Skip to main content

Table 2 DNA methylation methods for age estimation in forensic studies

From: Age estimation using DNA methylation technique in forensics: a systematic review

Reference and year Population Source of samples Age range (years) Sample size CpG coverage Genes Technique/† gDNA Statistical model Age prediction (MAD)
Weidner et al. 2014 Germany Blood 20–75 151 samples 3 CpGs ASPA, ITGA2B, PDE4C Pyrosequencing/ 500 ng MLRM 5.43
Xu et al. 2015a,b Chinese Blood 20–80 49 healthy females 6 CpGs ADAR, AQP11, ITGA2B, PDE4C EpiTYPER/ 1 μ SVRM 2.8
Zbiec-Piekarska et al. 2015a,b Polish Blood 2–75 300 male females 5 CpGs ELOVL2, C1orf132, TRIM59, KLF14, FHL2 Pyrosequencing/ 2 μg MLRM 3.4
Freire-Aradas et al. 2016 European Blood 18–104 725 male females 7 CpGs ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, C1orf132, chr16:85395429 EpiTYPER/ 300 ng MQTRM MAE ± 3.07
Park et al. 2016 Korean Blood 11–90 535 samples 3 CpGs ELOVL2, ZNF423, CCDC102B Pyrosequencing/ 500 ng MLRM ± 3.34
Hamano et al. 2016 Japanese Blood 0–95 22 healthy individuals, 52 cadavers 24 CpGs ELOVL2, FHL2 MS-HRM/ NA MLRM 7.44
Zubakov et al. 2016 Germany Blood 4–82 216 healthy males 8 CpGs ELOVL2, FHL2 EpiTYPER/ 500 ng MLRM 4.22
Cho et al. 2017 Korean Blood 20–74 100 healthy male females 5-13 CpGs ELOVL2, C1orf132, TRIM59, KLF14, FHL2 Pyrosequencing/ 500 ng MLRM 4.18
Naue et al. 2017 Netherlands Blood 18–69 208 samples 13 CpGs ELOVL2, TRIM59, F5, KLF14, DDO, GRM2, HOXC4, LDB2, MEIS1-AS3, NKIRAS2, RPA2, SAMD10, ZYG11A MPS, Sanger sequencing/ 300 ng RFR 3.24
Freire-Aradas et al. 2018 European Blood 2–18 209 healthy donors 6 CpGs FLJ40365, SDS, PGLYRP2, EDARADD, HKR1, KCNAB3, PRKG2, FLJ46365, ITGA2B, TOM1L1 EpiTYPER/ 300 ng MQTRM MAE ±0.94
Feng et al. 2018 Chinese Han Blood 15–75 390 males 9 CpGs ELOVL2, PDE4C, C1orf132, CCDC102 B, RASSF5, TRIM59, cg10804656 Pyrosequencing EpiTYPER/ 1 μg MLRM, SVRM, ANN 2.89
Spolnicka et al. 2018 Polish Blood 12–76 120 healthy, 190 with disease donors 5 CpGs ELOVL2, C1orf132, KLF14, FHL2, TRIM59 Pyrosequencing/ 1-2 μg ANN MAE 3.8 (healthy); 4.4–7.1 (disease)
Zbiec-Piekarska et al. 2015a,b Polish Blood and bloodstains 2–75 303 blood, 45 bloodstains 2 CpGs ELOVL2 Pyrosequencing/ 2 μg MLRM 5.03
Huang et al. 2015 Chinese Han Blood and bloodstain 11–70 40 blood, 20 bloodstains 5 CpGs ASPA, ITGA2B, NPTX2, Pyrosequencing/ 100 ng MRLM 7.87
Thong et al. 2017 Singapore Blood, bloodstain 3–80 145 blood, 26 bloodstains 32 CpGs ELOVL2, FHL2, KLF14, TRIM59, C1orf132 Pyrosequencing/ 500 ng blood, 50 ng bloodstain MLRM 4.8
Peng et al. 2019 Chinese Han Bloodstains 21–66 99 males 9 CpGs TRIM59, RASSF5, C1orf132, chr10:22334463/65, PDE4C, CCDC102B, ELOVL2 EpiTYPER/ 1 μg MLRM 2.94 < MAD <3.55
Lee et al. 2015 Korean Semen 20–73 31 males 3 CpGs TTC7B, NOX4, cg12837463 (no gene associated) SNaPshot/ 200 ng MLRM 4.2
Li et al. 2017 Chinese Han Semen 21–54 38 males 1 CpGs NOX4 Pyrosequencing/ 500 ng MLRM 4.08
Lee et al. 2018 Korean Semen 24–57 19 forensic case 1 CpGs NOX4 SNaPshot, Multiplex PCR/ 100 ng MLRM 4.8
