Work

AI-Assisted WES Variant Interpretation in Neurodevelopmental Disorders: A Narrative Review of Existing Tools

Year: 2026

Type: review

Abstract: Background/Objectives: Neurodevelopmental disorders (NDDs) comprise a heterogeneous group of conditions characterized by alterations in brain development and function, frequently associated with complex genetic architectures. The widespread implementation of next-generation sequencing, particularly whole-exome sequencing (WES), has substantially increased the identification of potentially disease-associated variants, shifting the major challenge from variant detection to their interpretation and prioritization. This review examines the emerging contribution of artificial intelligence (AI) to WES variant interpretation in NDDs, with particular emphasis on AI-assisted variant prioritization and protein structural modelling. Methods: We reviewed conventional computational and emerging AI-driven approaches applicable to different stages of WES interpretation, including variant pathogenicity and functional-effect prediction, evolutionary conservation, splicing assessment, phenotype-informed prioritization, and protein structure prediction. Their methodological principles, applications, and major limitations were critically examined within the context of neurodevelopmental genomics. Results: AI and machine-learning approaches increasingly integrate genomic, phenotypic, functional, and structural information, supporting phenotype-aware candidate prioritization and molecular contextualization of selected variants. However, computational outputs are affected by model-specific limitations and should not be considered independent evidence of pathogenicity. Their added value depends primarily on the complementarity and biological relevance of the information provided, rather than on predictor concordance alone. Conclusions: This narrative review highlights that AI-assisted WES interpretation should prioritize complementary, mechanism-relevant evidence over computational concordance. AI-assisted tools should therefore be selected according to variant class and disease mechanism and integrated with independent clinical, genetic, and functional evidence.

Source: Genes

DOI: 10.3390/genes17101276