Vibe Nylander

The paper uses targeted chromatin interaction profiling to identify causal genes at type 2 diabetes GWAS loci with higher sensitivity and resolution than genome-wide methods.

The paper creates TIGER, a resource integrating RNA-seq and genotyping data from human pancreatic islets to identify eQTLs and their associations with type 2 diabetes.

The paper develops a framework to determine tissue-of-action for type 2 diabetes GWAS signals by integrating gene expression and epigenomic data, revealing key tissues and enhancing functional interpretation.

The paper investigates how genetic variants affect gene expression in human pancreatic islets and identifies candidate genes linked to type 2 diabetes.

The paper uses deep learning models to predict regulatory variants in pancreatic islets and refines T2D association signals by integrating epigenomic data with genetic fine-mapping.

The paper integrates chromatin accessibility and DNA methylation data from human pancreatic islets to refine regulatory mechanisms at Type 2 Diabetes susceptibility loci.

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.

This study examines the electrophysiological and secretory properties of human β-cell lines EndoC-βH1 and -βH2, finding they closely resemble primary human β-cells in insulin secretion and ion channel behavior.