Genetic model predicts risk for common deadly diseases
Using data collected from the UK, the data suggests that up to 25 million people in the US may be at more than triple the risk of coronary artery disease…
A team of researchers have identified a new type of genome analysis that could identify large areas of the population with a high risk of developing serious common diseases – including breast cancer, type 2 diabetes and coronary artery disease.
The researchers, from the Broad Institute of MIT and Harvard, Massachusetts General Hospital (MGH) and Harvard Medical School, report the tests which uses data from millions of areas in the genome in order to determine the risk for five diseases. The tool can identify likelihoods of developing the diseases long before the appearance of symptoms.
Using data collected from the UK, the data suggests that up to 25 million people in the US may be at more than triple the risk of coronary artery disease, with millions more at a similar heightened risk for other conditions, based on their genetic variation alone.
The method is called polygenic risk scoring and could allow physicians to focus particular attention on individuals with an elevated risk of the diseases, enabling early intervention in order to prevent the disease.
The study raises questions about the application of this method, and how it can be further developed. The researchers identified that data mainly comes from individuals of European descent, and larger, more inclusive studies may be necessary.
“We’ve known for long time that there are people out there at high risk for disease based just on their overall genetic variation,” said senior author Professor Sekar Kathiresan, director of the Cardiovascular Disease Initiative at the Broad Institute, and director of the Centre for Genomic Medicine at MGH.
“Now, we’re able to measure that risk using genomic data in a meaningful way. From a public health perspective, we need to identify these higher-risk segments of the population, so we can provide appropriate care.”
In order to develop the appropriate algorithms to score the risk of developing disease, the researchers gathered data from large-scale genome-wide association studies to identify genetic variants associated with coronary artery disease, type 2 diabetes, atrial fibrillation, inflammatory bowel disease or breast cancer.
A computational model was designed for each disease, combining information from each of the variants into a single number – a polygenic risk score. This number could then be used to determine an individual’s risk, based on their genome.
The data was then tested on over data from over 400,000 individuals form the UK Biobank.
“These individuals, who are at several times the normal risk for having a heart attack just because of the additive effects of many variations, are mostly flying under the radar,” first author Dr Amit Khera explained. “If they came into my clinical practice, I wouldn’t be able to pick them out as high risk with our standard metrics. There’s a real need to identify these cases so we can target screening and treatments more effectively, and this approach gives us a potential way forward.”
The team suggests more research needs to be done to produce risk scores for other disease, and to include people of other ethnicities.
Despite this, the researchers propose the tool to be used in clinical settings, especially where the disease has a genetic component, is prevalent in the population and if knowing the genetic risk is useful in guiding preventative treatment.
“Ultimately, this is a new type of genetic risk factor,” said Prof Kathiresan. “We envision polygenic risk scores as a way to identify people at high or low risk for a disease, perhaps as early as birth, and then use that information to target interventions – either lifestyle modifications or treatments – to prevent disease. For heart attack, I foresee that each patient will have the opportunity to know his or her polygenic risk number in the near future, similar to way they can know their cholesterol number right now.”
The study was published in Nature Genetics.