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.

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!