Single-nucleotide polymorphisms, or SNPs, have long anchored genetic association studies. They can reveal inherited susceptibility, population structure, and variants linked with specific traits. Yet SNPs provide a largely static view of biology. They do not fully explain how aging, nutrition, inflammation, environmental exposure, and behavior influence gene regulation over time.
DNA methylation analysis adds this dynamic layer. Methyl groups attached primarily to cytosine-phosphate-guanine sites can affect transcription without altering the underlying DNA sequence. Because methylation patterns change across tissues and throughout life, they offer a valuable window into biological state.
The challenge is scale. A single methylation dataset may contain hundreds of thousands of measured sites, while each sample may also include genotype, transcriptomic, proteomic, clinical, and lifestyle variables. Traditio