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Verheij syndrome (VRJS) is a rare craniofacial spliceosomopathy presenting with craniofacial dysmorphism, multiple congenital anomalies and variable neurodevelopmental delay. It is caused by single nucleotide variants (SNVs) in PUF60 or interstitial deletions of the 8q24.3 region. PUF60 encodes a splicing factor which forms part of the spliceosome. To date, 36 patients with a sole diagnosis of VRJS due to disease-causing PUF60 SNVs have been reported in peer-reviewed publications. Although the depth of their phenotyping has varied greatly, they exhibit marked phenotypic heterogeneity. We report 10 additional unrelated patients, including the first described patients of Khmer, Indian, and Vietnamese ethnicities, and the eldest patient to date, with 10 heterozygous PUF60 variants identified through exome sequencing, 8 previously unreported. All patients underwent deep phenotyping identifying variable dysmorphism, growth delay, neurodevelopmental delay, and multiple congenital anomalies, including several unique features. The eldest patient is the only reported individual with a germline variant and neither neurodevelopmental delay nor intellectual disability. In combining these detailed phenotypic data with that of previously reported patients (n = 46), we further refine the known frequencies of features associated with VRJS. These include neurodevelopmental delay/intellectual disability (98%), axial skeletal anomalies (74%), appendicular skeletal anomalies (73%), oral anomalies (68%), short stature (66%), cardiac anomalies (63%), brain malformations (48%), hearing loss (46%), microcephaly (41%), colobomata (38%), and other ocular anomalies (65%). This case series, incorporating three patients from previously unreported ethnic backgrounds, further delineates the broad pleiotropy and mutational spectrum of PUF60 pathogenic variants.

Background and ObjectivesPathogenic variants in the neuronal sodium channel α1 subunit gene (SCN1A) are the most frequent monogenic cause of epilepsy. Phenotypes comprise a wide clinical spectrum, including severe childhood epilepsy; Dravet syndrome, characterized by drug-resistant seizures, intellectual disability, and high mortality; and the milder genetic epilepsy with febrile seizures plus (GEFS+), characterized by normal cognition. Early recognition of a child's risk for developing Dravet syndrome vs GEFS+ is key for implementing disease-modifying therapies when available before cognitive impairment emerges. Our objective was to develop and validate a prediction model using clinical and genetic biomarkers for early diagnosis of SCN1A-related epilepsies.MethodsWe performed a retrospective multicenter cohort study comprising data from patients with SCN1A-positive Dravet syndrome and patients with GEFS+ consecutively referred for genetic testing (March 2001–June 2020) including age at seizure onset and a newly developed SCN1A genetic score. A training cohort was used to develop multiple prediction models that were validated using 2 independent blinded cohorts. Primary outcome was the discriminative accuracy of the model predicting Dravet syndrome vs other GEFS+ phenotypes.ResultsA total of 1,018 participants were included. The frequency of Dravet syndrome was 616/743 (83%) in the training cohort, 147/203 (72%) in validation cohort 1, and 60/72 (83%) in validation cohort 2. A high SCN1A genetic score (133.4 [SD 78.5] vs 52.0 [SD 57.5]; p < 0.001) and young age at onset (6.0 [SD 3.0] vs 14.8 [SD 11.8] months; p < 0.001) were each associated with Dravet syndrome vs GEFS+. A combined SCN1A genetic score and seizure onset model separated Dravet syndrome from GEFS+ more effectively (area under the curve [AUC] 0.89 [95% CI 0.86–0.92]) and outperformed all other models (AUC 0.79–0.85; p < 0.001). Model performance was replicated in both validation cohorts 1 (AUC 0.94 [95% CI 0.91–0.97]) and 2 (AUC 0.92 [95% CI 0.82–1.00]).DiscussionThe prediction model allows objective estimation at disease onset whether a child will develop Dravet syndrome vs GEFS+, assisting clinicians with prognostic counseling and decisions on early institution of precision therapies (http://scn1a-prediction-model.broadinstitute.org/).Classification of EvidenceThis study provides Class II evidence that a combined SCN1A genetic score and seizure onset model distinguishes Dravet syndrome from other GEFS+ phenotypes.