There has been one previous report of a cohort of patients with variants in Chromodomain Helicase DNA-binding 3 ( CHD3 ), now recognized as Snijders Blok-Campeau syndrome. However, with only three previously-reported patients with variants outside the ATPase/helicase domain, it was unclear if variants outside of this domain caused a clinically similar phenotype. We have analyzed 24 new patients with CHD3 variants, including nine outside the ATPase/helicase domain. All patients were detected with unbiased molecular genetic methods. There is not a significant difference in the clinical or facial features of patients with variants in or outside this domain. These additional patients further expand the clinical and molecular data associated with CHD3 variants. Importantly we conclude that there is not a significant difference in the phenotypic features of patients with various molecular disruptions, including whole gene deletions and duplications, and missense variants outside the ATPase/helicase domain. This data will aid both clinical geneticists and molecular geneticists in the diagnosis of this emerging syndrome.

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PurposePhenotype 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.MethodsHere, 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.ResultsThe 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.ConclusionImage 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.

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