The Polygenic Risk Score in Clinical Pharmacy: From Genomic Architecture to Precision Drug Therapy: A Narrative Review
Main Article Content
Abstract
Background: Precision medicine increasingly relies on polygenic risk scores (PRS) to predict drug efficacy, safety, and therapeutic variability. Traditional monogenic pharmacogenetics, focused on individual gene variants, explains only 20-40% of drug response variation, leaving substantial "missing heritability." Polygenic models aggregating thousands of genome-wide variants offer improved prediction, yet clinical implementation in pharmacy practice remains inconsistent and incomplete. Objective: This narrative review synthesizes evidence on PRS methodologies, clinical applications across therapeutic domains, the pharmacist's role in precision drug therapy, barriers to adoption, and emerging technologies, to provide a comprehensive synthesis for pharmacy educators, practitioners, and health system leaders. Methods: A targeted literature search of PubMed, Google Scholar, and bioRxiv (2010-2025) identified 90 peer-reviewed articles addressing PRS genomic principles, calculation and validation methodologies, clinical applications in cardiovascular (statins, clopidogrel), psychiatric (antidepressants, antipsychotics), and endocrine (metformin) pharmacotherapy, pharmacy implementation frameworks, and ethical and regulatory considerations. Articles were selected based on methodological rigor, applicability to clinical pharmacy practice, and contribution to understanding of PRS mechanisms, validation, or implementation. Results: Polygenic models capture cumulative genetic burden and demonstrate moderate to good predictive accuracy across therapeutic domains (AUC 0.60-0.75), exceeding monogenic approaches. Clinical applications are emerging but largely research-stage; few pragmatic trials assess PRS-guided pharmacotherapy effectiveness versus standard care. Pharmacists' competencies as genomic consultants are well-articulated conceptually but underdeveloped operationally. Major barriers include economic constraints (cost of testing, lack of reimbursement), educational gaps (insufficient pharmacist training), methodological concerns (ancestry bias, limited population-specific models), and incomplete regulatory frameworks and implementation infrastructure. Literature distribution shows emphasis on clinical applications (31%) and PRS methodology (24%), with limited evidence on pharmacy implementation (20%) or health economic impact. Conclusion: PRS represent a paradigm shift toward precision pharmacotherapy but require systematic workforce development, health system investment, policy reform, and implementation science evidence to realize clinical and equitable impact
Article Details
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
1. Lu AY. Drug-metabolism research challenges in the new millennium: individual variability in drug therapy and drug safety. Drug Metab Dispos. 1998;26(12):1217-1222.
2. Visscher PM, Yengo L, Cox NJ, Wray NR. Discovery and implications of polygenicity of common diseases. Science. 2021;373(6562):1468-1473.
3. Ioannidis JP, Trikalinos TA, Khoury MJ. Implications of small effect sizes of individual genetic variants on the design and interpretation of genetic association studies of complex diseases. Am J Epidemiol. 2006;164(7):609-614.
4. Wray NR, Lin T, Austin J, McGrath JJ, Hickie IB, Murray GK, et al. From basic science to clinical application of polygenic risk scores: a primer. JAMA Psychiatry. 2021;78(1):101-109.
5. Stojanovska M. Pharmacogenomics Unleashed: Navigating the Intersection of DNA and Drug Therapies. In: Advances in Forensic Biology and DNA Typing. CRC Press; 2022. p. 305-323.
6. Hartl L, Fink MA, Beer-Hammer S. Pharmacology and Drug Targets–The Basis of Therapeutics. In: Principles of Biomedical Sciences and Industry: Translating Ideas into Treatments. 2022. p. 7-35.
7. Khoury MJ, Wagener DK. Epidemiological evaluation of the use of genetics to improve the predictive value of disease risk factors. Am J Hum Genet. 1995;56(4):835.
8. Johansson Å, Andreassen OA, Brunak S, Franks PW, Hedman H, Loos RJ, et al. Precision medicine in complex diseases—Molecular subgrouping for improved prediction and treatment stratification. J Intern Med. 2023;294(4):378-396.
