Predicting human age by detecting DNA methylation status in hair.

Area of Science:

• Forensic Science
• Genetics
• Epigenetics

Background:

• DNA methylation patterns change with age, offering a potential biomarker for age prediction.
• Previous age prediction models focused on blood, saliva, and semen, leaving hair unexamined.
• Forensic age estimation is crucial for criminal investigations and judicial processes.

Purpose of the Study:

• To investigate the potential of DNA methylation in hair follicles for chronological age prediction.
• To identify age-related CpG sites in human scalp hair.
• To develop and validate a DNA methylation-based model for age estimation in hair.

Main Methods:

• Measured methylation status of 10 age-related CpG sites using multiplex methylation SNaPshot.
• Analyzed 10 CpG sites from genes including LAG3, SCGN, ELOVL2, KLF14, C1orf132, SLC12A5, GRIA2, and PDE4C.
• Developed and compared four age-prediction models, including multiple linear regression.

Main Results:

• Identified 10 CpG sites strongly correlated with age in hair follicles, with four showing correlation coefficients > 0.7.
• A multiple linear regression model using 10 CpG sites achieved the best performance, with a median absolute deviation of 3.68 years.
• Found no significant differences in methylation based on sex, hair type, or hair color.

Conclusions:

• Chronological age can be reliably estimated from DNA methylation patterns in scalp hair follicles.
• This method allows for simultaneous human identification and age determination from a single hair sample.
• The developed method holds promise for forensic applications, enhancing investigative capabilities.