Objective Voltage‐gated sodium channels (SCNs) share similar amino acid sequence, structure, and function. Genetic variants in the four human brain‐expressed SCN genes SCN1A/2A/3A/8A have been associated with heterogeneous epilepsy phenotypes and neurodevelopmental disorders. To better understand the biology of seizure susceptibility in SCN‐related epilepsies, our aim was to determine similarities and differences between sodium channel disorders, allowing us to develop a broader perspective on precision treatment than on an individual gene level alone. Methods We analyzed genotype‐phenotype correlations in large SCN‐patient cohorts and applied variant constraint analysis to identify severe sodium channel disease. We examined temporal patterns of human SCN expression and correlated functional data from in vitro studies with clinical phenotypes across different sodium channel disorders. Results Comparing 865 epilepsy patients (504 SCN1A, 140 SCN2A, 171 SCN8A, four SCN3A, 46 copy number variation [CNV] cases) and analysis of 114 functional studies allowed us to identify common patterns of presentation. All four epilepsy‐associated SCN genes demonstrated significant constraint in both protein truncating and missense variation when compared to other SCN genes. We observed that age at seizure onset is related to SCN gene expression over time. Individuals with gain‐of‐function SCN2A/3A/8A missense variants or CNV duplications share similar characteristics, most frequently present with early onset epilepsy (<3 months), and demonstrate good response to sodium channel blockers (SCBs). Direct comparison of corresponding SCN variants across different SCN subtypes illustrates that the functional effects of variants in corresponding channel locations are similar; however, their clinical manifestation differs, depending on their role in different types of neurons in which they are expressed. Significance Variant function and location within one channel can serve as a surrogate for variant effects across related sodium channels. Taking a broader view on precision treatment suggests that in those patients with a suspected underlying genetic epilepsy presenting with neonatal or early onset seizures (<3 months), SCBs should be considered.

Variants in the SCN1A gene are associated with a wide range of disorders including genetic epilepsy with febrile seizures plus (GEFS+), familial hemiplegic migraine (FHM), and the severe childhood epilepsy Dravet syndrome (DS). Predicting disease outcomes based on variant type remains challenging. Despite thousands of SCN1A variants being reported, only a minority has been functionally assessed.We review the functional SCN1A work performed to date, critically appraise electrophysiological measurements, compare this to in silico predictions, and relate our findings to the clinical phenotype.Our results show, regardless of the underlying phenotype, that conventional in silico software correctly predicted benign from pathogenic variants in nearly 90%, however was unable to differentiate within the disease spectrum (DS vs. GEFS+ vs. FHM). In contrast, patch-clamp data from mammalian expression systems revealed functional differences among missense variants allowing discrimination between disease severities. Those presenting with milder phenotypes retained a degree of channel function measured as residual whole-cell current, whereas those without any wholecell current were often associated with DS (p = .024).These findings demonstrate that electrophysiological data from mammalian expression systems can serve as useful disease biomarker when evaluating SCN1A variants, particularly in view of new and emerging treatment options in DS. K E Y W O R D SDravet syndrome, electrophysiology, familial hemiplegic migraine, functional testing, GEFS+, patch-clamp, SCN1A *Andreas Brunklaus and Stephanie Schorge contributed equally to this study.

Missense variant interpretation is challenging. Essential regions for protein function are conserved among gene-family members, and genetic variants within these regions are potentially more likely to confer risk to disease. Here, we generated 2871 gene-family protein sequence alignments involving 9990 genes and performed missense variant burden analyses to identify novel essential protein regions. We mapped 2,219,811 variants from the general population into these alignments and compared their distribution with 76,153 missense variants from patients. With this gene-family approach, we identified 465 regions enriched for patient variants spanning 41,463 amino acids in 1252 genes. As a comparison, by testing the same genes individually, we identified fewer patient variant enriched regions, involving only 2639 amino acids and 215 genes. Next, we selected de novo variants from 6753 patients with neurodevelopmental disorders and 1911 unaffected siblings and observed an 8.33-fold enrichment of patient variants in our identified regions (95% C.I. = 3.90-Inf, P-value = 2.72 × 10 −11). By using the complete ClinVar variant set, we found that missense variants inside the identified regions are 106-fold more likely to be classified as pathogenic in comparison to benign classification (OR = 106.15, 95% C.I = 70.66-Inf, P-value < 2.2 × 10 −16). All pathogenic variant enriched regions (PERs) identified are available online through "PER viewer," a user-friendly online platform for interactive data mining, visualization, and download. In summary, our gene-family burden analysis approach identified novel PERs in protein sequences. This annotation can empower variant interpretation.