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 Automated, scalable quality control of heterogeneous exome sequence data [v1; not peer reviewed].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 The type 2 diabetes knowledge portal: a paradigm for the democratization of human genetic information [v1; not peer reviewed].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 A software platform facilitating community analyses of genetic datasets for complex disease [v1; not peer reviewed].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.

Design your own association analysis with our Genetic Association Interactive Tool (GAIT)

Genetic association analysis—identifying polymorphisms in the human genome that are correlated with altered risk of disease—is a powerful method for discovering disease mechanisms. These polymorphisms can indicate what goes wrong at the cellular level in the disease process, knowledge that is critically important for developing better diagnostics and therapies.

The Type 2 Diabetes Knowledge Portal offers a wealth of pre-calculated information on genetic associations between variants and type 2 diabetes (T2D) or other related traits. These results are computed using broadly defined groups of samples: either an entire sample set from a project, or ancestry-specific cohorts. This approach, while it generates very valuable results, masks effects that could only be detected in even more narrowly defined groups: for example, individuals within a certain range of age, body mass index, or cholesterol level. 

Until now, analysis of such fine-grained subsets of individual-level data has only been possible for expert geneticists with access to protected data. But our new Genetic Association Interactive Tool (GAIT) offers everyone an unprecedented amount of access to individual-level data along with an easy-to-use interface for analyzing genetic associations using custom subsets of samples and variants.

Two versions of GAIT are available in the Portal. One, on Variant pages (see an example) computes association statistics for the single variant featured on that page. The other, accessible on Gene pages (see an example) powers an interactive burden test that considers the collection of variants in or near a gene, or a selected subset of those variants. 

Where to find GAIT on Gene pages (left) and Variant pages (right)

The GAIT interface offers incredible flexibility for designing custom analyses. In the interactive burden test, you can filter variants by their predicted effects, or pick and choose individual variants to include. When creating sample sets for either single-variant association analysis or a gene burden test, you can specify a gender, set ranges for the values for multiple phenotypes, and choose principal components or phenotypes to use as covariates. And all these parameters may be set differently for different ethnic groups.

The GAIT interface displays phenotype values within the sample set and allows you to filter samples by multiple criteria

Once you set parameters of your choice, GAIT computes associations on the fly, based on individual-level data. To protect patient confidentiality, GAIT will not display results from sample sets consisting of fewer than 100 individuals.

To help you get familiar with this versatile tool, we’ve created a User Guide (download PDF) that summarizes all the details of the interface. Please give GAIT a try and let us know what you think!

New and updated data in the T2D Knowledge Portal

As members of the T2D Knowledge Portal team arrive in Vancouver for the American Society of Human Genetics meeting, we are pleased to announce that we have added a new data set to the Portal and made extensive updates to existing data sets. 

The new data set, named “CAMP GWAS” in the Portal, comes 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, and were analyzed by the Analysis Team of the Accelerating Medicines Partnership Data Coordinating Center (AMP-DCC) at the Broad Institute. The set adds individual-level genetic association data for type 2 diabetes (T2D), fasting glucose levels, and fasting insulin levels from nearly 3,500 samples to the Portal knowledgebase, and association data for more phenotypes will be added in the future.

CAMP data may be accessed on Gene and Variant pages in the Portal and via the Variant Finder, and may also be filtered and queried using the Genetic Association Interactive tool (GAIT).

Several other data sets in the Portal have been updated and improved:

• The size of the CARDIoGRAM GWAS data set has nearly doubled, now consisting of 184,305 samples, and the data analysis has been updated.
• The size of the CKDGen GWAS data set has also nearly doubled, to 133,814 samples; the data analysis has been updated; new subsets have been added that stratify serum creatinine associations by African American ancestry and stratify both serum creatinine and urinary albumin-to-creatinine ratio by the presence or absence of T2D.
• The data set previously named “DIAGRAM GWAS” in the Portal has been updated and re-named “DIAGRAM Trans-ethnic meta-analysis;” its sample size has increased to 149,821. Several new subsets have been added, including gender-stratified, MetaboChip, and fine mapping data.
• The GIANT GWAS data have been updated and European cohorts have been added for BMI and height traits.
• The GLGC GWAS data set has increased in size to 188,577 samples and has been updated.
• The number of samples in the MAGIC GWAS dataset has more than doubled, to 133,010; the data have been updated, and associations with 2 hour glucose, fasting glucose, and fasting insulin have been added for MetaboChip data.

