Cardiovascular Disease Knowledge Portal News

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&q

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 p