New datasets and many new phenotypes in the T2DKP

Today we release several new datasets, including associations for many new phenotypes and individual-level data for secure interactive analysis, to the Type 2 Diabetes Knowledge Portal.

The AAGILE GWAS dataset, from the African American Glucose and Insulin Genetic Epidemiology (AAGILE) Consortium, brings more diversity of ancestry to the T2DKP, with meta-analysis of fasting glucose and BMI-adjusted fasting insulin associations from over 20,000 African American individuals. These results were combined with associations for over 57,000 individuals of European ancestry from the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) in a trans-ethnic meta-analysis.

This release also adds two new diabetic kidney disease datasets from the SUMMIT (SUrrogate markers for Micro- and Macro-vascular hard endpoints for Innovative diabetes Tools) consortium. All of the more than 40,000 subjects in the "Diabetic Kidney Disease GWAS: subjects with T1D or T2D" dataset had either type 1 or type 2 diabetes. The study measured seven different renal phenotypes in these subjects, including four that are new to the T2DKP. Summary association results are available for the entire group and for sub-cohorts that separate T1D from T2D and European from Asian ancestry. A separate dataset from SUMMIT, "Diabetic Kidney Disease GWAS: subjects with T1D or T2D, ESRD vs. controls" is comprised of more than 5,600 diabetics, nearly 1,200 of whom had end-stage renal disease. These two datasets greatly expand the range of diabetic complications for which genetic association data are available in the T2DKP.

The T2DKP is federated, meaning that in addition to the Data Coordinating Center at the Broad Institute, some results are drawn from a sister site at the European Bioinformatics Institute (EMBL-EBI). This system allows data that may not leave Europe to be represented in the T2DKP. Six of the new datasets in this release are housed at the T2DKP Federated Node at EMBL-EBI.

The Hoorn Diabetes Care System (DCS) dataset includes associations for 12 different anthropometric, blood lipid, blood pressure, and liver and kidney function measures for a cohort of over 3,400 type 2 diabetics in the Netherlands.





The GoDarts project (Genetics of Diabetes Audit and Research in Tayside Scotland) recruits type 2 diabetics and matching controls in the Tayside region of Scotland. This release includes five new datasets from GoDarts, representing experiments performed using different arrays. Each experiment determined genetic associations for a wide variety of phenotypes, including two that are new to the T2DKP: levels of adiponectin and leptin, hormones that are associated with risk of T2D and obesity.

Results from all of these datasets may be searched using the Variant Finder tool and may be browsed:

• On Gene Pages in the Common variants and High-impact variants tables and in LocusZoom plots;

• On Variant Pages in the Associations at a glance section, the Associations across all datasets section, and in LocusZoom plots;

• From the View full genetic association results for a phenotype search on the home page: first select a phenotype, then select a dataset on the resulting page.


Individual-level data from the Hoorn DCS and GoDarts datasets also power secure interactive analyses using the Genetic Association Interactive Tool (GAIT) on Variant Pages. With the new additional data, nearly 61,000 individual-level samples are now available for custom association analysis.

Please take a look at the new results and contact us any time with questions or suggestions!

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):

• BioMe AMP T2D GWAS: 9,173 multi-ancestry samples from the Charles Bronfman Institute for Personalized Medicine BioMe Biobank;

• 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.

Those hoofbeats just might come from zebras

Image by Eric Dietrich via Wikimedia Commons

A physician in the 1940s wanted to convey to his students that the most obvious diagnosis is most likely to be the correct one, so he coined a saying that has become famous: “When you hear hoofbeats, think of horses not zebras.” Applying this concept to complex disease genetics, if a risk-associated variant causes a non-synonymous mutation in a coding sequence, the first hypothesis to consider is that it affects disease risk by altering the protein. But although this is often the case, one of the lessons we can learn from a large new study, published today and now available for browsing and searching in the T2D Knowledge Portal, is that we should not forget about zebras.

