This weekend, cardiovascular researchers from around the globe will be meeting in Chicago for the 2018 Scientific Sessions of the American Heart Association. Members of the Knowledge Portal Network team will be there to meet and talk with geneticists and biologists who use the Portals and get your input on how we can improve them.
Please come visit us at booth #2249 in the Exhibit Hall! We'll be there on Saturday, Nov. 10 from 11am-5pm; on Sunday, Nov. 11 from 10am-4:30pm; and on Monday, Nov. 12 from 10am-3pm.
Showing posts with label Type 2 Diabetes Knowledge Portal. Show all posts
Showing posts with label Type 2 Diabetes Knowledge Portal. Show all posts
Wednesday, November 7, 2018
Tuesday, October 23, 2018
New features and a new Portal released at ASHG
The Knowledge Portal team is back at work after a fantastic week at the American Society of Human Genetics meeting. We had many great conversations with researchers at our exhibit booth and at the Broad Institute exhibit booth, where we had a couple of guest spots. This year, we also held a workshop session on the Knowledge Portal Network and the Diabetes Epigenome Atlas (DGA), and about 80 people came to learn the basics of navigating the Knowledge Portals and the DGA. We were asked to provide the slides from that session, and they can be viewed here, but please note that they may not be easy to interpret without the accompanying oral presentation. We are working on creating both instructional webinars and short videos explaining different aspects of the Portals; stay tuned! And in the meantime, please contact us with any questions--we're here to help.
As usual, we released a number of new features on the Type 2 Diabetes Knowledge Portal in time for the ASHG meeting:
Calculated credible sets
Credible sets are useful because they assign to individual variants in a locus a probability of being causal for a phenotype. On Gene Pages (see an example), when viewing the type 2 diabetes phenotype, the Credible sets tab displays credible sets generated and published by Mahajan et al. (2018). However, credible sets have not been generated by researchers for phenotypes in the T2DKP other than T2D.
Now, the T2DKP provides calculated credible sets for all phenotypes. When viewing a phenotype other than T2D on the Gene page, the Credible sets tab is replaced by a Calculated credible set tab. This LocusZoom module, developed by our AMP T2D partners at the University of Michigan, automatically calculates posterior probabilities from p-values. Calculated credible sets include up to 10 variants; the credible interval covered by the set may vary, depending on the strength of associations across the region.
UK Biobank PheWAS
Recently, we added to the T2DKP another LocusZoom module for displaying phenome-wide associations. The PheWAS display, showing associations for a variant across all of the phenotypes included in the T2DKP, is the default visualization in the "Associations at a glance" section of Variant pages (see an example). Now, by checking the "Use UKBB data" box, you can view associations for a variant across about 1,400 UK Biobank phenotypes from an analysis performed by our AMP T2D partners at the University of Michigan.
Forest plot visualization of variant associations
We also provide yet another LocusZoom visualization on a separate tab of the "Associations at a glance" section of the Variant page. The Forest plot is an alternative way to visualize phenotypic associations for a variant. In addition to displaying the significance of associations, the Forest plot also shows the direction of effect and the confidence interval for variant associations.
Genetic Risk Score module
The T2DKP now includes an initial version of the Genetic Risk Score module. This is an instantiation of the same custom burden test that is found on Gene pages, but instead of using as input a set of variants across a gene, the module uses a set of 243 variants identified by Mahajan et al. (2018) that are significantly associated with T2D risk. The module draws on 9 different datasets, including 3 housed at the Broad Data Coordinating Center and 6 housed at the T2DKP Federated node at EBI. Just like the burden test, it allows you to choose a phenotype for analysis, adjust the set of variants if desired, filter the sample set by many criteria, and set custom covariates before running the analysis. The results obtained from this module can potentially reveal genetic relationships between phenotypes. The module is still under development, and we would appreciate your feedback on it!
New Knowledge Portal added to the network
At the ASHG meeting we unveiled the newest member of the Knowledge Portal Network: the Sleep Disorder Knowledge Portal (SDKP), for the genetics of sleep and circadian traits. There is currently one dataset for sleep genetic associations in the SDKP, "UK Biobank Sleep Traits GWAS," which includes chronotype, sleep duration, insomnia, daytime sleepiness, and nap traits. Additional association datasets are available for type 2 diabetes and glycemic traits, anthropometric traits, measures of kidney function, and psychiatric traits, and more sleep data will be added soon.
