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Connect with the Knowledge Portal Network team at #ASHG19

Attending the American Society of Human Genetics Annual Meeting next week? We are too, and we look forward to connecting with you in multiple venues:

Wednesday, October 16

Visit our booth (#131) in the exhibit hall from 10am-4:30pmAttend our Ancillary session:Translating Variant Associations to Functional Insights Using the Knowledge Portal Network
12:45-2:00 pm, Marriott Marquis Houston, Tanglewood room
Jesse Engreitz and Jason Flannick will speak, with an introduction from Noël Burtt and followup from Maria Costanzo.
Attend our presentation at the Broad genomics booth (#714) from 3-4pmAttend the talk by Lokendra Thakur, “Calculating principled gene priors for genetic association analysis.” 4:45-5pm, Room 317A, Level 3, Convention Center
Thursday, October 17

Visit our booth (#131) in the exhibit hall from 10am-4:30pmVisit the poster (#1657/T) by Ben Alexander, “Systematic comparison of different evidence sources for predicting GWAS effector genes” from 2-3pmVisit the poster (#1402/T) by D…

Mining insights from GWAS

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Genetic association data from genome-wide association studies (GWAS) are foundational for our understanding of complex diseases and traits. But in order to apply these results to diagnosis, drug development, and treatment, we need to identify the effector genes that explain those genetic associations. This is rarely straightforward: most SNPs associated with disease are located outside of coding regions of the genome, so that their impact on genes is not obvious; and even a variant located in a protein-coding gene may actually affect a different gene. And to complicate things further, a variant that is strongly associated with disease may not have a direct impact on a gene, but may rather be "along for the ride" with a tightly linked causal variant.

To help bridge the gap between genetic association results and the effector genes that are directly involved in disease, we are aggregating additional data types—for example, transcriptional regulation, tissue specificity, curate…

Join our instructional webinar July 18

Join us at noon EDT on Thursday, July 18 for an interactive workshop featuring gene-specific resources in the Knowledge Portal Network portals. We’ll first cover two new types of information on T2D gene associations: predictions of T2D effector genes, and gene-level T2D association scores. Then we'll delve into the Gene page with its comprehensive information for a wide variety of phenotypes, focusing on how the Knowledge Portals can help researchers prioritize genes within a GWAS locus for further investigation. See below for the agenda.

This session may be attended as an online webinar (connection information below) or in person at the Broad Institute in the 415 Main St Board room (mezzanine level), where lunch will be provided.

We hope you will attend and bring your questions and suggestions!


Agenda:

Introduction - Noël Burtt

Gene-specific resources in the Knowledge Portals - Maria Costanzo

Preview of upcoming features - Ben Alexander

Q & A - the T2DKP team


Connection Information…

Bottom line p-values now available in the CVDKP

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When genetic association analysis for a phenotype is performed in multiple studies, many different p-values representing the significance of that association are generated. How do we know which one is the most accurate?

To complicate things even further, the populations tested in different datasets often overlap with each other. How can we avoid double-counting associations?

Bottom line analysis provides an answer to both of these questions. It integrates results over multiple datasets and accounts for sample overlap between datasets to generate a single p-value representing the significance of the association between a variant and a phenotype.

Now, you can access bottom line p-values for individual variants on Variant pages in the Cardiovascular Disease Knowledge Portal as well as in the other portals of the Knowledge Portal Network: Type 2 Diabetes KP, Cerebrovascular Disease KP, and Sleep Disorder KP. To view bottom line p-values, open the "associations at a glance" secti…

GPS information for BMI and obesity now available in the CVDKP

Genome-wide polygenic scores (GPS) have great potential for helping to advance research on complex diseases and traits. Not only can they help predict individual genetic risk, but they can also help us understand the physiology of disease, by identifying groups at the extremes of risk whose clinical profiles can be studied or who may be enrolled in clinical trials.

Following up on their previous work that generated GPSs for five complex diseases, co-lead authors Amit Khera and Mark Chaffin, along with senior author Sekar Kathiresan and colleagues, have now developed a GPS for body mass index (BMI) and obesity, published today in Cell. To help promote obesity research, the authors have provided a file, now available for download from the Data page of the CVDKP, that lists the variants and weights that comprise the GPS.

To generate this GPS, Khera and colleagues started with a large, recently published genome-wide association study (GWAS) for BMI in more than 300,000 UK Biobank particip…

Faster access to tools from the CVDKP home page

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We've rearranged some of the links on the Cardiovascular Disease Knowledge Portal home page, as a first step towards offering a central location for analysis tools. The previous link to the Variant Finder tool has been replaced by a link to the new Analysis modules page:


The new page, shown below, offers access to two analysis tools.



The Interactive Manhattan plot allows you to choose a phenotype and view variant associations across the genome for that phenotype.  We've added phenotype selection options to both the Analysis modules page and the Manhattan plot page, making it easier to switch your view between phenotypes.  The default view on the Manhattan plot page shows the largest dataset for a phenotype, but when multiple datasets exist, you can select any one to display. For many datasets, LD clumping is available at several r2 thresholds. Clumping reduces redundancy due to association signals from linked variants, pinpointing the most strongly associated variant in a group…