9. Fusar-Poli L, Rutten BP, van Os J, Aguglia E, Guloksuz S. Polygenic risk scores for predicting outcomes and treatment response in psychiatry: hope or hype? Int Rev Psychiatry. 2022;34(7-8):663-675.
10. ARNAL SEGURA M. Machine learning methods applied to classify complex diseases using genomic data. 2024.
11. Martínez-Minguet D, Noel R, Simón AG, Pastor Ó. Challenges in clinical translation of polygenic risk score analyses: A systematic review. Genet Med. 2025:101662.
12. D'Souza A. The effectiveness of clinical nurse educators in the teaching of treatment and pharmacology.
13. Fortunate AA. Ethical, Legal, and Social Implications of Polygenic Risk Scores in Primary Care: Consent, Governance, and Trust.
14. Keat K. Advancing the Discovery and Implementation of Pharmacogenomics Using Genomic Biobanks and Artificial Intelligence. University of Pennsylvania; 2025.
15. Logsdon GA, Ebert P, Audano PA, Loftus M, Porubsky D, Ebler J, et al. Complex genetic variation in nearly complete human genomes. Nature. 2025;644(8076):430-441.
16. Piet G, Grundlehner A, Jongbloed R, Tamis J, de Vries P. SCAIRM: A spatial cumulative assessment of impact risk for management. Ecol Indic. 2023;157:111157.
17. Sha Y, Yu S, Cai Z, Zhang Z, Song K, Yang L, et al. Genome-Wide Analysis and Characterization of CYP450 Gene Family in Tobacco (Nicotiana tabacum L). Plant Mol Biol Rep. 2026;44(2):52.
18. Cai J, Yang Z, Zhao S, Ke X. Associations of dichlorophenol with metabolic syndrome based on multivariate-adjusted logistic regression: a US nationwide population-based study 2003-2016. Environ Health. 2023;22(1):88.
19. Wainschtein P, Zhang Y, Schwartzentruber J, Kassam I, Sidorenko J, Fiziev PP, et al. Estimation and mapping of the missing heritability of human phenotypes. Nature. 2026;649(8099):1219-1227.
20. Badwan BA, Liaropoulos G, Kyrodimos E, Skaltsas D, Tsirigos A, Gorgoulis VG. Machine learning approaches to predict drug efficacy and toxicity in oncology. Cell Rep Methods. 2023;3(2).
21. Grešová K, Martinek V, Čechák D, Šimeček P, Alexiou P. Genomic benchmarks: a collection of datasets for genomic sequence classification. BMC Genomic Data. 2023;24(1):25.
22. Rassmann S, Abashishvili L, Melikidze E, Sukhiashvili A, Lartsuliani M, Chkhaidze I, et al. Population-specific calibration and validation of an open-source bone age AI. Sci Rep. 2025;15(1):32673.
23. Arel-Bundock V, Greifer N, Heiss A. How to interpret statistical models using marginaleffects for R and Python. J Stat Softw. 2024;111:1-32.
24. Fahim YA, Hasani IW, Kabba S, Ragab WM. Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives. Eur J Med Res. 2025;30(1):848.
25. Hossam Abdelmonem B, Abdelaal NM, Anwer EK, Rashwan AA, Hussein MA, Ahmed YF, et al. Decoding the role of CYP450 enzymes in metabolism and disease: a comprehensive review. Biomedicines. 2024;12(7):1467.
26. OGIDI O, IYONSI OF. BIOCHEMISTRY OF DRUG METABOLISM: A REVIEW. Quantum J Med Health Sci. 2025;4(4):16-34.
27. Siemens A, Anderson SJ, Rassekh SR, Ross CJ, Carleton BC. A systematic review of polygenic models for predicting drug outcomes. J Pers Med. 2022;12(9):1394.
28. Bhavani G, Subbulakshmi P. Basics of pharmacology: drugs, mechanisms, and effects. Academic Guru Publishing House; 2025.
