Julien Thévenon

DiagAI, a machine-learning system, improves variant interpretation in genetic testing by prioritizing causal variants, outperforming existing tools with higher diagnostic yield and shorter variant shortlists.

The paper identifies biallelic variants in RNU4-2 causing a recessive neurodevelopmental syndrome with distinct white matter changes, expanding the known genotypic and phenotypic spectrum of RNU4-2-related disorders.

This study uses saturation genome editing to analyze RNU4-2 variants, identifying functional impacts and redefining the ReNU syndrome critical region.

The paper identifies monoallelic DHX9 variants as a cause of neurodevelopmental disorders and Charcot-Marie-Tooth disease, showing genotype-phenotype correlations and cellular dysfunction.

This paper evaluates exome sequencing as an alternative to chromosomal microarray for detecting copy number variants in rare disorders, showing improved diagnostic yield in prospective cases.

This paper develops a symptom interaction model to address heterogeneous phenotype reporting in genetic disease, improving gene prioritization by 42%.

Genome Alert! automatically detects changes in variant classifications and gene-disease associations over time, improving clinical interpretation of genomic data with minimal human effort.

The paper reclassifies DYRK1A variants using an integrative approach, showing some previously reported pathogenic variants do not cause obvious phenotypes.

The paper investigates why short-read variant callers fail to detect many insertions by analyzing callsets from long-read technologies and simulating the impact of insertion features on recall.

This study identifies DDX3X mutations as causing neurodevelopmental disorders by disrupting RNA metabolism and neurogenesis during fetal cortical development.

The paper identifies biallelic UNC45A mutations as the cause of O2HE syndrome, characterized by cholestasis, congenital diarrhea, hearing loss, and bone fragility.