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

Maulani, Chaerita; Auerkari, Elza Ibrahim

doi:10.1186/s41935-020-00214-2

Egyptian Journal of Forensic Sciences

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
Eipel et al. 2016	Germany	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
Bekaert et al. 2015a,b	Belgium	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)

†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

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