Leveraging next-generation phenotyping for ACMG classification from VUS to likely pathogenic in Mowat-Wilson syndrome

BackgroundNext-generation phenotyping (NGP) tools, such as GestaltMatcher, have revolutionized the diagnosis of rare genetic disorders through computational facial analysis. While NGP has been widely integrated into differential diagnosis workflows, its application in variant reclassification within the ACMG framework remains underexplored. MethodsWe applied GestaltMatcher to a pediatrics patient with a severe neurodevelopmental disorder, suspected Mowat-Wilson syndrome (MWS), and a de novo ZEB2 variant initially classified as a variant of uncertain significance (VUS). In addition to facial image analysis, we utilized the PEDIA framework, integrating Human Phenotype Ontology (HPO) terms and simulated exome data to refine variant prioritization. Bayesian likelihood modeling was used to establish Gestalt score thresholds for PP4 evidence levels (supporting, moderate, strong, and very strong). Brain MRI analysis was also performed to assess structural abnormalities characteristic of MWS. ResultsGestaltMatcher ranked MWS as the top differential diagnosis, and PEDIA integration further confirmed ZEB2 as the most likely disease-causing gene. Three of the patients four facial images met the PP4 moderate threshold, while one met PP4 supporting. Based on these findings, the ZEB2 variant was reclassified as Likely Pathogenic. MRI analysis revealed subtle corpus callosum thinning, consistent with MWS. Additionally, a validation case of an infant with molecularly confirmed MWS demonstrated the capability of GestaltMatcher to prioritize the diagnosis solely based on infant facial features. ConclusionThis study highlights the potential of NGP-driven facial phenotyping and multimodal integration in variant reclassification. The results support the broader application of AI-assisted phenotyping to improve diagnostic accuracy and ACMG-based variant interpretation, particularly in neurodevelopmental disorders with distinct facial features.