Mowat–Wilson syndrome (MWS) is a rare intellectual disability/multiple congenital anomalies syndrome caused by heterozygous mutation of the ZEB2 gene. It is generally underestimated because its rarity and phenotypic variability sometimes make it difficult to recognize. Here, we aimed to better delineate the phenotype, natural history, and genotype–phenotype correlations of MWS. In a collaborative study, we analyzed clinical data for 87 patients with molecularly confirmed diagnosis. We described the prevalence of all clinical aspects, including attainment of neurodevelopmental milestones, and compared the data with the various types of underlying ZEB2 pathogenic variations. All anthropometric, somatic, and behavioral features reported here outline a variable but highly consistent phenotype. By presenting the most comprehensive evaluation of MWS to date, we define its clinical evolution occurring with age and derive suggestions for patient management. Furthermore, we observe that its severity correlates with the kind of ZEB2 variation involved, ranging from ZEB2 locus deletions, associated with severe phenotypes, to rare nonmissense intragenic mutations predicted to preserve some ZEB2 protein functionality, accompanying milder clinical presentations. Knowledge of the phenotypic spectrum of MWS and its correlation with the genotype will improve its detection rate and the prediction of its features, thus improving patient care.

Purpose Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene. Conclusion Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.

Mowat-Wilson syndrome (MWS) is a genetic disease characterized by distinctive facial features, moderate to severe intellectual disability, and congenital malformations, including Hirschsprung disease, genital and eye anomalies, and congenital heart defects, caused by haploinsufficiency of the ZEB2 gene. To date, no characteristic pattern of brain dysmorphology in MWS has been defined. Through brain magnetic resonance imaging (MRI) analysis, we delineated a neuroimaging phenotype in 54 MWS patients with a proven ZEB2 defect, compared it with the features identified in a thorough review of published cases, and evaluated genotype-phenotype correlations. Ninety-six percent of patients had abnormal MRI results. The most common features were anomalies of corpus callosum (79.6% of cases), hippocampal abnormalities (77.8%), enlargement of cerebral ventricles (68.5%), and white matter abnormalities (reduction of thickness 40.7%, localized signal alterations 22.2%). Other consistent findings were large basal ganglia, cortical, and cerebellar malformations. Most features were underrepresented in the literature. We also found ZEB2 variations leading to synthesis of a defective protein to be favorable for psychomotor development and some epilepsy features but also associated with corpus callosum agenesis. This study delineated the spectrum of brain anomalies in MWS and provided new insights into the role of ZEB2 in neurodevelopment.Genet Med advance online publication 10 November 2016.