Kenneth E. Westerman

The paper identifies 16 genetic loci interacting with sleep duration to influence glycemic traits in over 480,000 individuals, revealing distinct biological mechanisms for short and long sleep.

The paper identifies estrogen-modulated SNVs associated with breast cancer risk and shows their interaction with reproductive factors using polygenic risk scores.

The paper identifies variance QTLs (vQTLs) and gene-environment interactions for body fat distribution using UK Biobank MRI and genetic data, with replication in external cohorts.

The paper develops a pipeline to compare polygenic scores (PGS) for detecting genetic interactions between adiposity and cardiometabolic risk factors, finding that interaction-based PGS (iPGS) most consistently identifies these interactions.

This study identifies twelve novel blood pressure loci and shows smoking modifies genetic effects on BP through immune-related pathways in over 1 million individuals.

The paper uses pathway-specific polygenic scores to identify gene-adiposity interactions affecting liver biomarkers, finding more specific and interpretable results compared to standard polygenic scores and GWIS.

The paper finds that functional variants in TAS2R38 associate with lower postprandial glucose levels, independent of dietary and lifestyle factors.

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 a decomposition approach to detect genetic modifiers of the relationship between dietary omega-3 fatty acids, plasma biomarkers, and hsCRP in the UK Biobank, improving statistical power.

The paper refines a gene-physical activity interaction affecting HDL-cholesterol by analyzing lipoprotein subfractions and exploring sex differences across multiple cohorts.

The paper conducts a large-scale rare variant GWAS meta-analysis of type 2 diabetes, identifying novel variants in monogenic diabetes genes and assessing their pathogenicity.

The paper introduces two tools, REGEM and METAGEM, to analyze gene-environment interaction summary statistics from GWIS studies, enabling efficient re-analysis and meta-analysis.

This study identifies genetic subtypes of type 2 diabetes across diverse ancestries, revealing ancestry-specific risk profiles and BMI thresholds.

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.

This paper clusters T2D genetic loci into ten groups using bNMF, identifying distinct biological mechanisms and clinical associations.

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 identifies variance-quantitative trait loci (vQTLs) and gene-environment interactions (GEIs) for cardiometabolic serum biomarkers using UK Biobank data.

The paper introduces a method to generate summary statistics for gene-environment interaction tests and a meta-analysis approach to combine results across studies, demonstrating its effectiveness in large-scale sequencing data.

The paper introduces a cloud-based workflow for rare variant analysis in whole genome sequencing studies, enabling scalable and reproducible analysis with functional annotations.

The paper identifies five novel gene-diet interaction effects on HbA1c using UK Biobank data, highlighting interactions with dietary patterns and traits.

GEM enables scalable gene-environment interaction analysis in large samples, identifying novel loci in a gene-sex interaction study.

PhyteByte predicts bioactivity of food compounds using machine learning, identifying potential agonists of PPARG and other targets.

The paper develops a DNA methylation-based risk score for cardiovascular disease and shows it predicts risk independently of traditional factors.

The paper develops a gene-diet interaction score to predict dietary fat response for cardiometabolic risk factors using the Women's Health Initiative cohort.

The paper identifies DNA methylation modules and regions associated with incident cardiovascular disease and cumulative risk factor exposure using data from the Women's Health Initiative and Framingham Heart Study Offspring Cohort.