Benjamin F. Voight

This study identifies cell type-specific eQTLs in the human pancreas using single-cell RNA-seq data and deconvoluted bulk RNA-seq data, revealing new genetic signals and associations with metabolic disease.

The paper introduces ARCHER-LD, a GPU-accelerated method to compute long-range linkage disequilibrium at biobank scale, achieving significant speedups over existing tools.

The paper identifies eQTLs and eGenes in tonsil-derived immune cells from children, linking them to childhood atopic diseases and expanding known genetic contributors.

The paper integrates multiple scRNA-seq datasets to create a comprehensive atlas of human pancreatic islets, identifying 13 cell populations and providing a resource for diabetes research.

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

This study analyzes chromatin accessibility QTLs (caQTLs) to better understand how genetic variants influence trait heritability through regulatory mechanisms.

The paper analyzes mutation spectra across 108 eukaryotic species, revealing cytosine transition mutability at CpG and CHG sites as key drivers of variation, with methylation levels not predicting mutation rates.

The paper integrates chromatin accessibility data with GWAS to identify regulatory mechanisms underlying blood lipid traits, finding 20% of signals lack proposed mechanisms.

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

The paper integrates GWAS data with a CRISPRi screen in osteoblasts to identify genes affecting bone mineral density and reveals pleiotropy across non-bone tissues.

The paper identifies pleiotropic loci across 1,167 traits using GWAS meta-analysis and shows they are enriched for functional annotations and improve polygenic score predictions.

The paper identifies 693 SNPs associated with transcription factor binding in liver ATAC-seq data by analyzing footprint quantitative trait loci (fpQTLs).

The paper performs genotype inference from aggregated chromatin accessibility data to identify caQTLs and their links to gene expression and complex traits.

This study identifies 91 risk loci for gallstone disease through a trans-ancestry GWAS, including 37 novel loci, and links them to metabolic and liver-related traits.

The paper identifies transitional cell states between alpha and beta cells in human islets using single-cell multiome data, suggesting potential for alpha-to-beta transdifferentiation.

This study identifies G6PC2's role in regulating glucagon secretion in α-cells, showing it controls glucose setpoint and suggests G6PC2 inhibitors could improve glycemic control.

This study evaluates the reliability of coronary artery disease polygenic risk scores across diverse populations, finding poor individual agreement despite equivalent population-level performance.

Population-stratified GWAS of 2,068 traits in 635,969 Million Veteran Program participants, 29% genetically non-European, yields 98,485 associations and 57,601 fine-mapped signals with largely shared cross-population architecture.

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

This study uses Mendelian randomization to assess the causal effect of height on cardiovascular diseases, finding that established risk factors largely explain the association, with minimal residual effect.

This paper optimizes the SAIGE algorithm using GPUs to accelerate GWAS on large datasets, achieving 20x speedup on the Million Veteran Program data.

This paper evaluates plasma biomarkers for causal association with peripheral artery disease using Mendelian randomization, identifying 19 biomarkers with significant genetic evidence for causality.

This paper introduces PhenoPLIER, a method that maps gene-trait and drug associations into gene modules to reveal disease mechanisms and drug targets.

Baymer is a Bayesian hierarchical model that estimates mutation probabilities using sequence context, showing improved accuracy and robustness compared to previous methods.

The paper identifies cell-type specific splicing quantitative trait loci (sQTLs) in iPSCs and HLCs and shows they contribute to lipid level variation beyond what is captured by gene expression alone.

This paper evaluates adding polygenic risk scores to the RECODe model for predicting cardiovascular outcomes in type 2 diabetes patients, finding no significant improvement in predictive performance.

The paper introduces a mediation model to estimate the effect size of hidden causal factors between SNPs and a continuous trait, using UK Biobank data to identify a potential hidden mediator for waist-hip ratio.

ColocQuiaL performs colocalization analysis between GWAS and eQTL/sQTL data to identify candidate causal genes for GWAS signals.

The paper develops TSABL predictors to identify causal variants in GWAS loci by leveraging trait-specific regulatory features and genomic annotations.

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.