Monday, September 18, 2017

All for one (population) and one for all

Type 2 diabetes (T2D) is a world-wide health problem, but it hits especially hard in Latin America, where incidence is higher than in many other parts of the world. To investigate the genetic basis for this difference, researchers from the U.S., Mexico, and Spain teamed up to look for genetic coding variants associated with T2D risk that are more common in people of Hispanic descent. In their recent paper (Mercader et al. 2017, Diabetes), the researchers discovered such variants and uncovered the molecular details of how one in particular affects T2D risk. Their results suggest a new avenue for drug development that could benefit diabetics of all ancestries. And surprisingly, although Hispanics have higher T2D risk, this variant actually protects against T2D.

In designing the study, Mercader and colleagues decided to focus on variants located within protein-coding sequences, whose effects can be more direct and more straightforward to test than those of variants outside genes. They used exome chip analysis, which considers only variants in protein-coding regions of the genome, to genotype both diabetics and non-diabetics of Hispanic descent from Mexico and the U.S. Their dataset, SIGMA exome chip analysis, is accessible in the T2D Knowledge Portal and described on our Data page.

To find variants that might differentially affect the Hispanic population, the researchers looked for T2D-associated variants that were common in Hispanics, but rare or low-frequency in people of European ancestry. The most significant variant in this category, rs149483638, is present at a minor allele frequency (MAF) of 17% in people of Hispanic ancestry, but has MAF of only 1%, 0.1%, and 0.02% in East Asian, African, or European ancestries, respectively.

Surprisingly, although enriched in this population that is more vulnerable to T2D, the rs149483638 effect allele is protective against T2D. People who are heterozygous for the effect allele (a T at position 2161530 of chromosome 11 rather than a C) have 22% decreased risk of T2D, while homozygous carriers have 40% decreased risk.

After the initial discovery, the investigators performed more analyses to verify whether rs149483638 was the causal variant in the region, and replicated the T2D association in independent datasets. All the results supported the hypothesis that this particular variant directly reduces T2D risk.

The variant is located in the IGF2 gene, which encodes a peptide similar to insulin that has previously been linked to growth disorders, obesity, and T2D. Alternative splicing generates two different isoforms of IGF2, and the protective allele disrupts a predicted acceptor site for the splicing event that would generate isoform 2. Could the absence of IGF2 isoform 2 be protective against T2D?

Mercader and colleagues performed further experiments to address the questions of whether the rs149483638 effect allele blocks the production of isoform 2 and whether this has an impact on T2D risk. In human cell culture, the protective allele did indeed block splicing at that site.

To see whether this happens in humans, the researchers tested tissue samples for the presence of isoform 2, and found that its expression was lower in people carrying the protective allele. Furthermore, among people who lacked the protective allele, those with T2D showed higher expression of isoform 2 in their visceral fat tissue than did those without T2D. Levels of isoform 2 in non-diabetics were also positively correlated with levels of HbA1c, which is an indicator of elevated blood glucose levels. No such correlations were seen for levels of IGF2 isoform 1.

Taken together, these results support the involvement of isoform 2 in the elevation of T2D risk, suggesting an intriguing possibility: could lowering levels of isoform 2 be an effective way to lower T2D risk?

If lowering isoform 2 levels were to be used as a T2D therapeutic, it would be important to know that this reduction had no adverse effects. Genetic data can shed light on this question as well. The authors looked in the Exome Aggregation Consortium (ExAC) database and in the clinical records of their study subjects, and saw no health effects other than lowered T2D risk in carriers of the protective variant. They also performed a phenome-wide association study (PheWAS) in the in Genetic Epidemiology Research on Aging (GERA) cohort, and saw no association of the T2D-protective allele with any of 18 medical conditions.

Thus it seems likely that loss of IGF2 isoform 2 would not be harmful, setting the stage for research into drugs that could specifically inhibit isoform 2 or block its production as a way to delay or treat the development of T2D.

These fascinating results have opened multiple avenues for future research. What is the specific biological role of IGF2 isoform 2 in T2D? It differs from isoform 1 only in that it carries an extra 56 N-terminal amino acids. Isoform 1 predominates, while isoform 2 is expressed at very low levels—although its highest expression is seen in pancreatic islets, liver, and fat, all tissues that are relevant for T2D. Elucidating the molecular details of this role will increase our understanding of the biological mechanisms in T2D. And from an evolutionary perspective, the question of how this protective variant came to be enriched in this population is an interesting one.

