Monday, June 18, 2018

See you at ADA!

The 78th Scientific Sessions of the American Diabetes Association are coming up in just a few days, and the T2D Knowledge Portal team will be there!

As usual, we'll have a booth in the exhibit hall. We'll be at booth #1075 from 10am to 4pm on Saturday and Sunday 6/23-24, and from 10am to 2pm on Monday 6/25. Come say hello, get a demonstration of the T2D, Cardiovascular Disease, or Cerebrovascular Disease Knowledge Portals, and pick up some of the T2DKP sticky notes that we'll be giving away!

Here's who you might find at the booth when you stop by:

There will also be presentations from several members of our group on Saturday, June 23:
  • Jason Flannick, PhD will give a talk on "The Type 2 Diabetes Knowledge Portal" at 11:30am.
Session: Quantifying Diabetes: Genomics, Electronic Health Records, and Automated Control
Location: W312
  • Jose C. Florez, MD, PhD, will moderate an interactive poster session, "Delving into Type 2 Diabetes Genetics", at 12:30 pm.
Location: Poster hall
  • Miriam Udler, MD, PhD will present "Genetic testing for Monogenic Diabetes--Whom to Test, What and How to Order?" at 2:15pm.
Session: Monogenic Diabetes Testing is Ready for Prime Time--Integrating Genetics into Your Practice
Location: W304E-H

We hope to meet you in Orlando!

Friday, June 1, 2018

New T2DKP features help distill knowledge from data

We are pleased to announce four new features in the Type 2 Diabetes Knowledge Portal that simplify the interpretation of genetic association data, making it easier to pinpoint variants and datasets that are informative for a disease or phenotype of interest.

"Clumping" variants by linkage disequilibrium

The first step in getting an overview of the results of a particular experiment is typically to plot variant associations vs. chromosomal location, in a so-called "Manhattan plot." These plots are available from the T2DKP home page after choosing a phenotype from the list:

After selecting a phenotype, you may select a dataset, and the Manhattan plot is displayed above a table of the top variants:

Now, in addition to selecting a dataset to view associations, you may select a threshold for linkage disequilibrium (LD) in order to reduce the number of linked variants that represent a single association signal. For example, without "clumping" variants by LD (r2 = 1), when viewing the DIAGRAM 1000G GWAS dataset there are 70 significantly associated variants in the IGFBP2 gene; but setting the most stringent LD threshold  (r2 = 0.1) reduces that number to just 8 variants by displaying only the most significant associations after clumping variants by LD. Intermediate LD thresholds of r2 = 0.2. 0.4, 0.6, or 0.8 may also be set, allowing more versatility in this analysis.

New Region page

The Gene page of the T2DKP (see an example) integrates and summarizes information about the associations of variants across the region of a gene. Now, you can see this integration and summation for any region of the genome, not just the areas surrounding protein-coding genes. Simply enter a chromosome and coordinates in the home page search box:

The resulting page resembles a Gene page. The traffic light integrates all associations across the region to give you an immediate indication of whether there are significant associations found in any of the datasets in the T2DKP. Further down the page, tools and displays let you drill down to the specifics for a phenotype or variant of interest. This new Region page provides a way to explore any part of the genome in great detail.

PheWAS graphic on the Variant page

Previously, the Variant page of the T2DKP displayed significant associations for each variant in a graphic that showed a color-coded box for each phenotype-dataset combination. But the rapidly increasing number of phenotypes becoming available from biobank studies has made this view unsustainably large. In its place, we have incorporated a phenome-wide association study (PheWAS) visualization developed at the University of Michigan. The graphic shows at a glance which phenotype associations are most significant for a particular variant. Mouse over a point to see more details.

All Associations graphic on the Variant page

The PheWAS graphic distills variant associations in order to highlight the most significant ones. But suppose you want to drill down to the details and explore associations in every dataset, viewing parameters like sample size, odds ratio, and more? There's a graphic for that too: our new All Associations interactive graphic, located in the "Associations across all datasets" section of the variant page. Start by using keywords to filter phenotypes. Filtering allows you to view one specific phenotype, several related phenotypes, or phenotypes in a broad category, such as glycemic phenotypes; both the graphic and the table below it change in response to phenotype filtering.  There are also options to filter by setting ranges of p-values and/or sample sizes.

