Shoa L. Clarke

The paper identifies 800 exome-wide significant rare coding variants associated with blood lipids in over a million diverse individuals, including 130 driven by non-European-like populations.

The paper investigates why the 9p21.3 CAD risk locus fails to replicate in African ancestry cohorts, finding ancestry-specific LD and haplotype diversity explain the lack of association.

This study identifies plasma proteomic signatures that modestly improve prediction of incident type 2 diabetes compared to clinical and genetic factors.

This study shows that angiographic CAD burden independently contributes to adverse cardiovascular outcomes, even when traditional risk factors are accounted for.

The paper identifies CXCL12 as a genetic driver of coronary artery anatomy variation through GWAS and mouse models, showing its role in fetal coronary development.

This paper develops a plasma proteomic signature for predicting atherosclerotic cardiovascular disease risk using UK Biobank data and finds it modestly improves prediction over clinical and genetic factors.

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

This study identifies 118 candidate causal genes and 21 novel lipid loci on the X chromosome through a multi-ancestry meta-analysis of blood lipid levels, revealing sex-biased effects and pleiotropy with cardiovascular and hormonal traits.

This study assesses the broad clinical manifestations of polygenic risk for coronary artery disease using genome-wide PRS in the Women's Health Initiative, finding associations with various biomarkers, outcomes, and unexpected cancer risks.

This paper integrates GWAS data with functional genomic datasets to identify regulatory mechanisms and causal genes underlying lipid-related traits.

The paper tests for genetic interactions affecting causal variant effect sizes in gene expression and LDL-C across European and African American populations, finding significant evidence in gene expression but not LDL-C.

The paper uses bidirectional Mendelian randomization to show LTL lengthening increases CHIP risk, while CHIP accelerates LTL shortening, with modest mediation between CHIP and CAD via LTL.

This study shows that FH genetic variants increase CAD risk even after adjusting for multiple LDL-C measurements from EHRs, indicating LDL-C data alone cannot fully explain the risk.