Forensic age prediction for saliva samples using methylation-sensitive high resolution melting: exploratory...

Area of Science:

• Forensic Science
• Genetics
• Molecular Biology

Background:

• Forensic age prediction is crucial for criminal investigations.
• Existing methods for age estimation from biological samples have limitations.

Purpose of the Study:

• To develop a novel, accurate, and cost-effective age prediction model for saliva samples.
• To validate the model's performance in real-world forensic scenarios.

Main Methods:

• Development of an age prediction model using methylation-sensitive high resolution melting (MS-HRM).
• Analysis of methylation profiles of ELOVL2 and EDARADD genes.
• Age prediction using support vector regression.
• Validation on intact saliva samples and saliva from cigarette butts.

Main Results:

• The model demonstrated high accuracy with a mean absolute deviation (MAD) of 5.96 years on training samples and 6.25 years on test samples.
• ELOVL2 was identified as a novel age predictive marker for saliva.
• The model performed adequately on saliva from cigarette butts (MAD = 7.65 years), unaffected by smoking habits.
• MS-HRM is a rapid (2 hours) and cost-effective method.

Conclusions:

• A novel MS-HRM-based age prediction model for saliva samples has been successfully developed and validated.
• This method offers a quick, cost-effective, and accurate approach for forensic age estimation.
• The model is applicable to challenging forensic samples, such as those found at crime scenes.