Polygenic Risk Scores: Advancements, Challenges and Future Directions in Precision Medicine

Kaname Zenke^*
*Correspondence: Kaname Zenke, Department of Hematology, Saga University, Saga, Japan, Email:

Introduction

Polygenic Risk Scores (PRS) have emerged as a groundbreaking tool in the field of genomics, offering the potential to significantly enhance our understanding of complex traits and diseases, while also paving the way for personalized medicine. These scores aggregate the cumulative effect of numerous genetic variants to provide a quantifiable measure of an individualâ??s genetic predisposition to a wide range of health conditions, including cardiovascular disease, diabetes, cancer, and mental health disorders. By synthesizing the small contributions of many genetic variants across the genome, PRS offer a more comprehensive prediction of disease risk compared to the traditional focus on single genetic variants. The potential applications of PRS in healthcare are vast, ranging from early disease prediction and prevention to targeted therapeutic interventions. However, despite their promise, the integration of PRS into clinical practice is fraught with challenges, including issues related to accuracy, generalizability across populations, and the complex interaction between genetics and environmental factors. As the field of genomics continues to evolve, it is essential to address these challenges to fully realize the potential of PRS in precision medicine [1].

Description

Polygenic Risk Scores have their origins in Genome-Wide Association Studies (GWAS), which have become an integral part of genetic research. GWAS have identified thousands of genetic variants associated with a wide variety of complex traits and diseases. However, each individual genetic variant identified by GWAS typically has a very small effect on the disease, making it difficult to predict disease risk based on any single variant alone. PRS solve this problem by aggregating the effects of multiple genetic variants, thereby providing a more comprehensive measure of an individual’s genetic risk. The methodology behind PRS involves summing the weighted effects of genetic variants across the genome, with each variant’s contribution to the score determined by its effect size as identified in GWAS. In this way, PRS allow for the quantification of genetic risk in a manner that can be applied to a wide variety of diseases and traits [2].

One of the most exciting aspects of PRS is their potential to transform precision medicine. By providing a personalized measure of genetic risk, PRS could enable healthcare providers to tailor prevention and treatment strategies to the individual’s genetic profile. For example, a person with a high polygenic risk score for cardiovascular disease might be more closely monitored for early signs of the disease or receive targeted interventions to reduce risk factors such as blood pressure or cholesterol levels. Similarly, in the context of cancer, PRS could help identify individuals who are at higher genetic risk for specific types of cancer, allowing for earlier and more frequent screenings. The ability to integrate genetic information into clinical practice could also enhance our understanding of complex diseases, revealing new insights into disease mechanisms and providing a foundation for the development of novel therapies [3].

Despite the potential benefits, the widespread implementation of PRS in clinical practice is not without its challenges. One of the key issues is the accuracy of PRS predictions. While PRS have shown promise in predicting disease risk, they are not perfect, and their predictive power can vary significantly depending on the trait in question. In some cases, PRS may explain a substantial portion of the heritable variation in a trait, while in other cases, they may only account for a small fraction of the total risk. For example, PRS for diseases such as type 2 diabetes and cardiovascular disease have shown moderate predictive accuracy, whereas PRS for complex traits like mental health disorders or certain cancers may be less predictive. The accuracy of PRS is influenced by several factors, including the quality and size of the underlying GWAS data, the number of variants included in the score, and the heritability of the trait. Traits with higher heritability tend to have more robust PRS, as the genetic contribution to these traits is stronger and more easily captured by the score [4].

Another major challenge in the use of PRS is their generalizability across diverse populations. Most GWAS have been conducted in populations of European ancestry, which means that the genetic variants identified in these studies may not be as relevant or informative for individuals of other ethnic backgrounds. There is growing recognition that the lack of diversity in genetic research limits the applicability of PRS, as genetic risk factors can differ significantly between populations. For example, certain genetic variants associated with diseases like hypertension or diabetes may be more prevalent in African or Asian populations than in individuals of European descent. To address this issue, there is an increasing push for more inclusive and diverse genetic research, including studies that focus on non-European populations. This is critical for ensuring that PRS are accurate and applicable to individuals from all ethnic backgrounds. Additionally, efforts are underway to develop multi-ethnic polygenic risk scores that incorporate genetic data from diverse populations, improving the accuracy and generalizability of the scores across different groups [5].

Conclusion

In conclusion, polygenic risk scores have the potential to revolutionize precision medicine by providing a personalized, data-driven approach to predicting disease risk and tailoring prevention strategies. However, there are several challenges that must be addressed before PRS can be widely integrated into clinical practice. These include improving the accuracy and generalizability of PRS, accounting for gene-environment interactions, and addressing ethical concerns related to privacy and discrimination. Despite these challenges, ongoing advancements in genomic research, data integration, and statistical modeling hold great promise for improving the utility of PRS in healthcare. As our understanding of the genetic architecture of complex diseases continues to evolve, polygenic risk scores are likely to play an increasingly important role in helping to predict disease risk, guide personalized interventions, and ultimately improve population health outcomes. The future of PRS in precision medicine is exciting, and as the field advances, we can expect to see a greater integration of genetic risk information into clinical decision-making, offering the potential for more effective and personalized healthcare.

Acknowledgment

None.

Conflict of Interest

None.

References

1. Kannel, William B., Thomas R. Dawber, Abraham Kagan and Nicholas Revotskie, et al. "Factors of risk in the development of coronary heart disease- six-year follow-up experience: the Framingham Study." Ann Intern Med 55 (1961): 33-50.

Google Scholar Cross Ref Indexed at

2. Cooney, Marie Therese, Alexandra L. Dudina and Ian M. Graham. "Value and limitations of existing scores for the assessment of cardiovascular risk: A review for clinicians." J Am Coll Cardiol 54 (2009): 1209-1227.

Google Scholar Cross Ref Indexed at

3. de La Harpe, Roxane, Christian W. Thorball, Claire Redin and Stephane Fournier, et al. "Combining European and US risk prediction models with polygenic risk scores to refine cardiovascular prevention: The CoLaus| PsyCoLaus Study." Eur J Prev Cardiol 30 (2023): 561-571.

Google Scholar Cross Ref Indexed at

4. Wei, Xiaoxia, Dianjianyi Sun, Jiaxin Gao and Jing Zhang, et al. "Development and evaluation of a polygenic risk score for lung cancer in never�smoking women: A large�scale prospective Chinese cohort study." Int J Cancer154 (2024): 807-815.

Google Scholar Cross Ref

5. Chen, Fei, Ravi K. Madduri, Alex A. Rodriguez and Burcu F. Darst, et al. "Evidence of novel susceptibility variants for prostate cancer and a multiancestry polygenic risk score associated with aggressive disease in men of African ancestry." Eur Urol 84 (2023): 13-21.

Google Scholar Cross Ref Indexed at