Vandana Shashi

The paper identifies domain-specific phenotypic effects of MACF1 variants outside the GAR domain, revealing broader neurodevelopmental and craniofacial anomalies.

The paper constructs a high-resolution human pangenome structural variant resource, identifies disease-associated SVs, and demonstrates its utility in filtering variants from probands and associating SVs with traits in a large cohort.

This paper introduces SimPheny, a framework that uses patient-to-patient phenotype matching to prioritize genes for rare disease diagnosis, outperforming existing methods in cases with limited phenotype associations.

This paper demonstrates that ensilication preserves DNA integrity at ambient temperature, enabling diagnostic-quality long-read sequencing without cold-chain infrastructure.

The paper identifies biallelic variants in RNU2-2 as a common cause of recessive neurodevelopmental disorders, showing higher prevalence than the dominant form in England.

The paper identifies de novo truncating variants in XRN1's C-terminal region causing lethal infantile mitochondrial cardiomyopathy by disrupting its interaction with EDC4.

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

The paper identifies chromatin regulatory genes as genetic modifiers of conotruncal heart defects in 22q11.2 deletion syndrome and links them to sporadic congenital heart disease.

AMELIE 3 automatically reanalyzes undiagnosed Mendelian disease patients using new literature, achieving 80% diagnostic yield with 0.5-1 alerts per patient per year.

This study describes 20 individuals with de novo SNAP25 variants, identifying intellectual disability and early-onset epilepsy as core symptoms of SNAP25-DEE, with overlaps with other SNARE complex genes.

This paper identifies pathogenic structural variants in rare disease patients using whole genome sequencing and reports their clinical relevance.

ClinPhen automatically extracts and prioritizes patient phenotypes from medical records to improve genetic disease diagnosis efficiency.

This study identifies damaging variants in IRF2BPL associated with neurological phenotypes in humans and shows functional conservation in fruit flies.