The motto of the Three Musketeers was "All for one and one for all," meaning that the group supports each member and each member supports the group. As this paper illustrates, this theme is also emerging in human genetics. By investigating distinct populations, we can not only learn about those specific populations but also gain knowledge to benefit all humankind.

Wednesday, August 30, 2017

Bringing the power of epigenomics to the T2DKP

Until recently, all of the results displayed in the Type 2 Diabetes Knowledge Portal (T2DKP) were based on genetic association data: the significance with which variants, or SNPs, occur in people’s genomes in conjunction with a disease or trait.

This information is hugely important for pinpointing regions of the genome that contribute to disease risk. It is now relatively straightforward to identify these regions, but it is still a large challenge to discover the mechanisms by which they act—especially for variants that are outside of coding sequences, without an obvious effect on the sequence of a particular protein. These non-coding variants, the most commonly seen in genetic association studies, are likely to affect tissue-specific gene regulation that could potentially be important to the disease process.

How can we overcome this challenge to find clues about the effects of these non-coding variants? Epigenomic data to the rescue!

Dr. Kyle Gaulton of the University of California at San Diego researches the transcriptional regulatory networks involved in type 2 diabetes by using epigenomic data in concert with genetic association data. He explains, "Regulatory elements control gene production and function, and are often highly specialized across cell and tissues and located far away from the genes they regulate. Molecular epigenomic hallmarks of gene regulation such as histone and DNA modifications, nucleosome depletion, chromatin conformation and DNA-protein interactions can pinpoint the precise genomic locations of regulatory elements. High-resolution epigenome maps of regulatory elements in pancreatic islets, liver, muscle, adipose and many other human tissues can then enable annotation of non-coding genetic variants and their potential gene regulatory functions. These maps are thus an invaluable component of determining how type 2 diabetes associated non-coding variants influence disease pathogenesis."

A recent paper from Dr. Gaulton and colleagues (Gaulton, KJ, et al. (2015) Nat Genet. 47:1415) illustrates the power of integrating these two data types. By combining information on transcription factor binding sites and tissue-specific chromatin states with genetic fine-mapping of T2D-associated loci, the authors elicidated the molecular mechanisms behind the effects of some T2D-associated variants, uncovering the role of the FOXA2 transcription factor in glucose homeostasis in T2D-relevant tissues.

Now, the T2DKP facilitates this type of analysis by presenting both genetic association and epigenomic data on Gene and Variant pages. We described the display of epigenomic data on Variant pages in a recent blog post. On Gene pages, epigenomic data are integrated into the LocusZoom display.

Locations of variants associated with T2D and chromatin states in pancreatic islets, across the SLC30A8 gene (partial view)

Below the plot of variant associations, chromatin states are displayed by default for the major T2D-relevant tissues. Using the pull-down menu at the top of the plot, you can choose from a diverse set to display other tissues and cell types. All of the details on how to use this interactive plot are included in our Gene Page guide.

This is only the first step for epigenomic data in the T2DKP. In the future, we plan to include additional types of epigenomic data that indicate chromatin accessibility and conformation. We will also add functionality; for example, for any given variant, you will be able to search for the tissues in which enhancer regions overlap the location of that variant.

As we actively develop this aspect of the T2DKP, we welcome your suggestions!

Thursday, August 17, 2017

New member of the Knowledge Portal family: the Cerebrovascular Disease Knowledge Portal

We are pleased to announce today’s launch of the Cerebrovascular Disease Knowledge Portal (CDKP), an open-access resource for the genetics of stroke built on the framework and infrastructure of the Type 2 Diabetes Knowledge Portal (T2DKP). The CDKP aggregates data from five large genome-wide association studies for stroke, and presents them along with GWAS results for T2D and other cardiometabolic and biometric phenotypes as well as epigenomic data from a wide range of tissues.

CDKP home page

Users of the T2DKP will find familiar interfaces in the CDKP, which offers the same three major entry points for exploring the data: Gene and Variant pages; the Variant Finder tool; and pages displaying genome-wide association results for each phenotype. Summary-level data are presented for browsing and searching, and researchers may perform custom analyses using individual-level data via the Genetic Association Interactive Tool (GAIT) or LocusZoom. Using the CDKP, T2D researchers can now check their favorite variants and genes for associations with a range of phenotypes related to cerebrovascular health and disease.

