Background: A subset of developmental disorders (DD) is characterized by disease-specific genome-wide methylation changes. These episignatures inform on the underlying pathogenic mechanisms and can be used to assess the pathogenicity of genomic variants as well as confirm clinical diagnoses. Currently, the detection of these episignature requires the use of indirect methylation profiling methodologies. We hypothesized that long-read whole genome sequencing would not only enable the detection of single nucleotide variants and structural variants but also episignatures.
Methods: Genome-wide nanopore sequencing was performed in 40 controls and 20 patients with confirmed or suspected episignature-associated DD, representing 13 distinct diseases. Following genomic variant and methylome calling, hierarchical clustering and dimensional reduction were used to determine the compatibility with microarray-based episignatures. Subsequently, we developed a support vector machine (SVM) for the detection of each DD.
Results: Nanopore sequencing-based methylome patterns were concordant with microarray-based episignatures. Our SVM-based classifier identified the episignatures in 17/19 patients with a (likely) pathogenic variant and none of the controls. The remaining patients in which no episignature was identified were also classified as controls by a commercial microarray assay. In addition, we identified all underlying pathogenic single nucleotide and structural variants and showed haplotype-aware skewed X-inactivation evaluation directs clinical interpretation.
Conclusion: This proof-of-concept study demonstrates nanopore sequencing enables episignature detection. In addition, concurrent haplotyped genomic and epigenomic analyses leverage simultaneous detection of single nucleotide/structural variants, X-inactivation, and imprinting, consolidating a multi-step sequential process into a single diagnostic assay.
Keywords: Developmental disorders; Episignatures; Long-read sequencing; Methylation; Methylome; Nanopore sequencing; Support vector machine; X-inactivation.
© 2024. The Author(s).