By 2030, every baby born in the UK could have their entire genome sequenced under a new NHS initiative to “predict and prevent illness”. This would dramatically expand the current heel-prick test, which checks for nine rare genetic conditions, into a far more extensive screen of hundreds of potential risks.
On the surface, the idea sounds like an obvious win for public health: spot problems early, intervene sooner and save lives. But genetic testing on this scale carries real risks, especially if the results are misunderstood or poorly communicated.
The new plan builds on a recent NHS pilot study that sequenced the genomes of 100,000 newborns in England to identify more than 200 genetic conditions. However, these tests don’t provide clear cut answers. They don’t offer a diagnosis or certainty, just an estimate of risk.
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A genetic result might suggest a child has a higher (or lower) probability of developing a certain disease later in life. But risk is not prediction. If parents, or even clinicians, misinterpret that nuance, the consequences could be serious.
Some families may come to see a child flagged as “at risk” as a patient-in-waiting. In extreme cases, they may treat a probability as a certainty; assuming, for instance, that a child “has the gene” and will inevitably become ill. That assumption could reshape how children are raised, how they’re treated and how they could see themselves.
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Alarming language
This isn’t speculation. Research shows that while some people understand risk scores accurately, many struggle with statistical information.
Words like “high risk” or “likely” are interpreted differently by different people and often more seriously than intended. Even trained doctors can overestimate what a positive test result means. When it comes to genomics, the line between “you might get sick” and “you will get sick” can blur quickly.
UK policymakers haven’t helped this confusion. Government messaging refers to “diagnosis before symptoms even occur” and “leapfrogging disease.” But this language overpromises what genomic data can do and downplays its uncertainty.
When testing is indiscriminate and communication unclear, the fallout can be wide ranging. Children identified as “high risk” may undergo years of monitoring, unnecessary medical appointments, or even treatment for diseases they never develop.
In some cases, this leads to physical harms, from unnecessary medications to procedures with side effects. In others, the damage is psychological: shaping a child’s identity around an anticipated future of illness. These psychological effects can be lasting. Being told you’re likely to develop a condition like dementia may influence how a person plans their life, even if that illness never materialises.
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False positives
There are also broader issues with applying this kind of screening to everyone. Risk based testing works best when it’s targeted; for example, among those with symptoms or a strong family history.
But in the general population, where most people are healthy, false positives can far outnumber accurate results. Even well designed tests can produce misleading outcomes when applied at scale.
This is a well-known statistical effect, discussed during the COVID pandemic. In populations where a disease is rare, even highly accurate tests produce more false positives than true ones. If DNA screening is rolled out universally, many families will be told their child is at risk when they are not.
These false positives can lead to a cascade of further tests, stress and unnecessary clinical interventions; all of which consume time and resources and may cause real harm.
This issue already affects adult testing. For example, Alzheimer’s tests that measure early changes in the brain work well in memory clinics, where patients already show symptoms. But when these same tests are used on the general population, where most people are healthy, they produce false positives in up to two-thirds of cases.
If genetic screening in newborns is rolled out in the same way, it could lead to similar problems: mislabelling healthy children as sick, and causing unnecessary worry and follow-up tests.
So what’s the solution? It’s not to abandon genetic testing altogether – far from it. When used carefully, genomic data can offer real benefits, particularly for patients with symptoms or in research settings. But if we’re going to roll this out to every newborn, the surrounding infrastructure needs to be robust.
That includes:
- Clear, consistent communication: Risk scores must be explained in ways that emphasise uncertainty, not oversold as definitive predictions.
- Support for parents: For consent to be truly informed, parents need help understanding that a genetic flag is not a diagnosis – and that many people with elevated risk never go on to develop the condition.
- Training for clinicians: Many doctors still lack the tools to interpret and explain genetic information accurately and responsibly.
- A national network of genetic counsellors are essential for supporting families through testing and interpretation. But current numbers in the UK fall far short of what universal newborn screening would require.
Genomic data holds great promise. But using it as a blanket tool for all newborns demands caution, clarity, and investment in communication and care. Without these safeguards, we risk turning healthy babies into patients-in-waiting.
Luca Stroppa, Postdoctoral fellow (“borsista di ricerca) at the University of Turin, former Postdoctoral Fellow on the project “Early Diagnosis – Handling Knowing”, University of St Andrews and Emilia Wilson, Lecturer in philosophy, School of English, Communication and Philosophy, Cardiff University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