29. Chaudhary U. Neuromodulation in neural circuits: neurotransmitters and synaptic plasticity. In: Expanding Senses using Neurotechnology: Volume 1–Foundation of Brain-Computer Interface Technology. Springer; 2025. p. 225-245.
30. Weiner DJ, Nadig A, Jagadeesh KA, Dey KK, Neale BM, Robinson EB, et al. Polygenic architecture of rare coding variation across 394,783 exomes. Nature. 2023;614(7948):492-499.
31. Verbeurgt P, Mamiya T, Oesterheld J. How common are drug and gene interactions? Prevalence in a sample of 1143 patients with CYP2C9, CYP2C19 and CYP2D6 genotyping. Pharmacogenomics. 2014;15(5):655-665.
32. Boy GA. Human-centered design of complex systems: An experience-based approach. Des Sci. 2017;3:e8.
33. Ebstein RP, Zhong S, Chark R, San Lai P, Chew SH. Genetics of Social Cognition in the Laboratory: Definition, Measurement, and.
34. Zhao M, Ma J, Li M, Zhang Y, Jiang B, Zhao X, et al. Cytochrome P450 enzymes and drug metabolism in humans. Int J Mol Sci. 2021;22(23):12808.
35. Choi SW, Mak TS-H, O'Reilly PF. Tutorial: a guide to performing polygenic risk score analyses. Nat Protoc. 2020;15(9):2759-2772.
36. Goldberg TE, Weinberger DR. Genes and the parsing of cognitive processes. Trends Cogn Sci. 2004;8(7):325-335.
37. Impellizzeri FM, Shrier I, McLaren SJ, Coutts AJ, McCall A, Slattery K, et al. Understanding training load as exposure and dose. Sports Med. 2023;53(9):1667-1679.
38. Wang RC, Wang Z. Precision medicine: disease subtyping and tailored treatment. Cancers. 2023;15(15):3837.
39. Cho T, Uetrecht J. How reactive metabolites induce an immune response that sometimes leads to an idiosyncratic drug reaction. Chem Res Toxicol. 2017;30(1):295-314.
40. Malki MA, Pearson ER. Drug–drug–gene interactions and adverse drug reactions. Pharmacogenomics J. 2020;20(3):355-366.
41. Hernaez R. Reliability and agreement studies: a guide for clinical investigators. Gut. 2015;64(7):1018-1027.
42. Ahern J, Thompson W, Fan CC, Loughnan R. Comparing pruning and thresholding with continuous shrinkage polygenic score methods in a large sample of ancestrally diverse adolescents from the ABCD Study®. Behav Genet. 2023;53(3):292-309.
43. Cantor RM, Lange K, Sinsheimer JS. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet. 2010;86(1):6-22.
44. Meuwissen T, Hayes B, MacLeod I, Goddard M. Identification of genomic variants causing variation in quantitative traits: A review. Agriculture. 2022;12(10):1713.
45. Smith AFM. Bayesian computational methods. Philos Trans R Soc Lond A. 1991;337(1647):369-386.
46. Saitou M, Dahl A, Wang Q, Liu X. Allele frequency differences of causal variants have a major impact on low cross-ancestry portability of PRS. medRxiv. 2022:2022.10.21.22281371.
47. Elgart M, Lyons G, Romero-Brufau S, Kurniansyah N, Brody JA, Guo X, et al. Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations. Commun Biol. 2022;5(1):856.
48. Pomales-Matos DA, Lyerly M, Rivera-Madera A, Echevarría-Bonilla OL, Álvarez-Cortés M, Henriquez-Quiñones SE, et al. Ancestry gaps in cardiovascular GWAS: a multi-database review of African representation in genomic studies. Front Genet. 2026;16:1647176.
49. Ding Y, Hou K, Burch KS, Lapinska S, Privé F, Vilhjálmsson B, et al. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat Genet. 2022;54(1):30-39.