Full details about all of these data sets are available on our Data page.

Because of compatibility issues with the updated data, we have temporarily removed the “GWAS results summary” section from Gene pages of the Portal. This feature will be restored within the next week.

As always with major updates, issues or bugs may have been introduced and we may not have found all of them during our routine testing. We encourage you to let us know of any problems that you encounter in using the Portal, and we welcome your questions and suggestions.

See you at ASHG 2016!

Members of the Type 2 Diabetes Knowledge Portal team will be attending the American Society of Human Genetics meeting next week in Vancouver, BC. You can catch us nearly every day of the meeting:

Tuesday 10/18

3 PM: Nöel Burtt will be one of the speakers in an informational session on the T2D Knowledge Portal and new funding opportunities offered by the Foundation for the NIH. Complimentary snacks, beer, and wine will be served! Please pre-register here.

Wednesday 10/19

10 AM - 4 PM: Find us in the exhibit hall at booth #428. We’ll be there to answer your questions and give tours and tutorials on the Portal.

Thursday 10/20

10 AM - 4 PM: We will again be in the exhibit hall at booth #428.

2 - 3 PM: Ryan Koesterer will present his poster on an automatic, scaleable quality control method for genetic association data that improves on current “gold-standard” methods (program #1943T).

2 - 3 PM: Maria Costanzo will present her poster giving an overview of data in the Portal and the global collaborative efforts behind its aggregation (program #329T).

Friday 10/21

10 AM - 4 PM: This is our last day in the exhibit hall at booth #428.

2 - 3 PM: Marcin von Grotthuss will present his poster on improving predictions of significant variants by taking protein structure into account (program #489F).

3 - 4 PM: Ben Alexander will present his poster on the software platform that powers the T2D Knowledge Portal user interface and custom analysis tools (program #1650F).

T2D Knowledge Portal staff attending ASHG

We look forward to meeting you at ASHG! If you have questions and cannot meet us any of these times, or if you won’t be at ASHG, our mailbox is always open at help@type2diabetesgenetics.org.

Come to a T2D Knowledge Portal information session at ASHG

The American Society of Human Genetics meeting is happening in Vancouver, B.C. in a little over two weeks! The Portal team will be presenting and exhibiting at multiple venues at ASHG, and the first event will take place immediately before the conference starts: an information session including an overview of the Accelerating Medicines Partnership in Type 2 Diabetes, a progress update on T2D Knowledge Portal functionality, and information on new funding opportunities. Complimentary hors d'oeuvres, beer and wine will be served!

Information session

Tuesday - October 18, 2016

3:00 pm - 4:00 pm PDT

Fairmont Waterfront


900 Canada Place Way


Vancouver, British Columbia

Please register here for this free event, hosted by FNIH.  Contact Nicole Spear at Nspear@fnih.org with any questions.

Watch this space over the next two weeks for a complete listing of opportunities to learn about the Portal and talk with the Portal team at ASHG!

New funding opportunities for T2D genetic research

The Foundation for the National Institutes of Health (FNIH) has released three new funding opportunities that aim to add to the growing body of data housed in the T2D Knowledge Portal. The new Request for Proposals (RFPs) solicit data on T2D related complications and individual level and whole exome sequencing data related to T2D.

FNIH awards will provide successful applicants with up to $200,000 per individual award for proposals to harmonize and transfer existing datasets and up to $500,000 per individual award for proposals that include the generation of new genotyping data. Awards will be made over two years and aim to enhance the NIH-funded T2D Knowledge Portal hosted by the Broad Institute at the Massachusetts Institute of Technology (MIT).

Responses to the FNIH Requests for Proposals are due by December 31, 2016. Details on the new funding opportunities can be found here. 

