Tuesday, March 6, 2018

T2DKP Winter Newsletter

The latest issue of our quarterly newsletter is now available. Download it here to find out what we've been up to!

Thursday, March 1, 2018

New release today, as the KPN moves to a regular release schedule

At the Knowledge Portal Network (consisting of the Type 2 Diabetes, Cardiovascular Disease, and Cerebrovascular Disease Knowledge Portals), we are establishing a regular bimonthly release schedule. Every other month, new data and features will be incorporated into the Portals. Today, we are pleased to announce the first of these releases.

New data in the Type 2 Diabetes Knowledge Portal

This release adds two new datasets to the T2DKP. The Diabetic Cohort - Singapore Prospective Study Program is a T2D case-control study to identify genetic and environmental risk factors for diabetes in Singapore Chinese. The DC-SP2 GWAS set, a meta-analysis of summary level T2D associations from 3,951 individuals, was contributed by Drs. Rob Martinus Van Dam, E Shyong Tai, and Xueling Sim from the National University of Singapore. They have also submitted individual-level data from this study to the Accelerating Medicines Partnership Data Coordinating Center (AMP DCC), and these data will be incorporated into the T2DKP after quality control and analysis are complete.

In addition to this set, we have incorporated the publicly available summary statistics from the DIAGRAM 1000G GWAS. This dataset, from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium, is a meta-analysis of 26,676 T2D cases and 132,532 control participants from 18 GWAS (Scott RA, et al. An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans. (2017) Diabetes 66:2888). Samples were imputed using the all ancestries 1000 Genomes Project reference panel.

More details about both of these datasets are available on our Data page.

New features specific to the Type 2 Diabetes Knowledge Portal

We have expanded the range of data available for interactive analysis by adding individual-level data from the CAMP GWAS, BioMe AMP T2D GWAS, and METSIM GWAS datasets to the dynamic analysis modules LocusZoom and GAIT (Genetic Association Interactive Tool). LocusZoom, powered by the Hail software developed at the Broad Institute as part of the AMP T2D project, allows you to perform custom association analysis while conditioning on specific variants or sets of variants.

GAIT offers alternative options for custom association analysis, such as filtering samples by their phenotypic characteristics (e.g., age, BMI, cholesterol levels) and choosing specific covariates. To date, seven different datasets comprised of over 67,000 samples are available for dynamic analysis in GAIT. These include datasets housed both at the AMP DCC (19k exome sequence analysis; CAMP GWAS; BioMe AMP T2D GWAS; METSIM GWAS) and at the EBI Federated node (EXTEND GWAS; Oxford Biobank exome chip analysis; GoDARTS Affymetrix GWAS).

We have also taken an initial step towards integration of the T2DKP with a new federated node, the T2DREAM database of epigenomic data relevant to T2D. In the near future, epigenomic data displayed in the T2DKP will be drawn dynamically from T2DREAM. In the meantime, we have added gene- and variant-specific links to T2DREAM from the re-styled External Resources section at the bottom of Gene and Variant pages.

New features for all Knowledge Portals

Some of the improvements in this release are visible in all the Portals of the Knowledge Portal Network. One of the most significant affects LocusZoom, the dynamic plot that displays variant associations along with their genomic coordinates, linkage disequilibrium, and other information. Previously, the only way to select a phenotype was to scroll through a long list. Now, a new phenotype filter lets you enter one or more search criteria and filter the list by those criteria. Once you have selected a phenotype, the datasets that include associations for that phenotype are presented for selection. Previously, only one dataset (the one with the largest sample size) was available for each phenotype; now, associations from all relevant datasets may be viewed in LocusZoom.

Portion of the updated LocusZoom interface, showing phenotype filtering capability.

The sample filtering panel of the user interface for the custom burden test and GAIT (Genetic Association Interactive Tool) has also been improved to make it more intuitive to use. The External Resources sections of Gene and Variant pages have been re-styled, and gene- and variant-specific links to PheWeb have been added. PheWeb displays phenotypes most significantly associated with the gene or variant, based on a GWAS for over 2,400 phenotypes in UK Biobank data that was performed by Ben Neale's group. Finally, the home pages of all the Portals have been redesigned to make the appearance of the disease-specific portals more distinct.