The new study, from a global coalition of scientists (Mahajan et al., Nature Genetics 2018), is an exome-wide association study that surveyed the T2D associations of variants within the protein-coding regions of the genome. Including more than 81,000 T2D cases, over 370,000 controls, and multiple ancestries, this study has a three-fold larger effective sample size than any previous study. Using p-value < 2.2 x 10^-7 as a threshold for significance across the exome, the authors found 69 significantly associated coding variants representing 40 distinct association signals in 38 loci—16 of which had not been previously associated with T2D risk.

To get a better idea of which variants in these loci were causal for T2D risk, the researchers performed fine mapping for 37 of the 40 significant signals. They meta-analyzed T2D associations for over 500,000 individuals of European descent, performed imputation, and then generated 99% credible sets for each signal—that is, sets of variants that are 99% likely to include the causal variant. To calculate the credible sets, they used an “annotation-informed prior” model of causality that took into account the distribution of associations for different variant impact classes and also the overlap of variants with putative enhancer elements.

The 37 association signals for which the authors generated credible sets were all due to coding variants that would cause changes in the sequence of the encoded protein. But surprisingly, the fine mapping analysis found that coding variants were likely to be causal for T2D risk at fewer than half of these loci.

One of these surprising results involves a gene that is well-known to be relevant to T2D: PPARG. Involvement of the PPARG protein in T2D is beyond doubt, since this ligand-inducible transcription factor is the target of thiazolidinedione drugs that are used to treat T2D. A common variant in PPARG, rs1801282, that causes a p.Pro12Ala change in the protein has been assumed to account for the T2D association, but there is little experimental evidence that this change affects PPARG function.

In the credible set generated in this study, the probability that rs1801282 is causal was not found to be particularly high. Included in this credible set along with rs1801282 are 19 non-coding variants. One of these was previously shown to affect a binding site for the transcription factor PRRX1 and to affect expression of PPARG2, a PPARG isoform. This suggests the intriguing possibility that the T2D risk in this locus is caused, partly or wholly, by variants affecting regulation rather than protein sequence.

A similar pattern, with partial causality due to non-coding variants, was seen at an additional 7 loci. And in 13 other loci, even though these loci were discovered via coding variant signals, non-coding variants had the highest probability of causing risk.

According to Professor Mark McCarthy of the University of Oxford, one of the principal investigators of the study, “Our study shows that we should not jump to conclusions when we see that one of our association signals includes a variant around which we can base an attractive mechanistic narrative. The “average” coding variant is more likely to be causal than the “average” noncoding variant, but even at the set of loci where we detect a significant coding variant association, it is as likely as not that the signal is driven instead by one of the non-coding variants nearby. By bringing together genetic and genomic data, we can improve our prospects for finding the causal variants at GWAS loci, but these should be the starting points for empirical studies not a destination in themselves.” Dr. McCarthy has written a commentary on this study; read it here.

So, in investigating complex disease genetics, it is still a good bet that a coding variant affects disease risk via altered protein sequence: at least in some parts of the world, hoofbeats are very often due to horses. But this study reminds us that it is always a good idea to look beyond the obvious hypothesis, and remember the zebras.

This paper includes many other discoveries, and we recommend that you read the paper to get the full story. We are pleased to announce that in addition to publishing the paper, the authors have made their results available to the T2D research community immediately upon publication, in the T2D Knowledge Portal.

The dataset in the T2DKP is named ExTexT2D (ExTended exome array genotyping for T2D) and includes associations for T2D, both unadjusted and adjusted for BMI. A description of the dataset along with a table listing the cohorts of the study subjects can be found on the Data page, and you can browse and search the ExTexT2D exome chip analysis dataset at these locations in the T2DKP:

• On Gene pages (see an example) on the Common variants and High-impact variants tabs
• On Variant pages (see an example) in the Associations at a glance section and the Association statistics across traits table
• Via the Variant Finder search
• View a Manhattan plot of associations across the genome by selecting “type 2 diabetes” or “type 2 diabetes adj BMI” in the View full genetic association results for a phenotype menu on the home page.

This dataset offers by far the largest sample size for exploring associations of low-frequency and common coding variants with T2D. The size of the study enabled evaluation of which coding variants mediate GWAS signals and which are simply "proxies" to the true causal variant, as revealed in the credible set analysis. With the addition of this dataset, the T2DKP offers in-depth information on two aspects of exome associations: common and low-frequency variant associations in ExTexT2D, and comprehensive coding variant associations in the 19K exome sequence analysis dataset (soon to include 50,000 exomes).

