Shiva Ganesan

The paper introduces GAVURD, a system to identify putative pathogenic non-coding variants in unresolved rare disease patients using trio whole genome sequencing and TAD data, and identifies six potentially causal variants in ten probands.

The paper analyzes a large DNM1 cohort to identify mutational hotspots and genotype-phenotype correlations in DNM1-related disorder.

The paper analyzes 11,125 trio exomes to explore the relationship between genetic variants and phenotypic features in neurodevelopmental disorders, finding significant clinical similarities and phenotypic gradients.

The paper identifies BSN variants as a cause of a new neurodevelopmental disorder with a broad phenotypic range, using clinical data and computational phenotyping.

This study identifies clinical features preceding genetic epilepsy diagnosis in EMR data from 32,000 individuals, showing early signs can inform earlier treatment.

The paper develops a Bayesian framework to integrate functional evidence into clinical variant interpretation for epilepsy-related ion channel genes, identifying key parameters that influence pathogenicity classification.

This study analyzes 52,143 individuals to characterize paediatric speech and language disorders, identifying genetic associations and clinical patterns.