50. Gibson G. On the utilization of polygenic risk scores for therapeutic targeting. PLoS Genet. 2019;15(4):e1008060.
51. Wand H, Lambert SA, Tamburro C, Iacocca MA, O'Sullivan JW, Sillari C, et al. Improving reporting standards for polygenic scores in risk prediction studies. Nature. 2021;591(7849):211-219.
52. Cross B, Turner R, Pirmohamed M. Polygenic risk scores: An overview from bench to bedside for personalised medicine. Front Genet. 2022;13:1000667.
53. Murray GK, Lin T, Austin J, McGrath JJ, Hickie IB, Wray NR. Could polygenic risk scores be useful in psychiatry? A review. JAMA Psychiatry. 2021;78(2):210-219.
54. Bai B, Liang J, Zhang G, Li H, Bai K, Wang F, editors. Why attentions may not be interpretable? Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining; 2021.
55. Speed D, Balding DJ. MultiBLUP: improved SNP-based prediction for complex traits. Genome Res. 2014;24(9):1550-1557.
56. Keys KL, Mak AC, White MJ, Eckalbar WL, Dahl AW, Mefford J, et al. On the cross-population generalizability of gene expression prediction models. PLoS Genet. 2020;16(8):e1008927.
57. Collister JA, Liu X, Clifton L. Calculating polygenic risk scores (PRS) in UK Biobank: a practical guide for epidemiologists. Front Genet. 2022;13:818574.
58. Linden A. Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. J Eval Clin Pract. 2006;12(2):132-139.
59. Atri P. Enhancing the reliability and accuracy of data pipelines through effective testing and validation strategies: A comprehensive approach. Eur J Adv Eng Technol. 2023;10(9):52-56.
60. Roden DM, Altman RB, Benowitz NL, Flockhart DA, Giacomini KM, Johnson JA, et al. Pharmacogenomics: challenges and opportunities. Ann Intern Med. 2006;145(10):749-757.
61. Vinci P, Panizon E, Tosoni LM, Cerrato C, Pellicori F, Mearelli F, et al. Statin-associated myopathy: emphasis on mechanisms and targeted therapy. Int J Mol Sci. 2021;22(21):11687.
62. Lopez J, Mark J, Duarte GJ, Shaban M, Sosa F, Mishra R, et al. Role of genetic polymorphisms in clopidogrel response variability: a systematic review. Open Heart. 2023;10(2).
63. Vasiliu O. The pharmacogenetics of the new-generation antipsychotics–A scoping review focused on patients with severe psychiatric disorders. Front Psychiatry. 2023;14:1124796.
64. Suleman N. Current understanding on Retinitis Pigmentosa: a literature review. Front Ophthalmol. 2025;5:1600283.
65. Tantray J, Patel A, Wani SN, Kosey S, Prajapati BG. Prescription precision: a comprehensive review of intelligent prescription systems. Curr Pharm Des. 2024;30(34):2671-2684.
66. Zhou K, Pedersen HK, Dawed AY, Pearson ER. Pharmacogenomics in diabetes mellitus: insights into drug action and drug discovery. Nat Rev Endocrinol. 2016;12(6):337-346.
67. Elsayed HH, Saleh RT. Review of: nutritional genomics and precision nutrition. Bull Natl Nutr Inst Arab Rep Egyp. 2024;64(2):204-234.
68. Singh O, Verma M, Dahiya N, Senapati S, Kakkar R, Kalra S. Integrating polygenic risk scores (PRS) for personalized diabetes care: advancing clinical practice with tailored pharmacological approaches. Diabetes Ther. 2025;16(2):149-168.
69. Minich DM, Bland JS. Personalized lifestyle medicine: relevance for nutrition and lifestyle recommendations. Sci World J. 2013;2013(1):129841.
70. Marques L, Costa B, Pereira M, Silva A, Santos J, Saldanha L, et al. Advancing precision medicine: a review of innovative in silico approaches for drug development, clinical pharmacology and personalized healthcare. Pharmaceutics. 2024;16(3):332.
