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.

Monday, June 3, 2019

See you at ADA next weekend!

The Type 2 Diabetes Knowledge Portal team will once again be presenting an exhibit booth at the 79th Scientific Sessions of the American Diabetes Association in San Francisco next weekend. This year, we're excited to be presenting along with our collaborators from the Diabetes Epigenome Atlas (DGA).

Stop by the booth (#2306) to get a personal, hands-on demonstration of the new tools and features, or just to say hello and let us know what new data and features you’d like to see in the T2DKP or DGA.

We’ll be there during all the exhibit hall hours:

Saturday, June 8:     10am-4pm
Sunday, June 9:       10am-4pm
Monday, June 10:    10am-2pm

Please email us if you would like to schedule a 1:1 tutorial session at a particular time, or just stop by our booth. We hope to see you there!

Wednesday, May 22, 2019

T2DKP now offers a T2D-specific exome sequence collection of unprecedented size

The largest known exome sequence analysis specific to a complex disease was published today in Nature, and all of the results are now freely available in the Type 2 Diabetes Knowledge Portal (T2DKP) to support researchers worldwide as they make decisions about how to prioritize potential T2D drug targets for investigation. The paper, “Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls” (Flannick et al.), describes a multi-ancestry analysis of both variant-level and gene-level genetic associations for type 2 diabetes.

The paper is the culmination of years of work from a global collaboration to generate exome sequences across five ancestry groups. The project began as an effort by the Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) consortium to perform exome sequencing and T2D association analysis for about 13,000 samples, and evolved into a consortium of consortia—about 30 international sites in all, including the GoT2D, ESP, SIGMA, LuCAMP, and ProDIGY consortia—that partnered to design a study including as many exomes as possible. The Accelerating Medicines Partnership in Type 2 Diabetes grew out of this effort, and today supports a wide range of genetic association and other studies aimed at elucidating the mechanisms behind T2D, as well as supporting the T2DKP to serve these results to the world.

The study included participants of African American, East Asian, European, Hispanic/Latino, and South Asian ancestry. The researchers sequenced exomes (the protein-coding regions of the genome) from these participants and performed gene-level association analysis in order to detect rare variants and uncover allelic series within genes. They also performed single-variant association analysis for a subset of the samples using genome-wide arrays and imputation. A comparison of the two methods confirmed that the strength of exome sequencing is its ability to identify informative, often rare, alleles that may yield clues to disease mechanisms, while array-based GWAS provides a more comprehensive picture of strongly associated loci.

The researchers found exome-wide significant gene-level T2D associations for three genes (MC4R, PAM, and SLC30A8). Replication of the gene-level associations in a meta-analysis of three independent exome sequencing datasets confirmed the significance of these associations and found exome-wide significance for a fourth gene, UBE2NL. The variant alleles uncovered in these genes are effectively “experiments of nature” that may subtly alter the structure, function, or stability of the gene products and could be very helpful in suggesting further research directions to discover the roles of these proteins in T2D risk.

But what of the other genes whose gene-level associations didn’t meet exome-wide significance? Suspecting that these associations could still provide valuable information, the authors decided to test whether these association scores were meaningful. They created sets of genes that were known or likely to have a role in T2D risk: for example, genes known to be T2D drug targets, genes in which mutations cause maturity onset diabetes of the young (MODY), or genes whose mouse homologs confer glycemic phenotypes when knocked out. In each set, genes in the sets had more significant gene-level T2D associations than would be expected by chance, suggesting that their scores were meaningful despite relatively low statistical significance. Analysis of additional sets of genes, for example those located in strongly T2D-associated GWAS loci, supported this conclusion.

Thus, although future studies with larger sample sizes will be needed to uncover strongly significant gene-level associations, the associations generated from this study can still provide evidence to support prioritization of research effort and resources. For example, the gene-level scores could help suggest which gene in a T2D-associated locus is most likely to be relevant to T2D. The series of variant alleles in individual genes that were identified in this study could help indicate whether it is gain or loss of protein function that affects T2D risk, an important piece of information for drug development.

So that researchers worldwide may benefit from these results, with agreement from all of the authors the results were made available in the T2DKP when the pre-print of the paper was posted to BioRxiv. “A main message of the paper is that rare variants potentially provide a much more valuable resource for drug development than previously thought,”  said Jason Flannick, first author on the paper. “We can actually detect evidence of their disease association in many genes that could be targeted by new medications or studied to understand the fundamental processes underlying disease. But because there is so much more information than just the variants in the genes cited in the paper, making all of the results available to everybody is critical for them to have the largest impact.”


In the T2DKP, this dataset is termed the AMP T2D-GENES exome sequence analysis set and is described on the Data page. The single-variant T2D associations may be browsed and searched throughout the T2DKP: on Gene and Variant pages, in Interactive Manhattan plots, and via the Variant Finder tool. The Genetic Association Interactive Tool (GAIT) for single variants and the custom burden test for genes provide secure interaction with the individual-level data from this set, allowing the user to filter samples and set custom parameters before performing on-the-fly association analysis.

The gene-level association scores are displayed in the T2DKP via two avenues. A new page lists genes with their association scores and other information such as the number of variants used to calculate the score. The variants comprising the scores may be filtered by any of 7 different categories, and the results of two different aggregation test methods are also available. Gene-level scores are also shown in the Gene Prioritization Toolkit on Gene pages. See our recent blog post for a description of this interface.

In addition to the sheer volume of these exome sequencing results, their open availability in the T2DKP is a remarkable milestone for the diabetes genetics research community. "I believe the T2D genetics community is setting examples both for human genetics, in data aggregation and joint analysis, and in its commitment to sharing of these results on an open platform enabling non-experts to make direct use of the results," says Noël Burtt, Director of Operations and Development for Knowledge Portals and Diabetes at the Broad Institute. The T2DKP team is proud to be a part of this collaborative effort.

Read the press release