Cross-Platform Application of DNA Methylation Age Estimation Model in Eastern Chinese Han Population

doi:10.12116/j.issn.1004-5619.2022.520405

Abstract

Abstract:

Objective To evaluate the forensic application value of an age estimation model based on DNA methylation in eastern Chinese Han population， and to provide a theoretical basis for exploring age estimation models suitable for different detection platforms. Methods According to the 6 age-related methylation sites in the published blood DNA methylation age estimation models of Chinese Han population， the DNA methylation level of 48 samples was detected by pyrosequencing and next-generation sequencing （NGS）. After submitting DNA methylation levels to the age estimation model， the DNA methylation ages were predicted and compared with their real ages. Results The 6 DNA methylation sites in both detection techniques were age-related， with an R² of 0.85 and a median absolute deviation （MAD） of 4.81 years when using pyrosequencing； with an R² of 0.84 and MAD of 4.41 years when using NGS. Conclusion The blood DNA methylation age estimation model can be used under pyrosequencing and multi-purpose regional methylation enrichment sequencing technology based on NGS and it can accurately estimate the age.

Key words: forensic genetic, epigenetics, age estimation, DNA methylation, pyrosequencing, next-generation sequencing

CLC Number:

Zi-wei WANG, Cheng-tao LI, Xi-ling LIU. Cross-Platform Application of DNA Methylation Age Estimation Model in Eastern Chinese Han Population[J]. Journal of Forensic Medicine, 2023, 39(5): 441-446.

Add to citation manager EndNote|Ris|BibTeX

URL: http://www.fyxzz.cn/EN/10.12116/j.issn.1004-5619.2022.520405

http://www.fyxzz.cn/EN/Y2023/V39/I5/441

Figures/Tables 6

References 21

1	VIDAKI A， KAYSER M. From forensic epigenetics to forensic epigenomics： Broadening DNA investigative intelligence[J]. Genome Biol， 2017，18（1）：238. doi：10.1186/s13059-017-1373-1 .
2	OSORIO F G， VARELA I， LARA E， et al. Nuclear envelope alterations generate an aging-like epigenetic pattern in mice deficient in Zmpste24 metalloprotease[J]. Aging Cell，2010，9（6）：947-957. doi：10.1111/j.1474-9726.2010.00621.x .
3	POLLINA E A， BRUNET A. Epigenetic regulation of aging stem cells[J]. Oncogene， 2011，30（28）：3105-3126. doi：10.1038/onc.2011.45 .
4	VIDAKI A， DÍEZ LÓPEZ C， CARNERO-MONTORO E， et al. Epigenetic discrimination of identical twins from blood under the forensic scenario[J]. Forensic Sci Int Genet，2017，31：67-80. doi：10.1016/j.fsigen.2017.07.014 .
5	LYONS M J， YORK T P， FRANZ C E， et al. Genes determine stability and the environment determines change in cognitive ability during 35 years of adulthood[J]. Psychol Sci， 2009，20（9）：1146-1152. doi：10.1111/j.1467-9280.2009.02425.x .
6	GREENWOOD T A， BEERI M S， SCHMEIDLER J， et al. Heritability of cognitive functions in families of successful cognitive aging probands from the Central Valley of Costa Rica[J]. J Alzheimers Dis， 2011，27（4）：897-907. doi：10.3233/JAD-2011-110782 .
7	CASTILLO-FERNANDEZ J E， SPECTOR T D， BELL J T. Epigenetics of discordant monozygotic twins： Implications for disease[J]. Genome Med， 2014，6（7）：60. doi：10.1186/s13073-014-0060-z .
8	SALAMEH Y， BEJAOUI Y， HAJJ N EL. DNA methylation biomarkers in aging and age-related diseases[J]. Front Genet， 2020，11：171. doi：10.3389/fgene.2020. 00171 .
9	FAN H L， XIE Q Q， ZHANG Z， et al. Chronological age prediction： Developmental evaluation of DNA methylation-based machine learning models[J]. Front Bioeng Biotechnol，2022，9：819991. doi：10.3389/fbioe. 2021.819991 .
10	FENG L， PENG F D， LI S F， et al. Systematic feature selection improves accuracy of methylation-based forensic age estimation in Han Chinese males[J]. Forensic Sci Int Genet，2018，35：38-45. doi：10.1016/j.fsigen.2018.03.009 .
11	PAN C， YI S H， XIAO C， et al. The evaluation of seven age-related CpGs for forensic purpose in blood from Chinese Han population[J]. Forensic Sci Int Genet，2020，46：102251. doi：10.1016/j.fsigen.2020.102251 .
12	李姗飞，彭付端，王建宁，等. 基于甲基化的年龄推断模型构建与效能评估[J].法医学杂志， 2019，35（1）：17-22. doi： 10.12116/j.issn.1004-5619.2019.01.004 .
	LI S F， PENG F D， WANG J N， et al. Methylation-based age estimation model construction and its effectiveness evaluation[J]. Fayixue Zazhi， 2019，35（1）：17-22.
13	聂燕钗，俞丽娟，管桦，等. DNA甲基化检测方法及其法医学应用研究进展[J].法医学杂志， 2017，33（3）：293-300. doi：10.3969/j.issn.1004-5619.2017.03.017 .
	NIE Y C， YU L J， GUAN H， et al. Research progress on the detection method of DNA methylation and its application in forensic science[J]. Fayixue Zazhi， 2017，33（3）：293-300.
14	TOST J， GUT I G. DNA methylation analysis by pyrosequencing[J]. Nat Protoc，2007，2（9）：2265-2275. doi：10.1038/nprot.2007.314 .
15	HOLMES E E， JUNG M， MELLER S， et al. Performance evaluation of kits for bisulfite-conversion of DNA from tissues， cell lines， FFPE tissues， aspirates， lavages， effusions， plasma， serum， and urine[J]. PLoS One， 2014，9（4）：e93933. doi：10.1371/journal.pone.0093933 .
16	LEONTIOU C A， HADJIDANIEL M D， MINA P， et al. Bisulfite conversion of DNA： Performance comparison of different kits and methylation quantitation of epigenetic biomarkers that have the potential to be used in non-invasive prenatal testing[J]. PLoS One， 2015，10（8）：e0135058. doi：10.1371/journal.pone. 0135058 .
17	ALIFERI A， BALLARD D， GALLIDABINO M D， et al. DNA methylation-based age prediction using massively parallel sequencing data and multiple machine learning models[J]. Forensic Sci Int Genet，2018，37：215-226. doi：10.1016/j.fsigen.2018.09.003 .
18	SANDOVAL J， HEYN H， MORAN S， et al. Validation of a DNA methylation microarray for 450 000 CpG sites in the human genome[J]. Epigenetics， 2011，6（6）：692-702. doi：10.4161/epi.6.6.16196 .
19	DAUNAY A， BAUDRIN L G， DELEUZE J F， et al. Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing[J]. Sci Rep， 2019，9：8862. doi：10.1038/s41598-019-45197-w .
20	POTABATTULA R， DITTRICH M， BÖCK J， et al. Allele-specific methylation of imprinted genes in fetal cord blood is influenced by cis-acting genetic variants and parental factors[J]. Epigenomics， 2018，10（10）：1315-1326. doi：10.2217/epi-2018-0059 .
21	SIMPKIN A J， HEMANI G， SUDERMAN M， et al. Prenatal and early life influences on epigenetic age in children： A study of mother-offspring pairs from two cohort studies[J]. Hum Mol Genet， 2016，25（1）：191-201. doi：10.1093/hmg/ddv456 .