The CDKP has two additional layers of functionality relative to the T2DKP, addressing particular needs of the stroke research community. A Downloads page provides files of summary statistics from recent stroke genetic association studies. And a home page link leads to the Precision Medicine Platform (PMP) of the American Heart Association Institute for Precision Cardiovascular Medicine, where authorized researchers may work with selected sets of individual-level data in a secure computing environment.

The Knowledge Portal (KP) framework was designed and built by a team at the Broad Institute as part of the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D), a public-private partnership that seeks to speed up the translation of genetic association data for T2D and related traits into actionable knowledge for new T2D treatments. In a collaboration with the International Stroke Genetics Consortium, funded by the National Institute of Neurological Disorders and Stroke, the Broad team incorporated stroke genetic data into the KP framework and customized the user interface for the stroke genetics research community.

This first application of the scalable, open-source KP software platform to a complex disease area other than T2D has paved the way for future collaborations to extend this platform to additional diseases, facilitating the translation of genetic data into actionable knowledge to improve human health.

Tuesday, July 11, 2017

Inaugural issue of the T2DKP quarterly newsletter

We've started a quarterly newsletter to keep you informed of the latest developments at the T2D Knowledge Portal. Download our Summer 2017 issue!

Monday, June 19, 2017

T2D Portal team at ADA 2017

Members of the T2D Knowledge Portal team returned last week from the 77th Scientific Sessions of the American Diabetes Association, inspired and invigorated by many great discussions with T2D researchers, educators, and clinicians.

In preparation for the conference, we set ourselves goals to add several new features to the Portal:

  • incorporate several new datasets and implement a new interactive Data page for exploring all datasets (see details)
  • add epigenomic data to shed light on the potential regulatory roles of genomic regions (see details)
  • implement a complete redesign of the Gene page that integrates multiple datasets to summarizes the significance of each gene to T2D and related phenotypes (see details)
  • connect with the new Federated Node of the Portal at EBI to provide seamless access to data housed there alongside data housed at the AMP T2D Data Coordinating Center at the Broad Institute (see details)

On the first day of the conference, Noël Burtt and Jason Flannick presented a mini-symposium focusing on the Portal to several hundred attendees.

This clearly generated a lot of interest, because our exhibit booth was a busy place for the next three days. 

T2D Portal team members at our exhibit booth

Multiple conversations happened at the booth!

We handed out a general guide to the Portal (download), and also presented a moderated poster (download).

At the booth, we especially enjoyed talking with people in the T2D field who are not geneticists but are simply curious about the genetics of T2D and the mission of the Portal. We encourage everyone to explore the Portal and to feel free to ask us any questions, even if they seem elementary. Please contact us any time with questions or feedback!

Monday, June 12, 2017

T2D Knowledge Portal now distills and summarizes genetic information for individual genes

The Type 2 Diabetes (T2D) Knowledge Portal presents genetic data relevant to T2D on two major types of page: Variant pages for individual variants, or SNPs; and Gene pages focusing on individual genes. Visual displays on Variant pages provide an immediate indication of the possible significance of each variant for T2D. But until now, Gene pages have presented large amounts of information from disparate sources without much integration or interpretation to guide the viewer.

Now, that has all changed with our release of the new Gene page. It guides researchers through an organized workflow that can help them take advantage of the aggregated data in the Portal to move from a variant of interest, to a gene of interest, to an assessment of the potential involvement of that gene’s product in T2D.

The central feature of the new Gene page is an at-a-glance display that summarizes the strength of the evidence for associations of the gene with T2D or related traits. An algorithm scans the comprehensive collection of datasets within the Portal to find data on variants in the gene, and the overall conclusion is shown by a “traffic light” icon. A green light indicates that there is strong evidence for association of at least one variant in the gene with at least one phenotype; a yellow light indicates that there is suggestive evidence, and a red light indicates that the data aggregated in the Portal contain no evidence for associations of variants within this gene.

Figure 1. Traffic light display for MTNR1B

Several sections of the page below the traffic light allow the user to drill down to much more information about the variants within the gene, their individual associations, and their collective impact on the disease burden of the gene. An interactive LocusZoom plot allows users to view the linkage disequilibrium relationships and associations from multiple datasets, with a wide variety of phenotypes, for common variants. The plot also displays the location of chromatin states, which can indicate the regulatory role of a region, in multiple tissues.

