Bringing the power of epigenomics to the T2DKP

Until recently, all of the results displayed in the Type 2 Diabetes Knowledge Portal (T2DKP) were based on genetic association data: the significance with which variants, or SNPs, occur in people’s genomes in conjunction with a disease or trait.

This information is hugely important for pinpointing regions of the genome that contribute to disease risk. It is now relatively straightforward to identify these regions, but it is still a large challenge to discover the mechanisms by which they act—especially for variants that are outside of coding sequences, without an obvious effect on the sequence of a particular protein. These non-coding variants, the most commonly seen in genetic association studies, are likely to affect tissue-specific gene regulation that could potentially be important to the disease process.

How can we overcome this challenge to find clues about the effects of these non-coding variants? Epigenomic data to the rescue!

Dr. Kyle Gaulton of the University of California at San Diego researches the transcriptional regulatory networks involved in type 2 diabetes by using epigenomic data in concert with genetic association data. He explains, "Regulatory elements control gene production and function, and are often highly specialized across cell and tissues and located far away from the genes they regulate. Molecular epigenomic hallmarks of gene regulation such as histone and DNA modifications, nucleosome depletion, chromatin conformation and DNA-protein interactions can pinpoint the precise genomic locations of regulatory elements. High-resolution epigenome maps of regulatory elements in pancreatic islets, liver, muscle, adipose and many other human tissues can then enable annotation of non-coding genetic variants and their potential gene regulatory functions. These maps are thus an invaluable component of determining how type 2 diabetes associated non-coding variants influence disease pathogenesis."

A recent paper from Dr. Gaulton and colleagues (Gaulton, KJ, et al. (2015) Nat Genet. 47:1415) illustrates the power of integrating these two data types. By combining information on transcription factor binding sites and tissue-specific chromatin states with genetic fine-mapping of T2D-associated loci, the authors elicidated the molecular mechanisms behind the effects of some T2D-associated variants, uncovering the role of the FOXA2 transcription factor in glucose homeostasis in T2D-relevant tissues.

Now, the T2DKP facilitates this type of analysis by presenting both genetic association and epigenomic data on Gene and Variant pages. We described the display of epigenomic data on Variant pages in a recent blog post. On Gene pages, epigenomic data are integrated into the LocusZoom display.

Locations of variants associated with T2D and chromatin states in pancreatic islets, across the SLC30A8 gene (partial view)

Below the plot of variant associations, chromatin states are displayed by default for the major T2D-relevant tissues. Using the pull-down menu at the top of the plot, you can choose from a diverse set to display other tissues and cell types. All of the details on how to use this interactive plot are included in our Gene Page guide.

This is only the first step for epigenomic data in the T2DKP. In the future, we plan to include additional types of epigenomic data that indicate chromatin accessibility and conformation. We will also add functionality; for example, for any given variant, you will be able to search for the tissues in which enhancer regions overlap the location of that variant.

As we actively develop this aspect of the T2DKP, we welcome your suggestions!

T2D Knowledge Portal now distills and summarizes genetic information for individual genes

The Type 2 Diabetes (T2D) Knowledge Portal presents genetic data relevant to T2D on two major types of page: Variant pages for individual variants, or SNPs; and Gene pages focusing on individual genes. Visual displays on Variant pages provide an immediate indication of the possible significance of each variant for T2D. But until now, Gene pages have presented large amounts of information from disparate sources without much integration or interpretation to guide the viewer.

Now, that has all changed with our release of the new Gene page. It guides researchers through an organized workflow that can help them take advantage of the aggregated data in the Portal to move from a variant of interest, to a gene of interest, to an assessment of the potential involvement of that gene’s product in T2D.

The central feature of the new Gene page is an at-a-glance display that summarizes the strength of the evidence for associations of the gene with T2D or related traits. An algorithm scans the comprehensive collection of datasets within the Portal to find data on variants in the gene, and the overall conclusion is shown by a “traffic light” icon. A green light indicates that there is strong evidence for association of at least one variant in the gene with at least one phenotype; a yellow light indicates that there is suggestive evidence, and a red light indicates that the data aggregated in the Portal contain no evidence for associations of variants within this gene.

Figure 1. Traffic light display for MTNR1B

Several sections of the page below the traffic light allow the user to drill down to much more information about the variants within the gene, their individual associations, and their collective impact on the disease burden of the gene. An interactive LocusZoom plot allows users to view the linkage disequilibrium relationships and associations from multiple datasets, with a wide variety of phenotypes, for common variants. The plot also displays the location of chromatin states, which can indicate the regulatory role of a region, in multiple tissues.

Figure 2. LocusZoom plot of the credible set of T2D-associated variants in MTNR1B (above) and chromatin state annotations for the region (below).

In the example shown above, the traffic light (Fig. 1) shows that variants in the MTNR1B gene encoding the melatonin receptor have one or more strong phenotypic associations (view the MTNR1B Gene page in the T2D Knowledge Portal). The table of common variants for MTNR1B (not shown) tells us that the most significantly associated variant is rs10830963. And a view of the LocusZoom plot for the credible set of variants associated with T2D (Fig. 2, top) shows that in fact the credible set for this region contains only rs10830963, further supporting its significance. The chromatin state annotations for this region (Fig. 2, bottom) provide evidence for a regulatory effect in pancreatic islets, consistent with a potential role in T2D. This information, easily found in the Portal today, replicates the results of a 2015 genetic analysis that required over 100 authors (Gaulton, KJ, et al. (2015) Nature Genetics 47:1415).

The new Gene page presents a lot of information and we can't cover it all in this space. But don't worry, we've created a guide to the page that explains every feature in detail. It's linked from the top of the page, or you can download it here.

With the inclusion of the new Gene page, the Portal now enables the rapid generation of testable hypotheses, by integrating, interpreting, and presenting information that previously could only be generated by coordinated research across a consortium. This new development brings the T2D Knowledge Portal project one step closer to informing the discovery of new targets and treatments for T2D.