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| Part of the Knowledge Portal Network team at our ASHG booth |
As usual, we released a number of new features on the Type 2 Diabetes Knowledge Portal in time for the ASHG meeting:
Calculated credible sets
Credible sets are useful because they assign to individual variants in a locus a probability of being causal for a phenotype. On Gene Pages (see an example), when viewing the type 2 diabetes phenotype, the Credible sets tab displays credible sets generated and published by Mahajan et al. (2018). However, credible sets have not been generated by researchers for phenotypes in the T2DKP other than T2D.
Now, the T2DKP provides calculated credible sets for all phenotypes. When viewing a phenotype other than T2D on the Gene page, the Credible sets tab is replaced by a Calculated credible set tab. This LocusZoom module, developed by our AMP T2D partners at the University of Michigan, automatically calculates posterior probabilities from p-values. Calculated credible sets include up to 10 variants; the credible interval covered by the set may vary, depending on the strength of associations across the region.
UK Biobank PheWAS
Recently, we added to the T2DKP another LocusZoom module for displaying phenome-wide associations. The PheWAS display, showing associations for a variant across all of the phenotypes included in the T2DKP, is the default visualization in the "Associations at a glance" section of Variant pages (see an example). Now, by checking the "Use UKBB data" box, you can view associations for a variant across about 1,400 UK Biobank phenotypes from an analysis performed by our AMP T2D partners at the University of Michigan.
 |
| New LocusZoom visualization shows variant associations across UK Biobank phenotypes |
Forest plot visualization of variant associations
We also provide yet another LocusZoom visualization on a separate tab of the "Associations at a glance" section of the Variant page. The Forest plot is an alternative way to visualize phenotypic associations for a variant. In addition to displaying the significance of associations, the Forest plot also shows the direction of effect and the confidence interval for variant associations.
 |
| Forest plot on the Variant page |
Genetic Risk Score module
The T2DKP now includes an initial version of the Genetic Risk Score module. This is an instantiation of the same custom burden test that is found on Gene pages, but instead of using as input a set of variants across a gene, the module uses a set of 243 variants identified by Mahajan et al. (2018) that are significantly associated with T2D risk. The module draws on 9 different datasets, including 3 housed at the Broad Data Coordinating Center and 6 housed at the T2DKP Federated node at EBI. Just like the burden test, it allows you to choose a phenotype for analysis, adjust the set of variants if desired, filter the sample set by many criteria, and set custom covariates before running the analysis. The results obtained from this module can potentially reveal genetic relationships between phenotypes. The module is still under development, and we would appreciate your feedback on it!
New Knowledge Portal added to the network
Wednesday, September 26, 2018
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!
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!
Monday, June 18, 2018
See you at ADA!
The 78th Scientific Sessions of the American Diabetes Association are coming up in just a few days, and the T2D Knowledge Portal team will be there!
As usual, we'll have a booth in the exhibit hall. We'll be at booth #1075 from 10am to 4pm on Saturday and Sunday 6/23-24, and from 10am to 2pm on Monday 6/25. Come say hello, get a demonstration of the T2D, Cardiovascular Disease, or Cerebrovascular Disease Knowledge Portals, and pick up some of the T2DKP sticky notes that we'll be giving away!
There will also be presentations from several members of our group on Saturday, June 23:
As usual, we'll have a booth in the exhibit hall. We'll be at booth #1075 from 10am to 4pm on Saturday and Sunday 6/23-24, and from 10am to 2pm on Monday 6/25. Come say hello, get a demonstration of the T2D, Cardiovascular Disease, or Cerebrovascular Disease Knowledge Portals, and pick up some of the T2DKP sticky notes that we'll be giving away!
Here's who you might find at the booth when you stop by:
There will also be presentations from several members of our group on Saturday, June 23:
- Jason Flannick, PhD will give a talk on "The Type 2 Diabetes Knowledge Portal" at 11:30am.
