Multi-omics. An emerging avenue of research for investigating the underlying architecture of human disease is the development of multi-omics approaches. Integration and analysis of large-scale data generated from genome sequencing alongside other -omics technologies including transcriptomics, proteomics, and metabolomics, enable a more comprehensive and nuanced insight into biological systems that underlie disease. However, in contrast to genomic data, the generation of multi-omics data remains expensive, time-consuming, and is typically limited in large-scale population studies. In a recent publication, Xu and collaborators developed a model predicting >17,000 multi-omic traits from genomic profiles across 50,000 people. Here is a brief review of their paper, with a focus on the relevance of developing multi-omics resources in 2023.
Language. In the recent years, there has been an emerging focus on the phenotypic characterization of genetic epilepsies and neurodevelopmental disorders. With a rise in large-scale studies leveraging massive and complex genetic and phenotypic datasets, understanding how we make sense of big data becomes critical. However, determining what are clinically meaningful findings and communicating the conclusions we make from these datasets remain a challenge. While we typically think about data in the scope of ‘n’s, probabilities, and p-values, there is understated value in the visualization of information. Here is a different way of how we think about scientific communication and how we can “make data speak in childhood epilepsies.”
FIRES. As a rare and severe epilepsy syndrome, febrile-infection related epilepsy syndrome (FIRES) is characterized by refractory status epilepticus (RSE) preceded by a febrile illness and often leads to prolonged hospitalizations, cognitive impairment, and intractable epilepsy. There are currently no clear causative etiologies identified in FIRES, and the underlying genetic architecture remains elusive. Here is a brief summary of our recent manuscript on the genetics of FIRES and refractory status epilepticus. This is what we learned about one of the most enigmatic conditions in child neurology.
Precision medicine. This post continues the discussion on how we can make sense of clinical data in the absence of outcomes in the context of precision medicine – a concept that drives much of what we do on a research basis. The fundamental idea is that clinical care in pediatric epilepsies can be personalized and tailored to underlying etiologies. With continual progress in gene curation and variant interpretation alongside clinical knowledge, we typically expect that treatment suggestions are immediately implemented after the discovery of the causative genetic etiology. For example, a child with early onset epileptic encephalopathy is found to have a gain-of-function variant in SCN8A and is almost immediately started on a sodium channel blocker such as Trileptal. However, to what extent is this the case? In the context of precision medicine, how precise are we exactly?
Phenotypic bottleneck. This is another post in the “phenotypic atomism series,” what has become our lab’s philosophy in how we think about and work with longitudinal clinical data. However, before we introduce another dimension to the phenotypic atom, let me first revisit the idea of the “phenotypic bottleneck” – a concept that had piqued my interest three years ago and led me to join the lab. In brief, in contrast to established pipelines for large-scale analysis of sequencing data, our ability to analyze clinical data at scale remains more limited. As a result, phenotypic characterization lags behind gene discovery, even with tremendous progress in the last few years. A major challenge stems from the inherent nature of working with multi-dimensional longitudinal clinical data: it can be sparse and incomplete at times. However, how much of the unknown is truly unknown?
Pandemic. This year’s Annual Meeting of the American Epilepsy Society (AES) was the 75th meeting, but it was a meeting like no other. #AES2021 was the first in-person meeting for the international epilepsy community with many international participants unable to join due to local restrictions and the US-based audience split between participating in-person and joining remotely. However, despite the unusual format, this year’s meeting was bustling and full of excellent science. Here are my five takeaways from AES 2021. 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