KCNQ2. I have to admit we have not written about KCNQ2 for a while, which does not do justice to the role of KCNQ2 in human epilepsies. KCNQ2-related epilepsies represent some of the most common genetic epilepsies and almost exclusively present with neonatal seizures. Historically, KCNQ2 was identified in families with self-limiting neonatal seizures. Subsequently, disease-causing variants were also identified in neonatal developmental and epileptic encephalopathies (DEEs). While self-limiting epilepsies were attributed to protein-truncating variants, KCNQ2-related DEEs are attributed to dominant-negative variants. However, as in many other DEEs, this conceptual black-and-white distinction is somewhat oversimplified, and the genotype-phenotype correlation in KCNQ2-related disorders is more complex. In a recent study, we assessed a total of 81 KCNQ2 variants’ functional effects in parallel, leading to some unexpected results about the function of disease-related KCNQ2 variants. Here is what this first large-scale electrophysiological analysis of an epilepsy-related ion channel told us. Continue reading
Semantic similarity. The phenotype era in the epilepsies has now officially started. While it is possible for us to generate and analyze genetic data in the epilepsies at scale, phenotyping typically remains a manual, non-scalable task. This contrast has resulted in a significant imbalance where it is often easier to obtain genomic data than clinical data. However, it is often not the lack of clinical data that causes this problem, but our ability to handle it. Clinical data is often unstructured, incomplete and multi-dimensional, resulting in difficulties when trying to meaningfully analyze this information. Today, our publication on analyzing more than 31,000 phenotypic terms in 846 patient-parent trios with developmental and epileptic encephalopathies (DEE) appeared online. We developed a range of new concepts and techniques to analyze phenotypic information at scale, identified previously unknown patterns, and were bold enough to challenge the prevailing paradigms on how statistical evidence for disease causation is generated. Continue reading
EMR. When we consider the natural history of rare diseases like the genetic epilepsies, we typically think about a lack of longitudinal data that contrasts with the abundant genetic information that is available nowadays – the so-called phenotyping gap. We typically suggest that we need to obtain this information in future prospective studies to better understand long-term outcome, response to medications, and potential early warning signs for an adverse disease course. However, a vast amount of clinical data is collected on an ongoing basis through electronic medical records (EMR) as a byproduct of regular patient care. In a recent study, our group built tools to mine the electronic medical records to assess the disease history of 658 individuals with known or presumed epilepsies using clinical information collected at more than 62,000 patients encounters across more than 3,200 patient years. Here is a brief summary of our first study on EMR genomics, an untapped resource that has the potential to improve our understanding of the genetic epilepsies. Continue reading
Early-onset epilepsies. In recent years, we have discovered several causative genes for severe epilepsies beginning in the first year of life, including KCNQ2, SCN2A, and STXBP1. Several studies have reported a high yield of diagnostic genetic testing, including NGS panel approaches and whole exome sequencing, particularly in patients with seizure onset in the neonatal period where detection rates are often reported to be above 50%. Two recent studies add to the growing pile of evidence that genetic testing, and in particular NGS-based testing methods, are valuable in the diagnostic workup of children presenting with seizures early in life. Will these two studies help push us towards a new consensus regarding genetic testing in epilepsy?