Matthias Thurner

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 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 genetic, genomic, and functional data to identify convergent biological pathways in type 2 diabetes, revealing enriched functional connectivity among causal genes.

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.