Polygenic risk scores enable clinicians and researchers to stratify patient populations by genetic risk, informing decisions on screening schedules, therapeutic selection, and preventive care before disease onset.
Most common diseases do not trace back to a single gene. Conditions like cancer, heart disease, and diabetes are polygenic, shaped by the cumulative effects of many thousands of small genetic changes that vary between individuals. This makes them fundamentally different from monogenic conditions like cystic fibrosis or Huntington's disease, where a single mutation is the primary driver.
When the Human Genome Project was completed, there was significant optimism that mapping the genome would reveal straightforward genetic causes for widespread conditions. Instead, researchers found that common diseases are influenced by the combined effects of many variants, each contributing a small amount of risk. Polygenic risk scores (PRS) emerged as a way to aggregate these individual effects into a single, interpretable measure of genetic predisposition.
Following the success of genome-wide association studies (GWAS) in identifying variants associated with disease, PRS provide a method for combining all the relevant variants an individual carries into one risk score. In this article, we explore the utility of PRS, drawing on research in personalized preventive care and treatment, including therapeutic intervention, early disease screening, and long-term healthcare planning.
Despite the recent growth of interest, polygenic risk scores are not a new concept. The first research exploring the predictive ability of genetic risk scores was published by B. Horne et al. in 2005. In 2013, N Chatterjee et al. projected that the "utility of future polygenic models will depend on achievable sample sizes, underlying genetic architecture and information on other risk-factors, including family history."
Since then, the scale of genomic studies and the sophistication of scoring algorithms have advanced considerably. PRS are now routinely applied across biomedical research. However, it is important to note that current PRS typically explain only a fraction of trait variance. Their value lies in correlating with genetic liability, the single largest contributor to phenotypic variation, rather than in providing deterministic predictions.
At its simplest, a polygenic risk score is a single number that summarizes the estimated effect of many genetic variants on an individual's likelihood of developing a particular condition. The term "polygenic risk score" (PRS) is used most often in the context of disease risk, though the broader concept is sometimes referred to as a polygenic score (PGS) or polygenic index (PGI).
Genome-wide association studies (GWAS) provide the foundational data for calculating polygenic risk scores. In a GWAS, researchers compare the genomes of thousands of individuals who have a disease against a control group to identify common genetic variants associated with the trait of interest.
Once variants are identified, each is assessed for the strength of its association with the disease and assigned a weight based on its measured effect. These weights are then combined into a scoring algorithm that sums the contributions of all relevant variants an individual carries. The resulting PRS provides insight into that person's relative genetic risk compared with the broader study population.
Running GWAS across diverse populations remains a priority for the research community. Research by the Broad Institute stresses the importance of diverse studies to develop "clinically meaningful risk predictors." The frequency of genetic variants differs between populations, which means a scoring algorithm developed primarily from data of European descent performs poorly when applied to individuals of other ancestries.
This has direct implications for how PRS are used in clinical research and trial design. If risk scores are not validated across the populations a study aims to enroll, the resulting stratification may be inaccurate, leading to misclassification of patients and unreliable eligibility assessments. Building more representative genomic datasets is essential for PRS to deliver equitable and clinically meaningful predictions across populations.
PRS enable the stratification of a population into those at high risk, moderate risk, or low risk for different conditions. Beyond clinical risk stratification, PRS are also used in research to assess shared etiology between phenotypes, evaluate the clinical utility of genetic data, and compare experimental outcomes between individuals with low and high PRS values.
In clinical and translational settings, researchers are applying this stratification in three key ways: to improve therapeutic intervention, inform early detection strategies, and support better lifestyle and healthcare decisions.
In conditions such as cancer, schizophrenia, and coronary artery disease, PRS have been shown to improve therapeutic intervention by identifying patients who stand to benefit most from specific treatments.
For example, Natarajan et al. found that "those at high genetic risk have a greater burden of subclinical atherosclerosis and derive greater relative and absolute benefit from statin therapy to prevent a first coronary heart disease event." Individuals in the top quintile of the polygenic risk score face a 30% increased risk for a coronary event but achieve a 46% relative risk reduction with preventative statin therapy. Those at intermediate risk (second to fourth quintiles) achieve a 26% relative risk reduction.
This type of finding illustrates a broader principle: PRS can help identify patients whose genetic risk profile means they are more likely to benefit from early or more aggressive treatment, even when traditional clinical indicators do not flag them as high risk.
Researchers have demonstrated the utility of PRS to make risk-based recommendations for disease screening, rather than relying on traditional age-based thresholds. This is particularly relevant for conditions like breast and prostate cancer, where screening guidelines are typically tied to age rather than individual genetic risk.
For example, the US Preventive Services Task Force guidelines recommend biennial screening mammography for women aged 50 to 74 years, unless an individual risk assessment indicates screening should take place earlier. P. Maas et al. developed a risk-based model, combining PRS with other clinical risk factors, to stratify a population of white women in the United States. The model identified 16% of the population with a genetic risk higher than an average 50-year-old, suggesting they would benefit from early screening starting at age 40. Conversely, 32% of the population could delay screening, as their disease risk at 50 was lower than that of an average 40-year-old.
As risk scores become integrated into clinical practice, PRS can help high-risk individuals make more informed lifestyle decisions. For example, across four studies involving 55,685 participants, people with a high genetic risk were able to offset their relative risk of coronary artery disease by nearly 50% by following a healthy lifestyle. This demonstrates that genetic risk is not deterministic; it can be meaningfully modified by behavior.
PRS also show potential for predicting the age of disease onset, supporting long-term healthcare and financial planning. R. Desikan et al. developed a PRS to classify populations into subgroups based on the age of Alzheimer's disease (AD) onset. Individuals in the top quartile had an average onset age of 75 years, compared to 95 years for those in the lowest quartile.
As the authors note, "from a disease management perspective, by providing an accurate probabilistic assessment regarding the likelihood of AD neurodegeneration, determining a genomic profile of AD may help initiate a dialogue on future planning. Finally, a similar genetic epidemiology framework may be useful for quantifying the risk associated with numerous other common diseases."
PRS are moving from research tools toward clinical application, particularly in well-studied conditions. For example, one study aims to demonstrate the actionability of risk scores for adjusting and stratifying screening recommendations in breast cancer. This reflects a broader shift toward integrating genetic risk into clinical decision-making.
However, several challenges remain before PRS can be reliably deployed at scale. As noted in Nature Protocols, despite the growing application of PRS, there are limited standardized guidelines for performing these analyses, which can lead to inconsistency between studies and misinterpretation of results. Key open questions include:
For precision medicine trials, these developments carry direct implications. PRS-based stratification can support more targeted patient identification, more refined eligibility criteria, and more efficient enrollment pathways. As scoring models improve and validation datasets expand, PRS are positioned to become a core component of trial design and patient stratification, particularly for common diseases where genetic architecture is polygenic and traditional biomarker-based approaches are insufficient.