Tuesday, October 8, 2019

Connect with the Knowledge Portal Network team at #ASHG19

Attending the American Society of Human Genetics Annual Meeting next week? We are too, and we look forward to connecting with you in multiple venues:

Wednesday, October 16
  • Visit our booth (#131) in the exhibit hall from 10am-4:30pm
  • Attend our Ancillary session:
Translating Variant Associations to Functional Insights Using the Knowledge Portal Network
12:45-2:00 pm, Marriott Marquis Houston, Tanglewood room
Jesse Engreitz and Jason Flannick will speak, with an introduction from Noël Burtt and followup from Maria Costanzo.
  • Attend our presentation at the Broad genomics booth (#714) from 3-4pm
  • Attend the talk by Lokendra Thakur, “Calculating principled gene priors for genetic association analysis.” 4:45-5pm, Room 317A, Level 3, Convention Center

Thursday, October 17
  • Visit our booth (#131) in the exhibit hall from 10am-4:30pm
  • Visit the poster (#1657/T) by Ben Alexander, “Systematic comparison of different evidence sources for predicting GWAS effector genes” from 2-3pm
  • Visit the poster (#1402/T) by Dylan Spalding, “Federating association analysis in type 2 diabetes to protect participants’ privacy” from 3-4pm

Friday, October 18
  • Visit our booth (#131) in the exhibit hall from 10am-3:30pm
  • Attend our presentation at the Broad genomics booth (#714) from 10-11am
  • Visit the poster (#2804/F) by Peter Dornbos, “The functional impacts of rare coding variants in 46,000 individuals on 23 quantitative phenotypes” from 2-3pm

Saturday, October 19
  • Attend the talk by Marcin von Grotthuss, “Public programmatic access to GWAS summary statistics and analytical methods.” 8:45-9am, Room 310A, Level 3, Convention Center

Tuesday, September 24, 2019

Learn about the Diabetes Epigenome Atlas at this week's webinar

Because integration with other data types can bring more meaning to genetic association data and spark insights into disease, we are working to develop connections between the Type 2 Diabetes Knowledge Portal (T2DKP) and the Diabetes Epigenome Atlas (DGA).  Learn more about this effort in our upcoming webinar at noon EDT on Thursday, September 26. We’ll demonstrate the current and planned connections between the T2D Knowledge Portal and DGA, and our guest speakers Kyle Gaulton and Parul Kudtarkar from DGA will provide an overview of the data and tools in the DGA resource.

This session may be attended as an online webinar (connection information below) or in person at the Broad Institute in the Cascades meeting room (11031) on the 11th floor of the 75 Ames St. building. We'll record the session and make it available on the T2DKP and on the Broad Institute YouTube channel for future viewing.

We hope you will attend and bring your questions and suggestions!

Future dates for our T2DKP Webinar Series:

Thursday, November 14, 2019
Thursday, January 16, 2020
Thursday, March 12, 2020

All events will take place at 12 noon Eastern time. We will send more details about each webinar as it approaches.

Connection Information:

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Meeting ID: 642 344 149


Wednesday, September 11, 2019

Learn about the T2D Knowledge Portal at #EASD2019

The Type 2 Diabetes Knowledge Portal team will be exhibiting next week at the European Association for the Study of Diabetes (EASD) conference in Barcelona. Please stop by our booth (M07) to get a hands-on tutorial and let us know which data and features would be most useful to your research.

We'll have team members there both from the T2DKP Federated Node at the European Bioinformatics Institute (EBI) and from the AMP T2D Data Coordinating Center (DCC) at the Broad Institute.  The Federated Node allows data that may not leave Europe due to privacy regulations to be queried remotely and securely via T2DKP tools and interfaces. Researchers anywhere in the world may browse and query data from either location, without even needing to know where the datasets reside. Stop by the booth and talk with us about adding your results to the T2DKP!



Thursday, September 5, 2019

T2DKP Newsletter available

A new edition of our periodic newsletter is now available. Download it here for the latest news about T2DKP data and features!

Wednesday, August 28, 2019

New T2DKP release brings new datasets and interfaces

Today's release of the Type 2 Diabetes Knowledge Portal includes many improvements:

  • updated and augmented results for 7 datasets, generated by re-analysis of individual-level data; 
  • multiple new datasets; 
  • a new interface displaying predicted trait- and disease-relevant tissues; 
  • new functionality in the custom burden test; 
  • and new video resources.

