Abstract
Background: Polygenic risk scores (PRS) have emerged as valuable tools in precision medicine by enabling the estimation of genetic predisposition to complex diseases. Derived from genome-wide association studies (GWAS), PRS aggregate the effects of multiple genetic variants to enhance disease risk stratification and support personalised healthcare. Despite their promise, clinical translation remains inconsistent, hindered by methodological, population, and equity challenges. Objective: This review systematically evaluates current evidence on the application, performance, and limitations of PRS in precision medicine. Methodology: A systematic literature search was conducted in PubMed, Scopus, and Web of Science for studies published between 2015 and 2024. Eligible studies included original research and systematic reviews assessing PRS construction, validation, and clinical utility across human disease contexts. Data were extracted on study design, disease domain, performance metrics, and ancestry representation. Study quality was appraised using the Newcastle-Ottawa Scale and AMSTAR 2 tools. Results: Ninety-two studies met the inclusion criteria. PRS showed moderate-to-high predictive performance (AUC 0.60–0.85) for diseases such as coronary artery disease, breast cancer, and type 2 diabetes, especially when combined with clinical risk factors. However, predictive accuracy was substantially lower in non-European populations. Fewer than 15% of studies included diverse ancestries, and methodological heterogeneity limited generalisability and clinical adoption. Conclusion: PRS holds significant potential to enhance risk prediction and preventive strategies in precision medicine. Nevertheless, disparities in population representation, lack of standardisation, and limited outcome-based studies currently constrain clinical translation. Greater emphasis on diverse genomic studies, harmonised methodologies, and real-world validation is needed.
References
1. Lewis CM, Vassos E. Polygenic risk scores: from research tools to clinical instruments. Genome Med. 2020;12(1):44.
2. Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–209.
3. Kurki MI, Karjalainen J, Palta P, et al. FinnGen: Unique genetic insights from combining isolated population and national health register data. medRxiv. 2022.
4. Mavaddat N, Michailidou K, Dennis J, et al. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. Am J Hum Genet. 2019;104(1):21–34.
5. Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nat Rev Genet. 2018;19(9):581–590.
6. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.
7. Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.
8. Wells GA, Shea B, O’Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 2014.
9. Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: A critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ. 2017;358:j4008.
10. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584–591.
11. Lambert SA, Gil L, Jupp S, et al. The Polygenic Score Catalogue: An open database for reproducibility and systematic evaluation. Nat Genet. 2021;53(4):420–425.
12. Inouye M, Abraham G, Nelson CP, et al. Genomic risk prediction of coronary artery disease in 480,000 adults. J Am Coll Cardiol. 2018;72(16):1883–1893.
13. Mavaddat N, Michailidou K, Dennis J, et al. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. Am J Hum Genet. 2019;104(1):21–34.
14. Läll K, Mägi R, Morris A, Metspalu A, Fischer K. Personalised risk prediction for type 2 diabetes: the potential of genetic risk scores. Genet Med. 2017;19(3):322–329.
15. Khera AV, Chaffin M, Aragam KG, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–1224.
16. Duncan L, Shen H, Gelaye B, et al. Analysis of polygenic risk score usage and performance in diverse human populations. Nat Commun. 2019;10(1):3328.
17. Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nat Rev Genet. 2018;19(9):581–590.
18. Lewis CM, Vassos E. Prospects for using risk scores in polygenic medicine. Genome Med. 2020;12(1):44.
19. Khera AV, Emdin CA, Drake I, et al. Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease. N Engl J Med. 2016;375(24):2349–2358.
20. Lambert SA, Gil L, Jupp S, et al. The Polygenic Score Catalog: An open database for reproducibility and systematic evaluation. Nat Genet. 2021;53(4):420–425.
21. Mars N, Koskela JT, Ripatti P, et al. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat Med. 2020;26(4):549–557.
22. Peterson RE, Kuchenbaecker K, Walters RK, et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell. 2019;179(3):589–603.
23. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584–591.
24. Allyse M, Robinson DH, Ferber MJ, Sharp RR. Direct-to-consumer testing 2.0: Emerging models of direct-to-consumer genetic testing. Mayo Clin Proc. 2018;93(1):113–120.
25. Torkamani A, Wineinger NE, Topol EJ. The personal and clinical utility of polygenic risk scores. Nat Rev Genet. 2018;19(9):581–590.
26. Lewis CM, Vassos E. Polygenic risk scores: from research tools to clinical instruments. Genome Med. 2020;12(1): 44.
27. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019; 51(4): 584 – 591.
28. Lambert SA, Gil L, Jupp S, et al. The Polygenic Score Catalogue: An open database for reproducibility and systematic evaluation. Nat Genet. 2021; 53(4): 420 – 425.
29. Mars N, Koskela JT, Ripatti P, et al. Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers. Nat Med. 2020; 26(4): 549 – 557.
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Copyright (c) 2025 Yohanna Usman, Ekwere Okon, Rosaleen Mcneil, Mohammed Bello, Amina Wazhi, Cecilia Edeh, Francis Shinku, Nanmwa Nden, Philip Nyango, Ali Shugaba