A new algorithm for predicting disease risk.

I pass on the text of this piece from Gina Kolata, and then the abstract of the article by Khera et al. she is referencing:

By surveying changes in DNA at 6.6 million places in the human genome, investigators at the Broad Institute and Harvard University were able to identify many more people at risk than do the usual genetic tests, which take into account very few genes.

Of 100 heart attack patients, for example, the standard methods will identify two who have a single genetic mutation that place them at increased risk. But the new tool will find 20 of them...The researchers are now building a website that will allow anyone to upload genetic data from a company like 23andMe or Ancestry.com. Users will receive risk scores for heart disease, breast cancer, Type 2 diabetes, chronic inflammatory bowel disease and atrial fibrillation...People will not be charged for their scores.

The abstract from Nature Genetics:

A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation. Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature, it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk. We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues.