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A robust cross-tissue DNA methylation model for forensic age estimation from oral samples.
Yuzhu Liu1, Maomin Chen1, Ya Li1
1Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, Hubei 430030, PR China.
Forensic Science International. Genetics
|July 29, 2025
Summary
This study developed a new DNA methylation model for accurate forensic age estimation from oral samples like saliva and buccal swabs. The robust cross-tissue model overcomes limitations of previous methods, offering reliable age predictions in real-world forensic cases.
Area of Science:
- Forensic Science
- Genetics
- Epigenetics
Background:
- DNA methylation is a key biomarker for estimating chronological age, crucial in forensic investigations.
- Existing DNA methylation models for age estimation face challenges with tissue specificity and cellular heterogeneity in oral samples (buccal swabs, saliva), impacting accuracy.
- Forensic applications require robust age estimation methods that are reliable across different oral sample types and conditions.
Purpose of the Study:
- To develop and validate a robust cross-tissue DNA methylation model for accurate forensic age estimation from oral samples.
- To overcome the limitations of tissue specificity and cellular heterogeneity in existing age prediction models.
- To identify DNA methylation markers with high cross-tissue stability for improved age estimation accuracy.
Main Methods:
- Quantified DNA methylation at 18 CpG sites in 216 paired buccal swab and saliva samples from Han Chinese individuals (aged 2-83 years).
- Systematically evaluated 32 model configurations, including varying CpG marker panels, age transformation, and tissue variable inclusion.
- Utilized quantile regression to develop an optimized 10-CpG model and validated its performance on independent datasets, including chewed gum samples, and under varying DNA input and storage conditions.
Main Results:
- An optimized 10-CpG quantile regression model achieved low mean absolute errors (MAEs) of 3.19 years (buccal swabs), 3.44 years (saliva), and 3.45 years (combined dataset) in cross-validation.
- The model demonstrated excellent performance on an independent validation set of chewed gum samples (MAE = 3.29 years).
- The model maintained reliable performance with low bisulfite-converted DNA inputs (as low as 5 ng) and after 31 days of environmental storage.
Conclusions:
- A methodologically sound and practically validated cross-tissue DNA methylation model for forensic age estimation from diverse oral samples has been established.
- This approach effectively addresses the challenges of tissue variability and cellular heterogeneity, offering a reliable solution for real-world forensic casework.
- The developed model provides a significant advancement in forensic age estimation, enhancing its applicability to commonly encountered oral-derived evidence.
