Greenwood Genetic Center (GGC) in collaboration with scientists from Canada have identified unique epigenetic signatures for nine neurodevelopmental disorders lending to a better method of diagnosis for disorders with much clinical overlap. The epigenetic signatures were developed through methylation analysis and were reported in The American Journal of Human Genetics .
The team investigated 14 neurodevelopmental disorders caused by genes that encode the cell's epigenetic machinery – the components that read, write and erase post-translational signals on DNA and histones and remodel the DNA. Of the 14 disorders evaluated, nine revealed unique methylation signatures including Sotos syndrome , Kabuki syndrome, CHARGE and ATRX.
"Many of the disorders have studied the clinical overlap, which makes sense of the genes causing each of these disorders," said Charles Schwartz, co-author of the study.
Charles Schwartz added, "What was interesting is that although the genes function is somewhat similar manners, nine of the disorders showed a unique epigenetic signature with minimal overlap."
The study authors suggest that these unique epigenetic signatures could be useful in concurrently screening for multiple syndromes with a high degree of sensitivity and specificity. Also, based on the degree of overlap between these signatures.
In addition, these unique signatures could also provide an innovative approach in solving ambiguous cases presenting with variants of uncertain significance from targeted gene sequencing, whole exome sequencing or whole genome sequencing. "Our results also led to the development of an epigenetic echo model for how these syndrome-specific signatures arise and are maintained," said Schwartz.
"The echo model states that an initial epigenetic dysfunction arises during early development, an broader methylation legacy or echo remains throughout the genome leading to the unique signature." This may help explain the variability and phenotypic overlap among these disorders. "
Benefits of this approach to diagnosis and variant classification include the lack of a need for parental samples and the potential of functional results to be obtained from blood samples rather than other inaccessible target tissues such as brain.
This method would also have the potential to identify a diagnosis when the mutation is in a noncoding region , and the classification of rare missense and in-frame variants that are often uninterpretable.
The team concludes that a single machine learning classification tool built on the combined signatures of these disorders, can accurately make the diagnosis . This method is not only distinguished from these other disorders, but also with a low probability of patients with other forms of intellectual and / or developmental delay.