We are pleased to provide access to these important new results. Please contact us with any questions or comments about these new data or the T2DKP in general!

Complete data description now available for T2DKP WES and WGS datasets

A new Data Descriptor publication from Jason Flannick, Christian Fuchsberger, Anubha Mahajan, and colleagues (Scientific Data 4, Article number: 170179 (2017) doi:10.1038/sdata.2017.179), presents absolutely everything there is to know about four large, important datasets that are included in the Type 2 Diabetes Knowledge Portal. These datasets are the product of the GoT2D and T2D-GENES consortia, large international groups that seek to uncover the genetic basis of type 2 diabetes.

The investigators took a variety of approaches to generate the most complete view of the genetic architecture of T2D available to date. They performed whole-exome sequencing on a group of 12,940 individuals of multiple ancestries (6,504 T2D cases and 6,436 controls) and whole-genome sequencing on 2,657 individuals of European descent, and tested the association of variants with T2D. They also used an exome chip to test coding variants in more than 80,000 people, and used imputation to test non-coding variants in an additional 44,000.

In total, the researchers sampled more than 120,000 genomes and identified more than 27 million single nucleotide polymorphisms, indels, and structural variants, testing their association with T2D. The new publication documents the experimental and analytical methods and results in complete detail. Analysis and interpretation of these data were also discussed in a previous publication (Fuchsberger, Flannick, Teslovich, Mahajan, Agarwala, Gaulton et al., 2016).

This comprehensive catalog of T2D associations is available for you to search and explore via the T2D Knowledge Portal. The datasets from this study are named as follows in the T2DKP:

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

All of these sets are described in more detail on our Data page, including lists of the cohorts studied and case/control selection criteria for each. Our Variant Finder tool searches all of these sets, and results from these datasets are displayed in various tables and interfaces on the Gene and Variant pages of the T2DKP.

The individual-level data in the 13K exome sequence set are also available for custom analysis via the Genetic Association Interactive Tool (GAIT) on Variant pages and the custom burden test on Gene pages. These tools allow researchers to interact with the individual-level data while protecting patient privacy. They access the 19K exome sequence analysis dataset, which includes the 13K exome sequence data from this study along with 6,000 additional exome sequences from the SIGMA and LuCamp consortia. Both tools allow you to filter samples by multiple criteria (for example, age, BMI, cholesterol levels of the subjects) and to choose covariates before running on-the-fly association analysis. The custom burden test also offers the ability to select the set of variants to consider in the analysis.

Please explore these datasets and, as always, let us know what you think!

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.

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!

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.

See you in San Diego!

Members of the T2D Knowledge Portal team are gearing up for the 77th Scientific Sessions of the American Diabetes Association, June 9-13 in San Diego, CA. We'll be releasing exciting new features of the Portal just before the conference, and we have a wide variety of presentations planned for each day.

On the opening day of the conference (Friday, June 9), join us for a mini-symposium that will present a comprehensive guide to the T2D Knowledge Portal and how you can use it to further your type 2 diabetes research. We will be exhibiting at booth #2452 on Saturday, Sunday, and Monday, and each day, genetics experts will be available at the booth to answer questions and discuss both the Portal and the genetics of T2D. On Saturday, members of the Portal team will participate in a moderated poster session, and posters will also be displayed on Monday. And on Sunday morning, our principal investigator, Dr. Jose C. Florez, will give a symposium presentation on "Mining the Genome for Therapeutic Targets."

Find the full details in the schedule below and follow us on Twitter (@T2DKP) for up-to-the-minute news throughout the conference. We're looking forward to meeting you!

