Clinical Feasibility of Long-Read WGS for DNA Methylation Signature Analysis

DNA methylation (DNAm) signatures have emerged as valuable diagnostic biomarkers for rare genetic disorders. To date, the most widely used approach for establishing and validating these signatures has relied on array-based technologies. However, in clinical diagnostics, there is a growing shift from short-read sequencing (SRS) toward long-read sequencing (LRS) technologies. Recent advances in platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) enable direct assessment of DNAm from native DNA, offering improved resolution and reduced technical bias compared to array-based technologies. In this study, we compared DNAm profiles generated by LRS with those obtained from DNAm arrays. DNAm profiles of two individuals with pathogenic KMT2D variants were analyzed using DNAm arrays, LRS using PacBio and ONT, and ONT multiplexed sequencing with adaptive sampling. A support vector machine (SVM) classifier trained on array data, as well as the public classification platform EpigenCentral, yielded correct predictions for all LRS samples, underscoring the potential of LRS platforms in DNAm-based diagnostics. Our results suggest that DNAm profiles generated by LRS align well with DNAm signatures established using DNAm arrays, supporting their feasibility in clinical and research applications with the added benefit of simultaneous methylation and sequence analysis.