Water. Three years ago, on Rare Disease Day 2023, I published a blog post comparing phenotypes to water, existing in three phases: solid, liquid, and vapor. At the time, I was trying to make a simple point. Rare diseases are not defined by their first description, nor only by a checklist of features. They are moving targets. Here is what has changed since then.
Category Archives: SYNGAP1
Signals in the noise – qEEG patterns in genetic epilepsies
qEEG. The electroencephalogram is one of the oldest tools in neurology. We use it every day to diagnose and monitor brain function, yet, even in the era of genomic medicine, most of our EEG interpretation still relies on visual inspection, a human reading of squiggled traces. In a recent publication in Neurology, we asked whether the information embedded in these signals could be measured more objectively in children with STXBP1, SCN1A, and SYNGAP1-related disorders. Here is the story on how we identified hidden signals in the EEG tracings of individuals with genetic epilepsies.
Revisiting SYNGAP1 through a disease-concept model
DCM. This is my second blog post on SYNGAP1-related disorders within a month, which highlights how fast the area of clinical trial readiness is moving for one of the most common synapse disorders. Over the last few years, we have become more and more attuned to the fact that readying genetic epilepsies for clinical trials requires a systematic approach. The first step in this process is a disease concept model (DCM). In a recent publication in Pediatric Neurology, the authors comprehensively mapped the lived experience of SYNGAP1-related disorders. Here is what the authors found.
The edge of SYNGAP1 – familial variants in a complex neurodevelopmental disorder
Bridge the gap. There are very few conditions that I have learned as much about in the last three years as SYNGAP1-related disorders. We just passed the critical milestone of 100 SYNGAPians followed in our clinical trial–grade natural history study (SYNGAP1 ProMMiS). In addition, our study using real-world data across more than 2,500 patient years came online earlier this year. We are becoming more familiar with the range of reflex seizures observed in this condition and the unique fingerprint on the sensory profile that characterize its behavioral features. In a recent publication, we explored another aspect of SYNGAP1 that had been neglected so far. We tried to understand what happens when SYNGAP1 variants run in families and what this means for trial readiness and precision therapies.
Phenotypes are like water – Rare Disease Day 2023
Phases. Today is Rare Disease Day. I would like to use this opportunity to explain some of the phenotype science that is critical for rare diseases. In contrast to common disorders, rare diseases face an unusual challenge. Once identified, the overall rareness of these condition poses the question of where phenotypes begin and where they end. For rare genetic disorders, is the phenotype of the first individual identified with a rare disease characteristic, or is there a larger spectrum that we should be aware of? Enter the various approaches to phenotype science that aim to decipher the full depth of clinical features associated with rare diseases. In order to understand the various approaches to rare diseases phenotypes, I would like to suggest a somewhat unusual analogy: phenotypes are like water.
The genetics of Doose Syndrome or Myoclonic Astatic Epilepsy
MAE. There are many distinct childhood epilepsy syndromes that we have become critically aware of in the genomic era as they are linked to prominent genetic causes, including Dravet Syndrome (SCN1A) and Epilepsy of Infancy with Migrating Focal Seizures (KCNT1). However, there are many other epilepsy syndromes where a genetic cause has long been suspected, but has remained elusive. One of the epilepsy syndromes that has largely remained unexplored is Doose Syndrome, also referred to as Myoclonic Astatic Epilepsy (MAE) or Epilepsy with Myoclonic-Atonic Seizures. In a recent study in Epilepsia, we explored the genetic architecture of Doose Syndrome and identified monogenic causes in 14% of individuals, including SYNGAP1, NEXMIF (KIAA2022), and SLC6A1. Our study suggests that Doose Syndrome is genetically heterogeneous, possibly with a distinct genetic landscape. Continue reading