Thursday, November 17, 2016

Collaborate with us!

One of the goals of the Type 2 Diabetes Knowledge Portal project is to bring together the world-wide T2D and genetics research communities to share data, knowledge, methods, and tools. In keeping with that goal, we welcome contributions of data to the Portal and we are also open to collaboration as we develop new and better ways to analyze and display data.

We’ve added a new page to the Portal, "Collaborate," that answers frequently asked questions about how to get involved. It includes links to our Data Submitter’s Guide and Data Transfer Agreement, gives an overview of the kinds of data we’re looking for, and tells you how to get in touch with our team.

The “Collaborate” page also links to information about funding opportunities offered by the Foundation for the NIH. Check this out if you’re interested in starting a new project to generate data for the Portal!

Monday, November 7, 2016

New MGH Cardiology and Metabolic Patient Cohort data in the T2D Knowledge Portal

We are pleased to announce a new data set in the T2D Knowledge Portal, from the MGH Cardiology and Metabolic Patient Cohort (CAMP). These data were contributed by Pfizer, Inc. as part of a public-private partnership to generate genotype data for a cardiometabolic and prediabetic cohort. This data set adds individual-level genetic association data for type 2 diabetes (T2D), fasting glucose levels, and fasting insulin levels from more than 3,500 samples to the Portal knowledgebase. Association data for additional phenotypes from this cohort will be incorporated in the future.

The inclusion of this data set in the T2D Knowledge Portal illustrates the uniqueness of the Accelerating Medicines Partnership, which brings together pharmaceutical companies and non-profit institutions with the goal of speeding up the discovery of new targets for treatment of T2D. The pharmaceutical partners in this collaboration have committed not only to providing funding, but also to sharing the data they generate. The CAMP data set contributed by Pfizer is the first set from a pharmaceutical partner to be made available in the Portal.

Another unique aspect of this data set is that it is the first to be included in the Portal with “Early Access Phase 1” status, which is assigned to new data. This status denotes that although analysis and quality control checks have been performed, the data are not yet considered to be in their final state. During the early access period, users may analyze the data but may not submit the results of these analyses for publication. Find the full details about the different phases of data release on our Policies page.

The CAMP cohort consists of 3,857 subjects who were recruited at the Massachusetts General Hospital Heart Center between 2008 and 2012. In addition to genotyping, the subjects had either vascular reactivity measurements (for T2D patients) or an oral glucose tolerance test (for patients not known to have T2D), and samples of their plasma and serum were analyzed. Most of the subjects were of European ancestry; about 10% were African American.

The analysis and quality control processes for this data set were performed by the Analysis Team of the Accelerating Medicines Partnership Data Coordinating Center (AMP-DCC) at the Broad Institute, and are completely transparent and fully documented. The experiment design and analysis are summarized on our Data page, and detailed reports are available for download. Going forward, all new data sets added to the Portal will be fully documented in this manner.

One intriguing—and somewhat puzzling—result from the analysis highlights the utility of incorporating data sets like this one into the Portal. The variant most strongly associated with T2D (at genome-wide significance) in this set is located in the major histocompatibility complex region near the HLA-C gene.

Known associations of genes in this region with type 1 diabetes, along with a high local recombination rate, make it challenging to interpret the meaning of this association. However, it certainly merits further investigation because of its genome-wide significance. The inclusion of this data set in the Portal, in the context of all other available data about T2D associations in the region, greatly facilitates the further analysis of this and other associations in the set.

The CAMP data may be accessed via multiple interfaces in the Portal. They are shown in tables of summary statistics and accessible in variant searches using the Variant Finder. Importantly, since the data are individual-level, samples may be filtered by various parameters and used for custom association analysis in our Genetic Association Interactive Tool (GAIT).

Find CAMP data at all of these locations in the Portal:

On Gene Pages (e.g.,  HLA-C) in the Variants & Associations table.
On Variant Pages (e.g., rs9468919) in the Associations at a glance section and in the Association statistics across traits table.
Via the Variant Finder tool, for the phenotypes T2D, fasting glucose, and fasting insulin.
Via the Genetic Association Interactive Tool (GAIT), which enables custom association analysis for either single variants (available on Variant Pages) or for the set of variants in and near a gene (Interactive burden test, available on Gene Pages).

Tuesday, November 1, 2016

View ASHG posters from the T2D Knowledge Portal team

Did you miss the American Society of Human Genetics 2016 Annual Meeting last month in Vancouver? Or did you attend, but weren’t able to get to our posters among the hundreds that were there? 


Now you can catch up on everything you missed from the Portal team. We’ve uploaded our posters to the open access publishing platform F1000Research, where you can view or download them. The Portal team presented four posters:


1. Automated, scalable quality control of heterogeneous exome sequence data. This poster presented by Ryan Koesterer, a member of the Analysis Team of the Accelerating Medicines Partnership Data Coordinating Center (AMP-DCC), describes a new, scalable method for quality control for exome sequence data, applied to data before they are incorporated into the Portal. 

Citation: Koesterer R, von Grotthuss M, Flannick J et al. Automated, scalable quality control of heterogeneous exome sequence data [v1; not peer reviewed]. F1000Research 2016, 5:2609 (poster) (doi: 10.7490/f1000research.1113354.1)


2. The Type 2 Diabetes Knowledge Portal: a paradigm for the democratization of human genetic information. This poster, from Portal content and community manager Maria Costanzo, presents an introduction to the Portal: its purpose, its content, and what kinds of questions it allows you to ask.


Citation: Costanzo MC and Accelerating Medicines Partnership: Type 2 Diabetes. The type 2 diabetes knowledge portal: a paradigm for the democratization of human genetic information [v1; not peer reviewed]. F1000Research 2016, 5:2607 (poster) (doi: 10.7490/f1000research.1113352.1)


3. A software platform facilitating community analyses of genetic datasets for complex disease. This poster from Benjamin Alexander, on the Portal software engineering team, describes the tools in the Portal that allow you to do both forward and reverse genetic analysis and even perform custom association analysis.

Citation: Alexander B, Duby M, Sanders M et al. A software platform facilitating community analyses of genetic datasets for complex disease [v1; not peer reviewed]. F1000Research 2016, 5:2608 (poster) (doi: 10.7490/f1000research.1113353.1)


4. Mapping variants to amino-acid changes in three-dimensional protein space improves aggregate association test power and suggests mechanisms of action. This poster, presented by Portal computational biologist Marcin von Grotthuss, illustrates a new method for evaluating the significance of variants by considering the protein structural context of the amino acids they encode. A long-term goal is to incorporate this analysis into the Portal.


von Grotthuss M, Florez JC, Flannick J et al. Mapping variants to amino-acid changes in three-dimensional protein space improves aggregate association test power and suggests mechanisms of action [v1; not peer reviewed]. F1000Research 2016, 5:2610 (poster) (doi: 10.7490/f1000research.1113355.1)


We hope you find these posters informative! Please let us know if you have any questions or suggestions.