年龄段/岁	男性	女性	合计
合计	24	24	48
3~20	6	6	12
>20~39	6	6	12
>39~60	6	6	12
>60	6	6	12

年龄段/岁	男性	女性	合计
合计	24	24	48
3~20	6	6	12
>20~39	6	6	12
>39~60	6	6	12
>60	6	6	12

CpG位点	CpG ID	基因	位置	扩增片段长度/bp
CpG1	-	ITGA2B	chr17：44，390，358	153
CpG2	cg14361627	KLF14	chr7：130，734，355	99
CpG3	cg06639320	FHL2	chr2：105，399，282	229
CpG4	cg04208403	ZNF423	chr16：49，491，896	113
CpG5	cg02228185	ASPA	chr17：3，476，273	104
CpG6	cg19283806	CCDC102B	chr18：68，722，183	197

CpG位点	CpG ID	基因	位置	扩增片段长度/bp
CpG1	-	ITGA2B	chr17：44，390，358	153
CpG2	cg14361627	KLF14	chr7：130，734，355	99
CpG3	cg06639320	FHL2	chr2：105，399，282	229
CpG4	cg04208403	ZNF423	chr16：49，491，896	113
CpG5	cg02228185	ASPA	chr17：3，476，273	104
CpG6	cg19283806	CCDC102B	chr18：68，722，183	197

CpG位点	焦磷酸测序	NGS	SNaPshot^[11]
CpG1	-0.705 7	-0.726 1	-0.666 1
CpG2	0.837 7	0.831 3	0.818 2
CpG3	0.515 2	0.915 4	0.789 2
CpG4	-0.859 0	-0.889 7	-0.535 5
CpG5	-0.770 3	-0.763 9	-0.628 6
CpG6	-0.848 4	-0.839 4	-0.870 4