Session: Quantifying Diabetes: Genomics, Electronic Health Records, and Automated Control
Location: W312
____________________________________
- Jose C. Florez, MD, PhD, will moderate an interactive poster session, "Delving into Type 2 Diabetes Genetics", at 12:30 pm.
Location: Poster hall
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- Miriam Udler, MD, PhD will present "Genetic testing for Monogenic Diabetes--Whom to Test, What and How to Order?" at 2:15pm.
Location: W304E-H
____________________________________
Friday, April 27, 2018
New T2DKP release adds individual-level data for interactive analysis
With the April release of the Type 2 Diabetes Knowledge Portal, we are increasing the number of datasets and samples available for interactive analysis via the LocusZoom and GAIT tools. These tools now access individual-level data from three additional datasets, all of which were quality controlled and analyzed at the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D) Data Coordinating Center (DCC):
- 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.
- 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.
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.
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!
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.
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.
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| 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 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.
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.
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:
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!
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!
Tuesday, November 14, 2017
Announcing the Cardiovascular Disease Knowledge Portal
We are pleased to announce the launch of the Cardiovascular Disease Knowledge Portal (CVDKP). Our collaboration with Dr. Patrick Ellinor, Dr. Sek Kathiresan, and their colleagues in the Atrial Fibrillation, Global Lipids Genetics, Myocardial Infarction Genetics, and CARDIoGRAMPlusC4D consortia has created a resource that offers world-wide open access to genetic and genomic information about atrial fibrillation, myocardial infarction, and related traits, with the goal of democratizing access to genomic data and accelerating cardiovascular genomics research.
The CVDKP is constructed on a software architecture originally developed for the Type 2 Diabetes Knowledge Portal (T2DKP), which is the central product of the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D). AMP T2D is a public-private partnership between the National Institutes of Health, the U.S. Food and Drug Administration, biopharmaceutical companies, and non-profit organizations that is managed through the Foundation for the NIH. AMP seeks to harness collective capabilities, scale, and resources toward improving current efforts to develop new therapies for complex, heterogeneous diseases.
The ultimate goal of AMP T2D is to increase the number of new diagnostics and therapies for patients while reducing the time and cost of developing them, by jointly identifying and validating promising biological targets for type 2 diabetes. The T2DKP furthers that goal by aggregating, harmonizing, and displaying genetic association and epigenomic results along with user-friendly analysis tools, allowing research biologists who would not individually be able to amass and manipulate these large datasets to glean insights from the data.
We are working towards these same goals for other complex diseases, by extending the platform and analysis tools constructed for the T2DKP. In partnership with the International Stroke Genetics Consortium, we recently created a Knowledge Portal for cerebrovascular disease (CDKP) based on the same infrastructure. Now, with the advent of the Cardiovascular Disease Knowledge Portal, we have a three-member Knowledge Portal Network for the genetics of cardiometabolic and cerebrovascular disease.
Data in the CVDKP directly relevant to heart disease include genetic associations with atrial fibrillation, electrocardiogram traits, plasma lipid levels, and myocardial infarction. Additional association datasets are available for type 2 diabetes and glycemic traits, anthropometric traits, measures of kidney function, and psychiatric traits. You may browse the complete list of datasets and their descriptions on the CVDKP Data page.
As for the Cerebrovascular Disease Knowledge Portal, in the CVDKP we also continue to work with the American Heart Association Precision Medicine Platform (PMP) to provide an additional avenue for accessing cardiovascular genetic data. Currently, summary statistics from the AFGen GWAS and AFGen exome chip analysis datasets are deposited in the PMP.
We welcome all suggestions, comments, questions, and submission of relevant datasets for the CVDKP. Please contact us at help@cvdgenetics.org!
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| CVDKP home page |
The CVDKP is constructed on a software architecture originally developed for the Type 2 Diabetes Knowledge Portal (T2DKP), which is the central product of the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D). AMP T2D is a public-private partnership between the National Institutes of Health, the U.S. Food and Drug Administration, biopharmaceutical companies, and non-profit organizations that is managed through the Foundation for the NIH. AMP seeks to harness collective capabilities, scale, and resources toward improving current efforts to develop new therapies for complex, heterogeneous diseases.