Please browse these new data and features, and let us know what you think!

Tuesday, February 6, 2018

Federation brings three new datasets to the T2DKP

Our mission at the Type 2 Diabetes Knowledge Portal (T2DKP) is to aggregate and analyze genetic association data relevant to T2D, and to make the knowledge that can be gleaned from these data available to researchers around the world. But it isn't possible to aggregate all of the relevant data in one place: privacy regulations at the institutional, regional, and national levels determine how these data are handled, and whether or where they can be transferred.

The T2DKP is supported by the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D),  a pre-competitive partnership among the National Institutes of Health, industry, and not-for-profit organizations, managed by the Foundation for the National Institutes of Health. Because AMP T2D seeks to facilitate discovery of new targets for T2D treatment by making as much data as possible available via the T2DKP, it funded the development of a mechanism for establishing interconnected federated nodes of the T2DKP that would enable researchers to interact with all of the data regardless of where they are located.

This goal was realized with the creation, by a team led by Thomas Keane and Dylan Spalding, of a federated node of the T2DKP at the European Bioinformatics Institute (EBI).  Data housed at the EBI node are stored in such a way that their specific privacy requirements are met, but they are made available for remote queries via T2DKP tools and interfaces. Results from such queries are served up alongside results from all of the datasets housed in the AMP T2D Data Coordinating Center (DCC) at the Broad Institute. Researchers may browse and query data from any location without even needing to know where they reside. This federation mechanism 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.

The first dataset to be incorporated into the Portal via the EBI federated node was the Oxford BioBank exome chip analysis dataset, which contains association data for glycemic, lipid, and blood pressure traits from over 7,100 subjects in Oxfordshire, U.K. The EBI Federated Node has now added three more datasets:

  • The EXTEND GWAS dataset, generated by Drs. Timothy Frayling and Andrew Wood and their colleagues, is comprised of 7,159 samples (1,395 T2D cases and 5,764 controls) from the Exeter EXTEND Biobank. It includes associations for a wealth of glycemic, anthropometric, cardiovascular, renal, and hepatic phenotypes--including many that are new to the T2DKP.
  • The GoDARTS Affymetrix GWAS dataset, from Dr. Colin Palmer and colleagues, includes summary-level statistics for associations with BMI and blood lipid levels from 3,307 diabetic participants in the Genetics of Diabetes Audit and Research Study in Tayside Scotland. In addition, individual-level data from over 17,000 subjects (including the set from which summary statistics were calculated) are available via the GAIT tool (see below). 
  • The Oxford BioBank Axiom GWAS dataset, from Dr. Fredrik Karpe and colleagues, includes associations for BMI and blood lipid levels from 7,193 participants, all healthy men and women between 30 and 50 years of age. It represents an additional analysis of the same samples contained in the Oxford BioBank exome chip analysis dataset.
These datasets are described in detail on our Data page. Summary results from all three sets are integrated into Gene and Variant pages in the T2DKP, and may also be viewed in the Manhattan plots accessible by searching for a phenotype from the T2DKP home page. The Variant Finder also queries these datasets.

The individual-level data behind all three of these datasets is accessible for custom association analysis in our Genetic Association Interactive Tool (GAIT) on Variant pages. Using this tool, researchers can filter samples to create a custom subset with defined characteristics such as age, gender, BMI, and other measures, and then run on-the-fly association analysis within that sample subset. Now, GAIT queries datasets both at the DCC and at the Federated node, using the same methodology for each, in a way that is transparent to users of the tool. The new Federated datasets bring the total number of individual-level samples available for custom analysis in the T2DKP to 67,768.

