Generation of DNA Methylation Signatures and Classification of Variants in Rare Neurodevelopmental Disorders Using EpigenCentral

Abstract

There are more than 700 genes that encode proteins that function in epigenetic regulation and chromatin modification. Germline variants in these genes (typically heterozygous) are associated with rare neurodevelopmental disorders (NDDs) characterized by growth abnormalities and intellectual and developmental delay. Advancements in next‐generation sequencing have dramatically increased the detection of pathogenic sequence variants in genes encoding epigenetic machinery associated with NDDs and, concurrently, the number of clinically uninterpretable variants classified as variants of uncertain significance (VUS). Recently, DNA methylation (DNAm) signatures, disorder‐specific patterns of DNAm change, have emerged as a functional tool that provides insights into disorder pathophysiology and can classify pathogenicity of variants in NDDs. To date, our group and others have identified DNAm signatures for more than 60 Mendelian neurodevelopmental disorders caused by variants in genes encoding epigenetic machinery. There is broad interest in both the research and clinical communities to develop and catalog DNAm signatures in rare NDDs, but there are challenges in optimizing study design considerations and availability of platforms that integrate bioinformatics tools with the appropriate statistical framework required to analyze genome‐wide DNAm data. We previously published EpigenCentral, a platform for analysis of DNAm data in rare NDDs. In this article, we utilize the published Weaver syndrome dataset to provide step‐by‐step protocols for using EpigenCentral for exploratory analysis to identify DNAm signatures and for classification of NDD variants. We also provide important considerations for experimental design and interpretation of DNAm results. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Exploratory analysis to identify disorder‐specific DNAm signatures Basic Protocol 2: Classification of variants associated with neurodevelopmental disorders

Missense variant in SRCAP with distinct DNA methylation signature associated with non‐FLHS SRCAP‐related neurodevelopmental disorder

“…Classification using machine learning model and EpigenCentral: Next, we classified the individual with SRCAP missense variant (Patient 1) along with eight control samples using the established machine learning models for non‐FLHS SRCAP ‐related NDD, FLHS, and 15 other NDDs using the online knowledgebase EpigenCentral (https://epigen.ccm.sickkids.ca) (Turinsky et al, 2020). Raw IDAT files of Patient 1 and eight control samples were uploaded to EpigenCentral and analyzed using the disease classification tool against 17 machine learning based classifiers specific to 17 distinct NDDs as previously described (Awamleh et al, 2022).…”

CRISPR/Cas9‐based functional genomics strategy to decipher the pathogenicity of genetic variants in inherited metabolic disorders

“…The use of additional omic approaches beyond WES/WGS, such as RNAseq, has improved the diagnostic workflow in terms of variant prioritization and also providing, in some cases, functional evidence to reduce the uncertainty of the identified variants. [3][4][5][6][7] However, in most cases, the elucidation of the physiological impact of a given VUS requires precise functional validation studies, which can be performed in patient samples, if available, or alternatively in more sophisticated models developed in the laboratory. As it can be inferred, one of the major bottlenecks in the diagnostic workflow of inherited genetic disorders is the determination of whether a VUS can be classified as a disease-causing variant or, on the contrary, as a benign variant not associated with disease risk.…”

“…By definition, VUS are those variants that have been identified by genetic testing for which enough information is not yet available to provide a useful clinical interpretation. The use of additional omic approaches beyond WES/WGS, such as RNAseq, has improved the diagnostic workflow in terms of variant prioritization and also providing, in some cases, functional evidence to reduce the uncertainty of the identified variants 3–7 . However, in most cases, the elucidation of the physiological impact of a given VUS requires precise functional validation studies, which can be performed in patient samples, if available, or alternatively in more sophisticated models developed in the laboratory.…”

Episignatures in practice: independent evaluation of published episignatures for the molecular diagnostics of ten neurodevelopmental disorders

“…Several research groups have repeatedly provided evidence of the effectiveness of this concept from a general point of view (18,(22)(23)(24)(25). More than 50 episignatures have been reported in the literature so far, in the context of more than 60 syndromes (11,20).…”