Insulin sensitivity comes into focus

Many different things can be seen in any landscape, depending on your focal point.
Image by Nicooo76 via Pixabay.

When photographing a landscape, different photographers choose different perspectives. Some capture a wide-angle view, while others focus on particular details.

It’s no different for researchers who use genome-wide association studies (GWAS) to investigate the genetic landscape of type 2 diabetes (T2D). A common perspective is to study the wide range of variants that are significantly associated with the presence of T2D in patients. But it can also be very informative to concentrate on individual traits related to the physiology of T2D. In a new paper in Diabetes, co-first authors Geoffrey Walford, Stefan Gustafsson, Denis Rybin, and fellow members of the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) took this focused perspective to discover associations of genetic variants with insulin sensitivity.

Along with reduced insulin levels, the loss of insulin sensitivity (often termed insulin resistance) is a major hallmark of T2D. When muscle, liver, and fat cells become less able to respond to insulin, blood glucose levels rise. Since this can contribute to development of T2D and exacerbate its symptoms, knowing which genetic variants are associated with sensitivity to insulin could be informative for understanding pathways that contribute to T2D risk.

But insulin sensitivity is difficult to measure. Earlier GWAS have used simple estimates of insulin sensitivity, such as fasting levels of insulin, and have discovered a handful of genetic variants that influence insulin sensitivity. The “gold standard” test, the euglycemic clamp, involves giving patients continuous infusions of insulin and glucose and monitoring their blood glucose every few minutes. It’s expensive and time-consuming—not a test that is practical to perform on the tens of thousands of subjects that are commonly used in GWAS.

The authors wondered whether they could instead use an index that combines several measurements, each relatively easy to make. It’s an index with a long name: the modified Stumvoll Insulin Sensitivity Index (ISI). Developed by Stumvoll and colleagues in 2001, this index can be derived in a variety of ways. The authors chose the ISI requiring just three measurements: fasting insulin levels; glucose levels two hours after a glucose load; and insulin levels two hours after a glucose load. This ISI is as good as or better than other estimates of insulin sensitivity and correlates well with the euglycemic clamp.

So the researchers looked for variants associated with the Stumvoll ISI in nearly 17,000 participants in the discovery phase of the work. They added another 13,300 in the replication phase, adding up to about 30,000 in the combined meta-analysis. Since obesity, measured by body mass index (BMI), can affect insulin sensitivity, the authors added BMI to some of their statistical models.

First, the authors found associations between the ISI and other variants already known to affect simple measures of insulin sensitivity. This provided reassurance that the ISI was properly detecting genetic influences on insulin sensitivity. After discovery, replication, and meta-analysis, two novel genetic variants were associated with ISI at genome-wide significance (P-value < 5.0 ×10^-8) in a model that tested the effect of the variant, age, sex, and the interaction between the variant and BMI: variant rs12454712, near the gene BCL2, and variant rs10506418, near the gene FAM19A2.

How might these variants affect insulin sensitivity? There’s a lot more work to be done before that question can be answered. Additional studies will need to clarify whether these variants, which are near BCL2 and FAM19A2, affect these or other genes, and then how these variants actually cause changes in insulin sensitivity. 

There are some clues already in the published literature. The variant rs12454712 near BCL2 has previously been found to be associated with T2D, supporting the hypothesis that this region of the genome contributes to T2D risk through reducing insulin sensitivity. And the gene itself (BCL2) has already been implicated in glycemic metabolism: inhibiting bcl2 improves glucose tolerance in a mouse model, while a drug that inhibits the protein product of the gene (BCL2) increases blood glucose levels in certain chronic lymphocytic leukemia patients. So there’s even more reason to suspect that the rs12454712 variant might affect insulin sensitivity via BCL2.

There is as yet no evidence linking the protein FAM19A2 function to glucose metabolism, so the jury is out on whether the variant rs10506418 affects FAM19A2 or some other nearby gene. 

By focusing on a detail of the T2D-related genetic landscape, this study has teased out two variants that may give us clues about the physiology of insulin sensitivity and the development of T2D. And that’s a valuable addition to our overall picture of T2D genetics!