Elucidating the clinical and molecular spectrum of SMARCC2-associated NDD in a cohort of 65 affected individuals

Purpose: Coffin-Siris and Nicolaides-Baraitser syndromes, are recognisable neurodevelopmental disorders caused by germline variants in BAF complex subunits. The SMARCC2 BAFopathy was recently reported. Herein, we present clinical and molecular data on a large cohort. Methods: Clinical symptoms for 41 novel and 24 previously published affected individuals were analyzed using the Human Phenotype Ontology. For genotype-phenotype correlation, molecular data were standardized and grouped into non-truncating and likely gene-disrupting (LGD) variants. Missense variant protein expression and BAF subunit interactions were examined using 3D protein modeling, co-immunoprecipitation, and proximity-ligation assays. Results: Neurodevelopmental delay with intellectual disability, muscular hypotonia and behavioral disorders were the major manifestations. Clinical hallmarks of BAFopathies were rare. Clinical presentation differed significantly, with LGD variants being predominantly inherited and associated with mildly reduced or normal cognitive development, while non-truncating variants were mostly de novo and presented with severe developmental delay. These distinct manifestations and non-truncating variant clustering in functional domains suggest different pathomechanisms. In vitro testing showed decreased protein expression for N-terminal missense variants similar to LGD. Conclusion: This study improved SMARCC2 variant classification and identified discernible SMARCC2-associated phenotypes for LGD and non-truncating variants, which were distinct from other BAFopathies. The pathomechanism of most non-truncating variants has yet to be investigated

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework

Several molecular and phenotypic algorithms exist that establish genotype–phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore’s ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype–phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally

Correction : Putting genome-wide sequencing in neonates into perspective

The original version of this Article contained an error in the spelling of the author Pleuntje J. van der Sluijs, which was incorrectly given as Eline (P. J.) van der Sluijs. This has now been corrected in both the PDF and HTML versions of the Article

Correction: Putting genome-wide sequencing in neonates into perspective

The original version of this Article contained an error in the spelling of the author Pleuntje J. van der Sluijs, which was incorrectly given as Eline (P. J.) van der Sluijs. This has now been corrected in both the PDF and HTML versions of the Article

A case series of familial ARID1B variants illustrating variable expression and suggestions to update the ACMG criteria

ARID1B is one of the most frequently mutated genes in intellectual disability (~1%). Most variants are readily classified, since they are de novo and are predicted to lead to loss of function, and therefore classified as pathogenic according to the American College of Medical Genetics and Genomics (ACMG) guidelines for the interpretation of sequence variants. However, familial loss-of-function variants can also occur and can be challenging to interpret. Such variants may be pathogenic with variable expression, causing only a mild phenotype in a parent. Alternatively, since some regions of the ARID1B gene seem to be lacking pathogenic variants, loss-of-function variants in those regions may not lead to ARID1B haploinsufficiency and may therefore be benign. We describe 12 families with potential loss-of-function variants, which were either familial or with unknown inheritance and were in regions where pathogenic variants have not been described or are otherwise challenging to interpret. We performed detailed clinical and DNA methylation studies, which allowed us to confidently classify most variants. In five families we observed transmission of pathogenic variants, confirming their highly variable expression. Our findings provide further evidence for an alternative translational start site and we suggest updates for the ACMG guidelines for the interpretation of sequ

PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework

Several molecular and phenotypic algorithms exist that establish genotype–phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals exists. We developed PhenoScore: an open-source, artificial intelligence-based phenomics framework, combining facial recognition technology with Human Phenotype Ontology data analysis to quantify phenotypic similarity. Here we show PhenoScore’s ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 37 of 40 investigated syndromes against clinical features observed in individuals with other neurodevelopmental disorders and show it is an improvement on existing approaches. PhenoScore provides predictions for individuals with variants of unknown significance and enables sophisticated genotype–phenotype studies by testing hypotheses on possible phenotypic (sub)groups. PhenoScore confirmed previously known phenotypic subgroups caused by variants in the same gene for SATB1, SETBP1 and DEAF1 and provides objective clinical evidence for two distinct ADNP-related phenotypes, already established functionally