Leveraging Next-Generation Phenotyping in Dysmorphology to Support Variant Interpretation in Mowat-Wilson Syndrome

Next-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.

We applied GestaltMatcher to a 4-year-old patient with an undiagnosed neurodevelopmental disorder, suspected Mowat-Wilson syndrome (MWS), and a de novo ZEB2 variant. In addition to facial image analysis, we used 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.

GestaltMatcher ranked MWS as the top differential diagnosis, and PEDIA integration further confirmed ZEB2 as the most likely disease-causing gene. Three of the patient's 4 facial images met the PP4 moderate threshold, while one met PP4 supporting. MRI analysis revealed subtle corpus callosum thinning, consistent with MWS. In addition, an exploratory case of an infant with molecularly confirmed MWS demonstrated the capability of GestaltMatcher to prioritize the diagnosis solely based on infant facial features.

This study highlights the potential of NGP-driven facial phenotyping and multimodal integration in dysmorphology. The results support the broader application of AI-assisted phenotyping to improve diagnostic accuracy, particularly in neurodevelopmental disorders with distinct facial features.