The graph plots p-value (vertical axis) vs. dataset sample size (horizontal axis) for each association. Points in the graph are triangular; whether the triangle points up or down indicates a positive or negative direction of effect, respectively. Mousing over a point shows you more details about the association and the dataset. This graphic can help you evaluate whether an association is likely to be real. As shown in the illlustration below, a genuine signal should increase in significance (i.e., decrease in p-value) with increasing sample size.

Stay in touch!

Like the rest of the T2DKP, these features are under continuous development. Please give them a try and let us know what you think.

Friday, May 11, 2018

T2DKP Spring Newsletter

The latest issue of our quarterly newsletter is now available. Download it here and get the latest!

Tuesday, May 8, 2018

NIDDK Workshop: Towards a Functional Understanding of the Diabetic Genome 2018

Recently, members of the T2D Knowledge Portal team were fortunate to participate in a fascinating workshop hosted by the NIDDKTowards a Functional Understanding of the Diabetic Genome. Speakers highlighted the diversity of ongoing research projects that aim to translate disease-associated variants into functional insights in type 2 diabetes.

The workshop featured presentations on multiple data types that can provide clues about the mechanisms by which sequence variants affect T2D risk. Many of these offer insights into transcriptional regulation: epigenomic chromatin modifications; tissue-specific RNA levels; eQTLs; transcription factor binding sites; long-range interactions between chromosomes that bring promoters and enhancers into proximity; and regulatory pathways. Others focus on downstream processes such as protein-protein interactions, biochemical pathways, and metabolomics.

It will be crucial to integrate all of these data types with genetic association data in order to get a complete picture of how particular genomic regions influence T2D biology, and at the T2DKP we are working towards incorporating as many of these data types as possible.

Although the presentations in this workshop were diverse, some common themes were evident. One was that although the insulin-secreting beta cells in pancreatic islets are hugely significant to T2D, and most T2D risk variants influence insulin secretion, current research projects are confirming and underscoring the importance of other tissues. Fat, liver, skeletal muscle (which comprises 40% of human body weight), and brain are all intimately involved in the development of T2D.

Another common theme for ongoing T2D research is that things may often be much more complicated than they first appear. A single genomic region associated with T2D risk may harbor multiple independent causal variants, each potentially having different regulatory effects, possibly affecting different tissues, and causing varied phenotypic consequences. Even if these variants alter a protein-coding sequence, they may not act through their effects on that sequence. These genetically complicated regions, such as those elucidated in FTO or TCF7L2, may be more common than we previously thought.

A third overall conclusion from the workshop is that model organism research can accelerate the investigation of candidate genes. The short life cycles of Drosophila and zebrafish, and the versatile genetic tools available for these systems, allow for rapid and systematic interrogation of gene function. Zebrafish glucose and lipid metabolism have much in common with those processes in human cells, and with their transparent bodies, zebrafish literally give us a window into pancreatic development.  In addition to being a well-developed model system, the mouse offers much greater genetic diversity than human, with about 40 million SNPs in the mouse genome as compared to about 10 million in the human genome.

At the T2DKP, efforts to integrate many of these data types are in progress, and integration of others is being planned. We continue to work towards making the T2DKP a comprehensive resource for the T2D research community, to help accelerate the translation of variant associations into knowledge about disease mechanisms and identification of potential drug targets.

Many of the presentations at the workshop featured web resources of potential interest to T2D researchers, listed below. The T2DKP is connected with the first, the Diabetes Epigenome Atlas. We are interested providing better connections between the T2DKP and other relevant resources. If you would be particularly interested in seeing links from the T2DKP to one of the resources below, or if you know of a resource that would be informative, we would love to hear your suggestions!

  • HaploReg: explore annotations of the noncoding genome at variants on haplotype blocks
  • ExPecto: tissue-specific gene expression effect predictions for human mutations
  • DeepSea: predict the cell type-specific epigenetic state of a sequence and the chromatin effects of sequence variants
  • GeNets: unified web platform for network-based analyses of genetic data
  • DCell: a deep neural network simulating cell structure and function

Wednesday, May 2, 2018

Join the Knowledge Portal Network team!

At the Knowledge Portal Network (currently consisting of the Type 2 Diabetes, Cerebrovascular Disease, and Cardiovascular Disease Knowledge Portals), we are looking for energetic, talented people to help us produce web portals that aggregate and serve genetic association results to the world in order to spark insights into complex diseases. There are positions open for a software engineer to help in developing and producing these web portals, and for a technical release manager to manage and coordinate tasks during production and maintenance of the portals.

The positions are located at the Broad Institute in Cambridge, MA, a dynamic and exciting work environment where cutting-edge science is applied to critical biomedical problems.

