Evaluation of DNA methylation episignatures for diagnosis and phenotype correlations in 42 Mendelian neurodevelopmental disorders

As these GEITP pages have often discussed, any multifactorial trait (e.g. type-2 diabetes, hypertension, schizophrenia) reflects contributions of genetics (differences in DNA sequence), epigenetic effects (DNA changes without sequence differences), environmental factors, endogenous influences (existing conditions such as heart and kidney disorders), and each person’s unique microbiome. Epigenetics can be further divided into DNA methylation, RNA-interference, histone modifications and chromatin remodeling. This article is about DNA methylation “signatures” (i.e. the cumulative DNA methylation patterns occurring at multiple CpG dinucleotides across the whole genome). Specific patterns in these methylomes of individuals with defined congenital syndromes have recently received particular attention in clinical settings.

It is now realized that DNA methylation patterns (‘episignatures’) — represent an early event during embryo development, and thus are present in numerous tissues of the affected individuals, including peripheral blood. The stability of these DNA-methylation patterns provides ground for their use in clinical diagnosis. Disorders studied so far have demonstrated that the observed episignatures are specific to the syndromes in which they were discovered, and that the observed patterns occur consistently across all individuals affected with the same disease (!!). This discovery makes it likely that DNA-methylation episignatures will have a great potential to diagnose numerous congenital disorders — a success that frequently cannot be achieved by conventional clinical and molecular assessments.

In April 2019, the first clinical genome-wide DNA-methylation assay, ‘‘EpiSign,’’ was launched; computational assessment of DNA-methylation data for 14 syndromes was shown to be highly successful, due to concurrent assessment of all the conditions and supervised and unsupervised classification algorithms. Undoubtedly, the number of conditions with episignatures to be included in this type of analysis is expected to rise exponentially. Authors [see attached article] evaluated 42 neurodevelopmental syndromes, and describe 34 distinct and reliable episignatures. Through development of a supervised classification algorithm, capable of simultaneous assessment of 34 episignatures — authors show that classification of closely related episignatures is also feasible.

Authors [see attached article] examined emerging patterns of overlap, as well as similarities and hierarchical relationships across these episignatures — to highlight their key features — as they are related to genetic heterogeneity, gene-dose effect, unaffected carrier status, and incomplete penetrance (penetrance of a disease-causing mutation is the proportion of individuals with the mutation who exhibit clinical symptoms). Authors demonstrate the necessity of multiclass modeling for accurate genetic variant classification, and they show how disease classification (using a single episignature at a time) can sometimes lead to classification errors in closely-related episignatures. Authors demonstrate the utility of this tool in resolving ambiguous clinical cases, and identification of previously undiagnosed cases, through mass screening of a large cohort of subjects with neurodevelopmental delays and congenital anomalies. This exciting study more than doubles the number of published syndromes with DNA-methylation episignatures. This novel episignature approach opens up new avenues for accurate diagnosis and clinical assessment in individuals affected by these disorders. 😊


Am J Hum Genet 5 Mar 2020; 106, 356–370

This entry was posted in Center for Environmental Genetics. Bookmark the permalink.