Monday, January 22, 2018

GWAS data re-analysis yields novel results about T2D risk

"Waste not, want not." The old proverb is about frugality, but a study published today gives it a whole new dimension. Lead author Sílvia Bonàs, directed by Josep Mercader and David Torrents and collaborating with many colleagues at the Barcelona Supercomputing Center, the Broad Institute, and other institutions (Bonàs-Guarch et al. (2018), Nature Communications 9), decided to investigate variants associated with type 2 diabetes (T2D) by re-analyzing existing GWAS data rather than initiating a new study.

This was a frugal strategy, conserving both time and resources. But the benefits of this approach went way beyond frugality. By aggregating multiple datasets and using unified, current methods for quality control, imputation, and association analysis, the researchers discovered nuggets of significant information that were not apparent in the original analyses of the individual sets. And all of these nuggets are freely available for browsing and searching in the T2D Knowledge Portal (T2DKP).

To amass these data, the researchers combined all of the individual-level T2D case-control GWAS data that were available from the European Genome-Phenome Archive (EGA) and the database of Genotypes and Phenotypes (dbGaP). After harmonization and quality control, data from 70,127 subjects (12,931 cases and 57,196 controls) remained, inspiring them to name the project "70KforT2D".

In the time since the original studies had been performed, better and more comprehensive reference panels for imputation had been generated by the 1000 Genomes and UK10K projects. By using both of these panels for imputation, the researchers were able to substantially increase the number of variants that could be imputed. They ended up with more than 15 million variants, including more than 5 million rare variants and over 1.3 million indels, which have previously been difficult to impute.

In performing association analysis, the authors took advantage of existing large datasets of T2D association summary statistics for meta-analysis, being careful to only combine non-overlapping samples. They also took advantage of the T2D Knowledge Portal to verify some associations for low-frequency variants that were located in coding regions and had suggestive, but not unambiguously significant, p-values. The significance of the T2D associations of these variants was confirmed by meta-analysis along with the associations seen in two large studies in the T2DKP (GoT2D exome chip analysis, with nearly 80,000 samples, and the 17K exome sequence analysis dataset with 17,000 samples).

The association analysis identified 57 loci associated with T2D risk at the genome-wide significance level or better (p-value ≤ 5x10e-8), seven of which had not previously been associated with T2D. The high quality of the data made it possible to fine-map the variants at each of these loci and construct credible sets. Many of the putative causal variants—including those in previously identified loci—were indels rather than single-nucleotide polymorphisms, underscoring the importance of an imputation procedure that discovers indels.

The T2D-associated loci discovered in this study give some tantalizing hints about genes potentially involved in T2D, and suggest new avenues for detailed wet-lab investigation. We can’t review all of them in this space, but one association is particularly interesting for the generalizable lessons it teaches us about case-control GWAS for T2D.

This association, which the authors validated and replicated using additional datasets, involves the X chromosome variant rs146662075. The risk allele confers a 2-fold elevated risk of developing T2D, in males. The variant appears to affect an enhancer that could regulate expression of AGTR2, a gene known to be involved in modulating insulin sensitivity—making it a very interesting subject for investigation with regard to T2D. More work is needed to figure out whether this is really a male-specific effect, or whether it was only detectable in males because imputation for the X chromosome is more accurate in males, who have only one copy of the chromosome.

The first lesson learned from this association is that the X chromosome harbors important loci, and deserves attention in association studies. While this seems obvious, since the X chromosome comprises 5% of the genome, it has been neglected in most studies to date.

The second lesson is that for an adult-onset disease like T2D, it’s very important to pay attention to the details of case-control classification. If there are young people in the control group, they may actually be future T2D cases, destined to develop the disease later in life. When the authors tried to replicate the initial discovery for this variant in different datasets, the associations were not as significant as expected. But after digging deeper into the experimental cohorts, they found that most of the replication datasets had many subjects younger than 55, which was the average age for T2D onset for these cohorts. Re-running the analysis after excluding controls younger than 55 and also excluding those who appeared to be pre-diabetic, based on an oral glucose tolerance test, brought the replication results into concordance with the discovery results and confirmed the significance of the rs146662075 association.

