# Toward precision medicine in SCN3A variants-associated encephalopathies and epilepsy: optimizing genetic diagnosis and molecular subregional effects

## TL;DR

This study improves the diagnosis of SCN3A gene variants linked to severe brain disorders and epilepsy by evaluating genetic algorithms and subregional effects.

## Contribution

The study provides systematic benchmarks for SCN3A variant interpretation and identifies optimal thresholds for accurate pathogenicity assessment.

## Key findings

- Pathogenic SCN3A variants are more likely to occur in transmembrane regions, indicating subregional effects.
- Deep-learning tools like AlphaMissense show high accuracy and robust discrimination for variant classification.
- Gene-specific thresholds significantly enhance the performance of multiple prediction algorithms.

## Abstract

Variants in SCN3A gene encoding the voltage-gated sodium channel Nav1. 3 have been associated with severe developmental and/or epileptic encephalopathies, characterized by early-onset, drug-resistant seizures, malformations of cortical development, and profound neurodevelopmental impairment. Rapid clinical interpretation of SCN3A missense variants remains challenging. This study aimed to explore potentially reliable indicators in reflecting the pathogenicity of SCN3A variants, thereby promoting genetic diagnosis.

The disease-associated and benign/likely benign SCN3A missense variants were systematically collected via two independent epilepsy geneticists to curate high-confidence dataset. The molecular subregional effects were analyzed to explore possible genotype-phenotype correlation. The diagnostic performance of nineteen commonly used algorithms was systematically evaluated using ROC analysis and confusion matrices metrics such as accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC).

A total of 20 pathogenic, affecting 37 patients, and 45 benign/likely benign SCN3A variants were included. Pathogenic SCN3A variants were statistically more located in transmembrane regions than in other regions, suggesting possible subregional effects. Deep-learning-based tools incorporating structural data, AlphaMissense, demonstrated superior balanced accuracy (>90%) and robust discrimination (AUC > 0.96). Meta-predictors, such as BayesDel_addAF and ClinPred, also showed high sensitivity but lower specificity at default thresholds. Notably, applying gene-specific optimal thresholds significantly improved performance across multiple tools.

This study provides systematic benchmarks for algorithms in SCN3A-related DEEs. Integration of reliable algorithms with gene-specific thresholds into clinical variant interpretation pipelines could possibly refine the pathogenicity assessment of missense variants, subsequently informing timely risk stratification and personalized therapeutic strategies for affected patients.