Andrea M. Oza

The paper develops a machine learning model to prioritize genes for newborn sequencing programs based on factors like evidence of disease natural history and treatment efficacy.

The paper curates 157 variants in hearing loss genes using disease-specific ACMG/AMP guidelines, resolving 69% of discordant variants and 24% of VUS variants into clear classifications.

The paper resolves the classification of two GJB2 variants as pathogenic for autosomal recessive nonsyndromic hearing loss through expert consensus.

The paper curates 164 gene-disease pairs for hearing loss, classifying their clinical validity to improve genetic testing accuracy.

The paper specifies ACMG/AMP variant interpretation guidelines for genetic hearing loss, updating and validating rules through expert curation of 51 variants.

The paper provides refined recommendations for applying the PVS1 ACMG/AMP variant criteria to loss of function variants, addressing gaps in the original guideline through evaluation with curated variants.

The paper curates clinically relevant transcripts for 109 hearing loss-associated genes to improve variant interpretation by classifying exons based on expression, variant frequency, and sequencing quality.