Find more details and apply for the software engineer or technical release manager positions at the Broad Careers site.

Friday, April 27, 2018

New T2DKP release adds individual-level data for interactive analysis

With the April release of the Type 2 Diabetes Knowledge Portal, we are increasing the number of datasets and samples available for interactive analysis via the LocusZoom and GAIT tools. These tools now access individual-level data from three additional datasets, all of which were quality controlled and analyzed at the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D) Data Coordinating Center (DCC):
  • CAMP GWAS: 3,628 multi-ancestry samples from the MGH Cardiology and Metabolic Patient cohort, generated by a public-private partnership between Pfizer Inc. and Massachusetts General Hospital;
  • METSIM GWAS: 8,791 European ancestry samples from the Metabolic Syndrome in Men study.
These individual-level data are available as "dynamic" datasets, powered by Hail software, in LocusZoom on Gene pages and Variant pages of the T2DKP, for the following phenotypes: 
  • BioMe AMP T2D GWAS: type 2 diabetes, BMI, diastolic blood pressure, fasting glucose, HbA1c, HDL cholesterol, LDL cholesterol, systolic blood pressure
  • CAMP GWAS: type 2 diabetes, BMI, fasting glucose, fasting insulin
  • METSIM GWAS: type 2 diabetes, BMI, diastolic blood pressure, fasting glucose, fasting insulin, HbA1c, HDL cholesterol, LDL cholesterol, systolic blood pressure
To perform interactive analyses on these data in LocusZoom, select one of the available phenotypes in step 1 and then choose a "dynamic" dataset in step 2.

When you click on a variant in the resulting LocusZoom plot, the option to condition on that variant appears in the tooltip:

Clicking on that link starts on-the-fly association analysis for the region while conditioning on that variant, which can reveal whether association signals are independent of each other. You can choose to condition on multiple variants. The variants of your choice are listed in the upper left-hand corner of the plot, and the list may be edited:

Individual-level data from these three datasets are also available for interactive analysis via the Genetic Association Interactive Tool (GAIT) on Variant Pages. After selecting one of the datasets, you will be able to choose a phenotype for association analysis, filter the sample pool by specifying a range of values for one or more phenotypes, choose custom covariates, and then run on-the-fly association analysis for your chosen subset of samples. Find all of the details about how to use this tool in our GAIT guide.

We hope that the increased ability to interact with individual-level data in the T2DKP will be helpful to your research. As always, we are happy to answer any questions about these or other data and tools; please contact us for help.

Tuesday, April 17, 2018

Developing a model for collaborative science: a mid-term perspective on the AMP T2D Partnership

In 2011, Dr. Francis Collins, Director of the National Institutes of Health (NIH), met with leaders in biomedical research to discuss a frustrating problem. Continual improvements in molecular biological and genomic techniques were generating an avalanche of data relevant to complex diseases, yet the translation of these data into insights about disease mechanisms and drug targets was unacceptably slow. It was clear that an entirely new paradigm for collaborative research would be needed to speed up the extraction of knowledge from data.

The result of these discussions was the creation of the Accelerating Medicines Partnership (AMP), one branch of which focuses on type 2 diabetes (T2D)—a life-threatening disease that affects hundreds of millions of people worldwide, whose incidence is growing, and whose progression cannot yet be effectively stopped or reversed. AMP T2D, a five-year project, includes the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); the pharmaceutical companies Janssen Pharmaceuticals, Eli Lilly and Company, Merck, Pfizer, and Sanofi; the University of Michigan; the University of Oxford; the Broad Institute; and other researchers around the globe. The Foundation for the National Institutes of Health (FNIH) also provides funding and coordination for the project.

Drawing on the strengths of both academia and industry, this public-private partnership brings together all stakeholders in a pre-competitive space to share data and combine resources, with the goal of validating new drug targets faster. Now in Spring 2018, roughly mid-way through the funding period, it is evident that this collaboration has resulted in remarkable progress on both scientific and collaborative fronts.

Genetic association data: the foundation of AMP T2D

Genetic association studies interrogate the genomes of individuals at millions of specific genomic positions to discover sequence variants that are correlated with the incidence of disease. From the outset, AMP T2D aimed to support the generation of unprecedented amounts of new genome-wide association study (GWAS), exome sequencing, and whole-genome sequencing data within the project as well as their aggregation with all relevant publicly available data. Originally, 5 sites were funded by the NIDDK to generate new data and deposit them into the AMP T2D Data Coordinating Center (DCC) at the Broad Institute. As the project evolved, another site was funded by the NIDDK and 8 more sites were funded by the FNIH. Additionally, an Opportunity Pool of funds from the NIDDK was created, allowing the AMP T2D Steering Committee to award smaller grants for complementary research projects in a flexible, science-driven manner.  Currently 10 Opportunity Pool projects are in progress, and more awards will be given in the future.

