ReviewGenes2026
SCN4A Channelopathies: From Disease Mechanisms to Variant Interpretation.
Review in Genes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
SCN4A encodes the skeletal-muscle voltage-gated sodium channel NaV1.4. Pathogenic variation in this gene produces fundamentally different disease mechanisms, including dominant alpha-pore gain of function, dominant S4 gating-pore currents, and reduced channel availability, with severe biallelic loss of function causing congenital myopathy and fetal hypokinesia. This heterogeneity makes variant interpretation difficult because formal variant classification, the direction of channel dysfunction, and patient-level disease attribution are closely related but distinguishable conclusions. In this review, we synthesize the clinical, genetic, electrophysiological, structural, and functional evidence relevant to SCN4A variant interpretation, with particular attention to missense variants of uncertain significance. We examine how inheritance, channel topology, phenotype, population data, segregation, RNA evidence, regional and residue-level context, and mechanism-matched functional assays can be integrated within the ACMG/AMP framework. Published pathogenic-enriched regions, gnomAD regional missense constraint, and same-residue observations may contribute to variant interpretation when the requirements of an applicable ACMG/AMP criterion are met; when they have not been specifically validated or calibrated for SCN4A, they are best used as contextual information to prioritize additional evidence generation. Based on this literature, we organize these evidence domains into a practical phenotype-first, mechanism-informed workflow for SCN4A variant interpretation. This workflow is not intended as an alternative to or extension of ACMG/AMP but as an SCN4A-specific application of established variant-interpretation principles in a gene associated with multiple inheritance patterns and directionally distinct disease mechanisms. After technical confirmation of a candidate variant, as well as phenotype and inheritance context guide selection of the relevant disease model, appropriately validated functional evidence may contribute directly to ACMG/AMP classification, while functional mechanism and patient-level disease attribution are documented as related but distinguishable interpretative outputs. The same approach applies when a variant is identified through genotype-first sequencing: interpretation should return to deliberate phenotyping, inheritance assessment, electrophysiological characterization, and consideration of the differential diagnosis before disease causality is inferred.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.