Friday, June 9, 2017

Mini-Symposium: A Researcher’s Guide to Exploring Diabetes Genetic Data in the Type 2 Diabetes Knowledge Portal
Chair: Mark McCarthy
11:30am - 12:30pm, Room 28

11:30-11:50am        Noël Burtt: Data, Analysis, and Tools in the Type 2 Diabetes Knowledge Portal
11:50am-12:10pm   Jason Flannick: Demonstration of Questions that Can Be Addressed Using the Portal

12:10-12:30pm       Question and Discussion Period


Saturday, June 10, 2017

• Exhibiting at booth #2452, 10am - 4pm

• Moderated Poster Session: Genetic Data, Pathways, and Variants for Type 2 Diabetes and Related Traits. 12:30-1:30pm, Hall B

Poster 1765-P
The Type 2 Diabetes Knowledge Portal: Accelerating Type 2 Diabetes Research through Community Access to Human Genetic Information and Tools
Presenter: Maria C. Costanzo

Poster 1766-P
Key Biological Pathways for Type 2 Diabetes Determined by Genetic Cluster Analysis on Related Traits
Presenter: Miriam S. Udler

Sunday, June 11, 2017

• Symposium presentation: Mining the Genome for Therapeutic Targets. 

Dr. Jose C. Florez

9:20-9:55am, Ballroom 20D

• Exhibiting at booth #2452, 10am - 4pm

Monday, June 12, 2017

• Exhibiting at booth #2452, 10am - 2pm

• Poster session, 12-1pm, Hall B

Poster 1765-P

The Type 2 Diabetes Knowledge Portal: Accelerating Type 2 Diabetes Research through Community Access to Human Genetic Information and Tools
Presenter: Maria C. Costanzo

Poster 1795-P
Type 2 Diabetes Gene Bioinformatically Identified by Variants Mapping to Amino-Acid Changes in Three-Dimensional Protein Space
Presenter: Marcin von Grotthuss

Introductory guide to genetic association analysis now available

P-values. Odds scores and betas. GWAS. Linkage disequilibrium. What does it all mean?

Human geneticists are, of course, intimately familiar with these concepts. But for people who are not human geneticists, just getting past the terminology can be frustrating. So we’ve written a basic primer and reference guide that can help users of the T2D Knowledge Portal understand the information presented in our interfaces and tools.

Our Introduction to genetic association analysis guide is available from our Resources page. Or download it here (PDF).

This guide provides a basic introduction to the rationale behind applying human genetic association studies to complex diseases like T2D, explains some of the parameters of genetic associations such as p-values and odds ratios, and describes the different types of experiment used to determine genetic associations.

Many thanks to Andrew Morris, University of Oxford, for his thoughtful review and helpful comments on this guide.

We would be happy to hear your suggestions for improvements and additions!

New Year, New Data: BioMe AMP T2D GWAS

We’re happy to announce the first addition of data to the Type 2 Diabetes Knowledge Portal in 2017: the BioMe AMP T2D GWAS data set. The generation of these data was funded by the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D), a collaboration between multiple stakeholders that aims to catalyze the clinical translation of genetic discoveries by producing and aggregating data, developing and implementing novel analytical methods and tools, and building infrastructure for data storage and presentation.

The BioMe AMP T2D GWAS data set is the first set to be entirely produced by the AMP T2D project, which supplied the funding and carried out every step of its production, from data generation to analysis, quality control, and presentation. Its immediate availability in the Portal, prior to publication, fulfills the mission of AMP T2D to speed up access to and utilization of new data.

These data were generated at the Charles Bronfman Institute for Personalized Medicine BioMe BioBank, a biorepository located at the Mount Sinai Medical Center (MSMC) in the upper Manhattan area of New York City. MSMC serves a diverse population of over 800,000 outpatients each year. Importantly, since many BioMe participants are African American or Hispanic Latino, this data set adds significant ethnic diversity to the Portal’s genetic association data.

The BioMe AMP T2D GWAS data set is comprised of about 13,000 unique individuals, 41.5% of whom are admixed American, 38% African American, and 20% European. Subjects were genotyped using at least one of three platforms: the Illumina Exome Array, the Illumina GWAS array, or the Affymetrix GWAS array. Their T2D status was assessed by an algorithm, and many additional traits were also measured.

