Despite the increasing use of genomic sequencing in clinical practice, the interpretation of rare genetic variants remains challenging even in well-studied disease genes, resulting in many patients with Variants of Uncertain Significance (VUSs). Computational Variant Effect Predictors (VEPs) provide valuable evidence in variant assessment, but they are prone to misclassifying benign variants, contributing to false positives. Here, we develop Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for missense variants trained using extensive diagnostic data available in 59 actionable disease genes (American College of Medical Genetics and Genomics Secondary Findings v2.0, ACMG SF v2.0). DeMAG improves performance over existing VEPs by reaching balanced specificity (82%) and sensitivity (94%) on clinical data, and includes a novel epistatic feature, the ‘partners score’, which leverages evolutionary and structural partnerships of residues. The ‘partners score’ provides a general framework for modeling epistatic interactions, integrating both clinical and functional information. We provide our tool and predictions for all missense variants in 316 clinically actionable disease genes (demag.org) to facilitate the interpretation of variants and improve clinical decision-making.

Despite an increasing use of genomic sequencing in clinical practice, interpretation of rare genetic variants remains challenging even in well-studied disease genes, resulting in many patients with Variants of Uncertain Significance (VUSs). Computational Variant Effect Predictors (VEPs) are currently used to provide valuable evidence in variant classifications, but they often misclassify benign variants, contributing to potential misdiagnoses. Here, we developed Deciphering Mutations in Actionable Genes (DeMAG), a supervised classifier for interpreting missense variants in actionable disease genes with improved performance over existing VEPs (20% decrease of false positive rate). Our tool has balanced specificity (82%) and sensitivity (94%) on clinical data, and the lowest misclassification rate on putatively benign variants among evaluated tools. DeMAG takes advantage of a novel epistatic feature, the partners score, which is based on evolutionary and structural partnerships of residues as estimated by evolutionary information and AlphaFold2 structural models. The partners score as a general framework of epistatic interactions, can integrate not only clinical but functional information. We anticipate that our tool (demag.org) will facilitate the interpretation of variants and improve clinical decision-making.

SummaryBasic helix-loop-helix genes, particularly proneural genes, are well-described triggers of cell differentiation, yet limited information exists on their dynamics, notably in human development. Here, we focus on Neurogenin 3 (NEUROG3), which is crucial for pancreatic endocrine lineage initiation. Using a double reporter to monitor endogenous NEUROG3 transcription and protein expression in single cells in 2D and 3D models of human pancreas development, we show peaks of expression for the RNA and protein at 22 and 11 hours respectively, approximately two-fold slower than in mice, and remarkable heterogeneity in peak expression levels all triggering differentiation. We also reveal that some human endocrine progenitors proliferate once, mainly at the onset of differentiation, rather than forming a subpopulation with sustained proliferation. Using reporter index-sorted single-cell RNA-seq data, we statistically map transcriptome to dynamic behaviors of cells in live imaging and uncover transcriptional states associated with variations in motility as NEUROG3 levels change, a method applicable to other contexts.