The ultimate goal of AMP T2D is to increase the number of new diagnostics and therapies for patients while reducing the time and cost of developing them, by jointly identifying and validating promising biological targets for type 2 diabetes. The T2DKP furthers that goal by aggregating, harmonizing, and displaying genetic association and epigenomic results along with user-friendly analysis tools, allowing research biologists who would not individually be able to amass and manipulate these large datasets to glean insights from the data.
We are working towards these same goals for other complex diseases, by extending the platform and analysis tools constructed for the T2DKP. In partnership with the International Stroke Genetics Consortium, we recently created a Knowledge Portal for cerebrovascular disease (CDKP) based on the same infrastructure. Now, with the advent of the Cardiovascular Disease Knowledge Portal, we have a three-member Knowledge Portal Network for the genetics of cardiometabolic and cerebrovascular disease.
Data in the CVDKP directly relevant to heart disease include genetic associations with atrial fibrillation, electrocardiogram traits, plasma lipid levels, and myocardial infarction. Additional association datasets are available for type 2 diabetes and glycemic traits, anthropometric traits, measures of kidney function, and psychiatric traits. You may browse the complete list of datasets and their descriptions on the CVDKP Data page.
As for the Cerebrovascular Disease Knowledge Portal, in the CVDKP we also continue to work with the American Heart Association Precision Medicine Platform (PMP) to provide an additional avenue for accessing cardiovascular genetic data. Currently, summary statistics from the AFGen GWAS and AFGen exome chip analysis datasets are deposited in the PMP.
We welcome all suggestions, comments, questions, and submission of relevant datasets for the CVDKP. Please contact us at help@cvdgenetics.org!
Wednesday, October 25, 2017
New phenotypes and physical activity stratification available in the T2DKP
We’ve recently updated one dataset and added another in the Type 2 Diabetes Knowledge Portal. Associations with multiple new phenotypes are now available for the BioMe AMP T2D GWAS dataset, and the new dataset "GIANT GWAS - stratified by physical activity" adds associations with anthropometric traits for cohorts stratified by gender and physical activity levels.
The BioMe AMP T2D GWAS dataset was first added to the T2DKP in early 2017, initially with three phenotypes (T2D, fasting glucose levels, and HbA1c levels). Deposition and analysis 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. This dataset was the first to be entirely produced within the AMP T2D project, including the deposition, analysis, quality control, and presentation of the data.
The 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 dataset adds significant ethnic diversity to the Portal’s genetic association data.
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. In this second phase of analysis, associations with seven traits were calculated: systolic and diastolic blood pressure; HDL and LDL cholesterol levels; creatinine levels and eGFR-creat; and BMI. A detailed analysis report for these associations may be downloaded from the BioMe AMP T2D GWAS section of our Data page.
The new GIANT dataset was generated by the GIANT (Genetic Investigation of Anthropometric Traits) consortium via a meta-analysis of genetic associations for BMI, waist-hip ratio, and waist circumference from more than 200,000 adults. Samples are stratified by sex, ancestry, and physical activity level (active or inactive). This work was published in a recent paper by Graff et al.
Data from both the BioMe and GIANT studies are available at these locations in the Portal:
Please check out the new data and contact us with any questions, comments, or suggestions.
The BioMe AMP T2D GWAS dataset was first added to the T2DKP in early 2017, initially with three phenotypes (T2D, fasting glucose levels, and HbA1c levels). Deposition and analysis 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. This dataset was the first to be entirely produced within the AMP T2D project, including the deposition, analysis, quality control, and presentation of the data.
The 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 dataset adds significant ethnic diversity to the Portal’s genetic association data.
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. In this second phase of analysis, associations with seven traits were calculated: systolic and diastolic blood pressure; HDL and LDL cholesterol levels; creatinine levels and eGFR-creat; and BMI. A detailed analysis report for these associations may be downloaded from the BioMe AMP T2D GWAS section of our Data page.
The new GIANT dataset was generated by the GIANT (Genetic Investigation of Anthropometric Traits) consortium via a meta-analysis of genetic associations for BMI, waist-hip ratio, and waist circumference from more than 200,000 adults. Samples are stratified by sex, ancestry, and physical activity level (active or inactive). This work was published in a recent paper by Graff et al.