In keeping with the spirit of open access, the authors provided the summary statistics from this work to the T2DKP even before publication. These results are incorporated into the T2DKP and are visible on Gene and Variant pages as well as searchable via the Variant Finder. The authors have also made the full summary statistics available for public download.

The novel and important findings from this study strongly reaffirm the value of data sharing. Not only are data sharing and re-analysis the right things to do for reasons of fairness, equity, and frugality; they can also spark new insights and move science forward in unexpected ways.

Friday, January 19, 2018

New METSIM dataset adds individual-level GWAS data to the T2DKP

The Finnish population is a valuable genetic resource. Having undergone multiple population bottlenecks, this relatively homogeneous population is enriched in low-frequency and loss-of-function variants. Even better, Finns are generally willing to participate in research studies, and many measures of their health are detailed in comprehensive electronic health records.

To take advantage of these characteristics, the METSIM (Metabolic Syndrome in Men) study (Laakso et al. 2017, J. Lipid Res. 58, 481-493) was initiated in 2005. Over 10,000 Finnish men were examined between 2005 and 2010. All of the subjects were phenotyped extensively, with an emphasis on traits associated with type 2 diabetes (T2D), cardiovascular disease, and insulin resistance, and their genotypes and exome sequences were determined. Subsets of the group have been characterized in more detail, with whole-genome sequencing and detailed analyses of transcripts and gene expression, DNA methylation, gut microbiome composition, and other phenotypes.

Now, you can easily access results from the METSIM cohort in the T2D Knowledge Portal. Variant associations with T2D, fasting glucose levels, and fasting insulin levels are available, both unadjusted or adjusted for body mass index. The individual-level data are also available for interactive analyses using our Genetic Association Interactive Tool (GAIT; see below), which allows you to design and run custom association analyses using custom subsets of the samples, while always protecting patient privacy. The addition of METSIM data brings to nearly 68,000 the number of samples available for analysis in GAIT.

The Foundation for the NIH and the Accelerating Medicines Partnership in Type 2 Diabetes were instrumental in bringing these data, generated by researchers in Finland and the U.S., to the T2DKP. Individual-level genotype data from 1,185 T2D cases and 7,357 controls were deposited into the Data Coordinating Center (AMP T2D DCC), and analysis and quality control were performed by the DCC analysis team. The experiment design and analysis are summarized on our Data page, and detailed reports that fully document the analysis are available for download.

The METSIM GWAS dataset currently has "Early Access Phase 1" status in the T2DKP, 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 full details about the different phases of data release on our Policies page.

Results from METSIM GWAS may be viewed at these locations in the T2D Knowledge Portal:

• On Gene Pages (e.g., MTNR1B) in the Common variants and High-impact variants tables and in LocusZoom static plots, for the phenotypes T2D, T2D adjusted for BMI, fasting glucose, fasting glucose adjusted for BMI, fasting insulin, and fasting insulin adjusted for BMI;

• On Variant Pages (e.g.rs579060) in the Associations at a glance section, the Association statistics across traits table, and in LocusZoom static plots;

• From the View full genetic association results for a phenotype search on the home page: first select one of the phenotypes listed above, and then on the resulting page, select the METSIM GWAS dataset.

Individual-level METSIM GWAS data may be used for custom interactive analyses using these tools in the T2DKP:

• Using the Variant Finder tool, you may specify multiple criteria and retrieve the set of variants meeting those criteria;

• Using the Genetic Association Interactive Tool (GAIT) on Variant Pages, you may select the METSIM GWAS dataset, choose one of 5 phenotypes for association analysis, choose custom covariates, and filter the sample pool by specifying a range of values for one or more of 8 different phenotypes, then run on-the-fly analysis.

Phenotypes available for association analysis of METSIM GWAS data in GAIT

Covariates available for selection when analyzing METSIM GWAS data in GAIT

Samples may be filtered by setting ranges for one or more of 8 phenotypes for the METSIM GWAS dataset

Wednesday, January 3, 2018

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!

Wednesday, November 15, 2017

T2DKP Fall Newsletter

The latest issue of our quarterly newsletter is now available. Download it here to find out what we've been up to!