Phenotypic analysis of 11,125 trio exomes in neurodevelopmental disorders https://t.co/vJEO0DTdeW #biorxiv_genetic

Advances in big data and omics: Paving the way for discovery in childhood epilepsies

Thanks to all our collaborators - @a_brunklaus @cmbosselmann @DLSneurocyclist @nupharm1 @LalDennis and many not found on Twitter - for their contributions!! https://t.co/SjJPDvBjll

So, we have curated functional data on 100s of ion channel variants... ...and defined how these mathematically fit into variant interpretation We hope this can be a meaningful addition for #genetic #diagnosis for more individuals with an #epilepsy-related #channelopathy! (7/7)

Here's a question @IngoHelbig posed back in summer 2020… How many individuals with #epilepsy #channelopathy can existing func. data apply to? For #SCN2A, across >300 individuals, we can now say that: More than 50% have func. info that could inform #geneticdiagnosis! (6/7)

For accessibility e.g., in clinical settings, we also: 1. built FENICS, a biomedical #ontology to capture our quantization of functional data in standard labels 2. deposited func. data on >250 variants in #SCN1A #SCN2A #SCN3A #SCN8A #KCNQ2 in #ClinVar using FENICS format (5/7)

Comparing benign variants from @nupharm1 lab w/ pathogenic variants... ...we defined the #ACMG/#AMP #PS3 criterion for #SCN1A #SCN2A #SCN3A #SCN8A #KCNQ2 Our results are part of the rule specifications of the #ClinGen #Epilepsy Sodium Channel #VCEP (4/7) https://t.co/ZlMDrnUo9o

In this manuscript, we explored that first question… ...on the implications of #electrophysiology data for #genetic #diagnosis We adapted @SarahBrnich et al’s #ClinGen rules framework @ClinGenResource... ...to multidimensional #voltageclamp data (3/7) https://t.co/6KxYc2hC7K

Functional data can help with important questions in #SCN1A #SCN2A #SCN3A #SCN8A #KCNQ2 disorders e.g.: - Is a variant #pathogenic? - What clinical features do we expect? - Do we expect a patient's #epilepsy to respond to sodium channel blockers? (2/7) https://t.co/IGvUYpoypm

Excited to share our new preprint!! Optimizing clinical interpretability of functional evidence in #epilepsy-related ion channel variants @IngoHelbig @nupharm1 🧵 Thread below! 👇 (1/n)

Our new preprint on #STXBP1 is now online! Delineating clinical and developmental outcomes in STXBP1-related disorders A huge thank you to @IngoHelbig and team https://t.co/9BIhppS8Am Here is a brief thread...

...all in all, an awesome presentation and congrats to Alicia! @IngoHelbig @SarahRuggieroGC @KimThalwitzer @AliciaH30833263

Delineating such subgroups can aid in clinical guidance... ...e.g., those with exon 10a variants have higher rates of refractory epilepsy, severe developmental delay, and cortical visual impairment... ...and nearly 70% of these individuals achieve some level of speech

We previously identified exon 10a as defining the primary brain #isoform of DNM1 and reported 10 individuals with variants in this exon and DNM1-related disorder - thread linked here: https://t.co/5tMBhlHyXX

Individuals with variants in exon 10a have a "disease within a disease..." ...more similar to each other than the rest of the DNM1 cohort... ...but still falling within the overall spectrum of DNM1-related disorders

"DNM1 is a strikingly homogeneous condition..." ...comparison of clinical presentations using @hp_ontology of three 'fully digitized' #genetic #epilepsies... ...individuals with DNM1 are more similar to each other than individuals with #SCN2A- or #STXBP1-related disorders

First, a look at the genetic landscape... ...missense variants are known to occur in the GTPase and middle domains... ...by comparing to population variant data, we can identify mutational hotspots... ...mapping each residue to an evidence level per #ClinGen #ACMG rules

Today, Alicia Harrison presented her exciting genetic counseling thesis work on #DNM1-related disorders! Assessment of clinical/phenotypic data across >70 individuals with this ultra-rare #genetic #epilepsy @IngoHelbig @SarahRuggieroGC @KimThalwitzer @CHOPDBHi @AliciaH30833263

Enriching representation learning using 53 million patient notes through human phenotype ontology embedding ⁦@hp_ontology⁩

Honored to have won best poster at the #AIinMedicine conference. Our work showed that through the electronic medical records, we can predict genetic epilepsy diagnoses years prior to a clinician. #Epilepsy, #Genetics, #HelbigLab, #CHOP, #ENGIN, #PennBioengineering