Journal: Frontiers in Molecular Biosciences

Article Title: Type-2 diabetes epigenetic biomarkers: present status and future directions for global and Indigenous health

doi: 10.3389/fmolb.2025.1502640

Figure Lengend Snippet: Comparison of candidate gene studies, microarrays, and methylome sequencing approaches for DNA methylation biomarker studies. Genome CpG coverage varies significantly across methods. Candidate gene approaches can typically assess tens to thousands of CpG sites, microarrays (such as Illumina 450 k and 850 k platforms) cover hundreds of thousands of CpG sites, and methylome-wide approaches capture nearly all 29.4 million CpG sites within the human genome (hg38, autosomes, X and Y). Base- and strand-level resolution highlights the ability to measure methylation at single-CpG resolution on individual DNA strands, and is only practically feasible with methylome sequencing. Microarrays provide site-level resolution based on reference genome sequence, but do not typically distinguish between strands, while candidate gene approaches are limited to specific loci typically without strand information. The potential for participant re-identification increases with the scale and resolution of the data. Sequencing-based methods pose a higher risk due to the comprehensive and individual-specific nature of the genomic data, requiring robust data governance and privacy protections. Microarrays and candidate gene studies present lower re-identification risks, as they capture less data and provide limited genomic context. Relative cost per sample reflects the resources needed for data production and analysis. Candidate gene approaches are the most cost-effective, while microarrays offer a balance of affordability, with methylome sequencing being the most costly due to sequencing and computational demands. Raw data sizes illustrate the storage demands of each method. Candidate gene studies generate minimal data (<10 MB per sample), while microarrays produce 16–20 MB per sample. In contrast, methylomes at 30x coverage produce approximately 110 GB of raw data per sample (compressed FASTQ format) and about 62 GB of mapped data (CRAM format), accounting for ∼10% data loss through PCR duplicates and read filtering, however this can be highly variable. The computational resources required increase with data complexity. Candidate gene and microarray studies can typically be processed on desktop computers or small servers, while methylome analyses often require servers or high-performance computing (HPC) environments. The shift to advanced computing infrastructure is driven by the large datasets and computationally intensive analyses associated with sequencing-based studies.

Article Snippet: The latest Illumina EPIC DNA methylation microarray (900 K) includes the addition of probes to study open chromatin as well as additional enhancer locations ( ).

Techniques: Comparison, Sequencing, DNA Methylation Assay, Biomarker Discovery, Methylation, Microarray