Modeling Methods and Influencing Factors for Age Estimation Based on DNA Methylation

Area of Science:

• Forensic science
• Epigenetics
• Biomarker discovery

Background:

• Accurate age estimation from biological samples is crucial in forensic investigations.
• DNA methylation status exhibits age-dependent alterations, providing a potential biomarker for age prediction.
• Current methods face challenges related to accuracy and influencing factors like tissue specificity.

Approach:

• This paper reviews established and emerging statistical models for DNA methylation-based age estimation.
• It examines various modeling techniques, including regression, support vector machines, neural networks, and random forests.
• The review also discusses factors influencing DNA methylation levels and their impact on age estimation accuracy.

Key Points:

• DNA methylation changes with age, offering a basis for forensic age estimation.
• Multiple statistical models exist for age prediction using DNA methylation data.
• Tissue specificity and other factors can influence methylation levels and model performance.

Conclusions:

• Developing robust DNA methylation-based age estimation models is significant for forensic science.
• Understanding influencing factors is key to improving the accuracy of these models.
• This review provides a reference for establishing reliable age estimation methodologies.