Not only has the number of genetic association studies increased since the inception of AMP T2D, but also the number of samples surveyed in each has grown dramatically, from typically under 100,000 to approaching 1 million today. The increased statistical power conferred by these large sample sizes has led to a huge increase in the number of loci found to be significantly associated with T2D, from about 70 at the start of the project to nearly 430.

Improvements in genomic technologies in the past few years have allowed AMP T2D collaborators to generate increasing amounts of sequencing data, which make it possible to comprehensively interrogate all alleles and to uncover rare variation. At the project’s start, T2D associations with exome sequences (covering the protein-coding regions of the genome) were available for about 13,000 samples, and no whole-genome sequencing studies had been published. Now, more than 2,600 whole genomes are available, and analysis of a set of 50,000 exomes—the largest disease-specific aggregation of exome sequencing data to date—is nearly complete. Importantly, many of the associations that have been newly discovered in sequencing studies involve relatively rare variants that affect protein-coding regions. It is often more straightforward to develop hypotheses about the impact of such variants than it is for variants outside of coding regions.

As the AMP T2D partnership has grown in prominence in the diabetes field, the DCC has been approached by investigators outside the project who want to contribute their data in order to aggregate and display them in the context of AMP T2D data. In early 2017, researchers in the 70kforT2D project, which found novel T2D associations by re-analyzing existing GWAS data, offered their results for integration into the DCC and display in the Type 2 Diabetes Knowledge Portal (T2DKP; see below) before publication.

70kforT2D GWAS was first pre-publication dataset to be added to the T2DKP from outside the AMP T2D partnership, and it was particularly appropriate that these scientists, whose results illustrate the value of data sharing, themselves chose to freely share their results. Incorporation of datasets into the AMP T2D DCC and T2DKP offers investigators the chance to take advantage of the expertise of the AMP DCC analysis team, apply cutting-edge analysis tools to their data, and display their results broadly to the T2D research community in the context of multiple datasets. The AMP T2D DCC is open to incorporating T2D-relevant datasets from all investigators (find details on contributing data here).

In addition to the datasets generated by AMP T2D partners and other T2D researchers, which focus on associations with T2D, glycemic measures, and T2D complications, the AMP T2D DCC also collects publicly available genetic association datasets for traits relevant to T2D, such as anthropometric measures, blood pressure and lipid levels, and heart and kidney disease.

Orthogonal data types to help identify and prioritize causal variants and genes

Finding genetic variants that are associated with T2D risk is critically important to understanding the genetics of T2D, but it is only a first step. The most significantly associated variant in a genomic region may not be the causal variant that is responsible for altered T2D risk. Researchers perform fine mapping to analyze genetic associations in specific regions of the genome and generate credible sets—that is, sets of variants that are predicted to include the causal variant. Mid-way through the AMP T2D funding period, emphasis among the data-generating partners is beginning to shift from simply generating association data to performing fine mapping and credible set analysis.

But even after predicting which sequence variations are responsible for altered risk, finding clues about how they affect risk requires integration with additional data types. Information about the functional importance of the genomic region where a variant is located—its relevance to gene expression, protein function, networks and pathways, metabolite levels, and more, all determined on a tissue-specific basis—can help prioritize genes and pathways for in-depth experimental investigation. These kinds of research were built into AMP T2D from the beginning, and as the importance of these data types became even clearer, several Opportunity Pool awards were given to projects focusing on complementary data types that shed light on the significance of genetic associations.

Several of these projects focus on generating tissue-specific epigenomic data: histone modifications, DNA methylation, chromatin conformation, transcription factor binding, 3-dimensional chromosome structure, and other data types. Epigenomic data can provide important clues about the mechanisms by which sequence variation affects T2D risk, particularly for variants that lie outside of protein-coding regions. For example, if a risk-associated variant is seen to disrupt a transcription factor binding site, this would support the hypothesis that the transcription factor and its target genes are relevant to T2D.

