James B. Meigs

This study investigates causal links between T2D genetic clusters and non-cardiovascular comorbidities, identifying potential drug repurposing opportunities.

The paper identifies genetic variants near SLC2A2 that influence glycemic response to glimepiride in type 2 diabetes patients.

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.

This study identifies 17 genetic loci associated with sleep duration and lipid levels, offering potential druggable targets for dyslipidemia in individuals with sleep disturbances.

This study integrates multi-omics data to identify genetic variants and molecular mechanisms associated with type 2 diabetes and highlights potential drug targets.

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.

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.

The paper demonstrates that TOPMed imputation improves the identification of rare variants associated with type 2 diabetes in Latino populations, leading to novel associations and better polygenic scores.

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

The paper develops a polygenic score incorporating rare and common variants to improve diabetes diagnosis using HbA1C levels, identifying more undiagnosed cases than chance.

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.

This paper develops a trans-ancestry polygenic risk score for type 2 diabetes and validates it across diverse populations to improve equitable risk prediction.

This paper develops a genome-wide polygenic score for chronic kidney disease that incorporates APOL1 risk genotypes and shows consistent performance across four major continental ancestries.

This study conducts a trans-ancestry GWAS using chronic ALT elevation as a proxy for NAFLD, identifies 77 significant loci, and validates 17 SNPs with histology and imaging data, revealing genetic links to metabolic and inflammatory traits.

The paper evaluates penetrance and variable expressivity of monogenic metabolic conditions using exome data from 77,184 individuals, finding that monogenic variants have larger effect sizes than polygenic scores but low penetrance.

This study performs a GWAS on random glucose measurements in 493,036 individuals, identifying 128 loci associated with glycaemic traits and providing insights into diabetes biology and treatment.

The paper identifies LOC157273 as a negative regulator of PPP1R3B and glycogen deposition in human hepatocytes, linking it to type 2 diabetes risk.

Multi-ethnic type 2 diabetes (T2D) meta-analysis of 228,499 cases and 1,178,783 controls finds 318 previously unreported loci, 804 expression-colocalized genes, and 13 SNP-by-T2D vascular-outcome interactions.

The paper identifies genetic variants affecting HbA1c levels in multi-ancestry cohorts, showing ancestry-specific impacts on diabetes diagnosis via HbA1c.

This study identifies nine novel DNA methylation sites associated with fasting glucose and insulin levels by integrating cross-omics data from multiple cohorts.

This study fine-maps type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, identifying 73 variants with >80% posterior probability of association.