Splice-site variants in neurology: from molecular mechanisms to clinical interpretation - a focused review.

Variants that disrupt pre-mRNA splicing represent a historically underrecognized proportion of pathogenic alleles in Mendelian neurological disorders, accounting for an estimated 15-30% of disease-causing variants. Conventional clinical genomic pipelines frequently overlook these variants because analysis and reporting have traditionally focused on protein-coding regions, with intronic and synonymous positions assigned lower priority. The central practical problem this review addresses is therefore not whether splicing matters in neurogenetic disease - that is well established - but how a splice-altering variant identified on a diagnostic report can be reliably identified, experimentally validated, and translated into a clinical diagnosis or therapeutic decision. Deep learning-based splice prediction tools such as SpliceAI and Pangolin, RNA sequencing, long-read transcriptomics, and minigene functional assays have collectively improved diagnostic yield in previously unsolved neurogenetic cohorts, particularly by identifying deep intronic pseudoexon variants and leaky canonical splice-site alleles. Nevertheless, accurate variant classification remains challenging: the Pathogenic Very Strong 1 (PVS1) criterion of the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) framework requires careful gene- and variant-specific adaptation when applied to splicing, prediction tools disagree with one another and are least reliable outside the core splice-site consensus, and RNA-based validation is constrained by the tissue- and developmental-stage-restricted nature of splicing itself. This review synthesizes the molecular basis of splice-altering variants, current standards for their nomenclature and computational prediction, the practical strengths and limitations of RNA-level validation, and their contribution to epilepsy, hereditary neuropathy, ataxia, dystonia, mitochondrial disease, neuromuscular disorders, and the newly recognized spliceosomal small nuclear RNA (snRNA) neurodevelopmental disorders. We place particular emphasis on the diagnostic and therapeutic decisions that depend on getting this interpretation right, on the unresolved limitations of current prediction and validation tools, and on priorities for embedding RNA-level diagnostics more routinely into clinical neurogenetic practice.