Evidence map›Paper›PMID 39633164›Full record

ArticleInternational journal of legal medicine2025

A methylation panel of 10 CpGs for accurate age inference via stepwise conditional epigenome-wide association study.

Yu Qian, Qianqian Peng, Qili Qian, Xingjian Gao, Xinxuan Liu, Yi Li, Xiu Fan, Yuan Cheng, Na Yuan, Sibte Hadi and 3 more

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in International journal of legal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Yu Qian *Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, China National Center for Bioinformation, Chinese Academy of Sciences, Beijing, China.
Qianqian Peng *CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
Qili Qian *CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
Xingjian GaoNational Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing, Jiangsu, China.
Xinxuan LiuKey Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, China National Center for Bioinformation, Chinese Academy of Sciences, Beijing, China.
Yi LiCAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
Xiu FanKey Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, China National Center for Bioinformation, Chinese Academy of Sciences, Beijing, China.
Yuan ChengKey Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, China National Center for Bioinformation, Chinese Academy of Sciences, Beijing, China.
Na YuanKey Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, China National Center for Bioinformation, Chinese Academy of Sciences, Beijing, China.
Sibte HadiDepartment of Forensic Sciences, College of Criminal Justice, Naif Arab University of Security Sciences, Riyadh, 11452, Kingdom of Saudi Arabia.
Li JinHuman Phenome Institute, Fudan University, Shanghai, China.
Sijia Wang *CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
Fan Liu *Department of Forensic Sciences, College of Criminal Justice, Naif Arab University of Security Sciences, Riyadh, 11452, Kingdom of Saudi Arabia. fliu@nauss.edu.sa.ORCID http://orcid.org/0000-0001-9241-8161

Funding

Naif Arab University for Security Sciences NAUSS-23-R17National Natural Science Foundation of China 81930056Science and Technology Service Network Plan KFJ-STS-QYZD-2021-08-001Science and Technology Service Network Plan KFJ-STS-ZDTP-079
6 · The paper itself

Abstract

Estimating individual age from DNA methylation at age associated CpG sites may provide key information facilitating forensic investigations. Systematic marker screening and feature selection play a critical role in ensuring the performance of the final prediction model. In the discovery stage, we screened for 811876 CpGs from whole blood of 2664 Chinese individuals ranging from 18 to 83 years of age based on a stepwise conditional epigenome-wide association study (SCEWAS). The SCEWAS identified 28 CpGs showing genome-wide significant and independent effects. Further restricting this panel to 10 most informative CpGs showed a tolerable loss of information. A linear model consisting of these 10 CpGs could explain 93% of the age variance (R

Indexed as

AgingCpG IslandsDNA MethylationEpigenomeGenome-Wide Association StudyAdolescentAdultAgedAged, 80 and overChinaEast Asian PeopleFemaleForensic GeneticsHumansLinear ModelsMaleAge predictionDNA methylationEast Asian populationEpigenome-Wide Association StudyForensic DNA phenotyping

Identifiers

PMID39633164

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.