The data were subjected to quality control and association analysis by the Analysis Team at the AMP Data Coordinating Center (DCC) at the Broad Institute. Variant associations with T2D, fasting glucose levels, and HbA1c levels were analyzed. The top results included both previously known and novel variants, with only a single variant reaching genome-wide significance: T2D association of the variant rs7903146, within the well-established T2D risk gene TCF7L2. Now that these results are available in the T2D Knowledge Portal, the ability to analyze them further in the context of all other available T2D association data may lead to additional insights.

The BioMe AMP T2D GWAS data currently has the “Early Access Phase 1” status that is assigned to new data. This status denotes that although analysis and quality control checks have been performed, the data are not yet considered to be in their final state. During the early access period, users may analyze the data but may not submit the results of these analyses for publication. Find the full details about the different phases of data release on our Policies page. More information about the data set, along with links to download even more detailed reports on its quality control and analysis, may be found in the BioMe AMP T2D GWAS section of our Data page.

BioMe AMP T2D GWAS data are available at these locations in the Portal:

• On Gene Pages (see an example) in the Variants & Associations table and the Minor allele frequencies across data sets table
• On Variant Pages  (see an example) in the Associations at a glance section and in the Association statistics across traits table
• Via the Variant Finder tool, for these phenotypes: type 2 diabetes; fasting glucose adjusted for age and sex; HbA1c adjusted for age and sex; and HbA1c adjusted for age, sex, and body mass index
• A "Manhattan plot" of associations across the genome may be seen by selecting one of the phenotypes above in the View full genetic association results for a phenotype scroll box on the Portal home page, and then selecting the BioMe AMP T2D GWAS data set.

As always, please contact us with any questions, comments, or suggestions.

New MGH Cardiology and Metabolic Patient Cohort data in the T2D Knowledge Portal

We are pleased to announce a new data set in the T2D Knowledge Portal, from the MGH Cardiology and Metabolic Patient Cohort (CAMP). These data were contributed by Pfizer, Inc. as part of a public-private partnership to generate genotype data for a cardiometabolic and prediabetic cohort. This data set adds individual-level genetic association data for type 2 diabetes (T2D), fasting glucose levels, and fasting insulin levels from more than 3,500 samples to the Portal knowledgebase. Association data for additional phenotypes from this cohort will be incorporated in the future.

The inclusion of this data set in the T2D Knowledge Portal illustrates the uniqueness of the Accelerating Medicines Partnership, which brings together pharmaceutical companies and non-profit institutions with the goal of speeding up the discovery of new targets for treatment of T2D. The pharmaceutical partners in this collaboration have committed not only to providing funding, but also to sharing the data they generate. The CAMP data set contributed by Pfizer is the first set from a pharmaceutical partner to be made available in the Portal.

Another unique aspect of this data set is that it is the first to be included in the Portal with “Early Access Phase 1” status, which is assigned to new data. This status denotes that although analysis and quality control checks have been performed, the data are not yet considered to be in their final state. During the early access period, users may analyze the data but may not submit the results of these analyses for publication. Find the full details about the different phases of data release on our Policies page.

The CAMP cohort consists of 3,857 subjects who were recruited at the Massachusetts General Hospital Heart Center between 2008 and 2012. In addition to genotyping, the subjects had either vascular reactivity measurements (for T2D patients) or an oral glucose tolerance test (for patients not known to have T2D), and samples of their plasma and serum were analyzed. Most of the subjects were of European ancestry; about 10% were African American.

The analysis and quality control processes for this data set were performed by the Analysis Team of the Accelerating Medicines Partnership Data Coordinating Center (AMP-DCC) at the Broad Institute, and are completely transparent and fully documented. The experiment design and analysis are summarized on our Data page, and detailed reports are available for download. Going forward, all new data sets added to the Portal will be fully documented in this manner.

One intriguing—and somewhat puzzling—result from the analysis highlights the utility of incorporating data sets like this one into the Portal. The variant most strongly associated with T2D (at genome-wide significance) in this set is located in the major histocompatibility complex region near the HLA-C gene.

Known associations of genes in this region with type 1 diabetes, along with a high local recombination rate, make it challenging to interpret the meaning of this association. However, it certainly merits further investigation because of its genome-wide significance. The inclusion of this data set in the Portal, in the context of all other available data about T2D associations in the region, greatly facilitates the further analysis of this and other associations in the set.