Figure 2. LocusZoom plot of the credible set of T2D-associated variants in MTNR1B (above) and chromatin state annotations for the region (below).

In the example shown above, the traffic light (Fig. 1) shows that variants in the MTNR1B gene encoding the melatonin receptor have one or more strong phenotypic associations (view the MTNR1B Gene page in the T2D Knowledge Portal). The table of common variants for MTNR1B (not shown) tells us that the most significantly associated variant is rs10830963. And a view of the LocusZoom plot for the credible set of variants associated with T2D (Fig. 2, top) shows that in fact the credible set for this region contains only rs10830963, further supporting its significance. The chromatin state annotations for this region (Fig. 2, bottom) provide evidence for a regulatory effect in pancreatic islets, consistent with a potential role in T2D. This information, easily found in the Portal today, replicates the results of a 2015 genetic analysis that required over 100 authors (Gaulton, KJ, et al. (2015) Nature Genetics 47:1415).

The new Gene page presents a lot of information and we can't cover it all in this space. But don't worry, we've created a guide to the page that explains every feature in detail. It's linked from the top of the page, or you can download it here.

With the inclusion of the new Gene page, the Portal now enables the rapid generation of testable hypotheses, by integrating, interpreting, and presenting information that previously could only be generated by coordinated research across a consortium. This new development brings the T2D Knowledge Portal project one step closer to informing the discovery of new targets and treatments for T2D.

Friday, June 9, 2017

Providing data access, ensuring data protection

Readers of this post probably don’t need to be convinced that genetic association data have enormous potential for helping us to understand and treat complex diseases like type 2 diabetes. Significant associations between variants and diseases can suggest genes, or regions of the genome, that could be important for disease risk or progression—and this knowledge could help us identify new drug targets.

The Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D) is a pre-competitive partnership among the National Institutes of Health, industry and not-for-profit organizations, which is managed by the Foundation for the National Institutes of Health. Its mission is to make genetic association data accessible to the worldwide biomedical research community via the Type 2 Diabetes Knowledge Portal, in order to facilitate discovery of new targets for T2D treatment. But it can be a challenge to aggregate genetic data. The privacy of the individuals who contributed their health status and genomic sequences must always be protected, and there are many layers of regulation to ensure this. Restrictions at the institutional, regional, and national levels determine how data are handled and whether they can be transferred.

Until now, all of the results displayed in the Portal have been derived from data housed at the AMP T2D Data Coordinating Center (DCC) at the Broad Institute, where the Portal website resides. But some of the valuable data generated outside the U.S. cannot be transferred to the DCC. To address this issue, AMP T2D funded the development of a mechanism that enables researchers to interact with all of the data: federation. 

Federation means that data are housed at a site (a “federated node”) that meets their specific privacy requirements, but are made available for remote queries via the Portal. Results from such queries are served up alongside results from all of the datasets housed in the AMP T2D DCC. Researchers may browse and query data from any location without even needing to know where they reside.

A federated node has now been created at the European Bioinformatics Institute (EBI) and may be accessed via the T2D Knowledge Portal. Today, Portal tools and interfaces can query both data housed at the AMP T2D DCC at the Broad Institute and data at the EBI federated node. 

According to Paul Flicek, a Senior Scientist and Team Leader of Vertebrate Genomics at EMBL-EBI, “A key mission of EMBL-EBI is to make data available to the widest possible community. Seamlessly accessing stored in multiple locations via a single portal helps ensure that the data we store from many projects are maximally useful for additional research.”

The first dataset to be incorporated into the Portal via the EBI federated node is the Oxford BioBank exome chip analysis dataset, which contains association data for glycemic, lipid, and blood pressure traits from over 7,100 healthy subjects in Oxfordshire, U.K. The dataset is described on our Data page. Portal users can interact with this dataset in the same way (and with the same speed) as with other datasets. 

“Diabetes is a global problem, and it will take research and innovation on a global scale if we are to tackle it effectively,” says Mark McCarthy, Robert Turner Professor of Diabetic Medicine at University of Oxford. “The success of our research on the genetics of diabetes depends on access to data generated by groups around the world. The federated portal provides an additional set of tools that will allow us to jointly analyse those data sets wherever they happen to be based.” 

Federation represents both an important technical advance in handling and protecting data, and a significant step forward in democratizing and improving access to genetic association results. And because it is generally applicable to any kind of genetic association data, it has the potential to have an impact beyond T2D research, facilitating the study of other complex diseases and traits.