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?
Natural History. Over the last few years, there has been a renewed interest in outcomes and natural history studies in genetic epilepsies. If one of the main goals of epilepsy genetics is to improve the lives of individuals with epilepsy by identifying and targeting underlying genetic etiologies, it is critically important to have a clear idea of how we define and measure the symptoms and outcomes that characterize each disorder over a lifetime. However, our detection of underlying genomic alterations far outpaces what we know about clinical features in most conditions – outcomes such as seizure remission or presence of intellectual disability are not easily accessible for large groups of individuals with rare diseases. In this blog post, I try to address the phenotypic bottleneck from a slightly different angle, focusing on how we think about phenotypes in the first place. Continue reading
Filling in. As Dennis is current fully engaged in the Helsinki meeting, I am filling in for him to present the most relevant publications in the field published in the last two weeks. This week’s publications were about functional studies, phenotype delineations, and novel gene findings. Continue reading
The backbone. As we have started a new round for BENCH introductory sessions with new collaborators, I thought that it might be timely to talk a little bit about our BENCH phenotype database and the concepts behind it. In addition to the purely technical aspects, there is a more fundamental question behind this: how do we want to document and store epilepsy phenotypes for research purposes, how do we find the balance between precision and efficiency? Continue reading