Jennifer A. Brody

This paper develops a machine learning framework to impute cross-platform proteomic data, improving replication of epidemiological findings and protein quality scoring.

The paper introduces the expected polygenic risk score (ePRS) framework to address population stratification in polygenic risk score analysis by modeling ancestral makeup, showing it provides equitable risk quantification across diverse populations.

This paper develops and validates multi-ancestry polygenic risk scores for type 2 diabetes prediction and complications across diverse populations.

cpiVAE improves cross-platform proteomics imputation between Olink and SomaScan, achieving higher correlation than KNN and WNN, and enabling better phenotype associations.

The paper identifies genes associated with obesity risk loci by integrating genetic and transcriptomic data from blood and brain tissues.

This study identifies 22 novel gene-sleep duration interaction loci for blood pressure traits across 811,405 individuals, highlighting distinct genetic influences of short and long sleep on cardiovascular health.

The paper introduces MultiSTAAR, a statistical framework for multi-trait rare variant analysis in WGS studies, identifying new lipid trait associations in diverse populations.

The paper identifies genetic loci and proteins associated with venous thromboembolism recurrence risk using GWAS and subgroup analyses across different thrombotic subtypes.

This study uses whole genome sequencing to identify 18 novel genetic loci associated with plasma fibrinogen levels, including variants more common in African populations and linked to liver-related traits and disease.

The paper develops a method to quantify somatic mutation burden in whole-genome sequencing data without paired-tissue samples and identifies genetic and phenotypic correlates of clonal hematopoiesis without known driver mutations.

This study develops multi-ancestry polygenic risk scores for venous thromboembolism using diverse GWAS data and shows improved predictive performance across European- and African-ancestry populations.

This paper conducts a multi-ancestry time-to-event GWAS to identify genetic variants associated with incident cardiovascular disease in people with type 2 diabetes, finding three novel loci and confirming known CAD variants.

This study shows that a polygenic risk score for coronary artery disease is associated with more severe atherosclerosis features in autopsy samples of sudden death cases.

This study identifies 1,289 genetic signals for type 2 diabetes in 2.5 million individuals, revealing distinct clusters linked to vascular complications and highlighting ancestry-specific genetic risk factors.

The paper identifies impactful rare variants by integrating multi-omic data from MESA, showing that combining expression and protein data increases the detection of rare stop and frameshift variants.

The paper validates novel telomere length regulation genes POP5 and KBTBD6 identified through trans-ancestry GWAS using functional experiments in blood cells.

The paper develops PACER to infer clonal expansion rates from single timepoint data and identifies TCL1A activation as a mediator of clonal hematopoiesis fitness.

The paper identifies a gene-diet interaction modifying the association between macronutrient intake and glycemic traits, highlighting challenges in detecting such interactions due to measurement error and sample size.

The paper presents a comprehensive catalog of structural variants across 138,134 individuals, identifying novel variants and their associations with cardio-metabolic and hematologic traits.

The paper introduces a framework for detecting noncoding rare variant associations using whole-genome sequencing data, applying it to lipid traits in the TOPMed program.

This study identifies 135 genomic loci associated with venous thromboembolism risk through cross-ancestry GWAS and provides insights into biological pathways and potential therapeutic targets.

The paper performs whole genome sequence analysis of plasma lipid levels in 66,329 individuals, identifying new lipid-associated alleles, including rare non-coding variants.

The paper shows that variant effect sizes on quantitative endophenotypes support pathogenicity in monogenic disease genes.

This paper uses gradient boosted trees to improve polygenic risk prediction by capturing non-linear genetic effects and SNP interactions, showing significant increases in variance explained for multiple complex traits.

The paper performs whole genome sequence association analysis of fasting glucose and insulin levels in diverse cohorts, identifying 22 significant variants across 16 gene regions, including rare variants.

The paper integrates DNA methylation with clinical risk factors to improve mortality prediction, identifying CpG biomarkers associated with all-cause, cardiovascular, and cancer mortality.

The paper estimates heritability for height and BMI using whole-genome sequence data and finds that rare variants contribute significantly to the missing heritability.