The CAMP data may be accessed via multiple interfaces in the Portal. They are shown in tables of summary statistics and accessible in variant searches using the Variant Finder. Importantly, since the data are individual-level, samples may be filtered by various parameters and used for custom association analysis in our Genetic Association Interactive Tool (GAIT).

Find CAMP data at all of these locations in the Portal:

• On Gene Pages (e.g.,  HLA-C) in the Variants & Associations table.
• On Variant Pages (e.g., rs9468919) in the Associations at a glance section and in the Association statistics across traits table.
• Via the Variant Finder tool, for the phenotypes T2D, fasting glucose, and fasting insulin.
• Via the Genetic Association Interactive Tool (GAIT), which enables custom association analysis for either single variants (available on Variant Pages) or for the set of variants in and near a gene (Interactive burden test, available on Gene Pages).

New funding opportunities for T2D genetic research

The Foundation for the National Institutes of Health (FNIH) has released three new funding opportunities that aim to add to the growing body of data housed in the T2D Knowledge Portal. The new Request for Proposals (RFPs) solicit data on T2D related complications and individual level and whole exome sequencing data related to T2D.

FNIH awards will provide successful applicants with up to $200,000 per individual award for proposals to harmonize and transfer existing datasets and up to $500,000 per individual award for proposals that include the generation of new genotyping data. Awards will be made over two years and aim to enhance the NIH-funded T2D Knowledge Portal hosted by the Broad Institute at the Massachusetts Institute of Technology (MIT).

Responses to the FNIH Requests for Proposals are due by December 31, 2016. Details on the new funding opportunities can be found here. 

Insulin sensitivity comes into focus

Many different things can be seen in any landscape, depending on your focal point.
Image by Nicooo76 via Pixabay.

When photographing a landscape, different photographers choose different perspectives. Some capture a wide-angle view, while others focus on particular details.

It’s no different for researchers who use genome-wide association studies (GWAS) to investigate the genetic landscape of type 2 diabetes (T2D). A common perspective is to study the wide range of variants that are significantly associated with the presence of T2D in patients. But it can also be very informative to concentrate on individual traits related to the physiology of T2D. In a new paper in Diabetes, co-first authors Geoffrey Walford, Stefan Gustafsson, Denis Rybin, and fellow members of the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) took this focused perspective to discover associations of genetic variants with insulin sensitivity.

Along with reduced insulin levels, the loss of insulin sensitivity (often termed insulin resistance) is a major hallmark of T2D. When muscle, liver, and fat cells become less able to respond to insulin, blood glucose levels rise. Since this can contribute to development of T2D and exacerbate its symptoms, knowing which genetic variants are associated with sensitivity to insulin could be informative for understanding pathways that contribute to T2D risk.

But insulin sensitivity is difficult to measure. Earlier GWAS have used simple estimates of insulin sensitivity, such as fasting levels of insulin, and have discovered a handful of genetic variants that influence insulin sensitivity. The “gold standard” test, the euglycemic clamp, involves giving patients continuous infusions of insulin and glucose and monitoring their blood glucose every few minutes. It’s expensive and time-consuming—not a test that is practical to perform on the tens of thousands of subjects that are commonly used in GWAS.

The authors wondered whether they could instead use an index that combines several measurements, each relatively easy to make. It’s an index with a long name: the modified Stumvoll Insulin Sensitivity Index (ISI). Developed by Stumvoll and colleagues in 2001, this index can be derived in a variety of ways. The authors chose the ISI requiring just three measurements: fasting insulin levels; glucose levels two hours after a glucose load; and insulin levels two hours after a glucose load. This ISI is as good as or better than other estimates of insulin sensitivity and correlates well with the euglycemic clamp.

So the researchers looked for variants associated with the Stumvoll ISI in nearly 17,000 participants in the discovery phase of the work. They added another 13,300 in the replication phase, adding up to about 30,000 in the combined meta-analysis. Since obesity, measured by body mass index (BMI), can affect insulin sensitivity, the authors added BMI to some of their statistical models.

