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.
Tag Archives: computational phenotyping
Walking in Memphis – TARGETing Epilepsy at St. Jude
Memphis, TN. Prior to this year’s AES meeting, the epilepsy genetics community descended upon St. Jude Children’s Research Hospital in Memphis. I had previously largely associated St. Jude with pediatric cancer treatment, but within the last few years, a large-scale pediatric neuroscience program was launched, putting Memphis on the epilepsy genetics map. And with Heather Mefford’s new lab, the program at St. Jude includes one of the major epilepsy genetics groups. While blogging about scientific meetings is always tricky, one particular quote from the first day struck me as particularly relevant for the current state of therapeutic development: “quick, but not too quick”. Here is where the field of epilepsy genetics and precision medicine finds itself at the end of 2022. Continue reading
Entering the phenotype era – HPO-based similarity, big data, and the genetic epilepsies
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
