Mary T. Brophy

This study evaluates cancer-specific polygenic risk scores (PRS) across diverse ancestries and finds they predict corresponding cancers with varying effectiveness and limited pleiotropy.

This study identifies correlates of risk for disinhibited behaviors in veterans using polygenic scores and electronic health records, finding strong associations with substance use disorders and related health outcomes.

The paper validates polygenic risk scores for schizophrenia, bipolar disorder, and depression using VA EHR data and shows their association with disease risk and hospitalization.

This paper uses deep learning to predict pancreatic cancer risk from clinical disease trajectories in two large datasets, achieving higher accuracy than models without temporal information.

The paper describes the design and performance of the MVP 1.0 custom Axiom array for genotyping a diverse cohort, achieving high-quality genetic data with substantial ancestral diversity.