71. Squassina A, Manchia M, Manolopoulos VG, Artac M, Lappa-Manakou C, Karkabouna S, et al. Realities and expectations of pharmacogenomics and personalized medicine: impact of translating genetic knowledge into clinical practice. Pharmacogenomics. 2010;11(8):1149-1167.
72. Shah RR. Pharmacogenetics in drug regulation: promise, potential and pitfalls. Philos Trans R Soc B. 2005;360(1460):1617-1638.
73. Brown JT, Goldwire M, Massmann A, Van Heukelom J, Pasternak A, Petry N, et al. The impact of pharmacogenomics on a pharmacy and therapeutic committee's formulary system management: An opinion of the pharmacokinetics/pharmacodynamics/pharmacogenomics practice and Research Network for the American College of Clinical Pharmacy. J Am Coll Clin Pharm. 2025;8(1):52-61.
74. Laing S, Mercer J. Improved preventive care clinical decision-making efficiency: leveraging a point-of-care clinical decision support system. BMC Med Inform Decis Mak. 2021;21(1):315.
75. Khude H, Shende P. AI-driven clinical decision support systems: Revolutionizing medication selection and personalized drug therapy. Adv Integr Med. 2025;12(4):100529.
76. Singh S, Stocco G, Theken KN, Dickson A, Feng Q, Karnes JH, et al. Pharmacogenomics polygenic risk score: Ready or not for prime time? Clin Transl Sci. 2024;17(8):e13893.
77. ALHAMDI M. Pharmacy Practice and Clinical Skills: Enhancing Patient-Centered Care.
78. Ali H. Artificial intelligence in multi-omics data integration: Advancing precision medicine, biomarker discovery and genomic-driven disease interventions. Int J Sci Res Arch. 2023;8(1):1012-1030.
79. Kullo IJ. Clinical use of polygenic risk scores: current status, barriers and future directions. Nat Rev Genet. 2025:1-18.
80. Guttmacher AE, Porteous ME, McInerney JD. Educating health-care professionals about genetics and genomics. Nat Rev Genet. 2007;8(2):151-157.
81. Feero WG, Kuo GM, Jenkins JF, Rackover MA. Pharmacist education in the era of genomic medicine. J Am Pharm Assoc. 2012;52(5):e113-e121.
82. Yau A, Abd Aziz AB, Haque M. Knowledge, Attitude and Practice Concerning Pharmacogenomics among Pharmacists: A Systematic Review. J Young Pharm. 2015;7(3).
83. Chapman CR, Mehta KS, Parent B, Caplan AL. Genetic discrimination: emerging ethical challenges in the context of advancing technology. J Law Biosci. 2020;7(1):lsz016.
84. Clayton EW, Evans BJ, Hazel JW, Rothstein MA. The law of genetic privacy: applications, implications, and limitations. J Law Biosci. 2019;6(1):1-36.
85. Doheny S, Clarke A, Carrieri D, Dheensa S, Hawkins N, Lucassen A, et al. Dimensions of responsibility in medical genetics: exploring the complexity of the "duty to recontact". New Genet Soc. 2018;37(3):187-206.
86. Nguyen HHK, Le HM, Le TQ, Dinh NTQ, Nguyen PA, Hoang DM, et al. Polygenic risk scores: Navigating the future of precision medicine through economic, ethical, and scientific advancements. iScience. 2026;29(1).
87. Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, et al. Polygenic risk score for cardiovascular diseases in artificial intelligence paradigm: a review. J Korean Med Sci. 2023;38(46).
88. Gupta A, Shah K, Shukla A. Bridging traditional risk factors and genetic insights: A review on polygenic risk scores in cardiovascular diseases. Adv Biomark Sci Technol. 2025.
89. Weitzel JN, Blazer KR, MacDonald DJ, Culver JO, Offit K. Genetics, genomics, and cancer risk assessment: state of the art and future directions in the era of personalized medicine. CA Cancer J Clin. 2011;61(5):327-359.
90. Suura SR. Integrating artificial intelligence, machine learning, and big data with genetic testing and genomic medicine to enable earlier, personalized health interventions. Deep Science Publishing; 2025.