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Related Experiment Video

Updated: Jul 4, 2025

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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Epigenetic-based age prediction in blood samples: Model development.

Gonul Filoglu1, Sumeyye Zulal Sımsek1, Gokhan Ersoy2

  • 1Department of Science, Institute of Forensic Sciences and Legal Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey.

Journal of Forensic Sciences
|February 3, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a DNA methylation model for accurate age estimation in the Turkish population. The 6-CpG model reliably predicts chronological age from blood samples, aiding forensic and clinical applications.

Keywords:
DNA methylationage estimationepigeneticsmethylation SNaPshot

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Area of Science:

  • Epigenetics
  • Genomics
  • Biogerontology

Background:

  • Aging is a multifaceted process involving genetic, epigenetic, and environmental influences, leading to physiological decline.
  • Epigenetic modifications, particularly DNA methylation, are crucial regulators of gene expression and are implicated in the aging process.
  • Accurate age estimation is vital for various applications, including forensic science and clinical diagnostics.

Purpose of the Study:

  • To develop and validate a novel epigenetic age estimation model for the Turkish population.
  • To identify specific CpG sites in DNA methylation patterns correlated with chronological age.
  • To assess the model's accuracy, reliability, and potential applications using blood samples.

Main Methods:

  • Selection of eight CpG sites (TOM1L1, ELOVL2, ASPA, FHL2, C1orf132, CCDC102B, cg07082267, RASSF5) based on age correlation.
  • Analysis of DNA methylation patterns in blood samples from 100 Turkish volunteers across different age groups.
  • Development of a predictive model using Multiple Linear Regression, focusing on a 6-CpG subset for optimal performance.
  • Validation through sensitivity analysis, training-test split, and fivefold cross-validation.

Main Results:

  • A 6-CpG model (excluding cg07082267 and TOM1L1) demonstrated high accuracy in age prediction (r=0.941).
  • The model explained 88% of age variance with low mean absolute error (MAE=4.07) and root mean square error (RMSE=5.73 years).
  • Reliable performance was observed with DNA inputs as low as 1 ng, with consistent accuracy across age cohorts and genders.

Conclusions:

  • The developed DNA methylation-based age estimation model is accurate and reliable for the Turkish population.
  • The model shows potential for practical applications in forensic investigations and clinical assessments.
  • Epigenetic markers provide a robust tool for estimating biological age and understanding aging processes.