To make these data accessible to researchers, one Opportunity Pool award supports the creation of the Diabetes Epigenome Atlas, which collects and displays epigenomic datasets relevant to T2D. In the near future, these data will be fully integrated with genetic association data in the Type 2 Diabetes Knowledge Portal (see below).

Other Opportunity Pool projects are concerned with processes downstream of gene expression. Discovering interactions between proteins implicated in T2D risk, for example, could help to uncover all of the players in pathways important for the development of T2D, increasing the number of potential drug targets. Determining the effects of variants on the levels of key metabolites can illuminate the metabolic pathways that change during the development of T2D. 

In addition to generating all of these orthogonal data types, AMP T2D partners are developing algorithms and using machine learning to classify and prioritize variants on the basis of the functional annotations that accompany them. Finally, other Opportunity Pool projects will use model organisms to test and validate drug targets that are suggested by these analyses.

Tools and methods to speed analysis and interpretation

At the inception of AMP T2D it was also clear that the development of new methods and tools would need to accompany the generation of data, and support for these activities was built into the program. One major technical effort has addressed an obstacle to global data aggregation: because of institutional and national privacy regulations, some datasets may not leave their site of origin to be aggregated with other datasets at the AMP T2D DCC. A group at the European Bioinformatics Institute has built a technical replicate of the DCC and knowledgebase, such that data stored there are equally as accessible for browsing, searching, and interactive analysis as are the data stored at the AMP T2D DCC at the Broad Institute. This federation mechanism allows global data accessibility even when data aggregation is not permitted.

Other efforts supported by AMP T2D are aimed at improving the speed and efficiency at which data can be taken in and analyzed. In one project, a data intake system is being developed that will streamline the process for both data submitters and for the DCC team, and will be applicable to data submission both at the Broad DCC and at other federated sites. Another project has created a software pipeline, LoamStream, that will largely automate quality control and association analysis of incoming data. Currently, LoamStream is in use for quality control of genotype data, and this has already greatly reduced the time required to process new datasets. Future work will extend the pipeline to association analysis and will also allow it to take in sequence data as well as genotype data.

A genetic association of a variant with T2D gains credibility if multiple independent studies replicate the association. Thus, it is important for researchers to be able to evaluate the weight of available evidence. But currently this is difficult to assess from the association datasets in the AMP T2D DCC, because many are based on overlapping sets of subjects. AMP T2D partners at the University of Michigan and University of Oxford are working on a method to take these overlaps into account and synthesize associations from multiple datasets into a “bottom-line” significance for association of a variant with T2D, which will aid in prioritizing variants for future work.

Multiple AMP T2D projects for analysis, interpretation, and custom interactive analysis of variant-phenotype associations are ongoing at the Universities of Michigan, Chicago, and Oxford, Vanderbilt University, and the Broad Institute. These projects are aimed at facilitating, in various ways, the path from variant associations to functional knowledge, and all have been or will be integrated into the T2D Knowledge Portal (see below).

Hail software offers a pipeline that speeds up the analysis of huge genomic datasets, while the gnomAD resource aggregates and harmonizes exome and genome sequences to provide a catalog of genetic diversity, in more than 100,000 humans, that aids in interpretation of variant associations with disease. A tool under development in the gnomAD project will display the effects of variants on protein structures as another way to deduce their potential impact.

Other analysis modules include gene-based association methods for using expression data to predict genes that may impact a phenotype (PrediXcan and MetaXcan), and a phenome-wide association study (PheWAS) method for visualization of the associations of a variant across multiple phenotypes, which is a crucial consideration during drug development. 

The interactive visualization tool LocusZoom will integrate many of these methods to display variant associations and credible sets, epigenomic and functional annotations, and phenotype associations across a genomic region as well as offering custom association analysis.

An example LocusZoom plot

AMP T2D Knowledge Portal: democratizing T2D genetic results for researchers world-wide

AMP T2D was founded on the idea that in order to truly accelerate progress, genomic information must be freely accessible to all scientists and presented in a way that is understandable by a broad range of researchers working on T2D biology, not only by human geneticists and bioinformaticians with special computational skills. So the roadmap for the project included not only data generation and analysis, but also the production of a publicly available web resource that would integrate data types, interpret the evidence, and present of all these results. 

While it is under continuous development, mid-way through the initial funding period the T2D Knowledge Portal (T2DKP) is already a well-established resource. Other web resources collect genetic association data, but the T2DKP is unusual in providing harmonized datasets to which a consistent analysis pipeline has been applied. Rather than simply cataloging datasets, it offers distilled and synthesized results along with their interpretation, to guide more detailed exploration of the evidence. And, unlike any other extant resource, it offers researchers the ability to perform interactive queries on protected individual-level data. 