Data from both the BioMe and GIANT studies are available at these locations in the Portal:
- On Gene pages (see an example) in the Common variants and High-impact variants tables and in LocusZoom static plots
- On Variant pages (see an example) in the Associations at a glance section and in the Association statistics across traits table, and in LocusZoom static plots
- Via the Variant Finder tool
- "Manhattan plots" of associations across the genome may be seen by selecting one of the phenotypes analyzed in these datasets in the View full genetic association results for a phenotype scroll box on the Portal home page
- Additionally, the BioMe data are available for sample filtering and custom association analysis via the Genetic Association Interactive Tool (GAIT) on Variant pages.
Please check out the new data and contact us with any questions, comments, or suggestions.
Monday, October 16, 2017
Learn about complex disease knowledge portals at ASHG 2017
Members of the Knowledge Portal team will be attending the American Society of Human Genetics meeting this week in Orlando, FL.
We'll be talking about the continuing progress of the Type 2 Diabetes Knowledge Portal, which has grown dramatically since ASHG 2016, with loads of new data and many new features. We'll also present our work towards expanding the T2DKP framework to other complex diseases, with the recent release of a new sibling portal for stroke genetics, the Cerebrovascular Disease Knowledge Portal.
You can catch us nearly every day of the meeting:
We'll be talking about the continuing progress of the Type 2 Diabetes Knowledge Portal, which has grown dramatically since ASHG 2016, with loads of new data and many new features. We'll also present our work towards expanding the T2DKP framework to other complex diseases, with the recent release of a new sibling portal for stroke genetics, the Cerebrovascular Disease Knowledge Portal.
You can catch us nearly every day of the meeting:
Wednesday 10/18
10 AM - 5 PM: Find us in the exhibit hall at booth #863. We’ll be there to answer your questions and give tours and tutorials on the Knowledge Portal Network.
10-10:30 AM: Demonstration of the Type 2 Diabetes Knowledge Portal at our booth, #863.
10:30-11 AM: Demonstration of the Cerebrovascular Disease Knowledge Portal at our booth, #863.
10-10:30 AM: Demonstration of the Type 2 Diabetes Knowledge Portal at our booth, #863.
10:30-11 AM: Demonstration of the Cerebrovascular Disease Knowledge Portal at our booth, #863.
2 PM - 4 PM: Ben Alexander will present poster #1186: The Type 2 Diabetes Knowledge Portal: Clearing a path from genetic associations to disease biology.
Thursday 10/19
10 AM - 5 PM: We will again be in the exhibit hall at booth #863.
10:30 - 11:30 AM: Portal team members will be available at the Broad Institute booth (#1037) for demonstrations and tutorials.
2-2:30 PM: Demonstration of the Type 2 Diabetes Knowledge Portal at our booth, #863.
2:30-3 PM: Demonstration of the Cerebrovascular Disease Knowledge Portal at our booth, #863.
10:30 - 11:30 AM: Portal team members will be available at the Broad Institute booth (#1037) for demonstrations and tutorials.
2-2:30 PM: Demonstration of the Type 2 Diabetes Knowledge Portal at our booth, #863.
2:30-3 PM: Demonstration of the Cerebrovascular Disease Knowledge Portal at our booth, #863.
4:15 PM–6:15 PM: Portal team members will be participating in Concurrent Invited Session #49:
Data Sharing, Analysis, and Tools to Catalyze Translation from Genomic to Clinical Knowledge
Room 330C, Level 3, Convention Center
Moderators: Benjamin Neale and Noël Burtt
Talks:
Serving genetic data and tools to the world - Jason Flannick.
The EGA as a platform for effective data sharing of human genetic and phenotype data -Thomas Keane.
Converting sequence data from over 140,000 people into rare disease diagnoses - Daniel MacArthur.
Assessing the phenome-wide consequences of genetically regulated molecular traits - Hae Kyung Im.
Friday 10/20
10 AM - 2:30 PM: This is our last day in the exhibit hall at booth #863.
We look forward to meeting you at ASHG! If you have questions and cannot meet us any of these times, or if you won’t be at ASHG, our mailbox is always open at help@type2diabetesgenetics.orghelp@type2diabetesgenetics.org.
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