Tuesday, October 18, 2016

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.

Friday, October 14, 2016

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.

Monday, October 3, 2016

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!

Thursday, September 15, 2016

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

Wednesday, August 10, 2016

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!

Monday, July 11, 2016

World-wide cooperation to address a world-wide problem

If you’re reading this post, you’re likely well aware that type 2 diabetes (T2D) is one of the biggest health problems we face and that its incidence is rising. Clearly, we need a better understanding of how T2D develops and what the risk factors are, along with more effective treatments.

Along with environmental and behavioral factors, variation in the human genome plays an important role in susceptibility to T2D. Mutations that alter gene expression or affect the function of proteins and noncoding RNAs can lead to differences in physiology and, ultimately, to differences in T2D risk. To begin to understand this, we first need to know which variants contribute to T2D and by how much. And for that, we need genetic association data—lots of it. Large amounts of data allow us to refine the genetic association map: reconfirming some previous signals, establishing that others are not significant, and adding evidence for or against the causal roles of variants.

Addressing this need, a study published today in Nature (Fuchsberger, Flannick,Teslovich, Mahajan, Agarwala, Gaulton et al.) presents the results of an international collaboration that has generated an unprecedented amount of T2D genetic data. As befits an approach to a huge problem, everything about this study is huge: the number of collaborators (more than 300, from 22 countries), the number of individual genomes sampled (120,000), the number of variants analyzed (tens of millions); and the number of funding organizations (more than 60). The result is the most comprehensive look at the genetics of T2D available to date.

One of the major projects described in the paper, led by the Genetics of Type 2 Diabetes (GoT2D) Consortium, was whole-genome sequencing for 2,657 people, half T2D cases and half controls. Whole-genome sequence analysis is the only way in which the influence of rare variants can be assessed comprehensively.

An open question in the T2D genetics community has been whether rare variants account for most of the T2D risk, or whether it is due to the effects of many common variants of small effect. This study begins to answer this question. It shows that most T2D risk can be ascribed to the modest effects of a large number of common alleles, and that there is likely no treasure trove of rare variants of large effect waiting to be found.
This project uncovered more than a dozen loci that were associated with T2D at genome-wide significance. Most were common variants, and some, such as the variant rs11759026 near CENPW, had not been seen before in genome-wide association studies. This study also called into question the previously identified associations of some variants and supplied better candidates for the actual T2D risk variant. For example, the noncoding variant rs10401969 had been associated with the CILP2 locus, but the additional data from this project now point to a linked missense variant in TM6SF2 as causal—an exciting finding, since TM6SF2 is involved in fat metabolism and could have a direct role in the development of T2D.
In another project reported by Fuchsberger and colleagues, combining exome sequence data from the T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) Consortium with the exome sequences obtained by the GoT2D project resulted in a data set of sequences from nearly 13,000 individuals, from five different ethnic groups.   Data sets stratified by different ancestries allow investigation of population-specific associations that might otherwise be obscured. The larger sample size and the focus on coding variation, with presumably larger effects on protein function, was another approach to maximize discovery of rare variants if such were present. Another benefit was to help implicate specific genes in previously associated genomic regions.
One variant identified by this approach has an immediately understandable relationship to diabetes: the rs2233580 variant causes a missense mutation in the PAX4 gene, which encodes a transcription factor that has been implicated in pancreatic islet differentiation. Interestingly, this is a common variant in East Asian populations but is nearly absent in the other ancestries studied. Other variants in the same gene have previously been associated with early-onset monogenic diabetes, so this result is a reminder that different mutations in same gene can have very different effects on the disease process. Other work in this study reaffirmed this conclusion for other genes.
The scale of this study is unprecedented, and we’ve only touched upon a small piece of it here. But something else is unprecedented about these data: they are available for anyone to explore, right now, in the T2D Knowledge Portal. Researchers don’t need to go to various sites to gather bits and pieces of the data, harmonize them, and analyze them; the data sets are globally accessible in the Portal along with pre-computed analyses and sophisticated tools for custom analyses.
The data sets from this study in the Portal are:

  • GoT2D WGS - whole-genome sequence data
  • GoT2D WGS + replication – whole-genome sequence data plus imputed genotypes
  • 13K exome sequence analysis
  • 82K exome chip analysis

All of these are described in more detail on our Data page. You can see a list of the cohorts and even view their case/control selection criteria. Our Variant Finder tool may be applied to all of these sets, and the Genetic Association Interactive Tool (GAIT) accesses the 17K exome sequence analysis data set that includes the 13K exome sequence analysis data from this study along with additional data from the SIGMA Consortium, previously published by Estrada et al. in JAMA. You’ll also see results from these data sets in various tables and displays on the Gene and Variant pages of the Portal.

In a review article that was also published today in Nature Reviews Genetics, Flannick and Florez advocate for the aggregation of genetic data in general, and the T2D Knowledge Portal in particular, as a way to democratize the study of T2D and accelerate discoveries that will improve patient care.

“Data from human genetics is highly valuable in identifying and validating the role of specific targets for development of new medicines,” said David Altshuler, who was previously the principal investigator at Broad for the T2D genetics studies and Portal at Broad, and is now Chief Scientific Officer at Vertex Pharmaceuticals.  “When government, non-profits and companies work together with patients to increase our knowledge of the genetic causes of disease, everyone benefits.”  

The Accelerating Medicines Partnership in Type 2 Diabetes funds the T2D Knowledge Portal as a means to facilitate collaboration, with the goal of benefitting patients with T2D world-wide. “Whether you are a biologist exploring a specific pathway in a model system, a pharmaceutical investigator examining an appealing drug target, or a clinician pondering whether a newly identified variant is the cause of a patient’s symptoms, having well curated human genetic data matched to carefully defined phenotypes at your fingertips should provide rapid insight and accelerate discovery,” said Jose Florez, the Chief of the Diabetes Unit at the Massachusetts General Hospital and a human geneticist at the Broad Institute, who leads one of the groups developing the Knowledge Portal. The deposition of the huge data sets from the Fuchsberger et al. study into the Portal has demonstrated that the processes in place for data intake, harmonization, and quality control are functional and can work at scale. We hope that other researchers and consortia will follow suit and help to make the Portal an even more powerful catalyst for new insights into T2D.

Monday, June 20, 2016

Report from New Orleans: 76th Scientific Sessions of the American Diabetes Association

Members of the T2D Knowledge Portal team braved extreme heat and humidity, as well as icy air conditioning, to attend the American Diabetes Association conference in New Orleans, LA. Our booth in the conference exhibit hall was a great way to interact personally with conference attendees and showcase the Portal. Many genetics researchers stopped by for one-on-one tutorials on our new tools and features. And clinicians and diabetes patients, even if they had no immediate use for genetic information, were happy to hear the goals of the project—to accelerate the identification of genes involved in T2D and, ultimately, to find new treatments and better understand the disease mechanism. 

We were pleased to welcome some special visitors to our booth: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Director Griffin Rodgers and Deputy Division Director Philip Smith. NIDDK is a major supporter of the T2D Knowledge Portal project.

Drs. Philip Smith (left) and Griffin Rodgers visit the Portal booth

Dr. Smith also made an video statement as part of the media coverage at ADA, eloquently explaining the rationale behind the Portal and the needs that it can address.

If you missed us at ADA, come visit us at our booth at the American Society for Human Genetics meeting next October! And if you can’t meet us in person, please feel free to email us at any time. We’re happy to answer questions or provide help in understanding the Portal data and tools.

Type 2 Diabetes Knowledge Portal team at ADA