Eipel et al. 2016 Germany Buccal swab 1–85 55 healthy donors 3 CpGs PDE4C, ASPA, ITGA2B Pyrosequencing/ 500 ng MLRM 4.3
Buccal swab 1–85 55 healthy donors 1 CpGs PDE4C Pyrosequencing/ 500 ng MLRM 5.2
Bekaert et al. 2015a,b Belgium Buccal swab 0–73 50 paternity cases 8 CpGs ASPA, PDE4C, ELOVL2, EDARADD Pyrosequencing/ NA MQDRM 3.32
Giuliani et al. 2016 NA Teeth (cementum, dentin, dental pulp) 17–77 22 anonymous healthy extracted teeth 32 CpGs ELOVL2, FHL2, PENK EpiTYPER/ 200 ng MLRM 1.2–7.07‡
Hong et al. 2017 Korean Saliva 18–73 226 male females 6 CpGs SST, CNGA3, KLF14, TSSK6, TBR1, SLC12A5, (PTPN7) SNaPshot/ 5-50 ng MLRM 3.13
Hong et al. 2019 Korean Saliva 18–65 95 male females 7 CpGs PTPN7, SST, CNGA3, KLF14, TSSK6, TBR1, SLC12A5, MPS, SNaPshot/ 200 ng MLRM, NN 3.69 (MLRM); 3.19 (NN)
Bekaert et al. 2015a,b Belgium Blood 0–91 169 deceased 37, living-donor male females 4 CpGs ASPA, PDE4C, ELOVL2, EDARADD Pyrosequencing/ 200 ng MQDRM 3.75
Teeth (Dentin) 19–70 29 extracted teeth male females 7 CpGs PDE4C, ELOVL2, EDARADD Pyrosequencing/ 200 ng MQDRM 4.84
Vidaki et al. 2017 NA Blood and saliva 11–76 46 blood, 265 saliva 16 CpGs CSNK1D, C21orf63, CASC4, SSRP1, FXN, P2RXL1, RASSF5, ERG, TRIP10, FZD9, KLF14, NR2F2, VGF, NHLRC1, SCGN, C19orf30 NGS/ 500 ng MLRM, GRNN MAE 7.45 (blood); 3.18 (saliva)
Alghanim et al. 2017 NA Blood and saliva 5–72 40 blood, 52 saliva 27 CpGs KLF14, SCGN, DLX5 Pyrosequencing/ 200-500 ng MLRM 6.6 (blood); 5.8–6.2 (saliva)
Hamano et al. 2017 Japanese Saliva, cigarette butts 1–73 197 healthy individuals, 16 cigarettes butts 2 CpGs ELOVL2, EDARADD MS-HRM/ NA SVRM 5.96 (saliva); 7.65 (cigarette butts)
Aliferi et al. 2018 NA Whole blood, saliva, semen 11–93 76 blood, 34 saliva 12 CpGs VGF, TRIP10, KLF14, CSNK1D, FZD9, C21orf63, SSRP1, NHLRC1, ERG, FXN, P2RXL1, SCGN MPS, Sequencing (MiSeq)/ 50 ng GRNN, R-models MAE 4.0 (blood); 7.3 (saliva)
Richards et al. 2019 Australian Blood, semen 22–84 28 samples 10-31 CpGs ELOVL2, CCDC102B, PDE4C MPS/ 200 ng MLRM 3.26 (blood); 4.10 (semen)
Jung et al. 2019 Korean Blood, saliva, buccal swabs 18–74 304 healthy donors 5 CpGs ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, TRIM59 SNaPshot/ 40-200 ng MLRM 3.48 (blood); 3.55 (saliva); 4.29 (buccal swab)
  1. †Input DNA for bisulfite conversion; ‡ depend on teeth area is analyzed; accuracy is indicated by MAD: mean absolute deviation (in years) from chronological age; MAE: median absolute error (in years); ANN: artificial neural network; GRNN: generalized regression neural networks; MLRM: multivariate linear regression method; MPS: massively parallel sequencing; MQTRM: multivariate quantile regression model, MQDRM: multivariate quadratic regression model; MS-HRM: methylation sensitive-high resolution melting, NGS: next-generation sequencing; RFR: random forest regression, an ensemble tool based on decision trees; R-models: statistical computing software to test 14 regression methods; SVRM: support vector regression model; NA: not available