First, the authors found associations between the ISI and other variants already known to affect simple measures of insulin sensitivity. This provided reassurance that the ISI was properly detecting genetic influences on insulin sensitivity. After discovery, replication, and meta-analysis, two novel genetic variants were associated with ISI at genome-wide significance (P-value < 5.0 ×10^-8) in a model that tested the effect of the variant, age, sex, and the interaction between the variant and BMI: variant rs12454712, near the gene BCL2, and variant rs10506418, near the gene FAM19A2.

How might these variants affect insulin sensitivity? There’s a lot more work to be done before that question can be answered. Additional studies will need to clarify whether these variants, which are near BCL2 and FAM19A2, affect these or other genes, and then how these variants actually cause changes in insulin sensitivity. 

There are some clues already in the published literature. The variant rs12454712 near BCL2 has previously been found to be associated with T2D, supporting the hypothesis that this region of the genome contributes to T2D risk through reducing insulin sensitivity. And the gene itself (BCL2) has already been implicated in glycemic metabolism: inhibiting bcl2 improves glucose tolerance in a mouse model, while a drug that inhibits the protein product of the gene (BCL2) increases blood glucose levels in certain chronic lymphocytic leukemia patients. So there’s even more reason to suspect that the rs12454712 variant might affect insulin sensitivity via BCL2.

There is as yet no evidence linking the protein FAM19A2 function to glucose metabolism, so the jury is out on whether the variant rs10506418 affects FAM19A2 or some other nearby gene. 

By focusing on a detail of the T2D-related genetic landscape, this study has teased out two variants that may give us clues about the physiology of insulin sensitivity and the development of T2D. And that’s a valuable addition to our overall picture of T2D genetics!

World-wide cooperation to address a world-wide problem

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

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

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

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

An open question in the T2D genetics community has been whether rare variants account for most of the T2D risk, or whether it is due to the effects of many common variants of small effect. This study begins to answer this question. It shows that most T2D risk can be ascribed to the modest effects of a large number of common alleles, and that there is likely no treasure trove of rare variants of large effect waiting to be found.

This project uncovered more than a dozen loci that were associated with T2D at genome-wide significance. Most were common variants, and some, such as the variant rs11759026 near CENPW, had not been seen before in genome-wide association studies. This study also called into question the previously identified associations of some variants and supplied better candidates for the actual T2D risk variant. For example, the noncoding variant rs10401969 had been associated with the CILP2 locus, but the additional data from this project now point to a linked missense variant in TM6SF2 as causal—an exciting finding, since TM6SF2 is involved in fat metabolism and could have a direct role in the development of T2D.

In another project reported by Fuchsberger and colleagues, combining exome sequence data from the T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) Consortium with the exome sequences obtained by the GoT2D project resulted in a data set of sequences from nearly 13,000 individuals, from five different ethnic groups.   Data sets stratified by different ancestries allow investigation of population-specific associations that might otherwise be obscured. The larger sample size and the focus on coding variation, with presumably larger effects on protein function, was another approach to maximize discovery of rare variants if such were present. Another benefit was to help implicate specific genes in previously associated genomic regions.

One variant identified by this approach has an immediately understandable relationship to diabetes: the rs2233580 variant causes a missense mutation in the PAX4 gene, which encodes a transcription factor that has been implicated in pancreatic islet differentiation. Interestingly, this is a common variant in East Asian populations but is nearly absent in the other ancestries studied. Other variants in the same gene have previously been associated with early-onset monogenic diabetes, so this result is a reminder that different mutations in same gene can have very different effects on the disease process. Other work in this study reaffirmed this conclusion for other genes.

The scale of this study is unprecedented, and we’ve only touched upon a small piece of it here. But something else is unprecedented about these data: they are available for anyone to explore, right now, in the T2D Knowledge Portal. Researchers don’t need to go to various sites to gather bits and pieces of the data, harmonize them, and analyze them; the data sets are globally accessible in the Portal along with pre-computed analyses and sophisticated tools for custom analyses.

The data sets from this study in the Portal are:

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

  

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

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

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

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