New genetic association results

Using the LoamStream genomic analysis pipeline, developed by the T2DKP team at the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D) Data Coordinating Center (DCC) at the Broad Institute, we have re-analyzed several sets of individual-level genetic association data that were generated by AMP T2D collaborators:
  • BioMe AMP T2D GWAS
  • CAMP GWAS
  • Diabetic Cohort - Singapore Prospective Study GWAS
  • FUSION exome chip analysis
  • FUSION GWAS
  • FUSION Metabochip
  • METSIM GWAS
The LoamStream software allowed T2DKP analysts to run these analyses rapidly, determining associations for many more phenotypes than were analyzed previously, and to use standard, state-of-the-art methods so that all of the results are comparable across datasets. Each dataset was analyzed for associations with glycemic, lipid, renal, anthropometric, and blood pressure phenotypes, and in addition, the type 2 diabetes cases in the BioMe AMP T2D GWAS set were analyzed for associations with three diabetic complications: chronic kidney disease, end-stage renal disease, and neuropathy. The Loamstream pipeline generates detailed Quality Control and Analysis reports, which may be downloaded from the dataset-specific sections of the T2DKP Data page.

Results from these re-analyses are integrated into the T2DKP and may be viewed on Gene and Variant pages and in Manhattan plots. They may be searched using the Variant Finder, and the individual-level data from the GWAS sets may be securely accessed for custom association analysis using the Genetic Association Interactive Tool (GAIT) on Variant pages.

We have also incorporated summary statistics from several studies into the T2DKP, including:
  • IVGTT-based Insulin Secretion GWAS (Wood et al. 2017): genetic associations for first-phase insulin secretion, as measured by intravenous glucose tolerance tests in over 5,500 multi-ethnic non-diabetic individuals. Several of the phenotypes measured are new to the T2DKP: acute insulin response, insulin secretion rate, and peak insulin response.
  • GIANT exome chip analysis (Turcot et al. 2018; Justice et al. 2019): genetic associations for BMI, height, and waist/hip ratio adjusted for BMI. BMI and height associations were determined in over 718,000 individuals, and waist/hip ratio in over 344,000.
  • Global Lipids Genetics Consortium exome chip analysis (Liu et al. 2017), with associations for plasma lipid levels in over 347,000 participants.
  • Chronic Inflammation GWAS (Ligthart et al. 2018): associations with plasma C-reactive protein, a measure of chronic inflammation, in more than 312,000 individuals.
  • COGENT-Kidney Consortium eGFR GWAS (Morris et al. 2019): associations with estimated glomerular filtration rate (eGFR) in over 204,000 subjects.

FOCUS on tissue enrichments

Although the genetic association results in the T2DKP identify sequence variants that are associated with the risk of developing T2D, it is rarely straightforward to identify the genes that are responsible for these associations, and in which tissues they act. Making connections between variants, effector genes, and tissues is essential for a better understanding of disease genes and pathways and for the development of new therapeutics.

To help researchers make these connections, we are assembling a toolkit of cutting-edge computational methods and applying them across all of the data in the T2DKP. The methods integrate GWAS data with transcriptomic data, tissue-specific gene expression results, eQTL data, and more to predict the probability of associations between variants, genes, phenotypes, and tissues. We present these results in interactive FOCUS (Find Orthogonal Computational Support) tables.

We previously added to the Gene page a Gene FOCUS table that presents results to help researchers evaluate candidate causal genes around a genetic association signal (read our blog post describing this interface). Now, we have added a Tissue FOCUS table presenting results that can suggest which tissues or cell types may be relevant for a disease or trait of interest. 

The table is currently accessible via a link on the home page:


To use the table, choose a phenotype of interest to see p-values for different tissues, denoting the significance with which variants associated with that phenotype are enriched in each tissue. The methods used for these predictions are DEPICT, GREGOR, and LD score regression (LDSR). Find complete details about the table and methods in our downloadable documentation.


Improvements to the custom burden test

Some sequence variants are biallelic or multiallelic: that is, the reference nucleotide may be substituted by two or more different nucleotides or indels.  Previously, in the custom burden test (found on T2DKP Gene pages) these variants were treated as a single allele. Now, the software underlying the custom burden test has been updated so that it treats multiple alleles separately, offering the ability to choose whether each allele of a multiallelic variant should be included in or excluded from the custom burden test. Stay tuned for other major improvements to the burden test, including the ability to use several different aggregation test methods, coming to the T2DKP in the near future!


New videos

Continuing with our series of short videos documenting various features of the T2DKP, we are releasing a new video that describes the interactive table of predicted T2D effector genes. The recording of our most recent webinar, covering gene-specific resources in the T2DKP, is also now available. Links to both videos can be found on the T2DKP home page and Resources page, as well as on the Broad Institute YouTube channel.


