With a few exceptions, the genetic tests used today detect only uncommon forms of disease. The tests identify rare variants in a single gene that causes the disease.

But most diseases aren’t caused by variants in a single gene. Often a hundred or more changes in genetic letters collectively indicate the risk of common diseases like heart attack, diabetes, or prostate cancer. Tests for these types of changes have recently become possible, and they produce what is known as your “polygenic” risk score. Polygenic risk scores are derived from the combination of these variants, inherited from your mother and father, and can point to a risk not manifest in either parent’s family history. We’ve learned from studies of many polygenic risk scores for different diseases that they provide insights we can’t get from traditional, known risk factors such as smoking or high cholesterol (in the case of heart attack). Your polygenic score doesn’t represent an unavoidable fate—many people who live into their 80s and 90s may harbor the risk for a disease without ever actually getting it. Still, these scores could change how we view certain diseases and help us understand our risk of contracting them.

A polygenic risk score might tell you that you’re at high risk for breast cancer and spur you to get more intensive screening.

Genetic tests for rare forms of disease caused by a single gene typically give a simple yes or no result. Polygenic risk scores, in contrast, are on a spectrum of probability from very low risk to very high risk. Since they’re derived from combinations of genome letter changes that are common in the general population, they’re relevant to everybody. The question is whether we’ll find a way to make proper use of the information we get from them. Can they inform us about changes to our lifestyle, or point to medications we should take or a screening test we should get, that might improve our chances of staying healthy?

Statin drugs are a good case study for this. They’re widely used, even though 95% of the people taking them who ­haven’t had heart disease or stroke get no benefit aside from a nice cholesterol lab test. We can use a polygenic risk score to reduce unnecessary statin use, which not only is expensive but also carries health risks such as diabetes. We know that if you are in the top 20% of polygenic risk for heart attack, you’re more than twice as likely to benefit from statins as people in the bottom 20%; these people can also benefit greatly from improving their lifestyle (stop smoking, exercise more, eat more vegetables). So knowing your polygenic risk might cause you to take statins but also make some lifestyle changes. (And a recent large-scale study in Finland showed that people with high heart-risk scores responded with lifestyle improvements at a much higher rate than those with low risk scores.)

And it’s not just about heart disease. A polygenic risk score might tell you that you’re at high risk for breast cancer and spur you to get more intensive screening and avoid certain lifestyle risks. It might tell you that you’re at high risk for colon cancer, and therefore you should avoid eating red meat. It might tell you that you’re at high risk for type 2 diabetes, and therefore you should watch your weight.

Yet despite growing evidence that polygenic risk scores are important, until recently there was no service allowing people to determine their own scores, even if they had invested in their own personal direct-to-consumer genetic profiling. We’re attempting to remedy that through the development of MyGeneRank, a free mobile app that estimates users’ polygenic risk for heart attack and stroke from their own genetic data. It also allows them to participate in a clinical trial to measure the influence of polygenic risk information on people’s behavior, as reported by them, and their heath data, captured by mobile sensors linked to their smartphones.

There are still some issues and controversies we need to deal with. Equal access is one major concern—especially given that the majority of genetic studies have been performed in populations of European ancestry. For now, it appears that the more powerful the predictions become, the less accurate they become with other populations.

In addition, genetic risk information is likely to make some people feel anxious or fatalistic (or might give others a false sense of security). Previous studies suggest that genetic risk information has a minimal influence on these psychological states, but many of those studies were done when the variations in risk you could get via polygenic factors were marginal. As our ability to separate people into increasingly different classes of genetic risk gets better, these issues may become more prominent.

Another challenge will be to convince people to forgo or delay medical interventions if they have a low risk of a certain condition. This will require them to agree that they’re better off accepting a very low risk of a catastrophic outcome rather than needlessly exposing themselves to a medical treatment that has its own risks. People tend to overestimate the likelihood of catastrophic events, so if polygenic scores are to achieve their full impact on health outcomes and health-care spending, we’ll need to find a way to effectively communicate those trade-offs.

And finally there are the privacy concerns. We need to maintain our current protections against genetic discrimination so that people can benefit from their own genetic information without having to worry that insurance companies will get access to that information and use it to raise their rates or deny coverage.

You can’t change your genetic risk. But you can use lifestyle and medical interventions to offset that risk. We can accelerate breast cancer screening for women with a high risk for the disease, and help people with borderline risk of heart disease to make decisions about whether to take statins or not. If we deliver and track the response to polygenic risk information, we can collect real-world evidence on how to optimize the use of that data to give safe and effective health advice.

In the near future your smartphone might feature technologies that monitor your physiological, genetic, environmental, and behavioral characteristics. And this information could be linked to virtual medical coaches and AI systems that can synthesize all that information and deliver you insights about your own health, on demand.

Ali Torkamani is director of genomic informatics at the Scripps Research Translational Institute. Eric Topol is a cardiologist and the author of books including the upcoming Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.