T2DKP home page

The Gene page of the T2DKP (see an example) illustrates the presentation of immediately understandable summary information along with the opportunity to drill down to the details. An algorithm considers the associations of all variants across a gene, for all phenotypes and in all datasets aggregated at the DCC, and calculates from them a “traffic light” signal for the gene: green to indicate that there is a significant association for at least one phenotype; yellow to indicate suggestive, if not highly significant, associations; and red to indicate that there is no evidence for association for any of the phenotypes considered in the T2DKP. Below this, tables and graphics invite users to explore all variants across the gene, their impacts on the encoded protein, and their associations, as well as their positions relative to epigenomic marks across the region in multiple tissues.

The T2DKP currently offers the ability to run custom, interactive association analyses using two different tools. In the LocusZoom visualization, users may choose one or more variants as covariates before performing association analysis. The Genetic Association Interactive Tool (GAIT) for single variant associations, which also powers the custom burden test for gene-level associations, is even more versatile, presenting the distributions of different characteristics of the sample set (age, sex, BMI, glycemic measures, blood lipid levels, and many more) and allowing users to filter the set by multiple criteria and to choose custom covariates before performing association analysis. Both of these tools allow analytical access to the individual-level data, whether housed at the Broad DCC or at the EBI federated node, in a secure environment so that data privacy is always protected.

Evolution of a collaborative environment

AMP T2D organization

The AMP T2D partnership is a multifaceted project (illustrated above) that embraces several aspects of basic research and combines them with building a product, the T2DKP. In connecting scientists both within and outside of consortia, in academia and in industry, working on genetic associations or functional studies, it is becoming the nexus of the T2D genetics community. Researchers are finding the T2DKP helpful for accessing even their own results and for viewing them in the context of multiple phenotypic associations and other complementary data types. Pharmaceutical partners are finding help via the Target Prioritization project, in which the tools and methods developed within AMP T2D are being used to prioritize a list of genes of mutual interest for further investigation.

Perhaps most importantly, AMP T2D has made researchers—both within and outside of the project—aware of the value of sharing data for representation in the context of all other relevant data. Only by compiling and interpreting all available information will we be able to make the best hypotheses about genes and pathways that are possible drug targets and prioritize them for in-depth functional investigation.

AMP T2D and beyond

In the remainder of the initial AMP T2D funding period, we expect continued progress in each of the areas discussed above. The data intake and analysis pipelines will be improved, and new data will be incorporated at an increasing pace—including data from the UK Biobank, which has generated association results for 500,000 genotyped subjects and more than 2,500 traits. Associations will be added for many more phenotypes related to T2D, including diabetic complications and longitudinal phenotype data that connect the development of various traits to the timeline of incident T2D.  Much more T2D-relevant epigenomic data will be available for query as well as for browsing, via dynamic connection with the Diabetes Epigenome Atlas. And entirely new data types (for example, metabolomic and proteomic data) arising from Opportunity Pool projects will be added to the T2DKP.

Ongoing work on tools and methods will result in the addition of many more interactive modules to the T2DKP. Researchers will be able to view PheWAS data; prune lists of variants by their linkage disequilibrium relationships; calculate credible sets and genetic risk scores with custom parameters; perform more versatile interactive burden tests; prioritize genes by pre-calculated association scores; overlay the positions of coding variants on protein structures to help assess their impact; and perform enrichment analysis on sets of loci to suggest pathways implicated in disease processes.

The Knowledge Portal platform developed for AMP T2D has already proved extensible to other complex diseases: in 2017, both the Cerebrovascular Disease and Cardiovascular Disease Knowledge Portals were launched. In the future, connections within the ecosystem formed by the T2D, Cerebrovascular, and Cardiovascular Portals will be improved, so that researchers can easily assess the impact of a variant or involvement of a gene for all of these related diseases. If funding and collaboration considerations allow, perhaps one day these Portals will merge into a single cardiometabolic disease genetics Knowledge Portal to accelerate the development of new therapeutics in this broader area.

Finally, the ultimate goal of this funding period is that by its end, the data generation, analysis, and interpretation will have facilitated the validation of multiple promising drug targets for further investigation. Given the rate of progress on multiple fronts, this seems a realistic goal. We hope that this unique collaborative environment will continue to accelerate T2D genetic research and will become a paradigm for other research communities.