the selected CpG sites. Using GRCh37 / hg19 as the reference genome, the selected CpG sites are cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, and cg03634854. S4, perform regression analysis on the methylation rate of the selected CpG sites and the age of the individuals to construct a regression model for inferring the age of individuals of unknown origin from semen or semen stains.

2. The method according to claim 1, characterized in that, Step S3 further includes: after obtaining the transformed DNA template treated with bisulfite, amplifying it using amplification primers corresponding to the CpG site to obtain amplification products.

3. The method according to claim 2, characterized in that, Step S3 further includes: after obtaining the amplification product, performing SNaPshot microsequencing on the amplification product using single-base extension primers corresponding to the CpG site to obtain the methylation rate of the CpG site.

4. The method according to claim 3, characterized in that, The 22 sites selected in step S3 are cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, cg03634854, cg03030301, cg04119405, cg25715498, and cg06. The sequence contains 22 CpG sites: 304190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357, and cg24812634. The amplification primers are the nucleotide sequences of SEQ ID NO:1 to SEQ ID NO:44 in the sequence listing, and the single-base extension primers are the nucleotide sequences of SEQ ID NO:45 to SEQ ID NO:66 in the sequence listing.

5. A system for inferring an individual's age from semen or semen stains, characterized in that, include: DNA extraction system for extracting genomic DNA from individual semen or sperm stains; A bisulfite treatment system for treating the genomic DNA with bisulfite; An amplification detection system was used to select 22 CpG sites in the genomic DNA and amplify the selected CpG sites to obtain the methylation rate of the selected CpG sites. Using GRCh37 / hg19 as the reference genome, the selected CpG sites were cg01789162, cg11262154, cg19998819, cg27231587, cg18037145, cg19983027, cg27111970, cg03634854, cg03030301, and cg041194. 05, cg25715498, cg06304190, cg06979108, cg12837463, cg20828122, cg12277678, cg13872326, cg20602007, cg25187042, cg21843517, cg04123357 and cg24812634; The data acquisition system performs regression analysis on the methylation rate of the selected CpG sites and the age of the individuals to construct a regression model for inferring the age of individuals of unknown origin from semen or semen stains.

6. The system according to claim 5, characterized in that, The amplification detection system uses amplification primers corresponding to the CpG site to amplify the site and obtain amplification products.

7. The system according to claim 6, characterized in that, The amplification detection system uses single-base extension primers corresponding to the CpG sites to perform SNaPshot microsequencing on the amplification products to obtain the methylation rate of the CpG sites.

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