Genetic analyzer-dependent DNA methylation detection and its application to existing age prediction models

Area of Science:

• Epigenetics
• Biomarker Discovery
• Forensic Science
• Gerontology

Background:

• DNA methylation is a key biomarker for estimating human age.
• The SNaPshot assay on genetic analyzers is used for DNA methylation analysis.
• Comparisons of DNA methylation data across different genetic analyzers are lacking.

Purpose of the Study:

• To evaluate differences in DNA methylation measurements between Applied Biosystems 3130, 3500, and SeqStudio genetic analyzers.
• To assess the impact of these differences on age estimation models.
• To develop a correction method for harmonizing data across analyzers for accurate age prediction.

Main Methods:

• Analyzed identical blood, saliva, and methylated DNA samples on three genetic analyzers (3130, 3500, SeqStudio).
• Compared methylation values at five specific CpG sites (ELOVL2, FHL2, KLF14, MIR29B2C, TRIM59).
• Developed regression functions to correct data from 3500 and SeqStudio based on 3130 data.

Main Results:

• Methylation values consistently decreased in the order: 3130 > 3500 > SeqStudio.
• Using uncorrected 3500 and SeqStudio data in a 3130-based age model introduced significant errors.
• Corrected data from 3500 and SeqStudio achieved age prediction accuracy comparable to the original 3130 data.

Conclusions:

• Genetic analyzer type significantly impacts DNA methylation measurements used for age estimation.
• A data correction strategy is essential for inter-analyzer compatibility in epigenetic age prediction.
• Harmonized DNA methylation data ensures reliable application of age estimation models across different platforms.