SCN1A pathogenic variants do not have a distinctive blood-derived DNA methylation signature

Abstract

DNA methylation signatures ("episignatures") can be used as biomarkers of genetic aberrations, clinical phenotypes, and environmental exposures in rare diseases. Episignatures are utilized in molecular diagnostics and can clarify variants of uncertain significance. A growing number of disease genes, including epilepsy genes, exhibit robust and reproducible episignatures. However, whether SCN1A, the most prominent epilepsy gene, has one or more episignatures has not yet been determined. We generated genome-wide DNA methylation data and performed episignature analysis on 64 individuals with Dravet syndrome due to pathogenic loss-of-function (LOF) variants in SCN1A and seven individuals with early infantile SCN1A developmental and epileptic encephalopathy due to pathogenic gain-of-function (GOF) variants in SCN1A, relative to a large reference database of controls and rare disease episignature-positive cohorts. We analyzed all samples with LOF variants together and performed separate analyses for missense, nonsense, and GOF variant cohorts. A reproducible blood-derived episignature was not evident in any of the cohorts using current analytical approaches and reference data.

Keywords: SCN1A; DNA methylation; Dravet syndrome.

FIGURE 1

DNA methylation profiling of SCN1A variants causing Dravet syndrome (DS): (A) all pathogenic SCN1A variants (n = 64), (B) pathogenic SCN1A nonsense variants (n = 28), and (C) pathogenic SCN1A missense variants (n = 23) used to train the model. In each panel, the top plots are heatmaps. No clear clustering was observed between cases (red), controls from different batches (blue), and controls from the shared batches (dark blue). The middle plots are multidimensional scaling. No clear separation was observed between cases (red), controls from different batches (blue), and controls from the shared batches (dark blue). The bottom plots are methylation variant pathogenicity (MVP) plots. MVP scores are presented for all the SCN1A cases as well as cases from 106 other neurodevelopmental disorders from the EpiSign Knowledge Database (EKD). The support vector machine (SVM) classifier was trained using SCN1A cases (“DS” for all variants, “nonsense,” and “missense”), matched controls, 75% of other controls from the EKD, and 75% of other neurodevelopmental disorders from the EKD (blue). The remaining 25% of controls and 25% of other neurodevelopmental disorders from the EKD were used for testing (gray). The model lacked specificity, as many samples from the other disorders also yielded high MVP scores.

FIGURE 2

DNA methylation profiling of SCN1A gain‐of‐function (GOF) variants causing early infantile encephalopathy. (A) Heatmap. Cases with pathogenic GOF variants in SCN1A (red) were clustered with controls from different batches (blue). No robust clustering was observed. (B) Multidimensional scaling plot. SCN1A GOF samples (red) did not separate from controls (blue). (C) Methylation variant pathogenicity (MVP) plot. The support vector machine classifier was trained using SCN1A GOF cases (n = 7), matched controls, 75% of other controls from the EpiSign Knowledge Database (EKD), and 75% of other neurodevelopmental disorders from the EKD (blue). The remaining 25% of controls and 25% of other neurodevelopmental disorders from the EKD were used for testing (gray). The model was not of full specificity, as cases from other disorders also yielded high MVP scores.