Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes.
Irene Miguel-EscaladaSilvia Bonàs-GuarchInês CebolaJoan Ponsa-CobasJulen Mendieta-EstebanGoutham AtlaBiola M JavierreDelphine M Y RolandoIrene FarabellaClaire C MorganJavier García-HurtadoAnthony BeucherIgnasi MoranLorenzo PasqualiMireia Ramos-RodríguezEmil Vincent R AppelAllan LinnebergAnette P GjesingDaniel R WitteOluf PedersenNiels GrarupPhilippe RavassardDavid TorrentsJosep Maria MercaderLorenzo PiemontiThierry BerneyEelco J P de KoningJulie Kerr-ConteFrancois PattouIryna O FedkoLeif C GroopInga ProkopenkoTorben HansenMarc A Marti-RenomPeter FraserJorge FerrerPublished in: Nature genetics (2019)
Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer clusters or super-enhancers. So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in three-dimensional (3D) space. Furthermore, their target genes are often unknown. We have created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers to their target genes, often located hundreds of kilobases away. It also revealed >1,300 groups of islet enhancers, super-enhancers and active promoters that form 3D hubs, some of which show coordinated glucose-dependent activity. We demonstrate that genetic variation in hubs impacts insulin secretion heritability, and show that hub annotations can be used for polygenic scores that predict T2D risk driven by islet regulatory variants. Human islet 3D chromatin architecture, therefore, provides a framework for interpretation of T2D genome-wide association study (GWAS) signals.
Keyphrases
- type diabetes
- genome wide
- endothelial cells
- transcription factor
- gene expression
- genome wide association study
- induced pluripotent stem cells
- cardiovascular disease
- copy number
- glycemic control
- dna methylation
- dna damage
- pluripotent stem cells
- single cell
- adipose tissue
- skeletal muscle
- machine learning
- blood pressure
- deep learning
- oxidative stress
- genome wide analysis