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

Tzung-Chien Hsieh; Dylan Todd; Taylor Warner; Kayla Blankenship; Dimah Saade; Hannah Weiland; Meghna Ahuja Bhasin; Hannah Klinkhammer; Jing-Mei Li; Peter Krawitz; Wei-Liang Chen; Ho-Ming Luk; Moon Ley Tung; Bharatendu Chandra

doi:10.1212/NXG.0000000000200384

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

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

Tzung-Chien Hsieh, Dylan Todd, Taylor Warner, Kayla Blankenship, Dimah Saade, Hannah Weiland, Meghna Ahuja Bhasin, Hannah Klinkhammer, Jing-Mei Li, Peter Krawitz, …

Neurology. Genetics, Vol.12(4), e200384

08/2026

DOI: 10.1212/NXG.0000000000200384

PMID: 42396398

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Abstract

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 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 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.

Details

Title: Subtitle: Leveraging Next-Generation Phenotyping in Dysmorphology to Support Variant Interpretation in Mowat-Wilson Syndrome
Creators: Tzung-Chien Hsieh - University Hospital Bonn
Dylan Todd - University of Iowa, Stead Family Department of Pediatrics
Taylor Warner - University of Iowa
Kayla Blankenship - University of Iowa Hospitals and Clinics
Dimah Saade - University of Iowa
Hannah Weiland - University of Bonn
Meghna Ahuja Bhasin - University of Bonn
Hannah Klinkhammer - University Hospital Bonn
Jing-Mei Li - University Hospital Bonn
Peter Krawitz - University Hospital Bonn
Wei-Liang Chen - Children's National
Ho-Ming Luk - Hong Kong Children's Hospital
Moon Ley Tung - University of Iowa
Bharatendu Chandra - University of Iowa
Resource Type: Journal article
Publication Details: Neurology. Genetics, Vol.12(4), e200384
DOI: 10.1212/NXG.0000000000200384
PMID: 42396398
ISSN: 2376-7839
eISSN: 2376-7839
Publisher: American Acacemy of Neurology
Language: English
Electronic publication date: 07/01/2026
Date published: 08/2026
Academic Unit: Stead Family Department of Pediatrics; Iowa Neuroscience Institute; Medical Genetics and Genomics; Neurology (Pediatrics)
Record Identifier: 9985179240902771

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