Kyle J. Gaulton

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 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.

The paper constructs a healthy human pancreas reference atlas to contextualize disease states and benchmark models.

The paper uses supervised machine learning to identify impaired mitochondrial quality control in β cells associated with type 2 diabetes using single-cell RNA sequencing data.

The paper describes the Common Fund Data Ecosystem (CFDE), which integrates NIH Common Fund data resources to enable discovery, analysis, and collaboration across heterogeneous datasets.

The paper identifies gene regulators of ER stress in beta cells and links them to type 2 diabetes risk through functional genomics and CRISPR screening.

This study develops a modified SARS-CoV-2 mRNA vaccine, G1-C, that enhances antibody and T cell responses and provides better in vivo protection against the virus.

This study examines the cell-type specific effects of opioid use disorder and HIV on the human brain, identifying changes in gene regulation, metabolism, and immune responses across different brain regions.

This study uses single-cell and spatial profiling to identify cellular and molecular drivers of beta cell dysfunction in the pancreas of cystic fibrosis patients.

This study shows that ATM-deficient pancreatic cancers reprogram the tumor microenvironment via TGF-β-driven myCAF enrichment, and targeting this stromal axis with chemotherapy improves therapeutic efficacy.

This study uses single-cell multiomics and spatial transcriptomics to identify gene regulatory programs in pancreatic cell types that drive type 1 diabetes progression.

This study performs a genome-wide association study of T1D stratified by HLA DR3 and DR4 status, revealing genetic and biological heterogeneity in T1D progression.

DNACipher predicts variant effects across cell types and assays, identifying imVars at most GWAS loci with support from experimental data.

This study uses single-cell multi-omics to identify cell type-specific regulatory networks and novel therapeutic targets in MASLD.

The paper integrates single-cell multi-omics and 3D genome data from human hearts to identify gene regulatory changes and causal genetic contributors in heart failure.

This study investigates islet cell dysfunction in type 1 diabetes by integrating electrophysiological, transcriptomic, and machine learning analyses of human islet cells from T1D donors.

This study identifies genetic risk factors for type 1 diabetes in individuals without high-risk HLA-DR3/DR4 haplotypes and develops a new risk score for better classification.

The paper examines sex and race differences in human pancreatic islets using scRNA-seq and snATAC-seq, finding sex-specific gene expression and chromatin accessibility differences in β cells that change with type 2 diabetes.

The paper identifies causal biomarkers for type 1 diabetes using protein QTLs and GWAS, revealing roles of pancreatic enzymes and immune processes in disease risk.

The paper uses single-cell multiome profiling to identify gene regulatory changes in pancreatic islets linked to diabetes risk factors like age, sex, and genetic variants.

This study identifies 165 T1D risk signals and develops a machine learning model, T1GRS, that improves risk prediction for T1D in Europeans and African Americans.

The paper identifies Neanderthal-derived genetic variants associated with asthma and shows one variant disrupts glucocorticoid receptor binding to an IL33 enhancer.

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

The paper creates an integrated gene expression map of pancreatic islet cell types from healthy and diabetic donors, identifying marker genes and differential expression patterns.

The paper introduces the T1D Knowledge Portal, a resource for querying genetic and functional data related to type 1 diabetes to aid in hypothesis generation and target discovery.

The paper integrates single-cell multiomic data with genetics to identify gene regulatory changes in beta cells associated with type 2 diabetes.

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

The paper identifies T1D risk genes and regulatory elements that influence beta cell survival under proinflammatory cytokine exposure using functional genomics and genetics.

The paper creates a chromatin accessibility atlas across 25 adult human tissues, identifying over 750,000 cis-regulatory elements and linking them to gene regulatory programs and disease-associated variants.

The paper identifies 491,818 candidate cis regulatory elements in adult mouse brain sub-types and links them to target genes and transcriptional regulators involved in various pathways.