For Premal Shah, PhD, CEO of clinical whole-genome analysis company MyOme, the idea that he might be at high risk for a heart attack is not abstract—it is personal history.
“My dad had a heart attack at 50 and is alive, my uncle dropped dead at 50, and my cousin dropped dead at 50,” Shah told Inside Precision Medicine. “I’m 49. It’s real. It’s absolutely real.”
Even in Shah’s case, traditional clinical risk calculators painted a reassuring picture. Standard cardiovascular assessments, based on cholesterol levels, blood pressure, and age, suggested little cause for alarm.
“If you plug in my LDL, HDL, and all the factors that go into the standard calculator, my doctor will tell you that I have a four percent risk of a heart event over the next 10 years—that’s fantastic! That’s actually below average for somebody my age,” Shah explained.
Genomics told a very different story.
“If you inflate my genetics, I’m now over 20% risk,” Shah said. “Now that I know this, and I’m on a low-dose statin, we can increase the dosage slightly. I’m scheduled to get a CT angiogram in January to measure the soft plaque in my arteries. These are the kinds of things that a clinical intervention allows me to do so that I don’t just walk around as a ticking time bomb.”
Cardiovascular disease, cancer, and other chronic conditions often develop quietly, shaped by a complex interplay of genetics, environment, and behavior. Increasingly, researchers argue that much of this risk is already encoded in our DNA—hidden in plain sight—waiting for the right analytical tools to be revealed.
Advances in next-generation sequencing (NGS), falling costs of whole-genome sequencing (WGS), and the rise of AI-driven genomic models are now converging to make that possibility real. The question is no longer whether genetic risk can be measured, but whether healthcare systems are ready to act on it—before symptoms appear.
The gap between conventional risk assessments and genetically informed risk is precisely where MyOme sees an opportunity to redefine preventive medicine. The company has partnered with Illumina to combine large-scale whole-genome sequencing with AI-integrated risk models designed to identify disease susceptibility earlier and more accurately than standard clinical tools.
From treating disease to preventing it
The collaboration reflects a broader shift underway in healthcare: moving from reactive treatment to proactive prevention. “If you think about preventative medicine and not what the health care system is today, which is treating diseases, it is about not waiting until it happens,” Shah said. “It’s not about treating patients when they get diagnosed with breast cancer.”
According to health economic modeling conducted by MyOme and its partners, widespread adoption of clinical WGS paired with AI-driven risk models could yield more than $200 billion in annual healthcare savings. The logic is straightforward: earlier detection enables earlier intervention, when treatments are less expensive and outcomes are better.
By identifying individuals at high genetic risk for conditions such as coronary artery disease, breast cancer, or metabolic disorders, clinicians can intervene years—or decades—before disease manifests. That may mean statins, enhanced imaging, lifestyle changes, or intensified screening protocols, many of which are already reimbursed under existing guidelines.
Shah emphasized that the value lies not only in identifying rare pathogenic variants, but in expanding clinical genomics into polygenic risk scores (PRSs), which capture the cumulative effect of many common genetic variants.
“What we’re finding is that people actually act on our PRS, especially if the addition of genomics and genetics puts them in a different category,” Shah said. “If they go from low- or intermediate-risk to high-risk for coronary, for example, they talk with their physicians, get started on statins, etc.”
The same logic applies to cancer risk. “Similarly, for breast cancer, a woman goes from 16% risk, which is better than average, a little more than average, until she reaches about 20%. She’s on track to get MRIs every year. These are all reimbursed things. That to me is wonderful.”
Faith in numbers
Skeptics may question whether $200 billion in annual savings is realistic. Shah is quick to note that the modeling assumptions are conservative. The analysis assumes 50% compliance with genetic testing across the U.S. adult population and 30% compliance with downstream interventions—far from universal adoption.
“If you have a heart event, which is very expensive,” Shah said, “but if you can get started on a high-dose statin, if you can make lifestyle and dietary changes, and if you can get screened for a CT angiogram, which costs several hundred dollars, you can prevent heart disease. That’s hundreds of thousands of dollars per incident.”
Scaled across millions of adults, those avoided events add up quickly. “When you think about this across millions of adults, a two-hundred-billion-dollar number is not crazy,” Shah said. “If everyone complied, the number would be much higher.”
Still, Shah stresses that the precise dollar figure is less important than the philosophical shift it represents. “That’s why it’s less about the number and more about this idea that we have to get away from this paradigm of treating disease and actually prevent and delay the onset of disease.”
Employers and health systems, he added, are beginning to see the return on investment. “Employers who we engage with and health systems that we engage with are starting to see the ROI benefit too. They see these numbers and say, ‘Wow, we can prevent people from getting the disease.’ That’s real money that we are saving, and more importantly, improving the outcomes.”
Putting prevention to the test
To move from modeling to evidence, MyOme is launching a major validation effort: the Proactive Health (MPH) Trial. The large-scale prospective study is designed to test whether combining WGS with AI-integrated risk models can demonstrably improve outcomes and reduce costs across common chronic diseases, cancers, and rare disorders.
“It’s a large-scale prospective trial that aims to prove that enhanced patient outcomes and substantial cost savings can be achieved from the use of whole genome sequencing combined with our AI-integrated genome-first approach to risk assessment for chronic conditions; these include cancers and also rare diseases,” Shah said.
“That’s the goal here: to use this number that we’ve shown to prove out that, yes, you can actually save a ton of money and improve outcomes.”
The trial is also intended to generate the kind of real-world evidence that insurers and policymakers demand. “The trial is also in part because any insights you get from the health economic standpoint or outcome standpoint, that’s what insurers and other bodies use to make decisions on coverage and payment.”
Details on endpoints and timelines remain under wraps. “We’re not prepared right now to discuss the strategy and when there are indications that we’re going to be talking about what the endpoints are.”
For Shah, the case for genome-first medicine ultimately comes down to one principle: informed action. “Getting an accurate risk assessment is the most important clinical intervention, as we’ve discussed,” Shah said, “because if I know what my roadmap is or my risk—and by the way, I do know now because of my own test—I’m going to act on it.”
If large-scale trials confirm that premise, whole-genome sequencing may no longer be viewed as a futuristic add-on to healthcare but as a foundational tool—one that shifts medicine from reacting to disease toward preventing it altogether.
