Interpretation of next-generation sequencing data of individuals with an apparent sporadic neurodevelopmental disorder (NDD) often focusses on pathogenic variants in genes associated with NDD, assuming full clinical penetrance with limited variable expressivity. Consequently, inherited variants in genes associated with dominant disorders may be overlooked when the transmitting parent is clinically unaffected. While de novo variants explain a substantial proportion of cases with NDDs, a significant number remains undiagnosed possibly explained by coding variants associated with reduced penetrance and variable expressivity. We characterized twenty families with inherited heterozygous missense or protein-truncating variants (PTVs) in CHD3, a gene in which de novo variants cause Snijders Blok-Campeau syndrome, characterized by intellectual disability, speech delay and recognizable facial features (SNIBCPS). Notably, the majority of the inherited CHD3 variants were maternally transmitted. Computational facial and human phenotype ontology-based comparisons demonstrated that the phenotypic features of probands with inherited CHD3 variants overlap with the phenotype previously associated with de novo variants in the gene, while carrier parents are mildly or not affected, suggesting variable expressivity. Additionally, similarly reduced expression levels of CHD3 protein in cells of an affected proband and of related healthy carriers with a CHD3 PTV, suggested that compensation of expression from the wildtype allele is unlikely to be an underlying mechanism. Our results point to a significant role of inherited variation in SNIBCPS, a finding that is critical for correct variant interpretation and genetic counseling and warrants further investigation towards understanding the broader contributions of such variation to the landscape of human disease.

Multiphase production log interpretation requires that the flow regime along hole in the wellbore is known. Flow regime is the cased-hole analogue of Iithology. Knowledge of the flow regime will help to interpret tool signals, will help to evaluate the flow rate on a per phase basis, and will reduce post processing load by at Ieast a factor of 10. Flow regime can be classified correctly by a neural net in up to 87% of all cases using 1/3 octave band spectra of flow generated sound. A neural net trained on commercially available tool data ('noise cuts') appears to be too sensitive to the wellbore inclination. Hence, application of automated neural net interpretation of noise logs requires a new generation of noise logging tools.