Megan M. Shuey

The paper examines how social determinants of health modify polygenic risk scores for cardiometabolic diseases and shows that integrating social and genetic factors improves disease prediction.

This study sequenced 35,024 individuals with predominantly African ancestry to address gaps in genomics and healthcare, finding under-documented disease variants and novel associations with drug adverse events and glycemic control.

This study identifies 21 novel genetic loci associated with keloids through a multi-ancestry GWAS, highlighting integrin signaling and fibrosis pathways.

The paper performs GWAS on cardiovascular outcomes in CKD patients, identifying stroke-associated SNVs but not replicating CHD findings, and finding genetic correlations with published GWAS.

The paper identifies 57 druggable targets for MASLD using genetic data and evaluates their therapeutic potential through Mendelian randomization and structural modeling.

The paper develops an integrated preclinical atherosclerosis database by extracting data from mouse models and provides tools for standardized analysis and integration with transcriptomics software.

This paper develops a genetically informed drug-repurposing pipeline for diabetes using EHRs and validates two anti-hypertensive classes as glycemic regulators.

This paper translates the PRS-CS polygenic risk score method to Julia, achieving 5.5x faster computation with maintained accuracy across nine traits.

This study examines the genetic links between baseline hematological parameters and risk of SARS-CoV-2 infection and severe COVID-19 using Mendelian randomization and GWAS data.