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Implementation of any new personalised health intervention requires more than evidence of clinical benefit for patients. Cost-effectiveness is one of the deciding factors in any new or improved clinical pathway.
Polygenic scores are no different. A polygenic score combines multiple genetic variants across the genome, each of which make a small contribution to risk of developing disease, into a single score. Combining genetic, environmental and lifestyle risk factors into a comprehensive risk model could be a powerful tool for refining screening and maximising health resource. The evidence required for determining the cost-effectiveness of polygenic scores has been unclear and recent studies aim to provide direction and better decision making for health policy.
Are polygenic scores cost-effective?
Cost-utility studies provide critical information for the costs and benefits of using any new test, typically building on existing public health and screening programmes, within a defined population. This analysis can tell health policy makers where the greatest value from these novel interventions is likely to be found to inform decision making.
Cost-effectiveness studies of polygenic scores in different healthcare settings are emerging in the literature. A recent systematic review of economic evaluations of polygenic scores explored the current evidence for implementation of polygenic scores in healthcare for different diseases. This review identified 24 cost-utility analyses of mostly high quality. The robust studies identified indicated a positive trend towards cost-effectiveness.
These addressed different disease contexts: 16 studies focused on cancer, five on cardiovascular diseases and three on other diseases. The studies focused on cancer mostly aimed to optimise screening, whereas the studies of other diseases most often looked at refining eligibility for preventative therapies. None of the identified studies explored the opportunity of polygenic scores to determine disease aggressiveness, although there is literature to support this and it may have value in some disease contexts.
This study had a number of interesting findings that should inform how we think about cost-effectiveness of polygenic scores going forward:
- What to count: it is not solely costs associated with the polygenic score test. Attention must also be given to delivery models, implementation and the potential socioeconomic impacts – such as increased anxiety or false reassurance from negative results.
- Real-world evidence: use of modelling can inform study design, but this is not a replacement for feasibility studies or randomised controlled trials to understand how implementation plays out in practice.
- On uptake: studies tended to assume 100% uptake and yet we know individual concerns – data privacy for one – and inequities in access to healthcare make this a lofty ambition.
- Shifting classification: polygenic scores are a new technology and as it evolves, individuals are likely to be reclassified above or below the risk threshold with implications for patients. Greater clarity is needed about how changes in risk clarifications affect individuals, including continuation of enhanced screening or preventative interventions, and best practice on how to communicate this uncertainty.
- Long-term gains: Studies often focused on a narrow definition of clinical benefit, but there are other long-term health and non-health benefits – particularly from encouraging lifestyle changes – best informed by real-world evidence.
Our work on polygenic scores
The pathway to implementing polygenic scores is far from straightforward. In the PHG Foundation report, ‘Evaluation of polygenic scores applications,’, we took a deep dive into medical test evaluation. Debate around how to determine clinical utility of polygenic scores is ongoing and is critical to achieve consensus on how to progress the field.
We have previously highlighted how researchers are exploring the use of polygenic scores in screening for cancer and cardiovascular disease. In this work, we provide much needed context of how the research into common genetic risk can be captured as a polygenic score and used within these disease groups.
We have distilled information in a series of briefing notes that provide a clear and concise understanding of fundamental issues around polygenic scores and potential ways to resolve them.
So, what does this mean?
Building the case for implementation of polygenic scores requires a thorough understanding of how they perform as a test in practice. This recent cost effectiveness review is a moment to reflect on what is the best study design, if cost-effectiveness is to be determined and how this contributes to demonstrating clinical utility. Certainly, hypothetical cohorts and simulations are not a replacement for real-world evidence.
Researchers are increasingly able to articulate how scientific and model validity can be established. And yet, gaps remain in the evidence that address how polygenic scores will be used as a test in clinical practice. This context is critical to assess performance and value for patients in healthcare. Ultimately, we need to be open about what evidence is needed to address these gaps. In a resource constrained health service, healthcare staff, public and patients deserve to know that these promised benefits reflect reality.
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