Monday, June 19, 2017

T2D Portal team at ADA 2017

Members of the T2D Knowledge Portal team returned last week from the 77th Scientific Sessions of the American Diabetes Association, inspired and invigorated by many great discussions with T2D researchers, educators, and clinicians.

In preparation for the conference, we set ourselves goals to add several new features to the Portal:

  • incorporate several new datasets and implement a new interactive Data page for exploring all datasets (see details)
  • add epigenomic data to shed light on the potential regulatory roles of genomic regions (see details)
  • implement a complete redesign of the Gene page that integrates multiple datasets to summarizes the significance of each gene to T2D and related phenotypes (see details)
  • connect with the new Federated Node of the Portal at EBI to provide seamless access to data housed there alongside data housed at the AMP T2D Data Coordinating Center at the Broad Institute (see details)

On the first day of the conference, Noël Burtt and Jason Flannick presented a mini-symposium focusing on the Portal to several hundred attendees.

This clearly generated a lot of interest, because our exhibit booth was a busy place for the next three days. 

T2D Portal team members at our exhibit booth

Multiple conversations happened at the booth!

We handed out a general guide to the Portal (download), and also presented a moderated poster (download).

At the booth, we especially enjoyed talking with people in the T2D field who are not geneticists but are simply curious about the genetics of T2D and the mission of the Portal. We encourage everyone to explore the Portal and to feel free to ask us any questions, even if they seem elementary. Please contact us any time with questions or feedback!

Monday, June 12, 2017

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.

Friday, June 9, 2017

Providing data access, ensuring data protection

Readers of this post probably don’t need to be convinced that genetic association data have enormous potential for helping us to understand and treat complex diseases like type 2 diabetes. Significant associations between variants and diseases can suggest genes, or regions of the genome, that could be important for disease risk or progression—and this knowledge could help us identify new drug targets.

The Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D) is a pre-competitive partnership among the National Institutes of Health, industry and not-for-profit organizations, which is managed by the Foundation for the National Institutes of Health. Its mission is to make genetic association data accessible to the worldwide biomedical research community via the Type 2 Diabetes Knowledge Portal, in order to facilitate discovery of new targets for T2D treatment. But it can be a challenge to aggregate genetic data. The privacy of the individuals who contributed their health status and genomic sequences must always be protected, and there are many layers of regulation to ensure this. Restrictions at the institutional, regional, and national levels determine how data are handled and whether they can be transferred.

Until now, all of the results displayed in the Portal have been derived from data housed at the AMP T2D Data Coordinating Center (DCC) at the Broad Institute, where the Portal website resides. But some of the valuable data generated outside the U.S. cannot be transferred to the DCC. To address this issue, AMP T2D funded the development of a mechanism that enables researchers to interact with all of the data: federation. 

Federation means that data are housed at a site (a “federated node”) that meets their specific privacy requirements, but are made available for remote queries via the Portal. Results from such queries are served up alongside results from all of the datasets housed in the AMP T2D DCC. Researchers may browse and query data from any location without even needing to know where they reside.

A federated node has now been created at the European Bioinformatics Institute (EBI) and may be accessed via the T2D Knowledge Portal. Today, Portal tools and interfaces can query both data housed at the AMP T2D DCC at the Broad Institute and data at the EBI federated node. 

According to Paul Flicek, a Senior Scientist and Team Leader of Vertebrate Genomics at EMBL-EBI, “A key mission of EMBL-EBI is to make data available to the widest possible community. Seamlessly accessing stored in multiple locations via a single portal helps ensure that the data we store from many projects are maximally useful for additional research.”

The first dataset to be incorporated into the Portal via the EBI federated node is the Oxford BioBank exome chip analysis dataset, which contains association data for glycemic, lipid, and blood pressure traits from over 7,100 healthy subjects in Oxfordshire, U.K. The dataset is described on our Data page. Portal users can interact with this dataset in the same way (and with the same speed) as with other datasets. 

“Diabetes is a global problem, and it will take research and innovation on a global scale if we are to tackle it effectively,” says Mark McCarthy, Robert Turner Professor of Diabetic Medicine at University of Oxford. “The success of our research on the genetics of diabetes depends on access to data generated by groups around the world. The federated portal provides an additional set of tools that will allow us to jointly analyse those data sets wherever they happen to be based.” 

Federation represents both an important technical advance in handling and protecting data, and a significant step forward in democratizing and improving access to genetic association results. And because it is generally applicable to any kind of genetic association data, it has the potential to have an impact beyond T2D research, facilitating the study of other complex diseases and traits.

Wednesday, June 7, 2017

New clues about variant effects: epigenomic data now available in the Portal

The T2D Knowledge Portal aggregates a wealth of genetic association data identifying variants that are associated with type 2 diabetes and related traits. These identifications show us that something within these genomic regions contributes to the risk of developing T2D. That’s an important first step, but in order to make use of this information to develop new T2D treatments, we need to figure out exactly what is causing the effect and how it relates to the disease process.

If a variant lies within a gene and changes a protein sequence, it can be relatively straightforward to formulate testable hypotheses about its effects. But most of the variants that are significantly associated with T2D—and with complex diseases in general—lie within noncoding regions of the genome and are likely to affect regulation of genes that could be far removed from the chromosomal position of the variant. It can be difficult to find clues about which genes are affected by these distant, noncoding changes, but now, we present a new type of data in the T2DKP that can help address this challenge.

The pattern of epigenetic modifications within a genomic region can provide important clues about its regulatory role. The distribution of these position-specific and tissue-specific marks—for example, covalent modifications of the histone proteins that package DNA—is characteristic of elements such as enhancers or transcription start sites. The Roadmap Epigenomics Consortium has developed methods for detecting these modifications genome-wide (hence the term “epigenomics”) and integrating their positional data, using the ChromHMM algorithm, to categorize genomic regions into “chromatin states”. The presence of these states in a given genomic region in different tissue types can give hints about whether that region might be involved in regulation of specific genes or pathways.

Now, you can view the tissue-specific chromatin states spanning the position of each variant on Variant pages within the Portal. We have incorporated epigenomic data from a study (Varshney et al., 2017) in which the locations of 13 distinct chromatin states were determined across a diverse set of cell lines and tissues, including pancreatic islets. The new “Epigenomic annotations” section of each Variant page (see an example) presents information about chromatin states in three different ways.

1. An interactive table listing chromatin states in this region, the tissue or cell line in which they were observed, and their genomic coordinates. Filter the table by chromatin state or by tissue to find states of particular interest.

2. A matrix displaying chromatin states by tissue type. This graphic gives a quick indication of chromatin states that are present in this region, across the whole panel of tissues.

3. A graphic showing the positions of chromatin states relative to the position of the variant.

These new features represent only the first phase of incorporating this new data type into the Portal. In the future, we will be adding more of these data along with more versatile interfaces for exploring them. Please check out our new epigenomic annotations and send us your feedback!