Friday, April 19, 2019

Bottom line p-values now available in the CDKP

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 Cerebrovascular Disease Knowledge Portal as well as in the other portals of the Knowledge Portal Network: Type 2 Diabetes KP, Cardiovascular Disease KP, and Sleep Disorder KP. To view bottom line p-values, open the "associations at a glance" section of the Variant page (see an example):



Choose to view "Bottom line analysis" in the PheWAS plot, and then mouse over a point to see the p-value:




We thank our colleagues at the University of Michigan, who developed the METAL method used in this analysis. Please note that this method as instantiated in the CDKP is experimental; be sure to compare the results with those from individual datasets, and contact us with any questions.

Thursday, April 18, 2019

GPS information for BMI and obesity now available

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 an open-access file listing the variants and weights that comprise the GPS. That file is now available for download from the Data page of our sister Knowledge Portal, the Cardiovascular Disease Knowledge Portal.

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 participants (Locke et al., 2015) and applied an algorithm that assigned a weight to each of 2.1 million variants, also taking into account factors such as the proportion of variants with non-zero effect size and the degree of correlation between a variant and its neighbors. They validated the GPS by applying it to nearly 120,000 additional UK Biobank participants, finding that the score was strongly correlated with measured BMI, and then applied it to four independent testing datasets.

We don't have space here to cover the many interesting details uncovered by the researchers, but overall, this work shows that a high GPS strongly predicts increased risk of severe obesity, cardiometabolic disease, and all-cause mortality. Those with the very highest GPS had a level of risk for obesity similar to that conferred by a rare monogenic mutation in the MC4R gene.

The GPS has the potential to be a powerful tool for people struggling with overweight and obesity. "Importantly, we are in the early days of identifying how we can best inform and empower patients to overcome health risks in their genetic background," said Khera in a press release from the Broad Institute. "We are incredibly excited about the potential to improve health outcomes."

We invite you to read the paper, take a look at the file of variants and weights freely available from the CVDKP Data page, and contact us with any questions!


Friday, March 1, 2019

Faster access to tools from the CDKP home page

We've rearranged some of the links on the Cerebrovascular 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.
  • The Variant Finder is a versatile tool that allows you to set multiple criteria (phenotype, p-value, size and direction of effect, and more) and retrieve the set of variants meeting those criteria.
The new Analysis Modules page will be the central access point for new analysis tools as they are developed, so check back often for updates!

The new Analysis Modules page will be the central access point for new analysis tools as they are developed, so check back often for updates!

Monday, January 28, 2019

New dataset added to the CDKP: WMHV GWAS

We are pleased to announce that a new dataset has been added to the Cerebrovascular Disease Knowledge Portal: Cerebral WMHV GWAS 2019, from the recently published study "Genetic variation in PLEKHG1 is associated with white matter hyperintensities" (Traylor et al. 2019). This genome-wide association study consisting of 11,226 individuals sparked the discovery of a locus at genome-wide significance in an intron of PLEKHG1.

Results from this study may be viewed and searched in the CDKP at these locations:

  • on Gene pages (view an example) for the "Cerebral white matter hyperintensities" phenotype
  • on Variant pages (view an example) in the Associations at a glance section, the Associations across all datasets graphic and table, and in LocusZoom static plots
LocusZoom plot of WMHV associations in the PLEKHG1 gene

  • from the View full genetic association results for a phenotype search on the home page: first select the cerebral white matter hyperintensities phenotype and then on the resulting page, select the Cerebral WMHV GWAS 2019 dataset.
Summary statistics may also be downloaded from the CDKP Downloads page.

Please check out these new results and let us know if you have comments or questions!