Upcoming webinar

Join us for our next webinar on Thursday September 26 at noon EDT! We'll announce the agenda and connection information in this space in the coming weeks.

Thursday, July 11, 2019

T2DKP webinar Thursday, July 18

Join us at noon EDT on Thursday, July 18 for an interactive workshop featuring gene-specific resources in the T2DKP. We’ll first cover two new types of information on T2D gene associations: predictions of T2D effector genes, and gene-level T2D association scores. Then we'll delve into the Gene page with its comprehensive information for T2D and many other phenotypes, focusing on how the T2DKP can help researchers prioritize genes within a GWAS locus for further investigation. See below for the agenda.

This session may be attended as an online webinar (connection information below) or in person at the Broad Institute in the 415 Main St Board room (mezzanine level), where lunch will be provided.

We hope you will attend and bring your questions and suggestions!


Agenda:

Introduction - Noël Burtt

Gene-specific resources in the T2DKP - Maria Costanzo

Preview of upcoming features - Ben Alexander

Q & A - the T2DKP team


Connection Information:

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Meeting ID: 619 080 603




Thursday, June 6, 2019

New T2DKP release features potential T2D effector genes

Today, in a new release of the Type 2 Diabetes Knowledge Portal, we present a distillation of many years of work from the global T2D research community: a list of the genes most likely to represent effectors for the development of T2D, based on a heuristic developed by Anubha Mahajan and Mark McCarthy that takes into account a variety of genetic and genomic evidence.

Identifying such candidate effectors is the goal of the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D), established in 2014. AMP T2D brought together stakeholders from government, academia, and industry in order to speed up translation of genetic data into insights about disease mechanisms and drug targets. The generation, aggregation, and analysis of unprecedented amounts of data in this collaborative effort has spurred efforts to develop methods for the systematic integration of data (see for example Fernandez-Tajes et al., 2019).

Now, by prioritizing and integrating multiple sources of evidence, Mahajan and McCarthy have classified genes according to the likelihood that they are involved in development of T2D.  The sources of evidence that they consider include genetic association data; functional genomic data such as eQTLs and chromatin conformation; mutant phenotype evidence from model organisms and knockdown screens in human cells; and other evidence gathered from the literature. The heuristic is described in detail in downloadable documentation.

Today's release of the T2DKP includes an interactive table that displays these classifications and allows you to view and explore all of the evidence underlying them.

Section of the Predicted T2D effector gene table. Columns are sortable, and columns containing combined evidence expand to show the individual evidence types comprising that classification.

When viewing this list, several caveats should be remembered. These are predictions only, and the strength of the predictions varies considerably among genes in the list. Also, any heuristic has limits, especially those developed in the absence of a clear "gold-standard" set, as this one was. Still, we hope that this list will be a valuable resource that can help suggest or support experimental directions for T2D researchers. We welcome feedback on the heuristic and the interface. Over the next year we plan to develop software to facilitate the generation and updating of these results.

Today's release of the T2DKP also includes 8 new datasets:

  • BioBank Japan GWAS (an overall set plus sex-stratified sets) bring to the T2DKP genetic associations for a wide range of phenotypes from over 190,000 individuals of East Asian ancestry. Phenotypes in these sets include many clinical measures as well as disease status for T2D, atrial fibrillation, and open-angle glaucoma.
  • Singapore Chinese Eye Study (SCES) GWAS, Singapore Malay Eye Study (SiMES) GWAS, and Singapore Indian Eye Study (SINDI) GWAS provide T2D associations for individuals of East Asian and South Asian ancestry.
  • Singapore Living Biobank GWAS datasets include associations with anthropometric and lipid traits for Chinese and Malay populations. 
All of these datasets are described fully on the T2DKP Data page.

Another new feature of today's release is that a link to standalone versions of our custom association analysis tools, the Genetic Association Interactive Tool (GAIT) and the Custom burden test, is now available on the Analysis Modules page. Both of these tools securely access individual-level data to compute on-the-fly genetic associations using custom parameters. GAIT, for single-variant association analysis, was previously only accessible on Variant pages; the Custom burden test for computing the disease burden across a gene was previously accessible only on the High-impact Variants tab of Gene pages. 

Finally, today's release includes a new instructional video that leads you through the features of the T2DKP Variant page. The video is listed on, and linked from, the T2DKP Resources page.




Check out our latest newsletter for more details about these and other recent additions to the T2DKP.