The workshop featured presentations on multiple data types that can provide clues about the mechanisms by which sequence variants affect T2D risk. Many of these offer insights into transcriptional regulation: epigenomic chromatin modifications; tissue-specific RNA levels; eQTLs; transcription factor binding sites; long-range interactions between chromosomes that bring promoters and enhancers into proximity; and regulatory pathways. Others focus on downstream processes such as protein-protein interactions, biochemical pathways, and metabolomics.
It will be crucial to integrate all of these data types with genetic association data in order to get a complete picture of how particular genomic regions influence T2D biology, and at the T2DKP we are working towards incorporating as many of these data types as possible.
Although the presentations in this workshop were diverse, some common themes were evident. One was that although the insulin-secreting beta cells in pancreatic islets are hugely significant to T2D, and most T2D risk variants influence insulin secretion, current research projects are confirming and underscoring the importance of other tissues. Fat, liver, skeletal muscle (which comprises 40% of human body weight), and brain are all intimately involved in the development of T2D.
Another common theme for ongoing T2D research is that things may often be much more complicated than they first appear. A single genomic region associated with T2D risk may harbor multiple independent causal variants, each potentially having different regulatory effects, possibly affecting different tissues, and causing varied phenotypic consequences. Even if these variants alter a protein-coding sequence, they may not act through their effects on that sequence. These genetically complicated regions, such as those elucidated in FTO or TCF7L2, may be more common than we previously thought.
A third overall conclusion from the workshop is that model organism research can accelerate the investigation of candidate genes. The short life cycles of Drosophila and zebrafish, and the versatile genetic tools available for these systems, allow for rapid and systematic interrogation of gene function. Zebrafish glucose and lipid metabolism have much in common with those processes in human cells, and with their transparent bodies, zebrafish literally give us a window into pancreatic development. In addition to being a well-developed model system, the mouse offers much greater genetic diversity than human, with about 40 million SNPs in the mouse genome as compared to about 10 million in the human genome.
At the T2DKP, efforts to integrate many of these data types are in progress, and integration of others is being planned. We continue to work towards making the T2DKP a comprehensive resource for the T2D research community, to help accelerate the translation of variant associations into knowledge about disease mechanisms and identification of potential drug targets.
Many of the presentations at the workshop featured web resources of potential interest to T2D researchers, listed below. The T2DKP is connected with the first, the Diabetes Epigenome Atlas. We are interested providing better connections between the T2DKP and other relevant resources. If you would be particularly interested in seeing links from the T2DKP to one of the resources below, or if you know of a resource that would be informative, we would love to hear your suggestions!
- Diabetes Epigenome Atlas (formerly T2DREAM): provides genomic and epigenomic data relevant to T2D
- Capture HiC Plotter (CHiCP): web application for visually integrating human population genetic data with promoter capture Hi-C data
- HaploReg: explore annotations of the noncoding genome at variants on haplotype blocks
- ExPecto: tissue-specific gene expression effect predictions for human mutations
- DeepSea: predict the cell type-specific epigenetic state of a sequence and the chromatin effects of sequence variants
- GeNets: unified web platform for network-based analyses of genetic data
- Attie Lab Diabetes Database: gene expression and diabetes-related clinical phenotypes in mouse
- DCell: a deep neural network simulating cell structure and function