Jonathan D. Mosley

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 paper introduces mixWAS, a one-shot, lossless algorithm for integrating distributed EHR datasets to identify cross-cohort genetic associations across multiple outcomes.

The paper develops a clinical algorithm to identify individuals with G6PD p.Val68Met variant at risk for diabetes undertreatment using routine lab measurements.

This study identifies 447 new genetic variants associated with prostate-specific antigen levels across multiple ancestries and shows improved cross-ancestry prediction of PSA, with implications for personalized prostate cancer screening.

This study investigates the clinical consequences of a genetic predisposition to lower white blood cell counts using a polygenic score and finds associations with bone marrow biopsy outcomes and treatment responses.

This study develops a polygenic score to adjust PSA levels, improving the detection of aggressive prostate cancer while reducing unnecessary biopsies.

The paper selects and validates ten polygenic risk scores for clinical use in diverse populations, focusing on chronic diseases and addressing disparities in predictive performance.

This paper uses Mendelian randomization to investigate the causal relationship between branched chain amino acids (BCAAs) and type 2 diabetes, finding that BCAAs are biomarkers of diabetes and higher insulin rather than mediators.

The paper identifies pathogenic variants in arrhythmia genes in eMERGE-III participants and reclassifies variants of uncertain significance using functional studies.

The paper introduces methods to clean and analyze clinical lab data linked to EHRs, finding polygenic scores associated with lipid levels and coronary artery disease biomarkers.

This study identifies genetic risk factors for major depressive disorder and loneliness as predictors of coronary artery disease risk, particularly in women.