AI-Assisted WES Variant Interpretation in Neurodevelopmental Disorders: A Narrative Review of Existing Tools.
- 1Oasi Research Institute-IRCCS, 94018 Troina, Italy.
- 2Department of Biological, Geological and Environmental Sciences, University of Catania, 95124 Catania, Italy.
- 3Department Medicine and Surgery, Kore University of Enna, 94100 Enna, Italy.
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.
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BibTeX
@article{treccarichi2026aiassisted,
title = {{AI-Assisted WES Variant Interpretation in Neurodevelopmental Disorders: A Narrative Review of Existing Tools.}},
author = {Simone Treccarichi and Mirella Vinci and Antonino Musumeci and Rosanna Galati Rando and Antonio Fallea and Desiree Brancato and Francesca Bruno and Concetta Federico and Salvatore Saccone and Maurizio Elia and Francesco Cali},
journal = {Genes},
year = {2026},
month = oct,
doi = {10.3390/genes17101276},
url = {https://europepmc.org/article/MED/42938392},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI-Assisted WES Variant Interpretation in Neurodevelopmental Disorders: A Narrative Review of Existing Tools.
AU - Simone Treccarichi
AU - Mirella Vinci
AU - Antonino Musumeci
AU - Rosanna Galati Rando
AU - Antonio Fallea
AU - Desiree Brancato
AU - Francesca Bruno
AU - Concetta Federico
AU - Salvatore Saccone
AU - Maurizio Elia
AU - Francesco Cali
PY - 2026
DA - 2026/10/14
JO - Genes
DO - 10.3390/genes17101276
UR - https://europepmc.org/article/MED/42938392
AB - 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.
SN - 2073-4425
ER -